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BACKGROUND Peanuts are widely grown in Brazil because of their great importance in the domestic vegetable oil industry and the succession of sugarcane, soybean and maize crops, contributing to soil conservation and improvement in agricultural areas. Thus, the present study aimed to determine the zoning of peanuts' climatic risk by estimating the water requirement satisfaction index (WRSI) for the crop in Brazil. We used a historical series of data on average air temperature and rainfall between 1980 and 2016. Reference evapotranspiration was estimated using the method of Thornthwaite, and we subsequently calculated crop evapotranspiration and maximum evapotranspiration. Water balances for all stations were calculated using the method of Thornthwaite and Mather, with an available water capacity in the soil of 15, 30 and 45 mm. The definitions of suitable, unfit and restricted areas and the planting season were performed using the WRSI. RESULTS Brazil has low climatic risk areas for growing peanuts throughout the year, except for winter. The country reveals that 88.19%, 97.93%, 99.16% and 39.25% of its area is suitable for planting peanuts on planting dates in spring, summer, autumn and winter, respectively. CONCLUSION Brazil has a large part of the areas favorable to the planting of peanuts. The maximum availability of soil water at a depth of 15, 30 and 45 mm does not influence regions with respect to peanut growing in Brazil. The states of Piauí, Ceará and Bahia are the most unsuitable on the winter planting date, with an average WRSI of 0.22. © 2021 Society of Chemical Industry

Thornthwaite climate classification indices are essential to interpret climate types in the state of the pantanal biome (Mato Grosso do Sul), simplifying calculation process and interpretation of climatological water balance by farmers. However, there are few studies found in the literature that characterize the climate of pantanel biome in different climatic scenarios. We seek to assess climate change using humidity index of Thornthwaite climate classification in pantanal biome. We used historical series of climate data from all 79 municipalities of Mato Grosso do Sul between 1987 and 2017, which were divided into microregions. Air temperature and precipitation were collected on a daily scale. Precipitation and potential evapotranspiration data allowed calculating water balance by the Thornthwaite and Mather method. We characterized all locations as wet and dry using aridity indices proposed by Thornthwaite. The global climate model used was BCC-CSM 1.1 developed at the Beijing Climate Center (BCC) with a resolution of 125 x 125 km. We used the scenarios RCP-2.6, RCP-4, RCP-6 and RCP-8.5 for analyzing 21st century projections (2041-2060 and 2061-2080 periods). Maps were generated from climate indices of Mato Grosso do Sul using kriging interpolation method with spherical model, one neighbor, and 0.25° resolution. The microregions showed different patterns regarding water balance components and humidity index. Humidity index had a mean of 15.94. The prevailing climate in the state of Mato Grosso do Sul is C2 (moist subhumid). The state of Mato Grosso do Sul has two well-defined periods during the year: a dry and a rainy period. Three climate types predominate in Mato Grosso do Sul and, according to the Thornthwaite classification, are B1 (humid), C2 (moist subhumid), and C1 (dry subhumid). Water characterization in Mato Grosso do Sul showed 234.78 mm year−1 of water surplus, 80.8 mm year−1 of water deficit, and 1,114.8 mm year−1 of potential evapotranspiration. Water deficit and potential evapotranspiration decrease as latitude increases. The climatic projections show, in all scenarios, reduce the area classified as umida in the state (B1, B2 and B3), besides adding the dry subhumid class (C1). The Scenario RCP 8.5 in 2061 - 2080 is the most worrisome situation of all, because the state can undergo major changes, especially in the pantanal biome region.

