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Agricultural artificial intelligence is increasingly shaped by a precision paradigm that treats good farming as the increasingly fine-grained optimization of inputs, outputs, risks, and operational decisions. This paradigm is attractive because it promises measurable efficiency, predictive control, and scalable decision support across complex farming systems. In climate-sensitive agriculture, however, the same orientation can narrow what counts as a good decision. The problem is that climate-sensitive farming is not merely a technical setting in which better data automatically produce better outcomes. Farmers must act under uncertainty, ecological variability, long time horizons, and ethically contested trade-offs involving productivity, resilience, livelihoods, and environmental stewardship. These conditions expose the limits of optimization as a sufficient guide for responsible agricultural AI. This article develops a conceptual framework that shifts the guiding principle of agricultural AI from precision to prudence. Prudence is understood as practical wisdom: the capacity to deliberate well about what is fitting, responsible, and contextually appropriate when action is necessary but certainty is unavailable. The framework therefore reorients AI from prescribing optimal decisions to supporting wise and accountable judgment. The article is based on a conceptual synthesis peer-reviewed publications across agricultural ethics, responsible innovation, precision agriculture, AI governance, climate adaptation, and science and technology studies. It critiques precision agriculture as an optimization paradigm, articulates prudence as a responsible AI principle, and develops a framework for climate-sensitive decision support. Two tables contrast the precision and prudence paradigms and present the proposed conceptual framework. The central argument is not that agricultural AI should abandon data, modelling, prediction, or decision support. Rather, responsible AI for climate-sensitive farming should use these capacities to enlarge human judgment rather than replace it. A prudent AI system helps farmers deliberate under uncertainty, compare trade-offs, question recommendations, and remain accountable for decisions that are ecological, social, and moral as well as technical.

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.

Artificial intelligence is increasingly embedded in smart farming through decision-support systems that integrate sensor data, remote sensing, machinery data, farm records, and predictive analytics. Yet agricultural decision-making cannot be reduced to automated recommendations because farm decisions are shaped by agronomic uncertainty, local field conditions, economic constraints, environmental goals, labor availability, machinery limitations, farmer experience, advisory relationships, and risk preferences. This conceptual framework article develops a farmer-centered account of human-in-the-loop intelligence for AI decision support in smart farming. The framework positions AI not as a substitute for farmer judgment but as one component of an iterative socio-technical decision process. It emphasizes farmer judgment, experiential knowledge, contextual validation, explainability, uncertainty communication, human oversight, trust calibration, advisor mediation, feedback, accountability, and adaptive decision-making. The article argues that human-in-the-loop intelligence is more than human approval of algorithmic outputs; it involves interpretation, questioning, correction, practical adaptation, and responsibility for situated decisions. By integrating agricultural AI, precision agriculture decision support, farmer knowledge, responsible innovation, and human-centered design perspectives, the manuscript contributes a structured conceptual basis for designing smart farming systems that preserve farmer agency while improving the practical relevance, transparency, and accountability of AI-supported decisions.