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

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.

💡 Research Summary

The paper tackles the “AgData Paradox” – the situation where valuable agricultural data remain locked in silos because producers distrust data consumers and existing systems lack true interoperability. To resolve this, the authors propose AgriTrust, a federated semantic governance framework that combines a multi‑stakeholder governance model with a formal semantic layer. The governance model rests on four pillars: (1) Data Sovereignty, giving data owners full control over who can access their data, under what conditions, and for what purposes; (2) Transparent Data Contracts, implemented as smart contracts that record terms, usage, and royalty settlements on a distributed ledger; (3) Equitable Value Sharing, a usage‑based royalty mechanism that directly rewards data producers; and (4) Regulatory Compliance, embedding national and international agricultural regulations into the system so that compliance checks can be automated.

The semantic layer is realized through the AgriTrust Core Ontology, an OWL‑based ontology that defines a shared vocabulary for production, processing, logistics, certification, tokenization, and value attribution. By representing concepts as RDF triples, the ontology enables true semantic interoperability: disparate platforms can exchange data without costly mapping because they speak the same “language”. The ontology is modular, allowing new regulatory or business concepts to be added as separate extensions without redesigning the whole model.

A key architectural decision is to remain blockchain‑agnostic. AgriTrust does not lock users into a single distributed ledger; instead, it provides adapters for multiple DLTs (Hyperledger Fabric, Ethereum, Corda, etc.). Data tokens carry standardized metadata and cryptographic hashes, while the choice of ledger is driven by performance, cost, and regulatory considerations. This multi‑provider approach prevents vendor lock‑in and lets participants keep legacy systems while only writing essential provenance events to the chain, thereby controlling storage costs.

The framework’s viability is demonstrated through three Brazilian supply‑chain case studies:

• Coffee (EUDR compliance) – Producers record origin and cultivation data on‑chain; an automated compliance engine checks EU Digital‑EU Regulation (EUDR) requirements in real time. Exporters reduced certification costs by roughly 30 % and gained stronger trust with European buyers.

• Soy (mass‑balance model) – The ontology captures production volumes and certified quantities. Smart contracts enforce a mass‑balance ratio, automatically blocking transactions that would exceed the certified amount and levying penalties for violations. Certification integrity stayed above 95 % and administrative effort dropped by 40 %.

• Beef (animal tracking) – Individual cattle are tokenized; movement events are logged, enabling end‑to‑end traceability. Certification bodies issue digital certificates directly from on‑chain data, cutting issuance time dramatically. Data providers earned an average 12 % increase in revenue through usage‑based royalties, while downstream actors received transparent provenance information.

Across all cases, AgriTrust delivered verifiable provenance, automated regulatory compliance, and new revenue streams for data owners, confirming that a governed, semantic approach can turn data sharing from a trust‑based dilemma into an automated, equitable operation.

The authors acknowledge limitations: scaling on‑chain storage for high‑frequency sensor data, maintaining consistency between off‑chain legacy systems and on‑chain records, and the need to extend the ontology for region‑specific regulations. Future work is outlined as (i) integrating lightweight zero‑knowledge proof techniques to protect privacy while preserving auditability, (ii) developing self‑evolving ontology mechanisms that can ingest new concepts automatically, and (iii) establishing cross‑ledger interoperability standards to enable seamless data flow across heterogeneous DLT networks.

In summary, AgriTrust offers a comprehensive blueprint that unites technical, institutional, and economic dimensions to build a trusted agricultural data economy. Its modular, blockchain‑agnostic, and ontology‑driven design makes it a candidate not only for agrifood but for any sector where data sovereignty, transparency, and regulatory compliance are critical.