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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 depends on distributed data from sensors, remote sensing, machinery, crop and livestock monitoring, weather services, and farm-management systems. Collaborative model development can use these resources, yet central collection may intensify privacy, ownership, access, control, cybersecurity, participation, and secondary-use concerns. This article proposes a conceptual federated learning architecture in which farms retain raw data locally while contributing model updates to coordinated cross-farm learning. The architecture integrates use-case definition, stakeholder and governance mapping, farm-data sensitivity assessment, provenance and quality review, local node configuration, local model training, protected update exchange, secure aggregation, global and local evaluation, cross-farm validation, heterogeneity assessment, privacy safeguards, security controls, drift monitoring, auditability, and farmer feedback. It distinguishes horizontal federated learning from conditional vertical and transfer configurations and connects these principles to bounded agricultural use cases, including crop monitoring, disease detection, yield prediction, remote-sensing interpretation, livestock monitoring, and human-reviewed decision support. Secure aggregation, differential privacy, homomorphic encryption, and trusted execution environments are treated as context-dependent safeguards rather than guarantees. The contribution links decentralized model development with governance, accountability, communication constraints, non-identically distributed data, validation, monitoring, and claim boundaries. It supports research and design but does not establish certification, legal compliance, superiority, farmer approval, operational safety, or deployment readiness.