{"title":"Holistic analysis of factors influencing the adoption of agroforestry to foster forest sector based climate solutions","authors":"Dagninet Amare , Dietrich Darr","doi":"10.1016/j.forpol.2024.103233","DOIUrl":null,"url":null,"abstract":"<div><p>Improving adoption rate is vital for realizing agroforestry innovations' financial and environmental benefits including fostering climate change adaptation and resilience efforts. Adoption rate of agroforestry innovations improves through feedback-enriched interventions. Yet, the lessons that decades of adoption research generated were only partially incorporated for improving prospective development interventions. Among others, application of reductionist approaches and rarity of holistic perspectives were primary causes for poor understanding of adoption contexts and subsequent incorporation in development programs. This study shows how to undertake holistic adoption empirical analysis by constructing Bayesian Belief Network (BBN). Findings revealed that household contexts consistently, followed by innovation attributes and system level features, influenced likelihood of adopting agroforestry innovations. BBN allowed discovery of the contribution of each variable and layer of variables on optimized adoption rate. Hence results suggested which (groups of) variables to focus when aiming to improve adoption results. Further testing hypothetical policy intervention allowed comprehension of potential outcomes. The approach consolidated the view that comprehensive assessment is essential for inclusive and actual understanding of adoption influencing factors. The stratification of farmers from discretization feature of BBN allowed potential of addressing all groups of farmers (e.g., poor, medium, rich, male decision-making dominated families), evading earlier concerns of development interventions benefitting only better-off farmers. Our findings proved that holistic analysis can better foster agroforestry innovations adoption by allowing targeted interventions and hence consolidated the forest sectors climate solution opportunities for smallholder farmers.</p></div>","PeriodicalId":12451,"journal":{"name":"Forest Policy and Economics","volume":"164 ","pages":"Article 103233"},"PeriodicalIF":4.0000,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Forest Policy and Economics","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1389934124000868","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
Improving adoption rate is vital for realizing agroforestry innovations' financial and environmental benefits including fostering climate change adaptation and resilience efforts. Adoption rate of agroforestry innovations improves through feedback-enriched interventions. Yet, the lessons that decades of adoption research generated were only partially incorporated for improving prospective development interventions. Among others, application of reductionist approaches and rarity of holistic perspectives were primary causes for poor understanding of adoption contexts and subsequent incorporation in development programs. This study shows how to undertake holistic adoption empirical analysis by constructing Bayesian Belief Network (BBN). Findings revealed that household contexts consistently, followed by innovation attributes and system level features, influenced likelihood of adopting agroforestry innovations. BBN allowed discovery of the contribution of each variable and layer of variables on optimized adoption rate. Hence results suggested which (groups of) variables to focus when aiming to improve adoption results. Further testing hypothetical policy intervention allowed comprehension of potential outcomes. The approach consolidated the view that comprehensive assessment is essential for inclusive and actual understanding of adoption influencing factors. The stratification of farmers from discretization feature of BBN allowed potential of addressing all groups of farmers (e.g., poor, medium, rich, male decision-making dominated families), evading earlier concerns of development interventions benefitting only better-off farmers. Our findings proved that holistic analysis can better foster agroforestry innovations adoption by allowing targeted interventions and hence consolidated the forest sectors climate solution opportunities for smallholder farmers.
期刊介绍:
Forest Policy and Economics is a leading scientific journal that publishes peer-reviewed policy and economics research relating to forests, forested landscapes, forest-related industries, and other forest-relevant land uses. It also welcomes contributions from other social sciences and humanities perspectives that make clear theoretical, conceptual and methodological contributions to the existing state-of-the-art literature on forests and related land use systems. These disciplines include, but are not limited to, sociology, anthropology, human geography, history, jurisprudence, planning, development studies, and psychology research on forests. Forest Policy and Economics is global in scope and publishes multiple article types of high scientific standard. Acceptance for publication is subject to a double-blind peer-review process.