Desarrollo y Validación Local de Funciones de Pedotransferencia para Parámetros Hídricos del Suelo en la Cuenca Hidrográfica del Río Una (SP): Un Enfoque Comparativo entre Modelos Lineales y Redes Neuronales Artificiales
Palabras clave:
sostenibilidad agrícola, cambio climático, agricultura de riego, seguridad alimentaria, PythonBarra lateral del artículo
Contenido principal del artículo
La predicción de la Capacidad de Campo (CC) y el Punto de Marchitez Permanente (PMP) es fundamental para el manejo eficiente del suelo y la sostenibilidad agrícola, como en la Cuenca Hidrográfica del Río Una (CHRU), ubicada en el estado de São Paulo, Brasil, destacada por su agricultura irrigada, que requiere un manejo adaptado a sus particularidades. Este estudio evalúa la eficiencia de Funciones de Pedotransferencia (FPT) generadas por Regresión Lineal Múltiple (RLM) y Redes Neuronales Artificiales (RNA) en la estimación de CC y PMP a partir de fracciones granulométricas y contenido de Materia Orgánica (MO). Se analizaron 35 muestras de suelo (0–20 cm), y los modelos se implementaron en entorno Python. La RLM destacó por su simplicidad y eficiencia, con R² de hasta 0.59 para CC y 0.87 para PMP, requiriendo menos variables y menor costo computacional. Por otro lado, las RNA alcanzaron R² de hasta 0.73 para CC, pero con mayor complejidad computacional y necesidad de bases de datos más extensas. Los resultados demuestran que ambas metodologías ofrecen soluciones adaptables para la estimación de parámetros hídricos del suelo, y su incorporación en sistemas de manejo agrícola es estratégica para aumentar la eficiencia en el uso del agua y promover prácticas sostenibles.
Abbasimaedeh, P. (2024). Soil liquefaction in seismic events: pioneering predictive models using machine learning and advanced regression techniques. Environmental Earth Sciences, 83(7), 189. https://doi.org/10.1007/s12665-024-11480-x
Abebaw, S. (2025). A Global Review of the Impacts of Climate Change and Variability on Agricultural Productivity and Farmers' Adaptation Strategies. Food Science & Nutrition, 13(5), e70260. https://doi.org/10.1002/fsn3.70260
Abdullah, G., Ahmad, M., Babur, M., Badshah, M., Al-Mansob, R., Gamil, Y. e Fawad, M. (2024). Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil. Scientific Reports, 14, 2323. https://doi.org/10.1038/s41598-024-52825-7
Abdulraheem, M., Chen, H., Li, L., Moshood, A., Zhang, W., Xiong, Y., Zhang, Y., Taiwo, L., Farooque, A. e Hu, J. (2024). Recent Advances in Dielectric Properties-Based Soil Water Content Measurements. Remote Sensing, 16(8), 1328. https://doi.org/10.3390/rs16081328
Amsili, J., van Es, H. e Schindelbeck, R. (2024). Pedotransfer Functions for Field Capacity, Permanent Wilting Point, and Available Water Capacity Based on Random Forest Models for Routine Soil Health Analysis. Communications in Soil Science and Plant Analysis, 55(13), 1967–1984. https://doi.org/10.1080/00103624.2024.2336573
Al-Shammary, A., Al-Shihmani, L., Fernández-Gálvez, J. e Caballero-Calvo, A. (2024). Optimizing sustainable agriculture: A comprehensive review of agronomic practices and their impacts on soil attributes. Journal of Environmental Management, 364, 121487. https://doi.org/10.1016/j.jenvman.2024.121487
Bahmed, I., Khatti, J. e Grover, K. (2024). Hybrid soft computing models for predicting unconfined compressive strength of lime stabilized soil using strength property of virgin cohesive soil. Bulletin of Engineering Geology and the Environment, 83, (46). https://doi.org/10.1007/s10064-023-03537-1
