Household welfare impacts of an agricultural innovation platform in Uganda

Beine Peter Ahimbisibwe,Eric Mungatana,Tahirou Abdoulaye,Solomon Asfaw,Arega Alene,Victor Manyong,John F Morton,Kate Wellard,Anton Bua,Shiferaw Feleke

doi:10.1002/fes3.225

Abstract

AbstractTechnical approaches to food production are important to the food security of growing populations in developing countries. However, strategic investments in research and farm‐level adoption require greater coherence in agricultural, societal, and local policies. The Agricultural Innovation System (AIS) and formation of the Cassava Innovation Platform (CIP) in Uganda were designed to stimulate interactions between researchers and farmers, leading to the development of improved cassava varieties through participatory plant breeding (PPB) and participatory variety selection (PVS). Moreover, the establishment of a community‐based commercialized seed system called Cassava Seed Entrepreneurship (CSE) has made an important contribution to the rapid multiplication and dissemination of clean planting materials in Uganda. The impact of CIP participation on rural household welfare was measured by household consumption expenditure per capita. The Endogenous Switching Regression (ESR) model was applied to data from a formal household survey conducted in the eastern, northern, and mid‐western regions of Uganda. The education, farm size, livestock size, access to credit, cost of cassava planting materials, access to extension service, access to training, and social group membership are significantly associated with CIP participation. CIP participation resulted in a 47.4% increase in household consumption expenditure. This important evidence highlights the need to promote agricultural innovation platform for improving rural livelihoods. Moreover, CIP participation has impact heterogeneity within the participant group that is conditional on household characteristics such as the gender of the household head, pointing to the need to tailor specific interventions and target specific groups within farm households.

Highlights

Agricultural research in sub-Saharan Africa (SSA) has long been dominated by a top-down approach in which technical innovations such as improved crop varieties were first developed in experimental stations and transferred to the farming communities for validation and adaptation (Pound & Conroy, 2017)
The initiative led to the development of new improved cassava varieties most preferred by smallholder farmers (Wellard, Chancellor, Okecho, Ndagire, & Mugarura, 2015) and establishment of a community-based, commercialized seed system, which contributed to the rapid multiplication, distribution, and uptake of disease-free planting materials
Following Rosenbaum and Rubin (2011), the propensity score of Cassava Innovation Platform (CIP) participation given a vector of observed covariates can be given as: P(Xi) = P(Pi = 1|Xi) = Xi + ui where P(Xi) is the propensity score of CIP participation; Pi is the vector of observed households' participation decision with a value of 1 for the household who reported participating in CIP and 0 otherwise, is a vector of parameters to be estimated; Xi represents the vector of preparticipating control variables which explain CIP participation, and ui is the error term that is independent of Xi and is symmetrically distributed about zero

Summary

| INTRODUCTION

Agricultural research in sub-Saharan Africa (SSA) has long been dominated by a top-down approach in which technical innovations such as improved crop varieties were first developed in experimental stations and transferred to the farming communities for validation and adaptation (Pound & Conroy, 2017). The difference in the expected outcomes from Equation (8a) and Equation (8b), referred to as the average treatment effect on treated (ATT), constitute the impact of CIP participation on consumption expenditure of participants. The difference in the expected outcomes from Equation (9a) and Equation (9b), referred to as the average treatment effect on the untreated (ATU), constitutes the potential impact of CIP participation on consumption expenditure of nonparticipants. Where P(Xi) is the propensity score (conditional probability) of CIP participation; Pi is the vector of observed households' participation decision with a value of 1 for the household who reported participating in CIP and 0 otherwise, is a vector of parameters to be estimated; Xi represents the vector of preparticipating control variables which explain CIP participation, and ui is the error term that is independent of Xi and is symmetrically distributed about zero. The regions are defined by dummy variables taking on the value of 1 if a household resides in the Midwestern or Northern regions, and 0 for the Eastern region

| RESULTS

Participants

Findings

| CONCLUSION