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The El Niño-Southern Oscillation (ENSO) phenomenon affects the global climate by changing temperature and precipitation patterns mainly in tropical climatic regions and median latitudes. Such event strongly influences agricultural activities and crop yields. The Niño Oceanic Index (ONI) of the US National Oceanic and Atmospheric Administration (NOAA) describes and monitors ENSO intensity from ocean temperature measurements. When ONI in the Niño 3.4 region was + 0.5 °C above normal or − 0.5 °C below normal for 5 consecutive 3-month running averages, El Niño (EN) or La Niña (LN) events, respectively, were established. The prediction of ENSO events is made by modeling at major global weather centers by atmosphere–ocean coupling models; however, no articles were found using decision tree classifier (DTC) for ENSO forecasting purposes. This modeling approach requires much less computational time and capacity. Furthermore, DTC can be sufficiently accurate for agricultural purposes. Thus, the objective of this research was to forecast as early as possible the El Niño and La Niña yearly events using a DTC technique from ONI data from 1950 to 2020. We used as input variables for DTC quarterly ONI values from 15 quarters prior the data of forecasting. The DTC showed an accuracy of 89%, 84%, and 78% to predict La Niña, El Niño, and neutral years, respectively, without training period. For validation, the accuracy was 100%, 79%, and 79% for La Niña, El Niño, and neutral years, respectively. The selected ONI quarters were July–August-September, January–February-March, and February–March-April of the previous year and January–February-March of the current year, allowing an 8-month advance forecast with an average accuracy of 78% (validation). [ABSTRACT FROM AUTHOR]

Forecasting the severity of plant diseases is an emerging need for farmers and companies to optimize management actions and to predict crop yields. Process-based models are viable tools for this purpose, thanks to their capability to reproduce pathogen epidemiological processes as a function of the variability of agro-environmental conditions. We formalized the key phases of the life cycle of Puccinia kuenhii (W. Krüger) EJ Butler, the causal agent of orange rust on sugarcane, into a new simulation model, called ARISE (Orange Rust Intensity Index). ARISE is composed of generic models of epidemiological processes modulated by partial components of host resistance and was parameterized according to P. kuenhii hydro-thermal requirements. After calibration and evaluation with field data, ARISE was executed on sugarcane areas in Brazil, India and Australia to assess the pathogen suitability in different environments. ARISE performed well in calibration and evaluation, where it accurately matched observations of orange rust severity. It also reproduced a large spatial and temporal variability in simulated areas, confirming that the pathogen suitability is strictly dependent on warm temperatures and high relative air humidity. Further improvements will entail coupling ARISE with a sugarcane growth model to assess yield losses, while further testing the model with field data, using input weather data at a finer resolution to develop a decision support system for sugarcane growers. [ABSTRACT FROM AUTHOR]

Pre-harvest yield forecasting is important for the sustainability of agricultural companies, enabling more sustainable economic decision-making. In the present study, we propose an approach based on the sum of degree days of the corn crop related to the dates of satellite images to organize the data of two crops generating predictive models with the k-nearest neighbors (KNN) and extreme gradient boosting (XGBoost). The field study was carried out in a commercial area during the 2017/18 and 2018/19 harvests. Spectral data were obtained from Sentinel-2 satellite images. After the correction and processing of the images, the values ​​of the spectral bands and the vegetation indices were obtained. For the development of the models, the images obtained throughout the cycle were divided into three classes of the mean weeks before harvest (WBH) from different degree-days (GD) during the cycle, in this study we adopted 12 combinations of data inputs to develop the models. In yield forecasting, we were able to forecast approximately 30 to 70 days before harvest (500 to 900 degree-days before harvest), in addition, the most accurate models were when the data used as driven variables were the spectral bands of the red, blue, green and nir collected from 800 to 1200 degree-days of the culture (WBH4). For the models developed, combined with WBH for yield forecast, it was possible to forecast yield with an average error of 0.503 t ha-1, and the greatest precision and accuracy occurred with the use of all variables RGB e Near-infrared. [ABSTRACT FROM AUTHOR]

