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systems research - the IDRC Digital Library - International ... systems research - the IDRC Digital Library - International ...
there were differences in income throughout the year. For example, CLA had a relatively stable income flow from October to March. This was not the case in CL6. However, for all clusters, the period April-September showed an inconsistent flow of income. The majority of farmers reported that their expenditures were higher than their income. In the case of CLA, expenditures were higher than income when compared with the other two clusters. Expenditures were relatively higher in October and March. On average, an annual deficit of P6,800 had to be met with loans. Loans obtained in kind, such as fertilizer from private dealers, were usually paid in the forin of rough rice. However, there were instances of positive net cash flows. Generally, a negative monthly cash flow occurred in July and from November to January. The high expenditure in July and November can be attributed to the purchase of farm inputs, hired labor costs, and education. Annually, all the clusters except CL8 had positive net cash floivs. Cluster CL4 had the highest farm income during the 12-mo period (May 1987-April 19S8), followed by CL6 and C U. The contribution of farm income to household income was highest in October anlollg farmsrs in CLA. In this case, 84% of total farm income was attributed to rice production. CONCLUSION The KABSAKA technology was not fully adopted in iloilo. Its adoption varied according to prevailing circumstances. In a way, the technology contributed some benefits to farmers in rainfed areas by providing them alternatives. o Classifying KABSAKA adopters as those who used DSR is no longer valid because filrnlers decide on the crop establishment method based on various factors, primarily, weather cond~tions. If crops cannot be established using DSR because of early rain, the fields must be rotovated before they can be planted to WSR. o Classifying technology adopters on the basis of level of adoption was a useful initial step to measure the impact of the KABSAKA technology. Differences between groups were examined in ternis of practices related to land preparation, crop establishment, weed control, insect control, and fertiIizer application. These differences were reflected in yield and net benefit and in the pattern of household income and expenditure. The data collected in 1987-88 revealed that CL4 farmers had adopted the KABSAKA technology more comprehensively and had proved to be better performers than farmers in the other clusters. Production-function analysis disclosed that cluster groupings shifted the production function. Cluster CL4 had the highest level of productivity.
o The classification of farmers on the basis of level of technology adoption does not permanently confine farmers to a particular group. They can be shifted from one group to another depending on their response and circumstances. o Another approach that needs to be considered is the classification of farmers on the basis of farm characteristics and the environment. This approach will determine whether farmers in a particular group have the same response when a particular change in the environment or technology occurs (e.g., late rainfall or increases in the cost of herbicide). o An attempt to analyze the data on a plot basis showed that farmers manage their farm plots in diverse ways that depend on farm characteristics. Because an average of the practices is provided when the analysis is aggregated at the farm level, this variability is not considered. The case study approach seems appropriate to address this issue. o The profits from rice production are not sufficient to sustain the development of the countryside. The pattern of household expenditures showed that farm income was mostly spent on household consumption. There were no investments made to improve farm level resources. The accumulation of assets was for household requirements (e.g., house repair and household appliances) rather than for farm resources. This pattern indicated that economic gains due to the new technology are inadequate to encourage further accumulation of capital among farmers. However, some farmers did purchase tractors and water pumps. o Institutional changes in one of the study areas (Barangay Pili) resulted in increased gains from the new technology. A cooperative was organized among the farmers in Pili to provide credit and marketing facilities. These changes were brought about only after the KABSAKA project was implemented. REFERENCES CITED Barlow C E, Jayasuriya S, Price E C (1983) Evaluating technology for new farming systems: case studies from Philippines rice farms. Internatic~nal Rice Research Institute, Los Baiios, Philippines. Medialdia M T S, Ranaweera N F C (1988) An assessment of the impact of KABSAKA technology (FSR) in Ajuy, Iloilo, Philippines. Paper presented at the 8th Annual Farming Systems Symposium, 9-12 Oct 1988, University of Arkansas, Fayetteville, Arkansas.
- 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 and 156: of the ratio of actual intake and r
- Page 157 and 158: Farming Systems Research and Extens
- 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: Herbicide is essential for DSR. How
- 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
- Page 207 and 208: stability and sustainability. 1nsti
- Page 209 and 210: o Limited feedback at all levels. A
- Page 211 and 212: Table l. Chronological sequcnce of
- Page 213 and 214: Table l. Chronological sequence of
- Page 215 and 216: Impact studies A number of studies
- Page 217 and 218: some simple statistical tests to as
- Page 219 and 220: assets). Patterns of food consumpti
- Page 221 and 222: Production functions for first-seas
- Page 223 and 224: and nonadopters indicate that there
- Page 225 and 226: E~zdowr~tcnt of l~ouselrol~f assets
- Page 227 and 228: variety, inadequate attention was p
- Page 229 and 230: Table 1. Farming system research si
- Page 231 and 232: Table 4. Comparison of levels of in
- Page 233 and 234: Table-6. Compariso~i of le\.els of
- Page 235 and 236: Table 9. Estimated production funct
- Page 237 and 238: Table 11. Factor shares of first-se
- Page 239 and 240: Table 13. Factor slirlres of third-
- Page 241 and 242: Table 15. Endownlent of farm assets
<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.