Impact of adopting multiple agricultural technologies on nutrition outcome in east africa: a multinomial endogenous switching regression approach

Abstract

Majority of the population of East African households are malnourished and much of the effort to address the problem of stunting, wasting and underweight have focused on interventions that are designed directly to address its immediate causes. It is expected that the adoption of multiple agricultural technologies such as improved beans varieties, bio-fortified maize variety, grafted fruit trees, and garden vegetable techniques can be a means by which malnourished rural households who may have less access to diverse meals, supplements, and fortified foods can enhance their balanced diet but malnutrition still remain a salient problem facing rural households in East Africa. This study determined the factors that affect the adoption of joint multi-agricultural technologies then analyze the impact of the best four combinations adopted in East Africa countries that is; improved beans variety, biofortified maize variety, grafted fruit trees, and use of garden vegetables techniques on the household nutrition outcome indicators of underweight (WAZ), wasting(WHZ) and stunting(HAZ). Where TC = base with no technology used, TC1 = Improved beans variety, biofortified maize variety, and grafted fruit trees, TC2 = Improved beans variety, biofortified maize variety, and garden vegetable techniques, TC3 = biofortified maize variety, garden vegetable techniques, and grafted fruit trees, TC4 = Improved beans variety, garden vegetable techniques, and grafted fruit trees. The study utilized a secondary household panel data of Kenya, Tanzania, and Uganda that was collected by IFRI for ten waves from 2007 to 2017 and each country with 500 households. This study utilized multinomial endogenous regression model so as to casual the impact of technology adoption and to correct for the self-selection bias. It was conceptualized that the decision to adopt a combination of multiple agricultural technologies (MATs) is modeled in consumer theory, specifically, a random utility framework. The latent model (U*jit) which describes the ith farmer’s behavior in adopting MATs j(j=1,...4) at time t over any alternative MATs combination was utilized in three stages. In the first stage, the analysis determined the factors for adopting multi-agricultural technologies using a multinomial endogenous switching regression. In the second stage, the inverse mills ration generated in stage one is used as linkage between adoption of technologies nutrition outcome, and on the third stage, the treatment effect was used to establish the relationship between adopters of the joint multiple agricultural technologies and non-adopters. The results show that year increase of the education of household head, general participation in community meetings and barazas increases the adoption of TC1 (45%), TC2 (44%), TC3 (25%), and TC4 (35%) respectively. The 1% percent increase in the adoption of joint technologies, the prevalence of stunting reduces by 17.4%, wasting 15.4%, and underweight by 16.8%. Results of the average treatment effects show that the households who adopted joint multiple agricultural technologies had a positive significant impact (HAZ β= .62, p<0.01), WAZ (β = .72, p<0.01), and WHZ (β = .74, p<0.01) which improves the nutrition status by HAZ (103%), WAZ (87%), and WHZ (84%). The best technology combination was TC3 which impacted all nutrition outcome at the highest percentage HAZ (25.8%), WHZ (24.2%), and WAZ (25.3%). Kenya(reference) had a higher significant propensity of adoption hence higher impact on nutrition outcome than Uganda (β = -.128, p<0.01) and Tanzania (β = -.155, p<0.01). This study concludes that adoption of multiple agricultural technologies improves household nutrition outcome. The household that adopted the joint multiple agricultural technologies had systematically higher nutrition outcome than the households who did not adopt even after controlling for all confounding factors. Among the three countries Kenya has a higher significant propensity on nutrition outcome. This study offers insight to policymakers, researchers, and extension workers regarding the advancement of factors suitable for joint technology combination to be adopted by the East Africa households. Consequently, this study recommends that household should focus on adopting the multiple agricultural technologies to improve their nutrition status. And more so focus more on the combination of TC3 (Biofortified maize variety, garden vegetable techniques, and grafted fruit trees) since it was the combination with greatest impact on nutrition outcome.