Articles | Volume 25, issue 4
https://doi.org/10.5194/nhess-25-1387-2025
https://doi.org/10.5194/nhess-25-1387-2025
Research article
 | 
10 Apr 2025
Research article |  | 10 Apr 2025

A data-driven framework for assessing climatic impact drivers in the context of food security

Marcos Roberto Benso, Roberto Fray Silva, Gabriela Chiquito Gesualdo, Antonio Mauro Saraiva, Alexandre Cláudio Botazzo Delbem, Patricia Angélica Alves Marques, José Antonio Marengo, and Eduardo Mario Mendiondo

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Cited articles

Battisti, R. and Sentelhas, P. C.: New agroclimatic approach for soybean sowing dates recommendation: A case study, Rev. Bras. Eng. Agr. Amb., 18, 1149–1156, https://doi.org/10.1590/1807-1929/agriambi.v18n11p1149-1156, 2014. a
Benso, M. R.: Climatic impact-drivers in the context of food security, Zenodo [data set], https://doi.org/10.5281/zenodo.12612860, 2024. a
Bray, E. A.: Plant response to water-deficit stress, Encyclopedia of Life Sciences, https://doi.org/10.1002/9780470015902.a0001298.pub2, 2007. a
Brazilian Institute of Geography and Statistics (IBGE): PAM – Municipal Agricultural Production, https://www.ibge.gov.br/en/statistics/economic/agriculture-forestry-and-fishing/16773-municipal-agricultural-production-temporary-and-permanent-crops.html?edicao=31814 (last access: 3 April 2025), 2023. a, b
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
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This study applies climate extreme indices to assess climate risks to food security. Using an explainable machine learning analysis, key climate indices affecting maize and soybean yields in Brazil were identified. Results reveal the temporal sensitivity of these indices and critical yield loss thresholds, informing policy and adaptation strategies.
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