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Artificial intelligence is increasingly embedded in precision agriculture through sensing, remote observation, predictive modelling, automated classification, and decision-support applications. Technical performance alone, however, is insufficient for determining whether these systems are responsibly designed or appropriately used. This article proposes an original governance framework that connects agricultural context definition, farm-data governance, transparency, explainability, accountability, sustainability assessment, farmer participation, human oversight, risk safeguards, implementation-readiness review, monitoring, and adaptive feedback. The framework treats transparency as a requirement for documenting data provenance, data quality, model assumptions, system limitations, and decision pathways, while explainability is positioned as a task-specific means of supporting interpretation rather than proof of model validity. Accountability is structured through explicit allocation of responsibilities among developers, platform providers, data stewards, advisors, farmers, and oversight actors. Environmental, social, and economic sustainability are assessed alongside digital inclusion, usability, local relevance, farmer autonomy, and trust-building conditions. The framework also addresses algorithmic opacity, bias, model drift, poor local calibration, data misuse, cybersecurity, automation dependence, accountability gaps, and implementation overclaiming. Its principal contribution is a layered, adaptive governance model for responsible agricultural decision support. The framework supports governance planning and research design but does not establish empirical validity, legal compliance, policy acceptance, farmer approval, sustainability performance, trust, certification, or deployment readiness.

Decision-support systems are increasingly positioned as mechanisms for translating agricultural data and analytical models into farm-management recommendations, yet their methodological quality cannot be judged through predictive performance alone. This article proposes an original methodological framework for designing and evaluating decision-support systems in precision agriculture. The framework integrates farm-management decision-context definition, stakeholder mapping, data-quality and provenance assessment, model and recommendation-logic documentation, explainability and interpretability requirements, usability and farmer-centered design, validation planning, decision-reliability assessment, adoption-barrier analysis, implementation-condition assessment, human oversight, and iterative feedback. Explainability is treated as a role-sensitive design requirement involving input visibility, output explanation, decision rationale, uncertainty communication, traceability, and appropriate explanation timing and format. Usability is addressed through farmer participation, co-design, workflow integration, accessibility, local relevance, manageable decision-support workload, training, and feedback mechanisms. Validation is extended beyond internal model testing to include external and local validation, calibration, error analysis, field-validation planning, drift monitoring, and review of the complete recommendation pathway. Adoption is interpreted as a contextual and voluntary process shaped by perceived usefulness, trust, affordability, infrastructure, digital capability, interoperability, support, and governance. Human oversight remains necessary because neither technical performance nor explanation quality independently establishes decision reliability. The framework contributes a structured method for coordinating design and evaluation activities while retaining explicit boundaries against claims of guaranteed decision quality, adoption, or deployment readiness.

Artificial intelligence is increasingly embedded in smart farming systems, yet explanation requirements are often treated as a single technical attribute rather than as stakeholder-dependent needs. This article develops a conceptual taxonomy of explainability requirements across agricultural decision contexts. The taxonomy approach distinguishes stakeholders by their roles, decision consequences, explanation purposes, required explanation objects, usability expectations, accountability needs, trust risks, and implementation boundaries. Farmers may require accessible, locally relevant, actionable, and uncertainty-aware explanations that preserve autonomy. Agronomists and advisors may require model logic, input relevance, data-quality information, output rationale, limitations, and evidence supporting professional validation. Regulatory stakeholders may require traceability, documentation, auditability, risk justification, and clear responsibility allocation. Platform providers, technology providers, data intermediaries, system operators, and data stewards may require lifecycle transparency, monitoring information, provenance records, version histories, and incident reconstruction. Supply-chain and sustainability actors may require interpretable provenance and decision boundaries without treating explanation as proof of quality, sustainability, or compliance. The taxonomy also identifies failures including under-explanation, over-explanation, misleading explanations, uncertainty omission, explanation overload, false confidence, provenance opacity, accountability gaps, and trust miscalibration. Its main contribution is a structured account of how explanation requirements change across stakeholders and decision settings. The taxonomy is conceptual and requires stakeholder validation, usability testing, governance assessment, and domain-specific evaluation before operational application.