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

 Corresponding author: Obunadike ThankGod Chiamaka. Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. AI-enhanced scenario planning for U.S. food trade policy: Anticipating global supply chain shocks and food insecurity risks Obunadike Thank God Chiamaka 1, * and Adedapo Alawode 2 1 Food Economics and Trade, Poznan University of Life Sciences, Poznan, Poland. 2 Department of Agricultural Economics and Agribusiness, New Mexico State University, USA. World Journal of Advanced Research and Reviews, 2025, 26(02), 943-962 Publication history: Received on 31 March 2025; revised on 06 May 2025; accepted on 09 May 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.26.2.1786 Abstract The increasing frequency of global supply chain disruptions—exacerbated by pandemics, geopolitical tensions, climaterelated 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. Keywords: AI Scenario Planning; Food Trade Policy; Supply Chain Shocks; Food Insecurity; U.S. Agriculture; Geopolitical Risk 1. Introduction 1.1. Background: U.S. Food Trade and Global Dependencies The United States plays a pivotal role in the global food supply system, acting both as a leading exporter of agricultural products and a significant importer of various food commodities. The U.S. agricultural sector exports more than $150 billion in products annually, including soybeans, corn, wheat, and dairy goods, serving as a critical pillar in global food security [1]. At the same time, the country depends on imports to meet domestic demand for tropical fruits, vegetables, seafood, and processed food items not produced locally or year-round. This two-way trade flow has created a highly interconnected system where any disruption can cascade through multiple supply chains. World Journal of Advanced Research and Reviews, 2025, 26(02), 943-962 944 These global dependencies have deepened as U.S. agribusinesses increasingly rely on overseas labor, inputs such as fertilizers, and multinational logistics networks. Trade agreements and regional partnerships have further integrated American producers and consumers into a dynamic international marketplace [2]. However, this interdependence also introduces vulnerabilities, especially when geopolitical tensions, trade barriers, or supply bottlenecks disrupt the equilibrium. For example, the reliance on imports from countries with volatile political climates or climate-sensitive agricultural outputs creates exposure to uncontrollable risk factors. Moreover, shifts in consumer preferences and population growth patterns have altered import-export balances. The U.S. now imports substantial quantities of processed food and organic products, further increasing reliance on international certification and supply continuity [3]. These complexities make it essential for policymakers to understand the systemic risks embedded in food trade networks. The growing entanglement of global supply chains in food trade necessitates the development of more agile, data-driven strategic planning tools. In this context, artificial intelligence (AI) emerges not only as a technological advancement but as a strategic imperative for forecasting, risk mitigation, and policy development in the evolving U.S. food trade ecosystem [4]. 1.2. Rise in Global Supply Chain Volatility In recent years, global supply chains have experienced unprecedented volatility, driven by a confluence of economic, environmental, and geopolitical disruptions. The COVID-19 pandemic starkly revealed the fragility of international logistics, with food imports delayed or blocked due to factory shutdowns, port closures, and transportation backlogs [5]. These disruptions led to product shortages, price spikes, and consumer panic—demonstrating how global dependencies can quickly turn into vulnerabilities. Trade conflicts and protectionist policies have further fueled uncertainty. Tariff impositions and retaliatory measures, particularly in U.S.-China agricultural trade, resulted in disrupted market access for key American exports, including soybeans and pork. The unpredictability of such trade actions makes it difficult for producers and importers to engage in long-term planning or sustain stable price margins [6]. Climate change is another compounding factor. Extreme weather events such as droughts, floods, and heatwaves are increasingly affecting harvests, livestock production, and transportation infrastructure globally. For instance, grain exports from drought-affected regions are often reduced or delayed, triggering ripple effects in dependent countries and global markets [7]. Cyberattacks and labor shortages have added further layers of complexity, creating bottlenecks in food processing and distribution systems. With the rise of just-in-time inventory systems, even small delays can result in significant disruptions across the supply chain [8]. In this volatile landscape, traditional supply chain models are proving inadequate. There is a growing need for predictive, adaptable systems that can identify stress points and recommend responsive policy measures in near realtime—a gap that AI technologies are increasingly being used to fill. 1.3. The Role of AI in Strategic Policy Planning Artificial Intelligence (AI) is rapidly emerging as a transformative tool in the domain of strategic policy planning, particularly in complex systems like food trade and supply chain management. AI enables real-time data integration, predictive analytics, and scenario modeling, equipping policymakers with deeper insight into interdependencies, vulnerabilities, and potential interventions. In the U.S. food trade context, these capabilities are increasingly critical as uncertainty and volatility challenge traditional decision-making