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Background: Climate influences the interaction between pathogens and their hosts significantly. This is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown‐eye spot, can reduce yields drastically. This study focuses on forecasting coffee brown‐eye spot using various models that incorporate agrometeorological data, allowing for predictions at least 1 week prior to the occurrence of disease. Data were gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais state. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown‐eye spot, identifying one with potential for advanced decision‐making. The top‐performing models were then employed in the next stage to forecast and spatially project the severity of brown‐eye spot across 2681 key Brazilian coffee‐producing municipalities. Meteorological data were sourced from NASA's Prediction of Worldwide Energy Resources platform, and the Penman–Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather water‐balance calculation. Six ML models – K‐nearest neighbors (KNN), artificial neural network multilayer perceptron (MLP), support vector machine (SVM), random forests (RF), extreme gradient boosting (XGBoost), and gradient boosting regression (GradBOOSTING) – were employed, considering disease latency to time define input variables. Results: These models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high‐yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low‐yielding scenarios. The incidence of brown‐eye spot varied noticeably between high‐ and low‐yield conditions, with significant regional differences observed. The accuracy of predicting brown‐eye spot severity in coffee plantations depended on the biennial production cycle. High‐yielding trees showed superior results with the XGBoost model (R2 = 0.77, root mean squared error, RMSE = 10.53), whereas the SVM model performed better under low‐yielding conditions (precision 0.76, RMSE = 12.82). Conclusion: The study's application of agrometeorological variables and ML models successfully predicted the incidence of brown‐eye spot in coffee plantations with a 7 day lead time, illustrating that they were valuable tools for managing this significant agricultural challenge. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: This research aimed to identify the agroclimatic zones in Brazil, excluding Rio Grande do Sul, that are suitable for olive (Olea europaea L.) cultivation, considering both climatic and topographical factors. Olives require specific conditions: moderate winter temperatures (7–15 °C), warmer summers (25–35 °C) and sufficient water during growth and fruit maturation. They can endure some drought, making them a viable option for agricultural diversification. Using daily meteorological data from 1989 to 2023 from NASA‐POWER, this study analyzed variables like air temperature (minimum and maximum) and rainfall. Key climate variables were the mean air temperature in winter (T_w), spring (T_s), summer (T_su) and autumn (T_a) and total annual precipitation (Prec). Criteria for suitability included: T_w between 5 and 20 °C, T_s between 15 and 23 °C, T_su between 15 and 30 °C, T_a between 15 and 22 °C, annual precipitation over 900 mm and altitude below 900 m. Geographic information system software and Python 3.8 were employed for data analysis and zoning. RESULTS: Results indicated that only 1.92% of the analyzed area, mainly in Minas Gerais, was suitable for olive cultivation. High temperatures and low rainfall in Brazil, particularly in the North and Midwest, make 59.56% of the country unsuitable for olive farming. Additionally, 18.58% of the land, mainly in the Northeast, faces challenges due to extreme heat (T_w) and insufficient water supply. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: Climate change is the main cause of biotic and abiotic stresses in plants and affects yield. Therefore, we sought to carry out a study on future changes in the agroclimatic conditions of banana cultivation in Brazil. The current agroclimatic zoning was carried out with data obtained from the National Institute of Meteorology related to mean air temperature, annual rainfall, and soil texture data in Brazil. The global climate model BCC‐CSM1.1 (Beijing Climate Center‐Climate System Model, version 1.1), adopted by the Intergovernmental Panel on Climate Change, corresponding to Representative Concentration Pathways (RCPs) 2.6, 4.5, 6.0, and 8.5 for the period 2050 (2041–2060) and 2070 (2061–2080), obtained through the CHELSA V1.2 platform, was chosen for the climate projections of the Coupled Model Intercomparison Project 5. Matrix images at a depth of 5–15 cm, obtained through the product of the SoilGrids system, were used for the texture data. ArcGIS version 10.8 was used to construct the maps. RESULTS: Areas favorable to the crop plantation were classified as suitable when air temperature TAIR was between 20 and 29 °C, annual rainfall RANNUAL between 1200 and 1900 mm, and soil clay content CSOIL between 30 and 55%. Subsequently, the information was reclassified, summarizing the classes into preferential, recommended, little recommended, and not recommended. The current scenario shows a preferential class of 8.1%, recommended of 44.6%, little recommended of 47.1%, and not recommended of 0.1% for the Brazilian territory. CONCLUSION: The results show no drastic changes in the total area regarding the classes, but there is a migration from these zones; that is, from tropical to subtropical and temperate regions. RCP 8.5–2070 (2061–2080) showed trends with negative impacts on arable areas for banana cultivation at the end of the century. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]