Key agricultural supply risks for global poverty and undernourishment: A systematic risk profiling approach

The intersection of agricultural production shocks—ranging from climate-induced yield failures to conflicts and policy responses—can trigger systemic volatility similar to the 2008 agrifood crisis. Standard Computable General Equilibrium (CGE) assessments typically rely on discrete deterministic counterfactuals that fail to capture the complex, correlated nature of real-world risk factors. This paper addresses a critical gap: How does the complex landscape of agricultural shocks shape global poverty and undernourishment? We propose a framework to move from isolated scenario analysis to a probabilistic "risk map" of global poverty using the Systematic Risk Profiling (SRP) method (Mukashov et al., 2026). Our approach relies on three pillars. Pillar 1: Monte Carlo Simulations. Utilizing historical data, we generate 10,000 large-scale Monte Carlo simulation scenarios of global agricultural production shocks across key global agricultural producers and vulnerable Global South regions. Pillar 2: Microsimulation Integration. We link the global CGE model with continuous household poverty and undernourishment microsimulation modules to dynamically update regional estimates. Pillar 3: Machine Learning for Risk Attribution. We employ Random Forest regression (Permutation Importance) to estimate the relative importance of sampled agricultural shocks on household welfare outcomes worldwide.