From Reactive Administration to Predictive Agricultural Governance: Artificial Intelligence and the Transformation of Risk Management in the Jordanian Ministry of Agriculture

Abstract This dissertation examines how artificial intelligence can strengthen risk management within the Jordanian Ministry of Agriculture in Amman by shifting institutional practice from reactive administration toward predictive governance. The study is grounded in a national context shaped by severe water scarcity, climate pressure, pest threats, supply vulnerability, and the need for more agile public-sector decision-making. Official Jordanian sources show that the Ministry of Agriculture has already committed itself to digital modernization through electronic agricultural services, smart applications, and increased digital service delivery, while Jordan’s National Food Security Strategy calls for regular and systemic data collection, monitoring, evaluation, reporting, and the digitization of food-security processes and services (Government Budget Department, 2025; Hashemite Kingdom of Jordan, 2021). At the same time, Petra reported in 2024 that Jordan launched a national food security management system designed to track food stocks, predict supply conditions, and support food-security decision-making across the Kingdom (Jordan News Agency, 2024). FAO’s Jordan country materials and regional documentation further confirm the structural vulnerability of the agricultural sector under conditions of water scarcity and limited arable land (FAO, n.d.; FAO, 2024a). The dissertation argues that Jordan has now moved beyond the stage where digitalization alone is sufficient. What is required is an integrated model of AI-enabled agricultural risk governance that combines interoperable data systems, predictive analytics, explainable decision support, human oversight, and governance assurance. This position is supported by recent academic and policy literature. NIST’s Artificial Intelligence Risk Management Framework identifies trustworthiness characteristics such as validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and fairness with harmful bias managed (NIST, 2023). OECD principles similarly emphasize transparency, explainability, human oversight, and accountability (OECD, 2024). Recent public-sector AI scholarship further argues that good governance of AI-supported public services must serve both effective administrative order and the just treatment and wellbeing of citizens (Mišić et al., 2025). The central contribution of this dissertation is the design of a Jordan-specific institutional model: the AI-Enabled Agricultural Risk Governance Framework (AI-ARGF). The framework is structured around five layers: integrated risk data, predictive analytics, explainable decision support, operational response, and governance assurance. It is intended to support the Ministry’s ability to anticipate threats, prioritize interventions, strengthen food-security monitoring, improve pest and climate risk response, and allocate scarce administrative attention more intelligently. The dissertation concludes that AI should not be treated as a procurement trend or an isolated technical tool. In the Jordanian Ministry of Agriculture, AI is best understood as a governable institutional capability that must be designed, supervised, explained, audited, and aligned with public value. Keywords: artificial intelligence, agricultural risk management, Jordan, Ministry of Agriculture, predictive governance, food security, public-sector AI Executive Summary Jordan’s agricultural sector operates under severe ecological and administrative pressure. FAO notes that Jordan has limited arable land due to aridity and water scarcity, while the National Food Security Strategy frames food security as a multi-institutional issue requiring governance, coordination, data systems, and strategic planning (FAO, n.d.; Hashemite Kingdom of Jordan, 2021). This combination of ecological exposure and institutional complexity makes agricultural risk management a strategic state function rather than a narrow technical exercise. The Ministry of Agriculture is already moving toward digital transformation. The 2025 budget chapter identifies the shift to a digital green economy, the launch of electronic agricultural services, and the use of applications and smart solutions in agriculture as explicit priorities. The same document refers to increasing the number of digital services and increasing users of the Ministry’s smart applications (Government Budget Department, 2025). Separately, the ARDI environmental and social systems assessment describes an improved portfolio of agriculture-relevant digital applications and services, including the development and management of an early warning system offering alerts to farmers in case of risks such as natural disasters (World Bank, 2022). Jordan’s AI policy environment creates an additional enabling condition. The Jordanian Artificial Intelligence Strategy and Implementation Plan 2023–2027 includes a five-year implementation plan with 68 projects and identifies practical AI deployment in priority sectors, including agriculture and digital government. The strategy brochure explicitly mentions the use of AI in the agriculture sector through UAVs to classify soil fertility and through early warning systems for frost affecting producers (Ministry of Digital Economy and Entrepreneurship, 2023). The dissertation’s main argument is that these developments remain fragmented unless they are organized into a single institutional architecture for AI-enabled agricultural risk governance. The proposed AI-ARGF model therefore translates Jordan’s digital and policy momentum into a ministry-wide design for predictive governance. It does so through five linked components: data integration, analytics, explanation, operational response, and governance assurance. The framework is intentionally designed for public administration rather than for farm-level automation alone. Its purpose is to improve anticipation, prioritization, coordination, and accountability inside the Ministry of Agriculture in Amman. The dissertation recommends that the Ministry establish AI governance before scale, begin with explainable high-value pilot use cases, treat human oversight as mandatory, and evaluate AI not only by prediction accuracy but also by workflow usability, transparency, documentation quality, and public-sector legitimacy. In practical terms, the strongest pilot domains are pest surveillance, food-security alert support, climate and frost warning, and risk-based inspection prioritization.