We developed models for simulating trends over time as functions of the thermal index and models for estimating the levels of infestation of the coffee leaf miner and coffee berry borer and the severity of disease for coffee leaf rust and cercospora, the main phytosanitary problems in coffee crops around the world. We used historical series of climatic data and levels of pest infestation and disease severity in Coffea arabica for high and low yields for seven locations in the two main coffee-producing regions in the state of Minas Gerais in Brazil, Sul de Minas Gerais and Cerrado Mineiro. We conducted two analyses: (a) we simulated the trends of the progress of diseases and pests over time using non-linear models. We only used the thermal index because air temperature is commonly measured by farmers in the regions. (b) We estimated the levels of pest infestation and disease severity using multiple linear regression, with the levels of diseases and pests as dependent variables and accumulated degree days (DD), coffee foliage (LF) estimated by DD and the number of nodes (NN) estimated by DD as independent variables. We used DD and LF = f (DD) and NN = f (DD) to predict diseases and pests with accuracy. MAPEs were 19.6, 5.7, 9.5, and 15.8% for rust, cercospora, leaf miner, and berry borer, respectively, for Sul de Minas Gerais. Establishing phytosanitary alerts using only air temperature was possible with these models. [ABSTRACT FROM AUTHOR]

The natural quality (NQ) of a coffee drink is defined as the one that is obtained in the year of bean production with the standard postharvest treatments of the region. The NQ varies with the climatic conditions during the crop cycle and with the drying period of beans in the sun. A well-formed bean can provide a good drink if well processed, but a bean grown during unfavourable environmental conditions will have poor quality regardless of the postharvest options. The goals of this study were to (1) increase our understanding of the relationship between meteorological elements (MEs) and NQ in coffee-producing regions, (2) identify the ranges of the MEs during the crop cycle that optimise quality, and (3) develop models to predict NQ based on the MEs. We used the two major regions of coffee production in the Brazilian state of Minas Gerais, the Cerrado Mineiro (CEMG), and the southern Minas Gerais (SOMG). We mapped the influences of ME on NQ successfully. Air temperature in November and December for SOMG and precipitation from November to January for CEMG were generally the most important MEs for NQ. Water deficiency, water storage, and rainfall became increasingly more important during winter (June to September) than during other seasons. The crop models were accurate, with errors < 7.9% for predicting NQ for all regions. These models used precipitation in June and December, the actual evapotranspiration in May, and the water deficit in April as the MEs for CEMG, and the rainfall in June and December, the water storage in April, and the actual evapotranspiration in May as the MEs for SOMG. [ABSTRACT FROM AUTHOR]

Disease and pest alert models are able to generate information for agrochemical applications only when needed, reducing costs and environmental impacts. With machine learning algorithms, it is possible to develop models to be used in disease and pest warning systems as a function of the weather in order to improve the efficiency of chemical control of pests of the coffee tree. Thus, we correlated the infection rates with the weather variables and also calibrated and tested machine learning algorithms to predict the incidence of coffee rust, cercospora, coffee miner, and coffee borer. We used weather and field data obtained from coffee plantations in production in the southern regions of the State of Minas Gerais (SOMG) and from the region of the Cerrado Mineiro; these crops did not receive phytosanitary treatments. The algorithms calibrated and tested for prediction were (a) Multiple linear regression (RLM); (b) K Neighbors Regressor (KNN); (c) Random Forest Regressor (RFT), and (d) Artificial Neural Networks (MLP). As dependent variables, we considered the monthly rates of coffee rust, cercospora, coffee miner, and coffee tree borer, and the weather elements were considered as independent (predictor) variables. Pearson correlation analyses were performed considering three different time periods, 1–10 d (from 1 to 10 days before the incidence evaluation), 11–20 d, and 21–30 d, and used to evaluate the unit correlations between the weather variables and infection rates of coffee diseases and pests. The models were calibrated in years of high and low yields, because the biannual variation of harvest yield of coffee beans influences the severity of the diseases. The models were compared by the Willmott's 'd', RMSE (root mean square error), and coefficient of determination (R2) indices. The result of the more accurate algorithm was specialized for the SOMG and Cerrado Mineiro regions using the kriging method. The weather variables that showed significant correlations with coffee rust disease were maximum air temperature, number of days with relative humidity above 80%, and relative humidity. RFT was more accurate in the prediction of coffee rust, cercospora, coffee miner, and coffee borer using weather conditions. In the SOMG, RFT showed a greater accuracy in the predictions for the Cerrado Mineiro in years of high and low yields and for all diseases. In SOMG, the RMSE values ranged from 0.227 to 0.853 for high-yield and 0.147 and 0.827 for low-yield coffee in the coffee borer forecasting. [ABSTRACT FROM AUTHOR]

