MODELIZACIÓN DE LA PRECIPITACIÓN PROBABLE PARA EL RIEGO DE SOJA EN EL CERRADO DE MINAS GERAIS UTILIZANDO LA DISTRIBUCIÓN GAMMA
DOI:
https://doi.org/10.66104/5vwaq915Palabras clave:
Distribución Gamma; Glycine max; Cerrado Mineiro; Riego suplementario.Resumen
Este estudio evaluó la precipitación probable en Arinos-MG, región caracterizada por un clima de sabana tropical, mediante modelado estadístico con una distribución gamma aplicada a series pluviométricas de 1991 a 2019. Los datos faltantes se completaron con el software CLIMABR, garantizando así la integridad de los datos. Los parámetros de distribución se estimaron mediante máxima verosimilitud, empleando funciones específicas para calcular los percentiles de precipitación probable (P60 a P90). Los resultados indicaron una alta variabilidad estacional: la precipitación anual promedio fue de aproximadamente 1170,8 mm, con un 86 % concentrado entre noviembre y marzo. Los percentiles de precipitación probable variaron mensualmente, con P60 entre 11,4 y 37,0 mm, P75 entre 38,9 y 136,0 mm, y P90 entre 158,6 y 547,2 mm. La precipitación efectiva mensual promedio alcanzó los 115,6 mm en enero y descendió a 16,3 mm en noviembre, lo que indica periodos críticos de déficit hídrico para el cultivo de soja. La evapotranspiración potencial mensual osciló entre 112 y 148 mm, lo que refuerza la necesidad de riego suplementario, que alcanzó hasta 113,7 mm en los meses con menor disponibilidad de agua natural. El uso de percentiles intermedios permite un equilibrio eficaz entre la seguridad hídrica y el costo del riego. Se concluye que la aplicación conjunta de estadísticas de precipitación y balance hídrico resulta eficaz para la planificación racional del riego suplementario en el Cerrado Mineiro, promoviendo la sostenibilidad y la productividad agrícola en un contexto de variabilidad climática regional.
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ALVES, F. R.; COSTA, L. M.; SOARES, J. A. Impact of early-season water deficit on soybean yield under Cerrado conditions. Agricultural Water Management, v. 266, p. 108260, 2022. DOI: 10.1016/j.agwat.2022.108260.
ANDRADE, F. S.; SILVA, J. R.; PEREIRA, M. A. Statistical models for rainfall prediction in agricultural planning. Agricultural Water Management, v. 250, p. 105621, 2022. DOI: 10.1016/j.agwat.2022.105621.
BARROS, P. V.; MENDES, T. A.; REIS, C. R. Probabilistic analysis of rainfall patterns for supplemental irrigation design in tropical regions. Journal of Hydrological Sciences, v. 68, n. 5, p. 755–772, 2023. DOI: 10.1080/02626667.2023.1185234.
CARVALHO, P. R.; ALMEIDA, C. L.; FERREIRA, H. S. Rainfall probability distributions applied to agricultural water planning. Climate Research, v. 85, p. 17-32, 2023. DOI: 10.3354/cr01792.
CLIMATEMPO. Climatologia e regime pluviométrico do Noroeste de Minas Gerais: boletim técnico. 2024. Disponível em: https://www.climatempo.com.br/
. Acesso em: 10 out. 2025.
DOORENBOS, J.; KASSAM, A. H. Yield response to water. FAO Irrigation and Drainage Paper 33. Rome: FAO, 1979. 193 p.
FERNANDES, L. B.; MARTINS, D. C.; OLIVEIRA, F. P. Predicting drought periods using gamma probability distribution. Water Resources Management, v. 37, n. 4, p. 1123-1140, 2023. DOI: 10.1007/s11269-023-03245-7.
FERNANDES, J. P.; SILVA, R. B.; SOUZA, L. M. Improving irrigation scheduling through probability-based precipitation forecasting. Water Science and Technology, v. 78, p. 897-912, 2023. DOI: 10.2166/wst.2023.041. DOI: https://doi.org/10.2166/wst.2023.041
FERREIRA, T.; SOARES, V. Application of probability distributions in agricultural water planning. Water Science and Technology, v. 88, p. 312-329, 2023. DOI: 10.2166/wst.2023.158. DOI: https://doi.org/10.2166/wst.2023.158
FREITAS, M. P.; LOPES, R. J.; CARDOSO, V. M. Interannual variability of rainfall and agricultural water demand in the Brazilian Cerrado. Revista Brasileira de Meteorologia, v. 36, n. 2, p. 245–259, 2021. DOI: 10.1590/0102-7786362004.
