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IRRl DISCUSSION PAPER SERIES NO. 15<br />
<strong>systems</strong> <strong>research</strong><br />
Edited by: N.F.C. Ranaweera
The <strong>International</strong> Rice Research Institute (IRRI) was established in 1960<br />
by <strong>the</strong> Ford and Rockefeller Foundations with <strong>the</strong> help and approval of <strong>the</strong><br />
Government of <strong>the</strong> Philippines. Today IRRI is one of (he 16 nonprofit in-<br />
ternational <strong>research</strong> centers supported by <strong>the</strong> Consultative Group on Inter-<br />
national Agricultural Research (CGIAR). The CGIAR is sponsored by <strong>the</strong><br />
Food and Agriculture Organization of <strong>the</strong> United Nations, <strong>the</strong> <strong>International</strong><br />
Bank for Reconstruction and Development (World Bank), and <strong>the</strong> United<br />
Nations Development Programme (UNDP). Its membership is comprised<br />
of donor countries, international and regional organizations, and private<br />
foundations.<br />
As listed in its most recent Corporate Report, IRRI receives support,<br />
through <strong>the</strong> CGIAR, from a number of donors including UNDP, World<br />
Bank, European Union. Asian Development Bank, Rockefeller Founda-<br />
tion, and Ford Foundation, and <strong>the</strong> international aid agencies of <strong>the</strong> fol-<br />
lowing governments: Australia, Belgium. Canada, People's Republic of<br />
China, Denmark. France, Germany, India, Indonesia, Is!amic Republic of<br />
Iran, Japan, Republic of Korea. The Ne<strong>the</strong>rlands, Norway, Philippines,<br />
Spain, Sweden, Switzerland, United Kingdom, and United States.<br />
The responsibility for this publication rests with <strong>the</strong> <strong>International</strong> Rice<br />
Research Institute.<br />
O <strong>International</strong> Rice Research Institute 1996.<br />
IRRl Discussion Paper Series<br />
The IRRI Discussion Paper Series was created as a flexible means for IRRI<br />
scientists to share information with specialized institutions and individu-<br />
als. Each paper is produced from camera-ready copy supplied by <strong>the</strong> au-<br />
thor and is processed through IRRI's Communication and Publications<br />
Services. The papers are read for typographical accuracy only and are not<br />
subjected to <strong>the</strong> normal IRRI editing or peer review processes.<br />
The series is intended to be a fast means of presenting preliminary<br />
results of <strong>research</strong> still in progress, but which could be of immediate use to<br />
olhers. 'Ihe series also contains special project reports, consortia and net-<br />
work reports, short proceedings or reports of meetings and workshops,<br />
recommendations from a particular workshop, and similar materials.<br />
IRRI invites feedback from readers, which will be useful to <strong>the</strong> au-<br />
thors when <strong>the</strong>y are refining <strong>the</strong>ir materials for formal publication in jour-<br />
nals or as monographs.<br />
<strong>International</strong> Rice Research Institute<br />
P.O. Box 933, Manila 1099. Philippines<br />
Fax: (63-2) 891 -1292<br />
Electronic mail: Postmaster@IRRI.CGNET.COM<br />
Telex: (In) 40890 RICE Ph4<br />
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Suggested Citation:<br />
Ranaweera NFC. 1996. Impact of farming <strong>systems</strong> <strong>research</strong>. IRRI Discussion Paper Series No. 15. <strong>International</strong> Rice Resarch Institute.<br />
P.O. Box 933. Manila. Philippines.
RECEIVED I RECU<br />
.-<br />
r b<br />
:'.i!~atlon Unit /<br />
S t c!is n de 1'6valuaf~on
Impact of farming<br />
<strong>systems</strong> <strong>research</strong><br />
PROCEEDINGS OF THE FINAL WORKSHOP<br />
ON IMPACT ASSESSMENT OF FARMING<br />
SYSTEMS RESEARCH<br />
Kandy, Sri Lanka<br />
9-13 December 1991<br />
Edited by: N.F.C. Ranaweera<br />
P.O. Box 933, Manila 1099, Philippines
Welcome address<br />
S .P.R. Weerasinghe<br />
(Director of Agriculture, Sri Lanka)<br />
Opening comments<br />
Contents<br />
F. A. Bernardo<br />
(Deputy Director General, <strong>International</strong> Rice Research Institute)<br />
Introductory address<br />
Hon. R.M. Dharmadasa Banda<br />
(Minister of Agricultural Development and Research, Sri Lanka)<br />
Overview<br />
N.F.C. Ranaweera<br />
(Division of Agricultural Economics and Planning, Department of Agriculture,<br />
Peradeniya)<br />
Farming <strong>systems</strong> <strong>research</strong> and its impact on farm families in two selected 1<br />
sites in Bangladesh--M. R. Siddiqui, M. R. Islam, N. P. Magor, N. U. Ahrned,<br />
and A. H. Khan<br />
Impact of farming <strong>systems</strong> <strong>research</strong> on selected farmers in Nepal: a case 2 8<br />
study of Pumdi Bhumdi--S.B. Ma<strong>the</strong>ma, K.D. Joshi, J. KC.<br />
Assessment of rice-fish farming system in Indonesia--M. 0. Adnyana, 5 9<br />
D. K.S. Swastika, and W. Sudana<br />
The impact of farming system <strong>research</strong> in Thailand--B. Shinawatara, 9 1<br />
C. Sukapong, P. Woodtikam, P. Deundao, P. Padermchai, and B. Ontuam<br />
Impact assessment of farming <strong>systems</strong> <strong>research</strong>-based technologies 129<br />
in <strong>the</strong> Philippines: <strong>the</strong> Isabela experience--R. R. Gonzaga, C. V. C. Barba,<br />
N. P. Gordoncillo, and N. F. C. Ranaweera<br />
Institutionalizing <strong>the</strong> farming <strong>systems</strong> <strong>research</strong> approach<br />
in Indochina--A. M. Mandac<br />
Impact assessment of farming <strong>systems</strong> <strong>research</strong> and development 165<br />
at <strong>the</strong> farm level: <strong>the</strong> case of KABSAKA technology in Iloilo, Philippines--<br />
V. T. Villancio, C. H. Manalo, M. L. V. I. Rebulanan, A. Sotomil,<br />
and N. F. C. Ranaweera<br />
Impact of farming <strong>systems</strong> <strong>research</strong> on <strong>the</strong> <strong>research</strong> and extension 185<br />
system: <strong>the</strong> case of <strong>the</strong> Philippines--V. T. Villancio, A. Punzalan, C. Hina,<br />
and V. R. Carangal<br />
vii<br />
ix<br />
. . .<br />
Xlll
Assessment of <strong>the</strong> impact of a farming-<strong>systems</strong> based technology<br />
site in Sri Lanka--N. F. C. Ranaweera, P. A. Samaratunga,<br />
J. M. K. P. Jayasinghe, and G. K. Renuka<br />
Impact assessment of farming <strong>systems</strong> <strong>research</strong> and development at<br />
<strong>the</strong> farm level: <strong>the</strong> rice-watermelon cropping system in Pangil,<br />
Laguna--V. T. Villancio, C. H. Manalo, M. L. Rebulanan, A. A. Arisgado,<br />
F. L. Matienzo, and N. F. C. Ranaweera<br />
Discussion and recommendations<br />
Participants<br />
Appendix: Acronyms
Welcome Address<br />
S. P. R. Weerasinghe1<br />
This is a historic occasion. It is <strong>the</strong> first time a serious attempt has been made to<br />
evaluate <strong>the</strong> benefits accruing to technologies developed within a farming <strong>systems</strong><br />
perspective. The presence of <strong>the</strong> Honorable R. M. Dharmadasa Banda, minister of<br />
Agricultural Development and Research, and his Secretary Mr. Dixon Nilaweera is<br />
a clear recognition of <strong>the</strong> importance attached to farming <strong>systems</strong> <strong>research</strong>. It is also<br />
a sign of appreciation to <strong>the</strong> efforts of scientists who contribute to <strong>the</strong> enhancement<br />
of agricultural productivity. Their <strong>research</strong> has a direct influence on <strong>the</strong> farmers who<br />
are <strong>the</strong> providers of food for our people. We are also privileged to have with us<br />
Dr. F. A. Bernardo, deputy director general of <strong>International</strong> Programs, <strong>International</strong><br />
Rice Research Institute (IRRI), Dr. V. R. Carangal, coordinator of <strong>the</strong> Asian<br />
Farming Systems Research Network at IRRI, and Dr. John Graham and Dr. John<br />
Hardie from <strong>the</strong> <strong>International</strong> Development Research Centre (<strong>IDRC</strong>), which<br />
provided <strong>the</strong> necessary funds to make this farming system activity a reality.<br />
These studies would not have come to fruition were it not for <strong>the</strong> concerted<br />
effort and dedication of scientists from <strong>the</strong> national programs in South and<br />
Sou<strong>the</strong>ast Asia and from <strong>the</strong> international centers. This workshop is an opportunity<br />
to critically review <strong>the</strong> impact of farming <strong>systems</strong> <strong>research</strong>, to identify <strong>the</strong> advances<br />
and weaknesses, if any, and to make recommendations to enable national<br />
agriculturil <strong>research</strong> <strong>systems</strong> to reorient future programs.<br />
Within <strong>the</strong> context of diminishing arable land per capita and declining<br />
incomes from farming, especially from rice, this workshop is most timely. Although<br />
science and technology have made rapid advances in <strong>the</strong> past few decades, <strong>the</strong><br />
application of new technologies in different farming situations has not had <strong>the</strong><br />
desired effects on agricultural production and farm income.<br />
Asian farmers have adopted a farming system that has gone through a<br />
process of evolution. The goals of <strong>the</strong>se farmers were subsistence for <strong>the</strong>ir families<br />
and preverltion of land degradation. Scientists have taken time to comprehend <strong>the</strong><br />
aspirations of farmers, but questions must be asked about <strong>the</strong> ways that advances in<br />
science and technology have been translated to farmers.<br />
Farming <strong>systems</strong> <strong>research</strong> should not be confined to <strong>the</strong> development of<br />
technology packages to increase production. Trade, postharvest handling and<br />
processing, input supply and marketing, and many o<strong>the</strong>r factors are integral to<br />
farming <strong>systems</strong> <strong>research</strong> and can be adopted by farmers to increase <strong>the</strong> production<br />
potential of <strong>the</strong>ir resource base. These areas have not been included in <strong>systems</strong><br />
<strong>research</strong> to <strong>the</strong> desired extent, but <strong>the</strong>y are of paramount importance to <strong>the</strong> farmer.<br />
'~irector of Agriculture, Sri Lanka.
A farmer can increase production, but if <strong>the</strong> farmer does not have <strong>the</strong> capacity to<br />
handle this production and cannot obtain reasonable prices for <strong>the</strong> outputs,<br />
frustration with <strong>the</strong> new technology will result, and <strong>the</strong> farmer will revert to <strong>the</strong><br />
traditional system.<br />
Agricultural scientists have a tremendous task ahead of <strong>the</strong>m. They must<br />
orient farming <strong>systems</strong> <strong>research</strong> to ensure that its application by farmers will result<br />
in a sustainable increase in production and incomes. At <strong>the</strong> same time, however,<br />
<strong>the</strong>y must ensure that <strong>the</strong>se technologies are in harmony with <strong>the</strong> environment and<br />
that <strong>the</strong>y do not contribute to its degradation.
Opening Comments<br />
After 15 yr of farming <strong>systems</strong> <strong>research</strong>, what has been achieved? Has <strong>the</strong>re been an<br />
impact? Should governments as well as national programs and international<br />
agricultural <strong>research</strong> centers continue to support farming <strong>systems</strong> <strong>research</strong>? An<br />
impact on family incomes is overdue.<br />
These impact studies on farming <strong>systems</strong> <strong>research</strong> take a hard look at<br />
technologies and <strong>the</strong>ir impact on <strong>the</strong> productivity of farmers. We also need to<br />
understand <strong>the</strong>ir impact on farm income and on <strong>the</strong> level of nutrition of farm<br />
families. Farming <strong>systems</strong> <strong>research</strong> must also address sustainability issues. The role<br />
of women, and gender issues in general, also have implications on sustainability.<br />
After all, farming <strong>systems</strong> in many developin countries involve <strong>the</strong> husband, <strong>the</strong><br />
wife, and <strong>the</strong> children. Farming is actually a amily activity, a family concern.<br />
We must, as well, have a hard look at <strong>the</strong> institutionalization of <strong>the</strong> farming<br />
<strong>systems</strong> approach to <strong>research</strong>. Have national agricultural <strong>research</strong> institutes adopted<br />
<strong>the</strong> farming <strong>systems</strong> approach in most of <strong>the</strong>ir <strong>research</strong> projects? Many institutions<br />
now automatically adopt an interdisciplinary approach to <strong>the</strong> analysis of crop<br />
problems and conduct of on-farm trials. We know that some countries have<br />
established farming <strong>systems</strong> <strong>research</strong> institutes. This is good because it is a form of<br />
institutionalization. But it is not good enough. The farming <strong>systems</strong> <strong>research</strong><br />
approach must become an integral part of agricultural <strong>research</strong> in most agricultural<br />
<strong>research</strong> programs.<br />
' ~ e ~ Director u t ~ General, <strong>International</strong> Rice Research Institute.<br />
B<br />
- vii -
Introductory Address<br />
Hon. R. M. Dharrnadasa ~ andal<br />
The Ministry of Agricultural Development and Research has collaborated with <strong>the</strong><br />
<strong>International</strong> Rice Research Institute for over 30 yr on a number of <strong>research</strong><br />
programs and exchanges of scientists. As a result of this collaboration, Sri Lanka has<br />
developed a number of rice technologies that, in no small way, have contributed to<br />
increased rice production and near self-sufficiency in rice. In addition, we have<br />
collaborated to develop technologies for crop diversification through <strong>the</strong> farming<br />
<strong>systems</strong> program.<br />
In a similar manner, <strong>the</strong> Department of Agriculture in Sri Lanka has been<br />
closely associated with <strong>the</strong> <strong>International</strong> Development Research Centre (<strong>IDRC</strong>) of<br />
Canada. <strong>IDRC</strong> has made major contributions to <strong>the</strong> development of our farming<br />
<strong>systems</strong> program for well over two decades. This study on <strong>the</strong> impact of farming<br />
<strong>systems</strong> <strong>research</strong> was funded by <strong>IDRC</strong>. <strong>IDRC</strong> has also provided leadership to<br />
develop technologies to improve grain legumes, develop oil seeds, and improve our<br />
library facilities.<br />
This workshop discusses <strong>the</strong> results of a 3-yr program and assesses <strong>the</strong> impact<br />
of some new technologies used by farmers. Asia has <strong>the</strong> largest number of people<br />
associated with agriculture. This agrarian population in Asia faces many problems,<br />
both in dealing with <strong>the</strong> availability of land per unit of household and in <strong>the</strong><br />
availabiljty of labor for cultivating <strong>the</strong> land.<br />
The increase in population in most Asian countries combined with <strong>the</strong><br />
decrease in land available for agriculture has resulted in a decrease in <strong>the</strong> ratio of<br />
land to people. In 1987, this ratio was 0.1 1 in Sri Lanka; it is expected to decrease to<br />
0.09 by <strong>the</strong> year 2000. In Pakistan, during this same period, <strong>the</strong> ratio will decrease<br />
from 0.19 to 0.14, in <strong>the</strong> Philippines from 0.14 to 0.10, in Thailand from 0.38 to 0.31,<br />
and in Bangladesh from 0.09 to 0.06.<br />
This reduction in <strong>the</strong> ratio of land to people results in a larger number of<br />
smallholders having small parcels of land. Therefore, <strong>the</strong> potential income-earning<br />
capacity of a farmer is reduced because of <strong>the</strong> limited productive capacity of <strong>the</strong><br />
land. Consequently, it is more difficult for <strong>the</strong> farmer to move ahead economically.<br />
It is in this context that current strategies and technologies must be assessed.<br />
Never<strong>the</strong>less, overall perforinance in <strong>the</strong> agricultural sector in Asia has been<br />
good. For example, during <strong>the</strong> past 15 yr, Sri Lanka has made remarkable<br />
achievements in food crop production. Rice production has increased from about<br />
1.5 million t in 1975 to 2.5 million t in 1990--an increase of 67% in 15 yr. This was<br />
mainly due to a 59% increase in yield (from 2.3 to 3.4 t/ha). The area under rice<br />
'~inister of Agricultural Development and Research, Sri Lanka.<br />
-h-
increased as well, but only nlarginally. As a result of this significant increase, Sri<br />
Lanka was able to reduce imports of rice and wheat flour from about 500,000 tlyr to<br />
below 300,000 tlyr. Imports of wheat flour were kept constant, while average per<br />
capita calorie consumption was maintained at <strong>the</strong> recommended level of 2,200<br />
calories per person. Credit for this achievement must go to our farmers and<br />
<strong>research</strong>ers.<br />
During <strong>the</strong> last decade, advances have also been made in o<strong>the</strong>r field crops.<br />
Average yields of maize have risen from less than 1 to 1.4 tlha, and total annual<br />
production has increased from 30,000 to 40,000 t. Yields of cowpea and green gram<br />
have improved from 0.6 tlha to more than 0.8 tlha, average yields of sweet potato<br />
from 2.5 to 7 tlha, and yields of cassava from 3 to 10 tlha. These increases have<br />
provided <strong>the</strong> population with Inore food at a relatively low price. Research in both<br />
rice and o<strong>the</strong>r crops has enabled farmers to obtain <strong>the</strong>se higher yields. An<br />
associated phenomenon has been <strong>the</strong> increased use of fertilizer and o<strong>the</strong>r inputs,<br />
which has provided <strong>the</strong> basis for increased intensities of cultivation. This<br />
phenomenon is seen around tlie world.<br />
This workshop looks at tlie jmpact of technology development from <strong>the</strong> point<br />
of view of its effects on <strong>the</strong> farrner. Despite all <strong>the</strong> production success, something<br />
still appears to be lacking. The farming population today is by and large poor, debt<br />
ridden, and malnourished. The incidence of poverty is very high to <strong>the</strong> point of<br />
being politically unsatisfactory. Farmers have always complained about <strong>the</strong>ir lack of<br />
access to inputs and, as a result, <strong>the</strong>ir inability to realize <strong>the</strong> potential of <strong>the</strong> new<br />
technologies. This is a serious problem. Some of <strong>the</strong> difficulties faced by <strong>the</strong> farmers<br />
are water availability, input supply, and adequate market facilities at reasonable<br />
prices. Some of <strong>the</strong>se issues may be beyond <strong>the</strong> purview of this workshop, but <strong>the</strong>y<br />
affect farmers in most Asian countries. Farmers are also aware that some of <strong>the</strong><br />
services that are provided are poor, [hat <strong>the</strong> timeliness and availability of inputs are<br />
unreliable, and that service is not continuous. It is not infrequent for farmers to be<br />
associated with development programs that generate new technology that is<br />
abandoned after some time ei<strong>the</strong>r because of termination of funding or transfer of<br />
personnel. Such situations must be looked at carefully.<br />
Sri Lanka has given <strong>the</strong> highest priority to programs that invest in human<br />
resources. The people are provided with direct access to resources with <strong>the</strong> aim of<br />
transforming <strong>the</strong> passive, <strong>the</strong> inert, and <strong>the</strong> excluded population into productive<br />
creators and owners of assets. 'The aim of this basic policy is growth with equity.<br />
Over <strong>the</strong> past decade, <strong>the</strong> agricult~~ral policy of Sri Lanka was directed toward<br />
achieving self-reliance in <strong>the</strong> basic requirements of food and increasing farmer<br />
income. This two-pronged strategy focused on developing large-scale irrigated<br />
settlements and commodity-oriented development programs that emphasized rice<br />
and a few o<strong>the</strong>r crops. The realization that farm incomes are needed to be enhanced<br />
has led to new policies [o diversify rural activities arid to provide farmers with Inore<br />
opportunities to optimize <strong>the</strong> use of <strong>the</strong>ir resources arid <strong>the</strong>reby maximize incomes.<br />
Our national policies have been focused primarily on increasing <strong>the</strong> supply of<br />
food and improving <strong>the</strong> standard of living, particularly of <strong>the</strong> rural population,<br />
through agricultural and rural develop~nent. Developing and transferring technology<br />
on regionally appropriate crops and cropping and farming <strong>systems</strong> and <strong>the</strong> provision
of production inputs at reasonable prices, were aspects that received attention. This<br />
enabled <strong>the</strong> farmers to increase productivity per unit area of land. To support and<br />
sustain this process, <strong>the</strong> government continued to streng<strong>the</strong>n <strong>the</strong> irrigation system of<br />
<strong>the</strong> country. Additional water resources were developed; <strong>the</strong> existing large- and<br />
small-tank irrigation <strong>systems</strong> were renovated, rehabilitated, and maintained; shallow<br />
and deep groundwater resources were harnessed through agro-wells and tube wells;<br />
farmers and farmer organizations were trained in water management; and <strong>the</strong><br />
irrigation infrastructure was maintained to maximize <strong>the</strong> use of this scarce resource.<br />
Creation of an institutional base to build a vibrant and well-articulated people's<br />
organization was followed by strong policy.<br />
Although marketing and prices for agricultural produce have been left<br />
largely to market forces, <strong>the</strong> government has ensured a reasonable price to farmers<br />
through a floor-price scheme. The government also facilitates <strong>the</strong> marketing of<br />
agricultural produce to ensure that farmers are protected.<br />
To attain sustainable growth with equity through a contin~~ous process of<br />
development, a conscious effort has been niade by <strong>the</strong> government to decentralize<br />
administrative power to <strong>the</strong> lowest possible operational level and to blend political<br />
leadership and administration to make decisions on resource allocation, monitoring<br />
programs, and implementation. Programs have also been aimed at environmental<br />
protection and poverty alleviation. For example, in our lead project (<strong>the</strong><br />
Janasaviya), <strong>the</strong> poverty-alleviation program seeks to invest in <strong>the</strong> poor and equip<br />
<strong>the</strong>m to move away from <strong>the</strong> poverty trap and to become partners in development.<br />
We have recognized that any program designed for those groups must necessarily be<br />
environmentally sustainable.<br />
Despite <strong>the</strong>se efforts, <strong>the</strong> pace of development, particularly in <strong>the</strong> agrarian<br />
sector, has been below expectations. Is this because some of <strong>the</strong> technologies that<br />
have been provided to <strong>the</strong> farmers are not appropriate to <strong>the</strong> social, economic, and<br />
cultural environment? Often, <strong>research</strong>ers, policymakers, and administrators claim<br />
that <strong>the</strong> technologies that have been developed are successful and that <strong>the</strong> farmers<br />
have benefited. However, if <strong>the</strong> effects are carefully analyzed, reality is far from this<br />
situation.<br />
Tn view of <strong>the</strong> mixed results of past policies, Sri Lanka is embarking on an<br />
innovative, forward-looking, aggressive policy in agricultural development. The<br />
overriding goal of agricultural development will continue to be directed toward<br />
achieving self-sufficiency in food (particularly rice). The principal aim of :igricultural<br />
development progriims will be to contribute to increase rural household incomes to<br />
levels comparable with those of families in o<strong>the</strong>r sectors of <strong>the</strong> economy. The main<br />
instruments will be <strong>the</strong> use of modern production and marketing techniques,<br />
diversification of <strong>the</strong> portfolio of crops, and expansion of off-farm sources of<br />
income.<br />
The challenges of providing adequate food and an improved living standard<br />
for <strong>the</strong> farming population must be considered seriously. A conflict in <strong>the</strong><br />
agricultural sector is <strong>the</strong> difficulty of providing food to <strong>the</strong> people at a reasonable<br />
price while ensuring a high quality of life for <strong>the</strong> farmers. High consumer prices may
esult in high incomes for farmers, but this is not always an acceptable option for<br />
consumers. However, cheap food, unless accompanied by very high production<br />
levels, cannot increase farmer income.
Overview<br />
Agricultural <strong>research</strong> in <strong>the</strong> past had <strong>the</strong> luxury that accountability to <strong>the</strong> majority<br />
clientele, namely <strong>the</strong> farmers, was not a high priority in <strong>the</strong> <strong>research</strong> agenda. More<br />
recently, because of <strong>the</strong> greater demand for technologies that are relevant to <strong>the</strong><br />
immediate ecological and socioeconomic environments, programs have focused<br />
<strong>the</strong>ir attention on <strong>the</strong> needs of farmers. This has been particularly true in <strong>the</strong> case of<br />
technologies developed within <strong>the</strong> framework of farming <strong>systems</strong> <strong>research</strong> (FSR), in<br />
which <strong>the</strong> concept of <strong>the</strong> whole farm and a <strong>systems</strong> perspective to <strong>the</strong> <strong>research</strong> has<br />
been adopted.<br />
Present-day agricultural <strong>research</strong> seeks to develop technologies that are<br />
sustainable over time, environmentally safe, and allow farmers to increase <strong>the</strong>ir<br />
incomes to achieve a better quality of life, whichever way it may be measured.<br />
During <strong>the</strong> last decade, pressures were placed on national programs to conduct<br />
agricultural <strong>research</strong> and development programs identified with FSR. <strong>International</strong><br />
donor agencies tended to provide finances for <strong>research</strong> only if it was considered<br />
within such a perspective.<br />
The adoption of FSR and <strong>the</strong> resulting technologies have resulted in<br />
significant increases of food production and in benefits to <strong>the</strong> <strong>research</strong> community.<br />
It has provided knowledge on effective methodologies and approaches to <strong>research</strong>,<br />
provided opportunities for more qualified human resources, through both formal<br />
and on-<strong>the</strong>-job trainings, and developed improved technologies.<br />
WHY STUDY IMPACT?<br />
With <strong>the</strong> maturing of <strong>the</strong>se programs and increased emphasis on streng<strong>the</strong>ning<br />
farming <strong>systems</strong> <strong>research</strong> in developing countries, a valid question is whe<strong>the</strong>r <strong>the</strong><br />
new technology has had <strong>the</strong> desired effects in terms of overall food production and,<br />
more significantly, real benefits to farmers. The question is <strong>the</strong>n asked: why study<br />
<strong>the</strong> impact of any program? This could be done for a number of reasons.<br />
Pure inquisitiveness<br />
There are <strong>research</strong>ers who look at <strong>the</strong> impact of any program purely from <strong>the</strong> point<br />
of view of inquisitive~less. They want to know, in general, <strong>the</strong> effects of <strong>the</strong> new<br />
technology.<br />
'~ivision of Agricultural Economics and Planning, Department of Agriculture,<br />
Peradeniya.
Investor concerns<br />
Usually investors are interested in <strong>the</strong> outcome of a new technology and its impact<br />
on various parameters that range from productivity to incomes. This is particularly<br />
true in <strong>the</strong> case of large-scale development programs that make significant<br />
investments in <strong>the</strong> agriculture sector. In <strong>the</strong>se instances not only <strong>the</strong> direct benefits<br />
are of interest, but also <strong>the</strong> indirect social and o<strong>the</strong>r multiplier benefits.<br />
Donor requirements<br />
Doilors that fund <strong>research</strong> and development programs are often interested in <strong>the</strong><br />
final impact of <strong>the</strong>ir investments. This is reflected in periodic reviews by project<br />
evaluation teams that go to <strong>the</strong> field to understand <strong>the</strong> benefits that farmers have<br />
obtained from <strong>the</strong> new technology. However, <strong>the</strong>se cursory visits do not adequately<br />
provide <strong>the</strong> information. An assessment of <strong>the</strong> benefits that <strong>the</strong> larger group of<br />
farmers have obtained from a particular program is of interest to donors.<br />
Feedback to <strong>research</strong>ers<br />
Within a <strong>research</strong> complex, <strong>the</strong> greatest beneficiary of an impact study will be <strong>the</strong><br />
<strong>research</strong>ers <strong>the</strong>mselves. They benefit by obtaining a better appreciation of <strong>the</strong><br />
benefits, or lack of benefits, from <strong>the</strong> perspective of <strong>the</strong> farmers. This is not usually<br />
done because, in most instances, <strong>research</strong>ers are satisfied with <strong>the</strong> knowledge that<br />
<strong>the</strong>ir <strong>research</strong> efforts are being practiced by farmers. Macrostatistics that indicate<br />
overall increases in production do not reflect <strong>the</strong> real picture in terms of differential<br />
impacts across different environments, or across different farmer groups and<br />
farming <strong>systems</strong>. This information can be obtained only from an in-depth impact<br />
study that analyzes <strong>the</strong> causes and effects of <strong>the</strong> use of a technology in a particular<br />
environment.<br />
IMPACT ON WHAT?<br />
Generally, economists involved in agricultural <strong>research</strong> tend to look at benefits from<br />
new technologies that reflect components of economic development. The issues can<br />
be categorized into micro and macro issues.<br />
Micro issues<br />
These issues are related more to direct effects in terms of increases in productivity,<br />
real incomes, and purchasing power of <strong>the</strong> farmers. These benefits are reflected in<br />
terms of a greater and more efficient use of inputs that fur<strong>the</strong>r increase productivity.<br />
Increases in <strong>the</strong> accumulation of capital assets, both farm and nonfarm, and, from a
nutritional aspect, changes in food intake that are reflected in better nutrition of <strong>the</strong><br />
farm family are also examined.<br />
Macro issues<br />
These issues generally relate to whe<strong>the</strong>r, during <strong>the</strong> process of adoption of <strong>the</strong> new<br />
technology, <strong>the</strong>re has been an effect on employment, education, and capital flows.<br />
Associated questions are whe<strong>the</strong>r supporting institutions, such as extension, credit,<br />
marketing, and input supplies, have responded to <strong>the</strong> demands of <strong>the</strong> new<br />
technology, and to what extent, if any, have agricultural policies (particularly<br />
pricing) been affected. In o<strong>the</strong>r words, have <strong>the</strong> adverse social and economic<br />
environments facing <strong>the</strong> farmer been reduced or eliminated through <strong>the</strong> provision of<br />
<strong>the</strong> support services that are normally needed for successful farm production?<br />
The influence of agricultural <strong>research</strong> on <strong>the</strong> overall development process is<br />
usually limited to providing only <strong>the</strong> technology. O<strong>the</strong>r support services and<br />
agricultural policies do not necessarily go in tandem with <strong>the</strong> technology<br />
requirements. This often leads to a large degree of nonadoption of <strong>the</strong> new<br />
technology.<br />
Although <strong>research</strong>ers portray FSR as a strategy for agricultural development,<br />
does it really provide <strong>the</strong> necessary answers to <strong>the</strong> multifaceted questions that are<br />
being faced at <strong>the</strong> level of national agricultural development? Research is<br />
concerned with exploring <strong>the</strong> uncertain. If <strong>the</strong> problem is successfully solved, <strong>the</strong><br />
probabilitj, of disseminating <strong>the</strong> <strong>research</strong> results in <strong>the</strong>ir exact form is low and <strong>the</strong><br />
chances of providing a positive effect on development with <strong>the</strong> equity goals<br />
identified is uncertain.<br />
Has <strong>the</strong> FSR approach provided adequate justification to be used to<br />
spearhead agricultural development programs that will provide farmers with<br />
adequate incomes to improve <strong>the</strong>ir quality of life? What this essentially asks is: what<br />
impact has <strong>the</strong> FSR approach had on <strong>the</strong> way of life of farmers from a development<br />
perspective? The ultimate aim of any improved agricultural system is to increase <strong>the</strong><br />
quality of life and improve <strong>the</strong> well-being of <strong>the</strong> farming community.<br />
An impact study of FSR technology could identify <strong>the</strong> strong and weak<br />
components of <strong>the</strong> technology itself, help make better resource-allocation decisions<br />
(particularly for <strong>research</strong> managers), provide alternate choices for <strong>the</strong> <strong>research</strong><br />
planning process, and identify <strong>the</strong> support services and policy measures that are<br />
needed to obtain real benefits from <strong>the</strong> technology. An analysis of <strong>the</strong> interplay of<br />
farm resources with consumption and o<strong>the</strong>r activities can provide insights to <strong>the</strong> real<br />
impact <strong>the</strong> new technology has had on farmers.
WORKSHOP OBJECTIVES<br />
This workshop is <strong>the</strong> culmination of a 3-year study in six Asian countries<br />
(Bangladesh, Indonesia, Nepal, Philippines, Sri Lanka, and Thailand) that<br />
investigated <strong>the</strong> impact of FSR on <strong>the</strong>ir national programs. The purpose of <strong>the</strong><br />
workshop was to present individual country experiences, understand <strong>the</strong> extent to<br />
perspectives have been incorporated into FSR activities, and provide<br />
future impact studies.
FARMING SYSTEMS RESEARCH AND ITS IhIPACT ON FARM FAMILIES<br />
IN W'O SELECTED SITES IN BANGLADESH<br />
M. R. Siddiqui, M. R. Islam, N. P. Magor, N. U. Ahmed, and A. H. ~han'<br />
The study was conducted to determine <strong>the</strong> extent of adoption of rice<br />
cropping <strong>systems</strong> technologies suitable to lowland rainfed areas, its<br />
impact on resource use, productivity, and income-expenditure<br />
patterns of selected farmers in two sites in Bangladesh. The sites are<br />
Kamalganj in <strong>the</strong> nor<strong>the</strong>ast and Sitakund in <strong>the</strong> sou<strong>the</strong>ast. The<br />
Kamalganj site which has rice surplus produces infrastructural<br />
facilities. Although Sitakund has access to major markets and off-farm<br />
employment, it is deficit in rice. Ninety farm families were randomly<br />
drawn from 6-7 villages in each site. Crop production per season and<br />
weekly income expenditure data were collected. Critical crop<br />
production factors such as planting time, varietal sequence, intensity<br />
of cropping, and fertilizer dosage were used in determining<br />
technology adoption. Aggregated scores of all <strong>the</strong>se factors were<br />
divided into three class intervals and <strong>the</strong> farmers were divided into<br />
low, medium, and high adopters. Results showed that <strong>the</strong> farmers<br />
adopted <strong>the</strong> technology. The productivity of <strong>the</strong> land and o<strong>the</strong>r<br />
resources varied positively in relation to <strong>the</strong> extent of adoption. Poor<br />
farmers (on <strong>the</strong> basis of farm size) were high adopters of <strong>the</strong> cropping<br />
<strong>systems</strong> technology and have invested more on required inputs. The<br />
extent of resource use in <strong>the</strong> production process was almost <strong>the</strong> same<br />
at both sites. But <strong>the</strong> yield was lower at Sitakund compared with that<br />
in Yamalganj due to variations in natural factors. The adoption of<br />
cropping <strong>systems</strong> technologies has generated more employment<br />
opportunities. The study suggests that infrastructural development at<br />
<strong>the</strong> initial stage and diversity in sources of income are essential in<br />
maintaining and improving <strong>the</strong> standard of living of <strong>the</strong> farming<br />
communities in <strong>the</strong> study areas.<br />
Bangladesh is primarily an agricultural country. More than 90% of its population is<br />
rural and about 75% of its total labor force is engaged in agriculture. Agriculture<br />
contributes approsim:ttely 56% of <strong>the</strong> gross domestic product (GDP) and provides a<br />
number of <strong>the</strong> raw materials required by <strong>the</strong> industrial sector. Altho~~gh agriculture<br />
is <strong>the</strong> most important economic activity in <strong>the</strong> country, Bangladesh does not produce<br />
enough food for its large population nor earn sufficient fore~gn exchange to allow<br />
importation of <strong>the</strong> needed food. Productivity per unit area of cultivated land is low<br />
because of <strong>the</strong> use of traditional production <strong>systems</strong> (e.g., use of local cultivars).<br />
This is a major constr;iint to attaining food self-sufficiency (Hoque 1978).<br />
'~an~ladesh Rice Research Institute, Gazipur, Joydehpur, Dhaka.
Rice is <strong>the</strong> major food crop and dietary staple in Bangladesh. Of <strong>the</strong><br />
13.5 million ha of cultivated area, rice covers about 10.6 million ha and is <strong>the</strong> only<br />
source of cash for many farmers (BRRI 1989). Research and development of<br />
appropriate rice technologies are needed to fit <strong>the</strong> existing farming <strong>systems</strong>, improve<br />
<strong>the</strong> nutritional status of <strong>the</strong> population, provide economic welfare to farm families,<br />
and meet <strong>the</strong> national food demand. To achieve <strong>the</strong>se objectives, <strong>the</strong> Bangladesh<br />
Rice Research Institute (BRRI) was established on 1 Oct 1970 as a semi-<br />
autonomous body.<br />
CROPPING SYSTEMS RESEARCH AND DEVELOPMENT AT BRRI<br />
The BRRI Cropping Systems Program focuses on increasing agricultural production<br />
in major agroecological environments through <strong>the</strong> efficient use of available farm<br />
resources and <strong>the</strong> adoption of improved cropping <strong>systems</strong> technologies. Farmer-<br />
participatory <strong>research</strong> in <strong>the</strong> villages has been <strong>the</strong> main approach of <strong>the</strong> program.<br />
The-program has addressed <strong>the</strong> following agroecological environments:<br />
rainfed upland, rainfed lowland, upland, irrigated lowland, dryland farming, and<br />
deepwater rice area. Attempts have been made to evaluate existing cropping<br />
patterns in terms of productivity, stability, and profitability. Consequently, improved<br />
cropping patterns and components of technologies have been designed and tested,<br />
and recommendations have been made for greater adoption.<br />
TECHNOLOGY FOR THE RAINFED LOWLAND ENVIRONMENT<br />
In Bangladesh, rice is cultivated under irrigated, rainfed, and deepwater conditions<br />
in four distinct rice seasons. These are aus, transplanted aman, broadcast or<br />
deepwater aman, and boro. Aus followed by transplanted aman rice is a major<br />
cropping system in <strong>the</strong> rainfed lowland environment, covering an estimated 3<br />
million ha. Rainfall and topography are important factors that influence <strong>the</strong><br />
adoption and productivity of this cropping system in <strong>the</strong> rainfed lowland<br />
environment. Aus rice is photoperiod-insensitive and generally grows under rainfed<br />
conditions ei<strong>the</strong>r as broadcast or transplanted crops from March to September.<br />
Aman rice is transplanted from July to September in areas where water depth does<br />
not exceed 0.5 m.<br />
Based on its extensive multilocation trials (MLT), <strong>the</strong> BRRI Rice Farming<br />
Systems Division has demarcated <strong>the</strong> land that can be used for this cropping system<br />
(aus followed by transplanted rice in aman) into three major target areas (Fig. 1).<br />
The onset and <strong>the</strong> duralion of wet (200 mm rain per month) and cool seasons, soil<br />
texture, suitability of <strong>the</strong> crop for <strong>the</strong> winter season, and socioecono~nic factors<br />
influence <strong>the</strong> choice of varietal combinations within a target area. The field duration<br />
and <strong>the</strong> establishment date of <strong>the</strong> aus crop, and <strong>the</strong> period between aus and aman,<br />
determine <strong>the</strong> date on which <strong>the</strong> aman rice crop can be transplanted. Within any<br />
single year, <strong>the</strong> establishment of <strong>the</strong> aus crop may be spread over a period of 4 -8<br />
wk. If rain starts late, <strong>the</strong> establishment of <strong>the</strong> aus crop will be delayed, which forces<br />
<strong>the</strong> aman crop to be planted in September. Socioeconomic factors, insufficient
labor, capital, and draft power spread <strong>the</strong> harvesting of aus and transplanting of<br />
aman over several weeks.<br />
The socioeconomic constraints limit <strong>the</strong> adoption of <strong>the</strong> BR1 - BR1 1<br />
cropping pattern by farmers. The aman crop is very sensitive to <strong>the</strong> date of<br />
establishment and to moisture stress. If <strong>the</strong> BR1 variety is not transplanted by 25<br />
May (ei<strong>the</strong>r because of <strong>the</strong> delayed onset of wet season or socioeconomic factors),<br />
<strong>the</strong> cropping pattern BR1 - BR11 may not be adopted in <strong>the</strong> central and eastern<br />
parts of Bangladesh. This feedback provided scope for fur<strong>the</strong>r refinement of <strong>the</strong><br />
technology and led to <strong>the</strong> establishment of cut-off dates for <strong>the</strong> various target areas.<br />
Accordingly, cut-off dates were suggested for <strong>the</strong> nor<strong>the</strong>ast area (target area 1)<br />
based on a predictive model (Magor et a1 1990) and for <strong>the</strong> sou<strong>the</strong>ast area based on<br />
observations (Fig. 1). Alternative cropping patterns were offered to farmers who<br />
failed to establish <strong>the</strong>ir aus crop by <strong>the</strong> cut-off date (Table 1).<br />
THE PROBLEM<br />
BRRI has recommended 26 high-yielding rice varieties suitable all over Bangladesh<br />
for specific seasons. The technologies generated by <strong>the</strong> Rice Farming Systems<br />
Division have been tested and proven to be productive and have been adopted in<br />
many target areas. For example, in Jamalpur, 100% and 85% of <strong>the</strong> farmers<br />
adopted modern rice varieties in <strong>the</strong> boro and transplanted aman seasons,<br />
respectively (Hoque et a1 1985). Farmers of Bhogra Village in <strong>the</strong> Gazipur District<br />
adopted recommended cropping <strong>systems</strong> technologies in more than 60% of <strong>the</strong>ir<br />
land and obtained 1 t/ha more per year than <strong>the</strong> nonadopters. Similar adoption<br />
studies were reported by Asaduzzaman (1979), Herdt and Garcia (1982), Hossain et<br />
a1 (1984), Dalrymple (1986), and Hossain (1987). All <strong>the</strong>se studies investigated only<br />
<strong>the</strong> rate of adoption of agricultural technologies and <strong>the</strong> factors that contributed to<br />
<strong>the</strong> adoption process. None attempted to determine <strong>the</strong> impact of adoption on<br />
income, consumption, and o<strong>the</strong>r expenditure patterns.<br />
This study attempted to determine <strong>the</strong> extent of adoption of <strong>the</strong> farming<br />
<strong>systems</strong> technology and its impact on income, consumption, and o<strong>the</strong>r expenditure<br />
patterns related to production in <strong>the</strong> farming community. The results were expected<br />
to provide basic information that would guide <strong>research</strong>ers and policymakers in <strong>the</strong><br />
efficient allocation of <strong>research</strong> resources.<br />
OBJECTIVES<br />
The specific objectives of <strong>the</strong> study are to determine <strong>the</strong> extent of adoption of<br />
improved cropping <strong>systems</strong> by farmers; to evaluate <strong>the</strong> impact of improved cropping<br />
<strong>systems</strong> on resource use and productivity; and to assess <strong>the</strong> impact of improved<br />
cropping <strong>systems</strong> on farm income, consumption, and o<strong>the</strong>r expenditure patterns.<br />
In view of <strong>the</strong> problems and objectives of <strong>the</strong> study, <strong>the</strong> following hypo<strong>the</strong>ses<br />
were tested: most farmers are adopters of improved cropping <strong>systems</strong>; <strong>the</strong> extent of
esource use and productivity varies according to <strong>the</strong> level of adoption; and income,<br />
consumption, and o<strong>the</strong>r expenditure patterns vary according to <strong>the</strong> extent of<br />
adoption.<br />
Approach<br />
METHODOLOGY<br />
This was an ex post evaluation of cropping <strong>systems</strong> <strong>research</strong> in Bangladesh. It was<br />
designed to compare farmers with different levels of adoption and to measure <strong>the</strong><br />
impact of cropping <strong>systems</strong> technology on productivity of resources, farmers'<br />
income, and expenditure patterns.<br />
Site selection<br />
Two sites (Kamalganj and Sitakund) were selected for <strong>the</strong> study. At Kamalganj,<br />
cropping <strong>systems</strong> <strong>research</strong> was conducted; while at Sitakund, multilocation testing<br />
was conducted. At both sites, small-scale pilot production programs had been<br />
launched and were productive. Both sites have 200 mm or more rainfall per month<br />
from May to September, sandy loam to clay loam soil, flood-free zone (i.e., a<br />
maximum level of 30 cm of water after heaw rainfall), and double-rice cropping<br />
system previously dominated by local varieties.<br />
Selection of villages and farmers<br />
Kamalgutij site. A survey was conducted in 13 villages within a radius of 5 km from<br />
<strong>the</strong> Kamalganj bazaar, to collect information on flooding, land holdings, and<br />
occupational structure of all households. Villriges prone to flooding were omitted.<br />
The remaining seven villages had similar land types. Households whose major<br />
source of income was agriculture were included in <strong>the</strong> sample. Proportionate<br />
random samples were drawn from each of <strong>the</strong> seven villages io obtain 90<br />
respondents.<br />
Sirctkzrtzti Sire. A household survey was concluctetl in 16 villages. The villages<br />
where agriculture was not <strong>the</strong> major occupation were excluded. Six villages were<br />
selected for <strong>the</strong> study. From <strong>the</strong>se six villages, 90 I~ouseholds were selected based on<br />
proportionate random sampling.<br />
Criteria to rate <strong>the</strong> extent of adoption<br />
Several factors affect <strong>the</strong> recommendations for rainfed lowland rice cropping<br />
<strong>systems</strong>. Factors such as timing, varietal sequence, intensity (single or double
cropping), and nitrogen and phosphorus application levels each constitute a part of<br />
<strong>the</strong> recommendation. For this reason, a scoring system that incorporated <strong>the</strong>se<br />
parameters was developed on a plot basis and <strong>the</strong>n cumulated for each farmer.<br />
Timing. In normal years (200 mm or more rain in May), farmers are advised<br />
to grow modern varieties on heavy textured soils in both <strong>the</strong> aus and aman seasons.<br />
Delays in <strong>the</strong> onset of <strong>the</strong> wet season and constraints in labor or draft power mean<br />
this recommendation must be adjusted to include a combination of modern and<br />
local varieties and single cropping. Alternative varietal sequences have been<br />
recommended to adjust to <strong>the</strong>se constraints (Table 1). The recommended sequence<br />
for a given transplanting date for a particular plot was given a score of 100. Whole-<br />
farm scores were weighted by area and expressed as a percentage.<br />
Vurietal seqrielzce. Irrespective of timing, a weighted score was gi~len to <strong>the</strong><br />
combination of varieties (Table 2). A farmer who grew a modern variety in aus<br />
followed by a modern variety in aman on a particular plot received a score of 100.<br />
Itrtetzsity. A double-crop sequence was given a score of 100; a single crop, 50.<br />
Fertilizer rute. Based on an intensive study of farm variety and fertilizer<br />
response, <strong>the</strong> NPK fertilizer recommendation is 60:40:20 for a nlodern rice variety<br />
and 40:20:0 for a local variety. Only N fertilizer was scored ancl calculated on <strong>the</strong><br />
basis of plot'percentage according to <strong>the</strong> varietal sequence. Recommended levels of<br />
N fertilizer are shown in Table 2. For example, a farmer who grew a modern rice<br />
variety in aus follo\sred by a modern variety in aman and used a total of 120 kg N/ha<br />
would receive a score of 100 for level of N fertilizer. Similarly, for <strong>the</strong> fallow - local<br />
rice variety pattern in aman, a farmer who used 50 kg N/ha would receive a score of<br />
100 for N level. However, a farmer could receive a score of more than 100 for a<br />
given plot by using more than <strong>the</strong> recommended level of input. Scores \seere <strong>the</strong>n<br />
cumulated on a whole-farm basis and weighted according to extent.<br />
dgr-eg'(~te scow.^. Different st~idies showed that varietal secluence, cropping<br />
intensity, and fertilizer rate are <strong>the</strong> most important factors responsil~le for annual<br />
crop yield per hectare (Hoque and Hobbs 1981). The correl;ltion matrices in Tables<br />
3 and 4 show similar relationships between total production and o<strong>the</strong>r factors suc.:~<br />
as varietal sequence, cropping intensity, and N fertilizer. However, <strong>the</strong> relatior:ship<br />
between total production and N fertilizer was not significant. This might be due to<br />
<strong>the</strong> low rate of fertilizer used by fdrmers. To calculate <strong>the</strong> adoption score, equal<br />
weights were given to <strong>the</strong>se three factors and <strong>the</strong> data were aggregated on a per year<br />
as well as per farm basis. Two crop years were included in <strong>the</strong> study and an average<br />
score over 2 yr was used to group <strong>the</strong> farmers according to <strong>the</strong> extent of adoption.<br />
At Kamalganj, <strong>the</strong> highest average score for 2 yr was 285 and <strong>the</strong> lowest was 156.<br />
Individuals having an aggregate score of 5 200, > 200 to 5 244, and > 244 were<br />
defined as low, medium, and high adopters, respectively. The same scoring<br />
technique was applied at Sitakund.
Determination of ado,)tion factors<br />
Regression analysis was done on <strong>the</strong> household survey data to identify <strong>the</strong> extent to<br />
which socioeconomic factors affected <strong>the</strong> decision to adopt modern rice<br />
technologies. The model used was:<br />
ADSC = F (FSIZE, POWER, AGE, EDUC, EXFA, POLND, ECONOMIC)<br />
where ADSC = aggre ated adoption score (9%); FSIZE = farm size (ha);<br />
POWER = number o f: animals used for power per household or pzr hectare; AGE<br />
= age of <strong>the</strong> respondent (years); EDUC = educational level of respondent (years of<br />
schooling); EXFA = experience in farming (years); POLND = percentage of rent<br />
in land; and ECONOMIC = number of effective family members per household or<br />
per hectare. The ordinary least square (OLS) technique was used and <strong>the</strong> best-fit<br />
model was selected on <strong>the</strong> basis of <strong>the</strong> sign and significance of <strong>the</strong> coefficients.<br />
Extent of resource use and productivity<br />
The enterprise budgeting technique was used to analyze <strong>the</strong> extent of resource use<br />
and productivity. Aus, aman, and aus and aman were considered as individual<br />
enterprises according to thc extent of adoption. The budgeting technique was used<br />
because it is relatively easy to ~lnderstand. A farm layout of <strong>the</strong> individual<br />
households was drawn and <strong>the</strong> size of <strong>the</strong> plots, tenurial status, and types of land<br />
were recorded. Crop production data on varietal sequences, dates of all operations,<br />
labor, animal power, and o<strong>the</strong>r inputs (e.g., fertilizers and insecticides) were<br />
recorded for each plot. Data on <strong>the</strong> use of family and hired labor were classified<br />
under niale, female, and children. Crop production data were collected mostly from<br />
a male adult (preferably <strong>the</strong> head of <strong>the</strong> household). Daily wage rate and weekly<br />
niarket prices of inputs and outputs within <strong>the</strong> project area were also recorded. Data<br />
on physical p;irameters such as rainfall and temperature were also recorded. Data<br />
on crop yield were collected from farmers and validated using crop-cut estimates.<br />
Different efficiency criteri:~ ivere calculated on a per hectare basi4 using current<br />
prices.<br />
Income sources and patterns of expenditure<br />
Because rural ho~iseliolds do not keep records of <strong>the</strong>ir activities, it is difficult to<br />
estimate activities done o11 a self-employment basis and expenditures that do not<br />
require cash transactions. Income and expenditure data were collected on a weekly<br />
basis to reduce <strong>the</strong> degrep, of inaccuracy. In kind transactions were valued using <strong>the</strong><br />
prevailing market price. Gifts received in cash or in kind were considered as income.<br />
Donations were considered as expenditures. Consumption from production was<br />
regarded as cash inflow (illcome) as well as cash outflow (expendi~ore). Validated<br />
income and expenditure data were aggregated on a yearly basis. Comparisons were<br />
made on a per farm as well as on a per hectare basis to standardize for land area<br />
and family members, respectively.
Management of <strong>the</strong> sites<br />
A site coordinator supervised each site and two local interviewers (male and<br />
female) collected <strong>the</strong> data. Employment of local female interviewers is essential for<br />
this type of data collection. It facilitated access to female members of <strong>the</strong> household.<br />
The coordinators visited <strong>the</strong> sites regularly and supervised <strong>the</strong> work of <strong>the</strong> field staff<br />
to ensure accurate collection and coding of data.<br />
RESULTS<br />
Socioeconomic characteristics of <strong>the</strong> households<br />
None of <strong>the</strong> households were landless. The average farm size was 0.95 ha in<br />
Kamalganj and 0.70 ha in Sitakund. At Kamalganj, average farm size was higher<br />
than <strong>the</strong> national average of 0.70 ha (BBS 1980). At both sites, high adopters are<br />
smallholders. On average, a household had six members at Kamalganj and seven at<br />
Sitakund. The average age of <strong>the</strong> respondent was 47 years at Kamalganj and 50<br />
years at Sitakund. At both sites, <strong>the</strong> education level of <strong>the</strong> respondents was <strong>the</strong><br />
primary level. Animal draft power was a major constraint in both sites. An average<br />
household at Sitakund had one animal and at Kamalganj two.<br />
The functional relationship between adoption of modern rice technologies<br />
and <strong>the</strong> so~ioeconomic characteristics of <strong>the</strong> respondents was also investigated.<br />
Extent of adoption<br />
Originally, 90 farmers from each site were selected for <strong>the</strong> study but some farmers<br />
refused to cooperate. In Kamalganj, 88 farmers participated in <strong>the</strong> study, while<br />
<strong>the</strong>re were 54 respondents in Sitakund. Most of <strong>the</strong> selected farmers at both sites<br />
adopted <strong>the</strong> rice production technologies. At Kamalganj, 18 farmers were low<br />
adopters, 47 were medium adopters, and 23 were high adopters. At Sitakund, 10<br />
were low adopters, 30 were medium adopters, and 14 were high adopters (Table 5).<br />
Low adopters owned fewer draft animals than <strong>the</strong> medium and high adopters at<br />
Kamalganj (Table 6). At Kamalganj, ei<strong>the</strong>r one buffalo or a pair of cattle were used<br />
for plowing. Therefore, one buffalo was considered equivalent to one pair of cattle.<br />
Factors of adoption: an econometric analysis<br />
Regression analvsis was used to determine which of <strong>the</strong> socioeconomic factors<br />
affected <strong>the</strong> decision to adopt niodern rice technologies (Table 7). The estimated<br />
model explains only 13% of <strong>the</strong> total variation in <strong>the</strong> adoption decision in<br />
Kamalganj and 38% in Sitakund. The relationship between adoption and <strong>the</strong><br />
explanatory variables (F ratio) was significant at <strong>the</strong> 1% level of probability. The
number of active family members per family and <strong>the</strong> number of animals per family<br />
were significantly and positively associated with <strong>the</strong> adoption of modern rice<br />
technologies at both sites. However, <strong>the</strong>re was an inverse relationship between farm<br />
size and ado tion of modern rice varieties. This was also found out in <strong>the</strong> studies of<br />
Lionberger (960)~ Juliano (1967), Copp and Sill (1968), Feliciano (1968), Madigan<br />
(1968), Guzman (1973), Islam (1986), and Hossain (1987).<br />
Yield, input use, and productivity according to adoption<br />
Enterprise budgeting places information about production and inputs in a common<br />
framework. It provides insights into <strong>the</strong> efficiency of input use and its impact on<br />
productivity. At both sites, aus followed by aman was <strong>the</strong> major cropping system.<br />
In 1989, <strong>the</strong> average rice yield in Kamalganj was 2, 396 kg/ha in aus, 2,561 in<br />
aman, and 4,957 in aus and aman (Table 8). Group comparisons showed that <strong>the</strong><br />
difference in yi-eld between low and high adopters was significant. High adopters<br />
obtained 1,127 kg more rice/ha per year than low adopters (Table 9). In 1990, <strong>the</strong><br />
average rice yield was 2,623 kg/ha in aus, 2,947 in aman, and 5,570 in aus and aman.<br />
Results of <strong>the</strong> group comparisons showed that <strong>the</strong> yield difference between low and<br />
high adopters was significant. High adopters obtained 1,105 kg more rice/ha than<br />
low adopters (Table 9). Increase in yield might be due to adoption of <strong>the</strong> rice<br />
technology and favorable wea<strong>the</strong>r conditions in both years.<br />
Average rice yields in 1989 at Sitakund were 1,625 kg/ha in aus, 2,639 in<br />
aman, and 2,639 in aus and aman (Table 8). Yield difference between low and high<br />
adopters was significant. High adopters obtained 1,440 kg more rice/ha per year<br />
than low adopters (Table 9).<br />
In 1990, average rice yield in aus season was 1,816 kg/ha, 1,435 in aman, and<br />
3,251 in aus and aman (Table 8). Group comparisons showed that yield difference<br />
between low and high adopters was significant. High adopters obtained<br />
1,293 kg more rice/ha than low adopters.<br />
At Sitakund, 1989 rice yield was low compared with yield in 1990 because of<br />
<strong>the</strong> drought at <strong>the</strong> beginning of <strong>the</strong> aus season and insect infestation during <strong>the</strong><br />
aman season.<br />
In <strong>the</strong> aus and aman seasons in 1989, thd intensity of using human labor,<br />
animal draft power, and fertilizers were highest for <strong>the</strong> high adopters followed by<br />
medium and low adopters (Table 10). Most of <strong>the</strong> high and medium adopters were<br />
smallholders who cultivated <strong>the</strong>ir land more intensively using family labor. Sitakund<br />
farmers used more hired labor and animal power compared with Kamalganj farmers<br />
because <strong>the</strong> opportunity for off-farm employment is higher at Sitakund. Sitakund<br />
farmers may have substituted family labor with hired labor and engaged in nonfarm<br />
activities. Farmers at t~oth sites used less fertilizer than <strong>the</strong> recommended rate
ecause of financial difficulties and lack of awareness. At both sites, high adopters<br />
used <strong>the</strong> highest fertilizer input, followed by medium and low adopters.<br />
A similar trend was observed in 1990 (Table 11). At both sites, total annual<br />
cost per hectare was highest in <strong>the</strong> case of hi h adopters followed by medium and<br />
low adopters. Returns to family labor, hired k abor, animal draft power, and material<br />
inputs were higher at Kamalganj. But <strong>the</strong> cost of rice production per kilogram was<br />
lower at Kamalganj; <strong>the</strong>refore, Kamalganj farmers were more cost-effective than<br />
Sitakund farmers. At Kamalganj, group comparisons showed no significant<br />
differences in net returns between low and medium adopters or between medium<br />
and high adopters. However, <strong>the</strong>re were significant differences in gross return<br />
between low and medium and between medium and high adopters (Table 12).<br />
At Sitakund, group comparisons showed significant differences in gross<br />
return and total cost between low and medium and low and high adopters. But <strong>the</strong><br />
net return was not significant.<br />
Adoption and its impact on income and expenditure<br />
The 1989 farm income of low adopters in Kamalganj was 58% of <strong>the</strong>ir total income,<br />
medium adopters was 54%, and high adopters 63%. In 1990, low adopters' farm<br />
income was 51%, medium adopters 59%, and high adopters 60%. While in<br />
Sitakund, <strong>the</strong> 1989 farm income of low adopters was 34% of <strong>the</strong>ir total income, 59%<br />
for medium.adopters and 50% for high adopters. The 1990 farm income of low<br />
adopters was 4396, medium adopters 59%, and high adopters 45%.<br />
The primary source of income for low and high ado~ters of rice technologies<br />
in Kamalganj was farming. In Sitakund, nonfarming activities were <strong>the</strong> major source<br />
of income and low adopters. While high adopters' dominant source of income were<br />
both farming and nonfarming activities (Table 13).<br />
At Kamalganj, <strong>the</strong> 1989 income from rice production was 78%, 82%, and<br />
85% of <strong>the</strong> total farm income for low, medium, and high adopters, respectively (data<br />
not shown). This increase in income was directly related to <strong>the</strong> adoption of <strong>the</strong><br />
technology. Similar results were obtained in 1990. The highest income from <strong>the</strong> sale<br />
of rice was obtained by high adopters. Income from livestock was <strong>the</strong> second most<br />
important source of farm income (about 10 % of <strong>the</strong> total farm income). Nonfarm<br />
income was about 40% of <strong>the</strong> total income. This decreased with <strong>the</strong> rate of adoption<br />
of rice technology. Service, business, and labor were <strong>the</strong> major sources of nonfarm<br />
income. At Sitakund, income from rice production was not directly related to<br />
adoption of technology. In 1989, <strong>the</strong> income from rice production was 65%, 32%,<br />
and 38% of <strong>the</strong> total farm income for low, medium, and high adopters, respectively.<br />
Again, high adopters gained <strong>the</strong> highest income from <strong>the</strong> sale of rice. Vegetable<br />
cultivation and livestock were <strong>the</strong> two most important sources of farm income at<br />
Sitakund. Nonfarm income at Sitakund was higher than in Kamalganj. Service,<br />
business, and overseas employment were <strong>the</strong> most important sources of nonfarm<br />
income.
At both sites, more than 73% of <strong>the</strong> total expenditure was spent on<br />
household requirements and <strong>the</strong> rest was spent on farm resources (Table 14).<br />
Farmers incurred <strong>the</strong> highest farm expenditure on hired labor and inputs.<br />
Expenditure on input use was directly related to <strong>the</strong> adoption of technology.<br />
Sitakund farmers spent more on hired labor because <strong>the</strong> wage rate was higher due<br />
to partial industrialization of <strong>the</strong> site. The highest household expenditure was for<br />
purchasing rice. This was inversely related to <strong>the</strong> adoption of technology (data not<br />
shown). At both sites, adoption of rice technology helped <strong>the</strong> farmers attain partial<br />
self-sufficiency in rice. In Kamalganj, less emphasis was given to education,<br />
medicine, and animal protein. But at Sitakund, high adopters spent more on<br />
education, medicine, and animal protein, followed by medium and low adopters.<br />
Similar patterns of income and expenditure were observed in 1990.<br />
Annual incomes and expenditures are presented in Table 14. Average results<br />
over 2 years indicated that cash balance at both sites was directly related to <strong>the</strong><br />
adoption of technology escept in <strong>the</strong> case of medium adopters at Kamalganj. In<br />
general, this indicates that farming is a profitable activity.<br />
POLICY IMPLICATIONS OF THE FINDINGS<br />
Adoption of cropping <strong>systems</strong> technologies had a substantial impact on <strong>the</strong> economy<br />
of <strong>the</strong> villages. Smallholders were high adopters of <strong>the</strong> cro ping <strong>systems</strong> technology.<br />
The high adopters did not apply <strong>the</strong> recommended dose o P fertilizer. The household<br />
survey indicated that small landholders had low purchasing power. The provision of<br />
credit facilities would enable fur<strong>the</strong>r adoption of modern rice technologies.<br />
Although <strong>the</strong> extent of resource use in <strong>the</strong> production process was almost <strong>the</strong><br />
same at both sites, <strong>the</strong> yield was lower at Sitakund because of variations in natural<br />
factors. Even under unfavorable conditions during 1989-90 in Sitakund, <strong>the</strong><br />
recommended technology outperformed <strong>the</strong> traditional practices. The study showed<br />
that high adopters used more hired labar as well as family lahor in <strong>the</strong> production<br />
process. Hired laborers are <strong>the</strong> landless or <strong>the</strong> small landholders. Therefore, an<br />
attempt should be made to improve <strong>the</strong> adoption of modern rice technologies<br />
through <strong>the</strong> extension services. This will generate more employment opportunities<br />
and increase <strong>the</strong> purchasing power of <strong>the</strong> landless and <strong>the</strong> sn~allholders.<br />
Although <strong>the</strong> farming <strong>systems</strong> included crops, livestock, homestead-forestry,<br />
and off-farm activities, <strong>the</strong> rice sector was <strong>the</strong> dominant component in <strong>the</strong> study<br />
areas. The rice cropplng system is subject t0 natural hazards. Therefore, attention<br />
must be give11 to o<strong>the</strong>r sources of income (e.g., intensive srnall-scale vegetable<br />
cultivation, goat rearing, and pond culture of fish) that could stabilize and increase<br />
income. The potential for nonagricultural income must :ilso be explored.<br />
At Kamalganj, <strong>the</strong>re were no rice processing centers. Farmers were forced to<br />
sell <strong>the</strong>ir grain to dealers at lower prices at <strong>the</strong> time of harvest to meet urgent cash<br />
needs. The development of small-scale local processing centers may streng<strong>the</strong>n <strong>the</strong><br />
position of <strong>the</strong> farmer.
REFERENCES CITED<br />
Asaduzzaman M (1979) Adoption of HYV rice in Bangladesh. Bangladesh Dev.<br />
Stud. 7(3): 23-49.<br />
BBS--Bangladesh Bureau of Statistics (1980) Bangladesh statistical year book.<br />
Dhaka, Bangladesh.<br />
BRRI--Bangladesh Rice Research Institute (1989) About BRRI. Gazipur,<br />
Bangladesh.<br />
Copp J H, Sill M L (1968) The function of information sources ir, <strong>the</strong> farm practices<br />
adoption proci.ss. Rural Sociol. J. 23: 146-147.<br />
Dalrymple, D G (19%) Development and spread of high-yielding rice varieties in<br />
developing countries. United States Agency for <strong>International</strong> Development,<br />
Washington, D. C.<br />
De Guzman A M (1973). Corolan rice farmers' response to change in cropping<br />
patterns: a case study. (mimeogr.)<br />
~eliciano'~ D (1968) Corrclates of productivity among tobacco f~rmzrs in Isabela.<br />
Ph D dissertation, University of <strong>the</strong> Philippines, Los Bafios, College, Laguna,<br />
Philippines.<br />
Herdt R W, Garcia L (1952) Adoption of modern rice technology: <strong>the</strong> impact of size<br />
and tenure in Sangladesh. <strong>International</strong> Rice Research Institute, P.O. Box<br />
933, Manila, Philippines. (mimeogr.)<br />
Hoque M Z (1978) A proposal for <strong>the</strong> extension of <strong>the</strong> Bangladesh Rice Research<br />
Cropping Systems Projects. Project proposal submitted to <strong>the</strong> <strong>International</strong><br />
Developinent Centre, Canada.<br />
Hoque M Z, Hobbs P R (1981) Rainfed cropping <strong>systems</strong> report of <strong>research</strong><br />
findings at Bhogra village, 1975-79. Bangladesh Rice Research Institute,<br />
Gazipur, Bangladesh.<br />
Hoque M Z, Nasiruddin M, Chowdhury N H, Hossain h4 (1985). Adoption and<br />
impact of modern varieties on rice production in Bangladesh. Proceedings of<br />
<strong>the</strong> workshop on experiences with modern rice cultivation in Bangladesh,<br />
Bangladesh Rice Research Institute, Bangladesh.
Hossain A M, Nur-E-Elahi, Nazrul I M M (1984) Farmer adoption study of<br />
recommended rainfed modern double rice cropping pattern technology<br />
under existing farming <strong>systems</strong>. Bangladesh Rice Research Institute,<br />
Joydebpur, Gazipur.<br />
Hossain M (1987) Farm size, tenancy, and land productivity: an analysis of farm<br />
level data in Bangladesh agriculture. Bangladesh Dev. Stud. 5(3):285-348.<br />
Islam M R (1986) Efficacy of deep-well pump irrigation system in improving<br />
rice-based farniing in Nueva Ecija. MS <strong>the</strong>sis, Central Luzon State<br />
University, Philippines.<br />
Juliano C P, Jr. (1967) The relationship between some characteristics of rice farm<br />
operators and adoption of some recommended practices in rice production in<br />
seventeen barrios of Laguna. MS <strong>the</strong>sis, University of <strong>the</strong> Philippines College<br />
of Agriculture, College, Laguna, Philippines.<br />
Lionberger H F (1960). Adoption of new ideas and practices. Iowa State University<br />
Press, Arnes, IA.<br />
Madigan F G (1968) The farmers said no. UP CRDC.<br />
Magor N P, Siddiqiii M K, Ahmed N U, h4iah N I, Amin R (1990) Creating<br />
computerized farm plans. Paper presented at <strong>the</strong> 1990 Asian Farming<br />
Systems Research and Extension Symposium, 19-22 Nov 1990, Bangkok,<br />
Thai1:ind.<br />
Magor N P (1986) Proceedings of high-yielding variety workshop. Bangladesh Rice<br />
Research Institute, Gazipur, Bangladesh.<br />
Miah S A, Mannan h1 A (1989) modern rice technology and its contribution to<br />
Bangladesh economy. Bangladesh Rice Research Institute, Gazipur,<br />
Bangladesh.
Table 1. Cropping pattern recommendations for rainfed lowland environments with heavy<br />
textured soils based on <strong>the</strong> aus transplanting cut-off dates.<br />
Aus transplanting Cropping pattern<br />
cut-off date Aus T. aman Remarks<br />
30 May BR1 BRllIBR10 Refined original<br />
recommended pattern<br />
20 May-<br />
30 Jun<br />
(1) short-<br />
duration<br />
local variety<br />
(2) short-<br />
duration<br />
modern variety<br />
After 20 Jun (1) fallow<br />
(2) modern<br />
Aus<br />
BRllIBR10 Aman transplanting to<br />
be completed within<br />
August<br />
Local Aman transplanting is<br />
expected to take place<br />
in September<br />
BR1 11BR10 Transplanting to be<br />
completed within July<br />
Fallow
Table 2. Crop varietal sequence score with recommended total N and P for<br />
each sequence.<br />
Varietal sequence Recommended<br />
fertilizer levela Intensity<br />
Aus Aman Score (kg NJha) score<br />
Modern<br />
Modern<br />
Local<br />
Fallow<br />
Modern<br />
Local<br />
Local<br />
Fallow<br />
Modern<br />
Local<br />
Modern<br />
Modern<br />
Fallow<br />
Local<br />
Fallow<br />
Local<br />
aIf at recommended fertilizer level, NFERT (nitrogen fertilizer) percentage<br />
score for <strong>the</strong> individual plot is 100.
