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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]

Identifying the climatic characterization of a region and its spatial and temporal variation, as well as its changes in the face of climate change events, is essential for agrometeorological studies because they can assist in the planning of strategies that reduce the negative impacts generated in the cultures exposed to critical climatic conditions. Thus, this study aimed to characterize the climatic conditions of the MATOPIBA region and its changes in scenarios of climate change using the classification index of Thornthwaite. Daily time series of rainfall and temperature data in the 1950–1990 period were used, covering 467 points over the studied region. The data set was used to estimate climatological water balance and climate index Thornthwaite (1948), and obtain the trends climatological according to IPCC (2014) climate change projections, with changes in the average air temperature (+ 1.5 °C and − 1.5 °C) and precipitation (+ 30% and − 30%). The MATOPIBA region is characterized by its humid, dry subhumid, and moist subhumid climate, with the rainy seasons, between October and April, and drought, from May to September, well defined. In MATOPIBA climate change scenarios, climatic extreme indices tend to alter the pattern, frequency, and distribution of climate class, which can increase climate risk and impact crop production. Therefore, the results obtained can be used to develop strategies to mitigate the vulnerability of crops to climate change conditions. [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]

Climate classification systems are tools that facilitate the analysis, grouping, delimitation, and dissemination of the climatic characteristics of a region, contributing to the circumscribing of areas suitable for the agriculture and to the validation of various models of climate change. In this study, Brazilian life zone types were identified by using the initial variant of the well-known Holdridge eco-climatological classification, under different climate change scenarios. To describe current conditions in Brazil, daily time series of temperature and precipitation for the period 1989–2019 are used, derived from the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources (NASA/POWER) platform. Climate change was considered through four alternative scenarios. According to the original classification, under current conditions, the dominant life zone type is the type tropical basal moist forest, covering 60.57% of the country. An increase in temperature can cause a decrease in the dominance of this life zone type, though approximately half of the country may be covered by this type. Furthermore, in case of an increase of 3 °C, the type tropical basal dry forest could already cover a third of Brazil. An increase of 30% in precipitation could cause the emergence of several new life zone types, while the extent of the type tropical basal moist forest could then decrease to 50.12%. Due to the high variability of the life zone types obtained, the Holdridge system can be considered a useful tool to illustrate climate change impacts. [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]

The present paper aims to compute climatological zones apt for the cultivation of pitaya based on trends in the occurrence of climate change events from the IPCC (Intergovernmental Panels on Climate Change) in Brazil. We used temperature and precipitation data from 4942 cities collected on the NASA/POWER platform (National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources) from 1990 to 2020 to elaborate on the current scenario. The climate change scenarios were obtained using the CHELSA platform (Climatologies at high resolution for the earth's land surface areas) and corresponded to the periods 2041–2060 and 2061–2080 associated with four IPCC climate change scenarios. The spatialization of the data occurred according to the bioclimatic classes designed to meet the thermal and water needs of the crop. In the current scenario, class B5 has a predominance of 37.07% of the country, characterizing the Midwest, Southeast, and Northeast regions, as well as the state of Paraná, as suitable for the cultivation of pitaya. Projections of temperature increase and reduction of accumulated rainfall were found throughout Brazil, but with greater impact in the North and Northeast regions, which had the greatest reduction of areas suitable for the cultivation of pitaya with a greater predominance of classes B8 and B9. In the South and Southeast regions, class B5 makes up a large part of the areas that remained suitable for the production of this fruit until 2080. The results suggest that climate change does not benefit the cultivation of pitaya in some regions of Brazil because the dimensions of the areas suitable for economic production are restricted. [ABSTRACT FROM AUTHOR]