Precipitation, in short periods of time, is a phenomenon associated with high levels of uncertainty and variability. Given its nature, traditional forecasting techniques are expensive and computationally demanding. This paper presents a soft computing technique to forecast the occurrence of rainfall in short ranges of time by artificial neural networks (ANNs) in accumulated periods from 3 to 7 days for each climatic season, mitigating the necessity of predicting its amount. With this premise it is intended to reduce the variance, rise the bias of data and lower the responsibility of the model acting as a filter for quantitative models by removing subsequent occurrences of zeros values of rainfall which leads to bias the and reduces its performance. The model were developed with time series from ten agriculturally relevant regions in Brazil, these places are the ones with the longest available weather time series and and more deficient in accurate climate predictions, it was available 60 years of daily mean air temperature and accumulated precipitation which were used to estimate the potential evapotranspiration and water balance; these were the variables used as inputs for the ANNs models. The mean accuracy of the model for all the accumulated periods were 78% on summer, 71% on winter 62% on spring and 56% on autumn, it was identified that the effect of continentality, the effect of altitude and the volume of normal precipitation, have an direct impact on the accuracy of the ANNs. The models have peak performance in well defined seasons, but looses its accuracy in transitional seasons and places under influence of macro-climatic and mesoclimatic effects, which indicates that this technique can be used to indicate the eminence of rainfall with some limitations. [ABSTRACT FROM AUTHOR]

ABSTRACT Climate classification systems ( CCS) are important tools to support a sustainable agriculture providing information about basic and general characteristics of a regional climate. The Köppen CCS ( KGT) is considered useful mainly in macroscale and Thornthwaite CCS ( TH), with a more complex symbology, in mesoscale for climatic determinations. Camargo (1991) ( CAM) proposed a new CCS trying to be as simple as Köppen and robust as TH nevertheless with a simple symbology. In the state of São Paulo, it is known that regions with distinct availability of seasonal and annual rainfall are classified similarly by KGT, bringing erroneous information to the agricultural credit system, sowing times and yield forecasts. A comparison of different CCS raises a better understanding of a regional climate in different spatial scales and of CCS themselves. Aiming to characterize the climate and the performance of the CAM the KGT, CAM and TH were applied in 334 locations in the state of São Paulo, Brazil. The most common CAM climates in the state were subtropical humid with dry winter ( ST-UMi) in the Centre and East region of the state, Tropical humid with dry winter ( TR-UMi) and Tropical subhumid with dry winter ( TR-SBi) in the north and west region, respectively. The total numbers of climates were 7, 19 and 26 from KGT, CAM and TH, respectively. The KGT had the ability in separating climates in macroscale, and CAM and TH in mesoscale. There was a tendency of the three CCS in separating warm and dry from cold and wet regions. The TH and CAM had the same performance (number of climates, NOC) in the extremes regions, in the hotter and dry (northern plateau) and the colder (mountainous regions, altitude > 900 m) corresponding to 52% of the state (178 locations). In the other areas TH had the double NOC than CAM. [ABSTRACT FROM AUTHOR]

Forecast is the act of estimating a future event based on current data. Ten-day period (TDP) meteorological data were used for modeling: mean air temperature, precipitation and water balance components (water deficit (DEF) and surplus (EXC) and soil water storage (SWS)). Meteorological and yield data from 1990-2004 were used for calibration, and 2005-2010 were used for testing. First step was the selection of variables via correlation analysis to determine which TDP and climatic variables have more influence on the crop yield. The selected variables were used to construct models by multiple linear regression, using a stepwise backwards process. Among all analyzed models, the following was notable: Yield = - 4.964 x [SWS of 2° TDP of December of the previous year (OPY)] - 1.123 x [SWS of 2° TDP of November OPY] + 0.949 x [EXC of 1° TDP of February of the productive year (PY)] + 2.5 x [SWS of 2° TDP of February OPY] + 19.125 x [EXC of 1° TDP of May OPY] - 3.113 x [EXC of 3° TDP of January OPY] + 1.469 x [EXC of 3 TDP of January of PY] + 3920.526, with MAPE = 5.22%, R² = 0.58 and RMSEs = 111.03 kg ha-1. [ABSTRACT FROM AUTHOR]