Bouma, J. (1989). Using Soil Survey Data for Quantitative Land Evaluation. Em Stewart, B. (Ed.), Advances in Soil Science (Vol. 9, pp. 177–213). Springer. https://doi.org/10.1007/978-1-4612-3532-3_4
Chen, S., Arrouays, D., Mulder, V., Poggio, L., Minasny, B., Roudier, P., Libohova, Z., Lagacherie, P., Shi, Z., Hannam, J., Meersmans, J., Richer-de-Forges, A. e Walter, C. (2022). Digital mapping of GlobalSoilMap soil properties at a broad scale: A review. Geoderma, 409, 115567. https://doi.org/10.1016/j.geoderma.2021.115567
Comitê da Bacia Hidrográfica do Rio Sorocaba e Médio Tietê. (2024). Relatório de Situação 2024. Ano base 2023. https://tinyurl.com/yt6f6d4e
Corral‐Pazos‐de‐Provens, E., Domingo‐Santos, J. e Rapp‐Arrarás, Í. (2018). Estimating the very fine sand fraction for calculating the soil erodibility K‐factor. Land Degradation & Development, 29(10), 3595–3606. https://doi.org/10.1002/ldr.3121
De Camargo, O., Moniz, A., Jorge, J. e Valadares, J. (2009). Métodos de análise química, mineralógica e física de solos do Instituto Agronômico de Campinas (Boletim Técnico, 106). Instituto Agronômico. https://tinyurl.com/35634trp
De Lima, M., Freire, F., Almeida, D., Almeida, B., Santos, R. e Marangon, L. (2025). Soil properties influence tree fall in a fragment of Atlantic Forest in Recife, Pernambuco, Brazil. Southern Forests: A Journal of Forest Science, 87(1–2). 69–81. https://doi.org/10.2989/20702620.2024.2444218
Departamento de Águas e Energia Eléctrica (DAEE). (s.d.). Banco de Dados Hidrológicos [Conjunto de dados]. https://tinyurl.com/3p3srys9
Du, X., Jian, J., Du, C. e Stewart, R. (2022). Conservation management decreases surface runoff and soil erosion. International Soil and Water Conservation Research, 10(2), 188–196. https://doi.org/10.1016/j.iswcr.2021.08.001
Dubreuil, V., Pechutti, K., Planchon, O. e Neto, J. (2017). Les types de climats annuels au Brésil: une application de la classification de Köppen de 1961 à 2015. EchoGéo, (41). https://doi.org/10.4000/echogeo.15017
El Behairy, R., El Arwash, H., El Baroudy, A., Ibrahim, M., Mohamed, E., Rebouh, N. e Shokr, M. (2024). An Accurate Approach for Predicting Soil Quality Based on Machine Learning in Drylands. Agriculture, 14(4), 627. https://doi.org/10.3390/agriculture14040627
Farooq, U., Ajmal, M., Li, S., Yang, J. e Ullah, S. (2024). Evaluation of Pedotransfer Functions to Estimate Soil Water Retention Curve: A Conceptual Review. Water, 16(17), 2547. https://doi.org/10.3390/w16172547
Fernandes, J., Portela, J., da Silva, E., Mota, P., Vieira, W., Cunha, W., Gomes, D., Fernandes, B., Silva, M., Dantas, M., Neitzke, P. e Antunes, L. (2025). Soil functioning and interrelations with hydrophysical attributes and organic and microstructural fractions in soils under land uses in the Brazilian Semiarid. Environmental Monitoring and Assessment, 197, 730. https://doi.org/10.1007/s10661-025-14151-x
Food and Agriculture Organization of the United Nations (FAO). (2020). The State of Food and Agriculture: Overcoming water Challenges in agriculture. https://doi.org/10.4060/cb1447en
Gee, G. e Bauder, J. (1986). Particle‐size Analysis. Em A. Klute (Ed.), Methods of Soil Analysis: Part 1 Physical and Mineralogical Methods (2nd ed., pp. 383–411). https://doi.org/10.2136/sssabookser5.1.2ed.c15
Georgiou, G. e Elkady, A. (2024). ANN-Based Model for Predicting the Nonlinear Response of Flush Endplate Connections. Journal of Structural Engineering, 150(5), 04024034. https://doi.org/10.1061/JSENDH.STENG-13119
Goodfellow, I., Bengio, Y. e Courville, A. (2016). Deep learning. MIT press.