This study aimed to estimate the number of generations and cycle duration of the southern red mite, coffee berry borer, and coffee leaf miner using the thermal index to assist in controlling these main coffee pests in the state of Paraná, Brazil. The data of maximum and minimum air temperature (°C) and precipitation (mm) of all municipalities in the state from 1984 to 2018 were collected from the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources (NASA/POWER). The reference evapotranspiration was estimated using the (Camargo Campinas IAC Boletim 116:9, 1971) method and the water balance was calculated using the method of (Thornthwaite C, Mather J (1955) The water balance publications in climatology, 8 (1). DIT, Laboratory of climatology, Centerton, NJ, USA). The basal temperature of each pest minus the average temperature of the years was used to calculate the degrees-day, the duration of the pest cycle, and the number of generations per year. The influence of altitude on the development of coffee pests was measured using the Pearson correlation. The thermal index is able to estimate the damage caused by coffee pests in the state of Pará, Brazil. Coffee pests show greater severity in the north of Paraná, in the regions with the highest temperatures. It is the same region that concentrates most of the coffee production of the state. The results of the life cycle and number of generations were interpolated for the entire state using the kriging method. Coffee pests showed the highest severity in the north region of the state of Paraná, more specifically in the Northwest, North Central, and West Central mesoregions. These regions have concentrated most of the state's coffee production. Mesoregions with the highest coffee production in the state showed higher susceptibility to coffee pests. Altitude showed a high correlation (r > 0.6) with the cycle variability and number of generations of coffee pests. The average cycles of the coffee berry borer, coffee leaf miner, and southern red mite are 24.13 (± 8.34), 45.64 (± 18.61), and 21.51 (± 3.51) days, respectively. The average annual generation was 16.67 (± 4.77), 9.02 (± 2.75), and 17.32 (± 2.63) generations, for the coffee berry borer, the coffee red mite, and the southern red mite, respectively. [ABSTRACT FROM AUTHOR]

Climate directly and indirectly influences agriculture, being the main responsible for low and high yields. Prior knowledge on yield helps coffee farmers in their decision-making and planning for the future harvest, avoiding unnecessary costs and losses during the harvesting process. Thus, we sought to predict coffee yield with regressive models using meteorological data of the state of Paraná, Brazil. This study was carried out in 15 localities that produce Coffea arabica in this Brazilian state. The climate data were collected using the NASA/POWER platform from 1989 to 2020, while the data of arabica coffee yield (bags/ha) were obtained by CONAB from 2003 to 2018. The Penman–Monteith method was used to calculate the reference evapotranspiration and the climatological water balance (WB) was calculated based on Thornthwaite and Mather (1955). Multiple linear regression was used in the data modeling, in which C. arabica yield was the dependent variable and air temperature, precipitation, solar radiation, water deficit, water surplus, and soil water storage were the independent variables. The comparison between the estimation models and the actual data was performed using the statistical indices RMSE (accuracy) and adjusted coefficient of determination (R2adj) (precision). Multiple linear regression models can predict arabica coffee yield in the state of Paraná 2 to 3 months before harvest. The maximum air temperature is the climate element that most influences coffee plants, especially during fruit formation (March). Maximum air temperatures of 31.01 °C in March can reduce coffee production. Wenceslau Braz, Jacarezinho, and Ibaiti presented the highest yields, with mean values of 32.5, 29.9, and 29.3 bags ha−1, respectively. The models calibrated for localities that have Argisol had the highest mean accuracy, with an RMSE of 2.68 bags ha−1. The best models were calibrated for Paranavaí (Latosol), with an RMSE of 0.78 bags ha−1 and R2adj of 0.89, and Ibaiti (Argisol), with RMSE and R2adj values of 3.09 bags ha−1 and 0.83, respectively. Paranavaí has a mean difference between the actual and estimated coffee yield of only 0.86 bags ha−1. The highest deviations were observed in Wenceslau Braz (9.17 bags ha−1) and the lowest deviations were found in Paranavaí (0.86 bags ha−1). The models can be used to predict arabica coffee yield, assisting the planning of coffee farmers in the northern region of the state of Paraná. [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]

