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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