IFPRI Webinar

How Can We Improve Food Security Monitoring in Conflict-Affected Regions? Machine Learning for Spatially Granular Food Security Mapping

Co-organized by IFPRI and the CGIAR Initiative on Foresight

March 25, 2025

Machine learning is transforming agricultural and food security research, enabling more accurate and timely insights. The International Food Policy Research Institute (IFPRI) is advancing data-driven approaches in various domains, including crop-type mapping, maize yield estimation, and boat detection. These innovations demonstrate the potential of machine learning in addressing complex challenges and informing policy decisions.

A key challenge in this space is food security monitoring in fragile and conflict-affected settings, where timely, granular data is often lacking but essential for policymakers, humanitarians, and researchers. Traditional methods, such as in-person household surveys, are often expensive, infrequent, and spatially coarse, limiting their ability to provide timely insights at local scales.

To address these challenges, IFPRI has developed a machine learning-based approach to estimate Food Consumption Scores—which is the most commonly used food security indicator by WFP and partners— at a granular village-tract level in Myanmar. This model leverages multiple data sources—including phone survey data, earth observation, crowd-sourced data, and GIS (Geographic Information System) datasets—to generate spatially explicit and near real-time food security assessments. During this seminar, we will discuss the development and application of this approach, the key data and modeling techniques used, and how this method can be scaled for other conflict-affected regions. We will highlight challenges such as data representativeness, feature selection, and model validation, and share insights into improving food security predictions. Finally, we will outline the broader implications of integrating machine learning with earth observation and survey data to support humanitarian efforts and policy decisions.

Moderator and Opening Remarks

Jawoo Koo, Senior Research Fellow, Natural Resource and Resilience Unit, IFPRI

Presentations

Joanna van Asselt, Associate Research Fellow, Development Strategies and Governance Unit, IFPRI

Zhe Guo, Senior GIS Coordinator, Foresight and Policy Modeling Unit, IFPRI

Links:

More about this Event: https://www.ifpri.org/event/how-can-we-improve-food-security-monitoring-in-conflict-affected-regions-machine-learning-for-spatially-granular-food-security-mapping/

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