frameworks [9]. Machine learning algorithms can analyze large volumes of structured and unstructured data—ranging from satellite imagery of crop yields to shipping logs, weather reports, and trade flows—to forecast disruptions before they manifest materially. For example, natural language processing tools can scan global news and policy briefings to detect emerging risks such as trade embargoes or disease outbreaks in agricultural zones [10]. AI-powered dashboards also support scenario planning by simulating the effects of policy changes, climate events, or logistical constraints on national food supply chains. These simulations help decision-makers evaluate the trade-offs and ripple effects of various interventions, including subsidies, import restrictions, or diversification efforts [11]. World Journal of Advanced Research and Reviews, 2025, 26(02), 943-962 945 Moreover, AI tools enhance collaboration between federal agencies, agricultural stakeholders, and logistics providers by enabling centralized, data-driven policy platforms. These platforms can offer early warning systems, risk maps, and optimized contingency strategies that reduce exposure to disruptions and ensure food security [12]. Ultimately, integrating AI into policy development enhances responsiveness, precision, and transparency. As global pressures on the food system intensify, leveraging AI becomes essential not only for crisis management but also for building long-term resilience in the U.S. food trade system. 2. The U.S. food trade landscape and global vulnerabilities 2.1. Key Commodities and Trade Partners The U.S. agricultural export portfolio is both diverse and strategically significant. Key commodities include soybeans, corn, wheat, beef, poultry, and dairy products, which together represent a substantial share of total exports. Soybeans are the most exported commodity by value, often driven by strong demand from Asia. Corn and wheat are critical staples that feed both human populations and livestock across the globe [5]. Meanwhile, high-value exports like beef, pork, and dairy have gained ground in premium markets, underscoring the competitiveness of the U.S. agri-food sector. Trade relationships with key partners underpin the stability and growth of these export flows. China, Mexico, and Canada rank among the top three destinations for U.S. agricultural exports. China’s demand for soybeans and pork, especially after domestic supply shocks such as the African swine fever outbreak, has made it a central trade partner in recent years. Canada and Mexico, through the United States-Mexico-Canada Agreement (USMCA), support high levels of integrated trade, especially in grains, fruits, vegetables, and meat products [6]. Additionally, Japan, South Korea, and the European Union remain critical for specialty exports and processed foods. The strong presence of U.S. food brands and long-standing diplomatic relations enhance access to these regulated, highincome markets. Trade in these regions is often governed by both tariff reductions and harmonization of safety standards [7]. The U.S. also relies on imports of products such as tropical fruits, nuts, coffee, and seafood from Latin America and Southeast Asia. These flows complement domestic production and respond to consumer demand for variety, availability, and seasonal continuity [8]. Understanding the nature and dependency of these bilateral and multilateral flows is essential for effective trade policy, especially amid evolving global risk dynamics. 2.2. Current Trade Agreements and Policy Instruments The U.S. food trade system operates under a complex web of trade agreements and policy tools that shape both export competitiveness and import access. Among the most pivotal is the United States-Mexico-Canada Agreement (USMCA), which replaced the North American Free Trade Agreement (NAFTA). USMCA preserves tariff-free access for most agricultural products and modernizes trade provisions related to biotechnology, sanitary standards, and dispute resolution—streamlining agricultural commerce across the continent [9]. Other key bilateral and multilateral agreements include the U.S.-Japan Trade Agreement, which reduces tariffs on beef, pork, and wine, and the U.S.-Korea Free Trade Agreement (KORUS), which has facilitated a steady increase in American grain, dairy, and fruit exports to South Korea. These agreements are crucial for maintaining competitiveness in highvalue markets, particularly where domestic subsidies or tariffs previously limited access [10]. Despite the benefits of trade liberalization, the U.S. also employs a range of policy instruments to protect domestic producers and manage market volatility. These include export subsidies, tariff-rate quotas, and sanitary or phytosanitary (SPS) measures that govern food safety and quality. The Farm Bill, reauthorized every five years, also contains provisions that impact trade, including crop insurance programs, export market development funding, and emergency food aid mechanisms [11]. The U.S. government frequently negotiates ad hoc trade arrangements to respond to emerging economic or political pressures. For example, during trade tensions with China, retaliatory tariffs led to expanded purchases from Brazil and Argentina, prompting the U.S. to offer subsidies and alternative market access programs to its affected farmers [12]. World Journal of Advanced Research and Reviews, 2025, 26(02), 943-962 946 Although trade agreements create frameworks for stability, their effectiveness depends on enforcement, diplomatic goodwill, and the adaptability of domestic industries. As global conditions evolve, including rising protectionism and regulatory divergence, trade policy must become increasingly agile, data-informed, and resilient to external shocks. 