A machine learning-based exploration of resilience and food security

dc.contributor.author	Villacis, Alexis H.;
dc.contributor.author	Badruddoza, Syed;
dc.contributor.author	Mishra, Ashok K.
dc.date.accessioned	2025-03-27T11:41:53Z
dc.date.available	2025-03-27T11:41:53Z
dc.date.issued	2024-12
dc.description.abstract	Leveraging advancements in remote data collection and using the Food Insecurity Experience Scale (FIES) as a proxy measure of resilience, we show that machine learning models (such as Gradient Boosting Classifier, eXtreme Gradient Boosting, and Artificial Neural Networks), can predict resilience with relatively high accuracy (up to 81%). Key household‐level predictors include access to financial institutions, asset ownership, the adoption of agricultural mechanization as evidenced by the use of tractors, the number of crops cultivated, and ownership of nonfarm enterprises. Our analysis offers insights to researchers and policymakers interested in the development of targeted interventions to bolster household resilience.
dc.identifier.citation	Villacis, Alexis H., Syed Badruddoza, and Ashok K. Mishra. 'A Machine learning‐based Exploration of Resilience and Food Security', Applied Economic Perspectives and Policy, vol. 46/no. 4, (2024), pp. 1479-1505.
dc.identifier.issn	ISSN 2040-5790
dc.identifier.issn	EISSN 2040-5804
dc.identifier.uri	https://nru.uncst.go.ug/handle/123456789/10219
dc.language.iso	en
dc.publisher	Wiley Periodicals, Inc
dc.title	A machine learning-based exploration of resilience and food security
dc.type	Article

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