Table 3. Correlation matrix used to score adoption at Kainalganj study site,<br />
Bangladesh (1989-90).<br />
Parameter Timing Varietal Intensity Fertilizer<br />
sequence (NI<br />
Varietal sequence 0.1922<br />
Intensity -0.0007 0.6515**<br />
N fertilizer -0.0878 0.0718 0.0552<br />
Total<br />
production -0.0287 0.4749** 0.5543** 0.1872<br />
per hectare<br />
Varietal sequence 0.2942"<br />
Intensity -0.0957 0.8028**<br />
N fertilizer -0.0129 0.1131 0.1154<br />
Total<br />
production -0.1036 0.4776** 0.5543"" 0.1886<br />
per hectare<br />
**significant at < 1 % level. "significant at l % level.
Table 4. Correlation illatsix used to score adoption at Sitakund study site,<br />
Bangladesh (1 989-90).<br />
Parameter Timing Varietal Intensity Fertilizer<br />
sequence (N)<br />
Varietal<br />
Intensity<br />
N fertilizer<br />
Total<br />
production<br />
per hectare<br />
Varietal<br />
Intensity<br />
N fertilizer<br />
Total<br />
production<br />
per hectare<br />
**significant at < 1 % level. '"significant at 1 R level.
Table 5. Distribution of households according to extent of adoption in two rainfed<br />
sites, Bangladesh (1 989-90).<br />
Extent of Kamalganj Sitakund<br />
adoption<br />
No. of % of total No. of % of total<br />
households households households households<br />
Low 18 20 10 18<br />
Medium 47 54 3 0 56<br />
High 23 26 14 26<br />
Total 8 8 100 54 100
-<br />
Table 6. Socioeconomic characteristics of households according to extent of adoption in two rainfed sites, Bangladesh<br />
(1 989-90).<br />
Socioeconomic<br />
characteristic<br />
PP-<br />
Family size (no.)<br />
Consuming unit (no.)<br />
Economic unit (no.)<br />
Age of respondent (yr)<br />
m<br />
l Farm size (ha)<br />
Marginal farms (%)<br />
Small farms (%)<br />
Medium farms (%)<br />
Large farms (%)<br />
Animal power (no. )<br />
Education level<br />
Kamalganj Sitakund<br />
Low Medium High All Low Medium High All<br />
6<br />
5<br />
4<br />
4 8<br />
1.10<br />
2<br />
8<br />
10<br />
-<br />
0.90<br />
Primary<br />
6<br />
5<br />
4<br />
46<br />
0.95<br />
3<br />
3 0<br />
19<br />
1<br />
1 .oo<br />
Primary<br />
6<br />
5<br />
5<br />
4 8<br />
0.83<br />
6<br />
13<br />
8<br />
-<br />
1.10<br />
Primary<br />
6<br />
5<br />
4<br />
47<br />
0.95<br />
11<br />
5 1<br />
3 7<br />
1<br />
1 .oo<br />
Primary<br />
9<br />
8<br />
6<br />
53<br />
1.03<br />
4<br />
6<br />
9<br />
-<br />
1.80<br />
Primary<br />
7<br />
6<br />
4<br />
49<br />
0.70<br />
16<br />
2 8<br />
l l<br />
-<br />
1.90<br />
Primary<br />
6<br />
5<br />
3<br />
50<br />
0.49<br />
13<br />
9<br />
4<br />
-<br />
1.50<br />
Primary<br />
7<br />
6<br />
4<br />
50<br />
0.70<br />
3 3<br />
43<br />
24<br />
-<br />
1.78<br />
Primary
Table 7. Ordinary least squares estimates of factors influencing <strong>the</strong> decision<br />
to adopt modern rice technologies in two rainfed sites, Bangladesh (1989-<br />
90).<br />
Variable<br />
Constant<br />
Farm size (ha)<br />
Economic unitlha<br />
Animal powerlha<br />
Age of <strong>the</strong> owner (yr)<br />
Experience in farming (yr)<br />
Education of respondents<br />
% sharedlrented land<br />
Loan receivedlha<br />
~2<br />
F ratio<br />
D.F.<br />
Estimates with standard errors<br />
Kamalganj Sitakund<br />
***significant at < 1 % probability level. **significant at < 5 % probability<br />
level. *significant at 10% probability level. ns=not significant. Figures in<br />
<strong>the</strong> paren<strong>the</strong>ses indicate standard errors.
Table 8. Extent of adoption and average rice yield (kglha) in two rainfed sites,<br />
Bangladesh (1989-90).<br />
Kamalganj Sitakund<br />
Extent of<br />
adoption Aus Aman Total Aus Aman Total<br />
Low<br />
Medium<br />
High<br />
Low<br />
All<br />
Medium<br />
High<br />
AI l
Table 9. Extent of adoption and comparison of yield differences at two rainfed sites,<br />
Bangladesh (1989-90).<br />
Extent of Kamalganj Sitakund<br />
adop tionl<br />
comparison Aus Aman Total Aus Aman Total<br />
Between<br />
low and medium<br />
Between<br />
medium and high<br />
Between<br />
low and high<br />
Between<br />
low and medium<br />
Between<br />
medium and high<br />
Between<br />
low and high<br />
**significant at < 1 % level. "significant at 5 % level. ns=not significant.
Table 10. Resource use and productivity according to extent of adoption in two rainfed<br />
sites, Bangladesh, aus + aman (1989).<br />
Resource use/ Kamalganj Sitakund<br />
productivity<br />
measure Low Medium High Low Medium High<br />
Labor (h) 1,330 1,417 2,072 1,285 1,980 2,207<br />
Family 1,048 1,139 1,748 1,041 1,642 1,625<br />
Hired 282 278 3 24 244 338 582<br />
Animal (h) 538 525 623 193 306 292<br />
Family 524 504 606 136 3 65 189<br />
Hired 14 2 1 17 5 7 4 1 103<br />
Fertilizer (kg)<br />
N 29 54 76 38 72 94<br />
P2°5 14 23 3 1 4 6 9<br />
Yield (kg) 4,410 4,882 5,537 1,814 2,627 3,254<br />
Gross return (taka) 25,018 27,692 31,206 13,269 18,209 20,548<br />
Total cost (taka)<br />
Full cost 10,671 12,000 15,572 10,477 16,201 18,114<br />
Cash cost 2,287 3,281 3,196 2,688 3,552 6,152<br />
Production cost (takalkg)<br />
Full cost 2.41 2.45 2.81 5.77 6.16 5.56<br />
Cash cost 0.5 1 0.67 0.57 1.48 1.35 0.89<br />
Net return (taka)<br />
Full cost 14,347 15,692 15,634 2,792 2,008 2,134<br />
Cash cost 22,731 24,411 28,010 10,581 14,647 14,096<br />
Returns to (taka)<br />
Family labor 3.74 3.75 2.79 1.70 1.67 1.5:<br />
Hired labor 11.18 12.29 10.65 5 .OO 3.24 2.2:<br />
Animal draft labor 5.44 5.98 5.18 21.79 16.35 3.2<br />
Material inputs 19.09 9.89 11.60 11.08 6.14 4.3
Table 11. Resource use and productivity according to extent of adoptiofi in two rainfed<br />
sites, Bangladesh, aus + aman (1990).<br />
Resource use1 Kamalganj Sitakund<br />
productivity<br />
measure Low Medium High Low Medium High<br />
Labor (h)<br />
Family<br />
Hired<br />
Animal (h)<br />
Family<br />
Hired<br />
Fertilizer (kg)<br />
N<br />
p205<br />
Yield (kg)<br />
Gross return (taka)<br />
Total cost (taka)<br />
Full cost<br />
Cash cost<br />
Prod cost (takalkg)<br />
Full cost<br />
Cash cost<br />
Net return (taka)<br />
Full cost<br />
Cash cost<br />
Returns to (taka)<br />
Family labor<br />
Hired labor<br />
Animal draft labor<br />
Material inputs
Table 12. Extent of adoption and differences in economic factors in two rainfed sites, Bangladesh (1989-90).<br />
Economic<br />
factor<br />
Aus 1989<br />
Gross return (takalha)<br />
Total cost (taka/ha)<br />
Net return (takalha)<br />
Aman 1989<br />
Gross return (takalha)<br />
Total cost (takalha)<br />
Net return (takalha)<br />
Aus + aman 1989<br />
Gross return (takalha per yr)<br />
Total cost (takalha per yr)<br />
Net return (takalha per yr)<br />
Aus 1990<br />
Gross return (takalha)<br />
Total cost (takalha)<br />
Net return (cakaiha)<br />
Aman 1990<br />
Gross return (takalha)<br />
Total cost (takalha)<br />
Net return (takatha)<br />
Aus + aillan 1990<br />
Annual gross return (takalha)<br />
Annual total cost (takalha)<br />
Annual net return (takalha)<br />
Kamalganj Sitakund<br />
Low and Medium Low and Low and Medium Low and<br />
medium and high high medium and high high<br />
"** si~nificant at < I '% level. ** sigiificant at 5% level. * siznificant at 10% level. ns = not significant.
Table 13. Summary of whole-farm cash flow according to extent of adoption in two rainfed<br />
sites, Bangladesh (1989-90).<br />
Income1<br />
expenses<br />
Total income (taka) 45,056<br />
Farm income 26,207<br />
Nonfarm income 18,849<br />
Total expenses (taka) 44,609<br />
Farm expenses 4,627<br />
Household expenses 39,982<br />
Cash balance (taka) 447<br />
Total income (taka) 42,668<br />
Farm income 21,666<br />
Nonfarm indome 21,002<br />
Total expenses (taka) 41,951<br />
Farm expenses 7,221<br />
Household expenses 34,730<br />
Cash balance (taka) 7 17<br />
Cash balance (1989 + 1990) 1,164<br />
Kamalganj Sitakund<br />
Low Medium High Low Medium High
Table 14. Summary of per hectare cash flow according to extent of adoption in two<br />
rainfed sites, Bangladesh (1989-90).<br />
Income1<br />
expenses<br />
Total income (taka) 40,959<br />
Farm income 23,824<br />
(58 %)<br />
Nonfarm income 17,135<br />
(42 %)<br />
Total expenses (taka) 40,553<br />
Farm expenses 4,206<br />
(10%)<br />
Household expenses 36,347<br />
(90%)<br />
Cash balance (taka) 406<br />
Total income (taka)<br />
Farm income<br />
Nonfarm income<br />
Total expenses (taka)<br />
Farm expenses<br />
Household expenses<br />
Cash balance (taka)<br />
Cash balance (1 989 + 1990)<br />
Kamalganj Sitakund<br />
Low Medium High Low Medium High
1. Target area delineation of rainfed lowlands for two rice cropping <strong>systems</strong> (aus-t.<br />
aman), Bangladesh.
IRlPACT OF FARMING SYSTEMS RESEARCH ON SELECTED FARMERS IN<br />
MIDHILLS OF NEPAL: A CASE STUDY OF PURlDI BHUMDI<br />
S. B. h.la<strong>the</strong>mal, K. D. ~oshi~, and J. KC'<br />
The Phumdi Bhumdi farming <strong>systems</strong> <strong>research</strong> site has been in<br />
operation for a number of years and has developed significant<br />
technologies for <strong>the</strong> farmers in <strong>the</strong> area. Extensive use of <strong>the</strong><br />
technology has been documented during <strong>the</strong> last 15 yr. The study to<br />
measure <strong>the</strong> impact of <strong>the</strong> farming <strong>systems</strong> program on <strong>the</strong><br />
productivity of different farm enterprises and farmers' income at <strong>the</strong><br />
site revealed that intervening farmers adopted recommendations both<br />
in <strong>the</strong> upland as well as <strong>the</strong> lowland areas and overall <strong>the</strong>re has been a<br />
positive impact, particularly in relation to nutrition aspects. The<br />
educational profile of <strong>the</strong> children at <strong>the</strong> site is also encouraging.<br />
To implement a <strong>systems</strong> approach, <strong>the</strong> Department of Agriculture (DOA) initiated<br />
a cropping <strong>systems</strong> program (CSP) in 1977 with support from <strong>the</strong> Integrated Cereals<br />
Project (ICP) funded by <strong>the</strong> United States Agency for <strong>International</strong> Development<br />
(USAID). The objective of <strong>the</strong> CSP was to evaluate station-generated technologies<br />
in terms of agronomic and socioeconomic performance and to develop<br />
modifications under specific production <strong>systems</strong> used by fdarmers in <strong>the</strong> hills and<br />
terai.<br />
To integrate different components of Nepalese farming <strong>systems</strong> into on-farm<br />
<strong>research</strong>, <strong>the</strong> Farming Systems Research and Development Division (FSRDD) was<br />
established in 1985 under <strong>the</strong> National Agricultural Research and Services Centre<br />
(NARSC). To give emphasis to hill agriculture, hill CSP sites (including Pumdi<br />
Bhumdi) were retained by <strong>the</strong> FSRDD and farming <strong>systems</strong> <strong>research</strong> (FSR) was<br />
carried out at <strong>the</strong>se sites. In 1990, <strong>the</strong> FSRDD was renamed <strong>the</strong> Central Farming<br />
Systems and Outreach Research Division (CFSORD) and given <strong>the</strong> mandate to<br />
coordinate station-managed outreach <strong>research</strong>.<br />
Little attention had previously been given to measuring <strong>the</strong> in~pact of<br />
<strong>research</strong> programs (including FSR) on agricultural development. This study<br />
attempted to determine <strong>the</strong> impact of FSR on selected hill farmers. The <strong>research</strong><br />
was conducted at <strong>the</strong> Pumdi Bhumdi FSR site by <strong>the</strong> Central Socio-Economic<br />
Research Division (CSERD) in collaboratiol~ with <strong>the</strong> CFSORD.<br />
'central Socio-Economic Research Division, Nepal.<br />
2~entral Farming Systems and Outreach Research Division, Nepal.
OBJECTIVES<br />
The objective of this study was to measure <strong>the</strong> impact of <strong>the</strong> farming <strong>systems</strong><br />
program (FSP) on <strong>the</strong> productivity of different farm enterprises and on farm<br />
incomes in Pumdi Bhumdi. The specific objectives were to describe <strong>the</strong> ethnohistory<br />
and <strong>the</strong> existing situation at <strong>the</strong> Pumdi Bhumdi FSR site; evaluate <strong>the</strong> adoption of<br />
<strong>the</strong> recommended FSR technologies by farmers; assess <strong>the</strong> impact of <strong>the</strong> FSR<br />
recommended practices on crop yields, forage and fodder production, and milk<br />
production; evaluate <strong>the</strong> changes in nutritional and educational aspects of <strong>the</strong><br />
monitored farm families; study <strong>the</strong> role of agricultural support services in <strong>the</strong><br />
transfer of FSR recommended technology to farmers; determine <strong>the</strong> links between<br />
<strong>the</strong> agricultural support services, farmers, and <strong>the</strong> FSR site coordinator; and identify<br />
constraints to adoption of recommended FSR technologies.<br />
JUSTIFICATION<br />
Many <strong>research</strong>ers, administrators, and policymakers argue that in spite of heavy<br />
investment in agriculture, <strong>the</strong>re has been little impact on farmers. A few <strong>research</strong>ers<br />
have attempted to measure <strong>the</strong> impact of <strong>the</strong> FSR program, but <strong>the</strong>y have<br />
concentrated only in <strong>the</strong> Terai region.<br />
Technology transfer from one site to ano<strong>the</strong>r in <strong>the</strong> hills is a challenge<br />
because of <strong>the</strong> enormous variability in agroecological and socioeconomic conditions<br />
of <strong>the</strong> farmers. Because of <strong>the</strong> heterogeneity of <strong>the</strong> farming <strong>systems</strong> in <strong>the</strong> hills,<br />
technologfi has had an impact only in some areas. However, assessment of <strong>the</strong><br />
impact of technology on hill farmers has been virtually neglected. This study<br />
attempted to determine <strong>the</strong> degree of adoption of recommended FSR technologies<br />
by <strong>the</strong> hill farmers of Pumdi Bhumdi.<br />
METHODOLOGY<br />
To achieve <strong>the</strong>se objectives, <strong>the</strong> study conducted a before and after survey and<br />
monitored six intervened and six control farmers. The criteria for selection of <strong>the</strong>se<br />
farms included representativeness within <strong>the</strong> farm-size class, but with <strong>the</strong> provision<br />
that milking buffalo shoulcl be present on <strong>the</strong> farm.<br />
Site selection<br />
Extensive farm monitoring on a whole-farm basis was carried out In 1984 at <strong>the</strong><br />
Pumdi Bhumdi FSR site. This site was proposed for <strong>the</strong> impact study for several<br />
reasons. First, when <strong>the</strong> CSP began in 1977, cropping <strong>systems</strong> <strong>research</strong> (CSR) was<br />
initiated at this site. The CSP was followed by <strong>the</strong> FSR approach in 1985. Therefore,<br />
on-farm <strong>research</strong> had been conducted continuously at this site for <strong>the</strong> last 13 yr.<br />
Second, Pumdi Bhumdi was representative of <strong>the</strong> western midhills of Nepal. Third,<br />
secondary data were available for <strong>the</strong> site from <strong>the</strong> previous work of <strong>the</strong> CSP and
new information collected during FSR work. Fourth, Pumdi Bhumdi was accessible<br />
by road at all times of <strong>the</strong> year.<br />
Farmer selection<br />
Before monitoring, 18 representative farmers were selected principally on <strong>the</strong> basis<br />
of <strong>the</strong>ir landholding (from 0.5 ha to 2.5 ha). The smallest farms (< 0.5 ha) and <strong>the</strong><br />
largest (>2.5 ha) were excluded to ensure that <strong>the</strong> farms best represented<br />
conditions at Pumdi Bhumdi. Of <strong>the</strong> 18 farmers, 12 farmers were selected to<br />
optimize resource use and focus on <strong>the</strong> crop-livestock activities on <strong>the</strong> farm. These<br />
farmers were divided into two groups. The FSR activities were concentrated in six<br />
farms and included detailed monitoring of existing farming activities. The remaining<br />
six farms were only monitored for <strong>the</strong>ir existing farming activities and included no<br />
intervention. The group of farms with FSR activities was <strong>the</strong> intervened group and<br />
<strong>the</strong> group with no farming intervention was <strong>the</strong> control group.<br />
The main criteria used to select representative farmers in each group were<br />
representativeness of <strong>the</strong> farm-size class; representative percentage of lowlands and<br />
uplands to be able to select farms with mixed land types (at least 20% of each land<br />
type); presence of kllarbur-i (pasture land) on which to plant fodder trees;<br />
representative number of milking animals (female buffaloes or cows);<br />
representative number of family members involved in full-time agriculture; and<br />
representative number of those willing to cooperate with <strong>the</strong> FSR program.<br />
The differences between <strong>the</strong> intervened and control farmers were as follows:<br />
B The intervened farmers participated and collaborated in <strong>the</strong> on-farm trials of<br />
<strong>the</strong> FSR program at <strong>the</strong> site; whereas, <strong>the</strong> control farmers nei<strong>the</strong>r<br />
participated nor collaborated in <strong>the</strong> on-farm trials;<br />
B ?'he intervened farmers were provided with free inputs (e.g., improved seeds,<br />
chemical fertilizers, insecticides, and pesticides) for <strong>the</strong> trials conducted by<br />
<strong>the</strong> FSR program. The performance of <strong>the</strong>se trials was closely monitored and<br />
supervised by <strong>the</strong> FSR staff. These trials were mainly managed by <strong>the</strong><br />
<strong>research</strong>ers with <strong>the</strong> assistance of <strong>the</strong> intervened farmers. The control<br />
farmers did not have access to free inputs from <strong>the</strong> FSR program and <strong>the</strong>ir<br />
existing farming activities were monitored without any intervention.<br />
B The intervened farmers were provided with technical services, training, and<br />
advice on improved farming practices. The control farmers did not have<br />
access to <strong>the</strong>se inputs. They learned about improved methods of cultivation<br />
and o<strong>the</strong>r FSR reco~nmended components (i.e., seeds and technical services)<br />
from <strong>the</strong>ir neighbors.
B<br />
The intervened farmers were approached frequently by <strong>the</strong> FSR staff;<br />
whereas, <strong>the</strong> control farmers were approached occasionally by <strong>the</strong> FSR staff<br />
merely to monitor <strong>the</strong>ir existing farm~ng activities.<br />
Data collection<br />
Although some data and information were taken from previous studies of <strong>the</strong> CSP,<br />
most of <strong>the</strong> data were collected from farm surveys and monitoring activities. Six<br />
intervened farmers and six control farmers were interviewed in detail to determine<br />
<strong>the</strong> degree of adoption of FSR recommended technologies. Site descriptions were<br />
based on secondary data collected from various sources.<br />
Analytical techniques<br />
A number of variables were used to measure <strong>the</strong> impact of FSR at Pumdi Bhumdi.<br />
To compare <strong>the</strong> before and after situations, cropping patterns, crop production,<br />
livestock enterprises, fodder and forage production, vegetable-based cropping<br />
patterns, and nutritional aspects were considered. However, no specific analytical<br />
technique was used because of <strong>the</strong> limited sample size. The collected data were<br />
compiled, and averages and percentages were computed.<br />
Site description<br />
lsir~lntion. Pumdi Bhumdi is located in <strong>the</strong> western midhills about 208 km<br />
Exist west it'b o Kathmandu (Fig. 1). It lies on <strong>the</strong> side of a hill whose elevation varies from<br />
750 to 1270 m. Average annual rainfall at <strong>the</strong> site is 4,000 mm. Crops are damaged<br />
by hail during both spring and fall. The total area of Pumdi Bhumdi is<br />
approximately 2,500 ha, and <strong>the</strong> cultivated area is about 1,012 ha. The total<br />
population in 1990 was 5,610. There are approximately 1,047 families and an<br />
average household has 5-6 family members who subsist on about 0.87 ha of<br />
cultivated land. A family has an average of three members available for farm labor.<br />
The existing farming <strong>systems</strong> and <strong>the</strong> interactions between different farm<br />
enterprises are shown in Figure 2. This figure shows that crops and livestock are an<br />
integral part of farming and that off-farm factors play an important role in<br />
supporting farming activities. The major cropping patterns during 1979 are also<br />
shown. In <strong>the</strong> lowland area, rice-based cropping patterns were <strong>the</strong> most common;<br />
whereas, in <strong>the</strong> upland area, maize-based cropping patterns were predominant.<br />
The survey resu!ts showed that <strong>the</strong> fertile upland areas have this<br />
predominant cropping pattern: maize intercropped with soybean followed by a relay<br />
crop of finger millet and a winter crop of wheat, mustard, or barley. The survey also<br />
showed that 51% of <strong>the</strong> upland area was devoted to triple cropping, 43% to double<br />
cropping, and only 2% to single cropping. In contrast, 55% of <strong>the</strong> lowland areas are<br />
devoted to a single crop of rice and 24% to maize - rice - wheat (DOA 1980). An
average family owns seven heads of livestock. The average number of livestock per<br />
family was 1.7 buffaloes, 0.6 bullocks, 0.1 cows, 0.8 calves, 1.6 sheep and goats, 2.4<br />
poultry, and 0.1 pigs (DOA 1980). A significant characteristic of Pumdi Bhumdi is<br />
<strong>the</strong> high percentage of farmers who do not own bullocks, which explains <strong>the</strong> power<br />
constraint faced by farmers during land preparation.<br />
Etllnollistoty of rlle sire. The settlement site was originally a dense forest. The<br />
site was inhabited by different ethnic groups. The Brahmins were numerically <strong>the</strong><br />
dominant caste. O<strong>the</strong>r castes who resided in <strong>the</strong> area were <strong>the</strong> Gurungs, Damaies,<br />
and Kamis. These castes migrated to this area many years ago. Agriculture was <strong>the</strong><br />
main occupation and livelihood of <strong>the</strong> people. The primary objectives of farming<br />
were to provide adequate food for subsistence and to save some seed for <strong>the</strong> next<br />
season.<br />
Farmers at <strong>the</strong> site faced various problems. It was reported that <strong>the</strong>re were<br />
hailstorms in 1948, 1949, 1979, and 1991. The hailstones were as big as pears. As a<br />
result, famine and diseases spread during <strong>the</strong>se years. Farmers were compelled to<br />
borrow money at high rates of interest.<br />
The migration rate seemed to be higher than <strong>the</strong> immigration rate. Few<br />
people, especially those of lower castes from <strong>the</strong> districts of Syangja and Baglung,<br />
immigrated to <strong>the</strong> village in search of employment. Because of various internal<br />
reasons (e.g., land problems and <strong>the</strong> exhaustion of natural resources), most of <strong>the</strong><br />
villagers migrated to <strong>the</strong> terai (mainly in <strong>the</strong> districts of Chitwan, Ranke, and<br />
Nawalparasi). The external factors that influenced migration from <strong>the</strong> villages were<br />
better employment opportunities available in terai, cheaper land, and better<br />
facilities for education, communication, transportation, and o<strong>the</strong>r infrastructure.<br />
Before <strong>the</strong> FSR program was initiated, <strong>the</strong> CSR program was launched in<br />
1977-78 to improve <strong>the</strong> socioeconomic conditions of <strong>the</strong> farmers. The main role of<br />
<strong>the</strong> CSP was to increase <strong>the</strong> yield of various crops by introducing improved varieties<br />
and new technologies at <strong>the</strong> local level. The CSR introduced some improved crop<br />
varieties (e.g., <strong>the</strong> Taichung-176, CH-4.5, and Khumal-3 varieties of rice; <strong>the</strong> Arun-2<br />
and Khumal Yellow varieties of maize; <strong>the</strong> RR-21 and UP 262 varieties of wheat;<br />
and <strong>the</strong> Kufri Jyoti varieties of potato). The cultural practices introduced were<br />
mainly improved methods of making ridges, efficient ways of weeding, line planting,<br />
application of chemical fertilizer, and mulching.<br />
During <strong>the</strong> CSP period, triple-cropping of rice - wheat - maize (R-W-M) was<br />
introduced in lowland areas where farmers'practiced rice - fallow - fallow (R-F-F).<br />
Most farmers in both groups reported that <strong>the</strong>y started to use chemical fertilizers<br />
only after CSP was introduced. After CSP was terminated, FSK began in 1985. In<br />
addition to crop varieties, <strong>the</strong> FSR program introduced new technologies for o<strong>the</strong>r<br />
components of <strong>the</strong> farming <strong>systems</strong> (e.g., vegetables, forage, and fodder).<br />
The introduced varieties included Khumal-3 (1985), Khumal-4 (1985),<br />
Khumal-5 (1986), Khumal-7 (1987), and Khumal-9 (1987) varieties of rice; Arun-4<br />
(1985), Manakamana- l and 2 (1987) varieties of maize; HS-94 (1985) and
Annapurna-l (1986) varieties of wheat; and Cardinal, a potato variety. In addition,<br />
<strong>the</strong> kitchen gardening program was initiated and farmers were convinced to<br />
establish year-round kitchen gardens near <strong>the</strong>ir houses. O<strong>the</strong>r activities introduced<br />
by FSP were improved methods of composting, application of pesticides, storage of<br />
seeds in improved metal bins, and storage of potato in locally made bamboo<br />
baskets.<br />
Description of fanners. The personal characteristics of <strong>the</strong> intervened and<br />
control farmers are given in Tables 1 and 2. The age of intervened farmers ranged<br />
from 35 to 63 yr. Two of <strong>the</strong> farmers had attended school for 6 and 10 yr. Average<br />
family size was 8, and <strong>the</strong> number of adults was between 1 and 5 per household. Of<br />
<strong>the</strong> 6 farmers, 2 were owners-cum-tenants and 4 were owners.<br />
The ages of <strong>the</strong> control farmers were between 36 and 50 yr, and one had 8 yr<br />
of schooling. Family size ranged from 5 to 8 with an average of 7 members. The<br />
number of adults was between 1 and 3 per household. Of <strong>the</strong> 6 farmers, 2 were<br />
owners-cum-tenants and 4 were owners.<br />
Frtrtlz clzaructeri.~tics. The farm characteristics of intervened and control farms<br />
are presented in 'Tables 3 and 4. The average size of land owned by both groups of<br />
farmers was nearly <strong>the</strong> same (0.96 ha intervened; 1.05 ha control). However, <strong>the</strong><br />
average area of cultivated land owned by <strong>the</strong> control farmers was slightly higher<br />
(0.90 ha compared with 0.77 ha). The average size of lowland area owned and<br />
cultivated hy both groups of farmers was higher than that of <strong>the</strong> upland area. A<br />
limited area of pasture land was owned by both groups. The average number of<br />
parcels per farm for both groups was similar (9.67 and 9.17). The average parcel size<br />
of intervened farmers was higher than that for control farmers (0.39 ha compared<br />
with 0.12 ha).<br />
FSR activities itztt-odlrced in <strong>the</strong> area. The following recommendations made<br />
by <strong>the</strong> FSR program at Pulr~di Bhumdi were based mainly on <strong>the</strong> results of activities<br />
carried out in <strong>the</strong> fields of intervened farmers from 1985 to 1990.<br />
¤ Crops. On <strong>the</strong> basis of problems identified by <strong>research</strong>ers, farmers, and<br />
extension workers at <strong>the</strong> site, component-based trials were conducted. These<br />
included varietal trials, fertilizer trials, and o<strong>the</strong>r management studies of<br />
various crops in farm fields. Components that gave promising results were<br />
recommended for use by farmers. These promising components were<br />
monitored on <strong>the</strong> basis of biological and socioeconomic factors (e.g.,<br />
improvements in variety, fertilizer rate, and cultural practices).<br />
H Livestock. Oats, Napier forage grasses, and ipil-ipil fodder trees were<br />
introduced and studied for <strong>the</strong>ir adaptability in <strong>the</strong> area. Similarly, <strong>the</strong><br />
performance of some o<strong>the</strong>r indigenous fodder tree species was studied.
H Horticulture. To have a more regular supply of vegetables throughout <strong>the</strong><br />
year, a study of vegetable production in a kitchen garden was conducted at<br />
<strong>the</strong> FSR site.<br />
m Training program. To familiarize farmers with FSR technologies and<br />
<strong>research</strong> methodologies, regular training was conducted at <strong>the</strong> FSR site.<br />
Cropping patterns<br />
IMPACT OF FARMING SYSTEMS RESEARCH<br />
In 1984-85, <strong>the</strong> predominant cropping patterns in <strong>the</strong> lowlands were rice - fallow -<br />
fallow (61 and 50% of <strong>the</strong> aggregated total area of <strong>the</strong> intervened and control farms,<br />
respectively), rice - wheat - maize (13 and 21%), rice - fallow - maize (10 and 1470),<br />
and o<strong>the</strong>r patterns (less than 1070) (Hawkins et al 1987). In 1988-89, after <strong>the</strong><br />
implementation of <strong>the</strong> FSR program at Pumdi Bhumdi, <strong>the</strong> cropping patterns used<br />
were different.<br />
In <strong>the</strong> lowland area, <strong>the</strong> predominant cropping patterns of <strong>the</strong> intervened<br />
farmers were rice - wheat - maize, rice - fallow - fallow, and rice - barley - fallow. On<br />
<strong>the</strong> control farms, <strong>the</strong> predominant cropping patterns were rice - wheat - maize, rice<br />
- fallow - fallow, and rice - mustard - maize (Table 5). In <strong>the</strong> upland areas, <strong>the</strong><br />
predominant cropping patterns were maize/finger millet - wheat (71 and 73%),<br />
maize/finger millet - mustard (20 and S%), and maize/finger millet - potato (5 and<br />
16%) in 1984-85. These patterns were chan ed into maize/finger millet - wheat,<br />
maize + vegetables - vegetables and maize7finger millet - mustard for both <strong>the</strong><br />
intervened and control farms.<br />
FSR has played a positive role in changing <strong>the</strong> cropping patterns from rice -<br />
fallow - fallow to rice - wheat - maize in <strong>the</strong> lowland areas and from maize/finger<br />
millet - potato to maize + vegetables - vegetables in <strong>the</strong> upland areas. Changes in<br />
<strong>the</strong> cropping patterns for <strong>the</strong> intervened and control farmers were identical both in<br />
<strong>the</strong> upland areas and <strong>the</strong> lowland areas. This is because <strong>the</strong> changes used by <strong>the</strong><br />
intervened farmers were followed later by <strong>the</strong> control farmers.<br />
Production<br />
Table 6 presents <strong>the</strong> farm practices and annual production of <strong>the</strong> different crops in<br />
<strong>the</strong> lowland and upland areas in 1984, and <strong>the</strong> productivity of <strong>the</strong> FSR-recom-<br />
mended technologies in 1985-90. Table 7 presents <strong>the</strong> crop yields obtained by <strong>the</strong><br />
farmers in 1988-89. A comparison of <strong>the</strong> average crop yields of intervened and<br />
control farmers shows a substantial increase in yield for rice and maize as a result of<br />
<strong>the</strong> recommended practices. The increase in rice yield of intervened farmers was<br />
43% more, and that of control farmers was 59% more. The yield was only 30%<br />
more than <strong>the</strong> rice yield in 1984. In <strong>the</strong> case of maize, <strong>the</strong> increase in yield was 55%
more for intervened farmers and 4.6% more for control farmers. The yield was 54%<br />
more than <strong>the</strong> yield of maize in <strong>the</strong> lowland areas in 1984. The yield potential of<br />
improved maize varieties under well-managed conditions exceeds 5 t/ha . Given this<br />
potential, <strong>the</strong> average yield of improved maize varieties obtained by <strong>the</strong> control<br />
farmers (4.8 t/ha) was acceptable. In <strong>the</strong> case of wheat, <strong>the</strong>re was no difference. In<br />
mustard, <strong>the</strong> difference in productivity was very small. A comparison between <strong>the</strong><br />
two groups was not possible for <strong>the</strong> o<strong>the</strong>r crops because each group was growing<br />
ei<strong>the</strong>r <strong>the</strong> recommended varieties or local varieties.<br />
In <strong>the</strong> upland area, improved varieties performed better than local varieties.<br />
The average yield of <strong>the</strong> local variety of maize was higher than that of <strong>the</strong><br />
recommended variety. The yield difference between <strong>the</strong> two varieties of mustard<br />
was not substantial.<br />
The differences in productivity were not reflected in <strong>the</strong> overall productivity<br />
of intervened farms compared with control farms. The reasons for this lack of<br />
difference appeared to be <strong>the</strong> partial adoption of improved practices by control<br />
farmers and <strong>the</strong> natural variation between <strong>the</strong> farms in each category.<br />
Adoption of crop varieties<br />
Both intervened and control farmers adopted improved crop varieties (Table 8).<br />
Intervened farmers adopted three different improved rice varieties (Khumal-3,<br />
Khumal-4, and Khumal-2); whereas, control farmers adopted two improved<br />
varieties of rice (Khumal-3 and Khumal-2). Intervened farmers cultivated Khumal-2<br />
on a 0.38 ha land, Khumal-3 on a 0.13 ha, and Khumal4 on 0.20 ha. The control<br />
farmers planted Khumal-2 on 0170 ha and Khumal-3 on 0.40 ha.<br />
The dominant improved maize varieties <strong>the</strong> farmers used were Khumal<br />
Panhelo, Manakamana-1, and Arun-2. Intervened farmers planted Khumal Panhelo<br />
on a 0.25 ha land, Manakamana-1 on 0.30 ha, and Arun-2 on 0.10 11a. The control<br />
farmers planted Khumal Panhelo on 0.23 ha, Manakamana-l on 0.13 ha, Arun-2 on<br />
0.03 ha, and Ganesh-2 on 0.03 ha. In <strong>the</strong> case of wheat, both intervened and control<br />
farmers adopted only RR 21, which was cultivated on 0.7 ha and 0.4 ha, respectively.<br />
Two varieties of improved mustard, Thulo Tori and Chitwan local, were each<br />
planted on 0.13 ha by intervened farmers; whereas, only Chitwan local was planted<br />
by <strong>the</strong> control farmers on 0.03 ha. The intervened farmers obtained seeds of<br />
different crop varieties from <strong>the</strong> Pumdi Bhumdi FSR site while <strong>the</strong> control farmers<br />
obtained improved seed varieties from <strong>the</strong>ir neighbors (intervened farmers), friends,<br />
and relatives.<br />
Despite <strong>the</strong> better performance of improved varieties under well-managed<br />
conditions, <strong>the</strong> farmers in Pumdi Bhumdi did not extend <strong>the</strong>ir areas under improved<br />
varieties for two main reasons: <strong>the</strong> higher risk of crop loss because of hailstorms<br />
(improved varieties require more inputs, <strong>the</strong>refore losses would be higher); and
improved varieties require higher levels of inputs, \vIiich all farmers could not<br />
afford. Both inputs and credit were not accessible to local farmers.<br />
Livestock enterprises and milk production<br />
The number of livestock owned by <strong>the</strong> monitored farniers is shown in Table 9. Both<br />
intervened and control farms owned a higher number of female buffalo than o<strong>the</strong>r<br />
animal species. These buffalo were often sold, exchanged, or lent when <strong>the</strong>y were<br />
pregnant or had young calves. Between 1984-85 and 1988-89, <strong>the</strong> number of buffalo<br />
calves, sheep and goats, and bullocks increased. The average number of female<br />
buffrllo, male buffalo, and co\vs were about <strong>the</strong> same, and all farmers raised local<br />
breeds.<br />
Milk production in Pumdi Bhumdi farms is an important source of income.<br />
Between 1984-85 and 1988-89, <strong>the</strong> average amount of milk sold per year of<br />
intervened farmers increased from 781 to 927 liters, while control farmers from 809<br />
to 845 liters (Table 10). Because of this increase in milk sales, <strong>the</strong> average income of<br />
intervened farmers increased by 74% and control farmers by 28%.<br />
Fodder and forage production and livestock feeding<br />
To reduce <strong>the</strong> deficit of animal feeds during <strong>the</strong> dry season, FSP introduced several<br />
types of forage and fodder (e.g., oats, Napier grass, and ipil-ipil trees) at Pumdi<br />
Bhumdi. Oats were tested in <strong>the</strong> cropping pattern in <strong>the</strong> lowland area, and Napier<br />
grass and ipil-ipil fodder trees were planted on terrace edges and pasture lands.<br />
Table l1 sho~~s that oats were not planted in <strong>the</strong> area before 1985. In 1988-<br />
89, intervened farmers planted oats on a 0.03 ha land while control farmers to a<br />
0.11 ha. FSR staff conducted <strong>the</strong> study on oat cultivation only in <strong>the</strong> fields of<br />
intervened farniers. However, <strong>the</strong> control farmers observed <strong>the</strong> better performance<br />
of oats and approached <strong>the</strong> Livestock Farm in Lamepatan, Pokhara, and intervened<br />
farmers for oat seeds. Oats have been reported to yield 7-8 t green fodder/ha and<br />
0.8-1.0 t seeds/ha, and feecling trials have indic:ited th:it milk production can be<br />
increased by 0.35-0.5 liter/d if 6-8 kg fresh oat grass/d is added to <strong>the</strong> diet (Si~lgh<br />
and Gautam 1980).<br />
Farmers in Pumdi Bhumdi grow many varieties of fod(lsr. A numher uf<br />
fodder saplings were distributed after <strong>the</strong> implementation of FSP. Table 13,<br />
compares <strong>the</strong> number of trees and saplings before FSP and n~~ml~cr of trees and<br />
saplings after FSP. There was an increase in <strong>the</strong> n~!rnber of fuddcr trees after <strong>the</strong><br />
implementation of FSP.
Ipil-ipil seedlings were distributed to both groups of farmers at <strong>the</strong> Pumdi<br />
~humdi FSR site by <strong>the</strong> Livestock Farm in Lamepatan, Pokhara with <strong>the</strong> help of<br />
FSR staff on-site. These seedlings were distributed to both groups not for trials but<br />
as a service to <strong>the</strong> farmers. Technologies for ipil-ipil cultivation were provided by<br />
<strong>the</strong> FSR staff to <strong>the</strong> intervened fariners only and performance was closely<br />
monitored.<br />
Plantations of ipil-ipil seedling gave mixed results in Pumdi Bhumdi. Ipil-ipil<br />
planted in pasture land showed minimal growth, although its performance on<br />
terrace edges was encouraging. Leucaena Ieucocephala could not be grown<br />
successfully in <strong>the</strong> area. Leucaena diversifolia was tested in 1987 and found to give<br />
good results. The general problems faced by farmers who grew ipil-ipil and Napier<br />
grass along <strong>the</strong> terrace edges were shading of crops and reduced soil fertility (Table<br />
13).<br />
Vegetable-based cropping pattern<br />
Before FSP, fariners usually grew vegetables in small areas during <strong>the</strong> winter season<br />
and a few cucurbits in <strong>the</strong> inaize crops. After FSP was introduced, kitchen gardening<br />
became well established and <strong>the</strong> farlners had a regular supply of vegetables<br />
throughout <strong>the</strong> year. The most cominon vegetables were sponge gourd, beans,<br />
pumpkin, bottle gourd, chilly, cauliflower, mustard, radish, cowpeas, snake gourd,<br />
potato, carrot, brinjal , cucumber, garlic, and peas. The most common vegetablebased<br />
patterns were maize/vegetables - vegetables and maize + vegetables -<br />
vegetables (Table 14). Since <strong>the</strong> introduction of FSP, <strong>the</strong> area under vegetables had<br />
also increased. The town of Pokhara is close to Pu~ndi Bhurndi; <strong>the</strong>refore, some<br />
far~ners started to sell green vegetables in <strong>the</strong> inarket to generate cash income.<br />
All intervened and control farmers grew vegetables mainly for home consumption.<br />
Because of <strong>the</strong> consumption of vegetables, most far~ners had nutritious diets.<br />
Fur<strong>the</strong>rmore, a few of <strong>the</strong> farmers had increased <strong>the</strong> number of buffalo, which<br />
increased <strong>the</strong> quantity of milk and ghee. Those farmers who had more milk and<br />
ghee, consumed milk and milk products, which fur<strong>the</strong>r improved <strong>the</strong>ir nutritional<br />
status.<br />
Education and training<br />
'I'he educational picture of Pumdi Bh~~mdi is encouraging. Although farmers had no<br />
schooling, many of <strong>the</strong>in give priority to education. Most children of <strong>the</strong>se farmers<br />
are finishing secondary school and many are attending college. However, most<br />
far~ners with higher education migrate to <strong>the</strong> towns and cities for o<strong>the</strong>r employment<br />
(Foster 1990).
The types of <strong>the</strong> training provided to <strong>the</strong> farmers and <strong>the</strong>ir reactions to <strong>the</strong><br />
training are shown in Table 15. The intervened group of farmers attended more<br />
training programs conducted by <strong>the</strong> FSP than <strong>the</strong> control farmers.<br />
Effectiveness of o<strong>the</strong>r FSR activities<br />
Most farmers in both groups said that <strong>the</strong>y had replaced <strong>the</strong> traditional technology<br />
with <strong>the</strong> improved technology introduced by <strong>the</strong> FSP. According to <strong>the</strong> farmers,<br />
knowledge about <strong>the</strong> adoption of chemical fertilizers, improved seeds, im roved<br />
fodder trees, and forage grasses took place only after <strong>the</strong> establishment o F <strong>the</strong> FSR<br />
in <strong>the</strong>ir site. Participation of both groups in various FSR activities (e.g., pro ram<br />
planning and decision making, selection of participant farmers, and training ", were<br />
reported to be highly satisfactory. Farmers In both groups felt that <strong>the</strong>ir suggestions<br />
and opinions in'solving <strong>the</strong> problems encountered during implementation of work<br />
plans and selection of <strong>the</strong> recommended technologies were well considered by <strong>the</strong><br />
FSP.<br />
However, both groups of farmers were not satisfied with <strong>the</strong> supply of inputs.<br />
Three of <strong>the</strong> six intervened farmers and two of <strong>the</strong> six control farmers reported that<br />
supplies of inputs by <strong>the</strong> concerned agency were insufficient and untimely.<br />
ROLE OF OTHER SUPPORT-SERVICE INSTITUTIONS IN THE TRANSFER<br />
OF FSR-IDENTIFIED TECHNOLOGY<br />
Extension service in <strong>the</strong> area<br />
The FSR and <strong>the</strong> Agriculture Development Office (ADO) have separate mandates<br />
and targets. From 1985 to 1990 <strong>the</strong>re was no extension agent assigned by <strong>the</strong> ADO<br />
in <strong>the</strong> Pumdi Bhumdi area. The linkage between <strong>research</strong> and extension was good<br />
but limited to training programs, participation in Samuhik Bhramnn<br />
(multidisciplinary team visits), and working group meetings.<br />
Credit supply<br />
Cash was a limiting factor for inputs (e.g., seed, fertilizers, and pesticides) required<br />
to adopt improved technology. However, credit to farmers was limited, and <strong>the</strong><br />
process was so complicated that farmers could not take advantage of this facility.<br />
The Agriculture Development Bank (ADB) at Pokhara provided loans for<br />
agricultural activities according to recommendations made by <strong>the</strong> ADO, Pokhara.
Input supply<br />
The Agricultural Input Corporation (AIC) was responsible for supplying inputs to<br />
<strong>the</strong> district headquarters while <strong>the</strong> cooperative societies and AIC dealers were<br />
responsible for <strong>the</strong> villages. However, timely supply of <strong>the</strong> required amount and<br />
types of seeds, fertilizers, and pesticides was always difficult. Pumdi Bhumdi was no<br />
exception. There was one AIC dealer to sell <strong>the</strong> inputs for cash, but irregular and<br />
insufficient supply of inputs was common.<br />
Marketing<br />
There was no established marketing system for agricultural products in <strong>the</strong> area.<br />
However, <strong>the</strong> government-owned Dairy Develop~nent Corporation has a milk<br />
collection center at Puindi Bhumdi. Therefore, farmers can sell <strong>the</strong>ir fresh milk<br />
directly to this center for cash. The farmers have to find <strong>the</strong>ir own markets for o<strong>the</strong>r<br />
commodities.<br />
Links among farmers, <strong>the</strong> site coordinator, and o<strong>the</strong>r related institnt ions<br />
The FSP at Pulndi Bhumdi sought to develop strong links with its line agencies at<br />
<strong>the</strong> district level to provide FSR-based technical support. Table 16 su~nmarizes <strong>the</strong><br />
links between <strong>the</strong> Pumdi Bhumdi FSR site and <strong>the</strong> ADO, <strong>the</strong> Livestock Research<br />
Farm, <strong>the</strong> Horticulture Research Farm, <strong>the</strong> District Forest Office (DFO), ADB,<br />
AIC, <strong>the</strong> Lumle Agriculture Research Center (LARC) , and <strong>the</strong> cooperative.<br />
The ADO provided resource persons to train <strong>the</strong> farmers, participated in<br />
working group meetings and <strong>the</strong> Salnuhik Bhraman (multidisciplinary team visits),<br />
and shared <strong>the</strong> responsibility of disseminating FSR-based technologies to o<strong>the</strong>r<br />
areas of <strong>the</strong> district. The livestock and horticulture <strong>research</strong> farms had strong<br />
coordination with <strong>the</strong> FSR program at Pumdi Bhu~ndi in <strong>the</strong> area of technical<br />
support. They provided test materials for on-farm <strong>research</strong> in livestock, fodder,<br />
forages, and vegetables.<br />
The DFO, ADB, and <strong>the</strong> cooperatives had weak links with <strong>the</strong> Pumdi<br />
Bhulndi FSR site. The involvement of AIC in <strong>the</strong> FSR was limited to <strong>the</strong> supply of<br />
inputs through a dealer in <strong>the</strong> area. Coordination with LARC was n~aintained by <strong>the</strong><br />
exchange of ~nformation on <strong>research</strong> findings, meetings, and visits.<br />
The FSR site office was active in identifying farm problems and in solving<br />
<strong>the</strong>se through close coordination with <strong>the</strong> different institutions in <strong>the</strong> district (Table<br />
17). Among <strong>the</strong> institutions, ADO acted as <strong>the</strong> extension catalyst between farmers<br />
and <strong>the</strong> FSR program. The linkage between <strong>the</strong> ADO and <strong>the</strong> farmers was close<br />
because <strong>the</strong> ADO is responsible for dissemination of FSR-based technologies. AS an
extension agency in <strong>the</strong> district, <strong>the</strong> ADO played a vital role in streng<strong>the</strong>ning <strong>the</strong><br />
FSR program and was aware of <strong>the</strong> importance of on-farm <strong>research</strong>.<br />
The livestock services center at Pumdi Bhumdi was responsible for <strong>the</strong><br />
supply of fodder saplings, forage seeds, and primary health care for <strong>the</strong> animals. In<br />
<strong>the</strong>se areas, <strong>the</strong>re was good understanding among <strong>the</strong> farmers, <strong>the</strong> livestock service<br />
center, and <strong>the</strong> FSR site coordinator.<br />
Finally, <strong>the</strong> FSR site coordinator and <strong>the</strong> farmers developed a close working<br />
relationship and effective links and collaboration. These factors contributed to <strong>the</strong><br />
positive impact of <strong>the</strong> FSR-recommended technologies on farmers in Pumdi<br />
Bhumdi.<br />
Constraints to adoption of FSR-recommended technologies<br />
The major problems in <strong>the</strong> adoption of FSR-recommended technologies were <strong>the</strong><br />
unavailability and untimely supply of inputs and lack of money to purchase <strong>the</strong><br />
inputs (Table 18). O<strong>the</strong>r problems included were: more labor was required to adopt<br />
<strong>the</strong> recommended cropping patterns and cultural practices; <strong>the</strong> late maturity and<br />
low palatability of <strong>the</strong> recommended varieties (especially maize); more seed was<br />
required for <strong>the</strong> improved varieties than for <strong>the</strong> local varieties; and <strong>the</strong>re were more<br />
diseases and insect problems during storage (especially maize and potatoes).<br />
The majority of <strong>the</strong> control farmers reported that <strong>the</strong>y did not have enough<br />
technical knowledge of <strong>the</strong> recommended practices because <strong>the</strong>y had no ready<br />
access to <strong>the</strong> information. To acquire more knowledge on <strong>the</strong> recommended<br />
technology, <strong>the</strong>y had to visit <strong>the</strong> FSR site office. However, because of inadequate<br />
time during <strong>the</strong> peak periods, <strong>the</strong>y did not have <strong>the</strong> time to visit <strong>the</strong> FSR site office.<br />
Some o<strong>the</strong>r constraints identified at <strong>the</strong> site were:<br />
m The main climatic constraints to increased cropping intensity were occasional<br />
drought and frequent hail storms.<br />
H Limited technology was available for most of <strong>the</strong> crops (e.g., hill crops, grain<br />
legumes, fruits, and vegetables) for on-farm testing which limited <strong>the</strong><br />
alternatives available to <strong>the</strong> farmers.<br />
¤ Climatic constraints (hail and drought) have rnade agricult~lre<br />
risky in this<br />
region; <strong>the</strong>refore, farmers were hesitant to make larger investments in crops<br />
(e.g., use chemical fertilizers or pesticides).
¤<br />
Weak <strong>research</strong>-extension linkages and poor support services (availability of<br />
credit and inputs, and marketing facility for agricultural products) have<br />
limited <strong>the</strong> transfer of technology and its adoption by farmers.<br />
Inadequate facilities (e.g., labor and physical facilities) for <strong>the</strong> FSR program<br />
was a constraint in disseminating <strong>the</strong> technology to <strong>the</strong> farmers in <strong>the</strong> area.<br />
CONCLUSION AND RECOMMENDATIONS<br />
The results of this study are encouraging because <strong>the</strong>y indicate that intervened<br />
farmers adopted FSR-recommended cropping patterns both in <strong>the</strong> upland and<br />
lowland areas. Most farmers adopted improved varieties of major crops and <strong>the</strong>ir<br />
cultural practices. Vegetable cultivation became very popular among intervened<br />
farmers, and <strong>the</strong>y devoted land to grow various types of vegetables. Farmers<br />
improved <strong>the</strong>ir nutritional status by consuming vegetables and generated cash<br />
income from vegetable sale. Farmers also adopted FSR-recommended species of<br />
fodder trees and grasses. The production and sale of milk increased substantially<br />
because of <strong>the</strong> increase in <strong>the</strong> number of animals due to <strong>the</strong> availability of suitable<br />
livestock fodder. Some control farmers have also adopted improved varieties of<br />
crops and fodder trees. These farmers have adopted <strong>the</strong> maize + vegetable -<br />
vegetables cropping pattern. These findings imply <strong>the</strong> suitability of <strong>the</strong> FSR<br />
approach in similar environments in <strong>the</strong> country.<br />
Based on <strong>the</strong> results of this study, <strong>the</strong> following recommendations are<br />
proposed to improve <strong>the</strong> economic conditions of hill farmers:<br />
¤ An FSR approach should be stressed in <strong>the</strong> development of all <strong>the</strong> major<br />
components of farming <strong>systems</strong> (e.g., crops, livestock, horticulture, and off-<br />
farm employment). A focus on only one component will cause imbalances.<br />
¤ There is a need to develop intercommodity and interdisciplinary links within<br />
<strong>the</strong> <strong>research</strong> system to integrate different components within FSR.<br />
II Research and extension should have joint responsibility to achieve <strong>the</strong> targets<br />
of FSP to make <strong>the</strong> impact of FSR more effective.<br />
¤ Agricultural inputs (e.g., improved seed, chemical fertilizer, credit, and<br />
extension services) must be made available on time, at <strong>the</strong> proper locations,<br />
and in sufficient quantities to ensure <strong>the</strong> adoption of improved technology.<br />
6 Close linkages among <strong>research</strong>, extension, and o<strong>the</strong>r related institutions<br />
should be established for effective transfer of FSR-recommended technology<br />
to <strong>the</strong> farmers.
m Extension and o<strong>the</strong>r production-support services (e.g., inputs and credit)<br />
should be oriented toward <strong>the</strong> promotion of a farming <strong>systems</strong> approach to<br />
agricultural development.<br />
m Production programs with FSR-based technology should be encouraged and<br />
implemented. They should be started in hilly areas where <strong>the</strong> dissemination<br />
of recommended technology is both feasible and acceptable to farmers.<br />
m An integrated package of practices for planting, harvesting, and marketing is<br />
also needed for major fruits and vegetables identified for cultivation in <strong>the</strong><br />
hills.<br />
Increases in livestock production and productivity depend primarily on <strong>the</strong><br />
enhancement of feed and fodder supply. Large-scale plantrng and proper<br />
maintenance of fodder trees and good pasture management are needed.<br />
Stall-feeding should be encouraged and free-grazing should be controlled.<br />
Farm manure is required in large quantities to increase <strong>the</strong> productivity of<br />
cereal and horticultural crops, but ~ts supply depends on increased<br />
production of fodder in hill areas.<br />
m Some constraints identified at <strong>the</strong> site (e.g., limited technology for hill crops,<br />
lack of infrastructure, a poor input-supply system, and lack of irrigation) must<br />
be overcome if FSR programs are to be fully adopted and <strong>the</strong>ir irnpact<br />
magnified.<br />
REFERENCES ClTED<br />
DOA--Department of Agriculture (1980) Cropping <strong>systems</strong> and livestock surve .<br />
Division of Agronomy, Department of Agriculture, Nepal. (unpublished{.<br />
Foster J H (1990) Evaluation of FSR/Nepal Impact Study. (unpublished).<br />
Hawkins R C et a1 (1987) Farm monitoring in Pumdi Bhumdi: summary and<br />
conclusions of a farming <strong>systems</strong> <strong>research</strong> strategy. Farming Systems<br />
Research and Development Division, Nepal.<br />
Singh B K, Gautam Y P (1986) Oat cultivation and its use as a green fodder for<br />
increasing buffalo milk production. Pages 463-477 ill Proceedings of <strong>the</strong> 2nd<br />
Monitoring Tour of Crop-Livestock Systems Research, Nepal and Indonesia,<br />
<strong>International</strong> Rice Research Institute, Manila, Philippines.
Table 1. Personal characteristics of <strong>the</strong> intervened group of farmers, Pumdi Bhumdi, Kaski District, Nepal (1988-89).<br />
Characteristics Farm A Farm B Farm C Farm D Farm E Farm F<br />
Age of <strong>the</strong><br />
respondent (years)<br />
Education (grade)<br />
Family size (No.)<br />
Male adults (No.)<br />
Female adults (No.)<br />
Children (No.)<br />
Tenure status Ownercumtenant<br />
*<br />
Data not available.<br />
Owner- Owner Owner Owner Owner<br />
cum-<br />
tenant<br />
Table 2. Personal characteristics of <strong>the</strong> controlled group of farmers, Pumdi Bhumdi, Kaski District, Nepal (1988-89).<br />
Characteristics Farm G Farm H Farm I Farm J Farm K Farm L<br />
Age of <strong>the</strong><br />
respondent (years) 50 48 36 46 44 3 8<br />
Education (grade) * * 8 * * *<br />
Male adults (No.) 1 3 2 1 1 1<br />
Female adults (No.) 2 3 3 2 2 1<br />
Children (No.) 4 1 1 4 5 3<br />
Tenure status Owner- Owner- Owner Owner Owner Owner<br />
cum- cum-<br />
tenant tenant<br />
*Data not available.
Table 3. Farm characteristics of <strong>the</strong> intervened group of farmers, Pumdi Bhumdi, Kaski District, Nepal (1988-89)<br />
Characteristics Farm Farm Farm Farm Farm Farm Average<br />
A B C D E F<br />
- - P<br />
Owned area (ha) 0.85 0.48 1.11 0.74 1.23 1.35 0.96<br />
Lowland (ha) 0.57 0.14 0.58 0.34 1.07 0.62 0.55<br />
Upland (ha) 0.23 0.09 0.31 0.19 l. l0 0.23 0.36<br />
Pasture land (ha) 0.05 0.25 0.22 0.2 1 0.06 0.50 0.22<br />
Cultivated land (ha) 1.10 0.23 0.89 0.53 1.17 0.82 0.77<br />
Lowland (ha) 0.91 0.14 0.58 0.34 1.07 0.59 0.61<br />
Upland (ha) 0.19 0.09 0.3 1 0.19 0.10 0.23 0.18<br />
No. of parcels 8.00 5.00 13.00 7.00 10.00 15.00 9.67<br />
Average parcel size (ha) 0.12 0.09 0.08 0.10 0.12 1.81 0.39<br />
Table 4. Farm characteristics of <strong>the</strong> controlled group of farmers, Pumdi Bhumdi, Kaski District, Nepal (1988-89).<br />
- -<br />
Characteristics Farm G Farm H Farm I Farm J Farm K Farm L Average<br />
Owned area (ha)<br />
Lowland (ha)<br />
Upland (ha)<br />
Pasture 1m.d (ha)<br />
Cultivated land (ha)<br />
Lowland (ha)<br />
Upland (ha)<br />
No. of parcels<br />
P
~ .-<br />
*Data not available.<br />
. .- , ..~ -. .. . -<br />
Table 5. Cropping patterns practiced by <strong>the</strong> farmers in Pumdi Bhumdi in 1984-85 (before FSR) and 1989 (after FSR).<br />
Intervened group<br />
(1984-85)<br />
Lowlands<br />
Rice - F - F (61%)<br />
R - wheat - maize (13%)<br />
Rice - F - maize (10 %)<br />
Uplands<br />
MaizeIFM - wheat<br />
MaizeIFM - mustard<br />
MaizeIFM - potato<br />
Controlled group Intervened group<br />
(1984-85) (1989)<br />
Controlled group<br />
(1989)<br />
Rice - F - F (50%) R - wheat - maize Rice - wheat - maize<br />
R - wheat - maize Rice - F - maize<br />
(2 1 %)<br />
Note: F = fallow; FM = finger millet; and Veg. = vegetables.<br />
Rice - F - F<br />
Rice - F - maize Rice - barley - fallow Rice - mustard - maize<br />
(14%)<br />
MIFM - wheat (73 %) MaizeIFM - wheat<br />
(71 %)<br />
MaizeIFM - wheat<br />
MIFM - mustard (8%) Maize + vegetables + veg. Maize + vegetables<br />
(20 %) + veg.<br />
MIFM - potato (16%) MaizeIFM - mustard MaizeIFM - mustard<br />
(5 %)
Table 6. Farm practices and production levels before FSR (1984) and potential of FSR recommended technology at Pumdi Bhumdi (1985-90).<br />
-<br />
Cropping patterns<br />
Lowlands<br />
Rice - wheat - maize<br />
Grain yield (tlha)<br />
Variety<br />
Fertilizer (NP kglha)<br />
Rice - fallow - maize<br />
Grain yield (tlha)<br />
Variety<br />
Fertilizer (NP kglha)<br />
Rice - wheat - fallow<br />
Grain yield (tlha)<br />
Variety<br />
Fertilizer (NP kglha)<br />
Rice - fallow - fallow<br />
Grain yield (tlha)<br />
Variety<br />
Fertilizer (NP kglha)<br />
Uplands<br />
MaizeIF. millet - wheat<br />
Grain yield (tlha)<br />
Variety<br />
Fertilizer (NP kglhaj<br />
MaizeIF. millet - potato<br />
Grain yield (tlha)<br />
Variety<br />
Fertilizer (NP kglha)<br />
MaizeIF. millet-mustard<br />
Grain yield (tlha)<br />
Variety<br />
Fertilizer (NP kglha)<br />
Maize + vegctablc - vegetable<br />
Grain yield (tlha)<br />
Variety<br />
Fertilizer (NP kglha)<br />
Farm practices and average FSR recommended technology and potential<br />
grain yield (tlha) in 1984 grain yield (tlha) in 1985-90<br />
Crop 1 Crop 2 Crop 3 Total Crop l Crop 2 Crop 3 Total<br />
4.7<br />
Arun 4<br />
40:20<br />
3.5<br />
Mana- l<br />
40:20<br />
3.4<br />
Mana- l<br />
40:20<br />
1.1 2<br />
Local RR 21<br />
0 40:20<br />
1.1 15 19.6<br />
Local Cardinal<br />
60:30<br />
1.2 0.5 5.1<br />
Local Chitwan L.<br />
20:20
Table 7. Average productivity of different crops in different land types at Pumdi Bhumdi. Nepal (1988-89).<br />
Lowland Upland<br />
Improved varieties Local varieties Improved varieties Local varieties<br />
Description Intervened Controlled Intervened Controlled Intervened Controlled Intervened Controlled<br />
farmers farmers farmers farmers farmers farmers farmers farmers<br />
Rice<br />
Grain yield (tha) 2.7 3.3 1.9<br />
Straw yield (tha) 3.1 2.7 3.3<br />
Wheat<br />
Grain yield (tha)<br />
Straw yield (tha)<br />
Maize<br />
Grain yield (tha) 1.5 4.9 1 .O 1.2 1.6 1.3 2.9 1.6<br />
Straw yield (tha) 3.3 10.5 3 .O 2.7 4.4 3.8 4.7 4.9<br />
Oat<br />
Fodder yield (tha) * 5.6 *<br />
Mustard<br />
Grain yield (th) 0.1 * 0.2 *<br />
Straw yield (tlha) 1.4 * 1.4 *<br />
Barley<br />
Grain yield (tha)<br />
Straw yield (tha)<br />
Potato<br />
Tuber yield (tha)<br />
Lentil<br />
Grain yield (tha) * 0.29 *<br />
Straw yield (tha) * 0.28 *<br />
Finger millet<br />
Grain yield (tha) * * 2.0 2.2<br />
Straw yield (tha) * * 6.9 7.0<br />
* Farmers growing ei<strong>the</strong>r recommended varieties or local varieties.
Table 8. Major crops, crop varieties, and area grown by intervened and controlled farmers at Pumdi Bhumdi, Nepal (1990-91).<br />
Improved variety Local variety<br />
Crops Name of variety Area No. of Variety Name of No. of Total<br />
(ha) grower introduced variety grower area<br />
farmers by (ha)<br />
Intervened<br />
Ricc FSP<br />
CSP<br />
FSP<br />
Maize Khumal yellow<br />
Manakamana- l<br />
Arun-2<br />
Wheat<br />
Mustard<br />
Finger millet<br />
Controlled<br />
Rice<br />
Chitwan local<br />
Thulo Tori<br />
Maize Khumal yellow<br />
Manakamana- l<br />
.4run-2<br />
Ganesh-2<br />
Wheat<br />
Mustard<br />
F. millet<br />
CSP<br />
FSP<br />
FSP<br />
Marshi pahelo<br />
Gurdi pahclo<br />
Sano pahelo<br />
Sano Seto<br />
CSP Sano Seto<br />
FSP<br />
FSP<br />
Sano Seto<br />
Sano Tori<br />
Okhale CSP Khalso ,md Dallc<br />
KR-2 1<br />
Chitwan Local<br />
Okhale<br />
FSP'<br />
CSP<br />
FSP<br />
CSP<br />
FSP<br />
FSP<br />
L ARC<br />
CSP<br />
FSP<br />
CSP<br />
Marshi pahelo<br />
Gurdi pahelo<br />
Sano pahelo<br />
Sano seto<br />
- ' --. Pm - C-,-nnino ~\,~trmq nroicct: LARC = Lumle Agricultural Research Center.<br />
Sano Seto<br />
Sano Tori<br />
Kl~also & Dalle
Table 9. Number and types of livestock owned by <strong>the</strong> monitored farmers, Pumdi Bhumdi, Kaski District (1988-89).<br />
Types of<br />
Livestock<br />
Male buffalo<br />
Female buffalo<br />
Cows<br />
Bullocks<br />
Buffalo calves<br />
Sheep and goats<br />
Chickens<br />
O<strong>the</strong>rs<br />
Average<br />
number/<br />
farm<br />
Intervened farm Controlled farm<br />
Types of<br />
breeds<br />
Local<br />
Local<br />
*<br />
Local<br />
Local<br />
Local<br />
Local<br />
*<br />
a~ses:<br />
1 = for home consumption, 2= for exchange, 3 = for sale.<br />
*Data not available.<br />
Average<br />
breeds<br />
Types of<br />
brceds<br />
Local<br />
Local<br />
Local<br />
Local<br />
Local<br />
Local<br />
Local<br />
*<br />
Breeding<br />
1+2+3<br />
1<br />
1+2+3<br />
1<br />
1 +3<br />
1<br />
*
Table 10. Production and use of milk by monitored farms, Pumdi Bhumdi, Kaski District, Nepal<br />
(1984-85 and 1988-89).<br />
Milk production and<br />
activities<br />
Intervened Controlled<br />
farm farm<br />
1984-85 1988-89 1984-85 1988-89<br />
Average milk sale (liter) 78 1 927 809 845<br />
per farm<br />
Average income (NPR)/farm 4,725 8,215 5,153 6,560<br />
Table 11. Fodder and forage production at Pumdi Bhumdi, Kaski District, Nepal (1988-89).<br />
Name of <strong>the</strong> fodder/ Average farm area Average farm area<br />
Types of farms forage grass before FSP (ha) after FSP (ha)<br />
Intervened Napier grass<br />
Oats<br />
Controlled Napier grass * 0.06<br />
Oats * 0.11<br />
* Data not available.
Table 12. Number and types of fodder trees grown by <strong>the</strong> farmers at Pumdi Bhumdi, Kaski District, Nepal<br />
(1988-89).<br />
Types of farms Name of<br />
before FSP<br />
Number added during -<br />
FSP<br />
fodder tree Tree Sapling Tree Sapling<br />
Intervened<br />
Controlled<br />
* Data not available.<br />
Badhar<br />
Pakhuri<br />
Birulo<br />
Kabro<br />
Chuletro<br />
Ipil-ipil<br />
Kutmiro<br />
O<strong>the</strong>rs<br />
Badhar<br />
Pakhuri<br />
Birulo<br />
Kabro<br />
Chuletro<br />
Ipil-ipil<br />
Kutmiro<br />
O<strong>the</strong>rs
Table 13. Major problems and constraints to growing fodder trees and forage grasses, Pumdi Bhumdi, Nepal<br />
(1990-91).<br />
Fodder trees Forage grasses<br />
Serial Typesa Intervened Controlled TypesC ' Intervened Controlled<br />
No. of farmy farmer of farmer farmer<br />
problems (FH) (FH) problems (FH) (FH)<br />
1 3,4 4 3 2,3 5 4<br />
3 1,4 0 2 173 0 1<br />
al- except for grass and firewood, little use for o<strong>the</strong>r purposes; 2- quantity of rass from trees is less than local trees;<br />
3- shadow effect on crops; and 4- difficult to protect from grazing animals. bg~~-frequency of households. '1-lack<br />
of market for surplus production; 2-low crop yieIds due to shadow effect; and 3-low crop yields because it exploits<br />
fertilizer.
