Search

The study of climatic variables in large scales with surface meteorological stations is limited due to the low density of these stations in many regions, possible sources of errors related to missing data, and uncertainties about the calibration sensors. Global gridded data (GD) systems can minimize these problems. Thus, studies that validate GDs with "ground truth" are important for several applications such as climate change. The objective of this study was to compare long series of surface data with 10-day estimates of average air temperature (T) and precipitation (P) using data from the European Center for Medium-Range Weather Forecast (ECMWF) and the National Aeronautics and Space Administration (NASA) for important agricultural locations in the states of Minas Gerais and São Paulo in Brazil. Despite the different spatial resolutions between ECMWF and NASA, the purpose of this paper was to evaluate the two data sources as they are readily available. The GD performance was evaluated by linear regression analysis. Analyses were performed for each meteorological variable for entire years and separated by seasons. The estimates of T from both ECMWF and NASA systems were accurate with the minimum Willmott concordance index (d) and RMSEp of 0.86, 0.37 °C, respectively, and precision with R2 0.61. The estimates of P had a minimum R2, d, and RMSEp of 0.48, 0.79, 2.15 °C respectively. The decreasing orders of (R2) were autumn > winter > spring > summer for T and winter > autumn > spring > summer for P, varying from 0.93 to 0.61 for T and from 0.77 to 0.48 for P. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) is one important agrometeorological parameter for hydrological studies and climate risk zoning. ET0 calculation by the FAO Penman–Monteith method requires several input data. However, the availability of climate data has been a problem in many places around the world, so the study of scenarios with different combinations of climate data has become essential. The aim of this study was to evaluate the performance of artificial neural network (ANN), random forest (RF), support vector machine (SVM), and multiple linear regression (MLR) approaches to estimate monthly mean ET0 with different input data combinations and scenarios. Three scenarios were evaluated: at the state level, where all climatological stations were used (Scenario I–SI), and at the regional level, where the Minas Gerais state was divided according to the climatic classifications of Thornthwaite (Scenario II–SII) and Köppen (Scenario III–SIII). ANN and RF performed better in ET0 estimation among the models evaluated in the SI, SII, and SIII scenarios with the following data combinations: (i) latitude, longitude, altitude, month, mean, maximum and minimum temperature, and relative humidity and (ii) latitude, longitude, altitude, month, mean temperature, and relative humidity. SVM and MLR models are recommended for all scenarios in situations with limited climatic data where only air temperature and relative humidity data are available. The results and information presented in this study are important for the agricultural chain and water resources in Minas Gerais state. [ABSTRACT FROM AUTHOR]

Brown eye spot (Cercospora coffeicola) is one of the main fungal diseases of coffee, leading to a significant drop in crop productivity and beverage quality in Brazil. The identification of potential risk areas for the development of the disease provides promising information for the management of the pathogen. This study aimed to elaborate an agroclimatic zoning for the incidence of brown eye spot on coffee under climate change scenarios, as suggested by IPCC (IPCC-AR5), in the main coffee-growing regions. Climate data of air temperature, precipitation, and relative humidity were collected from the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources (NASA/POWER) platform from 1989 to 2020 for 46 municipalities in the states of Paraná, São Paulo, Minas Gerais, Espírito Santo, Goiás, and Bahia. The ideal climate for brown eye spot occurrence consists of an air temperature (Tmean) between 18 and 30 °C, relative humidity (RHmean) > 90%, and leaf wetness duration (LWD) > 9 h. The number of hours of leaf wetness was determined by the sum of hours with relative humidity > 90%. Relative humidity was estimated for each hour of the day using air temperature and estimated data of mean dew point temperature, maximum relative humidity, and minimum relative humidity. Climate change scenarios were designed based on sets of climate simulations for the twenty-first century. Scenario S1 is the current scenario without changes, while scenarios S2 and S3 show Tmean + 1.5 °C and 3.0 °C, respectively, with RHmean without changes. Scenarios S4 and S5 present RHmean varying from − 30% to + 30%, respectively, with Tmean unchanged. In the current scenario (S1), Minas Gerais presented a predominance of 100% for low climate risk to brown eye spot in September. Paraná presented a medium risk in 76.15% of the state in April. Scenarios S2 and S3 showed significant changes, increasing the average fitness class in the study region, mainly in the states of São Paulo, Minas Gerais, Rio de Janeiro, and Paraná. Scenario S4 showed 100% predominance of the low-risk class. In contrast, S5 showed the occurrence of the high-risk class for the study region with a + 30% increase in relative humidity. Minas Gerais presented a predominance of the high-risk class for the development of C. coffeicola in 76%, 100%, 97.83%, 89.30%, 93.46%, 80.64%, and 57.77% from November to May, respectively. The presence of high relative humidity represents the main factor for the expansion of the high-risk class for the development of C. coffeicola. Producers knowing the months of the year and the places in Brazil with the highest incidence of brown eye spot will be able to prevent the disease in a more sustainable way, using more ecological products, such as the early application of copper. [ABSTRACT FROM AUTHOR]