Assessing the utility of machine learning for predicting food sufficiency: a case study in Malawi

Assessing the utility of machine learning for predicting food sufficiency: a case study in Malawi

This study explores the potential of applying machine learning (ML) methods to identify and predict areas at risk of food insufficiency using a parsimonious set of publicly available data sources. We combine household survey data that captures monthly reported food insufficiency with remotely sensed...

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Bibliographic Details
Main Authors: Andrew Tomes, Shahrzad Gholami, Didier Alia, Conor Hennessy, Dafeng Xu, Cecilia Bitz, Rahul Dodhia, Juan Lavista Ferres, C. Leigh Anderson
Format: Article
Language:English
Published: Cambridge University Press 2025-01-01
Series:Data & Policy
Subjects:
crop prices
food insufficiency
machine learning
Malawi
remote sensing
Online Access:https://www.cambridge.org/core/product/identifier/S2632324925100138/type/journal_article
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