Table 14. Impact of FSR on vegetable production in kitchen gardens, Pumdi Bhumdi, Kaski District, Nepal, 1988-89.<br />
Types of farms Vegetable species Cropping pattern Area (ha) Cropping pattern Technology adopted<br />
before FSP after FSP<br />
Intervened A sponge gourd, bean, Change in vegetable<br />
pumpkin, bottle gourd<br />
chilly,<br />
MIFM-Veg. 0.04 M +Veg. -Veg. production technique<br />
B Cauliflower, mustard,<br />
radish, chilly. * 0.013 M +Veg.-Veg.<br />
Improved seed<br />
Compost use<br />
C cauliflower, radish,<br />
bean, cowpea. MIFM-Veg . 0.06 M/Veg.-Veg.<br />
Improved varieties<br />
D sponge gourd, snake- Establishment of<br />
E<br />
gourd, pumpkin.<br />
winter vegetables +<br />
MIFM -Veg. 0.04 M +Veg. -Veg. kitchen garden<br />
More varieties<br />
potato, summer veg. MIFM -Veg. 0.06 M+Veg.-Veg.<br />
,<br />
VI<br />
W<br />
F Cauliflower, carrot,<br />
radish, mustard leaf MIFM-Veg. 0.025 MIFM-Veg .<br />
Improved variety,<br />
nursery<br />
I<br />
Controlled G radish, mustard, ' Application<br />
brinjal MIFM-Veg. 0.01 MIFM-Veg. of compost<br />
H sponge gourd, cucum- Improved variety,<br />
ber, pumpkin, radish. MIFM -Veg. 0.026 MIVeg . -Veg. nursery<br />
I cauliflower, pumpkin, Use of compost and<br />
mustard leaf, garlic. MIFM -Veg. 0.019 MIVeg. -Veg. improved practices<br />
J leafy mustard, pea, Increased vegetable<br />
potato MIFM -Veg. 0.023 M +Veg. -Veg. growing area<br />
K leafy mustard, bean, Improved varieties<br />
sponge gourd, potato. MIFM -Veg. 0.06 M+Veg.-Veg.<br />
L summer vegetables, Increased cropping<br />
winter vegetables. MIFM -Veg. 0.014 M +Veg. -Veg. intensity<br />
Note: M = maize; FM = finger millet; and Veg. = vegetables.<br />
- - -
Table 15. Training undergone by <strong>the</strong> farmers and <strong>the</strong>ir rating of its usefulness, Pumdi Bhumdi,<br />
Nepal (1990).<br />
Intervened Controlled<br />
Types of farmers usefulnessb farmers usefulnessb<br />
traininga (FH) (FH)<br />
al- kitchen gardening; 2- crop management; 3- livestock fee lng, fodder, and health management;<br />
4- mushroom cultivation; and 5- poultry bird management.' 1- very useful; 2- useful; and<br />
3- not useful. Note: FH - frequency of households.
Table 16. Links of <strong>the</strong> farming system <strong>research</strong> program to different organizations.<br />
Kinds of links/ Technical Provincial Supply of inputs Participation Extension<br />
organizations resource and materials in WGM and activities<br />
person Group treck<br />
ADO<br />
Livestock<br />
Research Farm<br />
Horticulture<br />
DFO<br />
ADB<br />
AIC<br />
LARC<br />
Cooperative<br />
Review (2)<br />
<strong>research</strong> (2)<br />
Review -<br />
<strong>research</strong> results<br />
Farmer<br />
Trainors (2)<br />
Fodder and<br />
forage<br />
(2)<br />
Seeds<br />
Tree saplings<br />
(1)<br />
Loan to<br />
needy farmers<br />
(1)<br />
Seeds, inputs<br />
(2)<br />
Seeds (1)<br />
Seeds (1)<br />
Participating<br />
(2)<br />
Participating<br />
(2)<br />
Participating<br />
-<br />
Extrapolate<br />
FSR findings<br />
Note: (1) indicates weak linkage; (2) indicates strong linkage; WGM = Working Group Meeting; Group treck is a<br />
multidisciplinary team visit in which <strong>the</strong> future course of action is discussed and decided; ADO = Agricultural Development<br />
Office; DFO; ADB = Agricultural Development Bank; AIC ; and LARC = Lumle Agricultural Research Center.
Table 17. Links between farmers and different institutions, hmdi Bhumdi, Nepal (1990-91).<br />
Role to Effectiveness of Participation<br />
Institution Relationship streng<strong>the</strong>n linkage and<br />
FSR communication<br />
- --<br />
Agriculture Development Yes Yes Strong Effective<br />
Office (ADO)<br />
Cooperative Yes Yes Weak No<br />
Livestock Service Center Yes Yes Strong Effective<br />
FSR Site Coordinator Yes Yes Strong Very<br />
Table 18. Problems faced by intervened and controlled farmers<br />
in <strong>the</strong> adoption of <strong>the</strong> improved technology recommended by <strong>the</strong><br />
FSR program, Pumdi Bhumdi, Nepal (1990-91).a<br />
Intervened<br />
farmers<br />
Controlled<br />
farmers<br />
2, 3 2, 3, 4<br />
al = lack of irrigation facility; 2 = unavailability and untimely supply<br />
of agricultural inputs; 3 = lack of money to purchase agricultural<br />
inputs; 4 = lack of technical knowledge; 5 = more labor required;<br />
6 = decline in soil fertility; 7 = disease and insect problems if stored<br />
for a long time; 8 = improved varieties destroyed by wild animals<br />
because of late maturity; 9 = less straw and poor taste; and 10 =<br />
requires more seed than local varieties.<br />
-
Capitol city<br />
1. Map of Nepal showing Pumdi Bhumdi FSR site.
----l<br />
CROP SUBSYSlLM<br />
rnurt<br />
L<br />
OTT tbRH<br />
'6<br />
Hourthold<br />
l !oOdl<br />
Lkbor<br />
u;~~;ltottd<br />
nanurt 1 1<br />
FRSIURE LAND<br />
( Wlirrbarl )<br />
I l<br />
I 1 m<br />
2. Existing farming <strong>systems</strong> and interactions between different farm<br />
enterprises at Pumdi Bhumdi.
ASSESSRIENT OF RICE-FISH FARMING SYSTEM IN INDONESIA<br />
h1.O. Adnyana, D. K. S. Swastika, and W. sudanal<br />
With <strong>the</strong> changing strategy of <strong>the</strong> Government of Indonesia to<br />
diversify <strong>the</strong> agricultural development program from a single<br />
commodity of rice, <strong>the</strong> technology of rice-fish was introduced<br />
primarily in West Java. Sobong District took <strong>the</strong> leadership in this<br />
technology with large numbers of farmers adopting this new farming<br />
system. A study of <strong>the</strong> impact of this technology indicated greater<br />
efficiency in input use, increased rice yields and farm incomes,<br />
increased consumption of fish as well as increased capital<br />
accumulation among <strong>the</strong> farmers. Based on <strong>the</strong> potential of this new<br />
technolo~, strong operational actions must be taken at <strong>the</strong> national<br />
level and <strong>research</strong> effort to fur<strong>the</strong>r improve <strong>the</strong> technology must be<br />
continued.<br />
The Government of Indonesia has changed <strong>the</strong> strategy of future agricultural<br />
development from a single-commodity approach to a more diversified approach to<br />
sustain rice self-sufficiency and increase farm income. Rice-fish farming system is an<br />
improved practice that makes more efficient use of agricultural resources in lowland<br />
rice areas.<br />
The rice-fish farming system (RFFS) has been traditionally practiced in<br />
IndonesiB since <strong>the</strong> middle of <strong>the</strong> 19th century. It was started with some trials<br />
conducted by students in <strong>the</strong> Cianjur and Singaparna Districts of West Java. The<br />
students tried to use <strong>the</strong> remaining water in <strong>the</strong> ricefields by rearing fish after <strong>the</strong><br />
wet season (WS) rice was harvested. They improved <strong>the</strong> farming system from rice -<br />
fallow to rice - fish. After <strong>the</strong>y graduated, <strong>the</strong>y introduced <strong>the</strong> rice-fish farming<br />
system and <strong>the</strong> rice + fish farming system (rice-cum-fish) to <strong>the</strong>ir respective villages.<br />
Fish production from ricefields rose from 19,082 t in 1968 to 29,120 t in 1979,<br />
and to 87,414 t in 1987 (CBS 1976-89). As shown in Table 1, <strong>the</strong> contribution of rice<br />
- fish to total fish production has increased annually. In 1987, RFFS accounted for<br />
47.3% of total freshwater fish production.<br />
The RFFS is considered relevant to future agricultural development<br />
programs of Indonesia because it sustains land productivity, increases incomes,<br />
and improves <strong>the</strong> quality of food of rural people (Fagi et a1 1989). An impact study<br />
on RFFS was conducted at Binong Sub-District, West Java. This study examined <strong>the</strong><br />
efficiency of use of inputs in rice production, labor allocation, rice yields, farm<br />
income, food and nonfood expenditures, and productive and nonproductive<br />
accumulation of assets.<br />
l ~ ~ efor n Agricultural c ~ Research and Development (BORIF), Bogor, Indonesia.<br />
- 59 -
West Java provincial le~el<br />
BACKGROUND<br />
Fish production in <strong>the</strong> RFFS decreased from 1974 to 1980, but increased from 1981<br />
to 1990. This was mainly due to <strong>the</strong> development of floating nets at <strong>the</strong> Saguling,<br />
Cirata, and Jatiluhur Dams for <strong>the</strong> growth of large-sized fingerlings. These<br />
fingerlings can be reared in ricefields, ei<strong>the</strong>r toge<strong>the</strong>r with rice or after rice harvest.<br />
Table 2 shows <strong>the</strong> production of freshwater fish and <strong>the</strong> share of fish produced from<br />
ricefields in West Java from 1974 to 1989.<br />
In 1990, <strong>the</strong> Provincial Government of West Java targeted 20,000 ha of<br />
irrigated land for RFFS. Credit of IRP50,000/ha, in addition to <strong>the</strong> amount<br />
allocated for rice cultivation, \tras provided to farmers who were willing to develop<br />
RFFS. In 1991, <strong>the</strong> targeted area was increased to 22,000 ha. Forty-eight percent of<br />
<strong>the</strong> total targeted area was concentrated in West Java. The success of <strong>the</strong> rice - fish<br />
farming intensification program in West Java encouraged <strong>the</strong> government to make<br />
RFFS a Bimas program at <strong>the</strong> national level. The targeted area of rice - fish farming<br />
in <strong>the</strong> dry season (DS) of 1991 and <strong>the</strong> WS of 1991-92 was 46,000 ha and covered 14<br />
provinces.<br />
In West Java alone, this program required about 1 million fingerlings over<br />
two seasons in 14 districts. Tasikmalaya required about 225,000 fingerlings and<br />
Cianjur 200,000. These are about 50% of <strong>the</strong> total number of fingerlings needed for<br />
<strong>the</strong> RFFS development program. West Java is targeted to have about 22,000 ha of<br />
wet land or 48% of <strong>the</strong> total area of RFFS in Indonesia.<br />
Subang District level<br />
Binong Subdistrict has a population of 97,658. and more than 8896 of it work in <strong>the</strong><br />
agricultural sector. The lo\vland area is <strong>the</strong> most productive agricultural land in<br />
West Java. It is located in <strong>the</strong> coastal area of Subang District. There are about<br />
12,929 ha of agricultural land and <strong>the</strong> majority (10,297 ha) is irrigated. Subang<br />
District is one of <strong>the</strong> main rice-producing regions in West Java.<br />
In 1978-79, <strong>the</strong> cultivation of fish in ricefields was introduced. This farming<br />
system consisted of three sub<strong>systems</strong>: fish grown with rice (rice-cum-fish), fish<br />
between rice crops, and fish grown after <strong>the</strong> rice (rotational fish). The most widely<br />
developed subsystem was rotational fish. The practice of running-water aquaculture<br />
also gradually increased. However, <strong>the</strong> s~lpply of fingerlings from <strong>the</strong> rotational fish<br />
farming system was inadequate and <strong>the</strong>re was a constant gap between demand and<br />
supply of fingerlings.<br />
To support <strong>the</strong> development of <strong>the</strong> RFFS, particularly fingerlings, a village<br />
extension model was developed in 1981-52 at Cicadas, Binong Sub-District.<br />
Running-water aquaculture was practiced simultaneously, which also required large-
sized fingerling (50-100 glfish). Therefore, <strong>the</strong> fingerlin~s produced in <strong>the</strong> RFFS<br />
had many optional market channels. Farmers who practiced floating-net fish culture<br />
often came to Binong to purchase fingerlings during <strong>the</strong> harvest season. This<br />
encouraged <strong>the</strong> farmers at Binong to practice <strong>the</strong> RFFS. By 1989, <strong>the</strong> RFFS had<br />
rapidly increased in Subang (Table 3).<br />
The Sukamandi Research Institute for Food Crops (SURIF) and <strong>the</strong><br />
Research Institute for Freshwater Fish (RIFF) in Bogor conducted experiments on<br />
<strong>the</strong> RFFS technology components and conducted on-farm <strong>research</strong> to improve <strong>the</strong><br />
technology. This <strong>research</strong> demonstrated that rice-fish culture had increased rice<br />
yields. The RFFS also reduced triple superphosphate (TSP), use of herbicides and<br />
insecticides, and labor for hand weeding, Results were consistent in experimental<br />
stations and on-farm trials.<br />
Three components of <strong>the</strong> RFFS practiced in <strong>the</strong> study area are as follows:<br />
H Rice-cum-fish or rice + fish: fry or fingerlings are raised toge<strong>the</strong>r with rice<br />
for about 40-45 d depending on <strong>the</strong> use of fish.<br />
H Sequential fish or fish in between: fish are cultivated after <strong>the</strong> harvest of WS<br />
rice and harvested before <strong>the</strong> DS rice is planted. This system is usually used<br />
to fatten <strong>the</strong> fingerlings before <strong>the</strong>y are cultivated in <strong>the</strong> rice-cum-fish system<br />
during <strong>the</strong> first DS.<br />
H Rotational fish culture (fish as palawija): after <strong>the</strong> harvest of DS rice, <strong>the</strong><br />
field is submerged to make a fish pond. This is usually done as an alternative<br />
to third-season crops (palawija crops), fish are raised for about 2-3 mo.<br />
If farmers who grow two crops of rice are considered, four farming system<br />
models are practiced over a period of one year in <strong>the</strong> study area. These farming<br />
<strong>systems</strong> are rice - rice - fallow, rice - rice - fish, rice + fish - rice + fish - fish, and<br />
rice t fish - fish - rice t fish - fish.<br />
The most commonly practiced rice - fish farming models are rice - rice - fish<br />
and rice + fish - fish - rice + fish - fish. The choice of farming model depends on <strong>the</strong><br />
availability of irrigation water. Rice - rice - fallow is still <strong>the</strong> major cropping pattern.<br />
The rice - cum - fish system offers better prospects in terms of water availability.<br />
Rotational fish culture depends on <strong>the</strong> water conserved by <strong>the</strong> Jatiluhur Reservoir<br />
and <strong>the</strong> Cimacam Dam. In this study, three farming models are evaluated to<br />
determine <strong>the</strong> impact of <strong>the</strong> RFFS: rice t fish - rice t fish - fish (FS 1); rice - rice -<br />
fish (FS 11); and rice - rice - fallow (FS 111).
OBJECTIVES<br />
The specific objectives of <strong>the</strong> study were to evaluate <strong>the</strong> historical profile of RFFS<br />
and to present ~ ts status in Indonesia and West Java; to determine <strong>the</strong> role of certain<br />
institutions in <strong>the</strong> development of RFFS; to identify <strong>the</strong> new technology developed<br />
within <strong>the</strong> farming system framework and <strong>the</strong> extent to which <strong>the</strong> technology had<br />
been adopted; to determine <strong>the</strong> degree to which <strong>the</strong> adoption of <strong>the</strong> RFFS had<br />
increased <strong>the</strong> efficiency of input use, rice production, net income of <strong>the</strong> farm<br />
households, and <strong>the</strong>ir purchasing power; to measure <strong>the</strong> degree to which <strong>the</strong><br />
purchasing power had improved <strong>the</strong> quality of <strong>the</strong> consumption pattern and <strong>the</strong><br />
accumulation of productive assets by <strong>the</strong> farm households; and to identify <strong>the</strong><br />
potential for, and constraints to, future development.<br />
Sampling<br />
METHODOLOGY<br />
Stratified random sampling was used to identify 20 farmer-cooperators from each of<br />
<strong>the</strong> three farming system models ( FS I, FS 11, and FS 111). Sixty farmers were<br />
intensively monitored for 2 yr.<br />
Data collection<br />
Rapid rural appraisal (RRA) was used to ga<strong>the</strong>r information on <strong>the</strong> institutional<br />
impact of FSR. Interviews were conducted with related key informants [i.e.,<br />
<strong>research</strong>ers from SURIF and <strong>the</strong> Freshwater Fishery Research Institute, Regional<br />
Office of Agricultural Ministry, Provincial Fishery Extension Service, Provincial<br />
Intensification Coordinator (Bimas), and District Fishery Extension Service, and a<br />
few farmers and traders at Binong, West Java]. Relevant secondary data were also<br />
ga<strong>the</strong>red from <strong>the</strong>se institutions. In addition, secondary data were collected from <strong>the</strong><br />
Central Bureau of Statistics (CBS) and Institut Pertanian Bogor (IPB).<br />
Data were collected on <strong>the</strong> history of RFFS, <strong>the</strong> progress and present status<br />
of RFFS, share of RFFS to freshwater fish production, <strong>the</strong> marketing system, <strong>the</strong><br />
su port system, constraints, <strong>the</strong> role of institutions in RFFS, and o<strong>the</strong>r related<br />
f<br />
in ormation. On-farm data were collected each day using an intensive record-<br />
keeping technique for a period of 2 yr. Data recorded during <strong>the</strong> first year included:<br />
input-output, food-consumption pattern, nonfood expenditures (e.g., education,<br />
health, recreation, and clo<strong>the</strong>s), productive assets (e.g., livestock, agricultural<br />
equipment, and bicycles), and nonproductive assets (e.g., radios and television). In<br />
<strong>the</strong> second year, a periodic record-keeping method (four times per season) was used<br />
for production activities.
Data analysis<br />
Financial analysis and econometric procedures were used to measure <strong>the</strong> impact of<br />
<strong>the</strong> RFFS.<br />
IMPACT ON FARMER WELFARE<br />
To evaluate <strong>the</strong> impact of <strong>the</strong> development of RFFS in <strong>the</strong> study area, <strong>the</strong><br />
technology used in each farming system was evaluated. This discussion focused on<br />
<strong>the</strong> inputs used and <strong>the</strong> outputs achieved (i.e., production of rice and fish, net<br />
income earned by <strong>the</strong> farmer, and food and nonfood expenditure patterns).<br />
Inputs<br />
The inputs (o<strong>the</strong>r than labor) used in each farming system model are presented in<br />
Table 4. There were significant differences in <strong>the</strong> levels of inputs used (TSP<br />
fertilizer, herbicides, and insecticides) in both <strong>the</strong> DS and WS. Lesser amounts of<br />
TSP were used in FS I and FS I1 than in FS 111. This indicated that <strong>the</strong> incorporation<br />
of fish, particularly in intercropping, significantly reduced <strong>the</strong> amount of TSP<br />
applied.<br />
The fingerling density and rate of TSP a plication recommended by <strong>the</strong><br />
Sukamandi Research Institute for Food Crops [SURIF) for RFFS were 2,000 fry/ha<br />
(weight of 10 g/fry) and 75 kg TSP/ha. The TSP application rate recommended by<br />
<strong>the</strong> extension service was 150 kg/ha. Although <strong>the</strong> technique of trench construction<br />
was adopted, farmers did not adopt <strong>the</strong> amount of TSP or <strong>the</strong> fingerling density<br />
recommended by SURIF. However, <strong>the</strong>y had adopted <strong>the</strong> rate of TSP recom-<br />
mended by <strong>the</strong> extension service, which was twice <strong>the</strong> rate recommended by <strong>the</strong><br />
<strong>research</strong> institute. In <strong>the</strong> case of fingerlings, <strong>the</strong> density ranged from 569 to 1,128<br />
fry/ha or about 28.4-56.4% of <strong>the</strong> recommended rate.<br />
The production of fish ranged from 321 to 464 kg/ha (equivalent rice)<br />
whereas, <strong>the</strong> production of fish at <strong>the</strong> on-farm <strong>research</strong> site averaged 1,500 kg/ha.<br />
Therefore, <strong>the</strong> transfer of technology from <strong>the</strong> <strong>research</strong> institute to <strong>the</strong> farmers<br />
needs to be intensified.<br />
The RFFS also reduced <strong>the</strong> amount of liquid insecticide and herbicide used<br />
in FS I and FS I1 in both wet and dry seasons (Table 4). For instance, in 1989-90,<br />
RFFS reduced <strong>the</strong> application of herbicide almost to zero in FS I and FS I1<br />
compared with FS 111, in which about 0.5 liter/ha were applied during <strong>the</strong> WS and<br />
DS. This indicates that <strong>the</strong> incorporation of fish in rice-based farming <strong>systems</strong><br />
reduced insect infestation and weed growth. However, fur<strong>the</strong>r <strong>research</strong> is required<br />
to evaluate <strong>the</strong> real impact of fish culture on rice production, particularly in relation<br />
to chemical inputs.
Labor requirement<br />
Labor requirements (men and women) for each activity within <strong>the</strong> 3 farming system<br />
models are presented in Tables 5 and 6. In general, <strong>the</strong> total number of hours<br />
required per hectare in FS I during <strong>the</strong> WS and DS was relatively higher than that<br />
required by FS I1 and FS 111.<br />
Men worked in almost all activities, particularly in land preparation, rat<br />
hunting, chemical spraying, weeding, and harvesting. Women performed rice<br />
planting, weeding, and harvesting. They spent more time in <strong>the</strong>se activities than<br />
men.<br />
Land preparation for rice farming and rice-fish farming was mostly done<br />
using hand tractors, which cost IRP49,000/ha in 1989-90 and IRP63,000/ha in 1990-<br />
91. However, manual labor was also used, particularly to hoe <strong>the</strong> corner plots of rice<br />
and to repair dikes and canals.<br />
In 1989-90, <strong>the</strong> use of labor for land pre aration was reduced. This indicated<br />
that cultivation of fish during <strong>the</strong> third season after two rice seasons) made land<br />
preparation easier.<br />
Rice and equivalent rice yields<br />
The average land size per family in FS I and FS I1 was 0.7 ha, while in FS I11 was<br />
1.1 ha. All yields and incomes were converted into hectares except in <strong>the</strong> case of fish<br />
in <strong>the</strong> second DS. The formula used was<br />
Price of fish Rp/kg of fish equiv kg<br />
Equivalent rice = X quantity of fish = X kg fish =<br />
Price of rice Rp/kg rice Price<br />
The rice yield and total equivalent rice yield in FS I were higher than <strong>the</strong><br />
yields in FS I1 and FS 111. This result was consistent in 1989-90 and 1990-91. The<br />
additional yield from rice - fish farming resulted from both fish production and<br />
cultivation of rice. Production figures for FS I, FS 11, and FS 111 are given in Table 7.<br />
The increase in rice yield in FS I and FS 11 in <strong>the</strong> WS may be due to <strong>the</strong><br />
residual effect of third-season fish cultivated in both farming <strong>systems</strong>. Fur<strong>the</strong>rmore,<br />
<strong>the</strong> high yield of rice in FS I may be due to <strong>the</strong> biological control of weed and insect<br />
infestation. Most of <strong>the</strong> RFFS areas were relatively less vulnerable to insect<br />
infestation and weed problems during <strong>the</strong> crop years 1989-91 and 1990-91. This was<br />
evident from <strong>the</strong> lower levels of insecticide, herbicide, and labor used for weeding<br />
and chemical application, compared with FS 111. In <strong>the</strong> DS, FS I also produced <strong>the</strong><br />
highest rice yield. However, <strong>the</strong> increase of 32% compared with FS 111 in 1989-90<br />
was reduced to 23% in 1990-91.<br />
P
In terms of yield equivalent to rice, in 1989-90, <strong>the</strong> total annual yield in FS I<br />
was 13,910 kg/ha and in FS I1 was 11,704. About 51% and 27% higher than <strong>the</strong><br />
annual yield of FS I11 (9,213 kg/ha). A similar situation was observed in 1990-91.<br />
The additional yield produced in FS I and FS I1 was contributed by fish production,<br />
particularly in FS I, in which fish culture was practiced three times a year. Fish<br />
cultivation may also have an indirect impact on soil fertility.<br />
Farm income<br />
The adoption of intensive RFFS affected <strong>the</strong> level of farm income by reducing <strong>the</strong><br />
level of inputs (e.g., insecticide, herbicide, TSP, and labor for weeding), increasing<br />
rice yields, and generating additional income from fish culture. The low level of<br />
input use reduced production costs, whereas, <strong>the</strong> increase in <strong>the</strong> production of rice<br />
and fish increased gross return and subsequently net income. The incomes of <strong>the</strong><br />
three farming system models are presented in Table 8.<br />
Food and nonfood expenditure pattern<br />
Data on food and nonfood expenditure patterns were collected during <strong>the</strong> first year<br />
of this study (1989-90) using an intensive record-keeping technique. Food and<br />
nonfood expenditure patterns per month and <strong>the</strong> monthly expenditures on fresh fish<br />
are presented in Tables 9, 10, and 11. The food and nonfood expenditure patterns<br />
were closely related to total income per farm family. On-farm income is one of <strong>the</strong><br />
sources of income that determines total income. On-farm income is not only<br />
determined by farming intensity but by land size.<br />
Farmers in FS III per capita expenditure on food (IRP18,877) was <strong>the</strong><br />
highest among <strong>the</strong> three farming system models although <strong>the</strong>y practiced a less<br />
intensive farming system (rice - rice - fallow). About 43.3% (IRP6,484) of <strong>the</strong> per<br />
capita expenditure on food is allocated to purchase rice, 15.3% (IRP2,893) for dry<br />
fish, and 15.2% (IRP2,877) for vegetables. Similarly, <strong>the</strong> per capita expenditure on<br />
nonfood items (IRP18,026) by families in FS I11 was also <strong>the</strong> highest among <strong>the</strong><br />
three farming models.<br />
Farming intensity increased per capita food and nonfood expenditures per<br />
month. For example, <strong>the</strong> average monthly per capita expenditure of <strong>the</strong> farm<br />
families belonging to FS I (IRP18,179) was higher than that of FS I1 (IRP14,123). A<br />
similar situation was observed in <strong>the</strong> nonfood expenditure pattern. The average<br />
monthly per capita expenditure on nonfood items in FS I (IRP14,OOl) was higher<br />
than <strong>the</strong> expenditures by families in FS I1 (IRP10,374). In o<strong>the</strong>r words, <strong>the</strong><br />
introduction of fish culture in FS I (rice+ fish - rice+ fish - fish) increased <strong>the</strong><br />
purchasing power of its farm families in relation to farm families belonging to FS I1<br />
(rice - rice - fish).<br />
In relation to consumption of fresh fish, <strong>the</strong> average monthly per capita<br />
expenditure of families in FS 1 (IRP1,220) was <strong>the</strong> highest among <strong>the</strong> three farming
system models. This indicated that <strong>the</strong> higher yield of fish in FS 1 (cultivated three<br />
times a year) increasecl mor~thly per capita fish consumption (Table 11).<br />
Asset accumulation<br />
Asset accumulation is <strong>the</strong> value of all goods owned by farmers. Assets are<br />
categorized as agricultural equipment (e.g., tractors, hoes, and motcicycles),<br />
livestock, nonproductive assets (radios, televisions, and furniture), and savings<br />
(jewelry and cash).<br />
Table 12 shows <strong>the</strong> value of <strong>the</strong> assets owned by farmers belonging to <strong>the</strong><br />
three farmin models. The total value of all assets owned by <strong>the</strong> farmers In FS I11<br />
during 1989- 8 0 and 1990-91 is hi her than <strong>the</strong> value of assets owned by farmers in<br />
FS I and FS 11. .Therefore, rice - f ish farming had little impact on <strong>the</strong> accumulation<br />
of assets, except in <strong>the</strong> case of FS 111. Accumulation of assets, total income of <strong>the</strong><br />
farm family, and farm size are closely interrelated. The average farm size in FS I11 is<br />
higher than in <strong>the</strong> o<strong>the</strong>r two models. In general, farmers belonging to FS I11 are<br />
better off than farmer: in FS 1 and FS 11; <strong>the</strong>refore, it is logical that <strong>the</strong>se FS 111<br />
farmers have more accumulated assets. However, farmers in FS I have more assets<br />
than FS I1 farmers. This indicated that rice - fish farming <strong>systems</strong> had made a<br />
positive impact on <strong>the</strong> accumulation of assets.<br />
BUDGETING STAGES<br />
The proposed model assumes that consumers follow a multistage budgeting process.<br />
Consumers are assumed to first allocate <strong>the</strong>ir income between savings and current<br />
expenditures (consumption). This stage is not rnodeled in this study. In <strong>the</strong> second<br />
stage, consumers are assumed to allocate <strong>the</strong>ir total current expenditure to two<br />
categories: food and nonfood commodities. The third stage involvzs <strong>the</strong> allocation<br />
of <strong>the</strong> total food expenditure to nine subgroups. The fourth stage involves <strong>the</strong><br />
allocation of nonfood expenditure to nine subgroups.<br />
Functional form<br />
The Linear Approximate Almost Ideal Demand System (LA/AIDS) was used to<br />
model budget allocation. Following Deaton (1988), <strong>the</strong> Marshallian demand<br />
equations, in share form, fcr <strong>the</strong> LA/AIDS demand system are<br />
Wi = ai + aij In (Pj) + Bi In (Y/P) for i, j = 1, 2 ,..., r [l]<br />
where Wi is <strong>the</strong> budget share of <strong>the</strong> ith commodity, Pj is <strong>the</strong> unit price of <strong>the</strong><br />
jth commodity, Y is <strong>the</strong> total expenditure, and P is Stone's price index, i.e.,<br />
In (P) = dk wli In (Pk) PI
A demographic variable (family size) was introduced into equation [l] by<br />
translating <strong>the</strong> intercept term. It was assumed that<br />
ai = aio + Ct ait Dt for i = 1, 2, ..., r<br />
where D denotes demographic variables. The resulting model was<br />
wi = aio + Ct ait Dt + Eij In (Pj) + Bi In (XIP) [41<br />
The relevant <strong>the</strong>oretical restrictions that can be imposed on this demand system<br />
are (Heien and Pompelli 1989)<br />
Data sourqe<br />
Symmetry: aij = aji; (i + j; i, j = 1, 2,. . . , r)<br />
Homogeneity: aij = 0; (i = 1, 2, . . ., r)<br />
Adding-up: Ci aio = 1 ; Ei bi = 0; Ci aij = 0; (t = 1, 2, . . . , v)<br />
The data on food and nonfood consun~ption from <strong>the</strong> impact study on FSR in Binong<br />
during <strong>the</strong> crop year 1989-90 were used in this analysis. Data were maintained for two<br />
rice seasons and a third season of crops and fish production within a year. Data were<br />
collected directly from <strong>the</strong> selected households. Information on individual household<br />
consumption was converted into per capita consumption per month. The aggregated<br />
data were divided by family size to obtain per capita consumption.<br />
All expenditures were classified into two major groups (food and nonfood<br />
items). Because economic <strong>the</strong>ory provides no guidance on <strong>the</strong> composition of food<br />
groups, <strong>the</strong> decision is usually made on an ad hoc basis by <strong>the</strong> <strong>research</strong>er. In this study,<br />
<strong>the</strong> proposed list of food groups and nonfood groups depended on <strong>the</strong> ability of farmers<br />
and consumers to recall <strong>the</strong> items <strong>the</strong>y had consumed.<br />
With regard to per capita expenditure of <strong>the</strong> selected households, food items<br />
were classified into nine subgroups: rice; meat; fresh fish; dry fish; eggs; vegetables;<br />
spices; cooking oil; and sugar, tea, and coffee. There were also nine subgroups of<br />
nonfood items: firewood and kerosene; soap and tooth paste; electricity; education;<br />
clo<strong>the</strong>s; taxes and o<strong>the</strong>r obligations; health; social activities and cultural ceremony; and<br />
asset accumulation.
Unit prices for all items were obtained by dividing <strong>the</strong> reported expenditure<br />
by <strong>the</strong> number of units. Some of <strong>the</strong> unit prices were computed as weighted prices.<br />
Following Heien and Pompelli (1989), a set of auxiliary regressions, which linked<br />
available prices to a set of dummy variables and to expenditure, was used to impute<br />
missing prices.<br />
To express all unit prices in <strong>the</strong> same matrix, both food and nonfood prices<br />
were scaled by dividing each by its corresponding mean value. In addition, all scaled<br />
unit prices were transformed into logarithms to obtain <strong>the</strong> linear form of <strong>the</strong><br />
demand model.<br />
EMPIRICAL RESULTS<br />
This discussion emphasizes <strong>the</strong> results of <strong>the</strong> economic analysis of <strong>the</strong> rice-fish<br />
farming <strong>systems</strong> and its impact on consumption patterns, <strong>the</strong> budget allocated for<br />
each item, and <strong>the</strong> magnitude of elasticities.<br />
Consumption pattern and budget share<br />
In general, FS I farmers had higher levels of per capita consumption than FS 11<br />
farmers. However, per capita consunlption by FS I farmers was lower than<br />
consumption by FS I11 farmers. This difference is evidently due to <strong>the</strong> fact that <strong>the</strong><br />
average land holding of <strong>the</strong> farmers in FS I11 was 1.1 ha compared with an average<br />
of 0.7 ha in <strong>the</strong> o<strong>the</strong>r two groups. The total per capita expenditure of <strong>the</strong> farmers in<br />
FS I was about 33.6% higher than farmers in FS 11, but about 12.5% lower than for<br />
farmers in FS 111.<br />
The budget share for food items in FS I (54.6%) is a little lower than in FS I1<br />
(56.770). However, <strong>the</strong> lowest share was in FS 111 (49.2%). The highest budget share<br />
for nonfood items was in FS I11 (50.8 96) followed by FS I (35.4%) and FS I1<br />
(43.3 %) (Table 13).<br />
Therefore, <strong>the</strong> poorer farmers in FS I and FS I1 spent more than 50% of<br />
<strong>the</strong>ir income on food items. On <strong>the</strong> contrary, farmers in FS 111, who had higher<br />
incomes, spent less than 50% of <strong>the</strong>ir income on food consumption. Therefore, <strong>the</strong><br />
impact of rice-fish farming <strong>systems</strong> will be most effective if it is developed among<br />
small-scale farmers under existing conditions.<br />
Food expenditure<br />
A summary of <strong>the</strong> expenditure pattern for food and <strong>the</strong> budget share of each food<br />
item are presented in 'Table 15. In all farming system groups, <strong>the</strong> largest part of <strong>the</strong><br />
expenditure on food was spent on rice. The second ancl third highest shares were for
dry fish and vegetables, respectively. The consumption of meat was relatively low in<br />
all three farming groups.<br />
With regard to <strong>the</strong> direct impact of <strong>the</strong> rice-fish farming <strong>systems</strong> on<br />
consumption of fresh fish, farmers in FS I consumed more fish (7%) than farmers in<br />
FS I1 (6%) and FS I11 (4%). O<strong>the</strong>r food items showed varied budget shares among<br />
<strong>the</strong> three farming system groups.<br />
Nonfood expenditures<br />
A summary of <strong>the</strong> nonfood expenditures and <strong>the</strong> budget share of each nonfood item<br />
are presented in Table 15. Within <strong>the</strong> nonfood category, <strong>the</strong> highest amount was<br />
spent on education. The impact of fish culture on quality of life was that farmers<br />
spent more on education. For example, farmers in FS I and FS I1 spent about 26%<br />
and 39% of <strong>the</strong>ir total nonfood expenditures on education compared with FS 111<br />
farmers who spent 1270. However, FS 111 farmers spent more on taxes and assets<br />
than farmers in FS I and FS 11.<br />
In relation to expenditures on social activities (e.g., cultural ceremony,<br />
recreation, and donations), <strong>the</strong>re is a remarkable difference between farmers in FS I<br />
and FS I1 although <strong>the</strong>y have <strong>the</strong> same social status. With <strong>the</strong> increased income<br />
from fish, FS I (fish cultivated three times a year), farmers spent more on social<br />
activities than FS I1 farmers. However, FS 111 farmers spent <strong>the</strong> highest budget share<br />
on social activities.<br />
Significant differences were also observed in expenditures on health services<br />
(e.g., visiting rural heklth services and family planning) between farmers in FS I and<br />
FS 11. Farmers in FS 111 spent <strong>the</strong> highest budget share on health services.<br />
Demand elasticities<br />
A summary of price and income elasticities for all budget categories is given in<br />
Tables 16, 17, and 18. These figures were compared using <strong>the</strong> estimated parameters<br />
of <strong>the</strong> LA/AIDS model. There was variability in <strong>the</strong> elasticities in all groups of<br />
consumers.<br />
The demand for food and nonfood groups appears to be unit price inelastic<br />
in all farming groups (Table 16). However, farmers in FS I11 are relatively more<br />
responsive (i.e., a coefficierlt of -0.664 for food and -0.748 for nonfood). Meanwhile,<br />
<strong>the</strong> demand for food and nonfood groups appears to be income elastic in all farming<br />
groups except for food in FS I (0.999 and 1.001).
Price elasticity. The demand for all food items appears to be price inelastic in<br />
all farming groups. The demand for rice is <strong>the</strong> most important in terms of food<br />
policy in <strong>the</strong> study area. Among all food items, <strong>the</strong> demand for rice is relatively<br />
more responsive to price variability in all farming groups (-0.390 in FS I, -0.403 in<br />
FS 11, and -0.306 in FS 111). Fur<strong>the</strong>rmore, <strong>the</strong> demand for fresh fish in FS I is <strong>the</strong><br />
most price inelastic among <strong>the</strong> three farming groups (-0.059 compared with -0.87 for<br />
FS I1 and -0.89 for FS 111). This may be due to <strong>the</strong> high degree of subsistence on<br />
<strong>the</strong>ir own production of fresh fish.<br />
Incot~ze elasticities. The demand for food items among all farming groups is<br />
mostly income elastic (coefficients of elasticities almost 1.0 except for some food<br />
items for which elasticity is oreater than 1.0). For example, <strong>the</strong> demand for meat in<br />
FS I is income elastic (1.8267. The demand for food commodity bundles (e.g., eggs<br />
and sugar, tea, and coffee in FS 111 and sugar, tea, and coffee in FS 11) are income<br />
elastic. In o<strong>the</strong>r words, <strong>the</strong> demand for those food iterns is likely to be determined<br />
by <strong>the</strong> farmers' income.<br />
Nonfood sector<br />
There is significant variability in <strong>the</strong> elasticities among nonfood items.<br />
Price elusticity. Demand in <strong>the</strong> nonfood sector is mostly price inelastic in all<br />
farming groups for health services. The demand for health services in FS I1 and FS<br />
111 is price elastic (coefficients of -1.324 and -1.025, respectively). The demand for<br />
assets is <strong>the</strong> most price elastic in all farming groups (coefficients of -1.377 in FS I, -<br />
1.751 in FS 11, and -1.751 in FS 111). This indicates that farmers would lessen <strong>the</strong>ir<br />
demand for assets (agricultural assets and savings) if <strong>the</strong>ir price increases. These<br />
assets include nonproductive assets such as radios and televisions.<br />
I~zcottze elusticity. The denland for firewood and kerosene, education, social<br />
activities and donations, and assets is income elastic in all farming groups. The<br />
demand for assets is highly income elastic (coefficients of 3.253- in FS 1. 4.533 in FS<br />
11, and 2.533 in FS 111). Social activities and donations showed a similar pattern.<br />
Role of extension, farmer participation, and ~~olicy<br />
Researchers and extension specialists can play a inajor role in improving <strong>the</strong><br />
conditions of small-scale farmers and encouraging policy. The technology developed<br />
by <strong>the</strong>se <strong>research</strong> projects have been transferred through multilocation testing; field<br />
days attended by extension specialists, <strong>research</strong>ers, pol~cymakers, and farmer<br />
groups; seminars and workshops; publications; and o<strong>the</strong>r media (radio and<br />
television).
CONCLUSION<br />
Rice-fish farming <strong>systems</strong> have been practiced since <strong>the</strong> middle of <strong>the</strong> 19th century.<br />
However, this farming system was traditionally practiced without appropriate<br />
technology, particularly without <strong>the</strong> construction of trenches. Farmers believed that<br />
trench construction would reduce <strong>the</strong> yield of rice. This study shows that trenches<br />
have no adverse effect on <strong>the</strong> production of rice.<br />
After <strong>the</strong> introduction of <strong>the</strong> new technology, farmers adopted <strong>the</strong> technique<br />
of trench construction but paid little attention to <strong>the</strong> amount of TSP or fingerling<br />
density. The fingerling density farmers used was 569-1,128 fry/ha. The production of<br />
fish ranged from 321 to 464 kg/ha (equivalent rice). This production is much lower<br />
than <strong>the</strong> level obtained under <strong>research</strong> conditions (average 1,500 kg/ha equivalent<br />
rice). Therefore, transfer of technology from <strong>the</strong> <strong>research</strong> institute to <strong>the</strong> farmers<br />
should be intensified. This could be done by streng<strong>the</strong>ning <strong>the</strong> links among<br />
<strong>research</strong>er, extension specialists, and farmers through on-farm <strong>research</strong>.<br />
Fish culture in rice-fish farming <strong>systems</strong> has several advantages: increased<br />
efficiency of TSP, herbicide, and pesticide use and better use of labor for weeding<br />
and spraying; increased rice and equivalent rice yield, increased net income;<br />
increased consumption of fresh fish; and increased accum~~lation of assets.<br />
The annual yield of rice and equivalent rice per hectare in FS 1 and FS I1<br />
were higher than in FS 111. The annual net income per hectare in 1989-90 of FS I<br />
was 89.5% arld of FS I1 was 60% higher than in FS 111. While in 1990-91, FS I was<br />
87.570 and FS 11 was 50.870 higher than in FS 111.<br />
Because FS I farmers in <strong>the</strong> irrigated areas cultivated fish three times a year,<br />
<strong>the</strong>ir per capita consulnption of fish was relatively higher than <strong>the</strong> consumption of<br />
FS 11 farmers, who cultivated fish once a year. There were significant changes in<br />
expenditures on food, particularly fresh fish, in all farming system groups. Significant<br />
changes were also observed in expenditures on nonfood items. The share of food<br />
expenditure to total expenditure is higher than nonfood expenditures in FS I and FS<br />
11. However, <strong>the</strong> opposite is true for consumers in FS 111.<br />
A significant variability in price and income eltlsticities of demand for food<br />
and nonfood items was found in all farming system groups. The demand for<br />
commodities within <strong>the</strong> food category is price inelastic in all farming groups, but <strong>the</strong><br />
demand for nonfood items varies. For example, <strong>the</strong> demand for education, health<br />
services, and assets in FS I is less elastic than in FS 11 and FS 111.<br />
Rice-fish farming has <strong>the</strong> potential for large-scale developrnent in Indonesia.<br />
It can be supported by government policy, external sup ort, farmer participation,<br />
and technology development. In spite of <strong>the</strong> prospects 1 or RFFS, <strong>the</strong>re are certain<br />
constraints. These are inconsistent supply of irrigation water, inadequate availability<br />
of fingerlings, natural hazards, and lack of appropriate processing and marketing<br />
<strong>systems</strong> of fish. To overcome <strong>the</strong>se problems, local governments (vrovincial and
district fishery extension services) and <strong>research</strong>ers must exert efforts to improve <strong>the</strong><br />
technology.<br />
Based on <strong>the</strong> potential, prospects, and constraints in <strong>the</strong> development of rice-<br />
fish farming <strong>systems</strong>. Appropriate actions are needed at <strong>the</strong> national level. These<br />
include <strong>the</strong> continuous improvement of <strong>the</strong> technology for hatching and rearing fry,<br />
extension and guidance for farmer groups, credit facilities, and improved marketing<br />
<strong>systems</strong>. Local governments may be needed to provide support services such as<br />
provision of irrigation water and credit for hatcheries.<br />
REFERENCES CITED<br />
CBS--Central Bureau of Statistics (1976-1988) Statistical yearbook of Indonesia.<br />
Jakarta.<br />
Deaton A (1988) Price elasticities from survey data: extensions and Indonesian<br />
results. Woodrow Wilson School, Princeton University. (mimeograph).<br />
Fagi A M, Suryapermana S, Syamsiah I (1989) Rice-fish farming system in lowland<br />
areas, <strong>the</strong> West Java case. Paper presented at <strong>the</strong> Asian Regional Workshop<br />
on Rice-Fish Research and Development. Central Luzon State University,<br />
Nueva Ecija, Philippines.<br />
Heien D, Pompelli G (1989) The demand for alcoholic beverages: economic and<br />
demographic effects. South. Econ. J. Agency for Agricultural Research and<br />
Development, Bogor, Indonesia.<br />
I
Table l. Area and production of freshwater (FW) fish and <strong>the</strong> share of RFFS to freshwater fish production, Indonesia<br />
(1 960-87).<br />
P P P P<br />
Area of Production of Production of Production of Total Production Share<br />
Year rice-fish rice-fish pond fish karamba* FW fish of RFFS<br />
(ha) (t) - (t> (t> (0<br />
1960<br />
1968<br />
1969<br />
1970<br />
1971<br />
1972<br />
1973<br />
1974<br />
1975<br />
1976<br />
1977<br />
1978<br />
1979<br />
1980<br />
1981<br />
1982<br />
1983<br />
1984<br />
1985<br />
1986<br />
1987<br />
Average<br />
Source: CBS 1975-89; Data for 1961-67 not available.<br />
* Karamba = Bamboo basket for keeping fish in <strong>the</strong> river or in <strong>the</strong> irrigation canals. It could also in <strong>the</strong> form of fence, fixed<br />
in <strong>the</strong> riverlirrigation canals.
Table 2. Production of freshwater fish in West Java (1974-90).<br />
Year Sawah Pond Running water Floating net Karamba Total Share<br />
(t> (t> (t> (t> (t> (t><br />
Average<br />
Source: CBS 1975-89 and Provincial Office of Fishery;<br />
* data not available.
Table 3. Area and production from rice-fish farming system, Subang, Indonesia (1982-89).<br />
Area<br />
P p<br />
Production<br />
Year Rice- Fish in Rotational Total Rice- Fish in Rotational Total<br />
cum-fish between fish fish cum-fish between fish Prod.<br />
(ha) (ha) (ha) (ha) (kg) (kg) (kg) (kg)
Table 4. Level of inputs used in each FS Model, Binong, Indonesia (1989-90 to 1990-91).<br />
Inputs<br />
1989-90<br />
Rice seed (kg) 27<br />
Fingerling (fish) 1,128<br />
Feed (kg) 11.7<br />
Urea (kg) 175<br />
TSP (kg) 153<br />
KCL (kg) 13<br />
ZA (kg) 0<br />
Liquid fertilizer (L) 0.06<br />
Carbofuran (kg) 2.3<br />
Herbicide (L) 0.05<br />
Rodenticide (g) 1 .O<br />
Insecticide (L) 0.7<br />
Land area (ha) (0.7)<br />
1990-91<br />
Rice seed (kg) 29<br />
Fingerling (fish) 723<br />
Feed (kg) 5.2<br />
Urea (kg) 191<br />
TSP (kg) 148<br />
KCL (kg) 55<br />
ZA (kg) 5.8<br />
Liquid fertilizer (L) 0.17<br />
Carbofuran (kg) 8.1<br />
Herbicide (L) 0.19<br />
Rodenticide (g) 1,225.0<br />
Insecticide (L) 1.2<br />
Land size (ha) (0.7)<br />
Wet season Dry season
l<br />
Table 5. Labor requirement for each FS Model, Wet Season, Binong, Indonesia (1989-90 to 1990-91).<br />
FS I FS I1 FS I11<br />
Activities Men Women Tract Total labor Men Women Tract Totallabor Men Women Tract Total labor<br />
(hlha) (hlha) (IRPIha) (hlha) (hlha)' (hlha) (1RPIha) (hlha) (hlha) (hlha) (IRPlha) (hlha)<br />
1989-90<br />
1 Land preparation 156 0 49,000 254 187 0 49,000<br />
2 Seedling 3 1 1 0 32 3 3 0 0 34 29<br />
3 Irrigation 2 0 0 29 24 0 0<br />
4 Rice planting 3 1 210 0 157 29 194 0<br />
5 Fish stocking 9 1 0 9 0 0 0<br />
6 Rat hunting 59 0 0 59 57 0 0<br />
7 Fertilizer application 24 0 0 24 26 1 0<br />
8 Weeding I 50 38 0 73 68 66 0<br />
9 Fish harvest 20 7 0 24 0 0 0<br />
l0 Chemical spray 14 0 0 14 23 0 0<br />
l 1 Rice harvest 187 220 0 3 19 135 196 0<br />
12 Postharvest 0 1 0 1 2 2 0<br />
4 Total (hlha) 610 478 49,000 995 584 259 49,000<br />
I Labor cost (Rplha) 305,000 143,400 49,000 497,400 292,000 137,700 49,000 478,700 299,000 135,300<br />
1990-91<br />
1 Land preparation 248 0 6,300 248 135 0 63,000<br />
2 Seedling 42 0 0 42 28 0 0<br />
3 Irrigation 28 0 0 28 16 0 0<br />
4 Rice planting 43 195 0 238 17 170 0<br />
5 Fish stocking 10 7 0 17 0 0 0<br />
6 Rat hunting 40 0 0 40 14 0 0<br />
7 Fertilizer application 37 6 0 43 20 1 0<br />
8 Weeding I 33 47 0 80 37 68 0<br />
9 Fish Harvest 20 19 0 39 0 0 0<br />
1OChemical spray 28 0 0 28 30 0 0<br />
1 1 Rice harvest 24 1 327 0 568 173 260 0<br />
12Posthmest 10 l l 0 2 1 11 1 0<br />
Total (hlha) 780 612 63,000 1,392 48 1 500 63,000<br />
Labor cost (Rplha) 390,000 183,600 63,000 636,600 240,500 150,000 63,000 453,500 316,000 162,000 63,000 541,000
Table 6. Labor requirement for each FS Model, Dry Season I, Binong, Indonesia (1989-90 to 1990-91).<br />
Activities Men Women Tract Total Labor Men Women Tract Total Labor Men Women Tract Total Labor<br />
(hlha) (hlha) (IRPIha) (hlha) (hlha) (hlha) (IRPIha) (hlha) (hlha) (hlha) (IRPIha) (hlha)<br />
1989-90<br />
1 Land preparation 207 0<br />
2 Seedling 3 6 0<br />
3 Irrigation 3 5 0<br />
4 Rice planting 24 151<br />
5 Fish stocking 7 2<br />
6 Rat hunting 39 0<br />
7 Fertilizer application 25 2<br />
8 Weeding I 30 46<br />
9 Fish Harvest l I 7<br />
lOChemical spray 15 0<br />
1 1 Rice harvest 179 205<br />
-1 12 Postharvest 27 19<br />
Total (hlha) 634 432<br />
Labor cost (Rplha) 3 17,014 129,733<br />
1990-91<br />
1 Land preparation 226 0<br />
2 Seedling 3 1 0<br />
3 Irrigation 65 0<br />
4 Rice planting 26 162<br />
5 Fish stocking 5 2<br />
6 Rat hunting 50 0<br />
7 Fertilizer application 26 7<br />
8 Weeding I 27 47<br />
9 Fish harvest 10 12<br />
IOChemical spray 22 0<br />
11 Rice harvest 208 25 1<br />
12Postharvest 19 2 1<br />
Total (hlha) 715 502<br />
Labor cost (Rplha) l 357,500 150,600<br />
1.and area (ha)<br />
I
Table 7. Rice and equivalent rice yield from rice-fish farming, Binong, Indonesia<br />
(1989-90 and 1990-91).<br />
WS DS I DS I1 Total<br />
rice equivalent rice rice equivalent riceEquivalent rice Total<br />
(kg) (kg) (kg) (kg) (kg) (kg)<br />
FS IIf 5,702 0 4,204 0 0 9,906<br />
(100%) (100%) (100%)<br />
Note: Average rice price: WS = RP25O/kg; DS I = RP280/kg; DS I1 = RP320/kg.<br />
Average fish price: WS = RP2,100/kg; DS I & DS I1 = RP2,200/kg.<br />
Figures in paren<strong>the</strong>ses = percentage of rice yield compared with FS 111.
able 8. Income analysis of each FS Model, Binong, Indonesia (1989-90 and 1990-91).<br />
1989-90<br />
FS I FS I1 FS 111<br />
WS DS I DS I1 Total WS DS I DS I1 Total WS DS I DS I1 Total<br />
Rice 1,761.840 1,390,230 0 3,152,070 1,409,760 1.320.840 0 2,730,600 1,273,200 1,055,160 0 2.328.360<br />
Fish 11 1,400 86,(i 171,470 369,470 0 0 253,128 253,128 0 0 0 0<br />
Gross reurn 1,873,240 1,476,830 17 1.470 3,521,540 1,409,760 1.320.840 253,128 2.983.728 1,273,200 1.055,160 0 2,328,360<br />
Material cost 176,526 165.355 40.450 382,331 102,338 141,783 15,373 259,494 141,141 168,012 0 309,153<br />
Labor cost 497.400 495,747 55,960 1.049,107 478,700 451.734 28.325 958,750 483,300 432,764 0 916.064<br />
Total cost 673,926 661,102 96,410 1.43 1,438 58 1.038 593.517 43.608 1.218.253 624,441 600.776 0 1,225,217<br />
Net returnlha 1,199,314 815,728 75,060 2,090.102 828,722 727,323 209,430 1,765,475 648,759 454.384 0 1,103,143<br />
1990-91<br />
Rice<br />
Fish<br />
Gross return<br />
Material<br />
Labor cost<br />
Total cost<br />
Net return<br />
Increase (%)
Table 9. Monthly food expenditure pattern (IRPIperson) of farm families in each farming system model, Binong, Indonesia<br />
(1989-90).<br />
Farming Rice Meat Fresh Dry Egg Vegetable Spices Cooking Sugar Total<br />
models fish fish oil coffee food<br />
FS I<br />
Average<br />
Maximum<br />
Minimum<br />
FS I1<br />
Average<br />
Maximum<br />
Minimum<br />
FS 111<br />
Average<br />
Maximum<br />
Minimum
Table 10. Nonfood expenditure pattern of farm families in each farming system model, Binong, Indonesia (1989-90).<br />
Farming Fire- Toothpaste Electricity Education Clo<strong>the</strong>s Taxes1 Health Donation Total<br />
models wood and soap obligations nonfood<br />
FS I<br />
Average<br />
Maximum<br />
Minimum<br />
FS I1<br />
Average<br />
Maximum<br />
Minimum<br />
& FS 111<br />
h,<br />
' Average<br />
Maximum<br />
Minimum<br />
Note: USD1 = IRP1,925
Table 11. Monthly consumption pattern (IRPIperson) of fresh fish by farm families in<br />
each farming system model, Binong, Indonesia (1989-90).<br />
Month FS I FS I1 FS I11<br />
November<br />
December<br />
January<br />
February<br />
March<br />
April<br />
May<br />
June<br />
July<br />
August<br />
September<br />
October<br />
Average<br />
Maximum<br />
Minimum
Table 12. The value of assets (IRP X thousand) owned by farm families in each FS<br />
Model, Binong, Indonesia (1 990-91).<br />
FSI FS I1 FS I11 FSI FS I1 FS 111<br />
Agricultural assets 108 176 181 96 3 8 49<br />
Livestock 6 8 6 6 82 194 265 193<br />
Nonproductive assets 168 3 9 124 1,138 936 1,303<br />
Savings 219 183 60 1 348 155 496<br />
Total 563 466 988 1,776 1,394 2,041<br />
Table 13. Monthly food and nonfood expenditures (IRPIperson) and budget share of<br />
farm families in each farming system model, Binong, Indonesia (1989-90).<br />
Food Nonfood Total<br />
a~alues in paren<strong>the</strong>ses are <strong>the</strong> budget shares. USDl = IRP1,925.
Table 14. Food expenditure pattern and budget share of farm families in each farming<br />
system model, Binong, Indonesia (1 989-90).<br />
Rice<br />
Meat<br />
Fresh fish<br />
Dry fish<br />
Vegetables<br />
Spices<br />
Cooking oil<br />
Sugarlcoffeel tea<br />
Total food<br />
a~ote: Values in paren<strong>the</strong>ses are budget share. USDl = IRP1,925.
Table 15. Nonfood expenditure patterns and budget share of farm families in each<br />
farming system model, Binong, Indonesia (1989-90).<br />
Electricity<br />
(16)<br />
Education<br />
Clo<strong>the</strong>s<br />
Health<br />
Asset<br />
Total nonfood<br />
aNote: Values in paren<strong>the</strong>ses are budget share. USDl = IRP1,925.
Table 16. Elasticities in food and nonfood sectors, Binong, Indonesia (1990-91).<br />
FS Model<br />
Income<br />
FS I<br />
FS I1<br />
FS I11<br />
Commodity<br />
Food Nonfood
Table 17. Elasticities of food commodities for own-price and income, Binong, Indonesia (1990-91).<br />
Income<br />
Rice Meat Fresh Dry Egg Vegetable Spices Cooking Sugarltea<br />
fish fish oil /coffee<br />
FS I 0.994 1.826 0.968 1 .OOO 1.058 1.017 1.008 1.037 0.979<br />
FS I1 0.953 1.040 0.935 0.965 1.059 0.991 0.987 1.051 1.331<br />
FS I11 0.953 0.940 0.935 0.965 1.600 0.981 0.977 0.949 1.781
Table 18. Elasticities of nonfood commodities for own-price and income, Binong, Indonesia (1990-91).<br />
Firewood/ Soap/ Electric Education Clothing Tax/ Health Donation Asset<br />
kerosene toothpaste Obligation<br />
Income<br />
FS I 1.046 0.933 0.832 1 .g58 0.892 0.526 0.589 1.398 3.252<br />
FS I1 1.189 0.976 0.703 2.191 0.655 0.199 0.503 2.749 4.533<br />
FS I11 1.197 0.979 0.991 1.095 0.655 0.199 0.986 2.749 2.533
l<br />
Oct k Jan Feb Mar Apr May Jun Jul ~ u g Sep<br />
Rice Rice / / Fish /<br />
RicrA Fish Kiw + Fish<br />
L-<br />
FIB = Fish in &.hn = Fish grow between wet and dry besson rice<br />
1. Rainfall partcrn and fanning niodcls in Binong, Indonesia.
THE IMPACT OF FARMING SYSTEMS RESEARCH IN THAILAND<br />
B. shinawatra1, C. sukapon$, P. ~oodtikam' P. ~eundao~,<br />
P. Padermchai , and B. Ontuam 3<br />
This paper examines <strong>the</strong> impact of two farming <strong>systems</strong> <strong>research</strong><br />
technologies introduced in Dok Kham Tai District, Phayao<br />
Province, Nor<strong>the</strong>rn Thailand. The two technologies, mungbean<br />
before rice and direct seedin of rice, were considered mature<br />
enough for extension. The ormer was to increase cropping<br />
intensity, farm income, and utilize beginning off-season moisture<br />
while <strong>the</strong> latter was to stabilize rice production in areas where<br />
<strong>the</strong>re was a problem concerning <strong>the</strong> unreliability of rainfall. The<br />
adoption of mungbean before rice was widespread with ups and<br />
downs. It was more appropriate in upper paddy where<br />
waterlogging was unlikely. Many farmers suffered losses from<br />
flooding in certain years. Average yields were found to be lower<br />
than expected (40-48 kg/rai versus <strong>the</strong> expected 88-161 kg/rai). A<br />
total of 10,046 rai was found planted in mungbean in 1991.<br />
Average cash gross margin per farm was 2,876 baht and 506 baht if<br />
family labor was costed. On a per rai basis, cash gross margin was<br />
271 baht and 91 baht with family labor included. Great price<br />
variability was also observed. While mungbean provided farmers<br />
some additional income, added organic matter and fertilizers to<br />
<strong>the</strong> soil, and yielded higher rice output in <strong>the</strong> next season, it can<br />
create delays and potential yield loss in <strong>the</strong> production of <strong>the</strong><br />
following rice crop. Direct seeding of rice was not as easily<br />
adopted by farmers in <strong>the</strong> study area as compared to mungbean<br />
before rice. When rainfall was adequate and regular, farmers did<br />
not see <strong>the</strong> need to direct seed. Transplanting of rice was still<br />
commonly practiced up to 1989. Direct seeding of rice became<br />
popular in <strong>the</strong> study area in 1990 onwards because rainfall was<br />
found to be usually inadequate for rice transplanting and labor<br />
costs high during peak demand periods. Direct seeding of rice with<br />
<strong>the</strong> use of an appropriate model of mechanical seeders introduced<br />
by <strong>the</strong> FSR project substantially reduced costs of rice planting. In<br />
1991, some 2,433 rai was planted to direct seeded rice in Dok<br />
Kham Tai, Phayao. Farmers found direct seeding convenient, easy<br />
to practice, and cost-effective. Many of <strong>the</strong>m acquired <strong>the</strong>ir own<br />
mechanical seeders. The practice is expected to expand at a rapid<br />
rate in <strong>the</strong> future both in this area and neighboring areas. The<br />
choice between mungbean before rice and direct seeding of rice<br />
sometimes has to be made but with proper time management of<br />
<strong>the</strong> two crops, farmers can ractice both technologies <strong>the</strong>reby<br />
enjoying much increased bene p its.<br />
'~ulti~le Cropping Center, Faculty of Agriculture, Chiang Mai University,<br />
Chiang Mai, Tha~land.<br />
2~arming Systems Research Institute, Thailand.<br />
3~hayap University, Chiang Mai, Thailand.
From 1980 to 1986, <strong>the</strong> Integrated Rainfed Farming Research and Development<br />
project was funded and supported by <strong>the</strong> United Nation Development<br />
Programme (UNDP)/Food and Agriculture Ogranization (FAO) in cooperation<br />
with <strong>the</strong> Royal Thai Government. The <strong>research</strong> project, which was initially<br />
conceived by <strong>the</strong> Department of Agriculture, was later expanded as a joint<br />
venture between <strong>research</strong> and extension. The Farming Systems Research<br />
Institute (FSRI) of <strong>the</strong> Department of Agriculture \vas <strong>the</strong> main agency that<br />
conducted and coordinated this project. The Department of Agricultural<br />
Extension, <strong>the</strong> Department of Li\lestock, and <strong>the</strong> Office of Agricultural<br />
Economics joined <strong>the</strong> project as cooperating agencies.<br />
The goal of <strong>the</strong> project was to improve <strong>the</strong> productivity and income of<br />
farmers in selected rainfed areas of major agroecological zones. The project was<br />
to develop farming <strong>systems</strong> technologies, including alternative cropping sjTstems<br />
and crop-livestock integration; introduce <strong>the</strong>se technologies in different<br />
agroecological zones ot selected rainfed areas; test <strong>the</strong>se technologies in <strong>the</strong><br />
field; and train staff of <strong>the</strong> FSRI in interdisciplinary farming <strong>systems</strong> <strong>research</strong><br />
(FSR), development, and extension. The project coirered nine pro\.inces: five in<br />
<strong>the</strong> north (Phavao, Chiang R:ii, Lampang, Phrae, and Sukothai), tlio in <strong>the</strong><br />
nor<strong>the</strong>ast (~ahasarakam arid Surin), and t\iro in <strong>the</strong> south (Phattalung and<br />
Nakhon Sri Thaminar:it).<br />
Because <strong>the</strong> project \vas farmer-oriented, <strong>the</strong> strategy for implementation<br />
underwent several changes, i.e., from purely cropping <strong>systems</strong>, to crop-livestock<br />
integration, to a whole-farm approach. The strategy of <strong>the</strong> project included:<br />
defining <strong>the</strong> target areas according to agroecological and crop-production zones;<br />
analyzing <strong>the</strong> existing farming practices and socioeconomic profiles of <strong>the</strong><br />
representative farmers in <strong>the</strong> rainfed areas; packaging and testing <strong>the</strong> developed<br />
technologies and evaluating <strong>the</strong>ir technical feasibility and socioeconomic<br />
viability; and e.utending pro\.sn viable technologies to more farmers.<br />
Figure 1 shows <strong>the</strong> co~~ceptual frameivork of <strong>the</strong> project, which includes<br />
<strong>the</strong> stages of FSR, <strong>the</strong> agencies in~lolved, and <strong>the</strong> approsin~ate time and<br />
conditions required for creation, de\felopment, and transfer of <strong>the</strong> technology.<br />
Mature technology is agroecologically feasible, economically viable. socially<br />
acceptable, and ready for extension. The role of <strong>the</strong> FSR <strong>research</strong>ers decreases<br />
over time; whereas, <strong>the</strong> role of extension officers and farmers increases as <strong>the</strong><br />
stages of multilocation testing, pilot production, and production programs are<br />
reached. The farming and cropping technologies tested in <strong>the</strong> target farms were<br />
developed in <strong>research</strong> stations riot only in Thailand but also in o<strong>the</strong>r countries<br />
and international <strong>research</strong> csnters.<br />
The 15 applied <strong>research</strong> packages or technologies were chosen to fit <strong>the</strong><br />
agroclimatic, socioeconomic, and indigenous farming practices in <strong>the</strong> project<br />
sites. Five technologies were introduced in <strong>the</strong> different regions.
Phayao<br />
Technology: three rice-based crops; two upland-based crops; two rice-based<br />
crops; and one stabilization crop.<br />
Cropping patterns: onion - rice - soybean; mungbean - rice soybean; sweet<br />
corn - rice - soybean; mungbean - rice - wheat; mungbean - rice - potato;<br />
mungbean - rice; corn/sorghum - mungbean; and direct seeded rice.<br />
Sukhothai<br />
Technology: three cotton-based crops; two cotton-based crops; and two soybean-<br />
based crops.<br />
Cropping patterns: soybean - cotton - blackgram multiple cropping;<br />
soybean - cotton relay cropping; sesame - soybean - sorghum; and baby corn -<br />
soybean - sorghum.<br />
Technology: two cassava-based crops; two rice-based crops; and one stabilization<br />
crop.<br />
Cropping patterns: cassava + peanut intercropping; jute - rice; and<br />
stabilization of cassava production through alley cropping and rotation cropping.<br />
Crop-livestock integration (Phayao, Mahasarakham)<br />
Technology: feed substitution for pigs; feed substitution for cattle; and feed<br />
mixing at village level.<br />
Implementation: use of by-products (rice bran, broken rice, corn, leafy<br />
vegetables, and water plants) as partial substitutes for commercial feeds for<br />
fattening pigs and cattle; and introduction of feed mixing equipment in <strong>the</strong><br />
village.<br />
When <strong>the</strong> project ended in 1986, <strong>the</strong> direct seeded rice and mungbean -<br />
rice <strong>systems</strong> were considered mature technologies. In addition, <strong>the</strong>re were four<br />
technologies at <strong>the</strong> multilocation testing stage and 29 at <strong>the</strong> on-farm trial stage.<br />
Four technologies were returned to <strong>the</strong> <strong>research</strong> station for fur<strong>the</strong>r study. Seven<br />
promising technologies were believed to be relevant to smallholders in rainfed<br />
areas. The technologies and <strong>the</strong>ir areas are summarized in Table 1.
THE 1'\VO ;MATURE TECHNOLOGIES<br />
Direct (dry) seeded rice (DSR) and mungbean before rice were mature<br />
technolog~es that u ~re adopted on a large scale because <strong>the</strong>y were simple and<br />
easy to practice. DSR was recommended for areas where rainfall was unreliable<br />
(e.g., in <strong>the</strong> nor<strong>the</strong>ast and in amphoe Dok Kham Tai, Phayao).<br />
Land preparation for DSR starts as soon as <strong>the</strong>re is adequate rainfall.<br />
Rice seeds should be coated with irisecticide to prevent insect damage before<br />
germination. Seeding should be done in June-July in <strong>the</strong> nor<strong>the</strong>rn areas and in<br />
August-September in <strong>the</strong> sou<strong>the</strong>rn areas. The rice varieties should be drought-<br />
tolerant and appropriate to local rainfall patterns. A mechanical seeder is<br />
recommended to facilitate weeding and o<strong>the</strong>r cultural operations. To support<br />
this recommendation, <strong>the</strong> project, through <strong>the</strong> Agricultural Engineering<br />
Division, developed l-row to 4-row mechanical seeders. Because manual labor<br />
was required to operate <strong>the</strong>se machines, farmers thought <strong>the</strong> task would be<br />
difficult. Therefore, <strong>the</strong>y resorted to draft animals but had little success. Finally,<br />
small tractors Lvere used to pull <strong>the</strong> seeder, and this was received with a fair<br />
degree of acceptability. Preemergence herbicides and chemical fertilizers were<br />
also recommended.<br />
In <strong>the</strong> nor<strong>the</strong>rn area, where conditions are drier, farmers failed to grow a<br />
good crop of transplanted rice (TPR) in about 2 out of every 3-4 yr. In this<br />
situation, DSR would have a positive impact because farmers would not need to<br />
wait for hea\y rains to establish <strong>the</strong> rice. Fur<strong>the</strong>rmore, DSR can withstand<br />
drought better than transplanted rice toward <strong>the</strong> end of <strong>the</strong> rainy season because<br />
of its early establishment and maturity.<br />
The estimated gross margin from DSR was about THB1940lha using <strong>the</strong><br />
recommended levels of inputs. The cost of inputs for DSR is less than for TPR.<br />
Direct seeded rice ivas more stable than TPR in rainfed areas. At amphoe<br />
(district) Dok Kham Tai, Phayao, where <strong>the</strong> cost of labor during rice<br />
transplanting was high. DSR was Inore profitable (UNDP/FAO 1986).<br />
hlungbean before rice was designed to capture early season soil moisture<br />
and to supplement farm income. hlungbean can be established before ei<strong>the</strong>r<br />
TPR or DSR, depending on local rainfall patterns. Land preparation for<br />
mungbean inust be done immediately after <strong>the</strong> onset of <strong>the</strong> wet season, i.e., in<br />
late April or early May. Seeding should be done immediately after land<br />
preparation using <strong>the</strong> same mechanical seeders used in DSR. However, <strong>the</strong> size<br />
of <strong>the</strong> slots must be adjusted (Suvanchinda et al 1980). The reconlmended<br />
mungbean varieties are Utong 1, VC 1178, Kampaengsaen 1, and<br />
Kampaengsaen 2. Utong 1, howei~er, had more problems than <strong>the</strong> o<strong>the</strong>r<br />
varieties. The yield of Utong 1 decreased whenever <strong>the</strong> field was waterlogged. It<br />
also had more than one flowering and pod-setting period, which prolonged <strong>the</strong><br />
harvest, increased labor for harvesting, and delayed <strong>the</strong> next crop. For all<br />
varieties, insecticide application was recommended whenever necessary;<br />
however, fertilizer was not needed.
