A meta-analysis of the adoption of agricultural technology in Sub-Saharan Africa

Both global poverty and hunger have increased in recent years, endangering progress towards accomplishing Sustainable Development Goals (SDGs) 1 and 2. The regression has been most pronounced in Sub-Saharan Africa (SSA). Meeting the SDG targets requires achieving resilient farm productivity. Although many farm management technologies exist to improve yields, farmers in SSA largely have not adopted these approaches. A long-standing literature about technology adoption identifies multiple hypotheses as to why farmers may or may not adopt new agricultural technologies, culminating in numerous micro-econometric studies. We analyse a metadata set capturing the findings of 164 published studies specifically focusing on SSA and show that 20 out of 38, or 53%, of the determinants commonly believed to influence technology adoption lack empirical support. Eighteen determinants—primarily related to information access, wealth, group membership and social capital, and land tenure—consistently influence adoption across studies. Wealth remains a significant determinant of fertilizer adoption, despite long-running subsidies in most countries, although it is decoupled from the adoption of improved seeds and alternative crop and nutrient management technologies. We highlight the foundational determinants of adoption and offer guidance to design effective interventions that can decrease poverty and hunger towards 2030.Author summary: Achieving SDG1&2 requires improved farm productivity in Sub-Saharan Africa (SSA). Although many agricultural technologies exist to improve yields, adoption remains low. We analyse a metadata set capturing the findings of 164 published studies focused on SSA that span nearly 30 years. We present the complexity of determinant-technology interactions for 3 technology groups using vote-count methodology, which can be subject to publication bias. We address this using sign-tests and establish that more than half of the determinants commonly believed to influence technology adoption lack empirical support. Access to general information (as opposed to narrowly focused practice specific information), wealth, and land tenure consistently influence adoption. Context specificity of technologies and determinants is illustrated by focusing on selected combinations with enough number of studies and important policy implications. Wealth remains a significant determinant of fertilizer adoption, despite long-running subsidies in most SSA countries, although it is decoupled from improved seed and alternative crop and nutrient management technology adoption. We highlight methodological recommendations to facilitate more rigorous meta-analyses in this increasingly complex literature to better guide effective intervention design to decrease poverty and hunger.