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Climate Classification System (CCS) is an important tool for validating climate change models, subsidizing the characterization of new areas suitable or unfit for agricultural activity according to future climate change scenarios. This study aims to classify the climate of the Brazilian territory in the various climate change scenarios of the IPCC through the Thornthwaite system (1948). We used a 30-year historical series of climatic data of average air temperature (°C) and rainfall (mm), obtained from the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources platform (NASA/POWER). Potential evapotranspiration (ETP) was estimated by the method of Camargo (1971); the climatological water balance (CWB) was calculated by the method of Thornthwaite and Mather (1955), using 100 mm of soil water storage capacity. CWB extracts were combined for classification by Thornthwaite (1948). The scenarios used were based on the IPCC (2014) projections and the study of Pirttioja et al. (2015). The Brazilian territory had an average air temperature of 22.20 °C (± 3.20) °C and annual precipitation of 1987 mm (± 725) mm. The climatic classification of Thornthwaite presented 108 climatic classes for the current scenario with a more significant predominance of the classes ArAʹaʹ, B4rAʹaʹ, and B3rAʹaʹ representing 20.54%, 15.62%, and 9.46% of the Brazilian territory, respectively. The climate class ArAʹaʹ had 39.20% in the North and 14.97% in the Midwest. The South region has a predominance of 24.31% for the class ArBʹ3aʹ. In the Southeast and Northeast, the climate classes B2rBʹ3aʹ and DdBʹ2aʹ represented 14.80% and 15.26% of the regions, respectively. The S5 scenario was considered more favorable to establishing crops, with 48.04% of Brazil represented by the climate class ArAʹaʹ. Furthermore, the most catastrophic scenarios for crops were S3 and S4, promoting Brazil a predominance of classes B3rAʹaʹ in 18.02% and B1rAʹaʹ in 21.04%, respectively, favoring the occurrence of arid and dry climates in large part of the Brazilian territory.

This study aimed to estimate the minimum and maximum monthly air temperatures in the sugarcane regions of Brazil. A 30-year historical series (1988-2018) of maximum (Tmax) and minimum (Tmin) air temperatures from the NASA/POWER platform was used for 62 locations that produce sugarcane in Brazil. Multiple linear regression was used for data modeling, in which the dependent variables were Tmin and Tmax and the independent variables were latitude, longitude, and altitude. The comparison between estimation models and the real data was performed using the statistical indices MAPE (accuracy) and adjusted coefficient of determination (R2adj) (precision). The lowest MAPE values of the models for estimating the minimum air temperature occurred mainly in the North during February, March, and January. Also, the most accurate models for estimating the maximum air temperature occurred in the Southeast region during January, February, and March. The MAPE and R2adj values showed accuracy and precision in the models for estimating both the maximum and minimum temperatures, indicating that the equations can be used to estimate temperatures in sugarcane areas. The Tmin estimation model for the Southeast region in July shows the best performance, with a MAPE value of 1.28 and an R2adj of 0.94. The Tmax model of the North region for September presents higher precision and accuracy, with values of 1.28 and 0.96, respectively.

BACKGROUND: Climate conditions affect animal welfare directly, influencing milk production. The Midwest region is the largest cattle-producing region in Brazil. The objective of this study was to elaborate on bioclimatic zoning for dairy cattle in the Midwest region of Brazil. Air temperature (Ta, °C) and relative humidity (%, RH) data from a 30-year historical series collected by the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources (NASA/POWER) platform were used. The Temperature and Humidity Index (THI) was determined for the hottest and coldest months. Milk production losses due to climate factors in the Midwest of Brazil for two daily production levels, 10 kg Milk (PL10) and 25 kg Milk (PL25), were estimated. RESULTS: The Midwest presented three THI classifications throughout the year: 'normal', 'alert', and 'critical alert'. The entire Midwest region was classified as 'normal' (THI < 70) between autumn and winter. The decrease in milk production (DMP) during the autumn and winter presented no loss for both production levels (PL10 and PL25).CONCLUSION: On the other hand, a 1 to 2 kg reduction in milk production was observed for cows with a PL25 production level between spring and summer in the southern Midwest region, while cows with a PL10 production level showed no reduction in milk production. Only the cities of Sinop and Cuiabá did not present a 'critical alert' during spring/summer for the risk of heat stress.

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