2.3. Vulnerabilities to Disruptions (Pandemics, Conflicts, Climate) The U.S. food trade system is increasingly vulnerable to a range of global disruptions, many of which lie outside the direct control of domestic policy. Pandemics, geopolitical conflicts, and climate-related events can significantly disrupt agricultural production, international logistics, and trade flows. The COVID-19 pandemic demonstrated how a public health crisis could escalate into a full-blown food supply chain emergency. Lockdowns, port restrictions, and labor shortages caused significant delays in both exports and imports, affecting perishables, inputs like seeds and fertilizer, and processing capacity [13]. Conflicts, both trade-related and military, also pose substantial risks. Escalating tensions between the U.S. and major trade partners—such as the U.S.-China tariff war—have resulted in retaliatory measures that disrupted billions in agricultural exports. Political unrest in key export or import regions, such as Eastern Europe or parts of the Middle East and Africa, can affect trade routes, market stability, and the safety of supply chain actors [14]. Additionally, the weaponization of food trade—through sanctions, export bans, or the politicization of SPS standards—creates uncertainties that are difficult to mitigate through traditional policy tools alone. Climate change represents a longer-term but increasingly acute threat. Droughts, floods, wildfires, and shifting weather patterns impact planting cycles, crop yields, and water availability. These effects are uneven across geographies, creating both surpluses and shortages that shift the global balance of supply and demand. For instance, heatwaves in key grain-producing regions have reduced output, forcing importers to seek alternative suppliers—often at higher costs and longer lead times [15]. The convergence of these vulnerabilities necessitates proactive policy and technological adaptation. Traditional forecasting models and trade policies are often too rigid or slow to respond to rapidly evolving threats. Leveraging realtime data analytics, AI-based forecasting, and dynamic trade risk assessments can help decision-makers build a more resilient food trade infrastructure capable of withstanding multi-dimensional shocks [16]. Figure 1 Global food trade flow map showing U.S. import/export dependencies World Journal of Advanced Research and Reviews, 2025, 26(02), 943-962 947 3. Traditional vs. AI-based scenario planning approaches 3.1. Overview of Strategic Foresight in Policy Strategic foresight refers to the structured exploration of potential future developments to inform present-day decisionmaking. In policy contexts, it enables governments to proactively anticipate long-term trends, emerging risks, and transformative opportunities, especially in complex and uncertain domains such as national food security and global trade. Unlike forecasting, which often projects a single outcome based on current trajectories, foresight embraces multiple futures and uses scenario development, horizon scanning, and expert elicitation to prepare for various contingencies [11]. Policymakers use strategic foresight to stress test assumptions, uncover blind spots, and design adaptive strategies that remain robust under different future conditions. For example, anticipating how demographic shifts, technological advancements, or climate change might reshape global food supply chains helps in crafting flexible trade and sustainability policies. Foresight does not predict the future but encourages systems thinking and resilience-building by considering low-probability, high-impact events alongside mainstream developments [12]. Government agencies, including the U.S. Department of Agriculture and international organizations like the OECD and FAO, increasingly integrate strategic foresight into food policy planning. These initiatives support decision-makers in identifying early warning signals and preparing for scenarios such as supply chain disruptions, geopolitical realignments, or abrupt shifts in dietary preferences. When combined with stakeholder engagement and interdisciplinary research, foresight tools foster more inclusive and forward-looking governance [13]. Despite its strengths, the value of strategic foresight is maximized when supported by dynamic and evidence-rich analytics. The rise of artificial intelligence and big data technologies presents new opportunities to strengthen foresight processes with more granular, timely, and adaptive insights into complex policy environments. 3.2. Limitations of Traditional Scenario Planning While traditional scenario planning has long been used to inform public policy and strategic decision-making, it suffers from several structural limitations that hinder its effectiveness in rapidly changing environments. One of the primary weaknesses is its reliance on static, predefined narratives that often fail to accommodate real-time developments or sudden disruptions. This rigidity limits the utility of such scenarios when policymakers must respond to fast-moving crises or complex, multi-dimensional risks like pandemics or cyberattacks [14]. Traditional scenarios are typically generated through expert workshops or Delphi methods, which, although valuable for identifying key drivers of change, are time-consuming and dependent on subjective judgment. These qualitative methods often fail to incorporate real-time data streams or dynamically model the interactions among economic, social, and environmental variables. As a result, many scenario exercises lack predictive precision and cannot adapt to unexpected developments or feedback loops [15]. Another limitation is the tendency to focus on linear extrapolations of the past rather than non-linear, emergent dynamics. For instance, standard food security scenarios may fail to account for the cascading effects of supply chain digitalization, AI-driven farming practices, or the geopolitical weaponization of agricultural trade. Moreover, traditional approaches seldom integrate uncertainty quantification, leaving policymakers unsure about the confidence or probability associated with different outcomes [16]. Given these constraints, conventional scenario planning tools are increasingly insufficient for navigating the volatility and interdependence of modern food trade systems. To remain relevant, they must evolve to incorporate real-time computation, machine learning, and probabilistic forecasting models. 