Search

Artificial intelligence is increasingly positioned as a driver of agri-food transformation through sensing, prediction, optimisation, automation, and decision support. Across farms, supply chains, advisory systems, and governance settings, AI-enabled tools are changing how agricultural information is gathered, interpreted, and translated into action. These changes create important opportunities, but they also raise deeper theoretical questions about what kind of intelligence agri-food systems should develop. Much discussion of agri-food AI emphasises productivity, efficiency, technical accuracy, and optimisation. Yet sustainable agri-food transformation cannot be judged only by whether AI increases yields, lowers costs, or accelerates decisions. It must also be evaluated by whether intelligent systems strengthen resilience, support equity, and respect ecological limits. This article develops a theory of sustainable intelligence in agri-food systems. Sustainable intelligence is defined as the AI-enabled capacity of agri-food systems to sense, interpret, learn from, and respond to changing conditions while balancing productivity, resilience, equity, and ecological care. The theory treats these four dimensions not as separate goals, but as interdependent constructs that must be continuously aligned. The article builds theory by integrating scholarship on digital agriculture, food-system sustainability, resilience thinking, equity, ecological responsibility, and responsible AI. It focuses on the mechanisms through which sensing, prediction, optimisation, feedback, participation, and governance can either mediate or intensify tensions among agri-food system goals. In doing so, it shifts attention from AI as a technical instrument to AI as a socio-technical balancing capacity. The proposed theory explains sustainable intelligence as an emergent property of data integration, adaptive feedback, participatory governance, ecological sensitivity, and accountability. It argues that AI-enabled agri-food systems become sustainable not when they maximise a single performance metric, but when they help actors recognise trade-offs, negotiate priorities, and preserve the conditions for just and ecologically responsible production. Sustainable intelligence therefore offers a theoretical foundation for agri-food futures that are productive, resilient, equitable, and ecologically caring.

Agri-food systems increasingly rely on analytics, artificial intelligence, machine learning, environmental monitoring, and decision-support tools to evaluate sustainability performance. These systems can estimate resource use, model environmental impacts, detect risks, and inform actions across farms, supply chains, markets, and governance settings. As analytics become embedded in sustainability decisions, their outputs increasingly shape how environmental problems are recognised and acted upon. Yet sustainability analytics does not become useful or responsible simply because it produces indicators, predictions, rankings, or dashboards. Environmental metrics may be technically sophisticated while remaining difficult for farmers, firms, auditors, regulators, and communities to interpret or challenge. This creates an explainability problem in which users need to understand not only what an analytic system reports, but also how the result was produced, what assumptions it contains, and what consequences may follow from acting on it. This article develops a conceptual framework for explainable sustainability analytics in agri-food systems. The framework treats explainability as a system-level requirement for linking environmental impact interpretation, risk explanation, responsibility attribution, human review, and accountability safeguards. It argues that sustainability analytics should be evaluated not only by predictive performance or measurement coverage, but also by whether it supports transparent, contestable, and context-sensitive decision-making. The article contributes a structured framework for interpreting environmental impact, explaining risk, attributing responsibility, and supporting accountable review in agri-food decision contexts. It positions explainable sustainability analytics as an approach that connects data, models, indicators, explanation mechanisms, human judgement, and governance safeguards. In doing so, it provides a foundation for sustainability analytics that is more interpretable, trustworthy, and actionable.

Agri-food systems increasingly rely on artificial intelligence for farm decision support, automation, quality control, sustainability monitoring, logistics, supply-chain coordination, risk prediction, and resource management. These applications promise productivity gains and improved responsiveness to environmental and market pressures. Yet their growing influence also creates new governance demands because AI systems can shape decisions that affect farms, firms, workers, consumers, ecosystems, and public institutions. Trustworthy AI in agri-food systems cannot be established through technical performance claims alone. A model may be accurate in a narrow test environment while remaining opaque, unsafe under changing field conditions, unfair across producer groups, or unable to substantiate sustainability claims. Certification therefore needs to move beyond reputational signalling and provide credible assurance that AI systems can be examined, challenged, monitored, and held accountable. This article develops a trustworthy AI certification framework for agri-food systems centred on auditability, safety, fairness, and sustainability-claim verification. The framework treats certification as a governance mechanism rather than a simple label or checklist. It identifies the evidence, assurance procedures, monitoring expectations, and institutional safeguards needed to make certification credible in technically complex and socially consequential agri-food settings. The article contributes a structured approach for regulators, certification bodies, technology providers, agri-food firms, farmer organisations, sustainability auditors, and public institutions. It argues that trustworthy AI certification should depend on verifiable evidence, independent auditability, lifecycle risk assurance, distributional accountability, environmental claim verification, and clear responsibilities across the AI value chain. Certification becomes meaningful only when it supports systems that are auditable, safe, fair, environmentally credible, and publicly accountable.