A Bayesian insight into improving national food security

Ujjwal Kc (), Lilly Lim-Camacho (), Rachel Friedman () and Steven Crimp ()
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Ujjwal Kc: Melbourne Institute of Applied Economic and Social Research, The University of Melbourne
Lilly Lim-Camacho: St Lucia, CSIRO
Rachel Friedman: Institute for Climate, Energy and Disaster Solutions (ICEDS), ANU
Steven Crimp: Institute for Climate, Energy and Disaster Solutions (ICEDS), ANU

Food Security: The Science, Sociology and Economics of Food Production and Access to Food, 2025, vol. 17, issue 5, No 10, 1206 pages

Abstract: Abstract The disruptions in food systems caused by extreme events have repeatedly challenged food security at multiple levels. Recently, the COVID-19 pandemic has exacerbated the vulnerabilities of existing global food systems and has resulted in food stress for an additional 145 million people. This paper addresses the critical need for enacting and strengthening policies targeted at securing food systems to achieve the Sustainable Development Goal (SDG) of Zero Hunger by 2030. We propose a novel systematic approach through the Bayesian network modeling framework to enhance national food security and build resilient food systems by effectively prioritizing areas where interventions are most critical and will have the greatest positive impact on investment. Our analysis utilizes annual data from the Global Food Security Index (GFSI) for Thailand from 2012 to 2020, which includes 59 indicators across four dimensions of food security. The GFSI data is sourced from international organizations including the FAO, WHO, World Bank, and others. Our results, supported by literature, showcase the Bayesian approach as an efficient and convenient decision-support tool that provides concrete and actionable recommendations for policymakers with clearly defined constraints and uncertainties. Further research could explore applying this approach to specific regional contexts, incorporating additional data sources to refine the prioritization of interventions.

Keywords: Bayesian network model; Food security; Food systems; Global food security index; Indicator; Zero hunger (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s12571-025-01566-0

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