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Understanding the adoption of climate-smart agriculture: A farm-level typology with empirical evidence from southern Malawi

机译:了解气候智能农业的采用:来自南马拉维南部的经验证据的农业水平类型

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Climate-smart agriculture (CSA) is increasingly important for advancing rural development and environmental sustainability goals in developing countries. Over the past decade, the international community has committed billions of dollars to support various practices under the banner of CSA. Despite this effort, however, CSA adoption remains low in many contexts. Lack of conceptual clarity about the range of potential farm-level CSA practices across contexts impedes understanding of CSA adoption in developing countries. Here we review relevant literature to develop a typology of farm-level CSA practices to facilitate analyses of CSA adoption. The typology consists of six categories, organized from least to most resource intensive: (1) residue addition, (2) non-woody plant cultivation, (3) assisted regeneration, (4) woody plant cultivation, (5) physical infrastructure, and (6) mixed measures. We use the typology to generate and test hypotheses about CSA adoption using primary household survey data from a large aidfunded CSA intervention area in southern Malawi. We then use recursive bivariate probit regression (controlling for endogeneity and selection bias) to estimate the effect of program participation on adoption across CSA categories. We find positive and statistically significant effects of program participation on adoption of CSA practices generally with the strongest effects on resource-intensive CSA categories. Results demonstrate the potential for wider application of the typology to build knowledge of the effectiveness of CSA promotion efforts across different social and environmental contexts. Our findings also suggest the importance of external support for the adoption of more resource-intensive CSA practices among rural households and communities in Malawi and elsewhere in the developing world. (C) 2019 Elsevier Ltd. All rights reserved.
机译:气候智能农业(CSA)对于推进发展中国家的农村发展和环境可持续发展目标越来越重要。在过去的十年中,国际社会已经承诺了数十亿美元来支持CSA横幅下的各种做法。然而,尽管有这种努力,但CSA采用在许多情况下仍然很低。缺乏概念清晰度,跨越背景潜在的农业级别CSA实践的范围阻碍了对发展中国家的CSA采用的认识。在这里,我们审查了有关文献,以开发农场级CSA实践的类型,以促进CSA采用的分析。类型学由六个类别组成,组织至少对大多数资源密集的:(1)残留量,(2)非木质植物栽培,(3)辅助再生,(4)木质植物栽培,(5)物理基础设施,和(6)混合措施。我们使用Texology使用来自Malawi的大型AIDFUNDED CSA干预区的主要家庭调查数据来生成和测试关于CSA采用的假设。然后,我们使用递归双变量概率回归(控制内核性和选择偏见)来估计方案参与在CSA类别中采用的效果。我们在方案参与通过对资源密集型CSA类别的最强烈影响的采用方案参与的积极和统计上显着影响。结果展示了更广泛地应用类型学的潜力,以建立在不同社会和环境背景上的CSA促进努力的有效性的知识。我们的调查结果还表明外部支持对Malawi以及发展中国家其他地方的农村家庭和社区采用了更多资源密集的CSA实践。 (c)2019年elestvier有限公司保留所有权利。

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