Search

Digital food systems promise to connect farms, processors, logistics providers, retailers, regulators, and consumers through continuous data exchange. In this vision, production conditions, quality records, traceability events, safety alerts, inventory signals, and sustainability indicators move across the food chain as shared informational infrastructure. Yet the operational reality remains far less integrated, with food data often fragmented across systems that cannot easily communicate. The central problem is that existing debates frequently treat interoperability as a technical matter of software connection, data standards, or platform implementation. Such accounts understate the systemic, organizational, and political conditions that make data exchange difficult even when digital technologies are available. The result is a persistent gap between the promise of farm-to-fork data continuity and the practical experience of disconnection. This article develops a theory of data frictions in digital food systems. Data frictions are conceptualised as points of resistance, incompatibility, and disconnection that interrupt data flow between heterogeneous food system actors. The objective is to explain why interoperability fails across farms, processors, retailers, and regulators despite growing investment in digital agriculture, traceability systems, blockchain platforms, and data governance frameworks. The article adopts a conceptual theory-building approach based on a synthesis of 26 peer-reviewed journal articles published between 2017 and 2025. It draws on information systems, food supply chain management, agricultural data governance, platform studies, and organizational theory. The synthesis does not present new empirical data but uses existing evidence to construct a theoretical explanation of interoperability failure. The theory identifies four interrelated categories of data friction: technical, semantic, organizational, and political. These frictions manifest differently across the farm-processor-retailer-regulator nexus but jointly undermine coordination, traceability, accountability, and digital transformation. The article argues that data frictions are not peripheral glitches but structural features of digital food systems, requiring governance innovation, shared standards, and incentive alignment across the chain.

Precision agriculture generates expanding volumes of sensor, machinery, remote-sensing, weather, soil, crop, livestock, operational, input-use, resource-use, and farm-management data. Although these data are commonly collected for immediate production purposes, they may also support sustainability reporting, environmental accountability, agricultural finance, insurance, and risk assessment. Such secondary use creates the possibility of a circular data economy in which curated farm data are responsibly reused across several applications and subsequently improved through feedback. This article proposes an original conceptual architecture for organizing these reuse pathways while preventing circularity from becoming uncontrolled extraction, opaque scoring, or unequal value capture. The architecture connects farm data generation and collection with quality and provenance review, metadata curation, consent and purpose limitation, data stewardship, interoperability, production analytics, sustainability reporting, finance and insurance applications, farmer protection, auditability, and feedback updating. It distinguishes analytical usefulness from implementation readiness and places governance gates before each consequential reuse pathway. Production reuse is treated as conditional decision support rather than a productivity guarantee; sustainability reuse is treated as bounded evidence rather than automatic reporting accuracy; and finance or insurance reuse is subject to data-quality, bias, fairness, transparency, and dispute requirements. The principal contribution is a structured circular data economy architecture that clarifies how agricultural data may circulate across domains while preserving farmer agency, contextual integrity, accountability, and explicit legal, financial, insurance, and deployment boundaries.

Artificial intelligence is becoming embedded in precision agriculture through data-intensive monitoring, predictive models, digital platforms, advisory systems, robotics, and automated machinery. These arrangements complicate accountability because consequential outputs may result from interactions among data providers, model developers, platform operators, advisors, machinery manufacturers, system integrators, service providers, and farm operators rather than from a single actor or technical component. This article proposes a conceptual accountability framework for examining AI liability issues across agricultural AI systems without treating accountability mapping as a substitute for legal interpretation. The framework begins with agricultural use-case definition and stakeholder-role mapping, then examines data-level duties concerning quality, provenance, access, control, privacy, cybersecurity, and governance. It further maps model-level responsibilities for documentation, validation, robustness, bias assessment, explainability, uncertainty communication, and drift monitoring. Platform and advisory layers are addressed through governance, interface design, monitoring, escalation, human review, professional interpretation, and farmer-autonomy requirements. Machinery, automation, and operational risks are incorporated through control-system responsibilities, sensor-to-actuator traceability, supervisory control, fail-safe design, maintenance, incident reporting, and operational boundaries. The framework also identifies liability gaps created by fragmented evidence, unclear validation ownership, platform ambiguity, ineffective human oversight, and incomplete traceability. Its contribution is a structured method for clarifying responsibility pathways, evidence needs, decision boundaries, and governance gaps across interconnected agricultural AI layers. The framework is conceptual and does not determine legal liability, establish compliance, certify safety, allocate blame, or demonstrate deployment readiness.