Ifpri Podcast

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