Short-duration rnungbean sown in April or May (before <strong>the</strong> main crop of<br />
rice in August) fits into <strong>the</strong> two-peak rainfall pattern of <strong>the</strong> country. However,<br />
mungbean is not recommended in low-lying areas of <strong>the</strong> ricefield where<br />
waterlogged conditions are likely to occur.<br />
Farmers who followed <strong>the</strong> recommended input levels for mungbean and<br />
rice obtained a gross margin of THB5,630/ha. This is about 100% higher than in<br />
fields where recommendations were not followed (Table 2).<br />
O<strong>the</strong>r promising technologies, such as jute before rice and kenaf before<br />
rice, were tested in four provinces in <strong>the</strong> nor<strong>the</strong>ast. The EEC- and USAID-<br />
funded projects in <strong>the</strong> area joined this project on <strong>the</strong> basis of <strong>the</strong>se two<br />
technologies.<br />
STUDY OBJECTIVES<br />
In 1988, <strong>the</strong> <strong>International</strong> Development Research Centre (<strong>IDRC</strong>), in coordi-<br />
nation with <strong>the</strong> <strong>International</strong> Rice Research Institute (IRRI), supported studies<br />
in several countries to investigate <strong>the</strong> impact of FSR. In Thailand, an impact<br />
study was conducted by social scientists from Chiang Mai University and Phayap<br />
University toge<strong>the</strong>r with personnel from <strong>the</strong> FSRI. The study area, amphoe Dok<br />
Kham Tai, Phayao, was selected under <strong>the</strong> guidance of <strong>the</strong> FSRI. The two<br />
mature technologies introduced by <strong>the</strong> Integrated Rainfed FR/D project were<br />
<strong>the</strong> subject of investigation. The main objective of <strong>the</strong> study was to determine<br />
<strong>the</strong> changes in productivity and income of farmers who adopted <strong>the</strong> new<br />
technologies in <strong>the</strong> rainfed areas of Dok Kham Tai. The specific objectives were<br />
to determine <strong>the</strong> level and extent of adoption of <strong>the</strong> new technology introduced<br />
in <strong>the</strong> project site and in neighboring areas; to estimate changes in net farm and<br />
household incomes resulting from <strong>the</strong> adoption of <strong>the</strong> new technology in relation<br />
to o<strong>the</strong>r sources of household income; and to examine <strong>the</strong> adoption of <strong>the</strong> new<br />
technology and its benefits to different groups of farmers.<br />
The study lasted 3 yr (1989-91). The first 2 yr were devoted to data<br />
collection and <strong>the</strong> last year to data analysis. Following <strong>the</strong> recommendations<br />
made during review meetings in Chiang Rai in November 1990, <strong>the</strong> study<br />
focused on <strong>the</strong> impact on institutions and communities. It was felt that without<br />
this aspect, impact assessment might be viewed as inadequate. Therefore, this<br />
study included <strong>the</strong> extent to which technologies and recommendations were<br />
adopted by extension officers and farmers; and <strong>the</strong> contribution of FSR to<br />
improvements in <strong>the</strong> capability of <strong>the</strong> <strong>research</strong> <strong>systems</strong> to meet <strong>the</strong> needs of its<br />
clients. However, greatest emphasis was given to <strong>the</strong> original objectives because<br />
this was <strong>the</strong> initial orientation of <strong>the</strong> study in 1989.
Conceptual framework<br />
STUDY METHODS<br />
To determine <strong>the</strong> impact of FSR technologies on <strong>the</strong> productivity and income of<br />
farmers in <strong>the</strong> rainfed areas, a causal relat~onship should be established. Had <strong>the</strong><br />
changes took place because of <strong>the</strong> new technologies and <strong>the</strong> operations of <strong>the</strong><br />
FSR project? In <strong>the</strong> case of mungbean before rice and DSR, cause and effect<br />
relationships could be established for income and productivity because both<br />
technologies were unknown in <strong>the</strong> area before <strong>the</strong> project was introduced.<br />
Changes in income and activities of <strong>the</strong> household could be directly attributed to<br />
mungbean because <strong>the</strong> land would o<strong>the</strong>rwise be left fallow. A comparison could<br />
also be made between DSR and TPR. O<strong>the</strong>r changes in <strong>the</strong> communities, such<br />
as living conditions, education, health, migration, and off-farm employment,<br />
were also studied.<br />
Data collection and analysis<br />
In 1989, DSR was no longer practiced by farmers in Dok Kham Tai. Earlier,<br />
however, it had been widely adopted in <strong>the</strong> area. In <strong>the</strong> district extension office,<br />
no mention was made of DSR and no statistics were collected. The team<br />
concentrated on <strong>the</strong> adoption of mungbean before rice, but was puzzled by <strong>the</strong><br />
disappearance of DSR from <strong>the</strong> area.<br />
A preliminary rapid rural appraisal followed by a formal survey was<br />
conducted during <strong>the</strong> first year (1989). A total of 160 households were<br />
interviewed using structured questionnaires. The sample was stratified as<br />
130 households within <strong>the</strong> project area, consisting of 100 adopterz of mungbean<br />
before rice and 30 nonadopters; and 30 households in amphoe Jun, Phayao, a<br />
district adjacent to but outside <strong>the</strong> project area, but had a similar agroeco!ogical<br />
environment.<br />
In <strong>the</strong> second year (l990), 30 households from <strong>the</strong> 100 households<br />
planting mungbean were selected for fur<strong>the</strong>r data collection. Household records<br />
on crop-production practices, farm income, and expenditures were maintained<br />
every 2 wk. Two <strong>research</strong> assistants were assigned to collect data and to record<br />
o<strong>the</strong>r socioeconomic changes in <strong>the</strong> communities. Data were analyzed using<br />
descriptive statistics.<br />
Dok Kham Tai study site<br />
Dok Kham Tai is a district in Phayao with an area of 77,800 ha. About 18,900 ha<br />
are ricefields and 5,800 ha are upland areas. The district is a long strip of land in<br />
<strong>the</strong> center of Phayao. It is bounded by mountain on <strong>the</strong> east. The area slopes to<br />
<strong>the</strong> west. The Mae Ing River runs across <strong>the</strong> northwest border, and <strong>the</strong> Rong
Chang River cuts through <strong>the</strong> middle of <strong>the</strong> district. Administratively, <strong>the</strong> district<br />
is divided into eight tambons (subdistricts). The experimental plots were in<br />
tambon Don Sri Chum and tambon San Koang.<br />
All of Don Sri Chum is essentially ricefields; whereas, San Koang has<br />
about 16% upland area. Don Sri Chum is largely lowland with a reasonably<br />
stable rice production (Patanothai 1983), except for areas near <strong>the</strong> Mae Ing and<br />
Rong Chang Rivers, which flood in some years. The rice area in San Koang is a<br />
mixture of both uplands and lowlands and has a higher percentage of upland<br />
rice. Its rice production is less stable. Tambons Ban Tham, Ban Pin, and Huey<br />
Lan are ricefields. They have relatively less rice area (63-71%) than tambons<br />
Dok Kham Tai and Don Sri Chum (97%).<br />
The estimated population of Dok Kham Tai in 1990 was 77,297. The<br />
majority are involved in rainfed farming. Rainfall averaged 1,052 mm from 1981<br />
to 1989 alld had a bimodal pattern with a lapse in June. Rainfall variability is a<br />
common problem in this district. Farms experience ei<strong>the</strong>r floods or drought<br />
every 2 yr and, as a result, <strong>the</strong> district is well-known for poverty and out-<br />
migration.<br />
Transpl;lnted rice is <strong>the</strong> main crop in <strong>the</strong> lowlands during <strong>the</strong> wet season<br />
(WS). In some years, farmers experience delayed or inadequate rainfall. In some<br />
tambon, it is possible to grow field crops (e.g., garlic and soybean) in <strong>the</strong><br />
ricefields during <strong>the</strong> dry season (DS). Most of <strong>the</strong> land, however, is left fallow in<br />
<strong>the</strong> DS until August. In <strong>the</strong> uplands, maize - mungbean is <strong>the</strong> most popular<br />
cropping pattern. Since 1984, when mungbean before rice was introduced, it has<br />
been widely practiced in <strong>the</strong> uplands. The cropping intensity in Jun is higher<br />
than in Dok Kham Tai. However, double cropping is possible in Dok Kham Tai.<br />
The district has a reputation for migration of young girls seeking off-farm<br />
jobs in <strong>the</strong> cities. They are usually employed in bars, massage parlors, night<br />
clubs, and bro<strong>the</strong>ls. Remittances from <strong>the</strong>se migrants result in ma:erial<br />
prosperity in <strong>the</strong> district in terms of big new houses, cars, and electrical<br />
appliances. Young men migrate to <strong>the</strong> cities or even overseas as construction<br />
workers.<br />
Exchange labor for farm work is uncommon in Dok Kham Tai.<br />
Additional labor is usually provided by nearby areas or districts. The wage rate<br />
increases during transplanting and harvesting when <strong>the</strong> demand for labor is high.<br />
Expansion of area planted to mungbean<br />
The <strong>research</strong> stages and <strong>the</strong> expansion of <strong>the</strong> area planted to mungbean before<br />
rice from 1982-83 to 1985-56 are shown in Table 3. Mungbean before rice was<br />
widely adopted during <strong>the</strong> pilot-production stage. Mungbean covered 62.4 ha in<br />
Phayao Province in 1985-86. In <strong>the</strong> same year, information collected by <strong>the</strong><br />
impact team suggested <strong>the</strong>re were 13.8 ha planted to mungbean in Dok Kham
Tai. Mungbean was introduced in Dok Kham Tai in 1983. From an experimental<br />
area of 9.6 ha in 1984 it espanded to about 1,600 ha in 1990 (Table 4). In 1987,<br />
658 households adopted mungbean in Dok Kham Tai (Table 5). However, <strong>the</strong><br />
cultivation of mungbean had been affected by frequent floods and waterlogging.<br />
In 1989, floods destroyed much of <strong>the</strong> mungbean and lowered farm income. As a<br />
result, many farmers abandoned <strong>the</strong> cultivation of mungbean a year later.<br />
In 1990, 1,376 ha in <strong>the</strong> upland areas of tambon San Kaong were planted<br />
to mungbean (Table 4), but much less was planted in <strong>the</strong> o<strong>the</strong>r tambons (11-S8<br />
ha). Although many farmers successfully planted mungbean, <strong>the</strong> price fluctuated<br />
widely and so did income. Many farmers abandoned mungbean cultivation in<br />
1989 because prices were low at THB6-8/k (Table 6). In 1991, however, <strong>the</strong><br />
price of mungbean increased to THB~o-I~,~~. Those who had continued to<br />
plant mungbean obtained high profits.<br />
The average area planted to mungbean per liousehold isas 2.1 ha in 1988<br />
and 2.2 ha in 1989. In 19S8, 19% of farmers planted mungbean on less than<br />
on 0.8 ha of land-. The rnaior,ity (-35%) planted on 0.8-1.6 ha. Only 14% planted<br />
on more than 3.2 ha (Tahie 7).<br />
Data from <strong>the</strong> field sune!, rei,ealed that <strong>the</strong> average yield of rnungbean<br />
was 250-300 kg/lia. This \+.as much lou'er than <strong>the</strong> estimated potential level of<br />
550-1000 kg/ha. About 10% of <strong>the</strong> farmers obtained an average yield of<br />
500 kg/ha or more. Most farmers (79%), however, obtained 375 &/ha or less in<br />
1988. Yields were generally higher for mungbean planted in small plots<br />
(0.5-0.6 ha) because of better management and more thorough harvesting than<br />
in <strong>the</strong> large plots (Table S). During harvesting (July-August), labor is scarce and<br />
expensive because it coincides with rice cultivation<br />
Costs and returns of mungbean bcforee rice<br />
Flooding in 1938 destroyed most of <strong>the</strong> niungbean crops especially in Dok Kham<br />
Tai. About 175" of <strong>the</strong> farniers surveyed had totally lost <strong>the</strong>ir crop. Flooding<br />
recurred in 1989 and many farmers gave up planting mungbean.<br />
Variable cash costs per household were, on <strong>the</strong> average, TFIB1.278;<br />
whereas, gross margin was TMB2,S70, excluding f~mily labor costs ('Table 8). If<br />
family labor costs \yere included, <strong>the</strong> gross margin was only THB506. The<br />
maximum area planted to niungbean per household was 8.7 11:~. 'T1:e ri?xiirnum<br />
gross margin per household &.as THB31,6S1 (e:icluding Iariiil!: labor costs) clnci<br />
THB22,700 (when farnily labor cost isas included). Farmsrs \;,lie here affected<br />
by floods suffered losses. One farmer lost TIiB5,510. Avera~c: Ivsscc due tc<br />
flooding were about THB70,Y.<br />
Farmers who had an average yield of about 375-500 1x/lia and planted on<br />
an average of 2.8 ha, had gross margins of THB11394 in 1988 (Table 8).<br />
Average gross margin (excluding family labor) per hectare was TtiB1,691 for all
yield ranges. When family labor costs were included, gross margin (2) per<br />
hectare was reduced to THB569. Gross margin per hectare could be THB3,750-<br />
5,100 if <strong>the</strong> yield was 375-500 kg/ha. This would be THB2,638-3,981 per hectare<br />
if family labor was deducted. This level of gross margin is lower than <strong>the</strong><br />
estimated level of THB5,625/ha.<br />
Additional details were collected on planted area, output per hectare,<br />
costs, and gross margin for mungbean before rice and were classified by farm<br />
size. Farms were categorized as small (0-1.6 ha), medium (1.7-3.2 ha), and large<br />
(greater than 3.2 ha). These categories were used as a proxy for socioeconomic<br />
status. Small-scale farmers planted an average of 1.0-1.2 ha to mungbean and<br />
obtained a yield of 306-319 kg/ha. Variable costs (cash) were THB681-925/ha.<br />
Gross mar in (excluding family labor) was THB2,175/ha in 1988 and<br />
THB1,000$ha in 1989 (Table 9). On a per farm basis, small-scale farmers<br />
obtained a gross margin of THB1,822 in 1988 and THB1,219 in 1989. However,<br />
large farms obtained lower yields (169-244 kg/ha) and lower gross margins<br />
(excluding family labor) than small-scale farmers. On average, <strong>the</strong>y obtained<br />
THB3,360-4,830 gross margin (excluding family labor) per farm. Some successful<br />
farmers obtained THB19,280-31,684 of gross margin (excluding family labor)<br />
from <strong>the</strong>ir mungbean crop. If family labor costs were included, <strong>the</strong>se farmers<br />
would obtain a gross margin of THB12,980-22,700.<br />
Opinions of farmers<br />
The 100 h~useholds surveyed in 1989 were surveyed again in 1990 to determine<br />
<strong>the</strong> opinion of <strong>the</strong> farmers about <strong>the</strong> performance of mungbean in 1989. The<br />
floods that occurred in <strong>the</strong> project area for two consecutive years had a negative<br />
effect on <strong>the</strong> attitudes toward mungbean. The majority (90%) of <strong>the</strong> households<br />
continued to plant mungbean before rice until 1989. These results were obtained<br />
when DSR regained popularity in 1990. Responses with regard to DSR were<br />
insufficient.<br />
Farmers continued to plant mungbean before rice because it provided<br />
money to support <strong>the</strong> following rice crop, it increased rice productivity, it<br />
increased household income, and prices were high in some years. The reasons<br />
why farmers abandoned <strong>the</strong> cultivation of mungbean were: floods, late rains that<br />
led to a conflict in timing of <strong>the</strong> mungbean harvest and rice transplanting (or<br />
DSR), increased insect problems, and low price in some years.<br />
The responses of farmers concerning <strong>the</strong> interrelationships of mungbean<br />
before rice and o<strong>the</strong>r components in <strong>the</strong> farming <strong>systems</strong> are as follows:<br />
Rice productivity. The majority of farmers reported positive effects on rice<br />
productivity. The 13% who reported negative effects attributed <strong>the</strong>se to<br />
delays in transplanting rice.
Land preparation for rice. The reactions of <strong>the</strong> farmers to <strong>the</strong> effect of<br />
rnungbean on land preparation were mixed. Although 32% cited ease in<br />
land preparation for <strong>the</strong> next crop, 24% reported difficulty in preparing<br />
<strong>the</strong> land for rice because mungbean plants were still in <strong>the</strong> field at <strong>the</strong><br />
time of land preparation.<br />
Weeds in rice crop. Only 8% reported positive effects of mungbean. The<br />
cultivation of rnungbean apparently introduced more weeds into <strong>the</strong><br />
ricefields.<br />
Soil fertility. Most farmers (74%) agreed that <strong>the</strong> planting of rnungbean<br />
improved <strong>the</strong> soil in <strong>the</strong>ir fields. The 3% who reported negative effects<br />
cited <strong>the</strong> need to apply fertilizers to improve soil fertility (soil analysis<br />
shown in Table 10).<br />
Livestock raising. Only 5% of <strong>the</strong> farmers reported a positive effect.<br />
Farmers observed that cows die from overeating mungbean. O<strong>the</strong>rs<br />
observed that <strong>the</strong>ir cows grazed on <strong>the</strong>ir neighbors' mungbeans, creating<br />
social conflicts.<br />
Upland mungbean. Forty percent reported positive effects of mungbean<br />
before rice on upland mungbean. There were no reports of negative<br />
effects.<br />
Mechanization. An increase in <strong>the</strong> degree of mechanization (i.e., use of<br />
threshing machine) was observed by 21% of <strong>the</strong> farmers. However, 72%<br />
reported no change in <strong>the</strong> level of mechanization.<br />
Labor use. More labor was used by 60% of <strong>the</strong> farmers to plant<br />
mungbean. There were no reports of a decline in labor use.<br />
Capital use. Most farmers (69%) agreed that mungbean cultivation<br />
increased capital requirements; 2470 reported no change.<br />
Income effect. Higher incomes were reported by 42% who grew<br />
mungbean; 29% experienced a reduction of income because of losses.<br />
Processing. Farmers sold <strong>the</strong> bulk of <strong>the</strong>ir mungbean prcduce to local<br />
merchants in <strong>the</strong> village. A small amount of mungbean was retained for<br />
home processing (e.g., bean sprouts and desserts).<br />
By-products. Some farmers used mungbean by-products for mushroom<br />
culture (17%) and for compost (9%). Many farmers, however, reported<br />
no knowledge of how to process or use <strong>the</strong> by-products.
Soil analysis<br />
The soil was analyzed to determine if changes in mineral components or soil<br />
texture might have affected <strong>the</strong> growth and yield of rice grown after mungbean.<br />
The field was classified into three areas: high terrace, middle terrace, and low<br />
terrace. In March 1991, 90 soil samples from <strong>the</strong> plots of mungbean - rice (30<br />
samples per terrace) and 30 soil sam les from <strong>the</strong> plots of fallow - rice (10<br />
samples per terrace) were collected P or soil analysis. Table 10 suggests that<br />
organic matter (OM), P, and K in <strong>the</strong> mungbean - rice cropping pattern were<br />
higher than in <strong>the</strong> fallow - rice pattern. The results from on-farm trials<br />
conducted in 1986-88 indicated that rice yields in <strong>the</strong> mungbean - rice cropping<br />
pattern were higher (3.75 t/ha) than those in fallow - rice pattern (3.3 t/haj<br />
(Nichai and Rasamee 1991).<br />
Expansion of direct (dry) seeded rice<br />
The UNDP/FAO project reported that <strong>the</strong>re had been an expansion of<br />
DSR (i.e., 1,484 ha in five provinces, of which 1,276 ha were in Phayao in<br />
1985-86) (Table 11). In 1989, <strong>the</strong>re was hardly any DSR areas in Phayao. Field<br />
<strong>research</strong>ers who introduced mungbean before rice and DSR in 1982 attributed<br />
<strong>the</strong> shift to TPR to good rainfall In 1986-88. DSR reenierged in 1990 on about<br />
160 ha because of late rains in 1989. However, in 1991, 163 households (about<br />
419 ha) adopted DSR. Many farmers ordered mechanical seeders and some<br />
went as far as Sukothai, a province approximately 300 km away, to purchase<br />
<strong>the</strong>se machines.<br />
Interviews with farmers in Dok Kham Tai revealed that <strong>the</strong> readoption of<br />
DSR in 1990-91 was due to <strong>the</strong> increased wage rate during transplanting. Some<br />
reported wage rates as high as THB100/d. This was nearly 7070 higher than <strong>the</strong><br />
average wage rate in o<strong>the</strong>r districts and in o<strong>the</strong>r periods of <strong>the</strong> year. The normal<br />
wage rate was THBGO-70/d. High wage rates were associated with delays in<br />
rainfall. When <strong>the</strong> rains come, <strong>the</strong> rush to transplant rice as early as possible<br />
creates stiff competition for labor and produces high wage rates. The labor<br />
requirement to transplant seedlings is about 19-31 d/ha. This is eq~iivalent to<br />
26-39 d of hired labor for an average farm (or approxin~ately THB2,500-4,000).<br />
However, if DSR is adopted and mechanical seeders are used, an average<br />
farmer would need 2 d to complete crop establishment. The cost of a seeder<br />
(THB6,OOO) could <strong>the</strong>refore be recovered in 2 yr. Farmers in areas where<br />
seeders are scarce hired seeders at THB188/ha.<br />
Many farmers would like to grow DSR on all of <strong>the</strong>ir land bzcause it is<br />
less costly. Given <strong>the</strong> rainfall pattern, farmers who adopted mungbean before<br />
rice found it difficult tu establish DSR in August because <strong>the</strong> soil was usually too<br />
wet. The mechanical seeder clogged easily. Late onset of rainfall rlelays <strong>the</strong><br />
mungbean crop and makes establishment of DSR almost impossible. A cut-off<br />
date for mungbean should <strong>the</strong>refore be established if DSR is to be successfully<br />
implemented. Some farmers purposely plowed <strong>the</strong>ir mungbean crops in early<br />
August before it was fully harvested to make way for DSR. In 1991, when <strong>the</strong>
price of mungbean was high, some farmers wondered whe<strong>the</strong>r <strong>the</strong>y had made<br />
<strong>the</strong> correct decision.<br />
The limited number of mechanical seeders was also a problem. In 1990,<br />
FSRI had only three units in tambon San Koang. There was long waiting lists for<br />
<strong>the</strong> use of <strong>the</strong>se machines that year. In 1991, 16 more machines were purchased.<br />
Large farm owners purchased <strong>the</strong>ir own mechanical seeders. Small- and average-<br />
size farm owners hired machines (with operators TEIBZOO/d; without operators<br />
THBlSOId). With <strong>the</strong> use of a one hand tractor, about 1.6 ha of land can be<br />
covered in a day. Although <strong>the</strong> availability of seeders improved in 1991, farmers<br />
still had to reserve <strong>the</strong> machines and wait for <strong>the</strong>ir turn.<br />
Currently, many farmers make provisions for doing ei<strong>the</strong>r DSR or TPR.<br />
They prepare rice nurseries alongside <strong>the</strong> plots prepared for direct seeding. This<br />
strategy provides a hedge against possible delays in transplanting if DSR cannot<br />
be established. If direct seeding is possible, <strong>the</strong>y can still sell <strong>the</strong>ir seedlings<br />
to o<strong>the</strong>r farmers.<br />
In a follow-up interview after <strong>the</strong> rice han-est in 1991, it was found<br />
that <strong>the</strong> business of renting seeders was becoming popular. Some farmers talked<br />
about purchasing additional seeders merely for this purpose. The 19 seeders in<br />
<strong>the</strong> area were shared by 163 farm households in 1991 (i.e., an a\.erage 22 ha per<br />
seeder). Farmers in <strong>the</strong> o<strong>the</strong>r tci)~lho)rs are also interested in <strong>the</strong> machines.<br />
Information on yield, costs, and gross margin of DSR were also collected.<br />
Only a few farmers were surveyed in 1990 because <strong>the</strong> practice was just being<br />
revived when <strong>the</strong> impact study was about to end. h4ost of <strong>the</strong> information<br />
concerning DSR in Dok Kham Tai in 1990-91 was obtained through informal<br />
interviews. Yields from TPR and DSR were comparable among small and<br />
medium farms (i.e., 2,588-2,688 kg/ha for DSR and 2,625 kg/ha for TPR). The<br />
weed problem in DSR was more common and severe than in TPR. This was one<br />
reason why farmers were slow to adopt DSR. The early adopters of DSR<br />
experienced severe weed problems. Farmers, ho\+.ever, minimized yield loss<br />
caused by weeds by more tt~orough land preparation (repeated tillage) and by<br />
alternating DSR and TPR.<br />
Among mungbean adopters, yields of TPR averaged 3,-t 19 kg,/ha in 1988.<br />
This was higher than <strong>the</strong> average output of DSR. Costs per hectare were also<br />
higher. Gross margin (excluding farm labor) was TFIB8,706/ha compared with<br />
THB5,331/ha for DSR. Gross margin (including farm labor) for TPR was<br />
THB7,581/ha but only THB3,700/ha for DSR. However, <strong>the</strong> Iligher yields and<br />
gross margins for TPR compared with DSR are not conclusive becalise yields<br />
were obtained during different periods.
Possible time conflict between mungbean and direct seeded rice<br />
Delayed establishment of <strong>the</strong> mungbean crop because of <strong>the</strong> late onset of rains<br />
poses a serious problem for DSR. If <strong>the</strong> onset of <strong>the</strong> rains is delayed until late<br />
May, farmers must make a choice between mungbean and DSR. If mungbean is<br />
to be planted and harvested, <strong>the</strong> rice crop must be transplanted. DSR could not<br />
be established in wet soil because <strong>the</strong> mechanical seeders clogged. However,<br />
TPR faces <strong>the</strong> risk of receiving inadequate rainfall for vegetative growth.<br />
The probability of <strong>the</strong> accumulation of an adequate amount of rain<br />
(75 mm) for crop establishment after <strong>the</strong> onset of <strong>the</strong> rainy season was<br />
determined. If mungbean is planted on 1 Apr, <strong>the</strong> probability of adequate<br />
rainfall is 0.1 (one in 10 years). If mungbean is planted on 21 Apr, <strong>the</strong>re is about<br />
a 0.7 probability of adequate moisture. A cumulative probability of a backward<br />
accumulation of 500 mm of rainfall is necessary for a rice crop (Morris and<br />
Zandstra 1978). Using this criterion and an 85-90 d growing season for<br />
mungbean, DSR planted about 11 Jul has a 0.7 probability of a backward<br />
accumulation of 500 mm of rain.<br />
If farmers decide to keep <strong>the</strong> mungbean crop and choose TPR, <strong>the</strong>y will<br />
encounter several consequences. They will experience a lower probability of<br />
adequate rainfall (50%) and will need rice seedlings for transplanting. Labor<br />
costs for transplanting will also be higher than for DSR.<br />
Currently, farmers in Dok Kham Tai are refining <strong>the</strong>ir knowledge about<br />
<strong>the</strong>se two technologies. If <strong>the</strong>y adopt both mungbean and DSR. They can derive<br />
greater benefits. Adopting any of <strong>the</strong> technologies can directly, or indirectly<br />
improve income by reducing costs.<br />
The probability of 10-d and 15-d dry spells in Dok Kham Tai were<br />
determined using 10-yr 1981-91) rainfall data. The probability of 10-d and<br />
15-d dry spells (< l mm i d) is 0.6 or 6 in 10 yr during late May and mid-June.<br />
Mungbean affected by dry spells had low productiv~ty in Dok Kham Tai.<br />
However, DSR requires a period of dry wea<strong>the</strong>r during seeding. If planted in<br />
mid-June, <strong>the</strong> probability of having dry spells is still high. However, if planted<br />
at <strong>the</strong> end of July, <strong>the</strong> probability drops to about 0.2 (two in 10 yr). It is not<br />
practical to use DSR in August because of <strong>the</strong> probability of 5-d dry spells<br />
(
manufacturers. The earlier models (e.g., l-row, 2-row, and even 3-row machines)<br />
were unsatisfactory when tested on farms. In 1985-86, when DSR was popular,<br />
<strong>the</strong> seeders used by project personnel were 2-row machines operated using<br />
manual labor. Fur<strong>the</strong>r modifications were done after a dialogue with farmers,<br />
FSRI field personnel, and <strong>research</strong>ers from <strong>the</strong> Division of Agricultural<br />
Engineering. In 1986, a 4-row model drawn by a small tractor was tested in<br />
tambon San Koang. This inodel is now popular in <strong>the</strong> study area.<br />
The role of Dok Kham Tai Agricultural Extension Office<br />
After <strong>the</strong> project ended in 1986, <strong>the</strong> Department of Agricultural Extension<br />
ceased to work with FSRI. It is unfortunate that <strong>the</strong> personnel of <strong>the</strong> District<br />
Office of Agricultural Extension in tambon Kaset were new appointees, thus,<br />
<strong>the</strong>y were unaware of <strong>the</strong> events that had taken place before 1986. They learned<br />
about mungbean before rice from farmers. but <strong>the</strong>y learned nothing about DSR.<br />
(Our team provided <strong>the</strong>m with information about <strong>the</strong> practice and popularity of<br />
DSR in Dok Kham Tai.) The agricultural data <strong>the</strong>y collected were highly<br />
aggregated. For example, information on mungbean from lowlands and uplands<br />
were aggregated into a single category. Similarly, DSR and TPR were taken as<br />
one category.<br />
Role of farmers in adaptation and testing new technologies<br />
Farmers in <strong>the</strong> area were active in testing new technologies. They observed <strong>the</strong><br />
performance of new technologies in <strong>the</strong>ir neigllborhood, sought new ways of<br />
doing things, and knew about sources of seed varieties and farm machinery. For<br />
example, a FSRI cooperator \vho planted mungbean (Utong 1) wanted new<br />
varieties that would perform better. He went to Kasetsart University in Bangkok<br />
to buy Kampaengsaen 1 and 2, which were not available in his area. Later, he<br />
found that Kampaengsaen 2 was more appropriate to his cropping system. He<br />
was also among <strong>the</strong> first farmers to test DSR, but he was not very successful<br />
because of weed problems.<br />
Threshing machines are essential for mungbean production. The project<br />
offered a threshing machine that worked well but was not yet available in <strong>the</strong><br />
market. A group of farmers traveled to Khon Kaen. Nor<strong>the</strong>ast Thailand. to<br />
purchase one for <strong>the</strong>ir own use and for renting at THBO.SO/kg. In 1?90, about<br />
5-6 more threshers were available in tambon San Koang. M~ingbean was initially<br />
planted using a mechanical seeder. Later, through dialogue with farnlers, it was<br />
agreed that broadcast seeding would be a better method.<br />
When problems concerning rainfall patterns and high labor costs in TPK<br />
were encountered, some farmers thought about <strong>the</strong> possibility of DSR. Farmers<br />
experimented with ways to control weed problems through good land<br />
preparation and alternation of DSR with TPR. They also sought information<br />
about obtaining 4-row seeders and traveled to Sukothai to purchase one.
Many of <strong>the</strong>se adaptations were spearheaded by advanced farmers,<br />
farmer leaders, and medium- to large-size farmers. These farmers have enough<br />
capital to invest in <strong>the</strong>ir farms, to experiment, and to try new methods.<br />
Eventually, <strong>the</strong> benefits of <strong>the</strong> new technologies diffuse to small farmers as well.<br />
Expansion of mungbean and DSR in neighboring areas<br />
In 1985, <strong>the</strong>re was an expansion in <strong>the</strong> area planted to DSR and mungbean<br />
before rice. For example, in Chiang Rai Province, <strong>the</strong>re were 17.6 ha of<br />
mungbean before rice, 7.2 ha of DSR; in Lampang province, 58.4 ha of<br />
mungbean, 159.0 ha of DSR; and in Phrae, 8.6 ha of mungbean and 41.8 ha of<br />
DSR. After 1985-86, statistics on mungbean and rice were aggregated in such a<br />
way that it was impossible to ascertain changes. Initial interviews revealed that<br />
expansion of <strong>the</strong> areas planted to <strong>the</strong>se crops was limited to <strong>the</strong> original sites.<br />
Adoption, however, was not substantial because of problems with marketing,<br />
inadequate <strong>research</strong> and extension support, and <strong>the</strong> need for machinery. The<br />
experience in Dok Kham Tai indicated that continuous support by <strong>research</strong> and<br />
extension personnel is critical to sustain adoption.<br />
SOCIOECONOMIC CONDITIONS OF ADOPTERS AND NONADOPTERS<br />
The disappearance of DSR in 1986-89 limited data collection on mungbean -<br />
rice adopters: Socioeconomic conditions were assessed among adopters and<br />
nonadopters of mungbean before rice both within and outside <strong>the</strong> project area.<br />
Table 12 shows important socioeconomic indicators of <strong>the</strong> three groups.<br />
Land size and distribution<br />
Adopters of mungbean before rice had larger ricefields and more upland areas<br />
than nonadopters. The average farm size was 2.2 ha among adopters and 1.8 ha<br />
among nonadopters both inside and outside <strong>the</strong> project area. For all farms<br />
(including <strong>the</strong> upland areas) <strong>the</strong> average size was 2.7-2.9 ha. Of <strong>the</strong> adopters,<br />
s~nall farmers (approximately 1 ha) constituted about 30% of <strong>the</strong> total. Small<br />
farmers who were nonadopters constituted 50-53% of <strong>the</strong> total and had about<br />
1.1 ha of land. The percentage of adopters who were medium and large farmers<br />
(33-3796) was greater than for nonadopters (16-33%).<br />
Land tenure<br />
Most adopters were owner-operators (96%) compared with 73-80% of<br />
nonadopters.
Net farm income per household<br />
The adopters of mungbean before rice had higher net farm incomes per<br />
household than nonadopters. Comparisons of net farm incomes of adopters and<br />
nonadopters indicated that <strong>the</strong> new technologies contributed to an improvement<br />
in income. Among small and medium farmers, adopters and nonadopters outside<br />
<strong>the</strong> project area had higher farm incomes than nonadopters ~vithin <strong>the</strong> project<br />
area. It appeared that farming in Dok Kham Tai was generally not as productive<br />
as it should have been even with improved technologies. Without new<br />
technologies, farm income in Dok Kham Tai was significantly less than in o<strong>the</strong>r<br />
districts, especially for small farmers. Farmers in Dok Kham Tai had high farm<br />
incomes only if <strong>the</strong>y operated large farms.<br />
Farm expenditure<br />
Farm expenditures were, on <strong>the</strong> average, higher among adopters than<br />
nonadopters both in and outside <strong>the</strong> project area, part~cularly for small- and<br />
medium-size farms.<br />
Off-farm and nonfarm occupation and income per household<br />
Adopters obtained more income from farming; whereas, nonadopters in <strong>the</strong><br />
project area derived income from nonfarm and off-farm sources. Off-farm<br />
income per household \rras higher for nonadopters in <strong>the</strong> project area<br />
(THB22,841) than for adopters (THB9,703) and for nonadopters outside <strong>the</strong><br />
project area (THB3,114). Off-farm employment sources included construction<br />
work, furniture making, vehicle repairs, sewing, farm Iabor, handicrafts, and<br />
processing and sale of farm products. Among nonadopters in Dok Kham Tai,<br />
salary-earning jobs were also available. Nonadopters in large farms both in and<br />
outside <strong>the</strong> project areas had higher farm irlconies than adopters. Nonadopters<br />
with large farms within <strong>the</strong> project area also had higher farm incomes because<br />
<strong>the</strong>ir average farm size \vas larger (6.2 ha) than that of adapters (4.8 ha). For <strong>the</strong><br />
nonadopters outside <strong>the</strong> project area, rice cultivation was more productive<br />
(3,806 kg/ha) than for adopters (3,250 kg/ha); <strong>the</strong>refore, <strong>the</strong> farni income of <strong>the</strong><br />
nonadopters was higher.<br />
The availability of off-farm and nonfarm occupations in <strong>the</strong> project area<br />
positively contributed to <strong>the</strong> adoption of labor-saving technology (e.g.,<br />
mechanical seeder). Given <strong>the</strong> availability of off-farm job opportunities in <strong>the</strong><br />
project area, it is expected that <strong>the</strong> potenti:ll to expand DSR (labor-sa\zitig) is<br />
greater than <strong>the</strong> potential for mungbean before rice (labor-usir?g).<br />
Labor use<br />
The introduction of mungbean before rice contributed to grzater use of<br />
labor in May-July. Without mungbean before rice, labor was not used except
during <strong>the</strong> establishment of nurseries for <strong>the</strong> rice. The harvest of mungbeans,<br />
however, sometimes conflicted with land preparation for WS rice. Some farmers<br />
left <strong>the</strong>ir mungbeans unharvested to prepare <strong>the</strong>ir land for <strong>the</strong> next rice crop.<br />
Migration of young people<br />
The improvement in farm income did not slow down <strong>the</strong> migration of young<br />
people to <strong>the</strong> cities. Among <strong>the</strong> adopters, 44% of <strong>the</strong> households had one or<br />
more members who migrated to <strong>the</strong> cities compared with only 33% among <strong>the</strong><br />
nonadopters. However, nonadopters outside <strong>the</strong> project area had <strong>the</strong> largest<br />
numbers of migrants (53%). The study team found that farming was increasingly<br />
becoming an activity for middle aged and older people in <strong>the</strong> community. In Dok<br />
Kham Tai, young men and women did little farm work. Scarcity of labor for farm<br />
work was especially noticeable and resulted in higher wages and <strong>the</strong> presence of<br />
labor from o<strong>the</strong>r districts.<br />
Livestock<br />
More adopters kept livestock than nonadopters.<br />
Education<br />
The level of education was evaluated on <strong>the</strong> basis of attendance of farm children<br />
in high schools and tertiary schools. Nonadopters had a greater percentage of<br />
children in tertiary schools.<br />
Health<br />
Of <strong>the</strong> adopters, 83% reported that household members were in good health,<br />
comj~ared with 70% among nonadopters outside <strong>the</strong> project area. Nonadopters<br />
within <strong>the</strong> project area were healthier (87%). These rough health indicators<br />
suggested that <strong>the</strong> population of Dok Kham Tai (whe<strong>the</strong>r adopters or<br />
nonadopters) were better-off than farmers in Jun district.<br />
Savings and loans<br />
Of <strong>the</strong> adopters, 30% had bank savings; whereas, only 28% of nonadopters in<br />
<strong>the</strong> project area and 20% outside <strong>the</strong> project area had bank savings. Equal<br />
numbers of adopters and nolladopters outside <strong>the</strong> project area borrowed money.<br />
In contrast, 47% of nonadopters in <strong>the</strong> project area borrowed morley. It seems
that although adopters were better off than nonadopters in terms of savings, <strong>the</strong>y<br />
were as much in debt as nonadopters.<br />
Assets<br />
Adopters had more household assets than nonadopters. For example, 61% of <strong>the</strong><br />
adopters had hand tractors compared with 22% of nonadopters in <strong>the</strong> project<br />
area. Adopters also had more livestock, trucks, motorcycles, bicycles, sewing<br />
machines, rice barns and livestock houses compared to nonadopters, especially<br />
those outside <strong>the</strong> project area.<br />
Improvement in standard of living<br />
About 92-93s of <strong>the</strong> households in all groups reported an improvement in <strong>the</strong>ir<br />
standard of living during tlie last S vr. Only 5% of adopters reported a<br />
deterioration in <strong>the</strong>ir standard of lhing compared with 8% among nonadopters<br />
in <strong>the</strong> project area and 7 9 among those outside <strong>the</strong> project area.<br />
Adopters of rnungbean - rice technology appeared to be more active in<br />
farming than nonadopters in <strong>the</strong> project area. Adopters also engaged in livestock<br />
enterprises more than nonadopters in <strong>the</strong> project area.<br />
Nonadopters outside <strong>the</strong> project area were as acti\,e as adopters and<br />
earned as much from fuming. Havever, <strong>the</strong>ir income frorn off-farm<br />
employment and li\.estock was less than <strong>the</strong> adopters. Their out-migration rate<br />
was also higher and <strong>the</strong>y depended more on remittances than adopters. Their<br />
health cond~tions and savings icere also slightly lower than adopters and<br />
nonadopters in <strong>the</strong> project area.<br />
hlungbean before rice<br />
CONCLUSION<br />
Although rnungbean before rice appeared to be a relativelv simple new<br />
technology that would provide farmers with supplemental jncome, a closer<br />
examination of <strong>the</strong> adoption of mungbean before.rice re\.ealed that it had tmtli<br />
strengths and weaknesses.<br />
Strengths<br />
¤ It provided supplemental income and helped ease capital requirements<br />
for <strong>the</strong> follo\ving rice crop.<br />
Q.<br />
.d
m It was easy to establish and required few inputs (including labor).<br />
m It fitted <strong>the</strong> bimodal pattern of rainfall, intensified <strong>the</strong> cropping system,<br />
and used labor when <strong>the</strong>re was little opportunity cost.<br />
m It positively contributed to soil fertility, rice yields, <strong>the</strong> use of by-products,<br />
and upland mungbean increased mechanization and livestock feeding.<br />
m It was widely adopted in rainfed upper ricefields in Dok Kham Tai.<br />
Weaknesses<br />
m The yield and income from mungbeans in <strong>the</strong> field were low compared<br />
with <strong>the</strong> expected level.<br />
m It was not suitable in lower ricefields that were prone to waterlogging and<br />
flooding.<br />
m In some years, prices of mungbean were low, which produced low income.<br />
m In some years, <strong>the</strong> onset of <strong>the</strong> rainy season was delayed, which resulted<br />
in a conflict between harvesting mungbean and planting rice. The growing<br />
season for mungbean had conflicted with <strong>the</strong> timing of DSR.<br />
m Recently, <strong>the</strong>re had been some insect problems with mungbean that<br />
require attention from <strong>research</strong> and extension personnel.<br />
Direct (dry) seeded rice<br />
The expansion of DSR was interesting in Dok Kham Tai. It was adopted,<br />
abandoned, and readopted in a period of 7 yr (1984-91). The period with a good<br />
rate of adoption was 4-5 yr after <strong>the</strong> project ended. The development of<br />
appropriate machinery, which took several years, was an important factor that<br />
contributed to adoption. Increased labor costs during mungbean harvesting and<br />
rice transplanting helped accelerate <strong>the</strong> process of adoption. DSR displayed <strong>the</strong><br />
following strengths and weaknesses.<br />
Strengths<br />
It substantially reduced <strong>the</strong> costs of planting rice.
W It produced a good stand of rice that escaped drought toward <strong>the</strong> end of<br />
<strong>the</strong> WS.<br />
H It reduced <strong>the</strong> risk of low rice yields because of dry spells that delayed<br />
transplanting operations.<br />
H It was simple and convenient, especially with <strong>the</strong> use of mechanical<br />
seeders and small tractors.<br />
H The level of yield obtained from direct seeding was comparable to<br />
transplanting.<br />
Weaknesses<br />
¤ It suffered from w.eeds, sometimes seriously.<br />
¤ It required mechanical seeders for planting operations, but sometimes<br />
<strong>the</strong>se mechanical seeders were scarce.<br />
H It could not be done if <strong>the</strong> soil was too \vet.<br />
The study of <strong>the</strong>se two mature technologies identified and recommended<br />
by <strong>the</strong> project reveals that even <strong>the</strong> so-called mature technologies require many<br />
adaptations. Adequate follow-up is needed. Continuity in <strong>research</strong> and links to<br />
extension must be ensured. Adaptation and testing of new technologies by<br />
farmers must be encouraged and recognized. The process of FSR is not simple<br />
and clear-cut. New problems arise, and new <strong>research</strong> topics must be generated<br />
from local problems. Constant interaction among FSR <strong>research</strong>ers, extension<br />
officers, and farmers is a must.<br />
At <strong>the</strong> same tirne, this case demonstrates <strong>the</strong> potentials and limitations of<br />
FSR scientists. FSR <strong>research</strong>ers can make important contri1)utions to adaptive<br />
<strong>research</strong> and can act as a link between experiment-station scienti$ts and farmers.<br />
However, when a technology is ready for extension. FSR <strong>research</strong>ers cannot<br />
adequately cope with <strong>the</strong> extension requirements of a large number of farmers.<br />
Nor can FSR <strong>research</strong>ers cope with <strong>the</strong> diverse ran6e of environments found in<br />
farms if <strong>the</strong> technology expands on a wide scale. Th~s limitation on <strong>the</strong> part of<br />
FSR scientists points to <strong>the</strong> important role of extension officers and farmers<br />
<strong>the</strong>mselves in fur<strong>the</strong>r adaptation of new technologies.
REFERENCES CITED<br />
Morris R A, Zandstra H G (1978) Land and climate in relation to cropping<br />
patterns. Pages 255 -274 itt Rainfed lowland rice: selected papers from<br />
<strong>the</strong> 1978 <strong>International</strong> Rice Research Conference. <strong>International</strong> Rice<br />
Research Institute, Manila, Philippines.<br />
Nichai T, Rasamee K (1991) Green manure in paddy rice cropping system.<br />
Paper presented at <strong>the</strong> Conference on <strong>the</strong> Progress on Biotechnology:<br />
Agriculture and Environment, 12-14 Nov 1991 at Chiang Mai Orchid<br />
Hotel, Chiang Mai, Thailand.<br />
Patanothai A (1983) A review of <strong>the</strong> rainfed crop production <strong>research</strong> and<br />
development project. Department of Agriculture, Thailand.<br />
Suvanchinda P, Nichai T, Suwan H, Hatsachai B, Vicharn V, Pracha D, Sompet<br />
K (1986) Pilot production in target areas in <strong>the</strong> rainfed agricultural<br />
project [in Thai]. Irz Proceedings of <strong>the</strong> Third Farming Systems Research,<br />
2-4 Apr 1986, Chiang Rai University, Thailand .<br />
UNDP/FAO -- United Nations Development Programme/Food and Agriculture<br />
Organization (1986) Integrated rainfed farming <strong>research</strong> and<br />
development Thailand. Project finding and recommendations.<br />
(~inp~blished).
Table l. The promising technologies (1986).<br />
~ -<br />
P<br />
P P Pp<br />
Package of technology - Status of technology No. of farmers Area covered Provinces<br />
in 1986 participating 1985-1986<br />
1985-1986<br />
1. Direct seeding of rice trials Multilocation testing, 645 1,522 ha 7<br />
preproduction<br />
2. Mungbean - rice cropping system Multilocation testing, 373<br />
preproduction trials<br />
3. Jute - rice cropping system Multilocation testing 24 3.8 ha 4<br />
4. Kenaf - rice cropping syslcln Multilocation testing 52 14.4 ha 4<br />
5. Crops - pig fattening system On-farm trials 6 139 m2 2<br />
G. Crops - duck in paddy fields On-farm trials 22 1,100 m' 1<br />
7. Cl-ops - pigs - ducks - fish On-farm trials 7 7,150 m2 2<br />
in farm ponds<br />
Source: [JNDPIFAO 1986 (figures converted from rai to hectares).
Table 2. Details of <strong>the</strong> two technologies presented by <strong>the</strong> project.<br />
Components of<br />
technology<br />
Agroclimatic<br />
suitability<br />
Direct seeding Mungbean - rice<br />
of rice cropping <strong>systems</strong><br />
Variety<br />
RD-6, RD-8 Utong 1, VC 1178<br />
RD-15, KDML-105 Kampaengsaen 1,2<br />
Fertilizer<br />
125 kglha 125 kglha<br />
(1 6-20-0) (1 6-20-0)<br />
62.5 kglha<br />
(2 1-0-0)<br />
Pest Control<br />
Furadan 3 1.25 kglha Furadan 18.75 kglha<br />
Saturn G Lanate 75 mL/ha<br />
25 kglha<br />
Machete<br />
Agroecological zone<br />
R3S6 R3S6<br />
Soil texture<br />
Clay to sandy Clay to sandy<br />
loam loam (upper paddy)<br />
Yield (kglha) 1,706-3,062 550-1,006<br />
Economic viability (THBIha)<br />
Production cost 4,113-6,938 7,913-11,169<br />
Gross revenue 4,944-8,875 12,344-16,800<br />
Gross margin 831-1,938 2,975-5,630<br />
Provinces introduced Phayao, Chiang Rai Phayao, Chiang Rai<br />
Phrae, Lampang, Phrae, Lampang,<br />
Phattalung, Surin, Phattalung, Nakorn<br />
Nakorn Sri Tharnmarat Sri Thammarat<br />
Source: UNDPIFAO 1986 (figures converted from rai to hectares).
Table 3. Mungbean before rice areas (1982-83 to 1985-86).<br />
Year Research stage Place Areas Yield (kglha)<br />
(ha) Mungbean Rice<br />
1982-1983 On-farm trials<br />
Phay ao<br />
1983-1984 On-farnl trials<br />
On-farnl trials<br />
1984- 1985 Multi-location<br />
testing<br />
Multi-location<br />
testing<br />
Multi-location<br />
testing<br />
On-farm trials<br />
1985-1986~ Pilot production<br />
Pilot production<br />
Pilot production<br />
Multi-location<br />
testing<br />
On-farm trials<br />
Amphoe Mae Jai,<br />
Amphoe Mae Jai<br />
Amphoe Mae Jai,<br />
Chiang Rai<br />
Amphoe Mae Jai<br />
Amphoe Mae Jai<br />
Amphoe Mae Ta<br />
Amphoe Kuan<br />
Kanon, Pattalung<br />
Phayao Province<br />
Chiang Rai<br />
Lampang Province<br />
Phrae Province<br />
Pattalung Province<br />
a~ncludes<br />
6 amphoes in Phayao Province; 6 amphoes in Chiang Rai Province; 9 amphoes in<br />
Lampang Province; 3 amphoes in Phrae Province; and 1 amphoe in Pattalung Province.<br />
Source: Suvanchinda et al. 1986 (figures converted from rai to hectares).
Table 4. Area (ha) planted to mungbean before rice in Dok Kham Tai, Thailand (1984-1991).<br />
San Koang<br />
Pa Sang<br />
Don Sri Chum<br />
Ban Tum<br />
Dok Kham Tai<br />
Nong Lom<br />
Huai Lan<br />
Ban Pin<br />
Total<br />
Source: Dok Kham Tai District Agricultural Extension Office (figures converted from rai to hectares).
Table 5. Number of farm households planting mungbean before rice.<br />
Tambon 1984 1985 1986 1987<br />
San Koang<br />
Pa Sang<br />
Don Sri Chum<br />
Ban Tum<br />
Dok Kham Tai<br />
Nong Lom<br />
Huai Lan<br />
Ban Pin<br />
Total<br />
-<br />
Source: Dok Kham Tai District Agricultural Extension Office.
Tablc 6. Prices 2nd yield of mungbcan bcfosc sicc.<br />
Price per kg 7 9 6-8 6-8 9-11 6-8 5-7 10-1 1<br />
Yield (kglha) 687.5"<br />
Utong 1 VC 1178 I
Table 7. Area planted to mungbean, Dok Kham Tai, Thailand (1988-89).<br />
Planted area<br />
(ha)<br />
Total<br />
average<br />
planted area (ha)<br />
Maximum (ha)<br />
Minimum (ha)<br />
Note: Figures converted from rai to hectares.<br />
No. of farmers % No. of farmers %<br />
1988 1989
Table 8. Area planted to mungbean, costs, and gross margin, Dok Kham Tai, Thailand (1988).<br />
Yield Planted area<br />
range (ha)<br />
(kg1 ha)<br />
Average (94 cases) 2.1<br />
Average for those who<br />
had some output<br />
(78 cases)2.1 1,377<br />
Variable cash<br />
cost<br />
(THBIha)<br />
Variable costs<br />
(including family<br />
labor)<br />
(THBIha)<br />
Gross margina<br />
(THBIha)<br />
Gross margin b<br />
(THBIha)<br />
b<br />
aGross margin is total value of production minus variable cash costs. Gross margin is total value of production minus variable<br />
cash costs and estimated cost of family labor.<br />
Note: Figures converted from rai to hectares.
Table 9. Area planted to mungbean, costs, and gross margin, Dok Kham Tai, Thailand (1989).<br />
Yield Planted area Variable cash Variable costs Gross margina Gross margin b<br />
range (ha) cost (including family (THBIha) (THBIha)<br />
(kg/ha) (THBIha) labor)<br />
(THBIha)<br />
AV (94 cases)<br />
AV for those who<br />
had some output<br />
(78 cases)<br />
Maximum<br />
Minimum<br />
'~ross margin is total value of production minus variable cash costs. b~ross<br />
margin is total value of production minus variable<br />
cash costs and estimated cost of family labor.<br />
Note: Figures converted from rai to hectares.
Table 10. Soil analysis of various lowland types under different cropping patterns, Dok-Kham Tai District (1991).<br />
Lowland type<br />
Low terrace . Middle terrace High terrace<br />
MO M1 MO M1 MO M1<br />
Texture Clay<br />
Loam<br />
Clay Clay Clay Loam<br />
Clay<br />
Note: MO = fallow - rice; M1 = mungbean - rice; figures in paren<strong>the</strong>ses are ranges.<br />
LR = lime requirement
Table 11. Area under dry seeded rice and rice yields (RCPRD project).<br />
Year Research stage Areas Yield (kglha)<br />
(ha)<br />
Dry seeded Wet<br />
198 1-1982 a On-farm trails<br />
1982-1983 a On-farm trails<br />
1983-1984 a Multilocation testing<br />
1984-1985 Multilocation testing<br />
1985-1986 Multilocation testing<br />
Average<br />
a1981-1982, 1982-1983, and 1983-1984 were areas in Dok Kham Tai. 1984-1985: Dok<br />
Kham Tai 306.3 ha; Mae Ta, Lampang Province, 1.9 ha; Mae Suai, Chaing Rai Province,<br />
1.9 ha; Kuan Kanoon, Pattalung Province, 4.0 ha. '1985-1986: includes 6 amphoes in<br />
Payao Province, 1,276 ha; 6 amphoes in Chiang Rai Province, 7.2 ha; 12 amphoes in<br />
Lampang Province, 159 ha: 3 amphoes in Phrae Province, 41.8 ha; 2 amphoes in Pattalung<br />
Province, 25.8 ha.<br />
Source: Suvanchinda et a1 1986 (figures converted from rai to hectares).
Table 12. Socioeconomic conditions of adopters and nonadopters both in <strong>the</strong><br />
project area and outside <strong>the</strong> project area.<br />
No. of farmers in sample<br />
Average size of farm (ha)<br />
Average size of upland (ha)<br />
Average land size (ha)<br />
Small farm (0- 1.6 ha)<br />
Medium farm (1.8-3.2 ha)<br />
Large farm ( > 3.2 ha)<br />
All farms (ricefield + upland)<br />
Land tenure (%)<br />
Land productivity<br />
(measured as average<br />
rice yield in kglha)<br />
Small farm (0-1.6 ha)<br />
Medium farin (1.8-3.2 ha)<br />
Large farm ( > 3.2 ha)<br />
Land distribution<br />
(percentage of group)<br />
Small farm (0-1.6 ha)<br />
Medium farm (1.8-3.2 ha)<br />
Large farm ( > 3.2 ha)<br />
Net farm income (THB)<br />
Small farm (0-1.6 ha)<br />
Medium farm (1.8-3.2 ha)<br />
Large farm ( > 3.2 ha)<br />
All farm sizes<br />
Off-farm income<br />
per household (THB)<br />
Small farm (0- 1.6 ha)<br />
Medium farm (1.8-3.2 ha)<br />
Large farm ( > 3.2 ha)<br />
All farm sizes<br />
Adopters Nonadopters<br />
In project Outside<br />
area project area<br />
-
continued.. .<br />
Table 12. Socioeconomic conditions of adopters and nonadopters both in <strong>the</strong><br />
project area and outside <strong>the</strong> project area.<br />
Adopters Nonadopters<br />
In project Outside<br />
area project area<br />
Total income (farm + off-fainl)<br />
Small farm (0-1.6 ha) 25,609 30,304 14,803<br />
Medium farm (1.8-3.2 ha) 28,934 40,083 32,844<br />
Larse farm ( > 3.2 ha) 48,110 61.510 49,589<br />
All farm sizes 34,320 38,589 27,292<br />
Off-Tarn], nonfarm occupation<br />
(%)<br />
Wage labor<br />
Salary earning job<br />
Commerce<br />
Average off-farm. nonfarm<br />
income by sources (THB)<br />
all farin sizes<br />
Wage labor 3,654 1,997 1,782<br />
Commerce 3?325 13,170 42 0<br />
Salary earning job 702 5,250<br />
Remittance 1,075 1,267 4,633<br />
Off-farm \\!age labor (TI-IB)<br />
Small farm (0-1.6 ha) 2,899 2,000 3,216<br />
Medium farm (1.8-3.2 ha) 2,106 2,967 188<br />
Large farm ( > 3.2 ha) 5,967 5 0 83<br />
All farm sizes 3,654 1,997 1,782<br />
Commerce (THR)<br />
Small farm (0-1.6 ha) 5,695 20,694 -<br />
Medium farm (1.8-3.2 ha) 2,581 667 625<br />
Large farm ( > 3.2 ha) 2,024 15,602 1,267<br />
All farm sizes 3,325 13,170 420<br />
Salary-earning job (THR)<br />
Small farm (0-1.6 ha) 2,333<br />
Medium farm (1.8-3.2 ha) 11,300<br />
Large farm ( > 3.2 ha) 1,900 -<br />
All farm sizes 5.250 -
a % Table<br />
continued.. .<br />
12. Socioeconomic conditions of adopters and nonadopters both in <strong>the</strong><br />
project area and outside <strong>the</strong> project area.<br />
Support from o<strong>the</strong>rs (THB)<br />
Small farm (0- 1.6 ha)<br />
Medium farm (1.8-3.2 ha)<br />
Large farm ( > 3.2 ha)<br />
All farm sizes<br />
Adopters Nonadopters<br />
In project Outside<br />
area project area<br />
Labor Utilization more same same<br />
Migration of young men and women<br />
(% of households<br />
in <strong>the</strong> group)<br />
Average number of persons in <strong>the</strong><br />
household who have migrated<br />
Livestock (q of households)<br />
Chickens<br />
Pigs<br />
Cows<br />
Buffalo<br />
Income from remittances<br />
(% of households<br />
in group)<br />
Education (observed)<br />
Sons and daughters able<br />
to go to secondary schools<br />
(% of households)<br />
Sons and daughters able<br />
to go to tertiary schools<br />
(% of households)
continued.. .<br />
Table 12. Socioeconomic conditions of adopters and nonadopters both in <strong>the</strong><br />
project area and outside <strong>the</strong> project area.<br />
Adopters Nonadopters<br />
In project Outside<br />
area project area<br />
Health<br />
Households<br />
reported in good health (%) 8 3 8 7 70<br />
Savings and Loans<br />
Farm households with<br />
bank savings (5%) 30 28 20<br />
Farm households with borroived<br />
money (72)<br />
Assets<br />
2-wheel tractors<br />
Pick-ups<br />
Motorcycle<br />
Bicycle<br />
Sewing machine<br />
4-wheel tractor<br />
Rice barn<br />
Television<br />
Livestock pens<br />
Impro\~ernent in <strong>the</strong> standard of<br />
living in 5 years (76)<br />
Better<br />
Worse<br />
Same<br />
Note: Figures con\lerted froin rai to hectares.
