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BACKGROUND: Coffee (Coffea arabica L.) is one of the most important commodities today, with a high economic value worldwide. Coffee leaf rust (Hemileia vastatrix Berk. et Br.) has been showing a high impact on Brazilian coffee trees among the various diseases that attack coffee. The climate has a great influence on the development of diseases, especially when fungi are the causal agents. This study aimed to carry out the zoning of climate favorability for coffee leaf rust in the traditional and main coffee‐producing regions of Brazil. The study was conducted in 13 locations in the states of Paraná, São Paulo, Rio de Janeiro, Espírito Santo, Minas Gerais, Goiás and Bahia. Air temperature and daily precipitation data for the current scenario were collected using the WorldClim version 2.1 platform for the last climatological normal and future climate change data. The ideal climate conditions for coffee leaf rust consist of a mean air temperature ranging from 21 to 25 °C and precipitation >30 mm per month. The Coupled Model Intercomparison Projects scenarios associated with the Intergovernmental Panel on Climate Change reports consisted of the Shared Socio‐Economic Pathways SSP‐1 2.6, SSP‐2 4.5, SSP‐3 7.0 and SSP‐5 8.5, the latter being considered one of the most catastrophic. All steps to carry out the suitability zoning were performed in a tool using the QGIS geographic information system software. RESULTS: Zoning for coffee leaf rust had three classes: favorable, relatively favorable and unfavorable. Currently, the largest coffee‐producing region in Brazil has 49.1% of its analyzed area classified as favorable, 39.2% as relatively favorable and 11.7% as unfavorable. In the current scenario, Patrocínio and Três Pontas are locations with high coffee production in which the favorable class is predominant. The state of Minas Gerais has an annual mean of 55.3% of its entire territory apt for the disease, with the highest occurrence between September and March. CONCLUSIONS: Climate change has a negative impact on the development of coffee leaf rust, mainly in the long term, as in the period of 2081–2100, in which the SSP‐5 8.5 scenario led to a decrease in the favorable and unfavorable areas and an increase in the relatively favorable areas of 9.8%, 18.6% and 71.5% for the Brazilian territory, respectively. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

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]

BACKGROUND: The loss of coffee leaves caused by the attack of pests and diseases significantly reduces its production and bean quality. Thus this study aimed to estimate foliation for regions with the highest production of arabica coffee in Brazil using nonlinear models as a function of climate. A 25‐year historical series (1995–2019) of Coffea arabica foliation (%) data was obtained by the Procafé Foundation in cultivations with no phytosanitary treatment. The climate data were obtained on a daily scale by NASA/POWER platform with a temporal resolution of 33 years (1987–2019) and a spatial resolution of approximately 106 km, thus allowing the calculation of the reference evapotranspiration (PET). Foliation estimation models were adjusted through regression analysis using four‐parameter sigmoidal logistic models. The analysis of the foliation trend of coffee plantations was carried out from degrees‐day for 70 locations. RESULTS: The general model calibrated to estimate the arabica coffee foliation was accurate (mean absolute percentage error = 2.19%) and precise (R2adj = 0.99) and can be used to assist decision‐making by coffee growers. The model had a sigmoidal trend of reduction, with parameters ymax = 97.63%, ymin = 9%, Xo = 3517.41 DD, and p = 6.27%, showing that foliation could reach 0.009% if the necessary phytosanitary controls are not carried out. CONCLUSION: Locations with high air temperatures over the year had low arabica coffee foliation, as shown by the correlation of −0.94. Therefore, coffee foliation can be estimated using degree days with accuracy and precision through the air temperature. This represents great convenience because crop foliation can be obtained using only a thermometer. © 2021 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

Copyright of Revista em Agronegócio e Meio Ambiente is the property of Revista em Agronegocio e Meio Ambiente and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Agrometeorological models have been applied to crop yield estimations, providing information for management. A limiting factor of these models is the input data, which come mostly from surface meteorological stations (SMS). We propose the use of gridded weather data provided by the European Center for Medium-Range Weather Forecasts (ECMWF) and the NASA as a viable and innovative alternative for coffee (Coffea arabica L.) yield estimation in Brazil. We made modifications in the coeffi- cients of the model proposed by Santos and Camargo, regarding the penalties for extreme temperatures and those related to different gridded data sources. The accuracy, measured by the mean absolute percentage error (MAPE), was 23.76, 24.61, and 22% for the calibrations with ECMWF, NASA, and SMS data, respectively. These high levels of MAPE were the result of the high biennality of the crop. The average tendency, measured by the systematic root mean square error, was an overestimation (or underestimation) of ±465 kg ha-1 by the ECMWF, ±411 kg ha-1 by NASA, and ±653 by the SMS models. The mean precision, measured by the nonsystematic root mean square error, was 186, 190, and 280 kg ha-1 for the ECMWF, NASA, and SMS models, respectively. These results indicate that coffee yield for São Paulo and Minas Gerais can be calibrated and estimated with the ECMWF and NASA gridded data. [ABSTRACT FROM AUTHOR]

