Baseline Study of Striga Control using IR Maize in Western Kenya

analysis using the same data set and this was consistent with the observed average maizeyield differences between male headed (957kg/ha) and female headed (750kg/ha) households.However, the results from the frontier analysis reported here do not necessarily imply thatmale headed households produced more maize on average than female headed households.Rather, it implies that male headed households are more likely to produce at the frontier thanfemale headed households and hence that the position of the maize production frontier isdetermined by male headed households. In either case, though, there is evidence of maizeproductivity differentials between male and female headed households.All coefficients of the district dummy variables are highly significant, indicating substantialmaize productivity differentials across locations in western Kenya. Kisumu and Nyandodistricts have significantly lower maize productivity relative to Bondo district. But Siayadistrict has significantly higher maize productivity than Bondo district. The results show thatfrom the four districts in Nyanza, Siaya has greater maize productivity potential. All districtsin Western are more productive than Bondo in Nyanza, with Bungoma having the highestmaize productivity followed by Vihiga, Teso and Busia. Siaya in Nyanza compares well withVihiga and Bungoma in Western in terms of maize production potential.6.2 Factors influencing maize production efficiencyThe estimate of the variance parameter, γ , associated with the variability of technicalinefficiency of maize production, is close to the maximum value of one and is significantlydifferent from zero. In Table 6.2, the significance of γ is shown by the high asymptotic t -ratio of 20. A generalised likelihood ratio test of the significance of the inefficiency effectswas also conducted and the results are presented in Table 6.3. The null hypothesis,Η0: γ=δ0=δ1= ... =δ7= 0 , tests whether the traditional average production function isappropriate as opposed to a frontier production function. As this is clearly rejected, the testresults confirm that the traditional response function is not an adequate representation ofmaize production in western Kenya, given the specifications of the translog stochastic frontierand inefficiency model. In other words, the inefficiency effects are significant in determiningthe level and variability of maize production in western Kenya. Furthermore, the nullhypothesis, Η0: δ1 = δ2 = ... = δ7= 0, tests whether the explanatory variables in the model forthe technical inefficiency effects have zero coefficients. This is again rejected, implying that,taken together, the explanatory variables have a significant impact on technical efficiency ofmaize production in western Kenya.Table 6.1. Maximum likelihood estimates of the translog stochastic frontier andinefficiency model for maize production in western KenyaVariable Parameter ML estimates T-ratios ElasticitiesStochastic frontierConstant β 05.601*** 8.800Land β 10.877*** 2.730 0.479***Labour β 20.107 0.392 0.201***Fertiliser β 3-0.140 -0.894 0.036***Oxen β 4-0.040 -0.180 0.278***Land × Land β 11-0.050 -0.770Labour × Labour β 22-0.079** -1.973Fertiliser × Fertiliser β 33-0.057 -1.013Oxen × Oxen β 440.119 1.40442