Abstract
Agriculture has been an essential and foundational activity for human societies since the dawn of civilization and nowadays serves as the backbone of economies worldwide. Efforts to understand and to enhance agricultural productivity are crucial for addressing global challenges and achieving sustainable development goals. This research study focuses on analyzing the factors influencing production of the flagship crops of the 32 states in Mexico. A regression tree model was employed as an explainable artificial intelligence technique to gain insights into the production patterns. The study utilized a dataset containing various agricultural variables, including territorial extension, precipitation mean, and temperature measurements across different months. Quantitative and qualitative approaches were employed to understand the significance of predictors. Through permutation importance analysis, it was identified that territorial extension, precipitation mean, and specific temperature measures, such as minimum temperature in January and mean temperature in November, had a substantial impact on crop production. Additionally, a visual analysis of the pruned regression tree further confirmed the importance of these predictors. The findings led to the formulation of seven production rules, which provide valuable guidance for agricultural decision-making. The results highlight the potential of the regression tree model as an explainable tool for understanding and predicting crop production.
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This work was supported by projects 13933.22-P and 14601.22-P from Tecnológico Nacional de México/IT de Mérida.
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Ruiz-Juárez, H.M., Castillo-Araujo, J., Orozco-del-Castillo, M.G., Cuevas-Cuevas, N.L., Cárdenas-Pimentel, F., Cáceres-Escalante, R. (2023). A Decision Tree as an Explainable Artificial Intelligence Technique for Identifying Agricultural Production Predictor Variables in Mexico. In: Mata-Rivera, M.F., Zagal-Flores, R., Barria-Huidobro, C. (eds) Telematics and Computing. WITCOM 2023. Communications in Computer and Information Science, vol 1906. Springer, Cham. https://doi.org/10.1007/978-3-031-45316-8_1
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