A DIGITAL GOVERNANCE FRAMEWORK FOR AGRICULTURAL SUPPLY CHAINS USING BLOCKCHAIN AND EXPLAINABLE ARTIFICIAL INTELLIGENCE

Abstract

Ensuring transparency, accountability, and regulatory compliance remains a significant challenge in modern agricultural supply chains due to fragmented information management and limited governance assessment capabilities. This study proposes a Digital Governance Framework that integrates blockchain technology, Explainable Artificial Intelligence (XAI), machine learning, and a Digital Governance Quality Index (DGQI) to support transparent and interpretable governance evaluation. The proposed framework was validated using an integrated dataset comprising 701 observations and 55 engineered variables. Experimental evaluation demonstrated that the proposed XGBoost model achieved an R² of 0.972, 98.93% classification accuracy, and a ROC–AUC of 0.985. Furthermore, the framework attained a mean DGQI score of 0.914 with 96.82% governance confidence, while the Hyperledger Fabric implementation sustained 842 transactions per second with an average transaction latency of 1.82 s. The proposed framework provides an accurate, interpretable, scalable, and trustworthy solution for strengthening digital governance and supporting evidence-based decision-making in agricultural supply chains.