The aim of this paper is to compare 18 reference evapotranspiration models to the standard Penman-Monteith model in the Jaboticabal, São Paulo, region for the following time scales: daily, 5-day, 15-day and seasonal. A total of 5 years of daily meteorological data was used for the following analyses: accuracy (mean absolute percentage error, Mape), precision (R2) and tendency (bias) (systematic error, SE). The results were also compared at the 95% probability level with Tukey's test. The Priestley-Taylor (1972) method was the most accurate for all time scales, the Tanner-Pelton (1960) method was the most accurate in the winter, and the Thornthwaite (1948) method was the most accurate of the methods that only used temperature data in the equations. [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]

Yerba mate (Ilex paraguariensis) is renowned for its nutritional and pharmaceutical attributes. A staple in South American (SA) culture, it serves as the foundation for several traditional beverages. Significantly, the pharmaceutical domain has secured numerous patents associated with this plant's distinctive properties. This research delves into the climatic influence on yerba mate by leveraging the CMIP6 model projections to assess potential shifts brought about by climate change. Given its economic and socio-cultural significance, comprehending how climate change might sway yerba mate's production and distribution is pivotal. The CMIP6 model offers insights into future conditions, pinpointing areas that are either conducive or adverse for yerba mate cultivation. Our findings will be instrumental in crafting adaptive and mitigative strategies, thereby directing sustainable production planning for yerba mate. The core objective of this study was to highlight zones optimal for Ilex paraguariensis cultivation across its major producers: Brazil, Argentina, Paraguay, and Uruguay, under CMIP6's climate change forecasts. Our investigation encompassed major producing zones spanning the North, Northeast, Midwest, Southeast, and South of Brazil, along with the aforementioned countries. A conducive environment for this crop's growth features air temperatures between 21 to 25 °C and a minimum precipitation of 1200 mm per cycle. We sourced the current climate data from the WorldClim version 2 platform. Meanwhile, projections for future climatic parameters were derived from WorldClim 2.1, utilizing the IPSL-CM6A-LR model with a refined 30-s spatial resolution. We took into account four distinct socio-economic pathways over varying timelines: 2021–2040, 2041–2060, 2061–2081, and 2081–2100. Geographic information system data aided in the spatial interpolation across Brazil, applying the Kriging technique. The outcomes revealed a majority of the examined areas as non-conducive for yerba mate cultivation, with a scanty 12.25% (1.5 million km2) deemed favorable. Predominantly, these propitious regions lie in southern Brazil and Uruguay, the present-day primary producers of yerba mate. Alarming was the discovery that forthcoming climatic scenarios predominantly forecast detrimental shifts, characterized by escalating average air temperatures and diminishing rainfall. These trends portend a decline in suitable cultivation regions for yerba mate. [ABSTRACT FROM AUTHOR]

Annatto, a globally valued crop, has multifaceted applications ranging from food coloring to medicine. While adaptable, its productivity is influenced by climate, making it vulnerable to global warming. This study investigates the agroclimatic suitability for annatto cultivation in Brazil, considering current and projected climate scenarios, highlighting potential impacts on agriculture, the economy, and environmental sustainability. This study aims to carry out the annatto (Bixa orellana L.) agroclimatic zoning for Brazil in the current scenario and the SSP5-8.5 climate change scenario for CMIP6. Air temperature and daily precipitation data for the 1970–2000 and for 2021–2100 for the IPSL-CM6A-LR model the SSP1-2.6, SSP2-4.5, SSP3-2.6 and SSP5-8.5 in GeoTIFF format. This study was carried out for the entire Brazilian territory. Future climate variables were obtained by the WorldClim 2.1 platform for the IPSL-CM6A-LR model in the periods 2021–2100 and the SSP1-2.6, SSP2-4.5, SSP3-2.6 and SSP5-8.5 scenario. Thus, zoning was carried out using geographic information systems software (QGIS), automated with the Python language. The annatto climate zoning for the current scenario showed five classes. The most predominant class was restricted due to excess water (42.9%), followed by the suitable class (32.5%), standing out the central Brazilian region: Mato Grosso do Sul (91.6%), Goiás (80.8%), and Tocantins (92.6%). Climate change affected negatively annatto climate zoning in all scenarios. The suitability class for the SSP5-8.5 scenario decreased in all periods, with means of 27.9% (2021–2040), 20.2% (2041–2060), 12.1% (2061–2080), and 6.5% (2081–2100). Climate changes significantly impact annatto cultivation areas in Brazil, particularly by 2081–2100, with a notable reduction in suitable zones. Key producing regions, like Monte Castelo-SP and Dracena-SP, face unsuitability, threatening local economies. The study underscores the urgency to explore annatto's adaptive capacity against the direst IPCC scenario (SSP5-8.5). [ABSTRACT FROM AUTHOR]