Abstract: BACKGROUND: Climatic conditions directly affect the maturation period of coffee plantations, affecting yield and beverage quality. The quality of coffee beverages is highly correlated with the length of fruit maturation, which is strongly influenced by meteorological elements. The objective was to estimate the probable times of graining and maturation of the main coffee varieties in Brazil and to quantify the influences of climate on coffee maturation. We used degree days to estimate flowering/graining periods (green fruit) and flowering/maturation periods (cherry fruit) for all cultivars. We evaluated the influence of climate on the time of maturity using Pearson correlation and nonlinear regression analysis and successfully mapped the influences of these elements. RESULTS: Arabica coffee matured up to 2–3 months earlier in São Paulo, where air temperatures (TAIR) were higher, than in Minas Gerais, which would allow earlier harvesting and the training of seedlings at the beginning of the rainy season. Catuaí‐Amarelo‐IAC‐62 cultivar needed 205–226 days between the end of flowering and maturation at locations with high TAIR and 375–396 days at locations with low TAIR. CONCLUSION: Water surplus and deficit were generally the most important variables for coffee maturation. Coffee matured faster in regions with high TAIR and evapotranspiration, moderate altitudes and deficits. Acaiá‐Cerrado‐MG‐1474 and Icatu‐Precoce‐Amarelo‐3282 were cultivars with an early cycle. © 2018 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Abstract: BACKGROUND: The geoviticultural multicriteria climatic classification (MCC) system provides an efficient guide for assessing the influence of climate on wine varieties. Paraná is one of the three states in southern Brazil that has great potential for the expansion of wine production, mainly due to the conditions that favour two harvests a year. The objective was to apply the geoviticultural MCC system in two production seasons. We used maximum, mean and minimum air temperature and precipitation for 1990–2015 for the state of Paraná. Air temperature and Precipitation were used to calculate the evapotranspiration and water balance. We applied the MCC system to identify potential areas for grapevine production for harvests in both summer and winter and then determined the climatic zones for each geoviticultural climate. RESULTS: Paraná has viticultural climates with conditions favourable for grapevine cultivation for the production of fine wines from summer and winter harvests. The conditions for the winter harvest provided wines with good coloration and high aromatic potential relative to the summer harvest. CONCLUSION: Chardonnay, Merlot, Pinot Blanc and Müller‐Thurgau were suitable for regions with lower air temperatures and water deficits. Pinot Blanc and Müller‐Thurgau were typical for the southern region of Paraná. © 2017 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Some forecasting techniques have been tested with crop models using various statistical analyses for generating future scenarios of yield (Y). Forecasting, however, can be achieved by simply using regression analysis and carefully selecting independent variables (IVs) with time displacement relative to the dependent variable. The early forecasting of Y is the vanguard of agronomic modeling, promoting improvements in planning, allowing more rational strategic decisions, and increasing food and economic security. Climatic variables are the most important factors controlling the yield and quality of coffee (Coffea arabica L.). We calibrated and tested agrometeorological models to forecast the annual Y of coffee for six traditional producing regions in the state of Minas Gerais, Brazil. We used multiple linear regressions, selecting IVs to maximize the period between the forecast of Y and the harvest for each locality. The IVs were monthly meteorological variables from 1997 to 2014: air temperature, rainfall, potential evapotranspiration, soil water storage, water deficit, and water surplus. The IVs were selected by testing all possible combinations in the domain and avoiding multicollinearity. The agrometeorological models were accurate for all regions, and the earliest forecasts were 6 and 5 mo before harvest for the producing locations of Guaxupé and Coromandel, respectively. The models for yield forecasting for Guaxupé included the water deficit in July and October and July precipitation for the high-yield season and the water deficit in April and September and October precipitation for the low-yield season. The models for yield forecasting for Coromandel included the November water surplus and February and September precipitation for the high-yield season and precipitation for January, April, and October for the low-yield season. [ABSTRACT FROM AUTHOR]

Copyright of Saúde e Pesquisa is the property of Saude e Pesquisa and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: The increasing demand in Brazil and the world for products derived from the açaí palm (Euterpe oleracea Mart) has generated changes in its production process, principally due to the necessity of maintaining yield in situations of seasonality and climate fluctuation. The objective of this study was to estimate açaí fruit yield in irrigated system (IRRS) and rainfed system or unirrigated (RAINF) using agrometeorological models in response to climate conditions in the eastern Amazon. Modeling was done using multiple linear regression using the 'stepwise forward' method of variable selection. Monthly air temperature (T) values, solar radiation (SR), vapor pressure deficit (VPD), precipitation + irrigation (P + I), and potential evapotranspiration (PET) in six phenological phases were correlated with yield. The thermal necessity value was calculated through the sum of accumulated degree days (ADD) up to the formation of fruit bunch, as well as the time necessary for initial leaf development, using a base temperature of 10 °C., Results: The most important meteorological variables were T, SR, and VPD for IRRS, and for RAINF water stress had the greatest effect. The accuracy of the agrometeorological models, using maximum values for mean absolute percent error (MAPE), was 0.01 in the IRRS and 1.12 in the RAINF., Conclusion: Using these models yield was predicted approximately 6 to 9 months before the harvest, in April, May, November, and December in the IRRS, and January, May, June, August, September, and November for the RAINF. © 2019 Society of Chemical Industry., (© 2019 Society of Chemical Industry.)