Cross-sectional analysis identifies factors influencing climate-smart agriculture adoption among farmers, implying policy intervention is needed.
The adoption of climate-smart agricultural practices addresses climate-change challenges while boosting productivity, enhancing adaptation, and mitigating greenhouse gas emissions. Despite its importance, adoption remains low in Ethiopia, and climate-change challenges are worsening. This paper identifies contextual factors associated with the adoption of climate-smart agricultural practices. Cross-sectional data were collected from 384 randomly selected smallholder farmers and analyzed using descriptive statistics and econometric methods. Among respondents, 61.72% adopted at least one climate-smart agricultural practice, and 38.28% did not. Regression results from a multivariate probit analysis revealed that climate-smart agricultural practices adoption is positively associated with smallholder farmers’ social networks, access to early warning systems, extension service, membership in agricultural unions, and participation in training, which enhance information flow and risk management capacity. In particular, stronger social networks are associated with higher adoption of climate-smart agricultural practices by enhancing information sharing, learning, and peer influence. Resource endowments, including land holding, livestock assets, and household income, further strengthen farmers’ ability to invest in and sustain such practices. In contrast, off-farm activities reduce the time, labor, and attention allocated to farming, thereby hindering the adoption of crop diversification practices. These findings require policy interventions to encourage smallholder farmers to adopt climate-smart agricultural practices. Specifically, strategies should focus on providing early and accurate climate-change information via various channels, offering targeted training on these practices and continuous extension consultancy services, and promoting and strengthening smallholder farmers’ social networks to increase peer learning and support.
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Daba et al. (2026) studied this question.
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