3.3. How AI Enhances Predictive Agility and Precision Artificial Intelligence (AI) significantly enhances the capacity of strategic foresight by addressing the limitations of traditional scenario planning and enabling more agile, data-driven policymaking. Through machine learning algorithms, natural language processing (NLP), and neural networks, AI can analyze vast, multidimensional datasets at high speed, identifying subtle patterns, anomalies, and leading indicators that human analysts might overlook [17]. This capacity is particularly valuable in the context of global food trade, where commodity flows, weather events, political decisions, and consumer behaviors are deeply interlinked and rapidly evolving. World Journal of Advanced Research and Reviews, 2025, 26(02), 943-962 948 AI enables the continuous updating of forecasts as new data becomes available, shifting foresight from static scenario construction to dynamic risk anticipation. For instance, real-time satellite data and climate models can be integrated with trade flows and yield forecasts to simulate the effects of drought in one region on global grain prices and availability. These predictive insights can inform timely interventions, such as pre-emptive import policy adjustments or strategic stockpiling [18]. Furthermore, AI models can generate probabilistic forecasts that quantify uncertainty. Tools such as Bayesian neural networks and ensemble models can express the likelihood of various outcomes, helping policymakers prioritize highimpact risks and allocate resources more effectively. This probabilistic thinking aligns well with strategic foresight principles by encouraging flexible, contingent planning rather than fixed-path assumptions [19]. AI also supports stakeholder inclusivity by visualizing complex data and scenarios through dashboards and decisionsupport tools. These platforms democratize access to insights, enabling collaboration across agencies and sectors. Ultimately, AI transforms strategic foresight into a real-time, adaptive process that enhances preparedness, responsiveness, and long-term resilience in food trade governance [20]. Table 1 Comparison of Traditional vs AI-Enhanced Scenario Planning Frameworks Feature Traditional Scenario Planning AI-Enhanced Scenario Planning Data Usage Limited, historical, often static Real-time, multidimensional, and continuously updated Scenario Generation Expert-driven, narrative-based Algorithmic, data-driven, dynamic Adaptability Infrequent updates, manual revisions Continuous learning and adaptive simulations Risk Quantification Qualitative or heuristic Probabilistic, with uncertainty bounds Stakeholder Involvement Workshop-based, episodic Scalable dashboards, live decision support Geographic and Commodity Resolution National-level focus Subnational and commodity-specific granularity Timeliness Periodic (e.g., annual exercises) On-demand, real-time recalibration 4. Key AI techniques for scenario modeling 4.1. Machine Learning for Demand and Price Forecasting Machine learning (ML) has become a powerful tool for forecasting food demand and price fluctuations in complex global trade systems. Traditional econometric models, such as ARIMA or linear regression, often rely on strong assumptions about data stationarity and linearity. In contrast, ML methods like random forests, support vector regression, and deep neural networks can uncover hidden patterns and nonlinear relationships within vast datasets, improving predictive accuracy in dynamic market environments [15]. In food trade policy, accurate demand and price forecasts are critical for planning imports, regulating subsidies, and avoiding both gluts and shortages. ML models can ingest real-time data from multiple sources—such as historical price trends, macroeconomic indicators, weather patterns, and trade volumes—to predict shortand long-term outcomes more effectively than static models. For example, neural networks have been successfully applied to forecast price volatility in commodity markets such as wheat, rice, and corn, capturing seasonal patterns and sudden shocks due to climate or geopolitical events [16]. Moreover, ML techniques can support subnational forecasting, helping policymakers understand consumption trends across different regions or demographic groups. This granular view supports targeted policy interventions, such as localized food assistance or infrastructure investment to address anticipated bottlenecks [17]. When integrated into supply chain management systems, ML forecasts enable better inventory planning, procurement decisions, and logistics coordination. Governments can use these models to pre-position strategic reserves or adjust import schedules, reducing costs and mitigating risks. By capturing the complexity of global food markets, ML World Journal of Advanced Research and Reviews, 2025, 26(02), 943-962 949 contributes to more agile, informed, and data-driven decision-making frameworks that are essential in today’s volatile economic landscape [18]. 