Climate change is a major problem for humanity, as it can drastically alter the current climate scenario, affecting mainly agriculture. In the state of Mato Grosso do Sul (MS), agribusiness is the main economic activity representing a large part of the state's GDP. Therefore, the aim of this study was to evaluate the influence of climate change on the climatological water balance in the Pantanal regions of Brazil. We used a 30-year historical series (1987–2018) of air temperature data (Tar, °C) and rainfall (P, mm) from the state of MS; climatic data were collected by the National Aeronautics and Space Administration platform/Prediction of World Wide Energy Resources - (NASA/POWER). Potential evapotranspiration (PET) was estimated using the Camargo (1971) method. The water balance (WB) was calculated using the Thornthwaite and Mather (1955) method, with soil water storage capacity equal to 100 mm. We calculated the aridity, hydric, and moisture indices for all municipalities in MS, and later classified according to Thornthwaite (1948). The scenarios used were based on the (IPCC 2014) projections. Air temperatures in the MS ranged from 22.5 to 27.6 °C in the current scenario; rainfall and PET have an average of 1400 mm annual−1 and 1188 mm annual−1, respectively. The WB of the state of MS has an EXC and DEF of 197.7 mm annual−1 and 64.2 mm annual−1, respectively. The predominant climatic type is C2 - subhumid. The highest values for SWS and EXC occur in scenarios S5, S10, S15, and S20, which are the most moisture scenarios. The highest DEF occurred in scenarios S1, S11, S16, and S21; these scenarios showed the driest climatic types. The northwestern region of the state, where the Pantanal is located, was the driest. In scenario S21, the climate of the state has a drastic change that makes several crops in the MS unfeasible, thus negatively influencing the fauna and flora of the Pantanal biome. [ABSTRACT FROM AUTHOR]

Improvement of planting season and crop growth time, considering climatic and soil needs of plants, is important to increase cassava (Manihot esculenta) production in Midwestern Brazil. Thus, we sought to develop an agricultural zoning for cassava cultivation in the Midwest of Brazil in different climate change scenarios. Mean air temperature and precipitation data from localities of the Midwest of Brazil were obtained from the Brazilian National Institute of Meteorology (INMET). Clay (%) data from localities of the Midwest of Brazil were obtained from SoilGrids. Regions where the air temperature was within the range from 20 to 27 °C were considered climatically favorable for commercial exploitation of cassava, in addition to precipitation between 1000 and 1500 mm year−1, and clay content was less than ≤ 35%. Moreover, regions with air temperature below 16 °C and above 38 °C, precipitation below 1000 mm and above 1500 mm year−1, and clay content > 35% were considered unsuitable for cassava cultivation. Raster or matrix images, corresponding to mean annual air temperature, annual precipitation, and clay (soil), were superimposed to create cassava suitability classes, according to crop requirements. The climate change scenarios were established by changing the air temperature (°C) and rainfall (mm). The air temperature was increased by 1.5, 3.0, 4.5, and 6.0 °C as adopted by Pirttioja et al. (Clim Res 65:87–105, 2015). We changed in precipitation − 30, − 15, + 15, and 30% according to the future projections simulated by the IPCC (2014). Maps were made using geographic information systems. In the states of Mato Grosso do Sul, Mato Grosso, and Goiás, mean precipitation was around 1200 to 4000 mm year−1. Northern Mato Grosso showed the highest annual precipitation, with values above 3500 mm. A large extension of the Midwest region of Brazil is climatically and soil favorable for cassava. The Midwest is a region with high rainfall, so we recommend planting in well-drained soils to avoid phytosanitary problems. Producers taking this care can plant cassava in 86.6% of the territory. The climate change scenarios demonstrated different Agriculture zonings for cassava in the Midwest of Brazil. With the increase in air temperature, greater marginal classes occurred, but cassava is resistant to this condition. But, this increase in temperature can reduce the cycle and consequently reduce production. [ABSTRACT FROM AUTHOR]