Gomes, G., Nery, L., Nicomedes, N., dos Santos, A. Toniolo, B., da Costa, R. e Silva, D. (2025). Impact of land use and land cover changes on sensible heat variability in a fragment of the Atlantic Forest biome. International Journal of Remote Sensing, 46(23), 9258–9284. https://doi.org/10.1080/01431161.2025.2580584
Hammad, M. (2024). Deep Learning Activation Functions: Fixed-Shape, Parametric, Adaptive, Stochastic, Miscellaneous, Non-Standard, Ensemble. arXiv, 2407.11090 https://doi.org/10.48550/arXiv.2407.11090
Hill, C., Du, L., Johnson, M. e McCullough, B. (2024). Comparing programming languages for data analytics: Accuracy of estimation in Python and R. WIREs Data Mining and Knowledge Discovery, 14(3), e1531. https://doi.org/10.1002/widm.1531
Hoerl, A. e Kennard, R. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. https://doi.org/10.1080/00401706.1970.10488634
Horuz, C., Kasenbacher, G., Higuchi, S., Kairat, S., Stoltz, J., Pesl, M., Moser, B., Linse, C., Martinetz, T. e Otte, S. (2025). The Resurrection of the ReLU. arXiv, 2505.22074. https://doi.org/10.48550/arXiv.2505.22074
Hou, C., Yi, Y., Song, J. e Zhou, Y. (2021). Effect of water-sediment regulation operation on sediment grain size and nutrient content in the lower Yellow River. Journal of Cleaner Production, 279, 123533. https://doi.org/10.1016/j.jclepro.2020.123533
Instituto Brasileiro de Geografia e estatística (IBGE). (2025). Cidades. https://tinyurl.com/547ww6xn
Ke, Z., Ma, L., Jiao, F., Liu, X., Liu, Z. e Wang, Z. (2021). Multifractal parameters of soil particle size as key indicators of the soil moisture distribution. Journal of Hydrology, 595, 125988. https://doi.org/10.1016/j.jhydrol.2021.125988
Kurnaz, T., Erden, C., Dağdeviren, U., Demir, A. e Kökçam, A. (2024). Comparison of machine learning algorithms for slope stability prediction using an automated machine learning approach. Natural Hazards, 120, 6991–7014. https://doi.org/10.1007/s11069-024-06490-8
Kwon, H., Son, J., Kim, D., Tamang, S. e Park, H. (2025). Optimizing thermal performance in internal passage cooling with extended rib: Applying response surface method with artificial neural networks. Applied Thermal Engineering, 258, 124570. https://doi.org/10.1016/j.applthermaleng.2024.124570
Lankford, B. e Orr, S. (2022). Exploring the Critical Role of Water in Regenerative Agriculture; Building Promises and Avoiding Pitfalls. Frontiers in Sustainable Food Systems, 6, 891709. https://doi.org/10.3389/fsufs.2022.891709
Lin, S., Zou, Y., He, Y., Xue, S., Zhu, L. e Ye, C. (2025). A Spatiotemporal Dynamic Evaluation of Soil Erosion at a Monthly Scale and the Identification of Driving Factors in Hainan Island Based on the Chinese Soil Loss Equation Model. Sustainability, 17(6), 2361. https://doi.org/10.3390/su17062361
Liu, L. e Ma, X. (2024). Prediction of Soil Field Capacity and Permanent Wilting Point Using Accessible Parameters by Machine Learning. AgriEngineering, 6(3), 2592–2611. https://doi.org/10.3390/agriengineering6030151
Mansour, R., Halwani, J., El-Dakdouki, M. e Mina, S. (2024). Seasonal assessment of surface water and sediments pollution in Rachiine River, Northern Lebanon, using multivariate statistical analysis. Heliyon, 10(19). https://doi.org/10.1016/j.heliyon.2024.e39016
McBratney, A. e Minasny, B. (2004). Soil inference systems. Developments in Soil Science, 30, 323–348. https://doi.org/10.1016/S0166-2481(04)30018-8