4.2. Natural Language Processing (NLP) for Trade Intelligence Natural Language Processing (NLP), a subfield of artificial intelligence, enables machines to process and interpret human language from unstructured text sources. In the context of food trade policy, NLP can serve as a powerful tool for trade intelligence by extracting actionable insights from news articles, policy documents, social media, and diplomatic communications. These sources often contain early signals of disruptions, such as export bans, regulatory shifts, or labor unrest, which may not yet be reflected in quantitative datasets [19]. By applying entity recognition, sentiment analysis, and topic modeling, NLP tools can monitor global narratives surrounding food markets and trade agreements. For instance, a sudden rise in negative sentiment toward wheat exports in major producing countries may signal a pending policy shift or domestic shortage. NLP models can alert policymakers to these trends in near real-time, supporting proactive responses to mitigate impacts on domestic food prices or availability [20]. Multilingual capabilities allow NLP systems to scan local media in multiple languages, increasing geographic coverage and contextual awareness. This is particularly valuable in tracking developments in politically sensitive or high-risk regions. Governments and international organizations can use NLP-driven dashboards to enhance situational awareness and improve diplomatic coordination in trade negotiations. When integrated with predictive models, NLP outputs can strengthen forecasting systems by adding qualitative, context-rich inputs. This synergy between structured and unstructured data sources enables a more comprehensive understanding of global trade dynamics, ensuring that food policy remains responsive to both data and discourse [21]. 4.3. Agent-Based Modeling and Reinforcement Learning Agent-based modeling (ABM) and reinforcement learning (RL) offer innovative frameworks for simulating food trade dynamics and testing the effectiveness of policy interventions under various conditions. ABM involves constructing virtual environments populated by autonomous agents—such as governments, traders, consumers, and producers— each with distinct goals, constraints, and adaptive behaviors. These agents interact based on defined rules, allowing the emergence of complex, system-level phenomena such as market fluctuations, supply chain bottlenecks, or cooperative alliances [22]. ABMs are particularly suited to exploring non-linear, path-dependent systems where top-down equations may fail to capture dynamic feedback loops. For example, policymakers can use ABMs to simulate the impact of export tariffs on soybean flows, observe how domestic producers and importers adapt, and identify unintended consequences such as regional food insecurity or price inflation [23]. This type of modeling enables robust scenario analysis, highlighting policy leverage points and trade-offs. Reinforcement learning complements ABM by enabling agents to learn optimal strategies through trial and error within simulated environments. In an RL framework, agents receive rewards or penalties based on the outcomes of their actions, allowing them to iteratively improve decision-making. Applied to food trade, RL algorithms can simulate supply chain optimization, import substitution strategies, or emergency response planning under uncertainty [24]. Together, ABM and RL create a sandbox for testing adaptive policies in volatile conditions. For instance, an RL agent representing a food security agency might learn the best timing and quantity for grain imports to stabilize prices while minimizing costs. These tools can also simulate competitive behaviors, such as trade retaliation or hoarding, enabling policymakers to anticipate geopolitical consequences. By capturing adaptive, decentralized decision-making, ABM and RL help bridge the gap between technical models and real-world complexity, offering flexible platforms for future-proofing food trade policy [25]. 4.4. Generative Models for Hypothetical Disruption Scenarios Generative models, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), are increasingly being applied in policymaking to simulate hypothetical disruption scenarios and assess resilience in complex systems like global food trade. These models learn the underlying structure of high-dimensional data and World Journal of Advanced Research and Reviews, 2025, 26(02), 943-962 950 generate synthetic outputs that resemble real-world phenomena, making them powerful tools for stress testing and risk forecasting [26]. In food policy, generative models can simulate low-frequency, high-impact events—such as coordinated export bans, cyberattacks on port infrastructure, or simultaneous crop failures across major producing regions. Traditional models may struggle with these outlier events due to data sparsity or restrictive assumptions. By contrast, generative models can produce realistic, data-informed scenarios that policymakers can use to test emergency preparedness plans or evaluate supply chain redundancy [27]. For example, a VAE trained on historical trade and price data can be used to generate alternate realities where specific disruptions occur, enabling the exploration of cascading effects on food availability, price volatility, and regional hunger risks. GANs, meanwhile, can generate synthetic climate anomalies to test the sensitivity of agricultural outputs and trade balances under extreme weather conditions [28]. These models also support data augmentation for rare-event training in machine learning pipelines, improving the robustness of predictive systems in crisis detection and response. When integrated with decision-support tools, generative scenarios help stress test procurement strategies, reserve