ABSTRACT

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.

摘要

本文绘制了气候智能型农业 (CSA) 技术的采用模式和决定因素。采用了结合主成分分析 (PCA) 和聚类分析的多变量分析方法，结果表明 CSA 的模式因家庭类型而异。资源丰富、经验丰富的农民大量使用轮作和最少耕作，这需要更多资源，而资源受限的集群则避开了这些。双障碍模型结果表明，CSA 的采用受到与柏油路的距离、天气信息的获取、牲畜收入份额和运输资产所有权的显着影响。户主性别、劳动力规模、扩展联系频率、信贷获取、获取天气预报、非农收入、与输入和输出市场的距离、交易者数量和资产所有权。该研究推荐了确保获得信贷和天气预报以及经常获得推广服务的政策。