Table 13. Soil analysis data of various lowland type with different cropping patterns at Dok-Kharn Tai District, Phayao Province, 1991<br />
Lowland type<br />
Element Low terrace Middlc tcrracc High terrace<br />
Texture Clay Clay Clay Clay Loam<br />
MO = fallow-rice<br />
M1 = mungbean-rice<br />
( ) = range<br />
Clay
l~ndicative figures only.<br />
'~ature technology is defined as agroecolcgically feasible, econcrnicaliy<br />
viable, and socially acceptable to farmers.<br />
1. Linkage between <strong>research</strong> and extension.
IMPACT ASSESSMENT OF FARMING SYSTEMS RESEARCH-BASED<br />
TECHNOLOGIES IN THE PHILIPPINES: THE ISABELA EXPERIENCE<br />
R. R. ~onza~a', C. V. C. ~ arba~, N. P. ~ordoncillo~, and N. F. C. an awe era'<br />
The apparent inability of conventionally developed agricultural technology to serve<br />
<strong>the</strong> needs of resource-poor farmers has led to <strong>the</strong> popularization of Farming<br />
Systems Research (FSR) as an alternative approach to <strong>research</strong> and development.<br />
During <strong>the</strong> last two decades, developing countries in Asia, Africa, and Latin<br />
America have adopted FSR and, most if not all, have institutionalized FSR in <strong>the</strong>ir<br />
National Research Systems (NARS). The FSR dornain is wide and varied. Activities<br />
in all phases of FSR are carried out in an interdisciplinary manner. Farmer<br />
participation is a key element. The approach requires a thorough understanding of<br />
<strong>the</strong> production <strong>systems</strong> of <strong>the</strong> farmer and <strong>the</strong> ways <strong>the</strong>y are conditioned by <strong>the</strong><br />
biophysical, socioeconomic, and political environments. The objective is to identify<br />
important farm production problerns and to design appropriate solutions that are<br />
not only technically and economically feasible but also sustainable. As such, FSR<br />
employs various tools, techniques, and methods from <strong>the</strong> socioeconomic and <strong>the</strong><br />
biological sciences.<br />
In spite of this methodological complexity, FSR has been accepted because it<br />
was deemed appropriate to <strong>the</strong> varied physical and socioeconomic conditions of<br />
farming environments in developing countries. Since <strong>the</strong>n, several FSR approaches<br />
and processes have emerged. It is imperative to assess <strong>the</strong> impact of technologies<br />
developed through FSR, not only to gauge <strong>the</strong>ir effectiveness and real contributions<br />
to small farmers, but also to make methodological improvements and redirect future<br />
efforts.<br />
The question, however, is by what measure should <strong>the</strong> contributions of FSR<br />
be assessed? In general, <strong>the</strong> explicit objective of FSR is to increase cropping<br />
intensity and improve cultural practices to achieve liigher output and factor<br />
productivity while increasing family income. It is implicitly assumed that <strong>the</strong><br />
improvement of <strong>the</strong> indicators of household welfare will follow. Ranaweera (1958)<br />
summarized <strong>the</strong> flow of <strong>the</strong> contribution of FSR to <strong>the</strong> different quality of life<br />
indicators (e.g., positive changes in purchasing power, asset accum~llation, quality of<br />
education, health, and household nutrition).<br />
The pathways to tliese quality of life indic~ltors exhibit complex relatio~~ships.<br />
Although it is con~monly mentioned that undernutrition is almost synonymous with<br />
poverty, and that income and purchasing power are key variables that allo\v <strong>the</strong><br />
household to purchase food items not produced on <strong>the</strong> farm and to diversify dietary<br />
'social Sciences Division, <strong>International</strong> Rice Research Institute, P.O. Box 933,<br />
hdanila, Philippines.<br />
"7nstitute of Human Nutrition and Food, College of Human Ecoloa, University of<br />
<strong>the</strong> Philippines Los Barios, College, Laguna, Philippines.
composition, increases in income can also esacerbate malnutrition. Jodha (1994)<br />
contends that commercialization can adversely influence <strong>the</strong> nutritional position of<br />
<strong>the</strong> farming household if it leads to an overall decline in <strong>the</strong> availability for<br />
consumption of general food commodities or of specific food items with high<br />
nutritive value. Indeed, <strong>the</strong> link between FSR technologies and <strong>the</strong> nutritional<br />
position of <strong>the</strong> household can be directly related to <strong>the</strong> consumption of 0~i.n-farm<br />
produce and indirectly related to <strong>the</strong> income path (Gonzaga et a1 1990).<br />
Although improvement in <strong>the</strong>se welfare vari;ibles is an ideal goal, it cannot<br />
be overemphasized that fulfillment of one Lvelfare indicator may lead to shortfalls in<br />
o<strong>the</strong>rs. The household makes decisions to meet its objectives according to <strong>the</strong> order<br />
of <strong>the</strong>ir priorities. Impact assessment <strong>the</strong>refore should not consider a failure to<br />
improve one or two welfare measures as a fdilure of FSR technologies in general.<br />
Assessment is an integral component of FSR: from <strong>the</strong> diagnostic and design<br />
phase (ex-ante), to <strong>the</strong> testing, preproduction, and production phases (on-going), to<br />
<strong>the</strong> extension and monitoring phase (ex-post). Although <strong>the</strong> parameters used in<br />
impact analysis for each FSR phase differ, each of <strong>the</strong>se ph;lses in\.olves<br />
measurements related to <strong>the</strong> impact of FSR technologies. During <strong>the</strong> ex-ante and<br />
on-going phases of impact assessment, <strong>the</strong> focus of analysis is on biological and<br />
technical feasibility, economic viability, and sociocultural acceptability of <strong>the</strong><br />
technoloby (Zandstra et a1 1981). Es-post assessment, ho\ive\/er,, goes beyond <strong>the</strong>se<br />
parameters and includes o<strong>the</strong>r indirect effects such as changes in real income levels,<br />
asset accumulation, educational and nutritional positions, and health of <strong>the</strong> farming<br />
households (Ranaweera 1985). In this study. impact assessment of indirect effects is<br />
limited to changes in household income and <strong>the</strong> nutritional position of <strong>the</strong><br />
household. In addition, measurable direct effects such as yield le\iels, input use, and<br />
changes in cropping patterns are assessed.<br />
Several approaches can be used to assess <strong>the</strong> impact of introduced technologies<br />
(Ranaweera 1988). One approach is <strong>the</strong> 11c.fot.c and ojtc>r approach. Different impact<br />
parameters are compared using information obtained before <strong>the</strong> introduction of<br />
technoloq and 5-10 yr after its introduction. This approach requires good baseline<br />
informatbn. In <strong>the</strong> absence of baseline inforniation. a wirlz il~ld ~t.it/zol,t technology<br />
approach can be used. Impact parameters from sites where <strong>the</strong> technology was<br />
introduced are compared with parameters collccted from sites where no<br />
technological innovation was introduced. The rocllt~olo~ udoptet. arid rzorltrliopter<br />
approach, in which two groups of farniers are classified :iccording to specified<br />
adoption rules, can be applied in a situation where it is diffic~~lt to find a site that is<br />
homologous to <strong>the</strong> project site.<br />
This study used a variant of <strong>the</strong> technology adopter and nonadopter<br />
approach. One dilemma was deciding on <strong>the</strong> domain of <strong>the</strong> impact assessment.<br />
Should it only recognize innovations carried out by <strong>the</strong> Regional Integrated<br />
Agricultural Research Systems (RIARS) in Cagayan Valley Region? Or, sho~lld it<br />
also take into account <strong>the</strong> contributions of o<strong>the</strong>r entities (e.g., seed companies),
whose goals nln counter to <strong>the</strong> objectives of <strong>the</strong> RIARS, and of <strong>the</strong> farmers<br />
<strong>the</strong>mselves, who continuously modify <strong>the</strong> technologies?<br />
Farmers were classified according to <strong>the</strong> degree of technology adoption,<br />
using a single criterion (croppin8 intensity). This classification of <strong>the</strong> degree of<br />
adoption allowed technological innovations from various sources to be considered.<br />
Biggs (1989) called this a nlulti'~le sorcrce of innovatio~z r?zodel that describes<br />
agricultural <strong>research</strong> and diffusion processes in <strong>the</strong> context of <strong>the</strong> historical,<br />
political, economic, agroclimatic, and institutional setting in which technological<br />
changes take place.<br />
The sample consisted of 105 farmers randomly selected from two villages and<br />
monitored for crop years 1989-91. Three farmers dropped out of <strong>the</strong> project;<br />
<strong>the</strong>refore, 102 farmers were used in <strong>the</strong> analysis. The farmers were classified<br />
according to degree of adoption using total cropping intensity of <strong>the</strong> farm (CI): low<br />
adopter < 1.3, medium adopter 1.3-1.7, and high adopter > 1.7.<br />
The observed cropping intensities ranged from a low intensity of 0.75 (some<br />
parts of <strong>the</strong> farm were not cultivated for at least one agricultural cycle) to a high<br />
intensity of 3.00. A cut-off C1 of < 1.30 (based on 2-yr averages) defined low<br />
technology adopters. The cut-off C1 was determined from a frequency distribution<br />
and a subjective judgment of <strong>the</strong> amount of land allocated by farmers to <strong>the</strong> new<br />
technologies (cropping patterns). A 30% increase in cropping intensity after about<br />
5 yr of exposure to <strong>the</strong> newr technologies is considered low. A 70% increase in C1 is<br />
considered high given <strong>the</strong> climatic situations that prevailed during <strong>the</strong> study period<br />
(i.e., late onset of rainfall and waterlogging during planting season).<br />
It was assumed that adoption of <strong>the</strong> rice - rice or mungbean - rice cropping<br />
patterns, in addition to <strong>the</strong> intensive maize-based cropping <strong>systems</strong> of <strong>the</strong>ir upland<br />
areas, was a sufficient condition to classify farmers as technology adopters The<br />
intuitive appeal of this approach was based on <strong>the</strong> argument that farmers who were<br />
not totally convinced of <strong>the</strong> qualities of <strong>the</strong> introduced technologies would not<br />
allocate all of <strong>the</strong>ir land holdings. The o<strong>the</strong>r extremes were that a fully convinced<br />
farmer would allocate all his kind; whereas, an unconvinced farmer would continue<br />
to follow traditional technology. A farmer's perception of <strong>the</strong> soilndness of a<br />
technoloby can, <strong>the</strong>refore, be nieasured to a large extent by <strong>the</strong> amount of land<br />
allocated to <strong>the</strong> new technoloky.<br />
O<strong>the</strong>r component technologies that are included with <strong>the</strong> introduced patterns<br />
were not included as criteria. This was not meant to disregard <strong>the</strong>ir critical<br />
contribution to <strong>the</strong> tot;il perfor~nance of <strong>the</strong> technology. In fact, <strong>the</strong>ir noninclusion<br />
was meant to avoid <strong>the</strong> effects of o<strong>the</strong>r exogenous variables on <strong>the</strong> question of<br />
adoption. For example, if a farmer failed to apply <strong>the</strong> correct level of fertilizer, this<br />
does not imply that <strong>the</strong> farmer was not convinced of its expected benefits, but<br />
perhaps was unable to afford <strong>the</strong> input cost. Similarly, a fariner might not apply <strong>the</strong><br />
optimum mix of inputs to a plot if less profit was expected (e.g., in an area that is<br />
likely to be adversely affected by flood or drought). Also, farmers whose established<br />
crop(s) are affected by drought or tlood will not apply <strong>the</strong>ir remaining inputs to<br />
minimize profit losses. All of <strong>the</strong>se are rational management decisions that could
not be captured if <strong>the</strong> criteria for farm groupings was based strictly on component-<br />
technology recommendations.<br />
Study area<br />
The studv area covered two villages in Isabela Province. The province is located in<br />
<strong>the</strong> nor<strong>the</strong>astern part of <strong>the</strong> Philippines, a region with high agricultural potential.<br />
This area has a unimodal rainfall pattern with an average annual rainfall of about<br />
2,200 mm. The area is situated along <strong>the</strong> typhoon belt. It experienced seven<br />
typhoons during crop years 1989-90 and three typhoons <strong>the</strong> following year. The<br />
region as a whole is usually isolated from <strong>the</strong> rest of <strong>the</strong> country for a few weeks<br />
when a strong typhoon hits <strong>the</strong> area. Prices of agricultural commodities are reduced<br />
when <strong>the</strong> area becomes isolated. Two land types exist in <strong>the</strong> study area: <strong>the</strong> river<br />
floodplains (uplands). lvhich are traditionally cultivated for maizs and upland crops,<br />
and <strong>the</strong> rainfed lowlands. which are traditionally culti\lated for rice.<br />
An average farming houlehold of six people cultivates about -7 ha, which are<br />
almost equally d~vided between <strong>the</strong> two land types (Table 1). It is uncommon to find<br />
farming households specializing in <strong>the</strong> cultivation of ei<strong>the</strong>r <strong>the</strong> rairlfed lowlands or<br />
<strong>the</strong> floodplains. The area is flood-prone during <strong>the</strong> monsoon and drought-prone<br />
during <strong>the</strong> dry season. In addition to upland crops and rice, farming households<br />
raise several head of water buffalo and cattle.<br />
Rainfed lowland area<br />
TRADITIONAL AYD INTRODUCED TECHKOLOGIES<br />
The traditional cropping pattern in <strong>the</strong> rainfed lowlands is fallow - rice. Rice is<br />
transplanted in August-September. The thrust of <strong>the</strong> now defunct RIARS of <strong>the</strong><br />
Department of Agriculture in Cagayan Valley was to establish two crop patterns.<br />
These patterns were designed to take advantage of <strong>the</strong> traditionally long f,<br />
cl 11 ow<br />
period before rice and <strong>the</strong> rainfall available during this period. To achieve this<br />
objective. RIARS introduced a short-duration upland crop (rnungbean) and dry<br />
seeded rice (DSR) before transplanted rice (TPR) in <strong>the</strong> wet season. Ei<strong>the</strong>r<br />
mungbean or DSR must be established during <strong>the</strong> onset of rainfall sonletime in rilid-<br />
April. This is followed by TPR in August-September. Table 2 shows <strong>the</strong> distribution<br />
of <strong>the</strong> present cropping patterns in <strong>the</strong> rainfed area. It suggests that <strong>the</strong> f~llow - rice<br />
pattern still predominates. The introduced patterns (mungbean - TPK and DSR -<br />
TPR) command a smaller share of <strong>the</strong> area. The sudden decline in area of <strong>the</strong>se two<br />
patterns in crop year 1990-91 can be attributed to <strong>the</strong> extended drought that<br />
affected <strong>the</strong> whole region. This indicates <strong>the</strong> instability of <strong>the</strong> introcluced<br />
technolosy.<br />
/<br />
On average, component technologies as implemented by <strong>the</strong> farmers were<br />
within <strong>the</strong> recommendation domain of RIARS (Table 3). In some cases, input levels<br />
exceeded <strong>the</strong> recomnlendations. The high seeding rate, particularly of mungbean, is
largely due to reseeding after early crop failure caused by flashfloods. Present yield<br />
levels are below those achieved after <strong>the</strong> preproduction phase of RIARS (RIARS<br />
19SS).<br />
Floodplains<br />
The traditional cropping pattern in <strong>the</strong> floodplains is maize - maize (Table 2). The<br />
first maize crop is established at <strong>the</strong> onset of <strong>the</strong> rains (April-May) and is followed<br />
by a second crop of maize in November-December. There are o<strong>the</strong>r minor but more<br />
intensive cropping patterns practiced by farmers using different cornbinations of<br />
upland crops. These are established on small patches of land for home consumption<br />
and <strong>the</strong> local market.<br />
The <strong>research</strong> focus of KIARS in this land type was on component<br />
technologies, with particular emphasis on <strong>the</strong> use of high-yielding varieties (HYVs)<br />
to replace traditional varieties. The present component technologies adopted by<br />
farmers far exceed <strong>the</strong> original recornmendations (Table 4). RIARS advocated high-<br />
yielding open-pollinated varieties of maize; however, private seed companies, with<br />
better logistics and <strong>the</strong> help of government extension personnel, promoted <strong>the</strong> use<br />
of hybrid seeds. 'The latter technology prevailed in this land type. Average yields,<br />
hoivever, remained low largely due to climatic vagaries.<br />
The establishment of maize in <strong>the</strong> upland areas (floodplains) coincided with<br />
<strong>the</strong> establishlnent of <strong>the</strong> introduced ~nungbean or DSR in <strong>the</strong> lowland in April-May<br />
and created cornfietition for farm resources (time, labor, and capital inputs). The<br />
maize crop in <strong>the</strong> upland area took precedence over crops in <strong>the</strong> lowland area.<br />
Farmers unanimously agreed that establishment of <strong>the</strong>ir maize crop was <strong>the</strong>ir first<br />
priority. Maize not only com~nands a better price and higher yield. but <strong>the</strong>re is less<br />
risk of crop failure. Mungbean in <strong>the</strong> lowland was susceptible to flashfloods,<br />
whereas, DSR was vulnerable to water stress. Resource-strapped farmers were<br />
unanimous that, if time permitted, any residual inputs would be devoted to <strong>the</strong><br />
cultivation of <strong>the</strong>ir 1owl;tnds.<br />
Lately, farmers have started to buy certified hybrid maize seeds in one<br />
season, and <strong>the</strong>n use selected F2 seeds for <strong>the</strong> next crop season. This was an<br />
apparent reaction to <strong>the</strong> high cost of hybrid seeds. Seed companies have now started<br />
to sell high-yielding open-pollinated maize seed.<br />
Changes in income<br />
RESULTS AND DISCUSSION<br />
Table 5 shows <strong>the</strong> diversity of income sources of <strong>the</strong> farming households in <strong>the</strong> study<br />
area. One revealing observation was <strong>the</strong> low earnings of low technology adopters<br />
relative to medium ancl high technology adopters. Income from th,o three major
crops (rice, maize, and mungbean) appeared unstable over two periods in all three<br />
categories of adopters. The FSR-pushed mungbean technology in <strong>the</strong> lowlands<br />
along with o<strong>the</strong>r crops (mostly vegetables in <strong>the</strong> upland area) accounted for <strong>the</strong><br />
difference in income betiveen <strong>the</strong> degrees of adoption in year one. Maize dominated<br />
in year two.<br />
The most important nonfarm income was wage earnings from nonfarm and<br />
off-farm labor re ardless of <strong>the</strong> extent of adoption. Total household income showed<br />
no significant dif f erences anlong <strong>the</strong> three categories of adopters although medium<br />
and high adopters appeared better off. The findings, in general, indicated that low<br />
farm earnings could be compensated through o<strong>the</strong>r means.<br />
Changes in expenditures<br />
Table 6 shows <strong>the</strong> comparison of cash expenditures of <strong>the</strong> farms. All farm groups<br />
apply relatively <strong>the</strong> same amounts of inputs. Fertilizer usage in crop years 1989-90<br />
reflects <strong>the</strong> only significant difference among <strong>the</strong> major items considered. Farmers<br />
in <strong>the</strong> area were applying inputs at rates almost equal to, if not above, <strong>the</strong><br />
recommended level.<br />
Table 7 shows nonfood household expenditures according to degrees of<br />
adoption. Except for loan repayment and educational expenses in crop year 1990-<br />
1991, all o<strong>the</strong>r major items of expenditure were not significantly different.<br />
Food expenditures reflect tastes and preferences and decision-making of <strong>the</strong><br />
family. Table 8 shows expenditures on major food items. O<strong>the</strong>r than expenditures on<br />
bean, no significant differences among <strong>the</strong> three categories of adopters were<br />
observed. The findings suggest that all households turn equally to <strong>the</strong> market for<br />
additional food.<br />
Changes in nutrient intake<br />
A food consumption survey, based on 24-h recall of food items consumed by <strong>the</strong><br />
family, was conducted for eight periods. The periods represented different farming<br />
activities determined at <strong>the</strong> village level. The intention ivas to capture seasonal<br />
variations in food intake. Nutrient adequacy was determined by taking <strong>the</strong> ratio of<br />
actual intake to <strong>the</strong> recommended daily allowance (RDA). The use of aggregate<br />
household consumption assumed that food and nutrient intake were equally shared<br />
among household members.<br />
Table 9 shows nutrient adequacy in all three categories of adopters over<br />
time. All groups exhibited inadequate energy intake. Protein intake was adequate<br />
only during <strong>the</strong> first two periods. Similarly, diet rating (a measure of <strong>the</strong> average<br />
intake of macronutrients and micronutrients) was inadequate in all periods.
lnadequacy of nutrient intake was prevalent whe<strong>the</strong>r <strong>the</strong> survey was conducted<br />
during lean or surplus periods. One surprising finding was that medium adopters<br />
tended to be better nourished than o<strong>the</strong>r groups of farmers. On average, energy<br />
intake of a medium adopter was above <strong>the</strong> 80% critical level. The o<strong>the</strong>r farm<br />
groups, low adopters in particular, fell short of <strong>the</strong> critical level. Protein intake,<br />
however, was above <strong>the</strong> 70% critical level for all groups.<br />
The nutritional status of <strong>the</strong> most vulnerable group of <strong>the</strong> household<br />
(children less than 6 yr of age) was used as a proxy for <strong>the</strong> general nutritional status<br />
of <strong>the</strong> household. The ratiogale is that in <strong>the</strong> short run, preschoolers are sensitive to<br />
even slight changes in food and nutrient intake. Table 10 shows <strong>the</strong> proportion of<br />
undernourishment in <strong>the</strong> three categories of adopters over time using <strong>the</strong> weightfor-age<br />
standard reference (relative to average weight of well-nourished<br />
population). The prevalence of undernutrition followed <strong>the</strong> trend in nutrient<br />
adequacy measure, i.e., <strong>the</strong> prevalence of undernourishment was relatively high<br />
during periods where adequacy in energy and protein intake were low in all tliree<br />
categor~es of adopters. All farm groups exhibited a high prevalence of moderate<br />
undernourishment, i.e., weights of preschoolers were 60-75% below <strong>the</strong> normal<br />
weight-for-age reference. The difference in undernourishment among <strong>the</strong> three<br />
categories of adopters was varied in all periods.<br />
Determinants of impact<br />
This analysis may not be sufficient to make a categorical statement on <strong>the</strong><br />
contributions of <strong>the</strong> introduced technologies. A Cobb-Douglas model (log-linear<br />
model) wasgestirnated simultaneously using <strong>the</strong> two-stage least square method<br />
(2SLS). The endogenous variables were productivity of rice, maize, and mungbean<br />
(kg/ha), total household income (P/household), and per capita nutrient intake. The<br />
estimated productivity of rice, maize, and mungbean enters <strong>the</strong> right hand side of<br />
ihe income model along with o<strong>the</strong>r exogenous variables. These exogenous variables,<br />
in turn, enter <strong>the</strong> models on nutrient intake as predicted income (Table 11).<br />
Impact on producti\.ity<br />
The farrlling households in <strong>the</strong> study area were engaged in mixed-crop farming (rice,<br />
mungbean, maize, arld some vegetable crops). An aggregate production function<br />
liornogenized by <strong>the</strong> prevailing market prices in <strong>the</strong> area m y be more appropriate<br />
to capture <strong>the</strong> overall productivity of <strong>the</strong> farm; however, an aggregate production<br />
function would hide <strong>the</strong> contribution of <strong>the</strong> individual commodities.<br />
Maize productivity<br />
The productivity of maize (kg/ha) can be explained by <strong>the</strong> rate of fertilizer<br />
application, seeds used, pesticide, preharvest labor, power-tiller rental for land<br />
preparation, farmer characteristics, and a dummy variable for year.
The explanatory Lwiable, nitrogen (N), explained a large portion of <strong>the</strong><br />
productivity in maize. A 1% increase in N application, while o<strong>the</strong>r factors being held<br />
constant, is expected to increase yield by 1.4%.<br />
The value of seeds used, a proxy for <strong>the</strong> amount and quality of seed used,<br />
made a positive significant contribution to maize yield. The result indicates that<br />
farmers Lvere correct to use hybrid maize (although expensive) over <strong>the</strong>ir traditional<br />
varieties or <strong>the</strong> open-pollinated maize, Lvhich ~vas being pushed by <strong>research</strong>ers, but<br />
was not readily available to <strong>the</strong> farmers.<br />
O<strong>the</strong>r sources of productivity were preharvest labor and <strong>the</strong> use of<br />
powertillers for land preparation. Farmers Lvere obsencd to do thorough land<br />
preparation and trying to maintain ~i.eed-free crops. Ltely, farmers have been<br />
observed substituting animal draft power for small and large power tillers,<br />
particularly in maize-based areas. The rental cost may not be <strong>the</strong> prime<br />
consideration. Timeliness in land preparation may be <strong>the</strong> most critical factor.<br />
The nonsignificant contribution of pesticides to maize >ield ma!/ indicate that<br />
farmers Lvere spraying <strong>the</strong>ir maize crop even if it was not necessary. It was common<br />
for farmers in this-area to establish an agreement Lirith local grain traders who<br />
provide <strong>the</strong>m with credit on <strong>the</strong> condition that <strong>the</strong> farmers sell a11 of <strong>the</strong>ir produce<br />
to <strong>the</strong> trader.<br />
Age of farmer, used as a prosy for long-term farming esperience, had a<br />
significant positi~~e impact on maize yield. Years of schooling, a proxy for stock of<br />
knowledge for effective implementation of <strong>the</strong> technoloby, did not significantly<br />
increase maize productivity. The intercept shifter ()fear dummy variable) suggested<br />
that maize yields, in general, were low during <strong>the</strong> first !,ear of <strong>the</strong> monitoring period,<br />
but <strong>the</strong> difference was not statistically significant.<br />
Rice product i ~itj<br />
The yield of rice (&/ha) was regressed with rate of fertilizer application, seeds<br />
used, pesticide, prehanest Iabor, power-tiller rental for land preparation, farmer<br />
characteristics, and a year d~lniriiy variable.<br />
The results indicated that N fertilizer, seed value (prosy for amount and<br />
quality), and pesticide cost had significant positive effects on rice Ilield. Preharvest<br />
labor and power-tiller rental made no significant contribution. However, farmer<br />
characteristics represented by age and years of schooling, were statistically<br />
significant.
Mungbean productivity<br />
The productivity of mungbean came mostly from pesticide application. Farmers<br />
were fully aware of <strong>the</strong> potential damage if insect pests were not controlled during<br />
<strong>the</strong> flowering stage of mungbean. Seeds did not significantly affect mungbean yield.<br />
Farmers had totally shifted to <strong>the</strong> new mungbean variety (MG-9) introduced by<br />
RIARS. Similarly, inputs, labor, and power tiller did not significantly affect<br />
mungbean yield. The intercept shifter (year dummy variable) indicated that<br />
mungbean yield in crop year 1989-90 was significantly higher than in <strong>the</strong> succeeding<br />
year.<br />
Impact on total household income<br />
The endogenous variable, total household income, represented <strong>the</strong> total earnings of<br />
<strong>the</strong> farm household from different sources. The explanatory variable, nonfarm<br />
income, was an arithmetic component of <strong>the</strong> endogenous variable in question;<br />
<strong>the</strong>refore, it was introduced in <strong>the</strong> model as a share of total income. Proxy variables,<br />
such as <strong>the</strong> number of hours spent on nonfarm economic activities, may be a more<br />
appropriate estimator. Different economic activities, however, yielded different<br />
levels of earnings. Total household income was explained by <strong>the</strong> technology<br />
variables (productivity in maize, rice, and mungbean, cropping intensities in lowland<br />
and upland areas of <strong>the</strong> farm, total land holdings, household size, and year dummy).<br />
Although productivity in rice and maize had a significant positive impact on total<br />
farm household income, mungbean contributed negatively (although <strong>the</strong> difference<br />
was not sta~istically significant).<br />
The significant negative effect of lowland cropping intensity compared with<br />
<strong>the</strong> positive impact of upland cropping intensity, reaffirmed <strong>the</strong> preference of <strong>the</strong><br />
farmers to intensively cultivate <strong>the</strong>ir upland areas ra<strong>the</strong>r than <strong>the</strong>ir lowlands.<br />
Similarly, total land holdings provided a significant positive impact on total<br />
household income.<br />
Household size had a significant positive effect on total income. Larger<br />
families tended to have an edge in generating cash income for <strong>the</strong> household. As<br />
expected, <strong>the</strong> value of landlord shares decreased total income. This particular<br />
variable represents tenurial status of <strong>the</strong> land, or land rent in general. Most farmers<br />
cultivated several parcels of land with different rental arrangements. Wea<strong>the</strong>r<br />
affected overall crop production during <strong>the</strong> first year of <strong>the</strong> study; <strong>the</strong>refore, total<br />
farm household income was affected that year (<strong>the</strong> significant negative sign for <strong>the</strong><br />
year dummy variable).<br />
Impact on adequacy of nutrient intake<br />
Adequacy in nutrient intake only considers <strong>the</strong> macronutrients (energy and protein).<br />
These macronutrients are still <strong>the</strong> most limiting in <strong>the</strong> diet of small farming<br />
households. The endogenous variables were calculated as a per capita intake instead
of <strong>the</strong> ratio of actual intake and reconlmended daily allowance (RDA) to ensure<br />
consistency in <strong>the</strong> use of <strong>the</strong> specified econometric model. Fur<strong>the</strong>rmore, <strong>the</strong> average<br />
adequacy in nutrients has yet to be satisfied.<br />
Per capita energy intake (calories) was determined by total household<br />
income, age and years of schooling of <strong>the</strong> mo<strong>the</strong>r, family size, and share of food<br />
expenditure of <strong>the</strong> total farm household budget. All exogenous variables had a<br />
significant positive impact on per capita energy intake. Characteristics of <strong>the</strong><br />
mo<strong>the</strong>rs such as educational attainment, a proxy variable for a\i.areness of good diet<br />
preparation, and age, representing long-term experience on <strong>the</strong> part of <strong>the</strong> mo<strong>the</strong>r,<br />
both positively contributed to increases in energy intake. Per capita protein intake<br />
(grams) was also explained by <strong>the</strong>se \,ariables. Total household income,<br />
expenditures on food. and characteristics of mo<strong>the</strong>rs had significant positi\.e impact<br />
on protein intake. The positi\.r effect of household size on energy and protein intake<br />
may indicate <strong>the</strong> capacity of <strong>the</strong> household to acquire enoush food.<br />
CONCLUSIOS<br />
If <strong>the</strong> explicit goal of FSR i~ to de\pelop a crop-nlis that incre~~ses producti\,ity of<br />
land ivhich can he translated into higher incomes. <strong>the</strong>n its implicit goal is to impro\,e<br />
<strong>the</strong> total welfare of <strong>the</strong> fartning households. This study assessed <strong>the</strong> i~npact of FSR<br />
on this basis.<br />
The study indicated that <strong>the</strong>re \vas a rnised imp;ict on <strong>the</strong> different \\.elfare<br />
measures of <strong>the</strong> fdrming households. It is probable that increases in rlourishnient<br />
\rere positiirely affected by <strong>the</strong> introduced technologies. The nutritional inipact of<br />
FSR technologies could be directl!! through an increase in household food supply as<br />
a result of an Increase in total production (represented by value of prod~rctivity in<br />
<strong>the</strong> model), or indirectly througli an increase in purchasing power as a result of<br />
increased total ho~15ehold incorne t7rought about by increased productivity. Changes<br />
in food supply, ho\i.e\:er, affect <strong>the</strong> n~.~tr~tional stat~~s of <strong>the</strong> household mernbers only<br />
to <strong>the</strong> extent that food consumption is affected. Altlioi~gh <strong>the</strong> technologies have a<br />
significant positive effect on per capita nutrient intake, <strong>the</strong> nutrient adequ;lcv<br />
indices suggest that houseliold intake still falls sliort of recli~ired daily nutrient<br />
intake. It appears, ho\\.ever, that sni;~ll farming households can hetter satisf! <strong>the</strong>ir<br />
protein requirement than <strong>the</strong>ir energy needs (o<strong>the</strong>r sti~dies have made similar<br />
observations). T\vo plausihle esplnnations ;ire offered: <strong>the</strong> m;li~i diet of Filipinos, is<br />
rice, which has a relatively high protein content; ;lnd Filipitios in gcner~ll. consume<br />
less fat. In <strong>the</strong> absence of baseline information, one can onl! ~pt~illtlte whe1l:er<br />
<strong>the</strong>re was an increase in nutrient intake or \chetl~er tllat inr;~ke I-enl;iins ; ~t <strong>the</strong> sL\iTltl<br />
level as that of <strong>the</strong> preproject period.<br />
As R:lvillon (1990) h;~s pointed out, <strong>the</strong> nutritional imp:!ct of a~ricultural<br />
changes depends on whetlier or not <strong>the</strong> vulnerable groups u'ithin <strong>the</strong> ho~~sehold<br />
benefit from <strong>the</strong>m. The prevalence of ~lnderno~lrishmeut among <strong>the</strong> preschoolers in<br />
all farm categories is noticeable. The nutritional st;ltus of <strong>the</strong> prrschoolers tends to<br />
follow seasonal fluctuations in <strong>the</strong> indices of nutrient adequacy. One limitation of<br />
<strong>the</strong> study is its inability to link direc.tly (at least within <strong>the</strong> specified econometric
model) <strong>the</strong> nutritional status of <strong>the</strong> preschoolers with <strong>the</strong> technology variables in <strong>the</strong><br />
model because <strong>the</strong> endogenous variable is qualitative in nature; and <strong>the</strong>re are many<br />
farmers within <strong>the</strong> sample who do not have preschoolers. Never<strong>the</strong>less, <strong>the</strong> authors<br />
believe that total income or food nutrient intake per se are necessary, but not<br />
sufficient conditions to alleviate undernourishn~ent. There are o<strong>the</strong>r exogenous<br />
factors that are as important (e.g., health and sanitation practices) but were not<br />
captured in <strong>the</strong> study.<br />
Income gains of <strong>the</strong> household still largely depend on <strong>the</strong> size of its farm<br />
holdings. The contribution of crop intensification technology in <strong>the</strong> lowland (as<br />
represented by mungbean - rice and <strong>the</strong> rice - rice cropping patterns) to inflows of<br />
household cash is in doubt. As long as <strong>the</strong>se technologies remain unstable, maize<br />
crop in <strong>the</strong> upland area is expected to be <strong>the</strong> main source of income.<br />
On <strong>the</strong> production side, <strong>the</strong> inputs farmers used were equal to and<br />
sometimes above <strong>the</strong> recommended level. Productivity, however, remains low<br />
relative to <strong>the</strong> expectations of FSR <strong>research</strong>ers at <strong>the</strong> end of technology generation.<br />
One ready explanation was <strong>the</strong> harsh wea<strong>the</strong>r conditions that prevailed during <strong>the</strong><br />
course of <strong>the</strong> impact-assessment study. Although <strong>the</strong> technology seems unstable, its<br />
actual contribution is already sizable. The evidence indicates that farm productivity<br />
could be increased by adopting introduced technologies. Considering <strong>the</strong> magnitude<br />
of <strong>the</strong> contribution of <strong>the</strong> technology variables, <strong>the</strong> results fur<strong>the</strong>r indicate that<br />
improvement on <strong>the</strong> introduced technology (i.e., improved stability) could fur<strong>the</strong>r<br />
enhance farm productivity.<br />
Because of limitations of available information, <strong>the</strong> assessment of <strong>the</strong> impact<br />
of FSR technologies on o<strong>the</strong>r welfare measures (e.g., education and wealth<br />
accumulation) was not attempted. Asset accumulation and education are long-term<br />
objectives of <strong>the</strong> household. To surmise that a link could be properly established<br />
between technology adoption (FSR) using cross-sectional data would produce<br />
misleading inferences (particularly because <strong>the</strong> introduced technologies were<br />
relatively young and <strong>the</strong> sample size was small). Time-series data with sufficient<br />
information on ho~~sehold asset possession before adoption wo~~ld provide a better<br />
understanding of <strong>the</strong> relationships of <strong>the</strong>se welfare measures to <strong>the</strong> technologies in<br />
question. Never<strong>the</strong>less, <strong>the</strong> results have provided insights on <strong>the</strong> contribution of<br />
FSR (or any agricultural technology) to some welfare measures which are given high<br />
priority by households.<br />
REFERENCES CITED<br />
Biggs S D (1989) A nlultiple source of innovation model of agricultural <strong>research</strong> and<br />
technology prumotion. Network Paper 6. Agricultural Administration Unit,<br />
Overseas Development Institute, Lorrdon.<br />
Gonzaga R G, Gordoncillo N P, Ranaweera N F C, Barba C V C (1990) Assessing<br />
nutritional consequences of FSR projects. Paper presented at <strong>the</strong> First Asian
Farming Systems Research and Extension Symposium, 19-22 Nov 1990,<br />
Asian Institute of Technology, Bangkok, Thailand.<br />
Jodha N S (1994) Discussant's Note on Commercialization in Agriculture.<br />
Ranaweera N F C (1988) The need to assess impact of farming <strong>systems</strong> <strong>research</strong><br />
(FSR) within <strong>the</strong> Asian context: an overview. Paper presented at <strong>the</strong><br />
Monitoring Tour Cum Workshop on Impact of Farming Systems Research in<br />
Selected Asian Countries, 25-30 Ap 1988, Amigo Terrace Hotel, lloilo City,<br />
Philippines.<br />
Ravallion M (1990) Income effect of undernutrition. Econ. Dev. Cultural Change<br />
38(3):489-5 15.<br />
RIARS--Regional Integrated Agricultural Research Systems (1988) Barangay pilot<br />
productitin program of improved cropping system. Paper presented at <strong>the</strong><br />
CVIARS/PCARRD Coordinated Research and Development Review, 8-10<br />
Jun 1988, Isabela State University, Isabela, Philippines.<br />
Zandstra H G, Price E C Jr., Litsinger J A, Morris R A (1981) A methodology for<br />
on farm cropping <strong>systems</strong> <strong>research</strong>. <strong>International</strong> Rice Research Institute,<br />
P.O. Box 933, Manila, Philippines.
Table 1. Basic characteristics of farm households, Isabela Province,<br />
Philippines (1989-91).<br />
Degree of Adoption<br />
Low Medium High<br />
Characteristics of farmers (1989)<br />
Age 37 40 38<br />
Educational attainment 6 6 6<br />
Farm Size (ha)<br />
Lowland<br />
Upland<br />
Total<br />
Table 2. Percentage distribution of cropping patterns in each crop year.<br />
Lowland<br />
Fallow - Rice<br />
Mungbean - Rice<br />
Rice - Rice<br />
O<strong>the</strong>r monocrop patterns<br />
Total<br />
Upland<br />
Corn - Corn<br />
Mungbean - Corn<br />
Monocrop pattern:<br />
Corn<br />
Mungbean<br />
O<strong>the</strong>r crops<br />
O<strong>the</strong>r 2-crop patterns<br />
O<strong>the</strong>r 3-crop patterns<br />
Total
Table 3. Input levels and productivity of crops under rainfed lowland conditions.<br />
Material inputs<br />
N (kglha)<br />
P (kglha)<br />
Insecticide cost (Plha)<br />
Herbicide cost (Plha)<br />
Seed (kglha)<br />
Labor Input: (hlha)<br />
Land preparation<br />
Power cost (Plha)<br />
Crop establishment<br />
Weed control<br />
O<strong>the</strong>r crop care<br />
Yield (kglha)<br />
Crop Mungbean 1st season 2nd season<br />
year before rice rice rice
Table 4. Input levels and productivity of upland crops under floodplain conditions.<br />
Crop 1st season 2nd season 2nd season<br />
year maize maize mungbean<br />
Material inputs<br />
N (kglha) 1989- 1990 80 75 22<br />
1990-1991 95 90 2<br />
P (kglha) 1989-1990 22 2 1 22<br />
1990-1991 2 1 22 -<br />
K (kglha)<br />
Insecticide cost (Plha) 1989-1 990 185 221 45 1<br />
1990- 199 1 216 242 3 13<br />
Seed (kglha) 1989-1990 17 17 19<br />
1990-1991 18 16 14<br />
Labor Input: (hlha)<br />
Land preparation 1989- 1990 72 100 89<br />
1990-1991 74 5 9 74<br />
Power cost (Plha) 1989- 1990 195 97 39<br />
1990-1991 242 220 154<br />
Crop establishment 1989- 1990 42 43 34<br />
1990-1991 38 3 8 3 5<br />
Weed control 1989- 1990 46 90 65<br />
1990-1991 7 7 100 7 1<br />
O<strong>the</strong>r crop care 1989- 1990 50 50 3 1<br />
1990-1991 5 6 52 22<br />
Yield (kglha) 1989-1990 2,313 2,655 435<br />
1990- 1991 2,709 3,503 3 85
Table 5. Comparison of annual household income (P) across degrees of adoption.<br />
Farm income<br />
Rice<br />
Maize<br />
Mungbean<br />
O<strong>the</strong>r products<br />
Subtotal<br />
Household Income<br />
Wages<br />
Remittances<br />
Loan proceeds<br />
O<strong>the</strong>rs<br />
Subtotal<br />
Total<br />
P P -<br />
Degree of adoption<br />
Year Low Medium High<br />
Note: Means followed by <strong>the</strong> same letter or its absence are not significailtly different.
Table 6. Comparison of annual farm expenses (P) across degrees of<br />
adoption.<br />
Labor wages<br />
Fertilizer<br />
Pesticide<br />
Seed<br />
Implements<br />
O<strong>the</strong>rs<br />
Total<br />
Degree of adoption<br />
Year Low Medium High<br />
Note: Means followed by <strong>the</strong> same letter or its absence are not significantly<br />
different.
Table 7. Comparison of annual nonfood household expenses (P) across degrees of<br />
adoption.<br />
Loan repayment<br />
Durables<br />
O<strong>the</strong>r household items<br />
Rentals<br />
Repairs<br />
Clothing<br />
Education<br />
Health<br />
O<strong>the</strong>rs<br />
Total<br />
Degree of adoption<br />
Year Low Medium High<br />
Note: Means followed by <strong>the</strong> same letter or its absence are not significantly different.
Table 8. Comparison of annual food expenditure (P) across degrees of adoption.<br />
Rice<br />
O<strong>the</strong>r cereal<br />
Root crops<br />
Sugar<br />
Beans<br />
Vegetables<br />
Fruit<br />
Meat<br />
Fish<br />
Dairy products<br />
Fats and oil<br />
O<strong>the</strong>r food items<br />
Total<br />
Degree of adoption<br />
Year Low Medium High<br />
Note: Means followed by <strong>the</strong> same letter or its absence are not significantly<br />
different.
Table 9. Comparison of percentage per capita nutrient adequacy and dietary<br />
rating across degrees of adoption.<br />
P P<br />
Degree of adoption<br />
Survey<br />
period Nutrient Low Medium High<br />
Sep 1989<br />
Oct 1989<br />
Jan 1990<br />
Mar 1990<br />
May 1990<br />
Jul 1990<br />
Oct 1990<br />
Aug 1991<br />
Average<br />
Energy<br />
Protein<br />
Diet rating<br />
Energy<br />
Protein<br />
Diet rating<br />
Energy<br />
Protein<br />
Diet rating<br />
Energy<br />
Protein<br />
Diet rating<br />
Energy<br />
Protein<br />
Diet rating<br />
Energy<br />
Protein<br />
Diet rating<br />
Energy<br />
Protein<br />
Diet rating<br />
Energy<br />
Protein<br />
Diet rating<br />
Energy<br />
Protein<br />
Diet rating<br />
Note: Means followed by <strong>the</strong> same letter or its absence are not significantly<br />
different.
able 10. Percentage distribution of nutritional status of children using weight-for-age<br />
across degrees of adoption.<br />
-<br />
Undernourished<br />
Survey Degree of Over-<br />
Period adoption Normal weight Mild Moderate Severe<br />
Sep 1989 LAW 26 13 47 7 7<br />
Medium 23 9 45 23 0<br />
High 23 6 36 32 3<br />
Oct 1989 Low 3 2 13 35 10 10<br />
Medium 24 2 45 22 7<br />
High 13 13 44 26 4<br />
Jan 1990 Low 3 2 33 57 4 4<br />
Medium 18 8 47 22 5<br />
High 26 4 3 7 33 0<br />
Mar 1990 Low 27 10 50 10 3<br />
Medium 19 2 46 26 7<br />
High 33 4 33 26 4<br />
May 1990 Low 33 3 3 3 3 28 3<br />
Medium 16 0 45 3 7 2<br />
High 20 0 38 38 4<br />
Jul 1990 Low 27 7 38 2 1 7<br />
Medium 24 3 34 39 0<br />
High 25 0 5 8 13 4<br />
Oct 1990 Low 28 12 44 16 0<br />
Medium 18 9 46 24 3<br />
High 3 3 8 38 2 1 0
Table 11. Log-linear models of impact determinate estimated using two-stage least square<br />
method (2SLS).<br />
A. Dependent Variable: Maize yield (kglha)<br />
Independent variables Coefficients<br />
Intercept<br />
Nitrogen fertilizer (kg Nlha)<br />
Seed cost (Plha)<br />
Pesticide cost (PI ha)<br />
Preharvest labor (hlha)<br />
Power rental (Plha)<br />
Land rental (Plha)<br />
Age of farmer (years)<br />
Schooling years of farmer<br />
Year dummy (1989-1990 = 1)<br />
R2<br />
B. Dependent variable: Rice yield (kglha)<br />
Independent variables Coefficients<br />
Intercept<br />
Nitrogen fertilizer (kg Nlha)<br />
Seed cost (Plha)<br />
Pesticide cost (Plha)<br />
Preharvest labor (ldha)<br />
Power rental (Plha)<br />
Land rental (Plha)<br />
Age of farmer (years)<br />
Schooling years of farmer<br />
Year duinmy (1989-1990 = 1)<br />
R2<br />
C. Dependent variable: Mungbean yield (kglha)<br />
Independent variables Coefficients<br />
Intercept<br />
Seed cost (Plha)<br />
Pesticide cost (Plha)<br />
Preharvest labor (hlha)<br />
Land rental (Plha)<br />
Age of farmer (years)<br />
Schooling years of farmer<br />
Year dummy (1989-1990 = 1)<br />
Note: *, **, and *** significant at 10%, 5%, and 1 %, respectively<br />
- 150 -<br />
Standard error<br />
Standard error<br />
Standard error
,<br />
!<br />
Table 11, continued.<br />
D. Dependent variable: total household income (log)<br />
Independent variables Coefficients<br />
Intercept<br />
A. Predicted maize yield<br />
B. Predicted rice yield<br />
C. Predicted mungbean yield<br />
Land rental (Plha)<br />
Ratio of ilonfarm income to total income<br />
Farm size (ha)<br />
Lowland cropping intensity index<br />
Upland cropping intensity index<br />
Household size (No.)<br />
Year dummy (1989-1990 = 1)<br />
E. Dependent variable: per capita protein intake<br />
Independent variables Coefficients<br />
Intercept<br />
D. Predicted total income<br />
Ratio of nonfarm income to total income<br />
Age of mo<strong>the</strong>r' (years)<br />
Schooling years of mo<strong>the</strong>r<br />
Household size (No.)<br />
Year dummy (1989-1990 = 1)<br />
F. Dependent variable: total household income (log)<br />
Independent variables<br />
Intercept<br />
D. Predicted total income<br />
Ratio food expenses to total expenses<br />
Age of mo<strong>the</strong>r (years)<br />
Schooling years of mo<strong>the</strong>r<br />
Household size (No.)<br />
Year dummy (1989-1990 = 1)<br />
Coefficients<br />
Note: *, **, and *** significant at 10%, 5%, and l%, respectively.<br />
Standard error<br />
Standard error<br />
Standard error
INSTITUTIONALIZING THE FARILIIIVG SYSTEhlS RESEARCH APPROACH<br />
IN INDOCHINA<br />
A. hl. iIlandac1<br />
Farming <strong>systems</strong> <strong>research</strong> (FSR) is a coordinated and integrated<br />
effort to develop tschnolop that will enable farmers to increase<br />
production. FSR addresses itself to each farm enterprise and to <strong>the</strong><br />
interrelationships among <strong>the</strong>m and between <strong>the</strong> farm and its<br />
environment. The <strong>research</strong> uses information about <strong>the</strong> various<br />
production and consumption <strong>systems</strong>, <strong>the</strong> animal production system,<br />
<strong>the</strong> cropping system, and <strong>the</strong> secondary production activities that add<br />
value to <strong>the</strong> primary product, to identify ways to increase <strong>the</strong><br />
efficiency with which <strong>the</strong> farm uses its resources. The <strong>research</strong> is<br />
carried out by a coordinated group of scientists from various<br />
disciplines:<br />
At least si~ distinct <strong>research</strong> and implementation phases<br />
characterize <strong>the</strong> FSR methodology: site selection and description;<br />
economic and biological component studies; design and testing of <strong>the</strong><br />
farming <strong>systems</strong>; multilocation testing; implementation of pilot<br />
production programs; and full production programs. The broad<br />
objective of this paper is to describe farming <strong>systems</strong> <strong>research</strong> in<br />
Indochina (Cambodia, Lao PDR, and Vietnam).<br />
Basic data for <strong>the</strong> Indochinese countries are given in Table 1. Cambodia has an area<br />
of 181,000 km2 comprising a large central plain, has one of <strong>the</strong> largest freshwater<br />
lakes in Asia (Lake Tonle Sap), and mountains in <strong>the</strong> nor<strong>the</strong>ast and southwest. The<br />
population is estimated at 7.2 million with an annual growth rate of about 37~. The<br />
average population density is 40 people/km2, with no more than 100 people/km2 in<br />
<strong>the</strong> more densely populated central plain and Tonle Sap provinces. The majority of<br />
<strong>the</strong> population is of Khmer origin. with minority ethnic groups making up less than<br />
5%. The birth rate is high (about 6%), however, <strong>the</strong> infant mortality is also hi~h (about 2%) because of poor sanitation, scarce medical services, and malnutrition.<br />
Lao PDR is a predominantly mountainous country coverifig 236,800 km2.<br />
There are plains bordering <strong>the</strong> Mekong River near Vientiane ancl Snvannakhet<br />
where most of <strong>the</strong> lowland rice is grown. The population is about 3.7 mil!ion. Lao<br />
PDR has one of <strong>the</strong> lowest population density in Asia (16 pe~ple/km2). Average<br />
'~nternational Programs Management Office, <strong>International</strong> Rice Research Institute,<br />
P.O. Box 933, Manila, Philippines.
population growth is 2.9%. The rural population of Lao PDR is among <strong>the</strong> poorest<br />
In <strong>the</strong> world and has an inadequate diet. The population is ethnically diverse, and<br />
some traditional customs may have constrained efforts in <strong>the</strong> past to provide social<br />
and economic development. The dominant ethnic group, <strong>the</strong> lowland Lao, resides in<br />
<strong>the</strong> river valleys and represents about 50% of <strong>the</strong> population. The remainder is<br />
distributed between <strong>the</strong> upland areas and mountain Lao, and comprises Khmu,<br />
Lamet, Hmong, Yao, Man, and tribal Tai.<br />
Vietnam covers an area of 329,600 km2. Over 40% of <strong>the</strong> country is forest<br />
and about 17% is cultivated for crops. Government policy aims to increase <strong>the</strong><br />
amount of land under cultivation. The country is divided into two main agricultural<br />
areas: <strong>the</strong> Red River Delta in <strong>the</strong> north and <strong>the</strong> Mekong River Delta in <strong>the</strong> south.<br />
A mountain range runs along <strong>the</strong> western boundary with Cambodia and Lao PDR,<br />
and along <strong>the</strong> nor<strong>the</strong>rn border with China. The population of Vietnam is<br />
approximately 63 million with an average density of 191 people/km2. Its population<br />
growth rate is about 2.5%. The population is basically rural and is concentrated in<br />
<strong>the</strong> two rice-growing deltas. In some nor<strong>the</strong>rn provinces, population densities are<br />
among <strong>the</strong> highest in <strong>the</strong> world.<br />
Economies<br />
The Cambodian economy functions at low capacity in nearly all sectors.<br />
Immediately following <strong>the</strong> fall of <strong>the</strong> Pol Pot regime in 1979, <strong>the</strong>re was a continued<br />
food shortage and a massive relocation of <strong>the</strong> population. A UN-sponsored relief<br />
operation costing US$213 million averted famine and assisted in resettlement. The<br />
former Soviet Union provided an additional US$300 million in aid.<br />
The Lao economy is poorly developed. Production is almost entirely agrarian<br />
and aims at self-sufficiency. Modernization of agriculture and <strong>the</strong> exploitation of<br />
mineral resources are constrained by shortages of skilled labor, domestic capital,<br />
and inadequate internal transportation and communications. Lao PDR is also<br />
landlocked, which is an additional constraint to its growth and development. The<br />
major objectives of its development plan are, first and foremost, self-sufficiency in<br />
food and improved infrastructure (e.g., roads, rural electricity, health, and<br />
education).<br />
Vietnam is predominantly an agricultural economy based on rice production.<br />
Crop cultivation accounts for about three-quarters of <strong>the</strong> gross value of agricultural<br />
product (GVAP), and roughly two-thirds of this, or almost 50% of total GVAP,<br />
consists of grain production (of which rice accounts for 85%). Food-grain<br />
production amounted to 19 million t in 1988, of which rice was 16.4 million t.<br />
Government policies in Vietnam are in transition. Several measures have<br />
been taken in recent years to liberalize agricultural production. There is a move<br />
toward independent planning by <strong>the</strong> provinces and <strong>the</strong> districts to exploit <strong>the</strong> private<br />
and open-market opportunities. In 1981, Vietnam departed from <strong>the</strong> collectivized
agricultural production system by introducing <strong>the</strong> individual-oriented contract<br />
system of production. In its initial form, <strong>the</strong> contract system restored <strong>the</strong> farm family<br />
as <strong>the</strong> basic production unit, while retaining compulsory production and<br />
procurement targets. However, it failed to provide security of tenure and market<br />
freedom. These deficiencies were corrected in April 1988 by Resolution No. 10,<br />
which liberalized all input and output markets and allowed households to maximize<br />
<strong>the</strong>ir own output and incomes.<br />
Agricultural production <strong>systems</strong><br />
Indochina is an important rice-growing region in Asia. Almost 8 million ha of rice<br />
are harvested annually. Vietnam ranks first in terms of both area (5.66 million ha)<br />
and yield (2.7 t/ha). Lao PDR has 679,269 ha of rice with an average yield of about<br />
2 t/ha. Cambodia, with an estimated yield of 1.1 t/ha from about 1.4 million ha, has<br />
<strong>the</strong> lowest rice yield in Asia.<br />
Rice dominates agriculture ill Indochina in terms of <strong>the</strong> number of people<br />
employed in cultivation, <strong>the</strong> area grown, and volun~e of production. In Cambodia,<br />
95% of <strong>the</strong> population makes its living from agriculture and 93% of <strong>the</strong> total<br />
cropped area is planted to rice. In Lao PDR, only 740,000 ha or 3.2% of <strong>the</strong> total<br />
area is cultivated. Therefore, <strong>the</strong> land-people ratio is at a low (0.2 ha/person). The<br />
rice area is about 86% of <strong>the</strong> total cropped area. In <strong>the</strong> Mekong River Delta in<br />
Vietnam, rice is <strong>the</strong> predominant crop (97.5%). The nor<strong>the</strong>rn provinces of Vietnam<br />
have a total rice area of 2,391,000 ha or about 74% of <strong>the</strong> total area of farm<br />
holdings, which makes rice <strong>the</strong> most dominant crop.<br />
Rice farming <strong>systems</strong><br />
Cambodia has four rice farming <strong>systems</strong> based on <strong>the</strong> degree of water control:<br />
rainfed lowland (including supplementary irrigated), upland, deepwater (floating),<br />
and dry season irrigated rice. About 80% of <strong>the</strong> rice area is grown in <strong>the</strong> rainfed<br />
lowland (Table 2). An almost equal proportion of floating and dry season irrigated<br />
rice is grown. Rainfed lowland is fur<strong>the</strong>r classified into high, medium, and low fields,<br />
which are grown with early- (90-120 d), medium- (120-150 d), and late-maturing<br />
(> 150 d) rice varieties (Cambodia-IRRI Project 1990a,b).<br />
The main cropping system in L20 PDR is based on a single wet season rice<br />
crop. Almost 60% of <strong>the</strong> total rice area is rainfed lowland; about 39% is planted<br />
under upland conditions and contributes about 25% of total rice production (Lao<br />
PDR-IRRI 1990). Traditional photosensitive varieties are widely grown and because<br />
of <strong>the</strong>ir long duration (140-180 d), <strong>the</strong>re is little opportunity to grow subsidiary crops<br />
ei<strong>the</strong>r before or after <strong>the</strong> rice crop.<br />
Compared with Cambodia and Lao PDR, Vietnam has more developed<br />
infrastructure. In <strong>the</strong> nor<strong>the</strong>rn provinces, 84% of <strong>the</strong> total area is irrigated lowland,<br />
9. '. i<br />
, c,<br />
1
12% shallow rainfed (0-50 cm), and 4% intermediate rainfed (50-100 cm)<br />
(Table 3) (Huke and Huke 1990).<br />
Multiple cropping <strong>systems</strong><br />
In Cambodia and Lao PDR, very little area is planted to a second rice crop,<br />
<strong>the</strong>refore, cropping intensity is very low. It is not known how much of <strong>the</strong> rainfed<br />
lowland rice area is planted to secondary crops after rice. Under upland conditions,<br />
land is usually left fallow after a single rice crop. However, limited cropping of<br />
mungbean and soybean has been observed in Cambodia (Nesbitt, 1991, pers.<br />
commun. ).<br />
In nor<strong>the</strong>rn Vietnam <strong>the</strong> dominant cropping pattern is spring-summer rice. In<br />
<strong>the</strong> Mekong River Delta, rice is produced during three major cropping seasons:<br />
Dong Xuan (winter-s ring), He Thu (summer-autumn or midseason), and Mua (wet<br />
season; long duration 7 .<br />
O<strong>the</strong>r crops have high potential in Indochina: sugarcane, coconut, sugar<br />
palm, pineapple, banana, orange, and o<strong>the</strong>r fruit crops. In addition, niungbean,<br />
sweet potato, soybean, and groundnut are important crops.<br />
FARMING SYSTEMS RESEARCH<br />
Integrated farming traditions are thousands of years old in Indochina, but are not<br />
fully reflected in production or in institutional support to agricultural production.<br />
Increased production of rice and increased productivity of rice-based farming<br />
<strong>systems</strong>, remain primary goals of <strong>the</strong> national plans in Cambodia, Lao PDR, and<br />
Vietnam. Crop selection has been guided by national policies and plans that stress<br />
rice production. Consequently, crop diversification has not occurred to any<br />
significant extent. During <strong>the</strong> past three decades, <strong>the</strong> development of farming<br />
<strong>systems</strong> <strong>research</strong> has mainly occurred in Vietnam, which is <strong>the</strong> focus of this section.<br />
Achievements<br />
There have been various programs and projects to support development plans and<br />
strategies in farming <strong>systems</strong> <strong>research</strong>. In <strong>the</strong> Red River Delta, where rice is <strong>the</strong><br />
dominant crop, short season varieties were introduced to enable farmers to grow a<br />
third crop (e.g., sweet potato, potato, or vegetables). A system of raising livestock<br />
(garden + fish pond + animal house) has been introduced under <strong>the</strong> household<br />
system. Rural industries and handicrafts have been emphasized for nonagricultural<br />
laborers, but difficulties were encountered because of lack of materials and<br />
marketing.
In <strong>the</strong> midwest plain, where <strong>the</strong>re are problem soils, zero tillage, raising<br />
soybean between rice, integration of rice and shrimp, raised beds for growing sandal<br />
trees, and raising shrimp in <strong>the</strong> canals have been studied. In <strong>the</strong> upland areas, where<br />
environmental degradation is a major problem, coffee trees have been planted<br />
across <strong>the</strong> slopes and green manure crops or legumes planted between <strong>the</strong> rows for<br />
<strong>the</strong> first three years. Different <strong>systems</strong> of land preparation have been tested for<br />
growing upland rice. Forest and abandoned lands were given to farmers.<br />
Studies in <strong>the</strong> Mekong River Delta focused on various rice-based farming<br />
<strong>systems</strong> models. Some of <strong>the</strong> models (e.g., rice-shrimp in fresh water, rice-shrimp in<br />
saline area, rice-fish in deepwater area, rice-fish integrated with fruit trees and beef<br />
cattle or dairy cattle, and rice-cash crops in floating rice areas) were initially<br />
developed by advanced farmers in <strong>the</strong> region.<br />
Technology transfer<br />
The transfer of technology was done mainly through demonstration plots.<br />
Researchers worked extensively with farmers to establish pilot farms in different<br />
ecological zones.<br />
Institutions<br />
The general level of interest in farming <strong>systems</strong> among scientists and planners in<br />
Vietnam is high. FSR in Vietnam is carried out by different agencies such as <strong>the</strong><br />
National Institute of Agricultural Sciences (NIAS), <strong>the</strong> National Institute of<br />
Agricultural Planning and Projections (NIAPP), <strong>the</strong> National Institute of<br />
Agricultural Economic Research (NAER), <strong>the</strong> Mekong Delta Farming Systems<br />
Research and Development Centre of <strong>the</strong> University of Cantho, and various<br />
universities. Vietnam is also an active participant in <strong>the</strong> regional project Farming<br />
Systems Development in Asia: Crop/Livestock/Fish Integration in Rainfed Areas.<br />
However, <strong>the</strong>re was a feeling that <strong>the</strong>re was fragmentation in <strong>the</strong> support to<br />
agriculture at <strong>the</strong> separate <strong>research</strong> centers. This led to a project proposal for<br />
coordinated farn~ing <strong>systems</strong> <strong>research</strong> and evaluation in Vietnam.<br />
Use of resource maps<br />
FARMING APPROACH<br />
General land-use maps have been extensively used in Vietnam for physical<br />
planning. The crops most suited to <strong>the</strong> given physical conditions (e.g., climate, soil,<br />
and irrigation regime) are chosen with no account of <strong>the</strong> relative economic value of<br />
various crops.
In Cambodia and Lao PDR, general land-use maps are being developed<br />
based on Landsat MSS data. Details are current1 being incorporated into <strong>the</strong>se<br />
maps to estimate <strong>the</strong> total and projected areas o ! land under various rice<br />
eco<strong>systems</strong>. The Cambodia-IRRI Rice Project has engaged <strong>the</strong> services of a landuse<br />
specialist to develop a comprehensive set of rice ecosystem maps for<br />
information ga<strong>the</strong>red from 1:50,000 topographic maps, land-use images, and<br />
available aerial photographs. A national rice-ecosystem map (scale 1: 1,000,000) and<br />
a set of more detailed provincial maps (scale 1:250,000) are being prepared.<br />
Site description<br />
Considerable time and effort were spent in conducting baseline surveys in<br />
Indochina. The output of <strong>the</strong>se surveys has been used for farming <strong>systems</strong><br />
characterization and as a basis for farming <strong>systems</strong> zoning at <strong>the</strong> district level in<br />
Vietnam. In addition, household surveys of farms have been conducted to get more<br />
detailed information to better understand farmer problems and to better plan<br />
agricultural development programs. The method compared <strong>the</strong> farming <strong>systems</strong> of<br />
low-performing farms with <strong>the</strong> <strong>systems</strong> used by high-performing farms. From <strong>the</strong><br />
results, agricultural package outlines were developed as potential intervention<br />
mechanisms to bring about rapid increases in production (Mandac 1990).<br />
Baseline surveys have similarly been completed in three rice eco<strong>systems</strong><br />
(rainfed lowland, deepwater, and irrigated) in Cambodia, and in rainfed and upland<br />
environments in La0 PDR (Lando and Solieng 1991, Schiller et a1 1991).<br />
Long-term cropping pattern and fertility<br />
The objective was to develop sustainable rice-based cropping <strong>systems</strong> that included<br />
increased production of nonrice crops and incorporation of grazing animals. In<br />
developing <strong>the</strong>se <strong>systems</strong>, emphasis was placed on ensuring that <strong>the</strong> workload of <strong>the</strong><br />
farm family was not unacceptably increased.<br />
The <strong>research</strong> was undertaken in <strong>the</strong> context of <strong>the</strong> whole farm (including on-<br />
farm or natural water storage and provision of feed for grazing animals). More<br />
specifically, <strong>the</strong> <strong>research</strong> involved experiments that addressed low soil fertility, pest-<br />
management strategies, and <strong>the</strong> introduction of proven appropriate models. An<br />
example of <strong>the</strong> work done in Cambodia is shown in Table 4.<br />
Component technologies<br />
The components of <strong>the</strong> farming <strong>systems</strong> currently undertaken in Indochina are<br />
shown in Table 5. They involve a range of activities, from varietal improvement to<br />
crop combinations.
Varietal irnprovernent. This componerlt seeks to develop consistent high-<br />
yielding rice varieties appropriate to different rice environments. This is being<br />
accomplished by continuing <strong>the</strong> collection, evaluation, and conservation of rice<br />
varieties and introducing o<strong>the</strong>r varieties adaptable to <strong>the</strong>se areas. An important<br />
activity is technology transfer through on-farm trials and field days.<br />
Integrated nuttient r?~urlagcmctlt. The focus of <strong>the</strong> integrated nutrient<br />
management program is to develop <strong>systems</strong> of nutrient management in various<br />
agroeco<strong>systems</strong> using green manure. legume crops, farm yard manure, crop residue,<br />
and inorganic fertilizers. Identification of suitable species, varieties of green manure<br />
and legume crops, and <strong>the</strong> incorporation of <strong>the</strong>se crop residues are being<br />
investigated. Research is also being conducted on applied fertilizers to gain<br />
maximum benefit from <strong>the</strong>ir use.<br />
Iiltegrufcdpcst r?zatlclger?zent. The main objectives of integrated pest<br />
management are to identify and determine <strong>the</strong> relative importance of <strong>the</strong> major<br />
insect pests, diseases, nematode, and weed problems affecting rice in Indochina; and<br />
to initiate component <strong>research</strong> on <strong>the</strong> more important pest problems.<br />
Ir77proved water ~?t~~n~g(:t?zctlt. The broad objective of improved water<br />
management is to develop methods of water management that make best use of<br />
water resources (as farm inputs and means of land reclamation and improvement)<br />
and increase farm productivity in various rice ecosystenls.<br />
IRRI APPROACH TO COUNTRY PROGRAMS<br />
The rationale for <strong>the</strong> IRRI country programs in Intlochina is based on <strong>the</strong> belief that<br />
strong natioxal <strong>research</strong> and extension programs are fundamental to increased<br />
productivity of rice and rice-based farming <strong>systems</strong>. The country and regional<br />
programs are designed to respond to <strong>the</strong> specific needs and problenls of each<br />
country. The type of help that IRK1 e\tends depends on <strong>the</strong> stage of development of<br />
<strong>the</strong> <strong>research</strong>, training, and development programs ill <strong>the</strong> individulil countries. IRRI<br />
has fornal or inform;~l rel:~tionships in both <strong>research</strong> and tr:iining with nearly all <strong>the</strong><br />
rice-growing countries in <strong>the</strong> world. Collaborr~tion is usually in <strong>research</strong> and<br />
training, however, IRRI is occasionallv invited to help plan <strong>the</strong> development of a<br />
national rice <strong>research</strong> institute.<br />
CONSTRAINTS TO FARMING SYSI'EMS RESEARCH<br />
The key constraints to farming <strong>systems</strong> <strong>research</strong> and development in Inclochina are<br />
lack of trained personnel, lack of <strong>research</strong> facilities and planning capacity, lack of<br />
effective technology transfer, and generally low level of infrastructure.
Cambodia, Lao PDR, and Vietnam lack trained scientists and technicians in<br />
sufficient numbers to provide an effective <strong>research</strong>-extension continuum. Cambodia<br />
and Lao PDR are much more seriously affected than Vietnam because <strong>the</strong>y have<br />
very few <strong>research</strong> workers with college degrees. In addition, extension services in all<br />
three countries are severely understaffed and are nonexistent in some areas. Again,<br />
Vietnam appears to be in a better position than Cambodia and Lao PDR in this<br />
regard.<br />
A major constraint in Cambodia is <strong>the</strong> absence of a national <strong>research</strong> center<br />
that will coordinate <strong>the</strong> activities of <strong>the</strong> Ministry of Agriculture (although similar to<br />
Lao PDR, work has commenced on a national agricultural <strong>research</strong> center). At<br />
present, <strong>the</strong> few <strong>research</strong> workers in Cambodia are located at three different<br />
centers, none of which has <strong>the</strong> potential to expand sufficiently beyond its present<br />
area of 8-10 ha. The main constraint here is <strong>research</strong> direction. Vietnam has a<br />
stronger <strong>research</strong> and development infrastructure than <strong>the</strong> o<strong>the</strong>r two countries.<br />
There is a particular need for assistance in <strong>the</strong> development of an effective<br />
system of technology transfer in both Cambodia and Lao PDR. Both countries have<br />
<strong>the</strong> beginnings of a province-based agricultural service dealing with seed and<br />
fertilizer distribution. However, without an effective <strong>research</strong> program, extension<br />
services have little improved technology to extend to <strong>the</strong> farming communities.<br />
Roads are very poor in Cambodia and Lao PDR. This situation results in<br />
transportation difficulties for personnel, equipment, and materials. Close to Phnom<br />
Penh and Vientiane, some improvements are being carried out, but <strong>the</strong> poor state of<br />
<strong>the</strong>ir national economies will preclude substantial improvement in <strong>the</strong> short term.<br />
Vietnam has a relatively well-established road network, particularly in <strong>the</strong> south,<br />
and does not have a major problem in this regard except, perhaps, in <strong>the</strong> uplands<br />
and highlands.<br />
CONCLUSION<br />
The main objective of agricultural development in Indochina is to produce enough<br />
food and to export goods and raw materials. The strategy is to use FSR to support<br />
national plans for agricultural development, rural development, and environmental<br />
protection.<br />
Cropping <strong>systems</strong> <strong>research</strong> in Indochina, particularly in Vietnam, has been<br />
going on for many years. In <strong>the</strong> 1960s, cropping system activities started with<br />
emphasis on <strong>the</strong> growing of three crops in areas where two crops were previously<br />
grown. In <strong>the</strong> 1970s, a large breeding effort was initiated on many crops including<br />
rice, maize, soybean, sweet potato, wheat, barley, and sorghum. Many ecological<br />
(soils and climate) and physiological studies were conducted to support <strong>the</strong>se<br />
activities. All <strong>the</strong>se contributed to <strong>the</strong> development of <strong>the</strong> technological components<br />
of farming <strong>systems</strong> in <strong>the</strong> region. Farming <strong>systems</strong> <strong>research</strong> in <strong>the</strong> 1980s focused on
solutions to problems under <strong>the</strong> basic conditions of disappearing lowlands and<br />
increasing population pressure.<br />
FSR is carried out by different agencies in Indochina. Apparently, <strong>the</strong>re is a<br />
lack of coordination, and <strong>the</strong>re is a strong need to integrate <strong>the</strong> activities of <strong>the</strong>se<br />
agencies to streng<strong>the</strong>n FSR in Indochina.<br />
REFERENCES CITED<br />
Huke R E, Huke E H (1990) Human geography of rice in Sou<strong>the</strong>ast Asia.<br />
<strong>International</strong> Rice Research Institute, Los Bafios, Philippines.<br />
IRRI--<strong>International</strong> Rice Research Institute, Cambodia-IRRI Project (l990a)<br />
Annual Report for 1989. IRRI, P.O. Box 933, Manila, Philippines.<br />
IRRI--<strong>International</strong> Rice Research Institute, Cambodia-IRRI Project (1990b)<br />
Inception Report. IRRI, P.O. Box 933, Manila, Philippines.<br />
IRRI--<strong>International</strong> Rice Research Institute, Indochina Program (1987) Project<br />
document. Phase 2. IRRI, P.O. Box 933, Manila, Philippines.<br />
IRRI--<strong>International</strong> Rice Research Institute, Lao PDR-IRRI Rice Research and<br />
Tanning Project (1990) Project docun~ent. IRRI, P.O. Box 933, Manila,<br />
Philippines.<br />
IRRI--<strong>International</strong> Rice Research Institute, Lao PDR -1RRI Rice Research and<br />
Training Project (1991) Progress report, Jan - Jun 1991. IRRI, P.O. Box 933,<br />
Manila, Philippines.<br />
Mandac A M (1990) Farming system report on streng<strong>the</strong>ning <strong>the</strong> NIAPP. Hanoi,<br />
Vietnam.
Table 1. Basic data on countries in Indochina.<br />
Land area (km') 181,000<br />
Population (million) 7.2<br />
Population density (people/km2) 40<br />
Population growth rate (%) 3 .O<br />
Climate Tropical<br />
Hottest month Mar- Apr<br />
Coldest month January<br />
Wettest illonth Oct (256 mm)<br />
Driest month Jail (8 mm)<br />
Cambodia Lao PDR Vietnam<br />
236,800 329,600<br />
3.7 63 .O<br />
16 19 1<br />
2.9 2.5<br />
Tropical Tropical<br />
April (23-34 OC) June (26-33 'C)<br />
January (14-28 'C) January (13-20 'C)<br />
June (302 mm) August (343 mm)<br />
December (3 mm) January (18 mm)
Table 2. Rice farming <strong>systems</strong> in Can-tbodia and Lao PDR.<br />
Rice farming system Cambodia Lao PDR<br />
Rice area as percentage of<br />
total cropped area<br />
Rainfed lowland<br />
Early<br />
Medium<br />
Late<br />
Irrigated<br />
Deepwarer<br />
Upland<br />
Total<br />
Average national yield (tlha)<br />
Note: Figures in paren<strong>the</strong>ses are percentages.<br />
Table 3. Rice farming <strong>systems</strong> in Vietnam (1990).<br />
Water<br />
regime<br />
Irrigated lowlarid<br />
Spring-Summer<br />
Summer- Autumn<br />
Winter-Spring<br />
Nol-<strong>the</strong>rn pro\~ilicesa Mekong River ~ elta~<br />
Area (000 ha) % Area (000 ha) %<br />
Rainfed lowlalid<br />
Shallow (0-50 cm) 2 84 12 300 12<br />
Intermediate (50-100 cm) 95 4 550 23<br />
Deepwater (floating) 300 12<br />
Total rice area -,- 3 391 100 2,400 100<br />
a~ource: Huke and Huke (1990). 'source: Vietnam-UNDP (1991).
Table 4. I,ong-term cropping patlcrns at I'rcy 1'hd;tu Sla~ion, Kampong Spcu I'rovince, Cambodia (Irrigated, 1990).<br />
TI-eatmcnt 'TIiird Crop" Firs1 crop 0<br />
First second Third Plant plant Grain Plant plant Grain<br />
crop crop cl-01' hcight population yield heighl population yield<br />
(cm) (no./rn2) ((/ha) (cm) (no. /m2) (t/ha)<br />
Rice Rice - 0 0 0 67 255 3.2<br />
Rice Mungbean Rice 75 200 1.3 7 7 252 3.2<br />
Rice Mung bean 33 5 8 0.05 7 2 250 3.2<br />
Rice Munghean 0 0 0 75 255 3.2<br />
Rice Mungbean Mungbean 3 5 62 0.03 6 8 202 3.1<br />
Rice Rice Rice 78 220 1.5 66 217 3 .O<br />
'planting date 13 Mar 1990; transplanting date 2 Mar 1990; harvest date 2 Jul 1990.<br />
b~lailtillg date 27 Jul 1990: transplanting dale 2 Aug 1990; harvest date 17 Nov 1990.
Table 5. Component-technology studies in Indochina.<br />
Varietal improvement<br />
Cultivar trials<br />
Germplasm collection/evaluation<br />
Hybridization<br />
Quality analysis<br />
Integrated nutrient nlanageinent<br />
Fertilizer responses (NPK)<br />
Green manure (legume) evaluation<br />
Integrated pest management<br />
Y ield-loss assessment<br />
Iniproved water management<br />
h'lixed farming<br />
Animal and fish<br />
Crop conibinations<br />
Rotations<br />
Intercroppilig combinations<br />
Cambodia Lao PDR Vietnam
IMPACT ASSESSXIENT OF FARhIIKG SYSTEhlS RESEARCH AND<br />
DEF-ELOPRIENT AT THE FARM LEVEL: THE CASE OF KABSAKA<br />
TECHNOLOGY IN ILOILO, PHILIPPINES<br />
V. T. ~illanciol, C. H. ~analol, hl. L. V. I. ~ebulanan',<br />
A. sotornill, and N. F. C. s an awe era^<br />
One of <strong>the</strong> most successful farming <strong>systems</strong> projects in <strong>the</strong> Philippines<br />
has been <strong>the</strong> KABSAKA project in Iloilo. The KABSAKA technology<br />
introduced <strong>the</strong> two three crops option from <strong>the</strong> traditional single crop<br />
practice that framers were involved in. This study attempted to<br />
quantify some of <strong>the</strong> benefits derived by <strong>the</strong> farring communities<br />
which adopted <strong>the</strong> technology. The study concludes that <strong>the</strong>re are no<br />
strict adopters; farmers use only specific components of <strong>the</strong><br />
technology particularly <strong>the</strong> ones beneficial to <strong>the</strong>m. Also, while <strong>the</strong><br />
income of adopter farmers is higher than <strong>the</strong> nonadopters, still <strong>the</strong><br />
incomes are inadequate to sustain <strong>the</strong> development in <strong>the</strong> countryside.<br />
KABSAU is <strong>the</strong> acronym for Kabusugan sa Kaumahan, an llonggo phrase<br />
meaning bortnty irz tlze fanlz. The KABSAKA project was initially a rice-based crop<br />
diversification strategy in <strong>the</strong> lowland rainfed areas that served as a basis for <strong>the</strong><br />
development of a cropping <strong>systems</strong> <strong>research</strong> methodology. Although <strong>the</strong> KABSAKA<br />
project may not have initially used <strong>the</strong> basic elements of farming <strong>systems</strong> <strong>research</strong><br />
(FSR), most of <strong>the</strong> FSR components were tested within <strong>the</strong> project at different<br />
stages of its development.<br />
The KABSAKA project had a farming <strong>systems</strong> perspective and included<br />
adaptive trials and pilot-production programs implemented in 1974-85 in <strong>the</strong> rainfed<br />
lowland areas of Iloilo. Two or more crops could be grown because of <strong>the</strong><br />
introduction of various components of <strong>the</strong> KABSAKA technology (which includes<br />
ezrly land preparation; use of early maturing varieties; direct seeding of <strong>the</strong> first rice<br />
crop; use of fertilizer, insecticide and herbicide; short turnaround period; and<br />
planting of upland crops, particularly legumes, in <strong>the</strong> drier areas where it is not<br />
possible to grow a second crop of rice).<br />
During <strong>the</strong> last 10 yr, several studies measured <strong>the</strong> impact of KABSAKA<br />
technoloa in Iloilo (Price 1982, Barlow et a1 1983, Wangwacharachul 1983). These<br />
studies indicated that <strong>the</strong> KABSAKA technology had increased cropping intensity,<br />
input usage, annual rice production, and farm income.<br />
a arm in^ System and Soil Resources Institute, College of Agriculture, University of<br />
<strong>the</strong> Philippines Los Bafios, Laguna, Philippines.<br />
2~ocial Sclences Division, <strong>International</strong> Rice Research Institute, P.O. Box 933,<br />
Manila, Philippines.