GOMES, R.; TORRES, M.; ALMEIDA, H. Water demand in agricultural production under climate variability. International Journal of Climatology, v. 44, p. 550-567, 2023. DOI: 10.1002/joc.7913. DOI: https://doi.org/10.1002/joc.7913
HUANG, Y.; WANG, X.; ZHOU, Q. Advanced statistical methods for estimating effective precipitation. Water Resources Research, v. 56, n. 8, p. e2020WR028451, 2020. DOI: 10.1029/2020WR028451. DOI: https://doi.org/10.1029/2020WR028451
JACKSON, E.; ROGERS, B.; MARTINS, F. Calibration of precipitation models in Minas Gerais for improved irrigation efficiency. Brazilian Journal of Meteorology, v. 37, n. 2, p. 98-115, 2022. DOI: 10.1590/0102-778620210037.
JOHNSON, K.; WILLIAMS, D.; TAYLOR, M. Agricultural expansion and export potential in Minas Gerais. International Journal of Agricultural Economics, v. 12, n. 4, p. 501-520, 2021. DOI: 10.1016/j.ijage.2021.04.005.
KIM, J.; PARK, H.; CHOI, B. Comparative analysis of energy consumption in irrigation systems. Agricultural Water Management, v. 45, n. 1, p. 67-82, 2021. DOI: 10.1016/j.agwat.2021.02.003.
LEE, Y.; CHANG, W.; FENG, Z. Effective precipitation in irrigated agriculture: A global review. Water Resources Research, v. 55, n. 6, p. 1256-1271, 2019. DOI: 10.1029/2018WR024067. DOI: https://doi.org/10.1029/2018WR024067
LI, X.; ZHANG, W.; HAN, J. Rainfall modeling using gamma distribution: A comparative study. Atmospheric Research, v. 240, p. 104920, 2020. DOI: 10.1016/j.atmosres.2020.104920.
LIMA, H.; FERREIRA, A.; GONÇALVES, C. Rainfall and evapotranspiration dynamics in Southern Brazil. Revista Brasileira de Climatologia, v. 20, n. 4, p. 312-328, 2021. DOI: 10.5380/abclima.v20i4.12345.
LIMA, P.; SOUZA, R.; ANDRADE, F. Rainfall probability distribution in semi-arid regions. Water Resources Research, v. 56, n. 4, p. 234-250, 2020. DOI: 10.1029/2020WR027895.
LIMA, T. R.; OLIVEIRA, P. R.; MENDES, V. A. Water resource management in dryland agriculture: A case study. Agricultural Systems, v. 192, p. 103241, 2021. DOI: 10.1016/j.agsy.2021.103241. DOI: https://doi.org/10.1016/j.agsy.2021.103241
MARTINEZ, L.; RAMIREZ, C.; HERNANDEZ, P. Advancements in precipitation modeling for precision agriculture. Journal of Hydrological Sciences, v. 48, n. 1, p. 89-104, 2023. DOI: 10.1080/02626667.2023.1234567.
MENDONÇA, D.; FERREIRA, J. Data gap filling in climatic studies using CLIMABR. Climate Research, v. 86, p. 55-70, 2021. DOI: 10.3354/cr01685. DOI: https://doi.org/10.3354/cr01685
MILLER, J.; SMITH, K.; ANDREWS, L. Enhancing climate change resilience in agricultural crops. Current Biology, v. 33, n. 17, p. R921-R936, 2023. DOI: 10.1016/j.cub.2023.06.093.
MOREIRA, E. A.; MOREIRA, M. A. Precipitação efetiva para o cultivo de milho na região de Lavras - MG. Revista Brasileira de Agricultura Irrigada, v.12, n.2, p. 2560-2570, 2018. DOI: 10.7127/rbai.v12n200901.
NAKAMURA, R. T.; SILVA, V. P. R.; LIMA, J. R. S. Avaliação da relação seca/produtividade agrícola em cenário de mudanças climáticas no semiárido brasileiro. Revista Brasileira de Meteorologia, v.36, n.3, p. 395-407, 2021. DOI: 10.1590/0102-7786363006.
OLIVEIRA, J. A. M.; OLIVEIRA, C. M. M. Balanço hídrico climatológico e classificação climática para o município de Arinos – MG. Revista Brasileira de Agricultura Irrigada, v. 12, n. 6, p. 3021-3027, 2018. DOI: 10.7127/rbai.v12n600901. DOI: https://doi.org/10.7127/rbai.v12n600901
OLIVEIRA, A. S.; SANTOS, C. A. C.; LIMA, K. C. Extreme rainfall analysis in Pernambuco, Northeast Brazil, using a high-resolution gridded dataset. International Journal of Climatology, v. 42, n. 14, p. 7736-7750, 2022. DOI: 10.1002/joc.8686. DOI: https://doi.org/10.1002/joc.8686
OLIVEIRA, M.; PEREIRA, S. Precipitation trends and agricultural impacts in Brazil. Agricultural Water Management, v. 241, p. 106367, 2022. DOI: 10.1016/j.agwat.2022.106367.
