Sustainability and adaptation dynamics in Global Food Security: A Bayesian Belief Network approach

Abstract

This study investigates the dependencies between the Global Food Security Index (GFSI) and indicators related to sustainability and adaptation using Bayesian Belief Network (BBN) models. Methodologically, the study utilizes the forward and backward propagation capabilities of BBNs in GeNIe software to identify critical indicators. It also examines the predictive power of these indicators by analyzing their mutual value of information in Hugin software. Data utilized in the analysis include GFSI data for 2022 and associated sustainability and adaptation indicators. Key findings reveal the critical importance of proactive risk management strategies, sustainable agricultural practices, and effective governance mechanisms in shaping food security outcomes. Disparities between high- and low-performing countries in GFSI outcomes underscore the need for tailored interventions to address systemic vulnerabilities. High-performing countries in the food security environment exhibit a very high probability of 93% of achieving high performance in the national agricultural adaptation policy. Conversely, low-performing countries exhibit a very high probability of 91% of achieving low performance in the agricultural water risk – quality indicator. The study contributes theoretically by uncovering the complex interactions within food systems and practically by offering insights for policymakers and practitioners to develop targeted interventions. The implications of the findings emphasize the importance of integrated approaches and evidence-based policymaking to address food security challenges effectively.