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Intelligent farming systems increasingly depend on data generated through sensors, machinery, satellites, digital platforms, advisory tools, farm management software, and artificial intelligence-enabled decision-support systems. These systems promise more precise monitoring, improved resource allocation, and better responsiveness to environmental and operational uncertainty. As farm operations become more data-intensive, farm data shift from being a by-product of agricultural activity to becoming a strategic asset within intelligent farming systems. This transformation creates governance problems that cannot be resolved through technical data management alone. Farm data raise contested questions about ownership, consent, access, interoperability, platform dependence, accountability, and farmer control. These questions are especially important because data generated on farms may be used beyond the original farming context for commercial analytics, platform development, benchmarking, insurance, input recommendation, credit scoring, or supply-chain governance. The objective of this article is to develop a data stewardship framework for intelligent farms. The framework is designed to clarify how ownership, consent, access, interoperability, accountability, and farmer-control mechanisms can be organised in ways that support responsible digital agricultural innovation. It treats data stewardship as a governance function that mediates between innovation, commercial data use, and the protection of farmers’ decision-making authority. The article is based on a governance framework synthesis  peer-reviewed articles published. It does not present new empirical data. Instead, it synthesises literature on digital agriculture, smart farming, agricultural data governance, platformisation, responsible innovation, interoperability, and farmer data rights to construct a practical and conceptual governance framework. The article maps farm data as a governance problem, analyses ownership and consent issues, examines interoperability and platform dependence, and proposes a stewardship framework for intelligent farms. Three tables summarise ownership and control issues, interoperability risks, and the proposed framework. Responsible intelligent farming requires data stewardship arrangements that protect farmer control while enabling trustworthy data sharing and innovation.

The contemporary food system generates data at nearly every point where biological production, industrial processing, logistics, retail exchange, and consumer behaviour intersect. Farms produce sensor, satellite, machinery, weather, input, yield, and management data, while processing plants generate quality, safety, traceability, energy, water, and waste records. Downstream actors produce further streams of information through cold-chain monitoring, inventory systems, point-of-sale records, household consumption patterns, and nutrition-related choices. Despite this expanding data environment, the food system remains poor in system-wide intelligence because most data are captured, governed, and analysed within isolated organisational and technical domains. Production analytics, manufacturing intelligence, supply chain visibility, and consumer analytics have developed unevenly and are rarely connected into a coherent information architecture. As a result, decisions continue to be optimised locally rather than across the whole food system. This article proposes a conceptual framework for integrating production, processing, distribution, and consumption analytics into a unified food system intelligence capability. Food system intelligence is defined as the capacity to connect data across food system stages, convert them into actionable knowledge, and support decisions that improve productivity, sustainability, resilience, quality, and consumer value simultaneously. The article does not present new empirical data but synthesises existing literature to construct an integrative framework. The framework identifies four core data layers corresponding to production, processing, distribution, and consumption, joined through an integration layer based on standards, shared semantics, interoperable platforms, data-sharing agreements, and governance mechanisms. It also emphasises feedback flows, especially the use of downstream consumption, waste, and demand data to inform upstream production and processing decisions. Conversely, upstream sustainability, provenance, and production-practice data can enrich downstream retail, labelling, and consumer choice. The central argument is that food system intelligence is not primarily constrained by the absence of data but by the absence of integration and governance. Building this capability requires technical interoperability, institutional trust, reciprocal value creation, and a shift from stage-specific analytics to whole-system reasoning. The proposed framework offers a roadmap for connecting food system data to decisions from farm to fork and from fork back to farm.

Smart farming increasingly depends on data generated by sensors, machinery, farm management systems, remote monitoring tools, digital platforms, and decision-support applications. These data flows support agronomic decision-making, resource efficiency, traceability, automation, advisory services, and supply-chain coordination. As farm operations become more data-intensive, farm data becomes a strategic resource rather than a neutral technical by-product. Farm data creates governance challenges because its production, interpretation, exchange, and reuse are distributed across multiple actors and technical systems. Farmers, technology providers, platform operators, agribusiness firms, advisors, researchers, regulators, and supply-chain actors may each hold different forms of access, control, and responsibility. This creates ambiguity around ownership claims, interoperability obligations, privacy safeguards, consent practices, accountability pathways, and farmer agency. This article develops a layered conceptual framework for farm data governance in smart farming ecosystems. The framework conceptualizes farm data governance as an interaction among data production, technical interoperability, ownership and control, privacy and consent, accountability mechanisms, platform governance, institutional oversight, and farmer agency. It argues that these layers must be analysed together because weaknesses in one layer can undermine governance capacity across the ecosystem. The article synthesizes scholarship on digital agriculture, smart farming, data ownership, interoperability, privacy, accountability, platform governance, farmer agency, and responsible innovation. Rather than treating farm data governance as a purely legal or technical matter, it develops a socio-technical model of how data flows, governance responsibilities, and institutional safeguards interact. This approach supports a more integrated understanding of responsible data governance in smart farming. The proposed framework explains how farm data governance can be organized across technical, organizational, legal, ethical, platform, and institutional layers. It highlights the need for interoperable systems, transparent contracts, meaningful consent, privacy-sensitive data practices, accountable platforms, institutional oversight, and farmer participation. The article contributes a farmer-centered governance model for smart farming ecosystems that are data-driven, trustworthy, and accountable.

Precision agriculture is increasingly organized around farm-generated data, connected machinery, remote sensing, digital advisory tools, analytics platforms, and data-sharing arrangements that reshape how decisions are made, value is created, and responsibility is distributed across agricultural systems. This article develops an original policy framework for farm data sovereignty in precision agriculture, treating sovereignty not as a simple ownership claim but as a governance condition shaped by farmer rights, access, control, consent, portability, transparency, interoperability, benefit-sharing, accountability, and institutional oversight. The framework approach is conceptual and literature-grounded, focusing on how farm data move through platform-controlled infrastructures, service contracts, advisory networks, cooperatives, research systems, and commercial analytics environments. The central policy problem is that farmers may generate data essential to precision agriculture while lacking practical ability to understand, access, transfer, audit, contest, or benefit from data and data-derived services. The article identifies key governance dimensions for fair, transparent, and interoperable farm data ecosystems, including contract fairness, platform accountability, data stewardship, privacy, cybersecurity, algorithmic transparency, dispute resolution, and collective governance. Its contribution is to provide a structured policy framework that helps policymakers, farm organizations, cooperatives, technology providers, advisory services, researchers, and platform developers diagnose governance gaps and design data policies that protect farmer agency while enabling responsible data sharing and innovation in precision agriculture.

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.