AI-enhanced scenario planning for U.S. food trade policy: Anticipating global supply chain shocks and food insecurity risks

Chiamaka, Obunadike Thank God and Alawode, Adedapo (2025) AI-enhanced scenario planning for U.S. food trade policy: Anticipating global supply chain shocks and food insecurity risks. World Journal of Advanced Research and Reviews, 26 (2). pp. 943-962. ISSN 2581-9615

The increasing frequency of global supply chain disruptions—exacerbated by pandemics, geopolitical tensions, climate-related events, and economic volatility—has exposed critical vulnerabilities in U.S. food trade policy. As the United States navigates complex interdependencies in agricultural imports and exports, traditional scenario planning methods often fall short in addressing the velocity and uncertainty of modern supply chain shocks. To strengthen national food security and resilience, there is an urgent need for intelligent, data-driven frameworks that can anticipate risks and support proactive policy formulation. This paper investigates the role of artificial intelligence (AI)-enhanced scenario planning in transforming U.S. food trade policy amid escalating global uncertainty. We present a multi-layered framework that integrates machine learning, agent-based modeling, and geospatial analytics to simulate diverse trade disruption scenarios—ranging from port closures and export bans to climate-induced yield losses. The proposed system leverages real-time data inputs such as trade flows, climate projections, and geopolitical signals to model cascading impacts across domestic supply chains and global food markets. Case studies illustrate how AI-enhanced tools can identify early warning signs, quantify ripple effects of trade policies, and optimize contingency strategies. Special focus is given to evaluating implications for low-income and food-insecure populations within the U.S., ensuring equitable outcomes in policy response. The study also discusses the importance of ethical AI governance, data transparency, and public-private collaboration in shaping responsive and inclusive food trade policy. In conclusion, AI-enhanced scenario planning offers a strategic imperative for safeguarding U.S. food systems against emergent threats, while fostering adaptive, forward-looking trade policy in an increasingly volatile global landscape.