Wangwacharachul (1984) examined <strong>the</strong> macro effects of <strong>the</strong> KABSAKA<br />
technology and concluded that <strong>the</strong> income structure h:id changed over a period of 5<br />
yr in favor of nonagricultural households in partially and fully irrigated areas. It had<br />
also improved <strong>the</strong> overall income of <strong>the</strong> rural sector. Barlow et a1 (1983) concluded<br />
that <strong>the</strong> economic impact of <strong>the</strong> new technologies depended on <strong>the</strong> resource level of<br />
<strong>the</strong> farmers. Under rainfed conditions where cash is scarce, low input levels are<br />
generally employed. Price (1982) discovered that <strong>the</strong> :idoption of KABSAKA<br />
technoloo reduced input use in relation to <strong>the</strong> recommendetl rate.<br />
The objective ot'tl~is study was to quantify <strong>the</strong> direct :ind indirect benefits of <strong>the</strong><br />
KABSAKA technology to farmers. The first part of <strong>the</strong> study was conducted in<br />
1957-88; <strong>the</strong> second part continued until 199 1. 'The first phase of <strong>the</strong> study focused<br />
on <strong>the</strong> degree of adoption of <strong>the</strong> ne\v technology, <strong>the</strong> impact of <strong>the</strong> new technology<br />
on overall production and income, <strong>the</strong> nonqi~antifiat)lc benefits (e.g., education and<br />
nutrition) that co!llti be attributed to rhe technology, and <strong>the</strong> role of support<br />
institutions in popul:irizjng tlie technology.<br />
The second phase of <strong>the</strong> study (1989-91) focused on finding several ways to<br />
classify adopters of <strong>the</strong> new technolop according to <strong>the</strong> level of adoption and,<br />
consequently, to determine <strong>the</strong> impact of <strong>the</strong> technology on farm household income<br />
and expenditures.<br />
The with and ~ifllolit approach was al~plied. 'l'he frarnev~ork proposed by<br />
Ranaweera (1988) was used to measure <strong>the</strong> impact of <strong>the</strong> adopted technology. This<br />
was based on <strong>the</strong> premise that <strong>the</strong> inlpxct of FSR (at <strong>the</strong> farming <strong>systems</strong> level or<br />
<strong>the</strong> farm household level) can he attributed to <strong>the</strong> technology that was generated.<br />
The impact of FSR can <strong>the</strong>n be measured as <strong>the</strong> difference between <strong>the</strong> adopters<br />
and <strong>the</strong> nonadopters \s.itli respect to selected in2pnct parameters.<br />
Selection of <strong>the</strong> study area<br />
The study area, Ajuy, Iloilo Province, \us selected on tlie basis of <strong>the</strong> extent of <strong>the</strong><br />
rainfed area, <strong>the</strong> extent of <strong>the</strong> KABSAKA project activity, average yield, tenurial<br />
status of farmers, and average landholding. Except for average yield, farmers in<br />
Ajuy were, more or less, similar to farmers in lloilo in all o<strong>the</strong>r aspects. The study<br />
initially covered three barangays or villages (Culasi, Pili, and San Antonio) in <strong>the</strong><br />
municipality. Ano<strong>the</strong>r t\vo barangays (Luca anci Poblacion) were added in <strong>the</strong><br />
second phase of <strong>the</strong> study.
Site description<br />
Iloilo is located in <strong>the</strong> sou<strong>the</strong>astern region of <strong>the</strong> Island of Panay. The municipality<br />
of Ajuy is situated in <strong>the</strong> nor<strong>the</strong>astern part of Iloilo, approximately 87 km from<br />
Iloilo City. Ajuy is composed of 34 barangays, three of which are islets.<br />
The topography of Ajuy is dominated by a mountain range (400 In in height)<br />
in <strong>the</strong> west that is covered with grasslands, shrubs, and secondary forests.<br />
Agricultural lands are located mostly along <strong>the</strong> foot of this mountain range and<br />
extend to <strong>the</strong> relatively flatter area along tlie sea coast. The soil type is generally<br />
sandy loam and clay loam.<br />
About 36% of <strong>the</strong> total area of <strong>the</strong> rnuniciprility is considered agricultural<br />
land, 41% of which is under rice-based farming <strong>systems</strong>. Of this' rice-based farming<br />
area, 90% is rainfed. O<strong>the</strong>r crops in <strong>the</strong> area are coconut, sugarcane, maize, root<br />
crops, and legumes.<br />
Fishing is a major source of income in <strong>the</strong> coastal areas. Fish ponds<br />
constitute 6% of <strong>the</strong> total area. Livestock is raised mainly for home consumption<br />
and as 3 supplenlentary source of income. The site belongs to <strong>the</strong> Type 111 climate,<br />
which is characterized by two distinct seasons. January-April is <strong>the</strong> dry period with<br />
an average rainfall of 230 mm/mo, May-December is tlie wet season (WS) with an<br />
average rainfall of 600 mm/mo. The monthly mean temperature is relatively<br />
constant (25.2 OC,in January, 28 'C in May). The monthly mean relative humidity<br />
ranges from abo~lt 75% in April to 84% in October.<br />
Selection of sample farms<br />
Many difficulties were encountered in <strong>the</strong> selection of san;ples that would represent<br />
adopters and nonadopters of <strong>the</strong> technology. F;irrners who had established <strong>the</strong>ir first<br />
crop using dry seeded rice (DSR) were classified as adopters. The o<strong>the</strong>r f~rmers<br />
were considered to be nonadopters. Initially, <strong>the</strong> selection of adopters of<br />
KABSAKA technology was based on <strong>the</strong> information provided by <strong>the</strong> Department<br />
of Agriculture (DA). However, <strong>the</strong>re were discrepancies, especially among farmers<br />
that changed categories year after year. Therefore, <strong>the</strong> farmers were cl:lssified<br />
according to <strong>the</strong> cropping system <strong>the</strong>y had adopted over <strong>the</strong> last 4 years. Sixty<br />
farmers were selected during <strong>the</strong> first phase of <strong>the</strong> study. Those who dropped out<br />
during <strong>the</strong> first year were replaced with ano<strong>the</strong>r 12 farmers.<br />
Data collection and monitoring<br />
Records on crop production and household transactions were maintained daily from<br />
March 1987 to April 1989. All crop production data were recorded on a plot basis<br />
using n farm activity sheet; whereas, household transaction dates were recorded on a
household basis on an income and expenditure sheet. Farmers were visited weekly<br />
to collect <strong>the</strong> completed record sheets.<br />
Seasonal surveys of crop production activities were conducted during <strong>the</strong> WS<br />
of 1989 and <strong>the</strong> dry season (DS) of 1990. Details of household income and<br />
expenditure were excluded from <strong>the</strong> survey.<br />
Methods of analysis<br />
Initial analysis was based on three farm-size categories. This did not reveal a<br />
significant difference in mean yield or inconle-expenditure patterns among adopters<br />
and nonadopters (Medialdia and Ranaweera 1988); <strong>the</strong>refore, fur<strong>the</strong>r analysis was<br />
done to compare <strong>the</strong> impact parameters between adopters and nonadopters. The<br />
impact indicators included input use, yield and pattern of household consumption,<br />
income, and expenditure. The partial budget technique was used to compare <strong>the</strong><br />
econoniic perfornlance of <strong>the</strong> t\vo groups.<br />
Cluster ai~alysis was used to classify <strong>the</strong> adopters of <strong>the</strong> new technology<br />
according to <strong>the</strong> level of adoption. Initially, plot-level data ra<strong>the</strong>r than farm-level<br />
data were used because farmers may apply different types of technology to plots<br />
with diverse characteristics (e.g., land tenure, Iandscilpe position, and soil texture).<br />
However, farm-level data were subsequently used for cluster analysis.<br />
Demographic characteristics of farmers and o<strong>the</strong>r farm-related inforrnation<br />
were also recorded along with farm-level data. Variables related to <strong>the</strong> different<br />
components of <strong>the</strong> KABSAKA technology were tabulated on a hectare basis or as a<br />
percentage of <strong>the</strong> area where <strong>the</strong> technology was used. For ex:imple, <strong>the</strong> percentage<br />
of <strong>the</strong> area planted using DSR was used to denote <strong>the</strong> extent to which <strong>the</strong> farmer<br />
adopted <strong>the</strong> recommended crop establishment methods. Likewise, <strong>the</strong> percentage of<br />
<strong>the</strong> area grown before <strong>the</strong> cut off date of <strong>the</strong> first crop and <strong>the</strong> percentage of <strong>the</strong><br />
area with a turnaround period of less than 16 d were used to denote <strong>the</strong> cut off date<br />
for sowing and <strong>the</strong> turnaround period, respectively. Inputs (fertilizer, insecticide,<br />
and herbicide) ancl yield were tabulated on a hectare basis. The VARCLUS<br />
procedure was used to group <strong>the</strong> variables for cluster analysis. About 106 variables<br />
were considered, but only 35 variables were used in <strong>the</strong> analysis (Table 1).<br />
An attempt was made to combine <strong>the</strong> b;isic practices related to a conlponent<br />
with f~rm-level characteristics, and to use <strong>the</strong>se as <strong>the</strong> classifying vari~~bles for<br />
cluster analysis. For example, for fertilizer usage as a cor-nponent technology, <strong>the</strong><br />
variables considered were <strong>the</strong> number of applicr~tions, <strong>the</strong> level of NPK in each<br />
application, and <strong>the</strong> value of inputs used. O<strong>the</strong>r components of <strong>the</strong> technology that<br />
were tested were crop establishment, weed control, and turnaround period. These<br />
components were tested on <strong>the</strong> premise that <strong>the</strong>se f~rnlers could be classified<br />
according to <strong>the</strong> level of technology adoption.
Cluster analysis \vas done using <strong>the</strong> CLUSTER procedure (Ward's Method)<br />
of SAS. The value was used to determine whe<strong>the</strong>r t\tro clusters were dissimilar.<br />
Two clusters with R2 values of more than 0.75 \\..ere considered to be one cluster.<br />
An R2 value of 0.75 may ei<strong>the</strong>r denote certain similarities or dissimilarities among<br />
clusters.<br />
The CANDISC procedure was used to derive canonical variables that<br />
summarized between-class variations. After <strong>the</strong> clusters were identified, each cluster<br />
was described according to <strong>the</strong> following parameters: plot and farm-level<br />
characteristics; level of adoption of <strong>the</strong> KABSAKA technology; productivity;<br />
profitability; household income; and expenditure patterns. The differences among<br />
<strong>the</strong>se parameters reflected <strong>the</strong> impact of <strong>the</strong> variations in technology adoption.<br />
ADOPTERS AND SONADOPTERS<br />
The first part of <strong>the</strong> study viewed <strong>the</strong> dichotomous aspect of technology adoption.<br />
Adopters used dry seeded rice (DSR) for <strong>the</strong> first rice crop; nonadopters did not.<br />
The <strong>research</strong>ers intentionally avoided <strong>the</strong> terms "high adopters" and "low adopters"<br />
to avoid being biased in relation to <strong>the</strong> technology promoted by <strong>the</strong> FSR project. It<br />
should be mentioned that FSR promotes experimentation and farmers should not<br />
be penalized for modifying <strong>the</strong> technology introduced by <strong>the</strong> project.<br />
Changes in farm practices<br />
A comparative study of <strong>the</strong> adopters and nonadopters showed significant differences<br />
with regard to farm practices associated with <strong>the</strong> KABSAKA technology. Adopters<br />
plowed and harrowed <strong>the</strong>ir fields three times. The nonadopters plowed only once. In<br />
1987-88, adopters planted 50% of <strong>the</strong>ir farm area to DSR and <strong>the</strong> balance to wet<br />
seeded rice (WSR) and transplanted rice (TPR). The date of crop establishment for<br />
adopters coincided with <strong>the</strong> date used by <strong>the</strong> non:idopters, who established <strong>the</strong>ir<br />
crop after <strong>the</strong> third week of June 1987. Although <strong>the</strong> land was prepared and<br />
supposedly planted to DSR, <strong>the</strong> rainfall during June prompted <strong>the</strong> adopters to use<br />
WSR in certain portions of <strong>the</strong>ir farms. The farm area of adopters that was planted<br />
to DSR increased to about 88% in 1988-89 and was established earlier (before <strong>the</strong><br />
third week of June) than <strong>the</strong> farm area of nonadopters. This was also true in<br />
1989-90.<br />
The seeding rate did not vary much, but adopters were more inclined to<br />
provide some allowances during <strong>the</strong> first crop. Farmers had to maintain higher<br />
seeding rates than recommended to provide an allowance for damage caused by<br />
golden snaiis.<br />
The turn-around period during 1957-88 was longer in <strong>the</strong> case of<br />
nonadopters (only 38% of <strong>the</strong> area was planted within <strong>the</strong> recommended period of<br />
15 d). The adopters had 66% of <strong>the</strong> area planted within 2 wk after harvest of <strong>the</strong>
first crop. In 1989-90, <strong>the</strong> adopters prepared <strong>the</strong>ir lands earlier (92% of <strong>the</strong> rice area<br />
was planted before <strong>the</strong> prescribed turnaround period of 15 d). The drought that<br />
occurred in <strong>the</strong> previous !!ear prompted <strong>the</strong> farmers to plant earlier.<br />
The second crop of rice was established primarily using WSR (81% of <strong>the</strong><br />
total area cultivated in 1987-88, and 96% in 1988-89 and 1989-90). Replanting was<br />
more common among nonadopters. About 13% of <strong>the</strong>ir land was replanted during<br />
<strong>the</strong> first crop in 1987-88; about 12% in 1988-89. Golden snails caused more damage<br />
during <strong>the</strong> second crop. Therefore, 27% of <strong>the</strong> farm areas of nonadopters had to be<br />
replanted in 1987-88 and 18% in 1988-89.<br />
The use of herbicide was considered an important component of <strong>the</strong><br />
KARSAKA technology, particularly for <strong>the</strong> DSR crop. Adopters applied an average<br />
of 1.0 liters/lia before <strong>the</strong> establishment of <strong>the</strong> first crop in 1987-88 compared \vith<br />
only 0.5 liters/ha for nonadopters. This rate was reduced to 0.7 liters/ha in 1988-89.<br />
For preemergence application, ado ters used 0.4 liters/ha of herbicide; whereas,<br />
nonadopters applied only 0.2 litersfha during <strong>the</strong> first crop in 1987-88. Adopters<br />
also used postemergence herbicide at <strong>the</strong> rate of 0.5 liters/ha during <strong>the</strong> first crop in<br />
1987-88 and 0.9 liters/ha in 1988-89. The cost of herbicide application was as high as<br />
P267 during <strong>the</strong> first crop of 1987-88. Despite <strong>the</strong> use of herbicide, manual weeding<br />
had to be done. Atfopters devoted relatively more time to weeding than<br />
nonadopters during <strong>the</strong>ir first rice crop.<br />
All farnlers used limited amounts of insecticide, ~vhicli indicated that insect<br />
pests were not a serious problem. However, in 1988-89, <strong>the</strong> nonadopters had to use<br />
1.2 kg ai/ha of insecticide because of a severe pest infestation. During that crop<br />
year, <strong>the</strong> infestation affected all crops except <strong>the</strong> DSR crops, which had been<br />
planted 2 wk earlier. The insect problem also affected <strong>the</strong> newly established WSR<br />
crops. The application of insecticide during <strong>the</strong> second rice crop did not vary<br />
between adopters and nonadopters.<br />
All farmers used less fertilizer than <strong>the</strong> recomn~ended level of 60-30-30.<br />
Commonly used fertilizers were urea and aninionium sulfate. The fertilizer costs<br />
were as high as P578 in 1987-88 and P764 in 1988-89 for <strong>the</strong> first crop of<br />
nonadopters.<br />
Average yield and profitability<br />
The yields obtained by 60 farmers before and after <strong>the</strong> introduction of KABSAKA<br />
were compared. The fidrmers were asked to recall production data 5 yr before <strong>the</strong><br />
KABSAKA project was initiated. Yield increased as a result of <strong>the</strong> adoption of<br />
KABSAKA technology. However, <strong>the</strong>re seemed to be no difference between<br />
adopters and nonadopters. The yield of adopters was even lower than nonadopters,<br />
except in 1989-90 (Table 2). This raises <strong>the</strong> Issue of <strong>the</strong> validit of <strong>the</strong> categorization<br />
of adopters based oo cropping <strong>systems</strong> (i.e., DSR versus WSR~ when dealing with a<br />
package of technology like KABSAKA.
A comparison of costs and returns on a per hectare basis for crop years<br />
1987-88 and 1988-89 showed that despite <strong>the</strong> h~gh production cost incurred by<br />
nonadopters, <strong>the</strong> adopters had lower net benefits compared to <strong>the</strong> nonadopters<br />
because of lower production (Table 3). However, in 1989-90, <strong>the</strong> adopters gained a<br />
little more than <strong>the</strong> nonadopters, but <strong>the</strong> difference was not significant.<br />
Farm household income and expenditure<br />
Household income and expenditures can be classified according to <strong>the</strong> source of<br />
income and nature of expenses. Sources of income are classified as those derived<br />
from farm and nonfarm sources. Expenditures are classified accordin to farm and<br />
nonfarm expenditures. Farm income includes income from <strong>the</strong> sale o k rice, livestock,<br />
and roduce from <strong>the</strong> home garden. Nonfarrn income includes income derived from<br />
P<br />
non arm activities and remittances from children. Farm expenditures are related to<br />
expenses in crop and animal production. Nonfarm expenditures include those<br />
related to food, basic household items, health, education, and o<strong>the</strong>r social<br />
obligations.<br />
There was a marked difference in <strong>the</strong> monthljl incomes of adopters and<br />
nonadopters. Nonadopters had a higher monthly farm income in 7 mo within a<br />
period of 11 mo (May 1987-Mar 1988). A higher farm income was observed among<br />
<strong>the</strong> nonadopters in October because of an increase in rice sales. A similar trend was<br />
observed in 198889 when <strong>the</strong> nonadopters had higher farm income in 6 mo within a<br />
period of 11 mo (Jun 1988-Apr 1989).<br />
A comparison of <strong>the</strong> cash and noncash incomes of adopters and nonadopters<br />
showed that adopters had a substantial amount of noncash income particularly in<br />
<strong>the</strong> months of June, July, and November. Noncash income, however, was not very<br />
substantial during <strong>the</strong> preceding year. Total annual cash income was similar for<br />
adopters and nonadopters. However, <strong>the</strong> pattern of monthly cash inflow varied.<br />
Adopters had a relatively stable monthly cash inflow from August 1987 to February<br />
1988 (Table 3).<br />
After September 1987, cash inflow was mainly derived from <strong>the</strong> sale of rice.<br />
For nonadopters, rice sales contributed to household income from October 1987 to<br />
May 1988. During a period of 12 1110, income from <strong>the</strong> sale of rice contributed 38%<br />
to <strong>the</strong> household income of adopters and 3770 to <strong>the</strong> household income of<br />
nonadopters.<br />
In 1988-89, <strong>the</strong> total cash inflows were almost <strong>the</strong> same as in <strong>the</strong> previous<br />
year. A peak occurred in December 1988 for both adopters and nonadopters.<br />
Among <strong>the</strong> nonadopters, rice sales contributed about 50% to <strong>the</strong> household income<br />
in October. Within a period of 11 mo, rice sales contributed about 24% to <strong>the</strong><br />
household income of adopters and 37% to <strong>the</strong> income of nonadopters.
Nonfarm expenses constituted <strong>the</strong> bulk of household expenditures,<br />
particularly during October and January, for both adopters and nonadopters in<br />
1987-88. The same pattern was observed in 1988-89, but expenditures were higher<br />
The adopters had a substantial cash outflo\v from July to December 1987.<br />
The cash outflow during <strong>the</strong> 12-mo period was P1,070 more than <strong>the</strong> cash inflow.<br />
The cash inflow among <strong>the</strong> nonadopters covered <strong>the</strong>ir cash outflow and provided a<br />
net cash flow of P1,268. The noncash outflow was significant among <strong>the</strong> nonadopters<br />
from October 1987 to April 1988. However, this trend was not observed in 1988-89.<br />
Except in December, cash and noncash expenses were evenly distributed throughout<br />
<strong>the</strong> year.<br />
Farm household asset accumulation<br />
The accumulation of assets was given special emphasis in <strong>the</strong> crop production survey<br />
of 1989-90. From April to September 1989, <strong>the</strong>re were only 12 farmers who acquired<br />
farm and household assets, most of which were household items such as gas stoves,<br />
clothing, blankets, and kitchenwares. One farmer bought a hand tractor engine, and<br />
two farmers had <strong>the</strong>ir houses renovated. During this period, adopters acquired<br />
assets worth P1,571; nonadopters spent about P523 on assets. Household assets<br />
were generally purchased with income gained from crops sales; o<strong>the</strong>r assets, such as<br />
tractor engines, were procured iitith loans.<br />
A similar trend was observed in a survey conducted during <strong>the</strong> second crop<br />
(October 1989-April 1990). There were 11 farmers who bought household assets<br />
and had <strong>the</strong>ir houses renovated. The adopters acquired household assets worth P809<br />
during this period, compared with P185 in assets acquired by nonadopters. One<br />
farmer obtained a loan to purchase a carabao; ano<strong>the</strong>r farmer acquired a loan to<br />
renovate his house. Income from crop and animal sales was used only for <strong>the</strong><br />
purchase of small household items and appliances.<br />
ALTERNATIVE TOOLS FOR CLASSIFYING<br />
TECHNOLOGY ADOPTERS<br />
The results of cluster analysis on data collected in 1987-88 revealed four dissimilar<br />
clusters. A partial tree diagram indicated that CL4 and CL7 were under CL2 with<br />
= 0.52; CL6 and CL8 were under CL3 with = 0.64 (Table 4). However, CL7<br />
had only two observations. The cluster analysis on <strong>the</strong> 1958-89 data showed 4 major<br />
clusters but 1 cluster had only 1 observation. For both cropping years, only 3 clusters<br />
with more than 5 observations were considered. Cluster analysis based on variables<br />
related to a particular component of technology was also used, but it produced <strong>the</strong><br />
same result as when <strong>the</strong> VARCLUS-selected variables were used.<br />
hlembership between clusters derived using data from each year were<br />
compared by considering <strong>the</strong> characteristics common to all clusters. There were 32
farmers who were common to both years. The association of <strong>the</strong> clusters derived<br />
using 1987-88 and 1988-89 data was significant using <strong>the</strong> chi-square test.<br />
Results showed that farmers transferred from one cluster to ano<strong>the</strong>r within<br />
<strong>the</strong> two periods. Differences in impact among <strong>the</strong> clusters in one period were not<br />
considered. Ra<strong>the</strong>r, <strong>the</strong> differences were compared between <strong>the</strong> two periods on <strong>the</strong><br />
assumption that farmers were subjected to more or less similar conditions in a<br />
particular period, whereas, conditions may vary widely between two periods.<br />
The results of cluster analysis for 1987-88 data were presented by comparing<br />
<strong>the</strong> clusters in <strong>the</strong> context of basic farm and household characteristics, farm<br />
practices, crop yield and profitability, and income and expenditure patterns.<br />
Production function models were also estimated to determine <strong>the</strong> influence of <strong>the</strong><br />
clusters on <strong>the</strong> production function.<br />
Farm characteristics<br />
The demographic characteristics of <strong>the</strong> clusters showed that CL4 farmers were<br />
younger and had less farming experience. However, <strong>the</strong>y had a higher educational<br />
level than farmers in CL6 and CB.<br />
Share tenancy is a dominant tenurial status among CL4 farmers (82%)<br />
followed by those in CL6 (58%). A more diverse tenurial status is found among<br />
farmers in CL8.*~he CL4 farmers used rice-based cropping <strong>systems</strong> in flat areas.<br />
Adoption of KABSAKA technology<br />
All farmers started plowing <strong>the</strong>ir lands on <strong>the</strong> last week of May, and crops were<br />
mostly established after <strong>the</strong> third week of June. Farmers in CL4 and CL8 plowed<br />
<strong>the</strong>ir fields twice for <strong>the</strong> first crop and <strong>the</strong>n, ei<strong>the</strong>r rotovated or harrowed using draft<br />
animals. About 58% of farmers, covering 79% of <strong>the</strong> area in CL4, cultivated <strong>the</strong>ir<br />
fields using rotovators. All <strong>the</strong> second crops were WSR except in <strong>the</strong> case of CL8<br />
where only about 51% of <strong>the</strong> area was planted to WSR. Farms in CL4 and CL6<br />
established <strong>the</strong>ir second crop as soon <strong>the</strong> first crop was harvested. About 73% of <strong>the</strong><br />
CL4 and 72% of <strong>the</strong> CL6 rice areas had a turn-around period of less than 15 d. A<br />
short turn-around period was possible because land preparation was accomplished<br />
with <strong>the</strong> aid of tractors (80% of <strong>the</strong> land in CL4 and 90% in CL6).<br />
Crop damage caused by golden snail infestation was a major problem in<br />
Ajuy. The damage to <strong>the</strong> second crop was greater than to <strong>the</strong> first crop. Rice<br />
seedlings, particularly DSR, are vulnerable to golden snails. During <strong>the</strong> second crop,<br />
farmers in CL4 had to replant about 50% of <strong>the</strong> area because of <strong>the</strong> damage caused<br />
by golden snails.
Herbicide is essential for DSR. However, it appeared that farmers in CL4<br />
were using less preemergence herbicide than farmers in CL6 and C U. Despite <strong>the</strong><br />
use of herbicide, manual weeding was still employed (i.e., as much as 70 h during<br />
<strong>the</strong> first crop). Hourever, <strong>the</strong>re was relatively less manual weeding during <strong>the</strong> second<br />
crop.<br />
Farmers in CL4 used less seed, insecticide, and herbicide during <strong>the</strong> first<br />
crop than farmers in CL6 and CL8 (Table 5). However, during <strong>the</strong> second crop,<br />
farmers in CL4 used more seed. The same trend was observed in expenditures on<br />
material inputs (e.g., fertilizer, insecticide, and herbicide). Among <strong>the</strong> clusters, CL6<br />
had <strong>the</strong> highest cost for hired !abor for <strong>the</strong> first crop, while CL8 had <strong>the</strong> lowest. A<br />
significant proportion of hired labor was used for replanting. On <strong>the</strong> whole, CL8<br />
farmers had <strong>the</strong> lowest cost.<br />
In terms of productivity, CL4 and CL6 had almost <strong>the</strong> same yield, while <strong>the</strong><br />
yield of CL8 was significantly lower. Cluster CL4 had <strong>the</strong> highest net benefit among<br />
<strong>the</strong> clusters, followed by CM. Cluster CL8 had <strong>the</strong> lowest net benefit (Table 6).<br />
ANALYSIS OF PRODUCTION FUNCTIONS<br />
The production-function model was estimated for each season in 1987-88. The<br />
estimate of <strong>the</strong> full model produced negative coefficients for nitrogen and potassium<br />
fertilizers, herbicide, and insecticide (Table 7). In <strong>the</strong> second round of estimation,<br />
<strong>the</strong> variables that did not have <strong>the</strong> expected positive sign were taken out of <strong>the</strong> full<br />
model. The values for both seasons were acceptable: 0.60 for <strong>the</strong> first crop and<br />
0.86 for <strong>the</strong> second crop. The elasticities for hired labor at <strong>the</strong> 0.02 level were<br />
significant for both seasons. However, <strong>the</strong> eiasticities for seeding rate were<br />
significant only during <strong>the</strong> second crop. The coefficients of <strong>the</strong> dummy variables in<br />
every cluster were significant for both seasons. This result shows <strong>the</strong> importance of<br />
<strong>the</strong> clusters in shifting <strong>the</strong> production functions. Based on <strong>the</strong> coefficients of <strong>the</strong><br />
dummy variables, CL4 had <strong>the</strong> highest coefficient of 1.24. CL6 had only 0.39.<br />
Cluster CL8 was used as <strong>the</strong> base cluster.<br />
Household income and expenditures<br />
The income and expenditure patterns of farm households were anrllyzed by<br />
examining <strong>the</strong> income and expenditure flow per month and its total for <strong>the</strong> year<br />
(Table 6). The period considered was May 1987 to April 1988. Cluster CL4<br />
appeared to have <strong>the</strong> highest income for <strong>the</strong> period (P17,248), CL6 had <strong>the</strong> lowest.<br />
This confirmed <strong>the</strong> good performance of <strong>the</strong> CL4 farmers as reflected in <strong>the</strong>ir<br />
output. The share of crop sales in farm income only averaged 37%. On a cluster<br />
basis, CL4 had 44% of farm income derived from rice.<br />
On average, income was distributed evenly, except in September and April<br />
when income was relatively higher. However, when cluster analysis was applied,
<strong>the</strong>re were differences in income throughout <strong>the</strong> year. For example, CLA had a<br />
relatively stable income flow from October to March. This was not <strong>the</strong> case in CL6.<br />
However, for all clusters, <strong>the</strong> period April-September showed an inconsistent flow<br />
of income.<br />
The majority of farmers reported that <strong>the</strong>ir expenditures were higher than<br />
<strong>the</strong>ir income. In <strong>the</strong> case of CLA, expenditures were higher than income when<br />
compared with <strong>the</strong> o<strong>the</strong>r two clusters. Expenditures were relatively higher in<br />
October and March. On average, an annual deficit of P6,800 had to be met with<br />
loans. Loans obtained in kind, such as fertilizer from private dealers, were usually<br />
paid in <strong>the</strong> forin of rough rice. However, <strong>the</strong>re were instances of positive net cash<br />
flows. Generally, a negative monthly cash flow occurred in July and from November<br />
to January. The high expenditure in July and November can be attributed to <strong>the</strong><br />
purchase of farm inputs, hired labor costs, and education. Annually, all <strong>the</strong> clusters<br />
except CL8 had positive net cash floivs.<br />
Cluster CL4 had <strong>the</strong> highest farm income during <strong>the</strong> 12-mo period (May<br />
1987-April 19S8), followed by CL6 and C U. The contribution of farm income to<br />
household income was highest in October anlollg farmsrs in CLA. In this case, 84%<br />
of total farm income was attributed to rice production.<br />
CONCLUSION<br />
The KABSAKA technology was not fully adopted in iloilo. Its adoption varied<br />
according to prevailing circumstances. In a way, <strong>the</strong> technology contributed some<br />
benefits to farmers in rainfed areas by providing <strong>the</strong>m alternatives.<br />
o Classifying KABSAKA adopters as those who used DSR is no longer<br />
valid because filrnlers decide on <strong>the</strong> crop establishment method based on<br />
various factors, primarily, wea<strong>the</strong>r cond~tions. If crops cannot be<br />
established using DSR because of early rain, <strong>the</strong> fields must be rotovated<br />
before <strong>the</strong>y can be planted to WSR.<br />
o Classifying technology adopters on <strong>the</strong> basis of level of adoption was a<br />
useful initial step to measure <strong>the</strong> impact of <strong>the</strong> KABSAKA technology.<br />
Differences between groups were examined in ternis of practices related<br />
to land preparation, crop establishment, weed control, insect control, and<br />
fertiIizer application. These differences were reflected in yield and net<br />
benefit and in <strong>the</strong> pattern of household income and expenditure. The<br />
data collected in 1987-88 revealed that CL4 farmers had adopted <strong>the</strong><br />
KABSAKA technology more comprehensively and had proved to be<br />
better performers than farmers in <strong>the</strong> o<strong>the</strong>r clusters. Production-function<br />
analysis disclosed that cluster groupings shifted <strong>the</strong> production function.<br />
Cluster CL4 had <strong>the</strong> highest level of productivity.
o The classification of farmers on <strong>the</strong> basis of level of technology adoption<br />
does not permanently confine farmers to a particular group. They can be<br />
shifted from one group to ano<strong>the</strong>r depending on <strong>the</strong>ir response and<br />
circumstances.<br />
o Ano<strong>the</strong>r approach that needs to be considered is <strong>the</strong> classification of<br />
farmers on <strong>the</strong> basis of farm characteristics and <strong>the</strong> environment. This<br />
approach will determine whe<strong>the</strong>r farmers in a particular group have <strong>the</strong><br />
same response when a particular change in <strong>the</strong> environment or<br />
technology occurs (e.g., late rainfall or increases in <strong>the</strong> cost of herbicide).<br />
o An attempt to analyze <strong>the</strong> data on a plot basis showed that farmers<br />
manage <strong>the</strong>ir farm plots in diverse ways that depend on farm<br />
characteristics. Because an average of <strong>the</strong> practices is provided when <strong>the</strong><br />
analysis is aggregated at <strong>the</strong> farm level, this variability is not considered.<br />
The case study approach seems appropriate to address this issue.<br />
o The profits from rice production are not sufficient to sustain <strong>the</strong><br />
development of <strong>the</strong> countryside. The pattern of household expenditures<br />
showed that farm income was mostly spent on household consumption.<br />
There were no investments made to improve farm level resources. The<br />
accumulation of assets was for household requirements (e.g., house repair<br />
and household appliances) ra<strong>the</strong>r than for farm resources. This pattern<br />
indicated that economic gains due to <strong>the</strong> new technology are inadequate<br />
to encourage fur<strong>the</strong>r accumulation of capital among farmers. However,<br />
some farmers did purchase tractors and water pumps.<br />
o Institutional changes in one of <strong>the</strong> study areas (Barangay Pili) resulted in<br />
increased gains from <strong>the</strong> new technology. A cooperative was organized<br />
among <strong>the</strong> farmers in Pili to provide credit and marketing facilities.<br />
These changes were brought about only after <strong>the</strong> KABSAKA project was<br />
implemented.<br />
REFERENCES CITED<br />
Barlow C E, Jayasuriya S, Price E C (1983) Evaluating technology for new farming<br />
<strong>systems</strong>: case studies from Philippines rice farms. Internatic~nal Rice<br />
Research Institute, Los Baiios, Philippines.<br />
Medialdia M T S, Ranaweera N F C (1988) An assessment of <strong>the</strong> impact of<br />
KABSAKA technology (FSR) in Ajuy, Iloilo, Philippines. Paper presented at<br />
<strong>the</strong> 8th Annual Farming Systems Symposium, 9-12 Oct 1988, University of<br />
Arkansas, Fayetteville, Arkansas.
Price E C (1982) SAS User's Guide: Statistics. SAS Institute, North Carolina.<br />
Ranaweera N F C (1988) The need to assess impact of farming <strong>systems</strong> <strong>research</strong><br />
(FSR) within <strong>the</strong> Asian context: an overview. Paper presented at <strong>the</strong><br />
Monitoring Tour Cum Workshop on Impact of Farming Systems Research in<br />
Selected Asian Countries, 25-30 Apr 1988, Arnigo Terrace Hotel, Iloilo City,<br />
Philippines.<br />
Wangwacharachul V (1984) Direct and indirect impact of <strong>the</strong> new cropping <strong>systems</strong><br />
technology and irrigation in a community economy: <strong>the</strong> case of Oton and<br />
Tigbauan municipalities, Iloilo Province, Philippines. Unpublished Ph D<br />
<strong>the</strong>sis, University of <strong>the</strong> Philippines Los Baiios, Philippines.
Table 1. List of \,ariables idc~l~lfit'd by VARCLUS procedure among 106 variables and<br />
used for cluster ana1)'sis. Aju! . Iloilo. Philippines (1987-88).<br />
\'ariable<br />
code<br />
YRSFARlI<br />
\VHLTR.\CT<br />
PL<br />
SPL<br />
AREA<br />
SLPl<br />
ST2<br />
T 3<br />
T1<br />
STI<br />
l'BWK20<br />
PLOW NI-<br />
SPLOWST<br />
PLPMA<br />
SPLPMA<br />
PLPMM<br />
I-I\VNT<br />
I-IiVPMA<br />
H\VPMhI<br />
RTNT<br />
SRTNT<br />
RTPhIM<br />
SPTPhIbI<br />
SiVSR<br />
TPR<br />
REPLTNG<br />
S REPLTNG<br />
FERTNT<br />
SFERTNT<br />
SHNDLBHR<br />
SAHERBQ<br />
AVEAI<br />
TURN<br />
AVEYIELD<br />
SAYIELD<br />
Years in farming<br />
Wheel tractor o\\.ned<br />
plot number<br />
2nd crop plot number<br />
2nd crop ar2a planted as percenl<br />
of Is1 crop arca planted<br />
2nd crop land position (flat)<br />
2nd crop tenant<br />
tenurial status (leaschuld)<br />
renut-ial sratus(o<strong>the</strong>rs)<br />
2nd crop tcnurial status (olliers)<br />
lst.c~.op planting bct'ose week 20<br />
1st crop plo\\'inz nu~iihes of rimes<br />
2nd crop pltnving niimbe~. of times<br />
Is1 c1.0~ plo\\.ing po\\,er animal source<br />
2nd crop plcwing po\vsr animal source<br />
1st crop plo\\,ins p\\-er machine sousce<br />
1st crop lia~.~~o\\li~ig number of times<br />
1st crop liar~.o\i~ing power animal source<br />
l sr crop liarr~\vi~lg power machine source<br />
1st crop I-otu\.atiny number of times<br />
2nd crop rotovalin$ number of times<br />
rotovatin_r po\\,er machine source<br />
2nd crop roto\~ating power<br />
n~achins source<br />
2116 crop wet seeded rice<br />
1st crop transplanting<br />
l st crop replantins<br />
2nd crop replan~iny<br />
1st crop fertilizer nu~uher of rimes<br />
2nd crop fertilizer number of times<br />
2nd crop u~eeding labor (h/Iia)<br />
2nd csop a\7erase l~erbicide quantity<br />
I st crop, average active ingred ient<br />
of insec~icide<br />
turn around period<br />
1st crop average yield<br />
2nd crop a\leraSe !:ield<br />
Mean Standard<br />
deviation
Table 2. Average rice yield (kglha) and profitability of rice production by adoptioil category, Ajuy Iloilo, Philippines (1987-90).<br />
First rice crop<br />
Average yield (kglha)<br />
Gross benefit ( P)~<br />
Total paid out cost (P)<br />
Net benefit (P)<br />
C,<br />
4<br />
\O<br />
I Second rice crop<br />
Average yield (kglha)<br />
Gross benefit (P)'<br />
Total paid out cost (P)<br />
Net benefit (P)<br />
a~4.50/kg of rice.<br />
Adopters Non adopters All Adopters Non adopters All Adopters Non Adopters All
Table 3. Conlparison of cash flow of adoprers and nonadopters, Ajuy, Iloilo, Philippines (1987-88 and 1988-89).<br />
Pcl-ccnlagc shar-c<br />
of rice ~o<br />
Cash inflow Cash outtlow Net cash inflow cash inflow<br />
Month Adopter N-adopter Adopter N-adopter Adopter N-adoptes Adopter N-adoptcr<br />
(n = 15) (n = 32) (11 = 15) (n = 42) (11 = 15) (n = 42) (n = 15) (n = 42)<br />
May 1.061<br />
June 490<br />
July 790<br />
Auzust 1,817<br />
September 1,23 1<br />
Octobcr 1,121<br />
Novelnber 1.74 1<br />
Decembcr 1,243<br />
January 1,478<br />
February 1,282<br />
March 930<br />
Aprii -<br />
Total 13.184<br />
May<br />
June<br />
-<br />
820<br />
July 1,071<br />
August 1.528<br />
September 1 ,l 19<br />
Octobel- 1.1 17<br />
November<br />
December<br />
993<br />
2,s 1 1<br />
January 794<br />
February 875<br />
March 565<br />
April 1,440<br />
'rota1 12.833<br />
Note: 1:i~urcs in paren<strong>the</strong>ses are not cash outflows.
Table 4. Partial I-esults of Ward's minimum variance cluster analysis,<br />
56 rice-based farmers, Ajuy, Iloilo, Philippines (1987-88).<br />
Cluster Clusters joined Number of<br />
farms
Table 5. Level of input use for <strong>the</strong> rice crop by clusters, 56 rice-based farmers,<br />
Ajuy , Iloilo, Philippines (1987-88).<br />
First rice crop<br />
Seeding rate (kglha)<br />
Fertilizer use (kglha)<br />
Urea<br />
11-14-14<br />
h? ixed<br />
Insecticide (kg ailha)<br />
Herbicide (literlha)<br />
Second rice crop<br />
Seeding rate (kglha)<br />
Fertilizer use (kglha)<br />
U sea<br />
14-14-14<br />
Mixed<br />
Insecticide (kg ailha)<br />
Herbicide (lirerlha)<br />
CL4 CL6 CL8 All<br />
(n = 15) (n = 22) (n = 17) (n = 56)
Table 6. Average rice yield (kglha) and profitability of rice crop by clusters, 56 rice-<br />
based farmers, Ajuy, Iloilo, Philippines (1987-88).<br />
First rice crop<br />
Average yield (kglha)<br />
Gross benefit (P)'<br />
Total paid-out cost (P)<br />
Net benefit (P)<br />
Second rice crop<br />
Average yield (kglha)<br />
Gross benefit (P)'<br />
Total paid-out cost (P)<br />
Net benefit (P)<br />
'~4.501kg of rice.<br />
CIA CL6 CL8 All<br />
(n = 15) (n = 22) (n = 17) (n = 56)
Table 7. Production elasticities of various production input variables in <strong>the</strong> estimated<br />
production function, 56 rice-based farmers, Ajuy, Iloilo, Philippines (1987-88).<br />
Variable Full model Reduced model<br />
1st Crop 2nd Crop 1st Crop 2nd Crop<br />
Intercept 5.34** 0.04 5.46* 1.13<br />
(8.45) (0.10) (8.24) (0.3 1)<br />
Seedins rate 0.04 0.48* 0.02 0.49**<br />
(1.33) (0.08) (0.57) (1.89)<br />
N fertilizer -0.07 0.05<br />
(-1.32) (0.042)<br />
P fertilizer 0.18 0.18<br />
(1.42) (0.42)<br />
K fertilizer -0.22 -0.19<br />
(-1.57) (-0.44)<br />
Herbicide -0.34* -0.60*<br />
(-2.39) (01.88)<br />
Insecticide -0.004 -0.22<br />
(-0.06) (-1.65)<br />
Hired labor 0.24** 0.36** 0.22** 0.40**<br />
(2.68) (1.75) (2.41) (1.97)<br />
P fertilizer<br />
F-v alue 9.41** 34.21** 12.22** 52,51**<br />
% *c<br />
Significant at 1 % level. Significant at 10% level.
IMPACT OF FARMING SYSTEMS RESEARCH ON THE RESEARCH AND<br />
EXTENSION SYSTEM: THE CASE OF THE PHILIPPINES<br />
V. T. ~illancio', A. ~unzalan', C. ~ina', and V. R. Caranga12<br />
The Farming Systems Approach has a key role in <strong>the</strong> strategy for <strong>the</strong><br />
development of <strong>the</strong> agricultural sector in <strong>the</strong> Philippines during <strong>the</strong> last<br />
two decades. Many of <strong>the</strong> institutions involved in <strong>research</strong> and<br />
development have adopted <strong>the</strong> concept and have developed<br />
methodologies over time to provide more meaningful and relevant<br />
technology to <strong>the</strong> farmers in <strong>the</strong>ir individual environments. The study<br />
highlights <strong>the</strong> developments and <strong>the</strong> operations of Farming Systems<br />
Development in <strong>the</strong> Philippines. analyses its impact, and concludes that<br />
fur<strong>the</strong>r emphasis should be provided to <strong>the</strong> streng<strong>the</strong>ning of <strong>the</strong> networks<br />
that exist as a rneans to fur<strong>the</strong>r enhance <strong>the</strong> FSR programs in <strong>the</strong><br />
country.<br />
A policy declaration has been made in <strong>the</strong> Philippines to use a farming <strong>systems</strong><br />
approach (FSA) as a general strategy to promote agricultural development. Executive<br />
Order 116 (30 Jan 1987), which provided for <strong>the</strong> reorganization of <strong>the</strong> Ministry of<br />
Agriculture and Food (MAF), mandated to "encourage people's participation in<br />
zgricultural development . . . and use a bottom up, self-reliant farming <strong>systems</strong> approach<br />
that will emphasize social justice, equity, productivity, and sustainability in <strong>the</strong> use of<br />
agricultural resoprces." The objective of this strategy was to "improve farm income and<br />
generate ivork opportunities for farmerslfishermen and o<strong>the</strong>r rural workers."<br />
The national thrust on farming system <strong>research</strong> and development (FSR&D)<br />
stemmed from <strong>the</strong> lessons and experiences gained by implementing various projects<br />
using <strong>the</strong> FSA. These projects began with multiple-cropping and cropping-<strong>systems</strong><br />
projects (1970s to mid-1980s) and have continued to <strong>the</strong> current farming <strong>systems</strong><br />
<strong>research</strong> and extension (FSR&E) projects. This paper highlights <strong>the</strong> historical<br />
development and operation of FSRSrD in <strong>the</strong>se projects and <strong>the</strong> response of <strong>the</strong>se<br />
projects to evolving patterns, concepts, and procedures. The effects of <strong>the</strong>se projects on<br />
small farms, <strong>the</strong>ir farming <strong>systems</strong>, farm households, and <strong>the</strong> community are also<br />
discussed. Finally, lessons, esperiences, and issues that affect <strong>the</strong> operations of <strong>the</strong>se<br />
projects are identified.<br />
'lowland-based Farming Systems Division, Farming Systems and Soil Resources<br />
Institute, College of Agriculture, University of <strong>the</strong> Philippines Los Bafios, Laguna,<br />
Philippines.<br />
2~sian Rice Farming Systems Network (ARFSN), <strong>International</strong> Rice Research<br />
Institute, P.O. Box 933, Manila, Philippines.
FROXl GREEN REVOLUTION TO FARhlING SYSTEhlS APPROACH<br />
The national thrust on FSR&D stemmed from <strong>the</strong> lessons and experiences gained<br />
from various projects that used <strong>the</strong> FSA (Table 1). These had several common<br />
characteristics: a focus on mu1 tiple cropping and cropping <strong>systems</strong> <strong>research</strong> and<br />
extension (R&E) in <strong>the</strong> rainfed lowland areas (e.g., MCEPP, KABSAKA, RADOS,<br />
MAhTBILAYAKA. and KASATINLU); <strong>the</strong> implementation of cropping <strong>systems</strong><br />
R&D as a component of an integrated development project (e.g., ABC project,<br />
CVIADP, NSIRDP. and PIADP); a focus on croppin <strong>systems</strong> RcYrE in <strong>the</strong> uplands<br />
and hilly areas (e. .. VRP and AUDP); a divergence T. rom activities oriented toward<br />
cropping <strong>systems</strong> P e.g., FSDP-EV and FSDP-B~col); and <strong>the</strong> development of<br />
regional and provincial capabilities to implement FSR&E activities, such as those<br />
done by <strong>the</strong> Regional Integrated Agricultural Research Systems (RIARS) and later<br />
by <strong>the</strong> AMP-ROS.<br />
The development of FSR&D in <strong>the</strong> Philippines was characterized by a shift<br />
from a comniodity-oriented strategy to a m~~ltifarni-enterprise approach. With <strong>the</strong><br />
intention of increasing <strong>the</strong> income of small-scale farmers, \rarious crop<br />
intensification and diversification programs were implemented. In 1972, a<br />
collaborative pilot project knolvn as <strong>the</strong> Multiple Cropping Extension Pilot<br />
Production Program (MCEPP) was launched by <strong>the</strong> University of <strong>the</strong> Philippines<br />
Los Baiios (UPLB) in collaborcltion with Central Luzon State University (CLSU)<br />
and Camarines Sur State Agricultural College (CSSAC).<br />
The strategy for diversification included expanding <strong>the</strong> farm area, increasing<br />
yield per unit area, and increasing <strong>the</strong> number of crops grown in an area for one<br />
year. To a large extent, <strong>the</strong> strategy focused on rice-based farming <strong>systems</strong>. This<br />
prompted <strong>the</strong> establishment of an interagency applied <strong>research</strong> project on rainfed<br />
and upland rice with o<strong>the</strong>r crops by <strong>the</strong> Department of Agriculture, IRRI, and <strong>the</strong><br />
Bureau of Agricultural Extension (BAEX).<br />
The government recognized <strong>the</strong> need to consolidate <strong>the</strong> efforts of various<br />
commodity programs and <strong>the</strong> successes of <strong>the</strong> pilot multiple cropping projects. Thus,<br />
<strong>the</strong> National hlultiple Croppirig Production Program (NhlCPP) was launched in<br />
1975. The program integrated <strong>the</strong> existing commodity progfams (hf-99, Masaganang<br />
Maisan, and GSK). Integration \vas designed along three d~niensions: <strong>the</strong> use of a<br />
cropping <strong>systems</strong> approach in crop productio~i, thc change of respo~isibility of<br />
agricultural technicians from sinsle-crop to multiple-crop cultivation on <strong>the</strong> farm,<br />
and <strong>the</strong> provision of credit facilities hy shifting from single-crop loans to loans for all<br />
crops grown lvithin a period of 1 yr.<br />
In a parallel development to <strong>the</strong> NMCYP, IRRI, BAEX, and <strong>the</strong> Bureau of<br />
Plant Industry (BPI) conducted cropping <strong>systems</strong> <strong>research</strong> in 1974 in <strong>the</strong> rainfed rice<br />
areas of Iloilo through <strong>the</strong> KARSAKA Project. This <strong>research</strong> demonstrated <strong>the</strong><br />
feasibility of t~vo cropping seasons for rice plus a third upland crop in <strong>the</strong> area.
The remarkable performance of <strong>the</strong> technology for a two-crop system under<br />
<strong>the</strong> KABSAKA Project in Iloilo encouraged multilocation testing of <strong>the</strong> technology<br />
in o<strong>the</strong>r parts of <strong>the</strong> country. This gave rise to o<strong>the</strong>r projects such as KASATINLU<br />
in South Cotabato (1979), MATISAYON in North Cotabato (1979),<br />
MANBILAYKA in Pangasinan, and ZAMDUGANI in Zamboanga del Sur.<br />
The lessons and experience generated from <strong>the</strong>se projects became <strong>the</strong> basis<br />
for <strong>the</strong> development of cropping <strong>systems</strong> <strong>research</strong> n~ethodology in <strong>the</strong> Philippines.<br />
During this period, fur<strong>the</strong>r testing of <strong>the</strong> cropping <strong>systems</strong> methodologies was<br />
undertaken in projects such as <strong>the</strong> Agusan, Bukidnon, Capiz (ABC) Settlement<br />
Project, Philippine-Australian Development Assistance Program (PADAP), Bicol<br />
River Basin Development Pro'ect (BRBDP), and Samar Integrated Rural<br />
Development Project (SIRDP~.<br />
Activities on cropping-<strong>systems</strong> <strong>research</strong> and development were expanded<br />
through <strong>the</strong> Rainfed Agriculture Development (Iloilo) Project (RADIP). This<br />
extended <strong>the</strong> KABSAKA project in Iloilo, and in 1951, established Rainfed<br />
Agriculture Development Outreach Sites (RADOS) in provinces with vast areas of<br />
rainfed rice: Ilocos Sur, Pangasinan, Mindoro Oriental, Leyte, Iloilo, Bohol, Davao<br />
Sur, South Cotabato, and North Cotabato. The site staff came from <strong>the</strong> Ministry of<br />
Agriculture and Food (MAF) and technical staff and support came from UPLB,<br />
IRRI, BPI, and NFAC. The lessons and experiences gained from RADOS were <strong>the</strong><br />
basis for <strong>the</strong> national technology verification program of <strong>the</strong> RIARS in 1983-88 and<br />
<strong>the</strong> Research and Outreach Sub-Project of <strong>the</strong> Accelerated Agricultural Production<br />
Project (AAPP-ROS) in 1958-91.<br />
The N'ational Technology Verification Program was <strong>the</strong> prime activity and<br />
thrust of <strong>the</strong> RIARS. The RIARS was created under <strong>the</strong> agricultural-<strong>research</strong><br />
component of <strong>the</strong> Agricultural Support Services Project (ASSP) in 1991. The<br />
objective of <strong>the</strong> project was to streng<strong>the</strong>n essential services in agriculture,<br />
particularly <strong>research</strong>. It also paved <strong>the</strong> way for <strong>the</strong> reorganization of <strong>the</strong> different<br />
bureaus and agencies of <strong>the</strong> DA (e.g., BAEX, BPI, BS, and BAI) to give more<br />
responsibility to <strong>the</strong> regional offices for faster dissemination of agricultural<br />
technologies. All efforts centered on cropping <strong>systems</strong> technologies because<br />
<strong>research</strong> developed from problem identification in <strong>the</strong> target sites to technology<br />
generation, adaptation, verification, and finally technology dissemination.<br />
Subsequently, crop-livestock integration activities were added in 1985.<br />
Although most efforts were geared toward cropping <strong>systems</strong> activities, s(?i-!zi<br />
projects also focused on FSR&D. These projects include <strong>the</strong> USAID-supported<br />
Farming Systems Development Project - Eastern Visayas (FSDP-EV), <strong>the</strong> Farming<br />
Systems Development Project - Bicol (FSDP-Bicol), and o<strong>the</strong>r projects under <strong>the</strong><br />
Rainfed Resources Development Project - I1 (RRDP).<br />
The historical development of FSR&D in <strong>the</strong> Philippines had three critical<br />
stages: multiple cropping, cropping <strong>systems</strong>, and farming <strong>systems</strong>. The KABSAKA<br />
project made valuable contributions to <strong>the</strong> development of cropping <strong>systems</strong> R&D<br />
(which was subsequently developed into FSR&D) and to <strong>the</strong> development of
ainfed areas. The RIARS project applied <strong>the</strong> methodology in <strong>the</strong> national program<br />
using local resources and expertise and external funding.<br />
IMPACT ON ORGANIZATION OF RESEARCH AND EXTENSION<br />
The evolution of FSR&D in <strong>the</strong> Philippines is characterized by <strong>the</strong> policies,<br />
organization, and management of <strong>the</strong> <strong>research</strong> and extension <strong>systems</strong> in <strong>the</strong> country.<br />
Various institutions integrated <strong>the</strong> FSA concept into <strong>the</strong>ir R&D activities at<br />
different stages (i.e., at <strong>the</strong> conceptual, structural, and operational levels). These<br />
institutions adopted <strong>the</strong> FSA concept, translated it into various programs, and<br />
developed an organizational structure that operated <strong>the</strong> FSRSrD projects.<br />
The impact of FSR&D on <strong>the</strong> organization 2nd management of <strong>the</strong> R&E can<br />
be seen in <strong>the</strong> way various RSLE institutions responded to <strong>the</strong> incorporation of<br />
FSR&D. Three types of units were created in <strong>the</strong> institutions to address FSR&D:<br />
units created as a separate institute in academic institutions, units within projects,<br />
and divisions or units within <strong>the</strong> DA.<br />
At UPLB, <strong>the</strong> Multiple Cropping Section (h4CS) was organized by <strong>the</strong><br />
Department of Agronomy in 1977 to implement <strong>the</strong> MCEPP. Likewise, with support<br />
from NFAC, <strong>the</strong> UPLB College of Agriculture Iaunchetl Comprehensive Farming<br />
Technology Support to Small Farmers (CFTSSF). In 1980, <strong>the</strong> MCS collaborated<br />
with <strong>the</strong> NFAC to coordinate <strong>the</strong> cropping <strong>systems</strong> program of <strong>the</strong> RADOS as part<br />
of RADIP. In <strong>the</strong> same year, staff from <strong>the</strong> UPLB-MCS were detailed to NFAC to<br />
constitute <strong>the</strong> Cropping Systems Diirision (NFAC-CSD), which was a reorganization<br />
of <strong>the</strong> former Multiple Cropping Section at NFAC. In 1983, <strong>the</strong> Farming Systems<br />
and Soil Resources Institute (FSSRI) was established. It pooled staff from <strong>the</strong> MCS-<br />
and <strong>the</strong> NFAC-supported Countryside Action Program (CAP). The FSSRI<br />
implemented <strong>the</strong> Small Farm Systems (SFS) Component of <strong>the</strong> World Bank-<br />
supported ASSP in 1933-1988.<br />
Anticipating, <strong>the</strong> role of <strong>the</strong> Regional State Colleges nnti Universities (SCUs)<br />
in <strong>the</strong> implementation of FSRJiD-related projects, staff from <strong>the</strong> SCUs were<br />
trained. This training started with <strong>the</strong> involvement of CLSU and CSSAC in <strong>the</strong><br />
MCEPP (1972- 1979).<br />
In 1980, staff from <strong>the</strong> Visayas State College of Agriculture (ViSCA) trained<br />
in cropping <strong>systems</strong> methodology with RADOS staff in anticipation of <strong>the</strong>ir future<br />
involvement in FSDP-EV in 1982. The ViSCA subsecluently est:~blished <strong>the</strong> Farm<br />
and Resource Management Institute (FARhlI) in 19S5 to address its farming<br />
<strong>systems</strong> thrust. Benguet State University (BSU) was likewise ir~volved with <strong>the</strong><br />
FSR&D component of <strong>the</strong> Highland Agricult~~ral Development Project (HADP),<br />
and attempts were made to establish an institute on farming systenis <strong>research</strong>.<br />
The farming-<strong>systems</strong> <strong>research</strong> component of <strong>the</strong> Philippine Council for<br />
Agriculture, Forestry, and Natural Resources Research and Developn~ent<br />
(PCARRD) was approved by <strong>the</strong> Governing Council on 23 Sep 1975. A Farming
Systems Research Network was organized. It consisted of 13 <strong>research</strong> centers and<br />
stations, with UPLB as <strong>the</strong> national center for farming <strong>systems</strong> <strong>research</strong>. Priority<br />
areas for <strong>research</strong> shifted from understanding <strong>the</strong> basic determinants and<br />
performance of farming svstems in <strong>the</strong> country (1976-80) to developing a crop-based<br />
farming <strong>systems</strong> approach (19S0-83).<br />
The Farming Systems Research and Development Network (FSRDN) was<br />
organized in 1989, composed of UPLB through FSSRI, DA through BAR and ATI,<br />
a network of SCUs in various regions of <strong>the</strong> country. The FSRDN facilitates <strong>the</strong><br />
exchange of expertise and information on FSR&D in <strong>the</strong> country. With <strong>the</strong> farming<br />
<strong>systems</strong> development project in Bicol and Eastern Visayas, a Site Research<br />
Management Unit (SRhlU) has been organized to formulate and implement <strong>the</strong><br />
farming program in each site selected within <strong>the</strong> region.<br />
In 1982, <strong>the</strong> Agricultural Research Office (ARO) was organized under <strong>the</strong><br />
Office of <strong>the</strong> Minister of <strong>the</strong> MAF to coordinate cropping <strong>systems</strong> <strong>research</strong><br />
programs, whereas, <strong>the</strong> KFAC-CSD coordinates production programs. The farming<br />
systeins thrust of DA was fur<strong>the</strong>r streng<strong>the</strong>ned with <strong>the</strong> reorganization of MAF by<br />
Executive Order 116 (19S7), which also created <strong>the</strong> Bureau of Agricultural<br />
Research (BAR). BAR assumed <strong>the</strong> responsibilities of ARO and was mandated to<br />
coordinate a11 ;igricultural <strong>research</strong> of <strong>the</strong> DA.<br />
The most recent development in agricultural RLQD was <strong>the</strong> enactment of <strong>the</strong><br />
local government code, which decentralized agricultural extension to local<br />
governments. More attention is being focused on <strong>the</strong> operations of FSRRrD at <strong>the</strong><br />
provincial and municipal levels. Although <strong>the</strong>re is already a certain degree of skill<br />
and an appreciation of FSRRrD as an approach nt <strong>the</strong> provincial level, more efforts<br />
must be focused on advising local governments about FSRRrD. This is a challenge<br />
that must be met in <strong>the</strong> coming decade.<br />
DEVELOPhrIENT OF FSRkD METHODOLOGIES<br />
The development of FSRLQD in <strong>the</strong> Philippines hr~s been influenced by global<br />
development of <strong>the</strong> approach. The development of methodologies is most apparent<br />
if all stages of FSRRrD are examined. In site selection, tools are being refined and<br />
include spatial maps and con~puter-aided tools such as Geographic Information<br />
Systems (GIS). Sites selected for FSR&D activities have shifted from rice-basecl<br />
environments to marginal areas (e.g., rainfed uplands and hilly areas).<br />
Site description and diagnosis has shifted from formal tools to less formal<br />
2nd rapid techniques such as Rapid Rural Appraisal (RRA) and o<strong>the</strong>r variants.<br />
Agroecosystem analysis, gender issues, diagnostic techniques, and ethnographic<br />
tools have enriched <strong>the</strong> tool kit that can be used.<br />
The design of R&E strategies has increasingly focused on farmer<br />
circumstances. Ex-ante evaluation of <strong>the</strong> technology is beginning to integrate
stability and sustainability. 1nstitution:il support, particularly marketing facilities, is<br />
being considered. The PCARRD-supported FSSRI and developed Agricultural<br />
Suitability and Evaluation S!.stems (ASES) in 1990. The use of biophysical<br />
parameters in ASES has already been initiated. These are now integrated with<br />
socioeconomic parameters.<br />
There is increasing participation of farmers and farmer groups in <strong>the</strong> testing<br />
and evaluation process. The FSDP-Bicol provided major innovation with <strong>the</strong><br />
Porbaran approach, in which farmers were provided with a range of technology<br />
options during a community planning workshop. The farmers <strong>the</strong>mselves dec~ded<br />
what to try and how to do it. The project staff served as facilitators. Farmer-to-<br />
farmer extension has also been effective in FSDP-EV and FSDP-Bicol.<br />
Some statistical methods are used to evaluate OFR, but economic analysis is<br />
still given extra weight. Risks, stability, sustainability, gender effects, and<br />
practicability are also gaining wide consideration. Efforts have been made by <strong>the</strong><br />
RIARS to train noneconomists in <strong>the</strong> tools and techniques of economic analysis.<br />
The dissemination stage has suffered a set-back because of a lack of<br />
extension-<strong>research</strong> links during <strong>the</strong> last four stages of FSRcYrE. Successful cropping<br />
<strong>systems</strong> have been tested and evaluated by <strong>the</strong> RIARS, but <strong>the</strong> results have not been<br />
disseminated because of a lack of extension support. Ano<strong>the</strong>r f~ctor is <strong>the</strong> emphasis<br />
given to <strong>research</strong> compared ivith extension.<br />
IMPACT OF FARhlING SYSTEhlS ON FARM HOUSEHOLDS<br />
Assessment of <strong>the</strong> impact of FSR'QE has g;lined attention over <strong>the</strong> last 5 yr. Impact<br />
studies place emphasis on <strong>the</strong> rate and degree of adoption of tlie technology and its<br />
impact on resource use, productivity, and income. h4inimal consideration is given to<br />
factors that indicate an i~nprovenient in <strong>the</strong> socioeconomic well-being of <strong>the</strong> farm<br />
population. Also, little attention has been gi\.en to tlie impact on conimunities and<br />
institutions.<br />
Impact studies include significant changes in <strong>the</strong> cropping patterns practiced<br />
by farmers. An increase in cropping intensity from 1.3 to 2.4 was observed among<br />
KABSAKA farmers when <strong>the</strong> KABSAKA technology was adopted. KASATINLU<br />
farmers increased <strong>the</strong>ir cropping intensity froni 1.9 to 2.57, as did farmers in <strong>the</strong><br />
Capiz Settlement Project.<br />
Ano<strong>the</strong>r aspect of impact is physical changes that occur in <strong>the</strong> fiirm after<br />
project participation. An impact evaluation study of RRDP reported an increase<br />
from 0.5 ha to 2.75 ha in tlie average size of farms cultivated by <strong>the</strong> beneficiaries.<br />
This change was attributed to production opportunities that had been brought about<br />
by <strong>the</strong> project. There was also increased use of idle lands for crop production,<br />
improved soil fertility, better watershed protection, and decreased soil erosion.
Ano<strong>the</strong>r impact has been on <strong>the</strong> ninnagement of inputs. There has been an<br />
increase in <strong>the</strong> use of commercial inputs, particularly fertilizer, pesticide, and<br />
herbicide. Various studies have shown hisher le\.els of fertilizer, pesticide, and<br />
herbicide use among farmers after <strong>the</strong> introduction of K,ABSAI(A. Also, 31% of <strong>the</strong><br />
KABSAKA farmers resorted to niechaniz:ition, i.e., <strong>the</strong> use of tractors for land<br />
preparation during <strong>the</strong> first rice crop, whereas, 50% of <strong>the</strong> area of <strong>the</strong> second rice<br />
crop was prepared using machinery.<br />
Farming <strong>systems</strong> performance has also retlected <strong>the</strong> impact of FSR&D<br />
projects. Increases in productivity and income were observed ainong project<br />
beneficiaries. For e~ample, with KABSAKA, annual rice production rose from<br />
1.2 t/ha to 8 t/ha. In <strong>the</strong> case of upland rice in Capiz, yield increased from 1.5 t/ha<br />
to 2 t/ha. Increased yield was also obsend among KASATINLU and<br />
MATISAYON farmers. The KABSAKA teclinology also allou~rld farmers to<br />
increase <strong>the</strong>ir labor productivity (i.e., average physical output per person-day).<br />
Based on <strong>the</strong> 1953 RADIP monitoring survey, l:lbor productivity increased by<br />
45.6%. Generally, farm income illcreases with increases in <strong>the</strong> level of production,<br />
but this may not necessarily increase net income bec:luse of <strong>the</strong> high costs associated<br />
with <strong>the</strong> recommended technologies. The KABSAKA project, however, reported<br />
higher net farm income among farniers after project participation. In <strong>the</strong> case of<br />
RRDP, average family income before and after :idoption of <strong>the</strong> technology rose<br />
from P1,278 to P2,049 per month. The integration of li\,estock into <strong>the</strong> farming<br />
system also increased farm inconie of farlners in RADIP.<br />
O<strong>the</strong>r impacts reported in 1990 by <strong>the</strong> RRDP Impact Ev:~luation Study<br />
included <strong>the</strong> increased motivation to plant crops, a\vareness of <strong>the</strong> potential of<br />
natural resource: in <strong>the</strong> comn~unity, lessened ju\.enile delinquency, encouragement<br />
to be industrious, and promotion of self-reliance anlong farmers.<br />
SOh4E ISSUES OF CONCERN<br />
During <strong>the</strong> 1990 National Rice-based FSRLSLE PI:i~ining Workshop, several issues<br />
were identified in <strong>the</strong> implementation of FSRLQE<br />
in RBFS:<br />
o Determination of recommendation domain :\lid extrapolation area. More<br />
effort is required to refine <strong>the</strong> use of GIS ancl ASES.<br />
o Too much reliance on RRA. Emphasis should be placed on <strong>the</strong> use of<br />
formal surveys and o<strong>the</strong>r data-ga<strong>the</strong>ring techniques that will aid<br />
monitoring, evaluation, and impact assessment.<br />
o Limited participation of farmers and extension specialists. Their<br />
participation must be encouraged.<br />
o Limited expertise on <strong>the</strong> technology options that are available. A<br />
mechanism is needed to provide updates on recent developments.
o Limited feedback at all levels. A mechanism is needed to encourage<br />
frequent interaction among project participants.<br />
o Tools for monitoring, evaluation, and impact assessment and <strong>the</strong> need to<br />
link <strong>the</strong>se activities as a continuum in FSR&E projects.<br />
o Cases of extension specialists turned <strong>research</strong>er. This attitude was<br />
encountered with RADOS and RIARS; <strong>the</strong>refore, <strong>the</strong>re is a need to<br />
instill <strong>the</strong> extension responsibilities in FSR&D staff and to develop a<br />
<strong>research</strong>-extension interface.<br />
o Limited appreciation of <strong>the</strong> contribution of farmers to FSRRrE by o<strong>the</strong>r<br />
development workers. There is a need for intensive training and<br />
information dissemination on FSRRrE.<br />
o Limited FSRRrE personnel because of <strong>the</strong> reassignment of trained<br />
personnel with <strong>the</strong> implementation of <strong>the</strong> local government code.<br />
Apart from <strong>the</strong>se issues and problems, o<strong>the</strong>r matters must be mentioned: <strong>the</strong> need<br />
for more practical and flexible approaches or methodologies for FSR&D, <strong>the</strong><br />
organization and managerial problenls associated with decentralized FSR&D, <strong>the</strong><br />
operations of a multidisciplinary and interdisciplinary team, reorientation from a<br />
technology-focused approach to problem (opportunity)-driven activities, indigenous<br />
technical knowledge and <strong>the</strong> changing environment, and technology<br />
commercialization and FSR&E.<br />
A COURSE OF ACTION<br />
This historical analysis of farnling <strong>systems</strong> <strong>research</strong> and development in <strong>the</strong><br />
Philippines has revealed <strong>the</strong> impact of <strong>the</strong> approach on <strong>the</strong> <strong>research</strong> and extension<br />
system of <strong>the</strong> country. The impact is reflectzd in <strong>the</strong> changes that have occurred in<br />
<strong>the</strong> focus, conceptual approacl~, organization, and management of <strong>the</strong> agricultural<br />
<strong>research</strong> and extension system. The <strong>research</strong> and extension system responded to <strong>the</strong><br />
evolution of farming <strong>systems</strong> <strong>research</strong> and development methodologies and<br />
concepts.<br />
The farming s stems <strong>research</strong> and development approach is still evolving.<br />
The potential of <strong>the</strong> ! arming <strong>systems</strong> <strong>research</strong> and development network to<br />
orchestrate this evolution is evident. The farming <strong>systems</strong> <strong>research</strong> and development<br />
could be mobilized to address <strong>the</strong>se issues by specifically supporting <strong>the</strong><br />
development of (1) ASES and related tools (e.g., GIS) to make use of <strong>the</strong> data bases<br />
that are generated a common framework for FS diagnosis and design that employs<br />
RRA and related techniques and o<strong>the</strong>r formal approaches, (2) techniques to<br />
integrate stability and sustainability analysis in farming <strong>systems</strong> development,<br />
(3) impact assessment methodology to tackle both micro and macro impacts, and
(4) a mechanism to link <strong>the</strong> commercialization of technology with farming <strong>systems</strong><br />
<strong>research</strong> and development.
