AI in Colombian Food Markets: Using Machine Learning to Address Price Crisis

Price shocks have long been a challenge for farmers in developing countries, posing a substantial threat to their investments and livelihoods when they encounter low prices at the time of harvest, often pushing them towards poverty. Local governments’ crisis responses often use inefficient, indiscriminate aid distribution. While local and regional governments can apply many tools to prevent sudden changes in crop prices, those tools tend to be expensive and difficult to implement in local communities. The principal objective of this study is to illustrate the feasibility of a cost-effective machine learning tool that predicts the most likely affected municipalities by a price shock, enabling local governments to effectively target assistance where it is needed. Two models were used in the article, a random forest and a decision tree algorithm. The findings suggest that, despite using a simple structure in both algorithms, the models were able to predict up to 79% of the municipalities affected by prices shocks. Furthermore, this article highlights that this relatively uncomplicated model structure can equip governments with accurate data, which could be employed in price crisis responses at a lower cost, thereby enhancing the efficiency of aid distribution.

Recommended citation: Nino Chaparro, G.E., Nino Chaparro, A., & Chaparro Pesca, J.A. (2023). AI in Colombian Food Markets: Using Machine Learning to Address Price Crisis. Económicas CUC, 27-10-2023.DOI: https://doi.org/10.17981/econcuc.Org.4818
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