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This paper maps adoption patterns and determinants of Climate-Smart Agriculture (CSA) technologies. A multivariate analysis approach that combined principal component analysis (PCA) and cluster analysis was employed and findings showed that patterns of CSA varied across household typologies. Resource endowed, experienced farmers have a high use of crop rotation and minimum tillage that require more resources while resource-constrained clusters shunned those. Double hurdle model results showed that adoption of CSA is significantly affected by distance to the tarred road, access to weather information, livestock income share and ownership of transport asset. Adoption intensity is significantly affected by the sex of household head, labour size, frequency of extension contact, credit access, access to weather forecasts, off-farm income, distance to input and output markets, number of traders and asset ownership. The study recommends policies that ensure access to credit and weather forecasts coupled with frequent access to extension services.

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