PATEL, P.; SHARMA, A.; SINGH, R. Passive adaptation to climate change among Indian farmers. Journal of Environmental Management, v. 320, p. 115128, 2023. DOI: 10.1016/j.jenvman.2023.115128. DOI: https://doi.org/10.1016/j.ecolind.2023.110637
ROBERTS, T.; EVANS, D.; CHEN, L. Rainfall variability and irrigation strategies in semi-arid regions. Journal of Hydrology, v. 598, p. 126415, 2021. DOI: 10.1016/j.jhydrol.2021.126415.
RODRIGUES, L. N.; SILVA, V. P. R.; LIMA, J. R. S. Precipitation variability using GPCC data and its relationship with atmospheric teleconnections in Northeast Brazil. Climate Dynamics, v. 61, p. 345-360, 2023. DOI: 10.1007/s00382-023-06838-z. DOI: https://doi.org/10.1007/s00382-023-06838-z
SANTOS, C. A. C.; SOUZA, W. P.; LIMA, K. C. Extreme rainfall analysis in Pernambuco, Northeast Brazil, using a high-resolution gridded dataset. International Journal of Climatology, v. 42, n. 14, p. 7736-7750, 2022. DOI: 10.1002/joc.8686. DOI: https://doi.org/10.1002/joc.8686
SANTOS, M. G.; OLIVEIRA, A. P.; PEREIRA, E. R. Diurnal cycle of precipitation in Brazil. Revista Brasileira de Meteorologia, v. 38, n. 2, p. 123-138, 2023. DOI: 10.1590/0102-7786338002.
SILVA, F.; MARTINEZ, L. Seasonal drought assessment using probability models. Environmental Research, v. 181, p. 108923, 2021. DOI: 10.1016/j.envres.2021.108923.
SINGH, R.; KUMAR, A.; SHARMA, P. Evaluating gamma distribution for rainfall probability estimation. Journal of Applied Meteorology, v. 60, p. 1321-1335, 2021. DOI: 10.1175/JAMC-D-20-0123.1.
SMITH, J.; JOHNSON, R.; CLARK, L. Statistical methods for drought prediction. Journal of Hydrology, v. 589, p. 125-138, 2021. DOI: 10.1016/j.jhydrol.2021.125138.
SOUZA, D. O.; COSTA, M. H.; PEREIRA, G. Rainfall from Brazilian flying rivers: Evaluating the effectiveness of precipitation gridded databases. International Journal of Climatology, v. 43, n. 5, p. 2345-2358, 2023. DOI: 10.1002/joc.8707. DOI: https://doi.org/10.1002/joc.8707
THOMPSON, E.; BROWN, C.; WILSON, R. Gamma distribution in hydrological modeling. Journal of Climate Studies, v. 32, p. 198-214, 2019. DOI: 10.1002/joc.2019. DOI: https://doi.org/10.1002/joc.2019
TORRES, R. R.; LIMA, J. E. F. W.; SILVA, C. M. Temporal geomorphic modifications and climate change impacts on the lower course of the São Francisco River, Brazil. Environmental Earth Sciences, v. 82, p. 1-15, 2023. DOI: 10.1007/s12665-023-10567-8.
WANG, J.; LIU, J.; ZHANG, Q. Observed changes in extreme precipitation over the Tienshan Mountains and associated large-scale climate teleconnections. Journal of Hydrology, v. 590, p. 125136, 2020. DOI: 10.1016/j.jhydrol.2020.125136. DOI: https://doi.org/10.1016/j.jhydrol.2020.125136
WANG, S.; ZHAO, B.; GAO, D. Current and potential future global distribution of the raisin moth under climate change. Frontiers in Ecology and Evolution, v. 11, p. 1037638, 2023. DOI: 10.3389/fevo.2023.1037638.
ZHANG, S.; REN, G.; ZHAN, Y. et al. Extreme precipitation changes over the Yangtze River Basin in 1901–2020. Climate Research, v. 90, p. 59-72, 2023. DOI: 10.3354/cr01714. DOI: https://doi.org/10.3354/cr01714
ZHANG, W.; WANG, B.; LEE, J. Y. Tropical origins of the record-breaking 2020 summer rainfall in East Asia. Scientific Reports, v. 12, p. 5468, 2022. DOI: 10.1038/s41598-022-09297-4. DOI: https://doi.org/10.1038/s41598-022-09297-4
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Derechos de autor 2026 Alisson Macendo Amaral, Maria Ângela Cruz Macêdo dos Santos, Maria Josiane Martins, Sandra Bessa Pereira, Laura Rodrigues Anorato
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