Mozaffari, H., Moosavi, A. e Nematollahi, M. (2024). Predicting saturated and near-saturated hydraulic conductivity using artificial neural networks and multiple linear regression in calcareous soils. PLOS ONE, 19(1), e0296933. https://doi.org/10.1371/journal.pone.0296933
Okewu, E., Misra, S. e Lius, F. (2020). Parameter Tuning Using Adaptive Moment Estimation in Deep Learning Neural Networks. Em O. Gervasi, et al. (Eds), Computational Science and Its Applications – ICCSA 2020 (pp. 261–272). Springer. https://doi.org/10.1007/978-3-030-58817-5_20
Pacheco, V., Bragagnolo, L. e Thomé, A. (2021). Artificial neural networks applied for solidified soils data prediction: a bibliometric and systematic review. Engineering Computations, 38(7), 3104–3131. https://doi.org/10.1108/EC-10-2020-0576
Padarian, J., Minasny, B. e McBratney, A. (2020). Machine learning and soil sciences: a review aided by machine learning tools. Soil, 6(1), 35–52. https://doi.org/10.5194/soil-6-35-2020
Piccoli, I., Camarotto, C., Squartini, A., Longo, M., Gross, S., Maggini, M., Cabrera, M. e Morari, F. (2024). Hydrogels for agronomical application: from soil characteristics to crop growth: a review. Agronomy for Sustainable Development, 44, 22. https://doi.org/10.1007/s13593-024-00958-4
Piccolo, A. e Drosos, M. (2025). The essential role of humified organic matter in preserving soil health. Chemical and Biological Technologies in Agriculture, 12, 21. https://doi.org/10.1186/s40538-025-00730-0
Qi, J., Cheng, P., Zhou, J., Zhang, M., Gao, Q., He, P., Li, L., Muga, F. e Guo, L. (2025). A Novel Model for Soil Organic Matter and Total Nitrogen Detection Based on Visible/Shortwave Near-Infrared Spectroscopy. Land, 14(2), 329. https://doi.org/10.3390/land14020329
Qiao, J., Zhu, Y., Jia, X., Huang, L. e Shao, M. (2019). Pedotransfer functions for estimating the field capacity and permanent wilting point in the critical zone of the Loess Plateau, China. Journal of soils and sediments, 19, 140–147. https://doi.org/10.1007/s11368-018-2036-x
Raja, M., Abdoun, T. e El-Sekelly, W. (2024). Exploring the Potential of Machine Learning in Stochastic Reliability Modelling for Reinforced Soil Foundations. Buildings, 14(4), 954. https://doi.org/10.3390/buildings14040954
Ramos, T., Darouich, H. e Gonçalves, M. (2023). Development and functional evaluation of pedotransfer functions for estimating soil hydraulic properties in Portuguese soils: Implications for soil water dynamics. Geoderma Regional, 35, e00717. https://doi.org/10.1016/j.geodrs.2023.e00717
Rawls, W., Brakensiek, D. e Saxtonn, K. (1982). Estimation of Soil Water Properties. American Society of Agricultural and Biological Engineers, 25(5), 1316–1320. https://doi.org/10.13031/2013.33720
Rehman, M., Abd Rahman, N., Ibrahim, A., Kamal, N. e Ahmad, A. (2024). Estimation of soil erodibility in Peninsular Malaysia: A case study using multiple linear regression and artificial neural networks. Heliyon, 10(7). https://doi.org/10.1016/j.heliyon.2024.e28854
Sadeghi, M., Casey, P., Carranza, E. e Lynch, E. (2024). Principal components analysis and K-means clustering of till geochemical data: Mapping and targeting of prospective areas for lithium exploration in Västernorrland Region, Sweden. Ore Geology Reviews, 167, 106002. https://doi.org/10.1016/j.oregeorev.2024.106002
Santos, A. (2024). Integração de parâmetros ambientais e machine learning na modelagem do solo para agricultura sustentável [Tese de Doutorado, Universidade Estadual Paulista]. Repositório institucional - Universidade Estadual Paulista.