Coffee production is significantly impacted by various diseases, predominantly those caused by fungi. One such notable disease in coffee crops is caused by the fungus Phoma spp. This pathogen leads to several symptoms detrimental to coffee plants, such as leaf lesions, drying of branches, and rotting of flowers and fruits. These symptoms often result in the dropping of the affected parts, subsequently leading to a decrease in the overall yield of the coffee crop. In response to this challenge, our objective was to develop a forecasting model for the incidence of Phoma leaf spot in Brazilian coffee crops, utilizing advanced machine learning algorithms. This approach is intended to predict disease outbreaks, thereby enabling timely and effective management strategies to mitigate the impact on coffee yield. The study was conducted in two stages: (1) calibration of machine learning models for locations (Boa Esperança, Carmo de Minas, Muzambinho, Varginha, Araxá, Araguari, and Patrocínio) with field data between 2010 and 2022; (2) Phoma leaf spot incidence forecast in municipalities of coffee-producing states in Brazil [Paraná (PR), São Paulo (SP), Rio de Janeiro (RJ), Espírito Santo (ES), Minas Gerais (MG), Goiás (GO), and Bahia (BA)]. Thirty-year climate data were retrieved from the NASA/POWER platform. Reference evapotranspiration was estimated by the Penman–Monteith method, generating water balance according to Thornthwaite and Mather (1955). To understand the effect of climate variables on the disease incidence, Pearson's univariate correlation was performed for each location. We used six algorithms to forecast the disease incidence, considering a 7-day latency period to define input variables. It is noteworthy that the evaluated locations present similar climatic conditions. Summer was the hottest and rainiest period, while winter was the coldest and driest. Annual averages of air temperature, cumulative rainfall, potential evapotranspiration, soil water storage, and incident radiation were 21.1 °C, 1208.9 mm, 1283.2 mm, 58.0 mm, 435.7 mm, and 18.1 MJ m2 day−1, respectively. The XGBoost model demonstrated superior performance for both high- and low-yield coffee trees, achieving an impressive precision (R2fit) of 0.46 and 0.51, respectively. Additionally, it exhibited high accuracy, with Root Mean Square Error (RMSE) values of 3.45% for high-yielding and 3.16% for low-yielding trees. In contrast, the multilayer perceptron (MLP) model displayed suboptimal results under both yield conditions. Given these findings, the XGBoost model proves effective in predicting the incidence of the disease at least 7 days ahead, based on the parameters applied in this study. [ABSTRACT FROM AUTHOR]

Highlights • EVAPO is the state-of-the-art app applied in agricultural or hydrological problems. • EVAPO is an app ready-to-use in practical situations where there is a scarcity of water. • EVAPO app is reliable because uses Penman-Monteith method to estimate potential evapotranspiration worldwide. Abstract In this study a new android app for smartphones to estimate potential evapotranspuration (PET) in real time, using gridded data from NASA-POWER, to any location in the world, would result in a more efficient irrigation and increase irrigation water conservation. The smartphone app called EVAPO uses meteorological data to calculate PET using the Penman–Monteith (FAO56) method. To evaluate performance of the proposed method, we compared PET estimated by the EVAPO with that estimated from climatic data from conventional surface meteorological stations. The accuracy, tendency and precision of the models were determined using the Willmott et al. (1985) concordance index (d), systematic root mean square error (RMSEs) and determination index (R2), respectively. The results obtained were satisfactory for all studied locations whit mean values of 0.67, 0.95 (mm) and 0.72 for d, RMSEs and R2, respectively. The app can be accessed in the Play Store (free): https://play.google.com/store/apps/details?id=br.com.maldonado.instantet0. [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]

