Adoption determinants of multiple climate smart agricultural technologies in Zimbabwe: Considerations for scaling-up and out

Using a multistage sampling technique, data were collected from 386 households in four districts of Zimbabwe to investigate current Climate Smart Agriculture (CSA) technology combinations smallholder farmers practising integrated crop-livestock farming are adopting, as well as the determinants of adoption. The study used two econometric techniques to address the objectives. Firstly, principal component analysis (PCA) was employed to identify the CSA technology combinations smallholder farmers adopted. Secondly, multinomial logistic regression model was then used to analyze the adoption of the constructed CSA technology bundles. The study identified three prominent technology bundles/combinations. The multinomial logistic selection model results reveal that observable household and market access characteristics influence the likelihood of a farming household adopting any CSA technology bundle. The results highlight that gender of household head, farm characteristics (soil type and labour size) and institutional factors (market access, information access and access to credit) are the main factors that determine the adoption of various CSA technology combinations. Thus, the study recommends that the government should design policies aimed at improving farmers' knowledge with regards to CSA, including early warning systems and programmes that enhance access to information, markets and credit.