management, and diplomatic responses under diverse and complex disruption profiles. Ultimately, generative models expand the strategic horizon of policymakers by enabling “what-if” analysis beyond historical precedent, fostering innovation in risk assessment and adaptive planning for global food trade [29]. Figure 2 Architecture of an AI-driven scenario planning system in food trade 5. Anticipating and modeling global supply chain shocks 5.1. Simulating Shock Events: Droughts, Export Bans, Conflicts Simulating shock events such as droughts, export bans, and geopolitical conflicts is crucial for building resilient food trade systems and informing policy design. These shocks often unfold unpredictably, yet their impacts can be devastating, cascading through global food networks with speed and intensity. Advanced simulation tools—particularly those powered by agent-based modeling, probabilistic forecasting, and scenario generation—enable policymakers to anticipate how these events might disrupt supply chains, affect market prices, and endanger food security [19]. Droughts are among the most frequent and impactful natural shocks to agriculture. By integrating satellite-derived climate data with crop models and trade flow databases, simulations can estimate reductions in yield and production, especially in key grain-exporting regions. These projections can be used to trigger early warnings for food-importing countries and guide import diversification strategies or the release of strategic reserves [20]. For example, modeling a drought in the Midwest United States can reveal potential downstream effects on corn prices, ethanol production, and livestock feed costs globally. Export bans represent another common policy-induced shock. Countries may implement temporary bans to preserve domestic food supply during times of crisis, but such actions often disrupt global trade flows and amplify scarcity in World Journal of Advanced Research and Reviews, 2025, 26(02), 943-962 951 food-importing nations. Simulations can help evaluate the global implications of these decisions, showing how food prices respond and how other exporters adjust their trade patterns in response [21]. Armed conflicts, especially in agriculturally productive regions, disrupt not only farming activities but also transport infrastructure and labor availability. Simulating these disruptions involves modeling trade rerouting, port closures, and commodity substitution. For example, disruptions in the Black Sea region can impact wheat and sunflower oil markets, prompting ripple effects in Africa and South Asia [22]. By reproducing these scenarios under varying intensities and durations, simulation tools help assess policy options such as subsidies, buffer stocks, or emergency import authorizations. They provide valuable insights that can improve preparedness and accelerate coordinated responses across governments and international agencies. 5.2. Cascading Effects Across Trade Networks Food trade networks are characterized by intricate interdependencies that amplify the effects of localized disruptions into global supply shocks. Understanding these cascading effects requires systems-level modeling that captures how shocks propagate across regions, commodities, and supply chain actors. Network-based simulations, which treat countries or trade hubs as nodes connected by trade flows, help reveal points of vulnerability, resilience, and risk amplification in real time [23]. When one country experiences a supply shock—due to drought, export restrictions, or labor strikes—the immediate effect is a reduction in export capacity. This leads to shortages or price increases for importing nations. However, the impacts seldom remain isolated. Importers must quickly seek alternative suppliers, often turning to countries with marginal excess capacity. This sudden demand spike can stress those secondary suppliers, leading to price inflation and supply rationing in unrelated markets [24]. Such chain reactions are especially pronounced for staple commodities like wheat, rice, and soybeans, where a handful of countries dominate global exports. For instance, a restriction on palm oil exports from a major supplier may result in increased global demand for soybean and sunflower oil, inflating prices across edible oil markets [25]. These secondary effects are difficult to detect without detailed simulations that model elasticity, substitution, and market reallocation dynamics. Cascading effects also manifest in logistics infrastructure, such as port congestion, shipping delays, and storage overflow. For example, if ports in one region become bottlenecked due to redirected flows, perishable goods may spoil, and landlocked nations may lose access to critical imports. Network simulations help visualize such stress points and test mitigation strategies like infrastructure scaling, transshipment agreements, or alternate corridor development [26]. Crucially, cascading effects are not only economic—they also include social and political dimensions. Rapid food price inflation has historically triggered social unrest, particularly in vulnerable regions. By modeling these secondand thirdorder impacts, trade policymakers can take a proactive approach to crisis prevention and systemic stability [27]. 