Predictive models of meteorological data through machine learning and their impact on the agroproduction of the Bolívar province, Ecuador
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Published:
Jul 17, 2026
Keywords:
Multivariate time series, vector autoregressive processes, Bayesian inference, recurrent deep learning.
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Abstract
Introduction. The inherent non-stationarity of Andean climate series limits the efficacy of classical linear models. Objective. To develop a comparative methodological framework integrating traditional statistical models (ARIMA/SARIMA) with Ensemble Learning algorithms (Random Forest, XGBoost) and deep neural networks (LSTM) for meteorological variable prediction, evaluating their causal impact on agroproduction via VAR modeling. Methodology. Quantitative longitudinal study with 1,642,217 observations (2016-2023). Robust preprocessing (Isolation Forest, KNN Imputer) and walk-forward temporal validation were applied. Multivariate analysis utilized unit root tests (ADF), Granger causality, and Forecast Error Variance Decomposition (FEVD). Results. Random Forest significantly outperformed SARIMA (RMSE 0.74 vs 1.66; R² 0.67 vs -0.63). The VAR (6) model evidenced significant Granger causality (p < 0, 001) between precipitation/temperature and corn yield, with a dynamic lag of 6 months. Conclusion. Non-parametric methods better capture the conditional heteroscedasticity of climate data, providing robust tools for precision agriculture. General Area of Study: Data Science and Artificial Intelligence. Specific area of study: Agroclimatic predictive modeling. Type of study: Original articles.
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Guanga Chunata, D. M., & Galvis Correa, A. A. (2026). Predictive models of meteorological data through machine learning and their impact on the agroproduction of the Bolívar province, Ecuador. ConcienciaDigital, 9(3), 60-77. https://doi.org/10.33262/concienciadigital.v9i3.3705
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References
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Benos, L., Tagarakis, A. C., Dolias, G., Berruto, R., Kateris, D., & Bochtis, D. (2021). Machine learning in agriculture: a comprehensive updated review. Sensors, 21(11), 3758. https://doi.org/10.3390/s21113758
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer, New York, NY, USA. https://link.springer.com/book/9780387310732
Chen, T., & Guestrin, C. (2016). XGBoost: a scalable tree boosting system [Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 785–794]. https://dl.acm.org/doi/10.1145/2939672.2939785
Consejo Nacional de Planificación. (2021). Plan nacional de desarrollo 2021-2025. https://iste.edu.ec/wp-content/uploads/2022/08/PLAN-NACIONAL-DE-DESARROLLO-2021-2025.pdf
Food and Agriculture Organization of the United Nations [FAO]. (2022). The state of food and agriculture 2022: leveraging automation for sustainable agrifood systems. https://openknowledge.fao.org/items/98a4c80a-b4d3-403c-8557-d8536c8316ee
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. https://mitpress.mit.edu/9780262035613/deep-learning/
Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://direct.mit.edu/neco/article-abstract/9/8/1735/6109/Long-Short-Term-Memory?redirectedFrom=fulltext&utm_source=researchgate.net&utm_medium=article
Huntingford, C., Jeffers, E. S., Bonsall, M. A., Christensen, H. M., Lees, T., & Yang, H. (2019). Machine learning and artificial intelligence to aid climate change research and preparedness. Environmental Research Letters, 14(12), 120001. https://iopscience.iop.org/article/10.1088/1748-9326/ab4e55?utm_source=researchgate.net&utm_medium=article
Hyndman, R. J. & Athanasopoulos, G. (2021). Forecasting: Principles and Practice. (3rd ed.) OTexts. https://research.monash.edu/en/publications/forecasting-principles-and-practice-3/
Instituto Nacional de Estadística y Censos [INEC]. (2023). Encuesta de superficie y producción agropecuaria continua 2023. https://www.ecuadorencifras.gob.ec/encuesta-de-superficie-y-produccion-agropecuaria-continua-2023/
Instituto Nacional de Meteorología e Hidrología [INAMHI]. (2020). Anuarios meteorológicos del Ecuador. https://servicios.inamhi.gob.ec/anuarios-metereologicos/
Kulyal, M., & Saxena, P. (2022). Machine learning approaches for crop yield prediction: a review [7th International Conference on Computing, Communication and Security (ICCCS), 1–7]. https://ieeexplore.ieee.org/document/10079240
Kumar Bandla, A., Nagendra Kumar, Y., & Sarada, B. (2025). A comprehensive analysis of machine learning techniques for crop yield prediction using remote sensing data [IEEE International Conference on Compute, Control, Network & Photonics (ICCCNP), 1–6]. https://ieeexplore.ieee.org/document/11233752
Kuradusenge, M., Hitimana, E., Hanyurwimfura, D., Rukundo, P., Mtonga, K., Mukasine, A., Uwitonze, C., Ngabonziza, J., & Uwamahoro, A. (2023). Crop yield prediction using machine learning models: case of Irish potato and maize. Agriculture, 13(1), 225. https://doi.org/10.3390/agriculture13010225
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., and Bochtis, D. (2018). Machine learning in agriculture: a review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674
Morales, A., & Villalobos, F. J. (2023). Using machine learning for crop yield prediction in the past or the future. Frontiers in Plant Science, 14, 1128388. https://doi.org/10.3389/fpls.2023.1128388
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830. https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven earth system science. Na-ture, 566(7743), 195–204. https://doi.org/10.1038/s41586-019-0912-1
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, A. S., Maharaj, T., Sherwin, E. D., Mukkavilli, S. K., Kording, K. P., Gomes, C. P., Ng, A. Y., Hassabis, D., Platt, J. C., … Bengio, Y. (2023). Tackling climate change with machine learning. ACM Computing Surveys, 55(2), 1–96. https://doi.org/10.1145/3485128
Serrano-Vincenti, S., Guamán-Pozo, J., Chuqui, J., Tufiño, R., & Franco-Crespo, C. (2025). Measuring the effects of climate change on traditional crops in tropical highlands, Ecuador. Frontiers in Sustainable Food Systems, 9(1447593). https://doi.org/10.3389/fsufs.2025.1447593
World Bank Group. (2020). Climate-smart agriculture: a call for action. https://documents.worldbank.org/en/publication/documents-reports/documentdetail/992021468197391264
Xie, F., Yan, H., Long, Y., Guo, H., Liu, H., & Yu, P. (2024). Weather prediction based on multivariate LSTM neural network model. Advances in Transdisciplinary Engineering. IOS Press. https://ebooks.iospress.nl/doi/10.3233/ATDE231201