AgriTrust: a Federated Semantic Governance Framework for Trusted Agricultural Data Sharing

TL;DR

AgriTrust is a federated semantic governance framework that enhances trust, interoperability, and compliance in agricultural data sharing through a multi-stakeholder model, semantic ontologies, and blockchain-agnostic architecture, demonstrated in Brazilian supply chains.

Contribution

This paper introduces AgriTrust, a novel federated semantic governance framework integrating ontologies and blockchain-agnostic architecture to improve trust and interoperability in agricultural data sharing.

Findings

Enables verifiable provenance and automated compliance.

Creates new revenue streams for data producers.

Successfully applied in Brazilian coffee, soy, and beef supply chains.

Abstract

The potential of agricultural data (AgData) to drive efficiency and sustainability is stifled by the "AgData Paradox": a pervasive lack of trust and interoperability that locks data in silos, despite its recognized value. This paper introduces AgriTrust, a federated semantic governance framework designed to resolve this paradox. AgriTrust integrates a multi-stakeholder governance model, built on pillars of Data Sovereignty, Transparent Data Contracts, Equitable Value Sharing, and Regulatory Compliance, with a semantic digital layer. This layer is realized through the AgriTrust Core Ontology, a formal OWL ontology that provides a shared vocabulary for tokenization, traceability, and certification, enabling true semantic interoperability across independent platforms. A key innovation is a blockchain-agnostic, multi-provider architecture that prevents vendor lock-in. The framework's viability is demonstrated through case studies across three critical Brazilian supply chains: coffee (for EUDR compliance), soy (for mass balance), and beef (for animal tracking). The results show that AgriTrust successfully enables verifiable provenance, automates compliance, and creates new revenue streams for data producers, thereby transforming data sharing from a trust-based dilemma into a governed, automated operation. This work provides a foundational blueprint for a more transparent, efficient, and equitable agricultural data economy.