5.3. Multi-Scenario Simulations for Policy Impact Assessment Multi-scenario simulation is a cornerstone of modern policy analysis, especially in domains marked by uncertainty and interdependence like international food trade. Unlike single-event modeling, which evaluates the impact of a predefined shock, multi-scenario simulations test a range of conditions—including compound events, recovery trajectories, and behavioral adaptations—to assess the robustness of policy decisions across potential futures [28]. Using probabilistic models, agent-based simulations, or system dynamics, policymakers can explore “what-if” questions under varying assumptions. For instance, a government might assess how simultaneously experiencing a domestic drought and an international export ban would affect national food security, foreign reserves, and trade balances. Each scenario offers different policy implications—requiring distinct responses such as scaling up food assistance, activating trade contingency plans, or adjusting tariff schedules [29]. Scenarios can also account for gradual trends, such as declining soil fertility or shifting dietary patterns, in addition to acute shocks. This allows policymakers to assess long-term investments like diversification of crop portfolios, infrastructure resilience, or regional trade integration. When combined with cost-benefit analysis, scenario modeling enables better prioritization of limited policy resources [30]. World Journal of Advanced Research and Reviews, 2025, 26(02), 943-962 958 need to invest in interoperable digital infrastructure, ensure cross-agency collaboration, and institutionalize AI into routine trade planning workflows. Future adaptations must focus on scaling these tools, enhancing transparency, and bridging data gaps to ensure equitable and resilient food systems capable of withstanding future shocks and strategic uncertainties [40]. 9. Challenges and ethical considerations 9.1. Data Gaps and Model Transparency Despite the growing integration of artificial intelligence into food trade policy, persistent data gaps and model opacity remain significant challenges. In many regions, particularly low-income and politically unstable areas, reliable data on agricultural production, prices, logistics, and consumption is either outdated, incomplete, or unavailable. This lack of coverage weakens the predictive power of AI tools and skews the accuracy of global food trade simulations [38]. Furthermore, many AI and machine learning models, particularly deep learning architectures, function as "black boxes"—providing predictions without clear explanations of how those outcomes were derived. For policymakers, this lack of model transparency impedes trust, interpretability, and accountability. When decisions involving subsidies, trade restrictions, or emergency aid are based on opaque algorithms, it becomes difficult to validate their fairness or assess their performance in hindsight [39]. Addressing these concerns requires building interoperable, open-source platforms that standardize food trade data collection and documentation. It also calls for adopting explainable AI techniques—such as model interpretability layers, decision trees, or SHAP values—that clarify variable importance and causal pathways. Transparent model documentation and auditable codebases are essential for aligning predictive systems with institutional norms and public oversight requirements [40]. 9.2. Bias, Fairness, and Equity in Predictive Tools As predictive tools become more prevalent in guiding food trade policy, concerns over algorithmic bias and fairness grow increasingly urgent. AI models trained on historical trade data may reproduce existing inequalities, such as favoring exporters with more established logistics infrastructure or underrepresenting smallholder farmers from developing nations. These biases can skew policy recommendations, further marginalizing vulnerable stakeholders [41]. Moreover, models that optimize purely for efficiency or market responsiveness may overlook equity-oriented outcomes, such as access to affordable food in remote regions or the economic viability of subsistence farming communities. Without deliberate attention to distributive fairness, AI systems risk reinforcing existing structural imbalances in global food trade [42]. Ensuring equity requires embedding fairness constraints directly into model design and training processes. This includes balancing performance across diverse population groups, explicitly modeling trade-offs between efficiency and social goals, and consulting marginalized communities in data governance and system design. Furthermore, impact assessments should be conducted to evaluate how AI-driven decisions affect different stakeholders—particularly in regions with limited bargaining power or voice in international trade forums [43]. Promoting fairness in AI for food trade is not only a technical challenge—it is an ethical and governance imperative for sustainable and inclusive food systems. 9.3. Legal and Governance Implications The application of AI in food trade forecasting and policy carries significant legal and governance implications, particularly as automated decisions increasingly influence market access, trade negotiations, and crisis responses. At present, there is limited regulatory clarity on the standards and liabilities associated with algorithmic decision-making in trade policy. This creates a gray zone where accountability for errors, bias, or unintended outcomes may be difficult to assign [44]. Issues such as data privacy, cross-border data sharing, and intellectual property rights also complicate AI deployment. Many food trade models rely on sensitive commercial data or geopolitical intelligence, raising questions about how such information is shared, protected, and used. Without clear legal safeguards, both data providers and governments may hesitate to engage in collaborative predictive analytics [45]. World Journal of Advanced Research and Reviews, 2025, 26(02), 943-962 959 Moreover, the adoption of AI must align with existing international trade agreements and frameworks, including WTO provisions on transparency, non-discrimination, and science-based decision-making. Any AI-derived policy