Soybean cultivation plays an important role in Mato Grosso do Sul and around the world. Given the inherent complexity of the agricultural system, this study aimed to develop climate-based yield prediction models using ML, considering the most correlated meteorological variables for each condition, test the best model with independent data, and define zones of higher soybean yield in Mato Grosso do Sul to recommend better planting sites. The study was carried out in two stages. First, meteorological and soybean yield data obtained from 47 locations in the state of Mato Grosso do Sul were used to calibrate the machine learning (ML) algorithms. Second, the best algorithm was used to predict soybean yields throughout Mato Grosso do Sul. Daily meteorological data of air temperature (T, °C), precipitation (P, mm), global solar irradiance (Qg, MJ m−2 day−1), wind speed (u2, m s−1), net radiation (Rn, MJ m−2 day−1), and relative humidity (RH, %) of the NASA-POWER system from 2002 to 2021 were used. The reference evapotranspiration (ETo) by the standard FAO method and water balance (WB) by Thornthwaite and Mather (1955) were calculated for each collection point. The MLs used in this stage consisted of multiple linear regression (MLR), multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBOOSTING), and gradient boosted decision (GradBOOSTING). The ML models were calibrated using 70% of the data selected for training and 30% for validation. Algorithms were evaluated by accuracy, precision, and tendency. All analyses were performed using Python 3.8 software. Climate variables showed high spatial and seasonal variability throughout Mato Grosso do Sul (MS). Pearson's univariate correlations between soybean yield and climate variables of the phenological period showed distinct relationships and different intensities. For instance, soil water storage (ARM) showed negative, neutral, and positive correlations in October, November, and December, respectively. The calibrated ML algorithms had a high precision and accuracy in both calibration and testing. For instance, the best model in the calibration was XGBOOSTING, which showed MAPE, R2, RMSE, MSE, and MAE values of 1.84%, 0.95, 2.06%, 4.24%, and 0.921%, respectively. Random forest (RF), extreme gradient boosting (XGBOOSTING), and gradient boosting (GradBOOSTING) were the most precise machine learning algorithms, with R2 values of 0.71, 0.62, and 0.62 in the test, respectively. [ABSTRACT FROM AUTHOR]

Knowing the number of workable days (NWD) with agricultural machinery during a crop is crucial to mitigate structural soil degradation in conditions of insufficient moisture. Although soil moisture is the most important, field planning often only involves precipitation. This study aimed to quantify NWD for the main sugarcane producing municipalities in Brazil across different seasons, considering the water balance. Two criteria were adopted to determine NWD: one includes days suitable for agricultural activities with daily precipitation less than 5 mm and soil water content between 40 and 90% of available water capacity, while the other only integrates precipitation (< 5 mm). Thirty years of daily climate data were collected for Brazilian locations to calculate the water balance. Cluster analysis was applied to group similar localities based on meteorological and water balance components. The study began with a statistical analysis of variability between groups and intragroup of meteorological and water balance elements. Subsequently, NWD maps were created for the past 30 years (characterization) and the past 10 years (planning), both for dry and rainy periods. A comparative analysis of the two criteria to account NWD was conducted. Over the last decade, significant drought trends led to an approximately 10-day increase in NWD nationwide during both dry and rainy seasons. The NWD criteria choice significantly impacted results, depending on the region of the country, reaching differences of up to 60 days within a total period of 90 days. The criterion considering soil water content tended to underestimate workable days but closely aligned with agricultural reality. [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]

