Baseline Study of Striga Control using IR Maize in Western Kenya

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5.5.2 Body Mass Index of mothersThe Body Mass Index (BMI) is a measurement of the nutritional status that is based on heightand weight (BMI = weight/height 2 ). It is used to compare and determine the health effects ofbody weight on human beings. A BMI score between 22 and 24 is considered normal. Belowthe lower limit, the individual is underweight; and above the upper limit, the individual isoverweight or obese.For western Kenya, the average BMI score indicates a normal situation for both Nyanza andWestern provinces (Table 5.10). However, a breakdown into BMI classes indicates that only46% of mothers were in a normal category of BMI, 36% were underweight and about 18%were overweight in Nyanza. In Western province, 61% of mothers were in a normal categoryof BMI, 27% were underweight and about 12% were overweight. The results show that moremothers were undernourished in Nyanza than in Western province.Table 5.10. Body Mass Index and BMI groupings of mothersNyanzaWesternN 364 461BMI (average) 21.54 21.9Normal (%) 45.9 60.7Underweight (%) 36.3 27.3Overweight (%) 17.9 11.9N = Number of mothers40
Chapter 6Empirical Analysis of Maize Production Efficiency in Western Kenya6.1 Stochastic production frontier estimatesThe maximum likelihood estimates of the parameters of the translog stochastic frontier andinefficiency model were obtained using FRONTIER 4.1 (Coelli, 1996) (Table 6.1). Thechoice of the empirical frontier production function was made based on the generalisedlikelihood ratio test following Coelli and Battese (1996), without having to impose anyfunctional form. The functional form of the stochastic production frontier and inefficiencymodel was thus determined by testing the adequacy of the Cobb-Douglas model against themore flexible translog model. The null hypothesis that the Cobb-Douglas model is anappropriate representation of the data, given the specifications of the translog, was highlyrejected (Table 6.2), indicating that the Cobb-Douglas model was actually not appropriate.Therefore, the translog stochastic frontier and inefficiency model was used as the preferredmodel for the analysis of maize production efficiency in western Kenya.The results indicate that maize output increases with land, labour, fertiliser and oxen power.The sizes of the elasticities of frontier output with respect to the inputs show the relativeimportance of the various factors of maize production. In this regard, maize output is mostresponsive to land and least responsive to fertiliser. The low production elasticity of fertiliserconfirms the observation that only 15% of the sample farmers used fertiliser. Moreover, mostof these farmers (70%) applied fertiliser below recommended rates. Low adoption andintensity of use of fertiliser could be associated with the increasing prices of fertiliser relativeto maize. On the other hand, the high production elasticity of land is consistent with the factthat land is a critical factor of production for smallholders, especially in western Kenya wherethere is a growing population pressure on land.The coefficients of the variables representing adoption of hybrid maize and Striga controltechnologies are both positive and significant, indicating that adoption of these technologiesincreased frontier maize output. That is, adoption of hybrid maize and Striga control practicesincreased maize production among the efficient farmers. The positive effect of hybrid maizeadoption is consistent with the survey result that adopters obtained average yields of about1.3t/ha compared with non-adopters who obtained only 0.8t/ha. Similarly, the positiveinfluence of Striga control on frontier maize output is consistent with the result that maizefarmers who used integrated Striga control practices obtained average maize yields over 1t/hacompared with the average 0.8t/ha for farmers who did not adopt this practice. Indeed, inwestern Kenya where Striga damage on maize yield could be as high as 75–100%, themarginal benefit of a control practice is high. The coefficient of the household headshipvariable is positive and significant at 10% level, indicating that male headed households hadhigher frontier maize output than female headed households. A separate model, constructedby interacting gender with all other variables, was estimated to test whether the sametechnology was available to male and female heads of households. None of the interactionterms was significant, and a generalised likelihood ratio test could not reject the nullhypothesis that the interaction terms are jointly equal to zero. This demonstrates that male andfemale headed households actually have a homogenous production technology, implying thatif their respective frontiers were to be estimated separately, they would have the same S-curveshape usually found in adoption studies of new crop varieities. This is consistent, for instance,with the observation that both female headed (12%) and male headed (20%) householdsadopted hybrid maize. Higher frontier output among male headed households implies anintercept shift in the frontier and hence a change in its placement. Significant genderdifferentials in maize output were found from a separate conventional production function41
Page 5 and 6: Acronyms and AbbreviationsAATF Afri
Page 7: control technologies. Promoting bot
Page 10 and 11: Figure 1.1. GIS maps showing sample
Page 12 and 13: ln(maizeoutput ) = β + β ln(Land
Page 15 and 16: Chapter 2Socioeconomic Characterist
Page 17 and 18: Figure 2.2. Major forms of financia
Page 19 and 20: Another interesting parameter on hu
Page 21 and 22: Chapter 3Crop Production and Produc
Page 23 and 24: Table 3.3. Seed use in NyanzaLong r
Page 25 and 26: Table 3.5. Crop productivity (kg/ha
Page 27 and 28: Chapter 4Striga and Striga Control
Page 29 and 30: N 402 400Uprooting (%) 82.3 86.0Bur
Page 31 and 32: of seed (Table 4.9). For the sustai
Page 33 and 34: Table 5.2. Household income in both
Page 35 and 36: Table 5.3. Logit model estimates of
Page 37 and 38: household members were used for thi
Page 39: nutritional status of children are
Page 43 and 44: Land × Labour β 12-0.011 -0.385La
Page 45 and 46: 50-75 45 53 54 5475-100 29 18 25 24
Page 47 and 48: Table 7.1. Types of development pro
Page 49 and 50: Chapter 8Conclusions and Recommenda
Page 51 and 52: promise for unlocking the maize pro
Page 53: Ray SC. 1985. Measurement and test

Baseline Study of Striga Control using IR Maize in Western Kenya

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