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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]

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]

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]

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]