BACKGROUND: We evaluated different machine learning (ML) models for predicting soybean productivity up to 1 month in advance for the Matopiba agricultural frontier (States of Maranhão, Tocantins, Piauí, and Bahia). We collected meteorological data on the NASA‐POWER platform and soybean yield on the SIDRA/IBGE base between 2008 and 2017. The ML models evaluated were random forest (RF), artificial neural networks, radial base support vector machines (SVM_RBF), linear model and polynomial regression. To assess the performance of the models, cross‐validation was used, obtaining the value of precision by R2, accuracy by root mean square error (RMSE), and trend by the mean error of the estimate (EME). RESULTS: The results showed that the RF algorithm achieves the highest precision and accuracy, with R2 of 0.81, RMSE of 176.93 kg ha−1 and trend (EME) of 1.99 kg ha−1. On the other hand, the SVM_RBF algorithm showed the lowest performance, with R2 of 0.74, RMSE of 213.58 kg ha−1 and EME of −15.06 kg ha−1. The average yield values predicted by the models were within the expected range for the region, which has a historical average value of 2.730 kg ha−1. CONCLUSION: All models had acceptable precision, accuracy and trend indices, which makes it possible to use all algorithms to be applied in the prediction of soybean crop yield, observing the particularities of the region to be studied, in addition to being a useful tool for agricultural planning and decision making in soy‐producing regions such as the Brazilian Cerrado. © 2021 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Background: The recent studies of the variations in the atmospheric column-averaged CO2 concentration ( X CO 2 ) above croplands and forests show a negative correlation between X CO 2 and Sun Induced Chlorophyll Fluorescence (SIF) and confirmed that photosynthesis is the main regulator of the terrestrial uptake for atmospheric CO2. The remote sensing techniques in this context are very important to observe this relation, however, there is still a time gap in orbital data, since the observation is not daily. Here we analyzed the effects of several variables related to the photosynthetic capacity of vegetation on X CO 2 above São Paulo state during the period from 2015 to 2019 and propose a daily model to estimate the natural changes in atmospheric CO2. Results: The data retrieved from the Orbiting Carbon Observatory-2 (OCO-2), NASA-POWER and Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) show that Global Radiation (Qg), Sun Induced Chlorophyll Fluorescence (SIF) and, Relative Humidity (RH) are the most significant factors for predicting the annual X CO 2 cycle. The daily model of X CO 2 estimated from Qg and RH predicts daily X CO 2 with root mean squared error of 0.47 ppm (the coefficient of determination is equal to 0.44, p < 0.01). Conclusion: The obtained results imply that a significant part of daily X CO 2 variations could be explained by meteorological factors and that further research should be done to quantify the effects of the atmospheric transport and anthropogenic emissions. [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]

Measurements of solar global radiation, wind speed, temperature and relative humidity of air were made in a coffee plantation (Coffea Arabica L. variety Icatu Vermelho- IAC 4045), grown under conditions of shading levels caused by grevillea trees (Grevillea robusta) in Mococa-SP region (21° 28′ S, 47° 01′ W, altitude 665m), between January and December 2005, aiming to show the effects of shading crop system on the microclimate. The results showed that there was a reduction of about 26% of solar global radiation in the shaded systems, with a monthly variation of 24-30%. It has been highlighted some differences in the transmittance of global radiation in the shaded coffee trees due to the spatial variation of grevillea canopy. There was a reduction of about 35% in the five days-averaged wind speed in the shaded system that also presented reduced maximum air temperature and a reduction in the vapor pressure deficit during day-light period, especially in the point sampled near to the grevillea trees. [ABSTRACT FROM AUTHOR]

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 (1989–2019) 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. [ABSTRACT FROM AUTHOR]

Highlights • The study permitted greenhouse irrigation management of sugarcane plantlets. • Crop evapotranspiration and coefficient were determined by weighing lysimeter. • Crop coefficient was correlated to cycle, growing degree-day and leaf area index. • Leaves pruning decreased crop coefficient for all treatments. Abstract The sugarcane pre-sprouted plantlets (PSP) production system is an innovative method to enhance crop multiplication rates. This system is greenhouse-grown, where correct irrigation management is essential to maintain the production quality. The purpose of this study was to determine the water requirement of three cultivars (CTC9005HP, RB966928, and SP87365) of pre-sprouted sugarcane plantlets, in which crop evapotranspiration (ETc) is a function of the product of the crop coefficient (Kc) and reference evapotranspiration (ETo). Three specifically built PSP-weighing lysimeters were used to determine ETc, while ETo was calculated by the FAO-56 method, making it possible to assess Kc values during the PSP cycle production. The ETc ranged from 3 to 6.9 mm d−1 for CTC9005HP, 3.1 to 6.8 mm d−1 for RB966928, and 2.9 to 6.6 mm d−1 for SP87365. Daily Kc values increased from 1.00 to 1.46 for CTC9005HP, 1.02 to 1.53 for RB966928, and 1.02 to 1.49 for SP87365. This research makes evident the importance of assessing appropriate Kc values in order to estimate crop water requirements and irrigation needs. Therefore, these results are appropriate for proper irrigation management in a greenhouse-grown sugarcane PSP. [ABSTRACT FROM AUTHOR]

