Adoption and impact of climate-smart agriculture technologies in integrated crop-livestock farming systems.
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In Zimbabwe, smallholder farmers who rely on rain-fed crop-livestock systems for their livelihoods face multiple constraints which include a shortage of labour, inadequate capital to purchase inputs, low soil fertility, pests, disease outbreak, and low productivity as a result of climate change and variability. Climate change has caused prolonged droughts, reduced rainfall amounts and changing rainfall patterns, threatening the welfare of agriculture-based households. Climate-smart agriculture (CSA) technologies have been promoted as a panacea to address the negative effects of climate change. To date, the adoption of CSA has been low and on small land sizes. However, to ensure maximum benefits from CSA and scale up adoption, a better understanding is required regarding smallholder farmer adoption patterns. This study mapped adoption patterns, analysed common CSA technology bundles, measured the impact of CSA on household welfare and modelled optimal enterprise mix for farmers adopting CSA. Data was collected through a cross-sectional household survey of 386 multistage randomly selected respondents from four districts in Zimbabwe. Analysis was done using multivariate statistical techniques of principal component analysis and cluster analysis as well as the Cragg double hurdle model, multinomial logistic regression model, endogenous Switching Regression model, Cost-Benefit Analysis, stochastic profit frontier model and multiobjective goal programming. The findings based on the PCA-Clustering analysis showed that patterns of CSA varied across the household typologies. Resource endowed and experienced farmers have a high use of technologies such as crop rotation and minimum tillage that require more resources while resource-constrained clusters avoided resource-intensive CSA technologies. The Cragg double hurdle model results showed that the adoption of CSA is significantly affected by distance to the tarred road, access to weather information, livestock income share, and ownership of transport assets. Adoption intensity is significantly affected by factors such as sex of household head, labour size, frequency of extension contact, access to credit, access to weather forecasts, off-farm income, distance to input and output markets, number of traders and asset ownership. In light of these findings, policies that ensure access to weather forecasts information, coupled with frequent access to extension officers by farmers and access to credit will go a long way in encouraging farmers to scale up the use of CSA. Additionally, government efforts should be directed towards input markets decentralization which can be done through policy incentives to the private sector which brings markets closer to farmers. Also, the establishment of tarred roads in rural areas will incentivise farmers to increase the adoption intensity of CSA while on the other hand attracting more traders to the rural areas. The multinomial logistic selection model results reveal that observable household and market access characteristics influence the likelihood of a farming household adopting three identified prominent technology bundles/combinations. The results highlight that household characteristics (gender of household head, labour size), farm characteristics (soil type), and institutional factors (market access, information access and access to credit) are the main factors that determine the adoption of various CSA technology combinations. The results encourage the government to design policies aimed at improving farmers’ knowledge with regards to CSA. These should include early warning systems and programs that enhance access to information, markets and credit. The econometric results of the Endogenous Switching Regression model showed that the soil fertility status of the fields and access to weather forecasts had a significant impact on the farmer’s decision to adopt CSA. The Average Treatment effect of the Treated and Average Treatment effect of the Untreated was positive and significant for adopters and non-adopters indicating that CSA adoption had resulted in a significant positive impact on the welfare of the farmers. Analysis of outcomes revealed that farmer and farm characteristics as well as market factors significantly affected household welfare. Household income with reference to adoption was significantly affected by factors such as education of household head, labour size, TLU, off-farm income and asset index. Food security was influenced by factors such as education of household head, TLU, access to safe water, access to sanitation, access to inputs and output markets. Results from the cost-benefit analysis revealed that maize performs best under CSA technologies. The cost-benefit analysis results point to the potential of CSA in positively influencing profitability as a result of reduced costs and improved productivity. The profit inefficiency model showed that extension contact, number of traders locally and adoption of CSA had significant negative coefficients implying that as these variables increase, profit inefficiency among maize growing farmers then decreases. The findings call for development practitioners to incorporate market linkages that bring buyers closer to the farmers and support for extension staff to be able to have frequent contacts with farmers. Results of the multi-objective goal programming model suggest a reduction in the area committed to field crops and point towards concentrating on high-value crops such as horticulture and larger ruminants such as cattle.