Table l. Chronological sequcnce of projects and activitics rclatcd to farming <strong>systems</strong> <strong>research</strong> and dcve1ol)mcnt in [he Philippines (1972-')l)."<br />
Y ca I- Prograni/pro.ject Agencies iavolved Fu~idi~ig source<br />
1972-79 Multiple Croppiiig Extension Pilot Production<br />
Progl-am (MCEPP) at Laguna, Nucva Ecijn,<br />
Camarines Sur, and Iloilo<br />
1974-85 National Multiplc Cropping Program (NMCP)<br />
1978-80 Cropping Systems Rcscarch ~utrcachProjccr<br />
a[ Tanauan, Batangas; Sta. Barbara,<br />
Pallpasinan. and Oton, Iloilo<br />
1077-8 1 Two Ricc Crop Systenis Projccts at Pangasinan<br />
(MANBILAYAKA), lloilo (KAESAKA), South<br />
Cotabato (KASATINLU), Zamboanga (ZAMDUGANI),<br />
and North Cotabaco (MATISAYON)<br />
Cropping Systcms 1'rogr;rm at thc hgusan,<br />
Bukidnon and Capiz Scttlc~ncnt Arcas (ABC<br />
project under <strong>the</strong> Second Rural Devclopmcnt<br />
L;III~ ScttIci11cnt Pro.jcc~)<br />
Sloping Agricultural Lilnd Technology (SALT)<br />
at Ba~lsalan, Davao del Sur<br />
Cropping/Farming Systems Research Outrcach<br />
Sitcs at Tugucgaraa, Cagi~yan; Guilnba<br />
Nucva Eci,ja and Clavcrin, hlismnis Oricntnl<br />
Rainfcd Agricultural Devclopment (Iloilo)<br />
Projcct (RADIP) - An Expansion of KABSAKA<br />
Rainfed Agricultural Devclopmcn~ Outrcach<br />
Si tcs (RADOS) at llocos Sur, Pangasinan,<br />
Mindoro Oricntal. Lcytc, Iloilo. Bohoi, Davao<br />
Sur, Soutli Cotabato, and North Cotabato<br />
- - -----p-pppppp<br />
UPLB. DA Rcgion <strong>IDRC</strong><br />
NFAC, IRRl<br />
M BLR C<br />
IKRI/DA<br />
NFAC<br />
IRRI/<strong>IDRC</strong><br />
COP ant1 Ford<br />
IRRI-PCARRD<br />
NFAC/<br />
World Bank<br />
World Bank<br />
World Bank<br />
1981-85 Multiple Cropping Projcct urldcr Philippine- D A AlDAU<br />
Australian Dcvclop~nent Assistance Program<br />
(PADAP)<br />
--
Table 1. Chroaological scqucncc of ill-ojccls and activities rc1;itcd fo Farming Sys~clns Rcsearch and Dcvclol)lllcnt in thc I'liilippi~~cs, 1972-0 1.<br />
Farming Systems Development Projec~ at Eastern<br />
Visayas (FSDP-EV)<br />
Palawan Integrated Area Development<br />
Project (PIADP)<br />
Phase I ( 1982-90) and Phase 11 ( 199 1-96)<br />
Farming Systems and Soil Resources<br />
Inslilulc (FSSRI)<br />
Regional Integrated Agricultural Research<br />
Syslcms (RIARS) under thc Agricultural<br />
Support Services Program (ASSP)<br />
Nor<strong>the</strong>rn Samar Intcgratcd Rural Development<br />
Project (NSIRDP)<br />
Philippilie Farmiiig Systelns R & D Progralll<br />
Farming Systems Dcvclopment Prqject at<br />
Bicol (FSDP-Bicol)<br />
Philippine Australian Rainfed Lowland Antique<br />
Project (PHARLAP)<br />
Rainfed Resources Development Program<br />
(RRDP)<br />
DA, NIA, DPWH ADUIEEC<br />
DENR, DOH, DSWD<br />
DAIARO, IRRI World Bank<br />
PCAliKD<br />
DA-V<br />
AIDAB<br />
USAID<br />
DAIARO, ANU ACIARIANU<br />
DA, PCARRD, SUCs USAID
Table l. Chronological sequence of projccts and activities related to Farming Systems Research and Development in <strong>the</strong> Philippines, 1972-91.<br />
1985-prcscnt Ccntrnl Visayns RcgionnI Projcct-l (CVRP) DAIDENR USAID<br />
1985-present Farm and Resources Management Institute<br />
(FARMI) at ViSCA<br />
1986-present Accelerated Agricultural<br />
Production-Rcscarcl~ and<br />
Outreach Subproject (AAPP-ROS)<br />
1987-present Highland Agricultural Development<br />
Project (HADP) DA-BSU<br />
1988-present Sorsogon Integrated Area Dcvclopmcnt<br />
Project<br />
1988-present Central Cordillera Agricultural Program<br />
(CEC AP)<br />
ADB<br />
DA/LGUs, NCO EEC<br />
DA World Bank<br />
1989-present Farming Systems R & D Network DA-ATI, FSSRl DAlBAKlSUCs DAIUSAID<br />
1989-present Sou<strong>the</strong>rn Mindanao Agricultural<br />
Project (SMAP) DAISCO EEC<br />
his list is an updated version of <strong>the</strong> co~npilation by R V Labios, January 1991 (Acronyms listed in Appendix).
ASSESSMENT OF THE IMPACT OF A FARMING SYSTEMS-BASED<br />
TECHNOLOGY SITE IN SRI LANKA<br />
N. F. C. Ranaweera, P. A. Samaratun 3, J. M. K. P. Jayasinghe,<br />
and G. K. Renuka B<br />
Sri Lanka has been actively involved in <strong>the</strong> development of new<br />
technologies within <strong>the</strong> perspective of farming <strong>systems</strong> over <strong>the</strong> last 15<br />
yr. Increasingly, questions have been asked as to <strong>the</strong> impact of such<br />
technologies to <strong>the</strong> farming community. Previous impact studies<br />
concentrated primarily on <strong>the</strong> adoption levels of <strong>the</strong> technologies and<br />
less in terms of actual impact. This study critically examines <strong>the</strong><br />
impact of a new technology at a particular site in Sri Lanka. Using<br />
production function techn~ques, <strong>the</strong> study concludes that <strong>the</strong>re were<br />
structural changes in <strong>the</strong> production process and in <strong>the</strong> personal<br />
incomes of farmers. However, <strong>the</strong>re were no significant changes<br />
observed in capital accumulation, nutritional status, and expenditure<br />
patterns between <strong>the</strong> adopters and nonadopters of <strong>the</strong> new<br />
technology.<br />
Agriculture in Sri Lanka faces problems of increased consumer demand for basic<br />
foods, high unemployn~ent in <strong>the</strong> rural sector, and diminished land resources.<br />
Strategies were developed to address <strong>the</strong>se problems by increasing <strong>the</strong> intensity of<br />
cultivation and diversifying <strong>the</strong> use of limited farm resources. Within this<br />
framework, a strategy for crop diversification combined with intensification of<br />
resource use was considered <strong>the</strong> key to stronger agricultural growth.<br />
FARMING SYSTEMS RESEARCH IN SRI LANKA<br />
These problems in agriculture required <strong>the</strong> developnlent of suitable component<br />
technologies (e.g., varieties with appropriate duration, crop combinations, and crop<br />
sequences) that were not only technically feasible but socially acceptable and<br />
econon~ically tenable. Rice and o<strong>the</strong>r field crops (OFC) are produced in Sri Lanka<br />
on small-scale family farms on which interactions between farm production, off-farm<br />
and nonfarm activities, and household consumption are highly significant. Initial<br />
studies began in 1976 at Walagambahuwa (a minor tank-based cropping system in<br />
<strong>the</strong> low-country dry zone) and Katupotha (in <strong>the</strong> low-country intermediate zone).<br />
Subsequently, <strong>the</strong> Department of Agricult~lre commenced farming <strong>systems</strong> <strong>research</strong><br />
(FSR) programs in o<strong>the</strong>r locations under <strong>the</strong> purview of six regional agricultural<br />
<strong>research</strong> stations (Table 1). Details of <strong>the</strong>se findings are described by Fernando<br />
(1979), Upasena (19Sla,b), and Sikurajapathy (1986).<br />
'~ivision of Agricultural Economics and Planning, Department of Agriculture,<br />
Peradeniya, Sri Lanka.
Impact studies<br />
A number of studies documented <strong>the</strong> adoption of <strong>the</strong> new technologies, but few<br />
concentrated on <strong>the</strong>ir impact. Ranaweera and Siripala (1980) and Ranaweera<br />
(1983) studied <strong>the</strong> impact of new cropping <strong>systems</strong> technology under minor-tank<br />
irrigation conditions and concluded that <strong>the</strong> impact had been primarily in terms of<br />
component technologies--bztter crop management and use of improved varieties.<br />
To e\.aluate <strong>the</strong> impact of FSR on real incorne or on household expenditure<br />
patterns, nutrition, education, accumulation of household and agricultural capital<br />
items, or o<strong>the</strong>r assets, a study was initiated in 1987 at two FSR sites--Uva<br />
Paranagarna and Katupotha.<br />
OBJECTIVES<br />
The spzcific objectives of <strong>the</strong> study were to examine <strong>the</strong> adoption of new technical<br />
practices by farmers and estimate <strong>the</strong> relative efficiency of resources used under<br />
traditional and new technologies; to study <strong>the</strong> impact of technology adoption on <strong>the</strong><br />
structure of crop-production functions, factor inco~nes, and personal incomes; and to<br />
examine <strong>the</strong> impact of <strong>the</strong> technology on nonq~~antifiablt: benefits such as better<br />
standards of nutrition.<br />
STUDY SITE<br />
The Uva Valley, represented by <strong>the</strong> Uva-Paranagama FSR site, has 25,000 smallscale<br />
vegetable farmers ~ ~llo operate, on average, on less than 0.5 ha and are<br />
responsible for nearly 60% of <strong>the</strong> total vegetable and potato production of <strong>the</strong><br />
country (Agricultural Implementation Programn~e 1990).<br />
Of thz 6,600 ha of 1o:vland ricefields in <strong>the</strong> region, about 6,400 ha are<br />
cultivated nith rice during <strong>the</strong> maha (January-May) season. A major portion (50%)<br />
of <strong>the</strong> lowland area is used for a second crop of vegetables and potatoes planted<br />
immediately after <strong>the</strong> rice crop. The balance of <strong>the</strong> land has problems with drainage<br />
or irrigation. A second crop of rice is usually grown if <strong>the</strong> problem is drainage; if<br />
poor irrigation is <strong>the</strong> problem, <strong>the</strong> field is left fallow. The third crop for <strong>the</strong> year in<br />
<strong>the</strong> lowlands coincides with <strong>the</strong> heavy rains and cold ten~perat~~res of October-<br />
December. This is <strong>the</strong> only period when nonirrigated ~~pland areas can be cultivated<br />
with a seasonal cash crop. The FSR site at Uva Paranagama represents <strong>the</strong> complex<br />
physical environment of <strong>the</strong> region.<br />
Patter~is of land use<br />
The topography of <strong>the</strong> Uva Brlsin ranges from undulating hills with gently sloping<br />
broad valleys to steep narrow inclines. The predominant soil is a red-yellow<br />
podzolic, and <strong>the</strong> valley bottom is a fertile alluvial fill. Terraced upland areas and
icefields are supplemented Lvith irrigation water from stream diversions. The land-<br />
use pattern can be broadly classified into four main categories.<br />
A'onimgated uplarzd areas. Annual cropping is limited to one vegetable or<br />
potato crop during <strong>the</strong> main rainy season (November-January). A few farmers grow<br />
a low input, short-term second crop of legumes.<br />
Imkated upland area aud borne garderzs. Two or three seasonal crops of<br />
vegetables or potatoes are cultivated according to <strong>the</strong> availability of supplementary<br />
irrigation facilities. A characteristic of <strong>the</strong> home garden is a mixture of perennial<br />
tree crops and livestock that is given preference around <strong>the</strong> home.<br />
Imkated Lowl17?1ds. These are fur<strong>the</strong>r classified as ricefields that receive<br />
assured irrigation water every year and ricefields that receive assured irrigation<br />
water on a 2-3 yr rotation. Just after <strong>the</strong> maha season, rice is cultivated in lowlands<br />
which are assured supplementary irrigation. Within <strong>the</strong> irrigated lowlands, poorly<br />
drained or imperfectly drained lowland areas, especially those situated below <strong>the</strong><br />
perennial water channel, are cultivated with rice during <strong>the</strong> second season.<br />
Ii'ell-drained lo~t~la?zds. If <strong>the</strong>se have assured irrigation, <strong>the</strong>y are usually<br />
cultivated with potato (<strong>the</strong> most profitable crop) immediately after <strong>the</strong> first rice crop<br />
(late June- mid-September). The third crop in <strong>the</strong> lowlands coincides with <strong>the</strong> heavy<br />
rains in November and December and <strong>the</strong> coldest period of <strong>the</strong> year. Only welldrained<br />
and moderately well-drained soils are cultivated with a third crop (where<br />
cattles are not a threat). Usually cattles kept in <strong>the</strong> highlands are allowed to graze<br />
on <strong>the</strong> lush grass in <strong>the</strong> lo~vland areas during this time of <strong>the</strong> year, which makes <strong>the</strong><br />
highlands available for a seed crop of potatoes.<br />
OBJECTIVES OF THE FSR PROGRAM<br />
In Uva Paranagama, <strong>the</strong> FSR program sought to shorten <strong>the</strong> time of <strong>the</strong> rice crop in<br />
<strong>the</strong> field frcjm 5.5 to 4.5 mo to allow a second crop to be stabilized and possibly a<br />
third crop to be grown and introduce new potato and vegetable varieties and new<br />
crop cultivars (e.g., soybeans and onions); to take best advantage of <strong>the</strong> environment<br />
by adjusting <strong>the</strong> time of lanting and recommend a fertilizer package to take<br />
advantage of residual ef P ects; and to improve patterns of labor use, particularly by<br />
using family labor more evenly throughout <strong>the</strong> year. Results of <strong>the</strong> <strong>research</strong> at Uva<br />
Paranagama are presented by Fernando (1979) and Upasena (1981a,b).<br />
CONCEPTUAL FRAMEWORK OF IMPACT STUDY<br />
Most studies of <strong>the</strong> impact of FSR on farm productivity and welfare are made<br />
between with and witllout, or udopter and nonadopter, groups of farmers. The<br />
common practice is to estimate a production function with a dummy variable to<br />
differentiate between farmers using and not using <strong>the</strong> technology and to ascertain<br />
<strong>the</strong> difference in productivity between <strong>the</strong> two groups. This is often accompanied by
some simple statistical tests to ascertain whe<strong>the</strong>r <strong>the</strong>re are significant differences in<br />
<strong>the</strong> welfare determinants of <strong>the</strong> farmers belonging to <strong>the</strong> two groups (Hafi and<br />
Ranaweera 1990). The rationale behind this approach is that <strong>the</strong>re is an impact on<br />
productivity if <strong>the</strong> dummy variable is significant. The significant differences found<br />
among welfare indicators are <strong>the</strong>n attributed to this impact on productivity.<br />
However, when a production function between two farmer groups is<br />
estimated using pooled data with a dummy variable, <strong>the</strong> implicit :issumption is that<br />
<strong>the</strong>re is only one set of structural parameters common to <strong>the</strong> two groups. The only<br />
difference, if <strong>the</strong> technolop had an impact, would be in <strong>the</strong> intercepts. This<br />
represents only partial reality because a substantial difference between <strong>the</strong><br />
technologies used by <strong>the</strong> t ~'o groups of farmers nlould most probably result in two<br />
different production functions with different parameters for ano<strong>the</strong>r variable. This<br />
necessarily changes <strong>the</strong> factor proportions and results in changes in factor incomes<br />
(factor shares) and consequzntly in personrll incomes.<br />
In <strong>the</strong> earlier approach, <strong>the</strong> differences in welfare indicators between <strong>the</strong> two<br />
groups of farmers are unquestioningly attributed to adoption of <strong>the</strong> new technology.<br />
The actual factors that link a technological change to a change in f:lrm welfare (i.e.,<br />
income generation and distribution parameters) are left totally unexamined because<br />
of a paucity of data<br />
In this study, three facets of this in1p:lct were considered:<br />
o If <strong>the</strong> key practices or inputs of <strong>the</strong> new technology package are adopted<br />
widely, it can be stated that <strong>the</strong>re has been an impact on agronomic<br />
practices (i.e., <strong>the</strong>re has been a technical inipact);<br />
o Adoption of some agronomic practices, or a technical impact alone, does<br />
not guarantee an economic impact. Economic impact req~lires <strong>the</strong><br />
adoption of practices to such a degree that a structural change is induced<br />
in <strong>the</strong> production functions of <strong>the</strong> farm; and<br />
o A structural change leads to changes in functional and, <strong>the</strong>reby, personal<br />
income distributions, which, in turn, vary .<strong>the</strong> levels of welfare and give<br />
rise to a welfare impact.<br />
The levels of adoption of various components of <strong>the</strong> tecllnology must 172<br />
examined to determine <strong>the</strong>ir agronomic impact on farm practices. To ascertain <strong>the</strong><br />
economic impact, an analysis is required of structur:ll ch:lnges in production<br />
functions and of changes in factor payments.<br />
If any welfare impact of <strong>the</strong> new technology exists, a c11:lnge in <strong>the</strong> le\fels and<br />
distribution of personal income would rnost probably be cliscernihle. The factor<br />
incomes are, <strong>the</strong>refore, converted to personal incomes by taking into account <strong>the</strong><br />
ownership of <strong>the</strong> various factors by farmers and by o<strong>the</strong>r individuals or institutions.
Changes in personal income are used as a major welfare indicator and as a means to<br />
substantiate <strong>the</strong> impact on o<strong>the</strong>r welfare indicators (e.g., accumulation of wealth<br />
and better nutrition).<br />
Sample selection<br />
Because of certain complexities at <strong>the</strong> Uva Paranagama site, a with and without<br />
methodology was used. At <strong>the</strong> initial FSR site, more than 90% of <strong>the</strong> farmers grew a<br />
short-duration rice variety and a third crop. This created a situation in which <strong>the</strong>re<br />
were hardly any nonadopters and, <strong>the</strong>refore, it was impossible to select a reasonable<br />
sample of nonadopters. Therefore, ano<strong>the</strong>r village (Medawala) about 10 km from<br />
<strong>the</strong> FSR site was selected as <strong>the</strong> second (without) site. Medawala had similar<br />
climatic and topographic conditions but had not been exposed to <strong>the</strong> FSR<br />
technology.<br />
Although <strong>the</strong> farnlers at <strong>the</strong> with site had been exposed to <strong>the</strong> FSR<br />
technology, some (10%) had not adopted it. There were also farmers in <strong>the</strong> without<br />
site who had observed <strong>the</strong> FSR technology and adopted it without persuasion from<br />
outsiders. Farmers at both sites were categorized into two groups: adopters and<br />
nonadopters. The adopters cultivated n short-duration rice variety and grew three<br />
crops per year. The nonadopters cultivated long-duration rice and cultivated two (or<br />
sonletin~es three) crops per year. In both groups, <strong>the</strong>re were farmers to whom FSR<br />
technology was formally introduced (<strong>the</strong> with-site farmers) and to whom formal<br />
exposure was not given (<strong>the</strong> ~vitliout-site farmers).<br />
Twenty-three farnlers from <strong>the</strong> with site and 14 farmers from <strong>the</strong> without site<br />
cooperated with <strong>the</strong> data collection. Of <strong>the</strong> total number of farmers from <strong>the</strong> two<br />
sites, 19 farmers were adopters and 18 farmers were nonndopters.<br />
Analytical tools<br />
As a preliminary analysis of farm productivity, simple arithmetic means of inputs<br />
and outputs were compared between <strong>the</strong> two groups of farmers. Mean yields and<br />
incomes from <strong>the</strong>se crops for <strong>the</strong> two groups of farmers were compared using a<br />
t-test. Whole-farm budgets for <strong>the</strong> three cropping seasons were coniputed to<br />
compare <strong>the</strong> productivity for <strong>the</strong> three different crops.<br />
To analyze structural changes in production activities, separate production<br />
functions were estimated using ordinary least sqiiares for <strong>the</strong> two groups of farmers.<br />
Changes in functional, and <strong>the</strong>reby personal, income distribution as a result of<br />
structural changes in <strong>the</strong> production pattern were obtained using <strong>the</strong> accounting<br />
method proposed by Herdt (1978). This analysis was conducted separately for three<br />
cropping seasons as well as for <strong>the</strong> full-year production cycle.<br />
Change in personal income was used as a major welfare indicator and as a<br />
means to substantiate impact on o<strong>the</strong>r welfare indicators (e.g., accumulation of farm
assets). Patterns of food consumption by farmers within a one-year period were<br />
taken into consideration to assess <strong>the</strong> impact of <strong>the</strong> new technology on nutrition.<br />
A pooled t-test was used to determine whe<strong>the</strong>r <strong>the</strong> differences in crilorie and<br />
protein-consuniption levels in <strong>the</strong> tn.0 groups were significantly different.<br />
Productivity anal3sis<br />
RESULTS AKD DISCUSSION<br />
Group averages of far~ner characteristics (i.e., age, number of years of schooling,<br />
and size of <strong>the</strong> farm family) of adopters and nonadopters in both study areas fell<br />
into narrow ranges. This indicated tllrit <strong>the</strong>re was ho~nogensity between <strong>the</strong> two<br />
oroups of farnlers in <strong>the</strong> tu.0 areas (Table 2). An average farmer was about 52-54 yr<br />
Eld, had about 4-7 yr of schooling, and supported a krinily of 5-6 members. Although<br />
<strong>the</strong> years of farming experience of adopters was nun~erically higher (about 32 yr)<br />
than nonadopters (25 yr), both groups had spent sufficiently long periods in<br />
agricultural production to fully understand <strong>the</strong>ir far111 practices. Therefore, a<br />
significant difference in production patterns of <strong>the</strong> tulo groups is highly unlikely to<br />
be induced by social background.<br />
The averxge farm size of an aclopter (0.81 ha) was ljigger than that of a<br />
nonadopter (0.76 ha). The average lo\vland size (0.42 ha) of adopters was also<br />
bigger than that of nonadopters (0.40 ha). Nonadoptsrs had larger highlands (0.40<br />
ha) than adopters (0.39 ha). hlajority of farmers in both groups had land holdings<br />
below 0.7 ha. Less than one-third of <strong>the</strong> farmers had land holdings larger than 1.0<br />
ha, and a slightly higher percentage of adopters owned holdings in this category.<br />
The distribution of loiipland by tenurial arrangement revealed that 42% of<br />
adopters and 43% of nonadopters were owner-operators (Table 3). Only a small<br />
percentage of farmers did not own lo\vlands. The f:irmers who did not own lands<br />
accounted for 22%- in <strong>the</strong> nonadopter group and 217c in <strong>the</strong> adopter group. Some<br />
farmers operated <strong>the</strong>ir o\4.n lowlands as \veil as some lowl:inds owned by o<strong>the</strong>rs<br />
(3770 of adopter farniers and 33% of no11:idopters). Ollly s small percentage of<br />
farmers in each group did not own hig1il;inds. Both adopters and non:ldopters<br />
operate \.cry slnall holdings and more than h;llf of <strong>the</strong>m are tenants. There is no<br />
substantial difference betn~een land ownership or tenurial patterns of aclopters and<br />
nonadopters.<br />
Input use in crop cullivntion in lowlnncis<br />
First-season rice cultivatiot~. Analysis of <strong>the</strong> first-season rice crop presents some<br />
interesting results. The cost of agrocheniicals used was significantly higher for<br />
nonadopters than adopters (Table 4). Although preharvest 1:ibor used was not<br />
different between <strong>the</strong> two groups of farmers, adopters used less labor for nursery<br />
and land preparation and Inore lrtbor for weeding. These differences can be<br />
attributed to land preparation using ani111;ll power or tractors.
The yield of rice was significantly higher for adopters than for nonadopters,<br />
and this higher yield was achieved at a lower total cost. Consequently, <strong>the</strong> net<br />
income obtained from rice cultivation by <strong>the</strong> adopters was higher than <strong>the</strong> income<br />
earned by nonadopters.<br />
Second-season potato cultivatiorz. In <strong>the</strong> case of potato, mean levels of<br />
preharvest labor used were significantly different and higher in nonadopters than in<br />
adopters (Table 5). The amount of labor used by adopters for weeding and fertilizer<br />
application indicated better weed control and better application of fertilizer. Potato<br />
ylelds for nonadopters were higher and resulted in a hlgher gross income. The costs<br />
of cultivation for nonadopters were higher than for adopters by a small margin.<br />
Therefore, nonadopters earned a substantially higher net income from <strong>the</strong> potato<br />
crop.<br />
Third-season vegetable clrltivatiol7. Preharvest labor for <strong>the</strong> third-season<br />
vegetable crop was similar for <strong>the</strong> two groups, except for a few crop-care operations<br />
(Table 6). Adopters used higher amounts of labor for weeding, pest and disease<br />
control, and harvesting. This may be related to <strong>the</strong>ir significantly higher yields.<br />
Nonadopters spent significantly more on seeds; whereas, adopters spent more on<br />
agrochemicals. Although <strong>the</strong> total cost of cultivation was higher in <strong>the</strong> adopter<br />
group than in <strong>the</strong> nonadopter group, <strong>the</strong> adopters earned higher net returns from<br />
<strong>the</strong>ir higher yields.<br />
An overall comparison of input use indicates that adopters often use fewer<br />
inputs and have managed to lessen <strong>the</strong>ir cost of culti\lation. Yield levels of adopters<br />
were higher for two of <strong>the</strong> three crops grown, which indicated that <strong>the</strong> average<br />
productivity of*<strong>the</strong> inputs was high. This led to higher g~oss and net returns for<br />
adopters (except in potato production). Returns to capltal and returns to labor were<br />
higher for adopters. This provided fur<strong>the</strong>r proof that <strong>the</strong> adopters were able to use<br />
<strong>the</strong>ir resources more efficiently (Table 7).<br />
Technological changes<br />
Production functions with several independent variables were estimated separately<br />
for <strong>the</strong> three cro ping seasons. The estimated production functions for all three<br />
crops were signi P icantly different (structurally) between <strong>the</strong> two groups of farmers.<br />
This indicates that <strong>the</strong> production functions of all three crops had undergone<br />
structural changes because of <strong>the</strong> impact of <strong>the</strong> FSR technology. In fact, only <strong>the</strong><br />
rice crop underwent a radical change; <strong>the</strong> old traditional rice varieties were replaced<br />
with improved varieties. The changes in potato and vegetable production resulted<br />
from a readjustment of <strong>the</strong> cropping system to <strong>the</strong> first change. This secondary<br />
change had a negative effect on <strong>the</strong> productivity of potatoes and a positive effect on<br />
<strong>the</strong> productivity of vegetables. Early planting of potato, which was a secondary<br />
change, exposed <strong>the</strong> crop to heavy winds and to deficient moisture during <strong>the</strong> period<br />
of tuber formation. Farmers suggested <strong>the</strong>se reasons for <strong>the</strong> lower yields. For<br />
vegetables, sufficient time was saved to allow for a longer duration and betterplanned<br />
vegetable crop, which increased yields.
Production functions for first-season rice<br />
For adopters and nonadopters, rice output was mainly determined by <strong>the</strong> area of<br />
land cultivated (Table 8). However, when land area and Iabor were included, both<br />
variables yielded coefficients \vith high standard errors. This was attributed to<br />
rnulticollinearity. The coefficient of correlation between <strong>the</strong>se variables was 0.75 for<br />
adopters. To circumvent this situation, labor use per unit area was entered in <strong>the</strong><br />
subsequent analysis. This produced stable coefficients for both land and labor.<br />
Never<strong>the</strong>less, <strong>the</strong> coefficient of <strong>the</strong> Iabor variable cannot be interpreted as <strong>the</strong><br />
production elasticity of labor.<br />
Althougll use of preharvest Iabor per unit area contributed significantly to<br />
rice output for nonadopters, it was not significant for adopters. The cost of fertilizer<br />
contribured significantly at <strong>the</strong> level of 0.01 for adopters and 0.1 for nonadopters.<br />
Production functions for second-season potato<br />
The area of land cultivated and <strong>the</strong> cost of fertilizers per unit area contributed<br />
significantly and positively to potato production (Table 9). As in <strong>the</strong> case of rice,<br />
fertilizer cost per unit area was used to avoid multicollinearity. The correlation<br />
coefficients between land area and fertilizer cost were 0.64 for adopters and 0.85 for<br />
nonadopters. The negative sign of <strong>the</strong> statistically significant coefficient of dunlrny<br />
variable 1 (with = 1; without = 0) in <strong>the</strong> production function for aclopters indicates<br />
that <strong>the</strong> farmers in <strong>the</strong> with site were technically less efficient than <strong>the</strong> farmers in<br />
<strong>the</strong> without site. This indicated that <strong>the</strong> farmers, who voluntarily used <strong>the</strong><br />
technology after having seen it, applied <strong>the</strong> technology more effectively than farnlers<br />
who were made adopters by persuasion. This dummy variable did not show any<br />
significant difference in <strong>the</strong> productivity of nonadopters, which is to be expected.<br />
The significant coefficient of dummy variable 3 (continuo~ls irrigation = 1;<br />
irrigation once in 2 yr = (1) in rhe production function of nonadopters showed that<br />
farmers who received continuous irrigation every year did better than farmers who<br />
received water once in 3 yr for <strong>the</strong> first-season rice crop. Dummy varirtble 3 is<br />
nonsignificant, however, in <strong>the</strong> production function for adopters. This cou Id be due<br />
to low variability of <strong>the</strong> dummy variable in <strong>the</strong> sample. Only one farmer did not<br />
have irrigation facilities in this group. Therefore, lack of continuous irrigation may<br />
be considered a constraint to <strong>the</strong> adoption of new technology.<br />
Dummy variable 4 (owner operators = l; tenants = 0) had a significant and<br />
negative coefficient for adopters, but a significant positive coefficient for<br />
nonadopters. Hypo<strong>the</strong>ses and empirical evidence suggest that tenant farmers<br />
produce more to ensure <strong>the</strong>y have a substantial portion of <strong>the</strong>ir produce left after<br />
paying a share to <strong>the</strong> landlord. However, <strong>the</strong>re is also evidence that owner-operators<br />
perform better because <strong>the</strong>y invest in <strong>the</strong>ir land over time for soil conservation.<br />
Never<strong>the</strong>less, <strong>the</strong>re is inadequate information available to explain <strong>the</strong> contrast in<br />
<strong>the</strong> behavior of <strong>the</strong> dummy variable for tenancy in adopter and nonadopter groups.
Production functions for third-season vegetables<br />
Land area mainly determined <strong>the</strong> output of third-season vegetables for adopters<br />
(Table 10). Although <strong>the</strong> estimated coefficients for area of land cultivated,<br />
preharvest labor/ha, and dummy variable 4 for <strong>the</strong> nonadopter group were not<br />
significant at conventional levels of significance, <strong>the</strong>y were larger than <strong>the</strong>ir standard<br />
errors. (Note that labor/ha was used to avoid <strong>the</strong> multicollinearity problem). The<br />
correlation coefficients between land area and labor were 0.58 for adopters and 0.55<br />
for nonadopters. The significant parameter estimates of dummy variable 1 in <strong>the</strong><br />
production function for vegetables grown by nonadopters indicated that <strong>the</strong> farmers<br />
who were adopters by persuasion were better than <strong>the</strong> farmers who voluntarily<br />
adopted <strong>the</strong> technology.<br />
Preharvest labor per unit area contributed significantly and positively, but<br />
dummy variable 4 had a coefficient with a negative sign in <strong>the</strong> production function<br />
of adopters. This significant parameter estimate of dummy v:iriable 4 indicated that<br />
<strong>the</strong> production of vegetables by tenant farmers was more efficient than production<br />
by owner-farmers. The tenancy arrangement was leasehold; <strong>the</strong>refore, it is possible<br />
that leased lands were cultivated with better care and intensity to obtain yields that<br />
were high enough to cover land rent.<br />
Functional and personal income distribution<br />
Factor-share analysis. It is assumed that each factor of production is paid its<br />
marginal-value product; <strong>the</strong>refore, <strong>the</strong> expenditures on all primary factors reflect<br />
<strong>the</strong> value <strong>the</strong>se factors add to total production. If long-term equilibrium prevails, <strong>the</strong><br />
total payments to each factor plus <strong>the</strong> payments on current inputs would exhaust <strong>the</strong><br />
total value of production. Never<strong>the</strong>less, because <strong>the</strong>se situations do not commonly<br />
prevail, <strong>the</strong>re is a difference between <strong>the</strong> total value of <strong>the</strong> product and <strong>the</strong> total<br />
cost, i.e., <strong>the</strong> net income or profit. Because management cannot be easily separated<br />
as a factor of production in rural agriculture, this difference really contains<br />
payments to management and profit. Therefore, it is more appropriately called a<br />
residual.<br />
Payments to all primary factors, <strong>the</strong> residual (value added), and payments to<br />
purchase current inputs show <strong>the</strong> functional or factor-income distribution. Because<br />
payments to current inputs go to <strong>the</strong> sectors that produce, <strong>the</strong> value added accrues<br />
to <strong>the</strong> factor owners in agriculture. Accordingly, <strong>the</strong> distribution of personal income<br />
of <strong>the</strong> various factor owners is determined by <strong>the</strong> levels of use of <strong>the</strong>ir resources.<br />
The personal income of peasant farmers in <strong>the</strong> current setting most probably<br />
consists of <strong>the</strong> residual, payments for farm-family labor, and payments to farmer-<br />
owned capital items. This analysis was conducted on <strong>the</strong>se basic principles.<br />
Factor sllure in first-season rice. Except for factor income of labor and<br />
residual, <strong>the</strong>re were no marked differences between adopter and nonadopter groups<br />
of farmers (Table 11). Adopters used more family and exchange labor, which are<br />
not paid for, and less hired labor. The nonadopters used more hired labor and less<br />
family and exchange labor. The negative values of <strong>the</strong> residuals for both adopters
and nonadopters indicate that <strong>the</strong>re were no profits and that <strong>the</strong>re were losses from<br />
rice alone. However, <strong>the</strong> (absolute) residual was higher for adopters than for<br />
nonadopters, which implies that <strong>the</strong> adopters suffered srnaller losses.<br />
Fr~ctor .rllrcrc in srcond-scrrrorz pottrto. Because pot2 to production requires a<br />
larger expenditure on current inputs, v;llue added amounts to only 65% of <strong>the</strong> total<br />
value of production for adopters and 60% for nonadopters (Table 12). This is ra<strong>the</strong>r<br />
low compared \\,ith <strong>the</strong> percentage of value added in <strong>the</strong> o<strong>the</strong>r two crops. Payments<br />
for labor in potato were relatively higher for adopters. Payments to o<strong>the</strong>r factors<br />
were approximately equal. As a result, nonadopters received a higher share of value<br />
added as residual than adopters, but <strong>the</strong>re were no large differences.<br />
Frrctor ~lrarc: irz tliird-srr~.~orz vegrtahle. The value of output is remarkably high<br />
in <strong>the</strong> case of aclopters. Expenditures on current inputs in absolute terms were<br />
higher u.ith adopters, but not to an extent cornniensur:tte with <strong>the</strong> productivity<br />
differences. As a result, <strong>the</strong> value added in absolute terms was higher for adopters.<br />
In proportional terms, <strong>the</strong> value added was 78547 for adapters and 77% for<br />
nonadopters (Table 13). Percentage factor incomes for labor, capital, and land were<br />
less for adopters, v. hich caused :l higher percentage residual than for nonadopters.<br />
Nonadopters hired niore Iallor t1i:in adopters and used more family and exchange<br />
Iabor.<br />
Fuctor sllrnrcls for tlle atlt~irtrl tl~t~~?e-crop cycle. Factor-s11are analysis for <strong>the</strong><br />
whole year showed that <strong>the</strong>re wits only a slight difference in <strong>the</strong> factor incomes<br />
between adopters and nonadopters (Table 14). The :t~ialysis for individual crops<br />
showed that adopters used more family and exchange Iabor and less hired Iabor;<br />
nonadopters used more hired labor than family and exchange Inhor. This is one of<br />
<strong>the</strong> major differences t11:it occurred after ndop tion of new FSR technology. After <strong>the</strong><br />
shares of income gained by different factors were netted, 4170 for aclopters and 42%<br />
for nonsdopters \%!ere left as residuals.<br />
Distribution of personal inconle<br />
First-sclr~son ricr. Because of <strong>the</strong> high productivity attained without a large increase<br />
in current input use, <strong>the</strong> production process for adopters resulted in a substantially<br />
higher percentage of value added than for nonadopters (Table 11). Rice production<br />
was not capitrll-intensive; <strong>the</strong>refore, only 6-796 of <strong>the</strong> total valt~e of production was<br />
obtained by <strong>the</strong> capital owner for both groups of farmers. The l~trgest portion of<br />
income (40%) was earned hy hired labor on nonadopters farms; whereas, <strong>the</strong><br />
portion was only 21% for adopters. As :I result of <strong>the</strong> use of higher amounts of<br />
family and exchange labor by adopters, <strong>the</strong>y received 70% of <strong>the</strong> total income<br />
generated by rice production as income, while nonadopters only received 50%. The<br />
remaining 30% of income generated by adopters and 50% generated by<br />
nonadopters accrued to o<strong>the</strong>rs, like hired Iaborerc and capit:iI owners.<br />
Second-scnron potuto. In <strong>the</strong> case of pot;tto, only about 1% of income went to<br />
<strong>the</strong> capital owner from adopters and nonadopters while <strong>the</strong> largest proportion of<br />
income (48% by adopters and 53% by nonadopters) accrued to <strong>the</strong> farmers as
esiduals (Table 12). Ultimately, <strong>the</strong> income shares of farmers were 93% of <strong>the</strong><br />
value added for adopters and 90% for nonadopters. The farm income of adopters is<br />
slightly less in absolute terms, although it is slightly more in terms of <strong>the</strong> proportion<br />
of value added.<br />
Tflird-season vegetable. Of <strong>the</strong> total value added in vegetable production,<br />
proportional income earned by hired laborers, capital owner, land owner, and family<br />
and exchanged labor were lower for adopters than nonadopters (Table 13). This led<br />
to higher percentages of income as a resldual(58%), and ultimately, a higher<br />
income share by <strong>the</strong> adopter farmers. In <strong>the</strong> production of vegetables, 90% of <strong>the</strong><br />
income generated by adopters and 78% of <strong>the</strong> income of nonadopters accrued as<br />
personal income and only a small percentage was paid out. In absolute terms, <strong>the</strong><br />
Inconies accrued to various factor owners connected to <strong>the</strong> adopters were higher<br />
because <strong>the</strong> value added for <strong>the</strong> same group is higher.<br />
Annual three-crop cycle. The values added in <strong>the</strong> three-crop annual cycles<br />
were very close in <strong>the</strong> two groups, with adopters having an additional SLR7,777/ha<br />
(Table 14). This resulted from a cancellation of <strong>the</strong> positive gains in rice and<br />
vegetable production by <strong>the</strong> negative gains in potato production. Only about 1-2%<br />
of <strong>the</strong> value added went to <strong>the</strong> capital owners and 15% went to tlie land owners<br />
from each group of farmers. Income from adopters to hired labor was 7% of value<br />
added; whereas, family and exchange labor captured 12%. For nonadopters, 11%<br />
went to hired Iabor and 8% to family and exchange labor. Except for proportions<br />
accrued to tlie farm family as payments for family labor, <strong>the</strong>re are no o<strong>the</strong>r salient<br />
differences in distributions of personal income. Residuals that remained with <strong>the</strong><br />
farmers amounted to 42% for adopters and 41% for nonadopters. Ultimately,<br />
adopters cry-ned a slightly higher percentage of value added (89%) as <strong>the</strong>ir personal<br />
income froni <strong>the</strong> production of crops within a l-yr period. Nonadopters earned only<br />
83 %.<br />
The income of adopters froni rice production improved slightly as n result of<br />
adopting tlie technology, but <strong>the</strong> effect was nullified by greater disadvantages to <strong>the</strong><br />
potato crop under <strong>the</strong> new farming system. The third crop of vegetables performed<br />
well under <strong>the</strong> new system and managed to equalize (in fact exceed nonadopters by<br />
a small margin) factor incon~es and personal incomes, with a slight reallocation of<br />
<strong>the</strong> cost of labor in hvor of farmers.<br />
\Velf;tre measures o<strong>the</strong>r than income<br />
Elidowl?tent of fan71 ar.rcts. Changes in personal income can cause changes in tlie<br />
endowment of farm and household assets. Table IS indicates that <strong>the</strong>re were no<br />
consistent differences in farm assets owned by <strong>the</strong> two groups of farmers. None of<br />
<strong>the</strong> adopters owned two-wheeler tractors, but one of 18 nonadopters owned a two-<br />
wheeler. Although 65 niammoties were owned by 19 adopters, only 13 were owned<br />
by l8 nonadopters within <strong>the</strong> year under study. Milking cows were owned by 13<br />
adopters, but only by 6 nonadopters. Storage boxes were owned by eight adopters<br />
and by three nonadopters.
E~zdowr~tcnt of l~ouselrol~f assets. Of <strong>the</strong> household assets that \\.ere<br />
considered, all adopters and 72% of nonadopters had radios (Table 16). More<br />
nonadopters o\\lned television sets than adopters. None of <strong>the</strong> nonadopters owned<br />
bicycles or motorbikes, but adopters did. A bus was owned by a nonadopter. There<br />
was no definite pattern ofdifferences in ownership of household assets. These<br />
findings are consistent with <strong>the</strong> marginally different personal incomes of adopters<br />
and nonadopters.<br />
hTzltritioti. Nutrition, which is one of <strong>the</strong> most important variables in<br />
measuring social well-being, plays a major role in enhancing health conditions and<br />
increasing <strong>the</strong> quality of labor. Calories and protein are <strong>the</strong> major components of a<br />
balanced diet.<br />
Each adopter consumed 2,126 cal/d; \\.hereas, each nonadopter consumed<br />
2,080 cal/d. Per capita consumption of protein per d:~y was 55 g for adopters and<br />
52 g for nonadopters. There \+.ere no significant differences in daily per capita<br />
calorie consuniption or protein consumption between adopters and nonadopters. No<br />
direct inipact of <strong>the</strong> technology on nutrition \\.;IS observecl.<br />
E.vpcnriitiir.c ur~rf .rrr\linss. Expenditures on food by both categories of farniers<br />
were very similar. Almost 40% of <strong>the</strong> total expenditure of adopters was spent on<br />
food consumption; whereas, nonadopters spent nearly 33% of <strong>the</strong>ir total<br />
expenditure 011 food (Table 17).<br />
Adopters had higher average savings than non:ldopters, l~ut <strong>the</strong> difference<br />
was not significant (Table 17). In <strong>the</strong> ahsence of any differe~ices in n~ltritional<br />
intake, <strong>the</strong> expenclitures and s:~vings tally well with <strong>the</strong> niargin:ll income advantage<br />
that farmers recei\.ed by adopting <strong>the</strong> new fClr~iiing sjJsterns technology.<br />
Limitations of <strong>the</strong> study<br />
The study, initiated in 1087-88, suffered :l break in d:itrl collection because of<br />
unstable conditions in <strong>the</strong> countrv. Therefore, some issues pertaining to <strong>the</strong> stability<br />
and sustainability of <strong>the</strong> te~Iiiiol6~~ could not be addressed. Howe\,er, <strong>the</strong> fact that<br />
overall cropping intensities \{,ere high, which ensured high f:lrni incomes, indicates a<br />
certain degree of stability o\.er time. The an;llysis of production functions for <strong>the</strong><br />
crops suffered from niulticolline:~rity. Therefore, input use per hectare had to be<br />
used in some cases as a proxy for <strong>the</strong> <strong>the</strong>oretically perfect ir:put [Ice per far~ii. This<br />
restricted <strong>the</strong> analysis to structural changes, but <strong>the</strong> cstirn;~tt.d coefficients could not<br />
be interpreted as production elr~sticities of inputs, and <strong>the</strong> :lnalysis could not be<br />
extended to determine returns to scale and factor shares. Therefore, <strong>the</strong> study of<br />
factor and income shares was limited to an accounting analysis at <strong>the</strong> average level.<br />
A more detailed analysis for above-average, average, and below-average farmers<br />
would have been preferable, but could not be conducted bec:luse <strong>the</strong> samples were<br />
limited to 19 adopters and 18 nonadopters.
CONCLUSION<br />
o There appears to be a certain degree of success in <strong>the</strong> new farming<br />
system because some farmers at a site where <strong>the</strong> farming system had not<br />
been introduced, adopted <strong>the</strong> system after <strong>the</strong>y observed its use.<br />
o When <strong>the</strong> adopter and nonadopter groups were compared, many<br />
practices were similar between <strong>the</strong> two groups. However, a major<br />
difference was that adopters planted a 4.5-mo rice variety and 3 crops per<br />
Y<br />
o Production-function analysis of individual crops showed that <strong>the</strong> adoption<br />
of <strong>the</strong> new technology caused structural changes in <strong>the</strong> production<br />
processes of all three crops. However, this had a positive effect for<br />
adopters only in rice and vegetables. Structural changes to potato<br />
production after adoption indicated that <strong>the</strong>re was a decrease in<br />
productivity because of increased exposure to physiological stress.<br />
o As a result of structural change, functional income distribution and value<br />
added for all three crops changed. The most salient feature was that use<br />
of family labor increased among adopters.<br />
o The personal income distribution from each crop changed in favor of<br />
adopters in rice and vegetable production and in favor of nonadopters in<br />
potato production.<br />
o When <strong>the</strong> three-crop annual cycle was considered as a system, <strong>the</strong>re was<br />
no significant difference in functional and personal income distribution<br />
between <strong>the</strong> adopter and nonadopter groups. The positive effects on rice<br />
and vegetable crops were canceled to a large extent by <strong>the</strong> negative<br />
effects on potato cultivation.<br />
o In <strong>the</strong> absence of income differences, changes in o<strong>the</strong>r related welfare<br />
indicators (i.e.,, farm and household capital accumulation, nutritional<br />
status, expenditures, and savings) were not observed.<br />
o The farming <strong>systems</strong> technology introduced in Uva Paranagama did not<br />
create an impact on <strong>the</strong> overall regional economy of Badulla and<br />
Bandar:lwela. Consequently, <strong>the</strong>re was no impact on <strong>the</strong> social welfare of<br />
adopters during <strong>the</strong> relatively brief period after its adoption, although<br />
<strong>the</strong>re were stn~ctural changes observed in individual crop production<br />
processes.<br />
Of <strong>the</strong> original ol3jectives of <strong>the</strong> FSR program, only enhancement of <strong>the</strong> use<br />
of family labor was achieved. During <strong>the</strong> introduction of <strong>the</strong> short-duration rice
variety, inadequate attention was paid to <strong>the</strong> possible negative effects on <strong>the</strong><br />
subsequent potato crop. The component teclinologies appear to have been<br />
developed in isolation of <strong>the</strong> rest of <strong>the</strong> farm.<br />
An important issue is whe<strong>the</strong>r <strong>research</strong> programs are adequately holistic to<br />
encompass <strong>the</strong> probable ramification of an introduced technology on a rural<br />
economy. How much disequilibrium and economic disarray can be caused by <strong>the</strong><br />
introduction of a certain innovation? It would be interesting to consider <strong>the</strong> effects<br />
beyond <strong>the</strong> geographical limitations of <strong>the</strong> farm. The problems could be viewed in<br />
<strong>the</strong> broader context of both farm and off-farm effects. This could lead to stronger<br />
policy analysis in <strong>the</strong> context of <strong>the</strong> whole family farm.<br />
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Fernando G W E (1979) Sri Lanka cropping <strong>systems</strong> program. Report of Cropping<br />
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Hafi A A B, Ranaweera N F C (1990) Assessing productivity and household impact<br />
of FSR/E in Uva Paranagama in Sri Lanka.<br />
Herdt R W (1976) Costs and returns for rice production in Luzon. Paper presented<br />
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<strong>International</strong> Rice Research Institute, Los Barios, Laguna, Philippines.<br />
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Sri Lanka. Paper presented at <strong>the</strong> <strong>International</strong> Rice Research Conference.<br />
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Sri Lanka. Paper presented at <strong>the</strong> Cropping Systems Conference,<br />
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<strong>systems</strong> project, Sri Lanka. Paper Presented at <strong>the</strong> 12th Cropping Systems<br />
Working Group Meeting, Burma.
Table 1. Farming system <strong>research</strong> sites in Sri Lanka.<br />
Locat ioll Zone Agroecological<br />
region<br />
1. Maha Illuppallama Dry zone, low country DL1<br />
2. Makandura Intermediare zone. low ILl & IL3<br />
counrry<br />
3. Baiidara\vela Intermediate zone, up IU3<br />
country<br />
4. Kilinochchi Dr!. zone. low country DL3 & DL3<br />
5. Karadian Aru Dry zone, low country D L2<br />
6. Ansunakolapalessa Dry zone, low country DL1 & DL5<br />
7. Galiiea~a Dry zone, lo\v country. DL 1<br />
irrisated conditions<br />
Table 2. Demo3rapIiic clinracteristics of adoprers and nonadopters.<br />
-<br />
Adoprers Nlnnadopters<br />
Age of rhe farmer (years) 51 (23)l' 52 (3.5)<br />
No cf !:ears of schooling (years) 3 (72) 7 (52)<br />
Experience in farmins (years) 32 (11) 25 i51)<br />
F:lmily size 6 (21) 6 (23;)<br />
('~u~nhers in paren<strong>the</strong>ses are coefficients 01' \.ariittion ( )<br />
-
Table 3. Distribution of farmers by tenurial arrangement (1990).<br />
Lowland<br />
Adopter (%) Nonadopter (%)<br />
Owner operator 42 (8) 44 (8)<br />
Owner + o<strong>the</strong>r tenurial<br />
arrangements<br />
O<strong>the</strong>r tenurial<br />
arrangements only<br />
Highland<br />
Owner operator<br />
Owner + o<strong>the</strong>r tenurial<br />
arrangements<br />
O<strong>the</strong>r tenurial<br />
arrangements<br />
Note: Figures in paren<strong>the</strong>ses refer to number reporting.
Table 4. Comparison of levels of inputs and outputs and <strong>the</strong> crop budget for <strong>the</strong> first rice crop<br />
of adopters and nonadopters.<br />
Inputs<br />
seeda<br />
Fertilizer cost<br />
Agrochemical cost 0<br />
O<strong>the</strong>r costs<br />
~abor~<br />
Nursery preparation<br />
Land preparation<br />
Planting<br />
Weeding<br />
Fertilizin:<br />
Pest and disease control<br />
Water management<br />
Hal-\.resting<br />
Hauling to threshing floor<br />
Threshing and wiru~owing<br />
(Total labor)<br />
Yield (kgld<br />
Total cost'<br />
Gross income"<br />
Net income"<br />
Adopter Nonadopter<br />
Physical Values Physical Values<br />
quantitieslha SLRIha quailtitieslha SLRIha<br />
days<br />
8.0<br />
66.3<br />
43.1<br />
31.3<br />
2.8<br />
0.8<br />
25.4<br />
42.7<br />
31.3<br />
25.3<br />
(267.0)<br />
3.860<br />
days<br />
15.4<br />
84.4<br />
44.4<br />
17.3<br />
1.4<br />
0.6<br />
19.6<br />
41.5<br />
23.6<br />
25.4<br />
(276.6)<br />
3,508<br />
"seed price of rice SLR7.18lkg. b~ipnifican<br />
at IOW level "Labor cost SLR6Oiday.<br />
'significant at 15 70 level. 'Co~nputed horn samplc means and not tested for statistical<br />
significance of differences.<br />
*<br />
*Pp<br />
.v
Table 5. Comparison of levels of inputs and output and <strong>the</strong> crop budget for <strong>the</strong> second-season<br />
potato crop of adopters and nonadopters.<br />
-<br />
Variable Adopter Nonadopter<br />
Inputs<br />
Seed costa<br />
Fertilizer cost<br />
Agrochemical cost b<br />
O<strong>the</strong>r costs<br />
~abor'<br />
Land preparation<br />
Planting<br />
Weeding<br />
Fertilizing<br />
Pest and disease control<br />
O<strong>the</strong>r operations<br />
Harvesting<br />
(Total labor)' '<br />
Yield (kg)<br />
Total costd<br />
Gross income d<br />
Net income d<br />
Physical Values Physical Values<br />
quantitieslha SLWha quantitieslha SLWHa<br />
days<br />
112.1<br />
19.8<br />
59.7<br />
12.9<br />
10.9<br />
21.2<br />
39.4<br />
(276.1)<br />
7,757<br />
days<br />
145.2<br />
28.3<br />
29.8<br />
8.2<br />
13.9<br />
33.2<br />
46.9<br />
(305.5)<br />
9,981<br />
?rice of seed potato SLR50.00lkg. b~ignificant<br />
at 1 % level. '~abor cost SLR60.00lday.<br />
Computed from sample means and not tested for statistical significance of differences.
Table-6. Compariso~i of le\.els of inputs and outputs and <strong>the</strong> crop budget for <strong>the</strong> third-season<br />
i.egetable crop of adopters and nonadopters.<br />
Variable Adopter Nonadopter<br />
Physical Values Physical Values<br />
Quantitieslha SLRIha Quantitieslha SLRIha<br />
Inputs<br />
Seed cost"<br />
Fertilizer cost<br />
Agrochemical cost 0<br />
O<strong>the</strong>r costs<br />
LaborC days days<br />
Land prsparatio~i 81.9 1,916 78.4 4,705<br />
Planting 15.9 956 16.4 986<br />
Weedins 45.5 2,728 32.7 1,964<br />
Fertilizing 8.4 506 6.1 383<br />
Pest and disease control 19.0 9.0 537.0<br />
O<strong>the</strong>r operations 31.3 2,060 28.9 1,735<br />
Harvesting 63.7 3,824 40.9 2,452<br />
(Total labor) (968.6) (16,123) (212.7) (12,762)<br />
Total costd 35.156 26,651<br />
Gross income'^^ 80,557 49,698<br />
Net iiicolne (sLRI~~)'~ 45.401 23,044<br />
p-- -<br />
"Significant at 5% level. '~i~~~ificnl~t at 108 level '~abor cost SLR60.00id d~omputed<br />
from sample means and not tesred for statistical significance of differences, e~ignificant at<br />
15% level.
Table 7. Returns to capital and labor for adopters and nonadopters.<br />
Rice<br />
Potato<br />
Returns to capital Returns to labor<br />
Adopter Nonadopter Adopter Nonadopter<br />
Vegetable 2.3 1.5 168 108<br />
Annual three-crop<br />
cycle 1.8 1.5 168 161<br />
Table 8. Estimated production functions for rice of adopters and<br />
nonadopters.<br />
Independent<br />
variable<br />
- -<br />
Constant 6.029**+*<br />
(5.52)<br />
LAREA<br />
LLB<br />
LFRT<br />
Adopt er Nonadopter<br />
Notes: l. Dependent variable is <strong>the</strong> production (kg) of rice per farm.<br />
2. Figures in paren<strong>the</strong>ses are t-values of <strong>the</strong> estimated<br />
coefficients.<br />
3, "*** significant at l % level; *** significant at 5% level; **<br />
significant at 10% level; and * significant at 15 % level.<br />
4. Definition of independent variables: LAREA = log value of<br />
land area in hectares; LLB = log value of days of prel~arvest<br />
labor per hectare; and LFRT = log value of total fertilizer<br />
cost in SLR.<br />
5. F-value of <strong>the</strong> chow test is 5.9****.
Table 9. Estimated production functions for potato of adopters and nonadopters.<br />
Independent<br />
variable<br />
Constant<br />
LAREA<br />
LFR<br />
Dummy 1<br />
Dummy 4 -0.514"<br />
(-1.633)<br />
= 0.62<br />
Adopter Nonadopter<br />
Notes: 1. The dependent variable is <strong>the</strong> potato production (kg) per farm.<br />
2. Figures in Paren<strong>the</strong>sis are t-values of <strong>the</strong> estimated coefficients.<br />
3. ****Significant at 1 % level; *** significant at 5% level; "*<br />
significant at 10% level; and * significant at l5 % level.<br />
4. Definitions of independent variables: LAREA = log value of land<br />
area in hectares; LFR = log value of total fertilizer cost per hectal-e<br />
in SLR; Dunlmy 1 = with site = 1; without site = 0: Dunlmy 3 =<br />
irrigation once in 2 years = 0; continuous irrigation = 1; and<br />
Dummy 4 = owner operator = 1; tenant = 0.<br />
5. F-value of <strong>the</strong> chow test is 3.41***.<br />
m,
Table 10. Estimated production functions for vegetable of adopters<br />
and nonadopters.<br />
Independent<br />
variable<br />
Constant<br />
LAREA<br />
LLB<br />
Dummy 1<br />
Dummy 4 -1.262***<br />
(-2.26)<br />
R2 = 0.60<br />
Adopter Nonadopter<br />
F = 5.03<br />
Notes: i. Dependent variable is <strong>the</strong> yield value (SLR) per farm.<br />
2. Figures in paren<strong>the</strong>ses are t-values of <strong>the</strong> estimated<br />
coefficients.<br />
3. ****significant at 1 % level; *** significant at 5% level;<br />
** significant at 10% level; and * significant at 15% level.<br />
4. Definitions of independent variables: LAREA = log value<br />
of area in hectares; LLB = log value of days of preharvest<br />
labor per hectare; Dummy 1 = with site = 1; without site<br />
= 0; and Duminy 4 = owner operator = 1; tenant = 0<br />
5. F-value of <strong>the</strong> chow test is 3.39***.
Table 11. Factor shares of first-season rice for adopters and nonadopters.<br />
Adopter Nonadopter Adopter Nonadopter<br />
Variable (SLRIha) (SLRIha) (%) (%)<br />
Ourput<br />
Current inputs<br />
Value added<br />
All labor<br />
Capital<br />
Famill. + exchange<br />
labor<br />
Hired labor<br />
Land<br />
Residual<br />
Farmers incomea<br />
"Farmers income = income of landowners and family exchange laborers' income<br />
and residual.
Table 12. Factor shares of second-season potato for adopters and nonadopters.<br />
Variable<br />
Output 152,724<br />
Current inputs 53,100<br />
Value added 99,624<br />
All labor 16.561<br />
Family + exchange<br />
labor 10,222<br />
Hired labor 6.339<br />
Capital 872<br />
Land 9,160<br />
Residual 73,03 1<br />
Farmers income 92,448<br />
Adopter Nonadopter Adopter Nonadopter<br />
(SLRIha) (SLRIha) (%l (%)
Table 13. Factor slirlres of third-season vegetables for adopters and nonadopters.<br />
Variable<br />
Output<br />
Current inputs<br />
Value added<br />
All labor<br />
Family + exchange<br />
labos<br />
Hired labor<br />
Capital<br />
Land<br />
Residual<br />
F~1-mers incoine<br />
Adopter Nonadopter Adopter Nonadopter<br />
(SLRIha) (SLRIha) (70) C%)
Table 14. Factor shares for <strong>the</strong> annual three-crop cycle of adopters and nonadopters.<br />
Adopter Nonadopter Adopter Nonadopter<br />
Variable (S LR/ ha) (S LRIha) (%) (%)<br />
Output 260,982<br />
Current inputs 72,591<br />
Value added 188,391<br />
All labor 48,707<br />
Family + exchange<br />
labor 3 1,064<br />
Hired labor 17,640<br />
Capital 3,715<br />
Land 27,480<br />
Residual 108,490<br />
Farmers income 167,100
Table 15. Endownlent of farm assets of adopters and nonadopters.<br />
T\i.o-wheel tractors<br />
Plows<br />
Sprayers<br />
Water pumps<br />
Rotary weeders<br />
Mammoties<br />
Milking cows<br />
Storage boxes<br />
Adopter<br />
(n = 19)<br />
Note: Fizures in paren<strong>the</strong>ses refer to number reporting<br />
Nonadopter<br />
(n = 18)<br />
Table 16. Household assets owned by adopters and nonadopters.<br />
Televisions<br />
Radios<br />
Sewing machines<br />
Bicycles<br />
Motorbikes<br />
Buses<br />
Adoptes Nonadopter<br />
(n = 19) (n = 18)<br />
Note: Figures in paren<strong>the</strong>ses refer to number reporting.
Table 17. Expenditures and savings of adopters and nonadopters<br />
Farm expenses (cash) 16,526<br />
Household expenses (17,984)<br />
Total expenses<br />
Savings<br />
Expenditures on food 11,782<br />
O<strong>the</strong>r expenses<br />
Adopter Nonadopter
IAIPACT ASSESShlEhT OF FARMING SYSTEMS RESEARCH AND<br />
DEVELOPMENT AT THE FARM LEVEL: THE RICE - WATERMELON<br />
CROPPING SYSTEhl IN PANGIL, LACUNA<br />
V. T. ~illanciol, C. H. hlanalol, Al. L. ~ebulanan'<br />
A. A. ~ris~ado', F. L. ~atienzo', and N. F. C. Ranaweera 2<br />
The <strong>research</strong> site, situated in <strong>the</strong> municipality of Pangil, province of Laguna, was<br />
part of a network of sites where technology verification trials and Barangay Pilot<br />
Production Programs (BPPP) were implemented. This cropping <strong>systems</strong> work was<br />
undertaken by <strong>the</strong> Regional Integrated Agricultural Research Systems (RIARS) of<br />
<strong>the</strong> Department of Agriculture (DA). The Pangil site represented rice-based<br />
cropping s!.stems under r:linfed conditions in Laguna.<br />
The RIARS activities in Pangil started in 1983 and addressed <strong>the</strong> rice-based<br />
and coconut-based farming <strong>systems</strong> in <strong>the</strong> area. Initial activities were conducted in<br />
barangays (villages) Isla and Balian where <strong>the</strong>re were three rice-based<br />
environments: rainfed, irrigated, and seasonally inundated areas. The rainfed areas<br />
are located in Isla and a part of Balian; whereas, <strong>the</strong> irrigated and seasonally<br />
inundated areas were confined to Balian.<br />
A rice - watermelon cropping system (RWCS) \\,as tested in <strong>the</strong> rainfed<br />
areas, a rice - rice cropping pattern in <strong>the</strong> irrigated areas, and a deepwater rice - rice<br />
cropping pattern in <strong>the</strong> seasonally inundated areas. Cropping <strong>systems</strong> trials on<br />
coconut + citrus followed in 1981.<br />
The RWCS was not very successful in Isla for 2 yr (1953-85) presumably<br />
because of heavy rainfall during <strong>the</strong> critical stages of \\.atermelon cultivation,<br />
especially during <strong>the</strong> fruiting stage. Six cooperators were selected for <strong>the</strong> initial<br />
cropping <strong>systems</strong> study. Results showed a favorable performance of <strong>the</strong> RCWS<br />
during <strong>the</strong> trial period (1986-85). In 1989, BPPP was launched to involve 28 farmers<br />
on an aggregate area of 29 ha.<br />
SITE DESCRIPTION<br />
Dambo is a village in <strong>the</strong> eastern region of Pangil, Laguna. The village is about 1.1<br />
km from <strong>the</strong> town and is accessible by a provincial road. Dambo consisted of 198<br />
households and had a total population of 1,015 in 1989. The total land area was<br />
armi in^ Systems and Soil Resources Institute, College of Agriculture, University of<br />
<strong>the</strong> Philippines Los Bafios, Laguna, Philippines.<br />
2~ocial Sciences Division, <strong>International</strong> Rice Research Institute, P.O. Box 933,<br />
Manila, Philippines.<br />
*
about 1,050 ha. Coconut occupied 46% of <strong>the</strong> area, and rice was planted on 38% of<br />
<strong>the</strong> land. The rest of <strong>the</strong> area was under pasture, forest, grassland, and farms<br />
planted with fruits (e.g., banana, lanzones, and citrus).<br />
The wet season (WS) is from June to November and <strong>the</strong> dry season (DS)<br />
from February to May. There are basically four land types that are characterized by<br />
differences in land form, soil type, water regime, and cropping <strong>systems</strong>. Along<br />
Laguna Lake is a seasonally inundated area called hot. This floodprone area has a<br />
clay soil with a slope of 0-2%. During <strong>the</strong> WS, grass (locally known as tikiw) and<br />
water lilies predominate. Rice is grown only in <strong>the</strong> DS.<br />
The up er area of <strong>the</strong> lake site (called tuhigul~) has a slope of about 0-2%<br />
and consists o p matala-tala clay soil. This is usually planted to rice - fallow, rice -<br />
rice, and RWCS.<br />
The upper region of <strong>the</strong> village has two distinct land types. The village is<br />
located in a narrow strip of relatively flat (about 5% slope) area planted with<br />
coconut and o<strong>the</strong>r fruit trees. The kaingin, which has a slope of about 25-65%, is<br />
slightly eroded. This area is planted with coconut and citrus and has patches of<br />
shrubs and grasslands.<br />
The total rainfed rice area is about 400 ha. During <strong>the</strong> impact assessment<br />
study, a large portion was planted with rice during <strong>the</strong> WS and left fallow during <strong>the</strong><br />
DS. In 1989,g<strong>the</strong> RWCS was practiced on about 27 ha. The Laot area was confined<br />
to a fallow - rice cropping system.<br />
Coconut cultivation occupies a large area but, compared with rice<br />
production, it is given little attention by farmers. Some farmers practice<br />
intercropping with banana and fruit trees (e.g., lanzones and citrus). Shifting<br />
cultivation is practiced in <strong>the</strong> kningin. Farmers plant root crops and vegetables in<br />
this area.<br />
Because <strong>the</strong> village is located along <strong>the</strong> lake, fishing is an iniportant activity.<br />
Sniall-scale swine and poultry raising are <strong>the</strong> most common animal enterprises.<br />
Handicrafts are <strong>the</strong> prime income-generating activity of women. O<strong>the</strong>r nonfarm<br />
income sources include jeepney driving, trading, logging, and carpentry.<br />
THE RICE - WATERMELON CROPPING SYSTEM<br />
The RWCS had been tested at Dambo since 1986. The favorable performance of<br />
<strong>the</strong> technology was reflected in increased yields and economic returns during <strong>the</strong><br />
2 yr of experimentation. The first rice crop of RWCS was grown during June-July.
Depending on <strong>the</strong> adequacy of rainfall, land preparation could be done as early as<br />
May. Seedlings were grown using <strong>the</strong> modified wet-bed method (dalakduk) as early<br />
as May to early June. Pregerminated seeds were sown on <strong>the</strong> seed bed at a thicker<br />
rate. Seedlings were not uprooted. Instead, <strong>the</strong> existing practice of Il~plup (cutting<br />
<strong>the</strong> seedlings in <strong>the</strong> form of turf) was used.<br />
The recommended seeding rate for high-yielding varieties (e.g., BPI-RI 10<br />
and IR62) was 75 kg/ha. Fertilization for rice was at <strong>the</strong> rate of 45-60-60 kg of<br />
NPK/ha. Insect control was recommended only when <strong>the</strong> economic threshold level<br />
was reached. The first rice crop was usually harvested in October-November.<br />
Dzcember is a rainy month; <strong>the</strong>refore, watermelon is not planted<br />
immediately after <strong>the</strong> rice crop is harvested. Farmers must wait until January to<br />
plant watermelon. Sugar Baby is <strong>the</strong> preferred variety. No elaborate lrmd<br />
preparation is done to cultivate waternielon. The recommended seeding rate is<br />
1 kg/ha spaced at 1.5 m along rows.<br />
A basal fertilizer (14-14-14) is applied at <strong>the</strong> rate of about 25 g/hill, and 1<br />
liter of poultry manure is used. A solution that contained 1 liter of urea for every 20<br />
liter of water was used for 12 to 16-d-old seedlings. The rate was gradually increased<br />
to 1 liter of urea, 2 liters of 14-14-14, and 0.5 liter of 0-0-60 mixed with 20 liter of<br />
water for 57 to 62-d-old plants. Watering was done daily. Insecticide was applied<br />
about 5 times during <strong>the</strong> growth period. Handweeding was done 1 nlo after planting.<br />
The first fruit was pruned and <strong>the</strong> next 2-3 fruit sets w.ere allowed to continue.<br />
hlelons \\.ere harvested in late March or April.<br />
METHODOLOGY<br />
Several data collection methods were used in this study: collection of secondary data<br />
from published and unpublished sources; hrm surveys; record keeping; case studies;<br />
and key-informant surveys. Data collected from secondary sources were those<br />
related to <strong>the</strong> characteristics of <strong>the</strong> site, <strong>the</strong> management recluirements, and <strong>the</strong><br />
performance of <strong>the</strong> R\VCS.<br />
Descriptive survey<br />
A survey of <strong>the</strong> rice-based cropping system was conducted in Dnrnbn, Pangil, in May<br />
1989. A list of farmers was provided by <strong>the</strong> agricult~iral technician and village<br />
officials. Fifty-one respondents were identified ancl intervie~ved. Farmers who<br />
adopted <strong>the</strong> RWCS were classified as adopters (26 farmers) and <strong>the</strong> rest (25<br />
farmers) were classified as nonadopters. Interviews were conducted using structured<br />
questionnaires designed to obtain information on existing farming <strong>systems</strong>,<br />
resources, problems, and constraints.
