Holistic analysis of factors influencing the adoption of agroforestry to foster forest sector based climate solutions

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.