A fuzzy logic-driven risk assessment framework for enhancing sustainable policy impact in smallholder Agri-food supply chains

The effectiveness of policies in smallholder agri-food supply chains is crucial for guaranteeing sustainability. However, there is a research gap in empirically evaluating the effectiveness of policies in this sector. This study proposes a fuzzy-logic-based risk assessment framework to evaluate policies. A fuzzy inference system (FIS) based risk assessment framework is developed to analyse policy performance under inherent uncertainty and complexity. The framework evaluates the risk of policy failure by modelling the relationship between the probability of failure and its consequences using fuzzy inference mechanisms. To our knowledge, this is the first FIS framework designed expressly for assessing policy interventions aimed at smallholder farmers. It enables more effective and data-driven decisions that directly support poverty reduction (SDG 1 ) and improved food security and sustainable agriculture (SDG 2 ) by identifying and mitigating high-risk policy failures. Applying this framework to the case study revealed that value chain development initiatives (PS 10 ) pose the highest risk of policy failure. This framework will be particularly useful for policymakers who may not be familiar with risk assessment, assisting them to better understand and managing the complexities inherent in policy implementation.