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Intelligent agriculture is often presented as a technological pathway for increasing productivity, reducing input waste, and improving farm-level decision-making. In this view, the farm is commonly treated as an optimisation target in which sensors, analytics, and automated recommendations are used to improve decisions at a particular moment. Although this perspective has advanced digital agriculture, it does not fully explain how farms accumulate experience, revise practices, and become more capable over time. This article argues that the intelligent farm should be conceptualised not only as a data-driven production system but also as a learning system. A learning system is one that senses changing conditions, interprets feedback, adapts action, and modifies its decision logic across repeated cycles of experience. This framing shifts attention from isolated optimisation events to the longer-term evolution of farm knowledge, adaptive capacity, and agronomic intelligence. The objective of the article is to develop a theory of the intelligent farm as a learning system. The theory integrates three mechanisms: continuous feedback, local adaptation, and agronomic intelligence evolution. Together, these mechanisms explain how intelligent farms can move from collecting data to building situated knowledge that improves future decision quality. The proposed theory shows that intelligent farms become smarter when feedback streams are interpreted through local context and converted into evolving decision rules. Two tables are included: one contrasts optimisation and learning system views of the intelligent farm, and the other presents the full theoretical model. The article concludes that treating farms as learning systems offers a more realistic and resilient paradigm for intelligent agriculture.

Food systems are increasingly embedding algorithmic traceability systems that combine blockchain infrastructures, AI-supported verification, IoT sensors, QR-enabled disclosure, and digital chain-of-custody tools. These systems are commonly promoted as instruments of transparency, fraud prevention, and consumer reassurance. Their deeper theoretical significance, however, lies in the way they reorganise the social foundations through which food becomes trusted. Existing approaches to food trust often emphasise interpersonal confidence, brand reputation, certification labels, and institutional credibility. Yet these approaches do not fully explain what happens when trust claims are mediated by automated records, distributed ledgers, sensor-generated evidence, and platform-based verification interfaces. The central problem addressed in this article is whether algorithmic traceability merely increases information or whether it changes the nature of food trust itself. This article develops an original theory of algorithmic food trust transformation. It argues that algorithmic traceability shifts food trust from a relational and reputational regime toward a system-mediated regime organised around visibility, verification, and institutional confidence. The article does not report new empirical data; instead, it builds theory by synthesising research on food trust, traceability, blockchain, digital transparency, certification, consumer behaviour, and food governance. The proposed theory identifies three sequential mechanisms. Algorithmic visibility makes selected aspects of the supply chain seeable; verification converts food claims into auditable assertions; and institutional confidence encourages consumers and market actors to rely on the functioning of technical and regulatory systems rather than on direct personal knowledge. These mechanisms do not eliminate trust, but they relocate it from persons and brands toward infrastructures, platforms, certifiers, and governance arrangements. The article contributes to food sociology and trust studies by showing that algorithmic traceability is not simply a technical improvement in supply chain information. It rewires the foundations of food trust and creates new vulnerabilities linked to data integrity, institutional legitimacy, platform control, and the opacity of verification systems. The theory therefore calls for a more critical understanding of digital food trust as a sociotechnical achievement rather than a natural consequence of transparency.

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.