Understanding the geochemistry and contamination of rivers affected by the rupture of tailings dams is paramount to emergency water resources management. This needs the long-term monitoring of tailings, sediments and water along the river, and the understanding of how the tailings interact physically and geochemically with the natural sediments and flowing water. An effort of this kind is uncommon because of its cost and opportunity. The purpose of this study was to help filling this gap using the rupture of B1 tailings dam (Brumadinho, Minas Gerais, Brazil, 25 January 2019) as example. To accomplish the objective, 31 parameters of tailings, sediment and water were monitored weekly for three years after the event, spanning impacted and non-impacted sectors of Paraopeba River, including a dam reservoir that is attenuating the tailings propagation downstream. Using hierarchical clustering analysis, the parameters were assembled into groups. Associations between parameters within the groups pointed to processes and drivers controlling the concentrations of Fe, Mn, Al, As, Pb and P in water. The results suggested reductive dissolution of Pb- and Mn-bearing minerals in the impacted and dam reservoir sectors as key process. In the rainy period, dissolved Mn concentrations in the impacted sector raised exponentially with temperature increase; in the dam reservoir, they showed episodic peaks triggered by thermal stratification-related pH decreases. In general, total concentrations > dissolved concentrations indicating predominant particle transport. Besides, concentrations were higher for Fe, Al and Mn, in the rainy season, and in the impacted sector. In the dry season, stream flow-controlled sediment resuspension triggered desorption of Fe and Al from sand grains raising their dissolved concentrations. In the rainy season, a pH declining trend probably related with erosion of acid soils in the watershed caused gradual increases of Fe, Al and P, desorbed from clays and silts. The main conclusion is that the B1 dam rupture left a strong fingerprint in the concentrations of Fe and Mn in the river, especially in the rainy season, which lasts and keeps the use of Paraopeba River suspended. [Display omitted] • Reductive dissolution elevate dissolved Mn concentrations in the rainy season (RS). • Tailings-related Fe adsorbs to sediment grain sizes and desorbs in the dry season. • Contaminant transport is predominant in the particulate phase. • The Igarapé barrier triggers temperature- and pH-dependent rises of Mn concentrations. • Erosion-related RS-pH declining trend elevate Fe, Mn and P, desorbed from clays-silts. [ABSTRACT FROM AUTHOR]

Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The original article can be found online at https://doi.org/10.1007/s00704-022-04341-9. [Extracted from the article]

Sweet potato is a tuberous root with versatility in food products, but also with applications in the energy industry, such as in ethanol production. Developing mechanisms to assess the performance of this crop is important, difficult, and costly, as its commercial product grows below ground. The use of remote sensing to evaluate the development of sweet potato has not yet been reported in the literature. In our study, we showed that spectral vegetation indices are good proxies to monitor the temporal dynamics of crop growth and differentiate phenological stages, regardless of the growing season. The development phases were divided into three stages according to the vegetation indices: (I) initial stage (<200 GDD), when vegetation has little influence on VIs; (II) growth stage (from 200 to 500 GDD), when vegetation has high influence on VIs due to its growth; and (III) stabilization stage (> 500 GDD), when major changes in VIs no longer occur because vegetative growth has ceased. Besides that, we found that these indices can predict crop yield before harvest. In two growing seasons, the smallest errors in yield estimates occurred during the growth stage. In the summer season with NDVI at 355 GDD with errors of 2.63 t ha−1 and in the winter season when GNDVI at 440 GDD had errors of 3.06 t ha−1. [ABSTRACT FROM AUTHOR]