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model) the nutritional status of the preschoolers with the technology variables in the model because the endogenous variable is qualitative in nature; and there are many farmers within the sample who do not have preschoolers. Nevertheless, the authors believe that total income or food nutrient intake per se are necessary, but not sufficient conditions to alleviate undernourishn~ent. There are other exogenous factors that are as important (e.g., health and sanitation practices) but were not captured in the study. Income gains of the household still largely depend on the size of its farm holdings. The contribution of crop intensification technology in the lowland (as represented by mungbean - rice and the rice - rice cropping patterns) to inflows of household cash is in doubt. As long as these technologies remain unstable, maize crop in the upland area is expected to be the main source of income. On the production side, the inputs farmers used were equal to and sometimes above the recommended level. Productivity, however, remains low relative to the expectations of FSR researchers at the end of technology generation. One ready explanation was the harsh weather conditions that prevailed during the course of the impact-assessment study. Although the technology seems unstable, its actual contribution is already sizable. The evidence indicates that farm productivity could be increased by adopting introduced technologies. Considering the magnitude of the contribution of the technology variables, the results further indicate that improvement on the introduced technology (i.e., improved stability) could further enhance farm productivity. Because of limitations of available information, the assessment of the impact of FSR technologies on other welfare measures (e.g., education and wealth accumulation) was not attempted. Asset accumulation and education are long-term objectives of the household. To surmise that a link could be properly established between technology adoption (FSR) using cross-sectional data would produce misleading inferences (particularly because the introduced technologies were relatively young and the sample size was small). Time-series data with sufficient information on ho~~sehold asset possession before adoption wo~~ld provide a better understanding of the relationships of these welfare measures to the technologies in question. Nevertheless, the results have provided insights on the contribution of FSR (or any agricultural technology) to some welfare measures which are given high priority by households. REFERENCES CITED Biggs S D (1989) A nlultiple source of innovation model of agricultural research and technology prumotion. Network Paper 6. Agricultural Administration Unit, Overseas Development Institute, Lorrdon. Gonzaga R G, Gordoncillo N P, Ranaweera N F C, Barba C V C (1990) Assessing nutritional consequences of FSR projects. Paper presented at the First Asian
Farming Systems Research and Extension Symposium, 19-22 Nov 1990, Asian Institute of Technology, Bangkok, Thailand. Jodha N S (1994) Discussant's Note on Commercialization in Agriculture. Ranaweera N F C (1988) The need to assess impact of farming systems research (FSR) within the Asian context: an overview. Paper presented at the Monitoring Tour Cum Workshop on Impact of Farming Systems Research in Selected Asian Countries, 25-30 Ap 1988, Amigo Terrace Hotel, lloilo City, Philippines. Ravallion M (1990) Income effect of undernutrition. Econ. Dev. Cultural Change 38(3):489-5 15. RIARS--Regional Integrated Agricultural Research Systems (1988) Barangay pilot productitin program of improved cropping system. Paper presented at the CVIARS/PCARRD Coordinated Research and Development Review, 8-10 Jun 1988, Isabela State University, Isabela, Philippines. Zandstra H G, Price E C Jr., Litsinger J A, Morris R A (1981) A methodology for on farm cropping systems research. International Rice Research Institute, P.O. Box 933, Manila, Philippines.
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IRRl DISCUSSION PAPER SERIES NO. 15
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RECEIVED I RECU .- r b :'.i!~atlon
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Welcome address S .P.R. Weerasinghe
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Welcome Address S. P. R. Weerasingh
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Opening Comments After 15 yr of far
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increased as well, but only nlargin
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esult in high incomes for farmers,
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Investor concerns Usually investors
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WORKSHOP OBJECTIVES This workshop i
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Rice is the major food crop and die
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esource use and productivity varies
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Determination of ado,)tion factors
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number of active family members per
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At both sites, more than 73% of the
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Hossain A M, Nur-E-Elahi, Nazrul I
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Table 2. Crop varietal sequence sco
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Table 4. Correlation illatsix used
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- Table 6. Socioeconomic characteri
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Table 8. Extent of adoption and ave
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Table 10. Resource use and producti
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Table 12. Extent of adoption and di
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Table 14. Summary of per hectare ca
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IRlPACT OF FARMING SYSTEMS RESEARCH
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new information collected during FS
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average family owns seven heads of
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H Horticulture. To have a more regu
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improved varieties require higher l
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The types of the training provided
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extension agency in the district, t
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m Extension and other production-su
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Table 3. Farm characteristics of th
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Table 6. Farm practices and product
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Table 8. Major crops, crop varietie
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Table 10. Production and use of mil
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Table 13. Major problems and constr
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Table 15. Training undergone by the
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Table 17. Links between farmers and