action— such as imposing import restrictions based on predictive risk—must be defensible under international law to avoid trade disputes [46]. To address these concerns, national and international institutions must develop AI governance frameworks specific to the food trade context, ensuring that transparency, legality, and ethical integrity are maintained throughout the lifecycle of predictive decision-making systems [47]. Table 4 Sample AI-Informed Policy Responses Under Different Stress Scenarios Stress Scenario AI Tool Applied Policy Response Enabled Severe export restriction in top wheat-exporting nations Bayesian forecast model Diversification of sourcing and emergency quota exemptions Climate-induced multi-country crop failure Generative scenario modeling Strategic reserve activation and price control mechanisms Port closure due to labor disruption Agent-based logistics simulation Alternative routing policy and temporary import waivers Trade conflict escalation with major partner NLP and sentiment analysis of diplomatic communications Bilateral renegotiation with fallback clauses Pandemic resurgence with global shipment delays ML predictive analytics with real-time logistics inputs Import timing adjustments and subsidy triggers for perishables 10. Conclusion Summary of Key Insights and Contributions This article has explored how artificial intelligence (AI), combined with advanced modeling techniques, can reshape the landscape of U.S. food trade policy. In an era defined by supply chain volatility, climate unpredictability, and geopolitical instability, traditional tools of policy planning—while foundational—are no longer sufficient for managing complex and dynamic global food systems. AI technologies such as machine learning, natural language processing, generative models, and agent-based simulations offer scalable solutions for predictive analytics, crisis response, and scenario planning. Key insights reveal that AI can significantly enhance demand forecasting, price volatility analysis, and disruption anticipation. From COVID-19-related supply chain breakdowns to the wheat shortage resulting from the Ukraine-Russia conflict, AI systems have already demonstrated value in supporting real-time policy decisions and resource reallocation. Moreover, AI augments trade negotiations and federal planning by enabling dynamic tariff adjustments, probabilistic risk simulations, and early-warning systems for market and supply shifts. The article also highlights challenges that must be addressed for AI’s full potential to be realized in this domain. These include data sparsity in emerging markets, opacity in complex models, the risk of algorithmic bias, and the need for ethical guardrails in policymaking. Despite these barriers, the opportunities for AI to support equitable, transparent, and resilient trade systems are profound. Through case studies, simulation examples, and policy recommendations, this article contributes a framework for integrating AI into the institutional fabric of food trade governance. It advocates for a shift from reactive to anticipatory strategies, empowering U.S. agencies to not only respond to crises but to proactively manage risk and strengthen the long-term resilience of domestic and global food systems. Call for Multi-Stakeholder Collaboration in AI Governance Effective AI integration into food trade policy cannot rest solely on government agencies or private-sector innovation. It requires a concerted, multi-stakeholder approach that brings together policymakers, technologists, agricultural producers, data scientists, academic institutions, civil society, and international organizations. Each actor plays a critical role in shaping not just the technical capabilities of AI systems, but also their ethical, legal, and social implications. World Journal of Advanced Research and Reviews, 2025, 26(02), 943-962 960 Collaboration must start with shared data infrastructure—standardized, secure, and interoperable across agencies and borders. Public-private partnerships can accelerate the development of open-access models, inclusive data collection efforts, and localized applications tailored to the needs of underserved regions. At the same time, academic and civil society actors should contribute to independent impact assessments, fairness audits, and transparency benchmarks that keep powerful tools accountable. Policy governance bodies must create participatory frameworks where all stakeholders have a voice in the design and deployment of AI systems. This includes integrating feedback from smallholder farmers, trade unions, and food security experts to ensure technology aligns with societal goals. Only through collective governance can AI evolve as a force for equitable and resilient food trade, addressing current vulnerabilities while preparing for future uncertainties. Final Thoughts on Future-Proofing U.S. Food Trade Policy Future-proofing U.S. food trade policy requires bold innovation grounded in adaptability and foresight. AI offers the tools to anticipate disruption, optimize responses, and foster resilience in the face of uncertainty. Yet its success hinges on transparent governance, inclusive collaboration, and the ethical use of data and algorithms. As risks become more complex and interconnected, policymakers must embrace AI not as a standalone solution, but as a strategic enabler embedded in a broader vision of food security, economic stability, and global cooperation. Proactive, intelligence-driven policy is no longer optional—it is essential for sustaining the future of food trade. Compliance with ethical standards Disclosure of conflict of interest No conflict of interest to be disclosed. References [1] Dhal SB, Kar D. 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