Enhancing the Quality, Safety, and Efficiency within the Value Chain of Non-Timber Forest Products: An Integrated ISM-Fuzzy MICMAC Methodology

Abstract

Non-Timber Forest Products (NTFPs) are essential to the livelihoods of forest-dependent communities, yet their value chains often suffer from inefficiencies, weak quality assurance, and food safety concerns due to unstructured pre- and post-harvest practices and limited market access. This study aims to identify, model, and analyze key factors that can enhance the quality, safety, and efficiency of NTFP value chains, providing a structured pathway toward sustainability. A comprehensive literature review and group interviews with 15 experts led to the identification of fifteen critical factors. These were analyzed using an integrated approach combining Interpretive Structural Modeling (ISM) and fuzzy MICMAC analysis. ISM was applied to establish hierarchical relationships and contextual linkages among factors, while fuzzy MICMAC addressed uncertainty in expert judgments and classified variables based on their driving and dependence power into independent, linkage, autonomous, and dependent clusters. The findings reveal that policy-level factors act as primary drivers within the system. Key operational elements, including quality management systems and improved post-harvest processing, were identified as linkage or dependent variables, indicating their strong reliance on foundational conditions. The integrated framework underscores the importance of proactive interventions, particularly strengthening institutional capacity and providing technical training to local communities. Overall, the study offers a practical roadmap for policymakers and practitioners to prioritize investments and transition from inefficient, traditional systems to resilient and sustainable NTFP value chains. It highlights the need to integrate traditional knowledge with modern quality standards and traceability mechanisms to ensure long-term sustainability and improved economic outcomes for rural populations.