MAIA: A Model-Based Approach for Blockchain Integration in Agri-Food Supply Chains

Agri-food supply chains are complex and distributed ecosystems involving heterogeneous actors, from primary producers to retailers and consumers. Ensuring information consistency and coordination among these actors is essential to improve traceability, efficiency, and transparency. In this context, Blockchain Technology (BCT) has emerged as a promising enabler for trustworthy and decentralized data management. Its ability to provide immutable and transparent records makes it particularly suitable for supporting traceability and accountability in agri-food processes. However, challenges emerge in complex, intertwined supply chains, where different chains may adopt heterogeneous blockchain platforms, each characterized by its own technological infrastructure, smart contract language, and approach to on-chain/off-chain data management. These differences hinder interoperability, scalability, and cost-effective data sharing. This issue is particularly relevant in agri-food ecosystems, where it is common for the same actor to participate in multiple intertwined supply chains. Despite its importance, the considered problem is still underexplored in the literature. We propose MAIA, a model-based methodological approach for blockchain Integration in intertwined Agri-food supply chains, covering the full lifecycle, from requirements elicitation to technology-agnostic modeling of resources and services, and finally to blockchain-specific implementation. Based on a batch-oriented model, the approach ensures a resource-oriented perspective and scalable integration across heterogeneous platforms. A proof-of-concept case study, validated on both Ethereum and Hyperledger Fabric, demonstrates its effectiveness in addressing integration challenges and, at the instantiation level, optimizing resource management in terms of costs, execution time, and scalability.