There is a wide variety of climates on the planet's surface, and climate classifications are tools for delimiting and describing prevailing climate types. Köppen classification relates types of climate to types of vegetation, while Camargo classification seeks to map climatic types based on thermal and water factors. In Brazil, the Midwest region is a major agricultural producer but still lacks detailed climate information. In this sense, we aimed to compare Köppen and Geiger (1928) with Camargo (1991) methods for climate classification in the Midwest of Brazil. For this purpose, we used data on daily global solar radiation; mean, maximum, and minimum air temperature; relative humidity; wind speed; and precipitation from 2160 weather stations, which were obtained from the NASA/POWER platform. Components of normal climatological water balance were calculated using the Thornthwaite and Mather (1955) method, with an available water capacity of 100 mm. Köppen and Geiger (1928) system uses data on mean annual temperature, annual precipitation, coldest month mean temperature, warmest month mean temperature, and driest month precipitation. The method of Camargo (1991), modified by Maluf (2000), uses the following meteorological elements: mean annual temperature, coldest month mean temperature, annual water surplus and deficit, and water deficit months. The similarity between classification methods was verified by agglomerative hierarchical clustering and Tukey's test at 95% reliability. The most predominant climate class according to Camargo (1991) was TR-UMi (humid tropical climate), representing 33.63% of the entire territory of the Midwest of Brazil. According to Köppen and Geiger (1928), six climate types were observed in the Midwest region, with a predominance of class Aw (tropical climate with dry winter), representing 58.50% of the entire region. While Köppen and Geiger (1928) showed a macroscale scope, Camargo (1991) classification had a mesoscale approach. The latter was more suitable for agricultural purposes, mainly because it provided information on prevailing water conditions in the region. [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]

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]

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]

Determining agro-climatic zones (ACZs) is traditionally made by cross-comparing meteorological elements such as air temperature, rainfall, and water deficit (DEF). This study proposes a new method based on correlations between monthly DEFs during the crop cycle and annual yield and performs a multivariate cluster analysis on these correlations. This ‘correlation method’ was applied to all municipalities in the state of São Paulo to determine ACZs for coffee plantations. A traditional ACZ method for coffee, which is based on temperature and DEF ranges (Evangelista et al.; RBEAA, 6:445-452, 2002), was applied to the study area to compare against the correlation method. The traditional ACZ classified the “Alta Mogina,” “Média Mogiana,” and “Garça and Marília” regions as traditional coffee regions that were either suitable or even restricted for coffee plantations. These traditional regions have produced coffee since 1800 and should not be classified as restricted. The correlation method classified those areas as high-producing regions and expanded them into other areas. The proposed method is innovative, because it is more detailed than common ACZ methods. Each developmental crop phase was analyzed based on correlations between the monthly DEF and yield, improving the importance of crop physiology in relation to climate. [ABSTRACT FROM AUTHOR]

Mechanization in coffee culture in Brazil is crucial for the optimization of production. However, growing demands of international markets for a low carbon productive chain trigger debates on producers with regard to hothouse gas emissions (GEE) due to their activities. Current study estimates the emission of CO2 equivalent (CO2 eq) per hectare in the main mechanized operations of coffee culture in the south of the state of Minas Gerais, Brazil. CO2 equivalent was estimated by a case study and employed parameters of GEE emission by methodology of GHG Protocol Agriculture. Results show that in coffee planting within convention modes, activities such as plowing, application of organic matter and burrowing contribute towards 73.92% of estimated emission of CO2 equivalent ha-1 during this stage, totaling 178.61 kg CO2 eq ha-1. In cultural treatments, operations in mechanized harvesting proved to be the greatest GEE emission sources, estimated at 123.81 kg CO2 eq ha-1. [ABSTRACT FROM AUTHOR]

Due to Brazil's huge extension and to different social, economic, demographic and cultural conditions, several regions in Brazil feature infectious and parasite diseases (IPDs). Current analysis assesses whether IPDs in the municipalities of the Health Regional Superintendence of Alfenas MG Brazil are related to specific risk factors. Current populated-based transversal, descriptive and quantitative study employs secondary data from the 2010 demographic census, by Pearson's co-relationship. All health and educational indicators significantly co-relate themselves with mortality due to IPDs, The water supply system and the number of sewerage and rainwater per network unit were the variables of basic sanitation indexes with the highest co-relation scores. In the case of economic indicators, the monthly income of half the minimum wage or without any monthly earnings significantly affected IPD-based mortality. Only the number of births weighing less than 2500g had any relationship to IPDs. Results showed environmental and sanitation conditions, coupled to the social and economic factors of residents in homes with such characteristics, plus the dissemination of basic measures for personal, home and community hygiene impact population's health. The latter influences morbidity and mortality by diseases of different etiologies, including IPDs. Knowledge on the prevalence and main associated variables may be a help in evaluating the problem and the planning and implementation of activities for the health and prevention of health-disease relationships. [ABSTRACT FROM AUTHOR]