A comparative description of <strong>the</strong> two groups was made on <strong>the</strong>ir basic<br />
characteristics, resources, farming <strong>systems</strong>, perceptions, problems, and constraints.<br />
Direct observation in <strong>the</strong> field was employed on parameters that would describe <strong>the</strong><br />
standard of living (e.g., <strong>the</strong> quality of housing and <strong>the</strong> availability and accessibility of<br />
community facilities).<br />
Case study<br />
The sunrey results were used to select <strong>the</strong> 10 farmers who will participate in <strong>the</strong><br />
study. Five were adopters of <strong>the</strong> RWCS and <strong>the</strong> o<strong>the</strong>r five were nonadopters.<br />
Data collection was done by requesting <strong>the</strong> farmer-participants to record<br />
<strong>the</strong>ir weekly farm and household activities. Monitoring forms were provided. These<br />
were collected every Tuesday. During <strong>the</strong> collection of forms, interviews with<br />
housewives or o<strong>the</strong>r members of <strong>the</strong> family were conducted to ga<strong>the</strong>r and clarify<br />
information that were not properly recorded. The form was usually maintained by<br />
<strong>the</strong> housewife.<br />
The minimum data collected for <strong>the</strong> case study were biophysical and<br />
socioecono~nic characteristics, cash and noncash transactions, household and<br />
nonfarm related activities, a diary of events that included reports on <strong>the</strong> state of<br />
farm enterprises and problems with farm management, and o<strong>the</strong>r information<br />
derived from direct field observation.<br />
The weekly monitoring forms were edited and coded. At <strong>the</strong> end of every<br />
cropping seasbn, <strong>the</strong> farmers' crop production was summarized to assess <strong>the</strong><br />
performance of <strong>the</strong> crop components and <strong>the</strong> cro ping <strong>systems</strong>. A resourceassimilation<br />
matrix was prepared to indicate <strong>the</strong> 1 ow of resources from one activity<br />
to ano<strong>the</strong>r. For exa~nple, income derived from <strong>the</strong> cultivation of watermelon was<br />
spent on watermelon cultivation as well as on rice and livestock production. The<br />
information in <strong>the</strong> resource-assimilation matrix was plotted in a resource-flow<br />
diagram. This diagram reflected <strong>the</strong> extent of financial integration that took place<br />
between activities.<br />
Cash-flow analysis was aggregated on a monthly basis. Cash inflow and cash<br />
outflow were categorized according to farm and nonfarm activities to examine <strong>the</strong><br />
role of RWCS. Household expenditure was considered as a nonfarm expense.<br />
Descriptive survey<br />
RESULTS AND DISCUSSION<br />
The major objective of <strong>the</strong> descriptive survey was to determine <strong>the</strong> degree of<br />
adoption of <strong>the</strong> RWCS introduced by <strong>the</strong> DA-RIARS at Dambo. The RWCS was an<br />
improvement on <strong>the</strong> existing rice - fallow cropping system.
Demograpllic cllaractehtics. The comparative study of <strong>the</strong> adopters and <strong>the</strong><br />
nonadopters showed <strong>the</strong>re were no significant differences in <strong>the</strong>ir general<br />
demographic characteristics. The average age of farmers was 46 yr, and <strong>the</strong>y had 6<br />
yr of formal schooling. Farm households had an average of 6 members. The average<br />
size of rice farms in <strong>the</strong> area was 1.4 ha. The farms of adopters were 0.6 ha larger<br />
than <strong>the</strong> farms of nonadopters.<br />
Fanning <strong>systems</strong>. Aside from <strong>the</strong> existing rice-based farming <strong>systems</strong>, some<br />
farmers practiced hilly land and coconut-based farming <strong>systems</strong>. Hilly land<br />
cultivation included perennial crops (e.g., coffee, citrus, banana, and o<strong>the</strong>r fruit).<br />
O<strong>the</strong>r important cropping <strong>systems</strong> practiced by <strong>the</strong> farmers were coconut<br />
intercropped with citrus, coffee, and vegetables. Of <strong>the</strong> 26 adopters, 61% practiced<br />
pure rice-based farming <strong>systems</strong>, 31% had a combination of rice-based and hilly<br />
land <strong>systems</strong>, and 8% had rice-based and coconut-based farnis. Adopters had more<br />
diversified farnis than nonadopters.<br />
Source of lahor aild po~~r. Labor for crop production was provided by <strong>the</strong><br />
farmer, family members, and hired and exchange laborers. Most of <strong>the</strong> farm<br />
operations (e.g., fertilizer and insecticide application) were carried out by <strong>the</strong><br />
farmer but activities such as planting and harvesting were mostly performed by<br />
family members. Lnbor for land preparation was usually hired.<br />
Hired labor and machinery were important resources in <strong>the</strong> community.<br />
Generally, farmers used small power tillers for land preparation. Only 37% of <strong>the</strong><br />
respondents owned draft animals. For watermelon production, hired or contract<br />
Iabor was <strong>the</strong> most common source of labor.<br />
The wage rate for hired labor varied according to <strong>the</strong> type of farm operation.<br />
The wage rate for land preparation using draft animals was P50/d. Hand tractors<br />
were rented at P700/ha.<br />
The inadequate number of tractors was identified as one of <strong>the</strong> farmers'<br />
constraints to rice farming. Only three tractors were available for rent. These were<br />
rented only when <strong>the</strong> tractor owners had completed <strong>the</strong>ir own land preparation.<br />
This affected <strong>the</strong> timely establisliment of crops.<br />
Source of capital. Local credit facilities were <strong>the</strong> most important sources of<br />
capital in <strong>the</strong> area. About 72% of <strong>the</strong> farmers were dependent on informal sources<br />
of credit; whereas, 18% derived capital from forms1 sources. Adopters used <strong>the</strong><br />
formal sources of credit because large amounts of capital were required for <strong>the</strong><br />
watermelon enterprise. The rural bank was <strong>the</strong> only source of formal credit of<br />
adopters, but it was <strong>the</strong> least important source for nonadopters. Fur<strong>the</strong>rmore, <strong>the</strong><br />
adopters had a viable farm enterprise, which enabled <strong>the</strong>m to pay for <strong>the</strong>ir loans.<br />
On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> nonadopters often obtained zero-interest loans from <strong>the</strong>ir<br />
relatives. O<strong>the</strong>r credit sources were friends, o<strong>the</strong>r farmers, and private money<br />
lenders. Some farmers did not obtain 1o:lns for fear of not being able to repay. Only
25% of <strong>the</strong> farmers generated savings from crop production. Earnings from<br />
livestock production were a common source of income among adopters.<br />
Production costs and incorne fro171 rice-based cropping glstem. The average<br />
cash expenditure in rice production during <strong>the</strong> first crop was P4,048/ha (Table 1).<br />
The highest capital outlay was spent on hired labor for both adopters (57%) and<br />
nonadopters (62%). Expenditures on fertilizer and seeds were relatively high. O<strong>the</strong>r<br />
expenses included hauling fees, insecticides, and herbicides.<br />
The average cost of watermelon production was P5,480/ha (Table 2).<br />
Fertilizer cost accounted for 43% of <strong>the</strong> total production cost, hired labor for 28%,<br />
and insecticides for 23%. Contract buyers bore <strong>the</strong> cost of harvesting, hauling, and<br />
transportation. Net returns were shared equally between <strong>the</strong> owner and <strong>the</strong> labor-<br />
contractor. The labor-contractor was given a cash advance that was deductible from<br />
<strong>the</strong> sale of <strong>the</strong> produce.<br />
The net income of <strong>the</strong> RWCS adopters was P33,036 while nonadopters<br />
gained P17,113 only (Table 3). Not all farmers could adopt <strong>the</strong> transplanted rice -<br />
watermelon cropping pattern because of its high capital requirement. Therefore,<br />
credit facilities and financial support programs are important components of <strong>the</strong><br />
project.<br />
Housetrold inconze and expenses. Income from on-farm, off-farm, and<br />
nonfarm activities were <strong>the</strong> major sources of capital for crop production. However,<br />
in most cases, <strong>the</strong> farmers could not meet <strong>the</strong> financial requirement of <strong>the</strong><br />
technology. Rice and watermelon were <strong>the</strong> two major crops that provided income<br />
and capital for household and farming activities. Income from coconut and hilly land<br />
cultivation also contributed to overall income.<br />
Fishing was a major source of income for 46% of <strong>the</strong> adopters and 52% of<br />
<strong>the</strong> nonadopters. O<strong>the</strong>r nonfarm activities included hog raising, tricycle driving,<br />
"sari-sari" store operation, coconut selling, rattan furniture manufacturing,<br />
carpentry, hiring out of labor, renting out hand tractors, and cottage industries.<br />
In terms of average monthly household expenditures, <strong>the</strong> adopters spent 16%<br />
more than <strong>the</strong> nonadopters. The estimated household expenses of adopters was<br />
P4,305; whereas, nonadopters S ent P3,620. The highest expenditure for both types<br />
of farmers was on food (40%), P ollowed by education of children (20%).<br />
Fann and housetiold assets.The adopters had more assets than nonadopters.<br />
About 35% of <strong>the</strong> adopters owned residential lands compared with only 16% of <strong>the</strong><br />
nonadopters. Regardless of <strong>the</strong>ir categories, all farmers had appliances (e.g.,<br />
refrigerators, radio cassettes, television sets, video recorders, stereo, sewing<br />
machines, and electric fans). One adopter owned a tricycle.
The major farm implements owned by <strong>the</strong> farmers were plows and harrows.<br />
More adopters owned farm assets and draft animals. However, two of <strong>the</strong> three<br />
hand tractors available in <strong>the</strong> area belonged to nonadopters. In general, adopters<br />
were financially more stable than nonadopters.<br />
Case study of farm households<br />
A case study of 10 farms was conducted to determine <strong>the</strong> impact of <strong>the</strong> rice -<br />
watermelon technology. The study focused on crop production and patterns of<br />
household income and expenditure. Selected demographic characteristics of <strong>the</strong><br />
farmers are. presented in Table 4.<br />
Fanuing.~~.~tcnzs. Among tlie farmers involved in <strong>the</strong> case study, adopters<br />
appeared to have cultivated more plots than nonadopters. The average farm size of<br />
<strong>the</strong> adopters (6.9 ha) was larger than <strong>the</strong> farms of nonadopters (1.7 ha). The farming<br />
<strong>systems</strong> practiced by <strong>the</strong> adopters were more diversified than those practiced by<br />
nonadopters. The adopters used both rice and coconut-based farming <strong>systems</strong><br />
(Table 5). The nonadopters primarily used rice-based farming <strong>systems</strong>.<br />
In 1989-90, adopters of rice - \ipatermelon used 8-7894 of <strong>the</strong>ir land for this<br />
system and <strong>the</strong> rice - rice cropping pattern on 20-50% of <strong>the</strong>ir land (Table 6). Most<br />
of <strong>the</strong> area devoted to rice-based farming <strong>systems</strong> by nonadopters was planted with<br />
<strong>the</strong> rice - rice cropping pattern (54-100%). Six farniers planted fallow - rice in hot.<br />
In 1990-91, almost all of <strong>the</strong> rice-based farming area was planted using <strong>the</strong> rice - rice<br />
cropping pattern. The only exception was a 3.0-ha plot planted with fallow - rice by<br />
one farmer in Laot. After <strong>the</strong> irrigation system becr~rne operr~tional in <strong>the</strong> early part<br />
of 1991, farmers planted rice instead of watermelon as <strong>the</strong> second crop.<br />
In 1989-90, <strong>the</strong> average area planted to R\VCS among <strong>the</strong> ~~dopters was<br />
0.65 ha; whereas 0.75 ha was devoted to <strong>the</strong> rice - rice and 1.20 ha was planted to<br />
fallow - rice. Nonadopters had an average area of 0.79 ha under <strong>the</strong> rice - rice<br />
cropping system. Three of <strong>the</strong> nonadopters implemented <strong>the</strong> fallow - rice pattern on<br />
an average of<br />
0.3 ha.<br />
The period of adoption of <strong>the</strong> technology varied among RWCS adopters.<br />
One farmer had been using this cropping pattern since <strong>the</strong> first experiment in 1987.<br />
Two o<strong>the</strong>rs had been planting RWCS since 1988 while ano<strong>the</strong>r started in 1989. One<br />
nonadopter implemented <strong>the</strong> rice - waternielon cropping pattern during <strong>the</strong> study.
Performance of RFYCS compared with rice-based cropping <strong>systems</strong><br />
In 1989-90, <strong>the</strong> study farmers planted 4 rice-based cropping patterns (i.e., RWCS,<br />
rice - rice, rice - fallow, and fallow - rice). However, in 1990-91, <strong>the</strong> only cropping<br />
patterns used were rice - rice and fallow - rice. The change in cropping pattern was a<br />
result of <strong>the</strong> restoration of <strong>the</strong> irrigation system during <strong>the</strong> later part of <strong>the</strong> second<br />
crop.<br />
The adopters planted a larger area to rice-based cropping <strong>systems</strong> than <strong>the</strong><br />
nonadopters. In 1990-91, adopters planted <strong>the</strong> rice - rice cropping pattern on an<br />
average of 2 ha; <strong>the</strong> nonadopters allocated 1 ha. However, in 1989-90, nonadopters<br />
planted a larger area to <strong>the</strong> rice - rice cropping pattern than <strong>the</strong> adopters (Table 7).<br />
On average, adopters obtained higher yields of rice and higher net returns than<br />
nonadopters. The nonadopters had higher input costs primarily because <strong>the</strong>y had to<br />
pay rent for <strong>the</strong> land.<br />
The costs (up to P3 1,353/ha) associated with waternlelon were purchased<br />
labor and material inputs. Contract labor wages varied depending on <strong>the</strong> net returns<br />
from <strong>the</strong> production of watermelon. The labor-contractor was paid 50% of net<br />
returns.<br />
In 1990-91, only two farmers practiced <strong>the</strong> fallow - rice cropping system in<br />
Laot. They gained a return above variable cost (RAVC) of P22,333.<br />
Contribution of R\VCS to household income and expenditures<br />
The adopters appeared to have more diverse sources of income. They posted a<br />
higher income from both farm and nonfarm sources. Farm income was derived<br />
mainly from <strong>the</strong> sales of rice, watermelon, coconut, and livestock. It contributed<br />
about 37-88% to <strong>the</strong> household income of adopters and 3549% for nonadopters in<br />
1990. The cultivation of watermelon contributed at least 21% to <strong>the</strong> household<br />
income of adopters and rice sales contributed a maximum of 37% (Table 8).<br />
Nonadopters apparently depended on <strong>the</strong> sale of rice, coconut, livestocl
Effect of RWCS on household cash flow<br />
Monthly cash-flow analysis included <strong>the</strong> farm and nonfarm economic activities of<br />
<strong>the</strong> household. Positive net cash flows were observed for all adopters except one,<br />
whereas, nonadopters had negative net cash flows.<br />
The favorable net cash flows of <strong>the</strong> adopters was attributed to <strong>the</strong> income<br />
gained from <strong>the</strong> production of watermelon. An improvement in <strong>the</strong> cash inflow was<br />
observed during April 1990 from <strong>the</strong> sale of watermelon and in November 1990<br />
because of <strong>the</strong> high yield of rice.<br />
The case of Ka Arturo<br />
To illustrate <strong>the</strong> adoption of <strong>the</strong> RWCS and its impact on <strong>the</strong> pattern of resource<br />
transfer among farm enterprises, <strong>the</strong> case of Ka Arturo was selected. He planted<br />
0.60 ha using <strong>the</strong> RWCS in 1959-90. From this cropping pattern, he harvested 1.6 t<br />
of rice (value P7,200) and 5.8 t of watermelon (P32,000). The value of production<br />
from <strong>the</strong> RWCS represented about 40% of <strong>the</strong> total value of his farm produce.<br />
In 1990, he earned approximately P20,000 from rice sales. About 44% of <strong>the</strong><br />
gross receipt was spent on household requirements, 20% on <strong>the</strong> production of rice,<br />
and 20% on livestock production. About P400 were spent on watermelon<br />
production. The expenditure on <strong>the</strong> production of watermelon included <strong>the</strong> share<br />
paid to <strong>the</strong> labor-contractor. Income from sales of watermelon was distributed as<br />
27% for household expenditure, 71% for watern~elon production, and <strong>the</strong> rest for<br />
livestock production.<br />
At <strong>the</strong> whole-farm level, Ka Arturo had diverse sources of income. Sale of<br />
crops and livestock appeared to be <strong>the</strong> dominant source. Children contributed about<br />
7% to <strong>the</strong> total household income. Proceeds from his informal credit financed about<br />
20% of his household expenditure.<br />
This case suggests that <strong>the</strong> integration of watermelon cultivrltion in <strong>the</strong><br />
farming system contributed enormously to overall resource use, income, and<br />
expenditure of <strong>the</strong> household. However, it appears that watermelon can only be a<br />
second alternative crop to rice. When conditions became favorable for rice, priority<br />
was given to <strong>the</strong> cultivation of a second crop of rice.
CONCLUSION<br />
The farmers in Pangil did not adopt <strong>the</strong> RWCS technology. They adopted<br />
watermelon as an alternative to rice during <strong>the</strong> DS. When <strong>the</strong>re was adequate<br />
rainfall, <strong>the</strong>y substituted rice for watermelon. Although <strong>the</strong> adopters practiced <strong>the</strong><br />
RWCS technology, almost all cultural practices and <strong>the</strong> management involved were<br />
carried out by a labor-contractor who had experience in watermelon production.<br />
The results suggest that <strong>the</strong> RWCS improved farm income. When <strong>the</strong><br />
adopters phased out <strong>the</strong> production of watermelon in 1991, <strong>the</strong>re was an apparent<br />
reduction in income. Farmers, however, were able to compensate for <strong>the</strong> losses<br />
through o<strong>the</strong>r sources. One of <strong>the</strong> farmers focused his effort on rice production and<br />
cultivated a larger area to make up for <strong>the</strong> loss of income from watermelon. This is<br />
most likely to happen if farmers have large farms. However, if farm size is a limiting<br />
factor, farmers will be inclined to plant watermelon after rice.<br />
The impact of watermelon production on income and expenditure patterns<br />
cannot be generalized. The case study revealed that income from <strong>the</strong> sale of<br />
watermelon was not necessarily reinvested in farm enterprises, but was spent on<br />
unproductive household items. The case study also revealed that farmers had o<strong>the</strong>r<br />
sources that could be used to augment <strong>the</strong>ir income if watermelon was not included<br />
in <strong>the</strong> farming system.<br />
Rice remained a stable component of <strong>the</strong> farming system with or without <strong>the</strong><br />
irrigation system. Citrus, however, has <strong>the</strong> potential to increase income. The RWCS<br />
is a very promising alternative only while <strong>the</strong> irrigation system is not operating.<br />
The experience in Dambo has implications for future development.<br />
Agricultural <strong>research</strong> should not merely focus on cropping <strong>systems</strong> work or deal only<br />
with rainfed agriculture. Research must focus on finding ways to integrate water<br />
management into <strong>the</strong> system. In Dambo, for example, years of cropping <strong>systems</strong><br />
<strong>research</strong> could be wasted because of <strong>the</strong> presence of an irrigation system.<br />
Consequently, farmers in <strong>the</strong> rainfed areas will pay more attention to <strong>the</strong> provision<br />
of <strong>the</strong> required water and will spend excessively on water-resource facilities. This<br />
situation has been experienced by farmers in <strong>the</strong> KABSAKA project where<br />
investments in costly water pumps have been made to guarantee adequate water<br />
supply. The money spent on <strong>the</strong>se facilities could have been invested in a much<br />
more profitable venture. Therefore, it is vital to establish cropping <strong>systems</strong> that will<br />
use limited water resources in <strong>the</strong> rainfed areas while providing reasonable income.
Table 1. Average cash expenses per hectare for rice production by type of farmer, first<br />
crop, 5 1 farmers, Dambo, Pangil, Laguna, Philippines (1989).<br />
Type of farm<br />
Adopters Nonadopters All farmers<br />
Value % Value % Value %<br />
Hired labor 2,281 57 2,544 6 2 2,413 60<br />
Fertilizer 5 84 15 469 11 5 27 13<br />
Insecticide 23 3 6 303 7 268 7<br />
Herbicide 132 3 192 5 162 4<br />
Seeds 525 13 46 1 11 493 12<br />
Hauling fee 22 1 6 150 4 186 4<br />
Total 3,976 100 4,119 100 4,048 100<br />
Table 2. Average cash expenses of 25 adopters per hectare<br />
for watermelon production. Dambo, Pangil, Philippines (1989).<br />
Hired labora 1,556<br />
Fertilizer 2,340<br />
Insecticide 1,273<br />
Planting material 311<br />
Total 5,480<br />
Value (P) Percentage<br />
a~ired<br />
laborers were paid on a monthly basis. Activities<br />
included crop establishment, fertilizer and insecticide<br />
application, cultivation, and watering of plants. Hauling<br />
and harvesting costs were shouldered by <strong>the</strong> contract buyers.
Table 3. Average net income per hectare by type of farmers, 5 1 farmers, Dambo,<br />
Pangil, Laguna, Philippines (1989).<br />
Adopters Nonadopters<br />
1st 2nd Total 1st Total<br />
crop crop crop crop<br />
rice watermelon rice<br />
Total yield (t) 4.8 3.94 4.3 4.3<br />
Average price<br />
4,250 5,596 4,940 4,940<br />
Total income 20,43 1 22,050 42,48 1 21,232 2 1,232<br />
Total costs 3,976 5,469 9,445 4,113 4,119<br />
Net income 16,455 16,581 33,036 17,113 17,113<br />
Table 4. Selected demographic characteristics of 12 farmers used in <strong>the</strong> case study of<br />
farm household, Dambo, Pangil, Laguna, Philippines (1991).<br />
Farmer Age Residence Farming Educational Household<br />
number (Y r) (Y r) (Y r) background size<br />
Grade V1<br />
High School<br />
3rd year HS<br />
Grade V1<br />
2nd year HS<br />
High School<br />
High School<br />
Grade I11<br />
Grade V1<br />
Grade IV<br />
High School<br />
Elementary<br />
'Farmers 10, 11 and 12 took turns in farming <strong>the</strong> 10th case study farm during <strong>the</strong><br />
period of <strong>the</strong> study.
Table 5. Selected clia~~acteristics of 10 case-study farms. Dambo, Pangil. Laguna.<br />
Pliilippines.<br />
Farmer No. or No. of Total fal-m Percentage of area planted<br />
nuii~ber pa see l plots area<br />
(113) RBFs" CBFS~ O<strong>the</strong>rs<br />
ice-bad Pal-min, tr . y. stems.<br />
b~ocnliu~-hased f.lstning s!xstems.<br />
'~a~itan .4co also se~\~ed as caretakes for a 35-ha coconut plantation.
Table 6. Percentage of area under various rice-based cropping <strong>systems</strong> planted by 10<br />
case-study farmers, Dambo, Pangil, Laguna, Philippines (1989-90).<br />
Farmer Total area Percentage of area planted<br />
number in RBFS RWCS Rice - rice Fallow - rice rice - fallow<br />
(ha)
Table 7. Comparative pcrforinance of various rice-bascd cropping sysleins by calegory of 10 case-study farmers, Dambo, Pangil, Laguna,<br />
Philippines (1989-91).<br />
Croplcropping Avei-agc Value of Total lictur~l above Average Value of Total Return above<br />
pattern area production variable variable area production variable variable<br />
planted cost cost planted cost cost<br />
Rice - fallow<br />
Fallow - rice<br />
Rice - rice<br />
l st crop<br />
2nd crop<br />
Rice - wateilnelon<br />
l st crop<br />
N<br />
P 2nd crop<br />
0<br />
1990-1991<br />
Fallow - rice<br />
Rice - rice<br />
1st crop<br />
2nd crop<br />
a~ncrease In area due to a parcel acquired by one fanner during <strong>the</strong> second crop.
Table 8. Percentage contribution of rice and watermelon to household income of nine<br />
case-study farmers, Dambo, Pangil, Laguna (1990).<br />
Farmer Total household<br />
~iumbee income Farm income Rice Watermelon<br />
reported (70) (%l<br />
aFal-mer 10, l1 and 12 were not included in this table.
hiem bers<br />
Discussion and Recommendations<br />
Group 1: Methodology<br />
R. Shand - Sri Lanka (convenor); R. Islam - Bangladesh; C. Barba - Philippines;<br />
V. Villancio - Philippines; M. Ahmed - ICLARM; J. Graham - <strong>IDRC</strong>;<br />
D. M. Ramiaramanana - Madagascar; R. Gonzaga - Philippines; P. A. Samaratunga<br />
- Sri Lanka; and G. K. Renuka - Sri Lanka.<br />
This group reviewed <strong>the</strong> lessons learned about methodology to suggest guidelines<br />
for future impact studies. The group agreed that <strong>the</strong> formulation and application<br />
of FSR impact methodology was readily distiriguishable from its predecessor, i.e.,<br />
comporient <strong>research</strong> with a <strong>systems</strong> perspective. The former included a range of<br />
impact variables. The challenge of developing a suitable methodology was raised<br />
frequently during <strong>the</strong> discussioris. The attempt at a comprehensive measurement<br />
of all impact variables was made in only one of <strong>the</strong> studies (Sri Lanka).<br />
Setting objectives<br />
In this project, seven objectives were agreed on at <strong>the</strong> initial workshop. Teams<br />
represented in <strong>the</strong> discussion group were unanimous in <strong>the</strong> view that attaining <strong>the</strong>ir<br />
targets, although a laudable objective, was far too ambitious, at least within <strong>the</strong><br />
structure of <strong>the</strong> project.<br />
A major reason was that, although <strong>the</strong> measurernent of impact was to focus<br />
on income levels and variables influenced by changes in income levels ra<strong>the</strong>r than<br />
on adoption, accurate measurement of adoption levels of <strong>the</strong> new technologies was<br />
a necessary precursor to fur<strong>the</strong>r analysis. Adoption measurement not only absorbed<br />
time and resources but, in many cases, produced problems because <strong>the</strong>re was no<br />
single or even several methodologies for this measurement. This had an impact on<br />
three studies in particular, but was a concern for all.<br />
Guideline 1: Do not set objectives for an FSR impact study on <strong>the</strong> assumptiori<br />
that a satisfactory location-specific methodolojg exists for mensu ring technology<br />
adoption.<br />
This particular issue was instrumental in reducing <strong>the</strong> number of objectives that <strong>the</strong><br />
teams attempted to achieve. There was full agreement on <strong>the</strong> basic need to<br />
measure <strong>the</strong> impact of technological change on productivity and income, but <strong>the</strong>re
was considerable doubt cast, in <strong>the</strong> absence of any evidence, that resultant income<br />
changes would have a significant impact on <strong>the</strong> income-dependent variables<br />
originally listed. This was argued on <strong>the</strong> basis that <strong>the</strong>re was a wide range of o<strong>the</strong>r<br />
variables, both short- and long-term, that might frequently relegate <strong>the</strong> indirect<br />
influence of technological change to an insignificant role. This was not suggested as<br />
an excuse for not attempting <strong>the</strong>se tests. Ra<strong>the</strong>r, it was felt that <strong>the</strong> study of <strong>the</strong><br />
interrelationships between income changes with technology and <strong>the</strong>se o<strong>the</strong>r<br />
variables was complex and would be a study in itself. It was generally agreed that a<br />
study such as <strong>the</strong> present one might best be restricted to impact on income levels<br />
and distribution. This should not dampen enthusiasm toward broader aims.<br />
Guideline 2: Objectives should be achievable within <strong>the</strong> normal limits of time and<br />
project resources.<br />
To achieve all of <strong>the</strong> original objectives, <strong>the</strong> projects would have to be expanded in<br />
time, finances, and team size. It might be preferable to confine this type of study to<br />
a single site on a pilot basis to enable methodological alternatives to be tested.<br />
Formulating hypo<strong>the</strong>ses<br />
The discussion on <strong>the</strong> need to formulate explicit hypo<strong>the</strong>ses for FSR impact studies<br />
was lively. Some argued that setting objectives was sufficient; o<strong>the</strong>rs argued that<br />
<strong>the</strong>y were implicit in <strong>the</strong>ir studies; o<strong>the</strong>rs said that <strong>the</strong> objectives were hypo<strong>the</strong>ses.<br />
There was also a view that if hypo<strong>the</strong>ses were established and subsequently rejected<br />
by <strong>the</strong> analysis, <strong>the</strong>n <strong>the</strong> studies would lose value. Contrary arguments were put<br />
forward that objectives and hypo<strong>the</strong>ses were not synonymous. Hypo<strong>the</strong>ses, although<br />
difficult to formulate, added great potential value to <strong>the</strong> studies because <strong>the</strong>y<br />
epitomized mainstream questions. Finally, it was argued that rejection of evide~ce<br />
could often be as valuable as acceptance.<br />
Guideline 3: Future FSR impact studies should formulate explicit important<br />
hypo<strong>the</strong>ses for testing.<br />
Identification of new technologies<br />
The studies showed <strong>the</strong> difficulties in identifying <strong>the</strong> newness of <strong>the</strong> technologies<br />
under study. Some were new in <strong>the</strong> study locatlon but were not necessarily new<br />
elsewhere. One was not new in time, but it had been recently adapted in various<br />
ways and needed definition. Ano<strong>the</strong>r found that <strong>the</strong> composition of <strong>the</strong> new<br />
technology had evolved under <strong>the</strong> influence of farmer adaptation in <strong>the</strong> course of<br />
<strong>the</strong> 3-year study. Ano<strong>the</strong>r study involved elements of social differentiation and<br />
evaluation.
Guideline 4: FSR impact projects should define a new technology selected for<br />
study in spatial and historical terms, and in its technical composition, social<br />
evolution, and adaptation.<br />
Selection of analytical techniques<br />
Selection of analytical techniques, as in any project, will be guided by <strong>the</strong> number<br />
and nature of hypo<strong>the</strong>ses and associated objectives. The issues of adopter and<br />
nonadopter arose several times. This required ingenuity by <strong>the</strong> individual <strong>research</strong><br />
teams to resolve <strong>the</strong> issue in <strong>the</strong> best way possible. Although open to some<br />
challenge, each technique proved useful under particular circumstances. Techniques<br />
for relating adoption, productivity, income, and household expenditures were more<br />
conventional and posed no great problems.<br />
Cash-flow analysis was used, and simple comparisons of a range of<br />
characteristics of adopter and nonadopter groups provided useful information. In<br />
several instances, choice of techniques was limited by sample size, which varied<br />
considerably between studies.<br />
Given <strong>the</strong> exploratory and pioneering nature of <strong>the</strong>se studies and <strong>the</strong> lack of<br />
an accepted FSR impact study methodology, no general guidelines have emerged<br />
for analytical techniques. The diversity of techniques actually applied in different<br />
circumstances shows <strong>the</strong> flexibility needed at this stage. The greatest versatility and<br />
sophistication was shown in <strong>the</strong> Sri Lankan study, which attempted <strong>the</strong> most<br />
comprehensive analysis. However, its results were restricted by small sample size<br />
and by <strong>the</strong> civil unrest in <strong>the</strong> country, which limited data collection to only l yr. No<br />
results, for example, were obtained on <strong>the</strong> relations between technology and welfare<br />
attributes.<br />
Key constmints. In this and any multilocation <strong>research</strong> project, <strong>the</strong>re are<br />
unanticipated random constraints that lower performance limits. The constraints are<br />
diverse and numerous and defy classification. They include (not exhaustively):<br />
o lack of baseline data on which to design surveys and comprehend <strong>the</strong><br />
socioeconomic and institutional setting of technological introduction<br />
and adoption;<br />
o unsatisfactory primary and secondary data, typically collected by<br />
agencies unrelated to <strong>the</strong> project unit (i.e., exteiision officers who<br />
aggregate data from <strong>the</strong> survey location);<br />
o <strong>the</strong> usual problems of household income management;
o restricted sample size, related to high number of drop-outs, or to<br />
unexpected statistics on adopters and nonadopters;<br />
o civil unrest that limit field surveys;<br />
o natural calamities such as drought, floods, and earthquakes that<br />
hamper field work and distort data (e.g., price);<br />
o complexities of allocation of land to crops (e.g.? <strong>the</strong> third-season crop<br />
in Bangladesh is divided into several plots of different crops, has<br />
mixed crops per plot, and has different crop mixtures per plot); and<br />
o changes in cropping <strong>systems</strong> during <strong>the</strong> course of <strong>the</strong> study, which<br />
amounted to an unanticipated induced technological change (private<br />
sector).<br />
Scope of methodolog for future impact studies on farming <strong>systems</strong> <strong>research</strong><br />
The issue of matching <strong>the</strong> scope of future impact study objectives to time and<br />
resources was examined. Agreement was reached on <strong>the</strong> following few additional<br />
points:<br />
o The need for a restatement of <strong>the</strong> value of forms of ex-ante analysis.<br />
These might include partial budgeting with explicit assumptions with<br />
regard to <strong>the</strong> impact of new technology on adoption, yields, projected<br />
costs, and prices. These assumptions can be made with transferable<br />
baseline data or, if necessary, with educated guesses. Sensitivity analysis<br />
may be added. O<strong>the</strong>r sources are rapid rural appraisal and <strong>the</strong> use of<br />
secondary sources of data.<br />
o The need for concurrent development of methodology to fulfil1 <strong>the</strong> needs<br />
created by designation of hypo<strong>the</strong>ses and objectives. This process was<br />
exemplified by <strong>the</strong> Sri Lanka study, hamstrung though it was in<br />
application.<br />
o The need to give serious consideration to <strong>the</strong> incorporation of additional<br />
assessments. These include equity and gender consequences at <strong>the</strong><br />
household level, <strong>the</strong> sustainability of benefits of technological adoption,<br />
and macro impacts (e.g., net capital flows and changes in input flows to<br />
<strong>the</strong> industry concerned).
hlem bers<br />
o The need to improve docunlentation to fur<strong>the</strong>r understand <strong>the</strong> processes<br />
and attendant costs of FSR impact studies.<br />
o The need to give careful consideration to <strong>the</strong> con~position of <strong>the</strong> FSR<br />
impact team, by bearing in mind <strong>the</strong> need for farmer participation<br />
throughout <strong>the</strong>se studies (i.e., <strong>the</strong>re must be an appropriate mix of<br />
biological and social scientists).<br />
o The need to emphasize <strong>the</strong> partnership that is required between farming<br />
<strong>systems</strong> <strong>research</strong>ers and extension officers to ensure that <strong>the</strong> extension<br />
staff are available and ready to cooperate in promoting <strong>the</strong> adoption of<br />
new technologies. The studies showed too often that <strong>the</strong>se links did not<br />
exist, especially when <strong>the</strong> two groups were in separate departments. In<br />
Thailand, <strong>the</strong> required links arose from personal contacts. In Isabela,<br />
Philippines, extension workers were diverted from FS <strong>research</strong>ers by<br />
technological change promoted by <strong>the</strong> private sector.<br />
Group 2: Policy<br />
V. T. Xuan - Vietnam (convenor); F. A. Bernardo - Philippines; S. A. Miah -<br />
Bangladesh; S. B. Ma<strong>the</strong>ma - Nepal; M. 0. Adnyana - Indonesia; J. Hardie - <strong>IDRC</strong>;<br />
R. Kirithaveep - Thailand; B. Shinawatra - Thailand; M. Agalawatte - Sri Lanka;<br />
and W. D. Dar - Philippines.<br />
Issues related to policy implications of <strong>the</strong> FSR approach were discussed at length.<br />
Based on <strong>the</strong>se discussions and <strong>the</strong> FSR lessons learned from <strong>the</strong> participating<br />
countries, several issues were raised and corresponding recommendations were<br />
made.<br />
Institutionalizing <strong>the</strong> FSR approach<br />
o To institutionalize <strong>the</strong> approach at both <strong>the</strong> national and grassroots<br />
levels, <strong>the</strong>re is an urgent need to sell and promote FSR-based ideas to<br />
policymakers. This can be achieved by acquainting <strong>the</strong> policymakers with<br />
<strong>the</strong> results of <strong>the</strong> FSR programs (i.e., impact studies and information and<br />
data on returns to investments). Various o<strong>the</strong>r advocacy efforts must also<br />
be pursued. A strong political will is a must.<br />
o The FSR approach must be adopted on a broader perspective. It is not<br />
sufficient to concentrate on <strong>the</strong> horizontal dimension; <strong>the</strong>re must be a<br />
focus on <strong>the</strong> vertical dimension as well. The vertical dimension would<br />
allow promotion of rural agri-based industries and provide opportunities
to accelerate <strong>the</strong> rural economy. Ano<strong>the</strong>r dimension that must be<br />
considered is sustainability.<br />
o FSR-based master plans are lacking in <strong>the</strong> participating countries.<br />
Therefore, master plans are needed to extrapolate FSR-based<br />
technologies into various country-specific policy recommendations.<br />
o The FSR approach is not a cure-all approach. Therefore, <strong>the</strong> commodity<br />
approach must be integrated into FSR.<br />
o Institutionalization of <strong>the</strong> FSR approach should take cost effectiveness<br />
into consideration. Countries have done this by following ei<strong>the</strong>r a soft or<br />
hard approach or a combination of <strong>the</strong>se two approaches. Fur<strong>the</strong>rmore, it<br />
is necessary to analyze <strong>the</strong> FSR approach in relation to social costs and<br />
profitability.<br />
o Extrapolation of FSR technologies should consider regional comparative<br />
advantages. It should also consider existing biophysical and<br />
socioeconomic environments across <strong>the</strong> regions to extrapolate FSR<br />
technologies.<br />
o The FSR approach is participatory and adopts a balance between a<br />
bottom-up and a top-down approach. To streamline and use <strong>the</strong> FSR<br />
approach in various environments, an attempt should be made to<br />
categorize <strong>the</strong>se environments. The FSR approach should be directed to<br />
a given environment on <strong>the</strong> basis of <strong>the</strong> suitability of available FSR-based<br />
technologies.<br />
Support services in a policy context<br />
Most parts of <strong>the</strong> policy context are a given, and FSR must be aware of this and<br />
adapt accordingly.<br />
o Resource maps, developed through geographical information <strong>systems</strong> and<br />
o<strong>the</strong>r means, are available to different zone environments for effective<br />
land use. These maps can be used to identify potential areas in which to<br />
extrapolate FSR technologies. Proper use of <strong>the</strong>se maps will speed up <strong>the</strong><br />
extrapolation process. Strong links and coordination among different<br />
agencies are needed to use <strong>the</strong>se maps effectively.<br />
o The supply of production inputs should be efficiently managed to ensure<br />
that <strong>the</strong>y are available in <strong>the</strong> right amount, at <strong>the</strong> right price, in <strong>the</strong> right<br />
quality, at <strong>the</strong> right time, in <strong>the</strong> right type, and in <strong>the</strong> right place. This will<br />
enhance and sustain <strong>the</strong> production system in any environment. However,
Members<br />
what is right depends on policies in <strong>the</strong> o<strong>the</strong>r areas (e.g., health,<br />
environment, and financing).<br />
o Accessibility of credit is important in <strong>the</strong> adoption of FSR-based<br />
technologies. However, most farmers do not have adequate collateral to<br />
get credit from institutions. To mitigate this problem, farmers should be<br />
encouraged to group <strong>the</strong>mselves to show <strong>the</strong>ir joint liabilities for access to<br />
credit. Risk-distribution strategies should be used. Provision of crop<br />
insurance is also needed to secure and sustain production <strong>systems</strong>.<br />
o A congenial marketing environment must be provided to allow producers<br />
to dispose of <strong>the</strong>ir products. Lack of an organized marketing system in <strong>the</strong><br />
rural sector puts farmers at a disadvantage. Groupings of farmers help to<br />
streng<strong>the</strong>n <strong>the</strong>ir bargaining power. Vertical links through <strong>the</strong><br />
development of rural agri-based industries create better marketing<br />
<strong>systems</strong>, employment, and distribution of marketing margins.<br />
o Infrastructure support to FSR-based technologies is part of <strong>the</strong> analysis of<br />
site conditions. Extrapolation to areas without an equivalent<br />
infrastructure is irresponsible. Development of FSR-based technologies<br />
in less advantaged and less favored areas will be more difficult because<br />
<strong>the</strong>se areas are likely to be Inore heterogeneous. However, social equity<br />
demands that <strong>the</strong> attempt be made.<br />
o Extension, education, and training are very important support <strong>systems</strong>.<br />
Active involvement of a11 major actors (e.g., <strong>research</strong>ers, extension<br />
workers, farmers, and <strong>the</strong> private sector) should be ensured from <strong>the</strong><br />
initial to <strong>the</strong> last stages of <strong>the</strong> FSR approach. Institutionalization of <strong>the</strong><br />
FSR approach must be encouraged in various educational institutions.<br />
Informal training in <strong>the</strong> FSR approach should be intensified and provided<br />
to extension workers, subject matter specialists, and middle-level<br />
managers.<br />
o Policy <strong>research</strong> on <strong>the</strong> impact and return on investment of FSR should be<br />
given priority.<br />
Group 3: Institutions<br />
S. Biggs - England (convenor); V. R. Carangal - Philippines; T. Bottema - Indonesia;<br />
M. Siddique - Bangladesh; D. K. S. Su~vastika - Indonesia; S. W. Almy -<br />
Madagascar; C. Sukapong - Thailand; A. Mandac - Philippines; J. M. K. P.<br />
Jayasinghe - Sri Lanka; W. Ratnayake - Sri Lanka; and K. A. Mettananda - Sri<br />
Lanka.
Institutionalization is used in a broad sense to reflect <strong>the</strong> need for <strong>the</strong> creation of<br />
long-term programs on FSR ra<strong>the</strong>r than short-duration projects. The word<br />
institution in its sociological sense refers to <strong>the</strong> relationships between actors to<br />
achieve certain things.<br />
FSR means many things to many people. However, FSR claims and aims to<br />
serve <strong>the</strong> farmer through assistance in problem solving. Impacts are by definition<br />
location-specific; however, <strong>the</strong> justification for FSR is often couched in terms of<br />
contributing to <strong>the</strong> achievement of national goals. It is often said that FSR is a way<br />
to get <strong>research</strong>ers into <strong>the</strong> field or a way to get <strong>the</strong> <strong>research</strong>ers to reach senior<br />
officials. It is acce ted that FSR is adaptive in nature, yet it is recognized that <strong>the</strong><br />
adaptive nature o its organizational form and management is less strong.<br />
F<br />
The macro - micro context<br />
The major strength of FSR is that it gives priority to <strong>the</strong> farmer and seeks to help<br />
solve <strong>the</strong> problems encountered by farmers. It addresses area-specific problems, <strong>the</strong><br />
solutions for which cannot necessarily be applied to larger areas. Given <strong>the</strong> fact that<br />
FSR is practiced and funded in <strong>the</strong> context of national development plans and<br />
<strong>research</strong> and development programs, it is hardly surprising that <strong>the</strong> goals of FSR<br />
activities are fitted into national objectives. This may carry a contradiction between<br />
FSR, which focuses on individual problem solving, and national programs that seek<br />
to reach national targets.<br />
Most cbuntries represented at this workshop adhere to national self-<br />
sufficiency goals with regard to major staples (Indonesia, Thailand, Sri Lanka,<br />
Bangladesh, and <strong>the</strong> Philippines). As <strong>the</strong> majority of cases show, FSR activities<br />
fitted national goals to expand production of rice as well as individual goals of<br />
farmers, which could be stated as expansion of income. Therefore, <strong>the</strong> existence of<br />
national targets is not necessarily in contradiction with individual goals and <strong>the</strong><br />
derived goals of FSR. However, it remains questionable whe<strong>the</strong>r on-farm <strong>research</strong>,<br />
as presented, can really be termed FSR in <strong>the</strong> pure sense because, by necessity, a<br />
priori project goals were derived from national goals. However, several successful<br />
adoptions, notably those in Indonesia and Thailand, benefited <strong>the</strong> farmers by<br />
increasing nonrice income and cutting cost, respectively, but <strong>the</strong>y did not expand<br />
rice production.<br />
Tl~e<br />
need to justify location-specific activities such as FSR in national terms<br />
is clear, but it is equally clear that attempts to pinpoint expected impact in national<br />
terms are by necessity somewhat forced. In fact, even location-specific impacts are<br />
difficult to substantiate. These observations point to <strong>the</strong> need for a priori<br />
recognition of <strong>the</strong> consequences and complexities accompanying FSR in <strong>the</strong> context<br />
of national plans and programs.
Decentralization and farming <strong>systems</strong> <strong>research</strong><br />
In most countries represented at <strong>the</strong> workshop, several shifts have occurred in<br />
<strong>research</strong> and development. First, by <strong>the</strong> late 1980s, <strong>the</strong> allocation and use of<br />
national resources became a dominant <strong>the</strong>me in <strong>research</strong> and development<br />
throughout <strong>the</strong> region. Ra<strong>the</strong>r than focus on comniodities per se, more attention<br />
was devoted to adaptive <strong>research</strong> in specific agroecological areas. This shift was<br />
accompanied by agroecological zoning and <strong>the</strong> creation of regional <strong>research</strong><br />
institutes, each with a specific mandate.<br />
Although this points to a form of decentralization, it should not be forgotten<br />
that in most countries this process was accompanied by a considerably streng<strong>the</strong>ned<br />
role for coordinating institutes and <strong>the</strong> creation of national apex agencies that were<br />
responsible for planning, budget allocation, and donor re1:ltions. This was <strong>the</strong><br />
second major shift.<br />
Decentralization and regional specialization of <strong>research</strong> into specific areas<br />
provided significant institutional opportunity for FSR. This is reflected in <strong>the</strong><br />
emergence of loosely structured FSR networks (e.g., in Vietnam) ;ind more<br />
formalized <strong>systems</strong> (e.g., in Thailand). An encouraging feature of this development<br />
is that government institutes, nongovernment organizations, and universities<br />
participate in <strong>the</strong>se networks and <strong>systems</strong>. Although <strong>the</strong>se structlires tend to emerge<br />
In response to somewhat inflexible government procedures, it is not unthinkable<br />
that such approaches, creative as <strong>the</strong>y are, could hinder long-term interest in <strong>the</strong><br />
practice of FSR because <strong>the</strong>y reduce <strong>the</strong> short-term need to properly institute<br />
programs.<br />
The situation in <strong>the</strong> region, however, is not uniform. In severai countries,<br />
FSR has become part and parcel of outreach institutes and even central <strong>research</strong><br />
agencies (e.g., in Indonesia and Sri Lanka). The success reported in <strong>the</strong> Indonesian<br />
case relates clearly to <strong>the</strong> existence of a well-defined FSR program and <strong>the</strong><br />
willingness of senior officials to take FSR activities seriously. However, a possible<br />
drawback could be that this FSR would be subject to national targets and plans,<br />
which \vould not automatically be beneficial to effective FSR. In view of <strong>the</strong><br />
variability in <strong>the</strong> organizational structure of <strong>research</strong> and development in <strong>the</strong> region,<br />
it is not possible to make sweeping statements and present firm conclusions. It is,<br />
for example, not :lt all certain that <strong>the</strong> prevalent shift to rewurce rn:inagement will<br />
indeed lead to more efficient use of resources and expanded private and social<br />
benefits. At <strong>the</strong> same time, <strong>the</strong> reduced emphasis on long-term, commodity-focused<br />
programs may hamper <strong>the</strong> creation of basic technology. Whatever ~ilill happen, it is<br />
to be foreseen that <strong>the</strong> resource-focused drive will continuz because of <strong>the</strong><br />
increased importance that society gives to environmental factors.
The interface role of farming <strong>systems</strong> <strong>research</strong><br />
Almost every country uses different terms for FSR and its component methods<br />
(rapid rural appraisal, on-farm trials, and pilot production projects). Each country<br />
has important political reasons for its choice of terms. For example, "production<br />
programs can only be done by <strong>the</strong> Indonesian Extension Service, so <strong>the</strong> FSR team<br />
does a Development Research Project", or FSR in <strong>the</strong> Francophone scientific<br />
community means <strong>the</strong> systematic political analysis of <strong>the</strong> national agricultural<br />
economy, <strong>the</strong>refore, <strong>the</strong> FSR program becomes an "on-farm <strong>research</strong> program"; or<br />
multilocation tests have been done on rented farmland because of a lack of regional<br />
stations; <strong>the</strong>refore, "on-farm trials" become "participatory trials" to underline <strong>the</strong><br />
difference in methods.<br />
Most of <strong>the</strong>se terminological variations relate to <strong>the</strong> fact that FSR as a<br />
distinct institutional entity is a newcomer to national scenes and is an interface<br />
between well-established bureaucracies. Its aim is to solve problems, not to carry<br />
out a predetermined set of actions, although it has a core methodology of its own.<br />
Successful programs have been seen to act as intermediaries between <strong>the</strong> station-<br />
based <strong>research</strong> establishment and extension services, marketing boards, and<br />
irrigation authorities. Although <strong>the</strong>y are usually based in <strong>the</strong> <strong>research</strong> service, <strong>the</strong>y<br />
do not confine <strong>the</strong>mselves to traditional scientific <strong>research</strong> methods and sometimes<br />
find <strong>the</strong>mselves under attack for encroaching on <strong>the</strong> turf of ano<strong>the</strong>r agency or<br />
operating outside <strong>the</strong>ir <strong>research</strong> mandate.<br />
Management of farming <strong>systems</strong> <strong>research</strong>: pulling strings and straws<br />
A number of issues have emerged concerning <strong>the</strong> organization and management of<br />
FSR project and programs:<br />
o Flexibility in <strong>the</strong> application of FSR principles. In many cases, it was<br />
observed that successful projects changed <strong>the</strong> technologies and <strong>the</strong><br />
emphasis of <strong>the</strong> FSR activities as <strong>the</strong>y proceeded. In cases where a<br />
staged approach appeared to have been followed, it was found that <strong>the</strong><br />
program had continuously monitored and reformulated its program in<br />
accordance with <strong>the</strong> changes in policy, development, and context.<br />
o Clarification and agreement on FSR objectives. Most of <strong>the</strong> FSR projects<br />
had a wide range of differing and sometimes conflicting objectives. The<br />
expectations of many projects were over-ambitious. A management<br />
lesson from <strong>the</strong> case studies was that greater emphasis should be given<br />
during FSR planning to <strong>the</strong> projection of expected outcomes and to <strong>the</strong><br />
methods of monitoring and assessing <strong>the</strong>se outcomes. These issues should<br />
include how to assess <strong>the</strong> extent of FSR influence on <strong>research</strong> station<br />
priorities and on <strong>the</strong> streng<strong>the</strong>ning and monitoring of farmer<br />
experimentation.
o More program and less project funding of FSR. In many successful FSR<br />
projects, it was found that <strong>research</strong> managers had additional funds and<br />
resources from o<strong>the</strong>r sources. Some successful FSR cases were <strong>the</strong> result<br />
of support after <strong>the</strong> project had been completed. A lesson from <strong>the</strong>se<br />
experiences was that FSR projects should not be seen in isolation from<br />
o<strong>the</strong>r <strong>research</strong> and developnlent activities. A second lesson was that<br />
some FSR projects Livere terminated at <strong>the</strong> time when <strong>the</strong> group or project<br />
was becomlng most useful.<br />
o Farmer reszarch: <strong>the</strong> need for monitoring and streng<strong>the</strong>ning. The country<br />
studies reveal an important and somewhat trivial fact. The issue is that in<br />
several cases, FSR interventions and improved technologies were based<br />
on already existing practices and commodity choices of farmers. The<br />
cases of rlce in Thailand, fish in ricefields in Indonesia, and potato in Sri<br />
Lanka illustrate that activities were defined and executed on <strong>the</strong> basis of<br />
esisting trends.<br />
It is noticeable that in Indonesia and Thailand, interventions were<br />
successful, although <strong>the</strong> courses of <strong>the</strong> events leading to success were<br />
quite different. In Sri Lanka, <strong>the</strong> suggested technological change was not<br />
accepted because <strong>the</strong> fdrmers had already esperimented with, and<br />
rejected, <strong>the</strong> component technology.<br />
Ano<strong>the</strong>r observation was made with regard to <strong>the</strong> diversity of actual<br />
iinprovenients. In Thailand, <strong>the</strong> shift in <strong>the</strong> factor market and increased<br />
labor costs determined <strong>the</strong> viability of mechanized direct seeding, in fact,<br />
a move toward intensification. In Indonesia, in contrast, <strong>the</strong> inclusion of<br />
fish with rice could be considered as a type of intercropping, which<br />
intensified prodi~ction and increased land productivity.<br />
Efforts in Thailand and Indonesia led to improvement in component<br />
technolow; whereas, in Sri Linka, FSR confirmed <strong>the</strong> experience of <strong>the</strong><br />
farmers, G-hich was based merely on observing existing practices and<br />
trends. The importawe of monitoring and streng<strong>the</strong>ning farming<br />
practices is underlined.<br />
o The outgoing approach. FSR is a ~nultidisciplinary approach to solve farm<br />
problen~s. But shortage of skilled labor in different disciplines may inake<br />
it difficult to form a FSIi group in many developing countries.<br />
Sometimes, national institutes have governn~ent permission to conduct<br />
<strong>research</strong> on a particular crop or enterprise. In such a case, FSR can only<br />
be conducted by using resource persons. The host institute should<br />
<strong>the</strong>refore go to o<strong>the</strong>r organizations to find <strong>the</strong> required resource persons<br />
and divide <strong>the</strong> work responsibilities anlong <strong>the</strong>m. Opportunities should<br />
also be divided among <strong>the</strong> collaborators. The implementing organization<br />
should not try to show that <strong>the</strong>y are in charge. This approach has been<br />
quite successful in FSR in Bangladesh, Thailand, and Indonesia. O<strong>the</strong>r<br />
organiz:itions that are conducting FSR can follow this outgoing approach<br />
to include o<strong>the</strong>r institutions in <strong>the</strong>ir FSR process.
o Assessing <strong>the</strong> outcomes of FSR. Most of <strong>the</strong> analytical tools used to<br />
assess <strong>the</strong> impact of FSR were biased toward evaluating income<br />
generation. There was no uniform relation between adoption of<br />
technology and expanded income. This suggests <strong>the</strong> need to look beyond<br />
hard indicators of welfare. If only hard ind~cators are observed, less<br />
obvious but important benefits could be missed. This was reflected in<br />
many of <strong>the</strong> impact studies. Some benefits may not be totally reflected in<br />
<strong>the</strong> incomes of <strong>the</strong> direct recipient, and some benefits may take longer to<br />
show results. There could also be indirect benefits that are difficult to<br />
quantify. Therefore, when starting a project or activity, a conceptual<br />
framework should be developed to capture <strong>the</strong> expected benefits in terms<br />
of specific objectives of <strong>the</strong> components of <strong>the</strong> FSR technology package.<br />
All activities should contain a self-evaluation mechanism.
Bangladesh<br />
Mr. RaGqul Islam<br />
Bangladesh Rice Research Inslitute<br />
Gazipur, Joydebpur<br />
Dhaka<br />
Dr. Siddique Ali Miah<br />
Bangladesh Rice Reseafrch Institute<br />
Gazipur, Joydehpur<br />
Dhaka<br />
Mr. Mohinur Siddique<br />
Bangladesh Rice Research Institute<br />
Gazipur, Joydcbpur<br />
Dhaka<br />
England<br />
Dr. Stephen Biggs<br />
School of Development Studies<br />
Universi~y of East Anglia<br />
Norwich h'R 4715<br />
Indonesia<br />
Dr. Made Oka Adnyana<br />
Agency for Agricultural Research and<br />
Development<br />
BORIF, P.O. Box 368/Boo, J1. Tentara<br />
Pelajar, No. 3A, Bogor 16114<br />
(d/h jl. Cimanggu)<br />
Ir. Taco Boltema<br />
CGPRT Centre, Jalan Merdeka<br />
135 Bogor 16111<br />
Ir. Dewa Ketut Sadra Swastika<br />
Agency for Agricultural Research and<br />
Development<br />
BORIF , P.O. Box 36S/Boo, J1. Tentara<br />
Pelajar, No. 3A, Bogor 161 14<br />
(d/h jl. Cimanggu)<br />
Participants<br />
Madagascar<br />
Dr. Sussn Almy<br />
Madagascar-IRRT Rice Research Projcct<br />
B.P. 4151<br />
Antananariva (101)<br />
Ms. Daniele Marie Ramiararnanana<br />
Interna~ional Rice Research Institute<br />
P.O. Box 933, Manila<br />
Philippines<br />
Nepal<br />
Dr. Sudarshan Ma~herna<br />
Central Socio-Economic Research Division<br />
National Agricultural Research Council<br />
Khumaltar, Lalipur<br />
Philippines<br />
Dr. Corazon Barba<br />
College of Human Ecoloby<br />
University of rhe Philippines Los Baiios<br />
College, Laguna<br />
Dr. William Dar, Director<br />
Bureau of Agricultural Research<br />
Department of Agriculture<br />
Diliman, Quezon Ciry<br />
V. Villa~lcio<br />
Farn~ing Systems and Soils Research Insriru~e<br />
University of rhc Philippines Los Barios<br />
Collcge, Laguna<br />
Thailand<br />
Dr. Benchaphun Shi!lawatra<br />
Chiang Mai University<br />
Chisng Mai 50002
Mr. Chalerm Sukapong<br />
Farming Systems Research Institute<br />
Department of Agriculture<br />
Bangkok 10900<br />
Mr. Rasamee Kirithaveep<br />
Farming Systems Research Institute<br />
Department of Agriculture<br />
Bangkok 10900<br />
Vietnam<br />
Dr. Vo-Tong Xuan<br />
University of Cantho<br />
Mekong Delta Farming Systems Research and<br />
Development Centre, Cantho<br />
Haugiang<br />
<strong>IDRC</strong><br />
Dr. John Graham<br />
Agriculture, Food, and Nutrition Sciences<br />
Division<br />
<strong>IDRC</strong>, Tanglin P.O. Box 101<br />
Singapore 9124<br />
Dr. John Hardie<br />
Agriculture, Food, and Nutrition Sciences<br />
Division<br />
<strong>IDRC</strong>, P.O. Box 8500, Ottawa<br />
Canada KIG 3H9<br />
Dr. Mahfuzudin Ahmed<br />
First Floor, House No. 363, Road 27<br />
New DOHS, Mohakhali<br />
Dhaka, Bangladesh<br />
Dr. Clive Lightfoot<br />
<strong>International</strong> Center for Living Aquatic<br />
Resources Management,<br />
MC P.O. Box 1501, Makati, Metro Manila<br />
Philippines<br />
IRRI<br />
Dr. Virgilio Carangal<br />
Asian Rice Farming Systems Network<br />
<strong>International</strong> Rice Research Institute<br />
P.O. Box 933, Manila<br />
Philippines<br />
Mr. Raymundo Gonzaga<br />
Social Sciences Division<br />
<strong>International</strong> Rice Research Institute<br />
P.O. Box 933, Manila<br />
Philippines<br />
Mr. Abraham Mandac<br />
<strong>International</strong> Programs Management Office<br />
<strong>International</strong> Rice Research Institute<br />
P.O. Box 933, Manila<br />
Philippines<br />
Sri Lanka<br />
Dr. Fredrick Abeyratne<br />
Resource Management Division<br />
AR&TI Wijerama Mawatha<br />
Colombo 7<br />
M. Agalawatte<br />
Division of Agricultural Economics and<br />
Planning<br />
Department of Agriculture<br />
Peradeniya<br />
Mr. N.K. Atapattu<br />
Division of Agricultural Economics and<br />
Planning<br />
Department of Agriculture<br />
Peradeniya<br />
Ms. J.M.K.P. Jayasinghe<br />
Division of Agricultural Economics and<br />
Planning<br />
Department of Agriculture<br />
Peradeniya<br />
Mr. K.A. Mettananda<br />
Agricultural Research Station<br />
Maha Illuppallama
Dr. N.F.C. Ranawecru<br />
Di\ision of .-lgricul[l~ral Econo~nics and<br />
Planning<br />
Department of Agriculture<br />
Pcradeniya<br />
Mr. W. Rarnayakc<br />
Diiision of Technolag Transfer<br />
Department of Agriculture<br />
Peradeniya<br />
hls. G.K. Rcnuka<br />
Diikion of Xgricul~ural Econoniics and<br />
Planning<br />
Dcpartmcnt of Agricullure<br />
Peradeniya<br />
hlr. P.A. Ssmaratu~lga<br />
Division of Agricultural Economics and<br />
Planning<br />
Dtpnrtmcnt of Agriculture<br />
Prradeniys<br />
Dr. Rick Slla~~d<br />
Residence oi thc Australia High Conlniissioner<br />
S5 Horron Placc<br />
Colon~bo 7<br />
DI-. M. Sikurajnpatliy<br />
Division oi Sccd and Plant h.la~erials<br />
Dcpartmenl of Agriculture<br />
Peraclc~li!,:i<br />
Dr. D.E.F Surawecra<br />
Division of Agricultural Economics and<br />
Pla~i~iing<br />
Dcparlmc~lt of Agriculture<br />
Peradeniya<br />
Dr. N. Vignarnjah<br />
Addl. DD (Research) Division of Research
AAPP-ROS<br />
ABC Project<br />
ACIAR<br />
ACSN<br />
ADB<br />
ADS<br />
AIDAB<br />
ANU<br />
ARFSN<br />
ARO<br />
ASES'<br />
ASSP<br />
AT1<br />
AUDP<br />
BAEX<br />
BAR<br />
BP1<br />
BRBDP<br />
BS<br />
BSU<br />
CAP<br />
CECAP<br />
Appendix: Acronyms<br />
Accelerated Agricultural Production Project<br />
Research and Outreach Sub-Project<br />
Agusan, Bukidnon, Capiz Settlement Project<br />
Australian Centre for <strong>International</strong> Agricultural<br />
Research<br />
Asian Cropping Systems Network<br />
Asian Development Bank<br />
Agricultural Development Specialist<br />
Australian Development Assistance Bureau<br />
Australian National University<br />
Asian Rice Farming Systems Network<br />
Agricultural Research Office<br />
Agricultural Suitability and Evaluation Systems<br />
Agricultural Support Services Project<br />
Agricultural Training Institute<br />
Antique Upland Development Project<br />
Bureau of Agricultural Extension<br />
Bureau of Agricultural Research<br />
Bureau of Plant Industry<br />
Bicol River Basin Development Project<br />
Bureau of Soils<br />
Benguet State University<br />
Countryside Action Program<br />
Central Cordillera Agricultural Project
CFTSSF<br />
CLSU<br />
CSD<br />
CSSAC<br />
CVIADP<br />
CVRP-I<br />
DA<br />
DA-V<br />
DENR<br />
DOH<br />
DPWH<br />
DSWD<br />
EEC<br />
FARM1<br />
FS<br />
FSA<br />
FSDP-EV<br />
FSDP-Bicol<br />
FSR&D<br />
FSRDN<br />
FSR&E<br />
FSSRI<br />
Comprehensive Far~rring Technology Support to<br />
Small Farmers<br />
Central Luzon State University<br />
Cropping Systems Division<br />
Camarines Sur State Agricultural College<br />
Cagayan Valley Integrated Area Development<br />
Project<br />
Central Visayas Regional Project-I<br />
Department of Agriculture<br />
Department of Agriculture - Visayas<br />
Department of Environment and Natural<br />
Resources<br />
Department of Health<br />
Department of Public Works and Highways<br />
Departnrent of Social Welfare and Development<br />
European Econolnic Community<br />
Farm and Resource Management Institute<br />
Farming Systems<br />
Farming Systems Approach<br />
Farming Systems Development Project-Eastern<br />
Visayas<br />
Farming Systems Development Project-Bicol<br />
Farming Systems Reser~rcl~ and Development<br />
Farming Systems Research and Development<br />
Network<br />
Farming Systems Research and Extension<br />
Farming Systems and Soil Resources lnstitu te
GIS<br />
GOP<br />
GSK<br />
HADP -<br />
HYV<br />
IAPMP<br />
<strong>IDRC</strong><br />
IMPAS<br />
IRRI<br />
KABSAKA -<br />
KASATINLU -<br />
LGU -<br />
MAF -<br />
MBLRC<br />
MCEPP<br />
R'ICS<br />
NFA<br />
NFAC<br />
NGA<br />
NGO<br />
NI A<br />
NIST<br />
NMCPP -<br />
OFR -<br />
geographic information <strong>systems</strong><br />
Government of <strong>the</strong> Philippines<br />
Gulayan Sa Kalusugan<br />
Highland Agricultural Development Project<br />
high-yielding varieties<br />
Integrated Agricultural Production and<br />
Marketing Project<br />
<strong>International</strong> Development Research Centre<br />
impact assessment<br />
<strong>International</strong> Rice Research Institute<br />
Kabusogan sa Kaumahan<br />
Kasaganaan sa Tigang na Lupa<br />
local government unit<br />
Ministry of Agriculture and Food<br />
Mindanao Baptist Livelihood and Rural Center<br />
Multiple Cropping Extension Pilot Production<br />
Program<br />
Multiple Cropping Section<br />
National Food Authority<br />
National Food and Agriculture Council<br />
National Grains Authority<br />
nongovernment organization<br />
National Irrigation Administration<br />
National Institute of Science and Technology<br />
National Multiple Cropping Production Program<br />
on-farm <strong>research</strong>
PIADP<br />
PCA<br />
PCARRD<br />
PHARLAP<br />
PSC<br />
PTA<br />
RADIP<br />
RADOS<br />
RBFS<br />
R&D<br />
R&E<br />
RIARR<br />
RRA<br />
RRDP<br />
SCO<br />
SCUs<br />
SFS<br />
SIRDP<br />
SMAP<br />
SRMU<br />
TA<br />
TD<br />
Philippine Australian Development Assistance<br />
Program<br />
Palawan Integrated Area Development Project<br />
Philippine Coconut Authority<br />
Philippine Council for Agriculture, Forestry and<br />
Natural Resources Research and Development<br />
Philippine Australian Rainfed Lowland Antique<br />
Project<br />
Philippine Sugar Commission<br />
Philippine Tobacco Administration<br />
Rainfed Agricultural Development (Iloilo) Project<br />
Rainfed Agricultural Development Outreach Sites<br />
rice-based farming system<br />
<strong>research</strong> and development<br />
<strong>research</strong> and extension<br />
Regional Integrated Agricultural Research<br />
System<br />
rapid rural appraisal<br />
Rainfed Resources Development Project<br />
Special Concerns Office of DA<br />
state colleges and universities<br />
small farm <strong>systems</strong><br />
Samar Integrated Rural Development Project<br />
Sou<strong>the</strong>rn Mindanao Agricultural Project<br />
Site Research Management Unit<br />
technology adaptation<br />
technology dissemination
TG<br />
TV<br />
UPLB<br />
USAID<br />
VISCA<br />
\m<br />
- technology generation<br />
- technology verification<br />
- University of <strong>the</strong> Philippines Los Baiios<br />
- United States Agency for <strong>International</strong><br />
Development<br />
- Visayas State College of Agriculture<br />
- World Bank
Production Team<br />
Editor : Michael Graham<br />
Cop!.editors : Liza Gel isan<br />
Tess Rola<br />
Sheila Siar<br />
Publication Coordinator : Anly Quintos<br />
Typesetting : Arlene dela Cruz<br />
Lourd Ann Erasga<br />
Ruth Funtanilla<br />
Amy Quintos<br />
Gebs Quintos
IRRl Discussion Paper Serles<br />
NO. 1.<br />
NO. 2.<br />
NO. 3.<br />
NO, 4.<br />
No. 5.<br />
NO. 6.<br />
NO. 7.<br />
No. 8.<br />
No. 9.<br />
NO. 10..<br />
NO. 11.<br />
No. 12.<br />
No. 13.<br />
No. 14.<br />
Ma<strong>the</strong>ny EL, Raab RT, Navarro EL. eds. 1994. Current<br />
status and future directions of rimlated group valning<br />
programs in Asia.<br />
Qu~ck GR, Yabes S, eds. 1994. Mlcroenterprlse develop<br />
ment--small-scale farm equipment manufacturing:<br />
entrepreneurship and employment.<br />
Oenn~ng CL. 1994. Farmers as customers: a service<br />
management appcoach to designing an agricultural<br />
<strong>research</strong> and development Institution.<br />
Senadhira D, ed. 1994. Rlce anQ problem so~ls In South<br />
and Sou<strong>the</strong>ast Asta.<br />
Calvero SB, COakleY SM, McDanlel LR, Teng PS. 1994. A<br />
wea<strong>the</strong>r factor searching program for plant pathlogical<br />
studles: Wlndow Pane Vefslon WlB0003.<br />
Devendra C, Sevilla C, eds. 1995. Cropanlmal Interactton.<br />
RaMnundO ME. Mamarll CP. Araaon EL 1995. Characterlstlcs<br />
and classlflcation of wetland rlce soils In India,<br />
indonesla, and malland.<br />
Qukk GR, Paris TR, eds. 1995. Enhancing incomes of rural<br />
women through suitably engineered <strong>systems</strong>.<br />
Tlnsley RL. 1995. Rice <strong>research</strong> and production In <strong>the</strong><br />
Commonwealth of Independent States (CIS).<br />
MOO* K, ed. 1995. Constraints, opportunities, and<br />
innovations for wet seeded rice.<br />
Pingali PL, Paris TR, eds. 1995. Competition and conflict<br />
in Asian agricultural resource management: ~sses,<br />
options, and analytical paradigms.<br />
Zheng K, Humg N, Bennett 1, Khush GS. 1995. PCR6ased<br />
marker-assisted select! on In rice breeding.<br />
Cuyno RV, Lumanta MF, Mmza MR, Carretas AC. 1996.<br />
What lRRl personnel say about me matrix management<br />
system 5 years after its adoption.<br />
Yap IV, Nelson RJ. 1996. WlnBoot: a program for Worn ing bootstrap anabsis of blnary data to determine <strong>the</strong><br />
canfldence llmRs of UPGMA-based dendroprams.