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PROJECT TOPIC AND MATERIAL ON DIFFERENTIAL IMPACTS OF CONSERVATION AGRICULTURE TECHNOLOGY OPTIONS ON HOUSEHOLD INCOME IN SUB-SAHARAN AFRICA
The Project File Details
- Name: Differential Impacts Of Conservation Agriculture Technology Options On Household Income In Sub-Saharan Africa
- Type: PDF and MS Word (DOC)
- Size: [70 KB]
- Length:  Pages
Conservation agriculture (CA), which consists of minimum soil disturbance, crop residue retention and crop rotation, is claimed to generate a number of agronomic, economic and environmental benefits. Recognising these potential benefits, CA is widely promoted in efforts towards sustainable agricultural intensification. However, there has been an intense debate about its suitability in smallholder farming environments, and this has stimulated a growing interest in the adoption and impacts of CA technologies in sub-Saharan Africa (SSA). Using survey data from maize-growing households in nine SSA countries, this paper seeks to add to the extant literature by examining the drivers and welfare impacts of individual and combined implementation of the three components of CA. We employ inverse-probability-weighting regression-adjustment and propensity score matching with multiple treatment estimators. Overall, results show that adoption of a CA technology significantly increases total household income and income per adult equivalent. Disaggregating the CA components, we find that adoption of the components in combination is associated with larger income gains than when the components are adopted in isolation, and the largest effect is achieved when households implement the three practices jointly. Nevertheless, implementation of the full CA package among the sampled households is very low, with an average adoption rate of 8%. We identify key factors that might spur increased adoption, including education, secure land rights, and access to institutional support services. Results further show that the determinants and impacts of the CA components vary considerably among the study countries, suggesting location specificity of CA. Our results are consistent across alternative estimators.