Introduction
Artificial intelligence (AI) is rapidly transforming agricultural and food systems, enabling new forms of data-driven decision-making, automation, and predictive analytics across the agrifood value chain. AI-enabled tools are increasingly applied in precision agriculture, autonomous machinery, climate forecasting, and digital traceability systems, with demonstrated potential to improve productivity, resource efficiency, and sustainability (Mana et al., 2024; Wu et al., 2025).
However, the rapid integration of AI into agriculture raises a central governance challenge: how can regulatory systems effectively oversee AI technologies that are dynamic, data-intensive, and embedded across diverse agricultural contexts? This paper addresses how existing and emerging regulatory frameworks can govern AI systems in agriculture and food systems, and what adaptive regulatory approaches are needed to manage their evolving risks while supporting innovation.
Agriculture provides a particularly important setting for adaptive governance because AI systems operate within highly dynamic environmental, biological, and economic conditions. Machine-learning models used for irrigation, pest detection, autonomous machinery, and supply-chain management may perform differently across seasons, regions, crop systems, and climatic conditions. As a result, governance challenges often arise after deployment rather than during initial approval. Static regulatory approaches based on fixed technologies may struggle to address evolving risks, making adaptive governance especially relevant for agricultural AI applications.
AI systems introduce new risks related to data privacy, algorithmic bias, system opacity, and unequal access to digital infrastructure, which may exacerbate existing inequalities within food systems (World Bank, 2025; Yuan & Sun, 2025; Ragany et al., 2026). Despite rapid advances in digital agriculture, empirical and policy research on AI governance in agriculture remains limited relative to sectors such as finance and healthcare, where regulatory frameworks and institutional responses have been more extensively studied. This gap is particularly important given the sector’s unique characteristics, including environmental variability, decentralized decision-making, and the critical role of food systems in economic and social stability. Recent reviews similarly argue that while AI offers substantial opportunities for improving agricultural productivity and resilience, governance frameworks must address privacy, algorithmic bias, unequal digital access, transparency, liability, data quality, and stakeholder trust (Omotayo et al., 2025; Ragany et al., 2026).
Existing AI governance frameworks have increasingly adopted risk-based approaches, such as those embodied in the European Union’s AI Act, which calibrate regulatory oversight according to system risk and potential harm (OECD, 2023; Val, 2025). However, these frameworks have largely been developed for cross-sectoral applications and face limitations when applied to agriculture, where AI systems interact with environmental and biological processes and fragmented institutional arrangements.
This paper makes three contributions to the emerging literature on AI governance. First, it extends predominantly cross-sectoral AI governance frameworks by situating them within the institutional and environmental context of agriculture. Second, it develops a functional, case-based analytical framework showing how AI governance is activated through existing regulatory regimes rather than through standalone AI statutes. Third, it identifies concrete adaptive regulatory mechanisms such as lifecycle monitoring, embedded compliance, and standards-based oversight that can be operationalized in real-world agricultural applications.
This article adopts a qualitative, multi-method approach combining regulatory analysis, comparative case studies, and cross-sector comparison. Three agricultural AI applications are examined to explore how governance challenges emerge across different technological contexts. Detailed methodological procedures are described in Section 2. The analysis focuses primarily on the United States, where AI governance is emerging through a combination of agency guidance, technical standards, and sector-specific regulation. The European Union is an important comparative benchmark because the EU AI Act is the most comprehensive risk-based AI regulatory framework currently in force.
The paper proceeds as follows. Section 2 describes the analytical approach and case-study methodology. Section 3 reviews the evolving policy landscape for AI governance in agriculture, focusing on regulatory developments in the United States and the European Union, as well as international frameworks. Section 4 presents three case studies—drone-based monitoring, AI-driven irrigation, and blockchain-enabled traceability—to illustrate how AI governance operates in practice. Section 5 draws comparative lessons from adaptive regulatory approaches in other sectors, including financial technology and digital health. Section 6 concludes with implications for the design of adaptive AI governance frameworks in agriculture and food systems.
This project was made possible through the support of Grant 63641 from the John Templeton Foundation. The opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of the John Templeton Foundation. For more information, visit The Next Frontier: Rethinking Regulation in an Era of Rapid Innovation.