To build climate-resilient livestock systems, public policies must be informed by bioclimatic zoning, enabling region-specific interventions and more efficient resource allocation. This study aimed to conduct bioclimatic zoning for dairy cattle farming in the state of Maranhão, Brazil. Big data analysis techniques and predictive geostatistical modeling were applied to historical (2012–2023) and future climate scenarios under intermediate (RCP4.5) and high-intensity (RCP8.5) greenhouse gas emissions. Kriging maps of THI revealed a decreasing north–south thermal gradient, with values exceeding 80 during critical years. Milk yield losses were more pronounced in high-producing cows, reaching up to 5 kg/cow/day under extreme heat. Areas identified as drought-prone exhibited spatial patterns consistent with THI distributions. The projections indicate that, under the RCP 4.5 scenario, over 60% of Maranhão will exhibit average THI values between 78 and 81 by the end of the century. Under the RCP 8.5 scenario, large areas of the state are expected to reach THI values above 86. Under these conditions, estimated milk production losses may exceed 4 kg/cow/day for moderate-yielding animals and 9 kg/cow/day for high-yielding ones, respectively. The results reinforce the importance of bioclimatic zoning to support informed policymaking in the context of climate change.

Resumo A utilização dos dados de reanálise atmosférico global (ERA-Interim), produzidos pelo European Centre for Medium Range Weather Forecasts - ECMWF se torna uma importante ferramenta para regiões de produção agrícola, principalmente em áreas escassas de estações meteorológicas. Objetivou-se neste trabalho, caracterizar a variabilidade espacial e sazonal hídrica do estado do Mato Grosso do Sul usando dados de reanálise meteorológica do ERA-Interim. O estudo foi realizado no Mato Grosso do Sul, localizado na região Centro-Oeste do Brasil. Os dados decendiais de precipitação pluvial e temperatura média do ar foram obtidos pelo ERA-Interim entre os anos de 1989 e 2015, totalizando 566 pontos. Estimou-se o armazenamento de água no solo, a deficiência hídrica e o excedente hídrico a partir do balanço hídrico normal de Thornthwaite e Mather (1955). No Mato Grosso do Sul ocorreu variação espacial e sazonal dos componentes do balanço hídrico. A variação sazonal do armazenamento hídrico do solo ocorreu com a mudança das estações do ano. A região sul do estado do Mato Grosso do Sul apresenta os maiores recursos hídricos, com DEF reduzido, ARM próximo de 100% e EXC elevados. Os maiores excedentes hídricos ocorrem no período de dezembro a março, enquanto, de agosto a outubro ocorrem os menores valores de excedentes hídricos no estado.

Know the precision of potential evapotranspiration models in different agronomic and climatic conditions is useful for irrigated agriculture. Therefore, we aimed to compare 18 methods of estimation of ETP with the Penman-Monteith (FAO-56) method, at different time scales for the State of Mato Grosso do Sul. Time series of climatic data were used on a daily scale between 1983 and 2018 from 22 locations in the state of Mato Grosso do Sul. ETP estimation models tested were: Benevidez-Lopez, Blaney-Criddle, Camargo, Hamon, Hargreaves, Hargreaves-Samani, Jensen-Haise, Jobson, Kharrufa, Linacre, Makkink, Penman, Priestley-Taylor, Radiation, Romanenko, Tanner-Pelton, Thornthwaite, and Turc. These models were compared with Penman-Monteith in daily, weekly, and monthly scales. The comparison between the ETP estimation models and the Penman-Monteith model was performed by the statistical indices: accuracy (MAPE) and precision (R²aj). Estimation methods showed differences in efficiency over time scales. The best performances of the models were on the daily scale. For daily scale, methods of Priestley-Taylor, Hargreaves, Hamon, and Makkink present the best values of accuracy and precision for the State of Mato Grosso do Sul. In the weekly scale, the most accurate methods are Hamon and Makkink, while for monthly scale the best methods are Makkink and Priestley-Taylor.