综合荟萃分析和专家知识,确定埃塞俄比亚气候智能型农业做法的优先次序

Zenebe Adimassu , Degefie Tibebe , Wuletawu Abera , Lulseged Tamene
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引用次数: 0

摘要

埃塞俄比亚不同的农业生态区正在倡导各种气候智能型农业(CSA)做法,以提高农业部门应对气候变化的可持续性、复原力和生产力。对这些 CSA 实践进行优先排序和包装,对于扩大气候变化减缓工作的影响至关重要。通过战略性地选择和优先考虑这些做法和技术,可以将资源有效地分配给最有可能减少温室气体排放、提高抗灾能力和促进可持续发展的活动。然而,确定满足弱势农民需求、适合当地具体情况的气候智能型实践并确定其优先次序仍具有挑战性,这往往受到主观评估和有限认识的阻碍。本文旨在利用研究成果和专家知识的结合,提高确定 CSA 实践优先次序的准确性和客观性。具体步骤如下i) 使用 CSA 优先级评估框架,根据 CSA 支柱(生产率、适应性和减缓)确定各种农业生态中的 CSA 实践并确定其优先级;ii) 采用元分析方法确定各种 CSA 实践对 CSA 实践三大支柱的影响大小;iii)根据效应大小类别对效应大小值进行重新调整和排序;以及 iv)进行相关性分析以评估两种方法之间的关系,最后取平均值进行整合并确定 CSA 实践的最终排名。总体而言,我们发现两种方法的排序之间存在微弱的相关性,导致专家对 CSA 实践的排序与元分析结果不匹配。使用元分析方法时,只有 35% 的 CSA 实践在两种方法中的排名相同,40% 的 CSA 实践更有可能被专家排名,而 25% 的 CSA 实践更有可能被元分析方法排名。这意味着专家们高估了各种 CSA 实践对生产力、土壤流失、径流和土壤有机质等各项指标的影响。综合两种方法的排名有助于在不同农业生态区域有针对性地采取 CSA 实践。根据综合排名,针对该国六大农业生态区采取了多项 CSA 实践。这些不同的 CSA 实践提高了生产率,增强了适应性,并从大气中封存了二氧化碳。根据这些 CSA 实践的可用性,可以对这些实践进行各种组合包装。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating meta-analysis and experts’ knowledge for prioritizing climate-smart agricultural practices in Ethiopian

Various climate-smart agricultural (CSA) practices are being advocated in different agroecological zones of Ethiopia to enhance the sustainability, resilience, and productivity of the agricultural sector in response to climate change. Prioritizing and packaging these CSA practices are essential to amplify the impact of climate change mitigation efforts. By strategically selecting and prioritizing these practices and technologies, resources can be allocated effectively to activities with the highest potential for reducing greenhouse gas emissions, bolstering resilience, and fostering sustainable development. However, identifying and prioritizing climate-smart practices that cater to the needs of vulnerable farmers and are tailored to specific local contexts remains challenging, often hindered by subjective assessments and limited awareness. The objective of this paper was to enhance the precision and objectivity of prioritizing CSA practices by leveraging a combination of research findings and expert knowledge. The steps included the following: i) a CSA prioritization assessment framework was used to identify and prioritize CSA practices across various agro-ecologies based on the CSA pillars (productivity, adaptation, and mitigation); ii), a meta-analysis approach was employed to determine the effect size of various CSA practices on the three pillars of CSA practices; iii), the effect size values were rescaled and ranked based on effect size categories; and iv), correlation was performed to assess the relationship between the two approaches, and finally, average values were taken to integrate and determine the final rank of CSA practices. Overall, we found out that there were weak correlations between the ranks of the two approaches, resulted in a mismatch between the ranks of CSA practices by experts and meta-analysis results. Using the meta-analysis approach, only 35% of the CSA practices were equally ranked by both approaches, 40% of the CSA practices were more likely ranked by experts, while 25% of the CSA practices were more likely ranked by the meta-analysis approach. This implies that experts overestimated the effect of various CSA practices on various indicators of productivity, soil loss, and run-off and soil organic matter. Integrating the ranks of the two approaches helped to target CSA practices across various agro-ecological zones. According to the combined ranking, several CSA practices were targeted to six major agro-ecological zones in the country. These various CSA practices increase productivity, enhance adaptation, and sequester carbon dioxide from the atmosphere. Based on the availability of these CSA practices, it is possible to package various combinations of these practices.

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