Santos, A., Silva Junior, A., Nery, L., Gomes, G., Toniolo, B., Silva, D. e Lourenço, R. (2025a). Random forest algorithm applied to model soil textural classification in a river basin. Environmental Monitoring and Assessment, 197, 330. https://doi.org/10.1007/s10661-025-13786-0
Santos, A., Nery, L., Tondato Arantes, L., Toniolo, B., Silva, D. e Lourenço, R. (2025b). Performance of Random Forest in predicting soil loss based on values calculated by USLE. Earth Sciences Research Journal, 29(4), 379–386. https://tinyurl.com/4tcmv95h
Saxton, K., Rawls, W., Romberger, J. e Papendick, R. (1986). Estimating Generalized Soil‐water Characteristics from Texture. Soil Science Society of America Journal, 50(4), 1031–1036. https://doi.org/10.2136/sssaj1986.03615995005000040039x
Silva, D. (2016). Proposta metodológica para elaboração de um índice espacial de sustentabilidade ambiental aplicado a bacias hidrográficas [Tese de Doutorado, Universidade Estadual Paulista]. Repositório institucional - Universidade Estadual Paulista.
Silva, D., Filho, J., de Oliveira, R. e Lourenço, R. W. (2018). Proposta metodológica para análise espacial de nutrientes do solo em bacias hidrográficas. Sociedade & Natureza, 30(3), 85–107. https://doi.org/10.14393/SN-v30n3-2018-5
Silva, L., Amorim, R., Filho, E., Bocuti, E. e da Silva, D. (2023a). Pedotransfer functions and machine learning: Advancements and challenges in tropical soils. Geoderma Regional, 35, e00720. https://doi.org/10.1016/j.geodrs.2023.e00720
Silva, D., Oliveira, R., Simonetti, V., Toniolo, B., Sales, J. e Lourenço, R. (2023b). Creation of an environmental sustainability index for water resources applied to watersheds. Environment, Development and Sustainability, 25, 11285–11305. https://doi.org/10.1007/s10668-022-02527-9
Silva Junior, A., Santos, A. e Silva, D. (2025). Aplicações do Geoprocessamento na Gestão e Conservação do Solo: Análise Integrada da Granulometria do solo e do Fator K em Sub-Bacias Hidrográficas. Ateliê Geográfico, 19(3), 349–376. https://doi.org/10.5216/ag.v19i3.81589
Smagin, A. (2025). A physically based model for the entire soil water retention curve. Soil Science Society of America Journal, 89(2), e70054. https://doi.org/10.1002/saj2.70054
Štrbac, S., Stojić, N., Lončar, B., Pezo, L., Ćurčić, L., Prokić, D. e Pucarević, M. (2024). Heavy metal concentrations in the soil near illegal landfills in the vicinity of agricultural areas—artificial neural network approach. Journal of Soils and Sediments, 24, 373–389. https://doi.org/10.1007/s11368-023-03637-1
Tang, L. (2024). Extraction study of soil mineral ions and microbial influences on phosphorus and sulfur cycling based on principal component analysis. Journal of Combinatorial Mathematics and Combinatorial Computing, 127b, 6405–6419. https://doi.org/10.61091/jcmcc127b-351%20%20%20%20
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
Tisdall, J. e OADES, J. (1982). Organic matter and water‐stable aggregates in soils. Journal of Soil Science, 33(2), 141–163. https://doi.org/10.1111/j.1365-2389.1982.tb01755.x
