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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.

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