Abstract:

[Objective] Data across the agricultural industrial chain are generated throughout production, processing, storage, logistics, sales and supervision. Such data feature dispersed stakeholders, long‑link processes, multi‑source heterogeneity, and high privacy‑ and business‑information sensitivity. Nevertheless, current agricultural big‑data platforms and standalone technologies including blockchain‑enabled traceability, privacy‑preserving computation and federated learning cannot systematically resolve bottlenecks in cross‑entity data circulation, such as data ownership confirmation, controlled utilization, audit tracing and value‑oriented collaboration. Accordingly, this paper proposes a trusted data‑space framework tailored for agricultural production, circulation, supervision and service collaboration scenarios to satisfy requirements for trusted cross‑entity data circulation. It elaborates the framework's layered architecture, core operational mechanisms and implementation paths, offering systematic references for agricultural data sovereignty protection, controlled data sharing, audit tracing and value collaboration. [Methods] Based on the national standard Technical Architecture of Trusted Data Space and the characteristics of the agricultural industry chain, including multiple stakeholders, long-chain processes, limited computing capacity at edge nodes, and data heterogeneity, a domain-adaptation approach was adopted to construct the overall framework and analyze its operating mechanisms. The framework design focused on semantic interoperability, connector-based controlled interaction, privacy-preserving computation, and blockchain-based evidence preservation. A self-developed integrated service platform was then used for preliminary scenario-based analysis, combining about 120 000 food safety sampling and monitoring records from a city during 2023－2025 with the business workflow of an organic farm. The analysis covered standardized data access and governance, risk profiling, on-chain evidence preservation, trusted traceability, permission control, abnormal request interception, and audit tracing. [Results and Discussions] A four-layer architecture was established, consisting of an infrastructure and data resource layer, a trusted data space core layer, a data capability support layer, and an agricultural industry chain application ecosystem layer. The operating mechanism formed a closed loop jointly driven by deep semantic interoperability, connector-based controlled interaction, value co-creation through privacy-preserving computation, and dynamic trust supported by blockchain and smart contracts. Under this framework, ontology models, metadata, and knowledge graphs supported concept alignment, structural mapping, and contextual disambiguation; connectors, digital contracts, and usage control policies constrained usage purposes, invocation frequency, field scope, output forms, and prohibited behaviors; privacy-preserving computation and federated learning enabled cross-entity collaborative analysis without centralizing raw plaintext data; and blockchain recorded contract hashes, log hashes, result digests, and abnormal interception records for auditable tracing. The platform-based scenario description showed that the framework could support standardized data access, risk profiling, on-chain evidence preservation, role-based permission control, abnormal operation interception, and audit tracing in agricultural and food safety risk governance scenarios. In the organic-farm workflow, the mechanism was reflected by keeping raw data off-chain, storing key digests on-chain, controlling data use within authorized environments, and retaining auditable records of the process. Compared with conventional centralized agricultural data platforms, this framework emphasized physical distribution with logical integration and extends security protection from access control to continuous usage control after cross-entity interaction. The four-dimensional implementation pathway further indicated that institutional rules, lightweight connectors and privacy-preserving components, specialized data intermediaries, and progressive pilot deployment should advance in coordination. [Conclusions] The proposed trusted data space framework provides a systematic approach to controlled data circulation, data sovereignty protection, auditability, and value collaboration among multiple stakeholders in the agricultural industry chain without requiring centralized aggregation of raw plaintext data. It can serve as a reference for the circulation of agricultural data elements and the collaborative transformation of the agricultural industry chain, while providing a basis for integration with the national data infrastructure system.

Key words: agricultural industry chain, data circulation, trusted data space, data element circulation, data connector, federated learning, agricultural informatization system