Veloso, M., Rodrigues, L. e Filho, E. (2024). Pedotransfer functions for estimating hydraulic conductivity and soil moisture in the Cerrado biome. Revista Brasileira de Engenharia Agrícola e Ambiental, 28(12), e278960. https://doi.org/10.1590/1807-1929/agriambi.v28n12e278960
Wang, C., Wu, Y., Xie, L., Yang, Z., Tian, J., Yu, F., Ren, J. e Li, S. (2025). Estimating Soil-Water Characteristic Curve From the Particle Size Distribution With a Novel Granular Packing Model. Water Resources Research, 61(2), e2024WR037262. https://doi.org/10.1029/2024WR037262
Wang, L., Guo, M., Chen, Z., Zhang, X., Zhou, P., Liu, X., Qi, J., Wan, Z., Xu, J. e Zhang, S. (2024a). Quantifying the contributions of factors influencing the spatial heterogeneity of soil aggregate stability and erodibility in a Mollisol watershed. Catena, 239, 107941. https://doi.org/10.1016/j.catena.2024.107941
Wang, K., Zhou, J., Tan, M. L., Lu, P., Xue, Z., Liu, M. e Wang, X. (2024b). Impacts of vegetation restoration on soil erosion in the Yellow River Basin, China. Catena, 234, 107547. https://doi.org/10.1016/j.catena.2023.107547
Weber, T., Weihermüller, L., Nemes, A., Bechtold, M., Degré, A., Diamantopoulos, E., Fatichi, S., Filipović, V., Gupta, S., Hohenbrink, T., Hirmas, D., Jackisch, C., de Jong van Lier, Q., Koestel, J., Lehmann, P., Marthews, T., Minasny, B., Pagel, H., van der Ploeg, M… Bonetti, S. (2024). Hydro-pedotransfer functions: a roadmap for future development. Hydrology and Earth System Sciences, 28(14), 3391–3433. https://doi.org/10.5194/hess-28-3391-2024
Wösten, J., Lilly, A., Nemes, A. e Le Bas, C. (1999). Development and use of a database of hydraulic properties of European soils. Geoderma, 90(3–4), 169–185. https://doi.org/10.1016/S0016-7061(98)00132-3
Xiong, M., Sun, R. e Chen, L. (2018). Effects of soil conservation techniques on water erosion control: A global analysis. Science of the Total Environment, 645, 753–760. https://doi.org/10.1016/j.scitotenv.2018.07.124
Yang, J. e Long, Q. (2024). A modification of adaptive moment estimation (adam) for machine learning. Journal of Industrial and Management Optimization, 20(7), 2516–2540. https://doi.org/10.3934/jimo.2024014
Ye, C., Zheng, G., Tao, Y., Xu, Y., Chu, G., Xu, C., Chen, S., Liu, Y., Zhang, X. e Wang, D. (2024). Effect of Soil Texture on Soil Nutrient Status and Rice Nutrient Absorption in Paddy Soils. Agronomy, 14(6), 1339. https://doi.org/10.3390/agronomy14061339
Zhang, J., Miao, Q., Xue, Y., Li, Z. e Cui, Z. (2020). Managing soils and crops for sustainable agricultural intensification in coastal saline zones. Agronomy Journal, 112(4), 3076-3088. https://doi.org/10.1002/agj2.20249
Ziviani, M., Costa, E., Alves, A., Pinto, L., Pereira, M. e dos Anjos, L. (2024). Pedotransfer Functions in the Assessment of Fragile Mountainous Areas: Carbon and Nitrogen Stocks in Mountains in Southeastern Brazil. Revista de Gestão Social e Ambiental, 18(8), e06052. https://doi.org/10.24857/rgsa.v18n8-018
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