Economywide effects of climate‐smart agriculture in Ethiopia

Climate‐smart agriculture (CSA) is an approach for transforming and reorienting agricultural systems to support food security under climate change. Few studies, however, quantify at the national scale CSA's economic effects or compare CSA to input‐intensive technologies, like fertilizer or irrigation. Such quantification may help with priority setting among competing agricultural investment options. Our study uses an integrated biophysical and economic modeling approach to quantify and contrast the economywide effects of CSA (integrated soil fertility management in our study) and input‐intensive technologies in Ethiopia's cereal systems. We simulate impacts for 20‐year sequences of variable weather, with and without climate change. Results indicate that adopting CSA on 25% of Ethiopia's maize and wheat land increases annual gross domestic product (GDP) by an average 0.18% (US$49.8 million) and reduces the national poverty rate by 0.15 percentage points (112,100 people). CSA is more effective than doubling fertilizer use on the same area, which increases GDP by US$33.0 million and assists 75,300 people out of poverty. CSA and fertilizer have some substitutability, but CSA and irrigation appear complementary. Although not a panacea for food security concerns, greater adoption of CSA in Ethiopia could deliver economic gains but would need substantial tailoring to farmer‐specific contexts

Farm household typology and adoption of climate-smart agriculture practices in smallholder farming systems of southern Africa

© 2018 African Journal of Science, Technology, Innovation and Development Enhancing adoption rates of climate-smart agriculture practices and their impact on livelihoods requires promotional persistence, complemented by a thorough socioeconomic analysis that recognizes the heterogeneity of smallholder farmers. Farm typologies are a useful tool to assist in understanding and unpacking the wide diversity amongst smallholder farmers to improve both up- and out-scaling of climate-smart agriculture practices. Our study typifies farm households in southern Africa based on socioeconomic factors prompting adoption of climate-smart agriculture practices. We use a combination of principal component analysis for necessary data reduction and cluster analysis to identify typical farm households and their socioeconomic characteristics. It is evident from our results that various socioeconomic factors define clusters and can be associated with adoption and use of climate-smart agriculture practices in smallholder farming. We conclude that farm typology identification is an important step towards the promotion of climate-smart agriculture practices in smallholder agriculture. These typologies provide essential ammunition to support efforts and policies aimed at improving adoption by recognizing heterogeneities in the targeted populations. In addition, we conclude that the multivariate analysis provides useful tools suitable for identifying the important socioeconomic characteristics of households influential in determining adoption of climate-smart agriculture practices