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----l CROP SUBSYSlLM rnurt L OTT tb
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West Java provincial le~el BACKGROU
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OBJECTIVES The specific objectives
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Labor requirement Labor requirement
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system models. This indicated that
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Unit prices for all items were obta
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Price elasticity. The demand for al
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district fishery extension services
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Table 2. Production of freshwater f
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Table 4. Level of inputs used in ea
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Table 6. Labor requirement for each
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able 8. Income analysis of each FS
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Table 10. Nonfood expenditure patte
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Table 12. The value of assets (IRP
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Table 15. Nonfood expenditure patte
Page 105 and 106: Table 17. Elasticities of food comm
Page 107 and 108: l Oct k Jan Feb Mar Apr May Jun Jul
Page 109 and 110: From 1980 to 1986, the Integrated R
Page 111 and 112: THE 1'\VO ;MATURE TECHNOLOGIES Dire
Page 113 and 114: Conceptual framework STUDY METHODS
Page 115 and 116: Tai. Mungbean was introduced in Dok
Page 117 and 118: Land preparation for rice. The reac
Page 119 and 120: price of mungbean was high, some fa
Page 121 and 122: manufacturers. The earlier models (
Page 123 and 124: Net farm income per household The a
Page 125 and 126: that although adopters were better
Page 127 and 128: W It produced a good stand of rice
Page 129 and 130: Table l. The promising technologies
Page 131 and 132: Table 3. Mungbean before rice areas
Page 133 and 134: Table 5. Number of farm households
Page 135 and 136: Table 7. Area planted to mungbean,
Page 137 and 138: Table 9. Area planted to mungbean,
Page 139 and 140: Table 11. Area under dry seeded ric
Page 141 and 142: continued.. . Table 12. Socioeconom
Page 143 and 144: continued.. . Table 12. Socioeconom
Page 145 and 146: l~ndicative figures only. '~ature t
Page 147 and 148: composition, increases in income ca
Page 149 and 150: not be captured if the criteria for
Page 151 and 152: crops (rice, maize, and mungbean) a
Page 153 and 154: The explanatory Lwiable, nitrogen (
Page 155: of the ratio of actual intake and r
Page 159 and 160: Table 3. Input levels and productiv
Page 161 and 162: Table 5. Comparison of annual house
Page 163 and 164: Table 7. Comparison of annual nonfo
Page 165 and 166: Table 9. Comparison of percentage p
Page 167 and 168: Table 11. Log-linear models of impa
Page 169 and 170: INSTITUTIONALIZING THE FARILIIIVG S
Page 171 and 172: agricultural production system by i
Page 173 and 174: In the midwest plain, where there a
Page 175 and 176: Varietal irnprovernent. This compon
Page 177 and 178: solutions to problems under the bas
Page 179 and 180: Table 2. Rice farming systems in Ca
Page 181 and 182: Table 5. Component-technology studi
Page 183 and 184: Wangwacharachul (1984) examined the
Page 185 and 186: household basis on an income and ex
Page 187 and 188: first crop. In 1989-90, the adopter
Page 189 and 190: Nonfarm expenses constituted the bu
Page 191 and 192: Herbicide is essential for DSR. How
Page 193 and 194: o The classification of farmers on
Page 195 and 196: Table 1. List of \,ariables idc~l~l
Page 197 and 198: Table 3. Conlparison of cash flow o
Page 199 and 200: Table 5. Level of input use for the
Page 201 and 202: Table 7. Production elasticities of
Page 203 and 204: FROXl GREEN REVOLUTION TO FARhlING
Page 205 and 206: ainfed areas. The RIARS project app
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stability and sustainability. 1nsti
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o Limited feedback at all levels. A
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Table l. Chronological sequcnce of
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Table l. Chronological sequence of
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Impact studies A number of studies
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some simple statistical tests to as
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assets). Patterns of food consumpti
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Production functions for first-seas
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and nonadopters indicate that there
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E~zdowr~tcnt of l~ouselrol~f assets
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variety, inadequate attention was p
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Table 1. Farming system research si
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Table 4. Comparison of levels of in
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Table-6. Compariso~i of le\.els of
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Table 9. Estimated production funct
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Table 11. Factor shares of first-se
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Table 13. Factor slirlres of third-
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Table 15. Endownlent of farm assets
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IAIPACT ASSESShlEhT OF FARMING SYST
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Depending on the adequacy of rainfa
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Demograpllic cllaractehtics. The co
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The major farm implements owned by
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Effect of RWCS on household cash fl
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Table 1. Average cash expenses per
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Table 5. Selected clia~~acteristics
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Table 7. Comparative pcrforinance o
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hiem bers Discussion and Recommenda
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Guideline 4: FSR impact projects sh
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hlem bers o The need to improve doc
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Members what is right depends on po
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Decentralization and farming system
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o More program and less project fun
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Bangladesh Mr. RaGqul Islam Banglad
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Dr. N.F.C. Ranawecru Di\ision of .-
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CFTSSF CLSU CSD CSSAC CVIADP CVRP-I
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PIADP PCA PCARRD PHARLAP PSC PTA RA
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Production Team Editor : Michael Gr
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