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Background <p>Food insecurity affects a large proportion of the world's population, with enormous disparities to the disadvantage of low-income countries, especially those in sub-Saharan Africa. In this context, public decision-makers, funders, and NGOs urgently need streamlined indicators on households in precarious situations to better orient their policies and aid. However, existing food security monitoring tools focus on isolated dimensions of the problem, and some remain static. We aim to fill these gaps by focusing on a composite metric of food security to explore associations and identify variables with predictive relevance for food security outcomes using machine learning techniques.</p> Methodology <p>This paper applies machine learning methods to a panel dataset from Burkina Faso to explore which agroecological practices are associated with improvements in food security and have an impact on the prediction of food security outcomes.</p> Results <p>Overall, the trained models recorded accuracies varying from 64% for the Multi-Layer Perceptron model to 72% for the assembly models, with learning times generally &lt; 1&#xa0;min, except for the SVM model, which recorded around 30&#xa0;min, suggesting good potential for these models to identify food-insecure households. Our results show that agroecological intensification through a combination of multiple agroecological practices, such as livestock and crop species diversification, plays a key role in predicting households’ food security status. Indeed, households practicing agroecology with good species diversification recorded a food insecurity rate of 6.6%, compared with 15% for conventional households, and pairwise differences in the prevalence of food security are significant (p-values most often &lt; 0.01). The partial dependence plots show that the probability of belonging to the food-secure class increases by 3% as species richness increases or as livestock are integrated.</p> Conclusion <p>The performance of the algorithms using a simplified measure of food security suggests that machine learning model-driven approaches could significantly improve food insecurity crisis response.</p>

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Examining the role of agroecology in food security prediction: evidence from Burkina Faso using machine learning methods

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月10日

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  • Theodore Nikiema,
  • Pamela Giselle Katic,
  • Sylvain Kpenavoun Chogou,
  • Eugene C. Ezin

摘要

Background

Food insecurity affects a large proportion of the world's population, with enormous disparities to the disadvantage of low-income countries, especially those in sub-Saharan Africa. In this context, public decision-makers, funders, and NGOs urgently need streamlined indicators on households in precarious situations to better orient their policies and aid. However, existing food security monitoring tools focus on isolated dimensions of the problem, and some remain static. We aim to fill these gaps by focusing on a composite metric of food security to explore associations and identify variables with predictive relevance for food security outcomes using machine learning techniques.

Methodology

This paper applies machine learning methods to a panel dataset from Burkina Faso to explore which agroecological practices are associated with improvements in food security and have an impact on the prediction of food security outcomes.

Results

Overall, the trained models recorded accuracies varying from 64% for the Multi-Layer Perceptron model to 72% for the assembly models, with learning times generally < 1 min, except for the SVM model, which recorded around 30 min, suggesting good potential for these models to identify food-insecure households. Our results show that agroecological intensification through a combination of multiple agroecological practices, such as livestock and crop species diversification, plays a key role in predicting households’ food security status. Indeed, households practicing agroecology with good species diversification recorded a food insecurity rate of 6.6%, compared with 15% for conventional households, and pairwise differences in the prevalence of food security are significant (p-values most often < 0.01). The partial dependence plots show that the probability of belonging to the food-secure class increases by 3% as species richness increases or as livestock are integrated.

Conclusion

The performance of the algorithms using a simplified measure of food security suggests that machine learning model-driven approaches could significantly improve food insecurity crisis response.