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Forests in Brazil play a crucial role in maintaining ecological balance and the environment, but this has been threatened by deforestation, forest fires, and the effects of climate change. Among these, forest fire has happened frequently in areas where there have been changes in land use for activities of agriculture and livestock farming, that motivate the destruction of forests particularly in biomes like the Amazon and Cerrado. In those biomes, the forest fires initiated to clear land for pasture are the most worrying because promote ecological and socioeconomic consequences, contributing to greenhouse gas emissions and impacts the regional flora and biogeochemical cycles. As a strategy to understand and identify areas at risk of forest fires, this study aimed to develop a risk zoning framework for fire hotspots in the biomes of Brazilians. This framework combines multiple variables, incorporating factors like physical terrain, land use, and climatic data, to assess the potential fire risk. The areas with greater fire risk are located in the Caatinga, Cerrado, and Pantanal biomes, in which the physical and climate variables influence directly in incidence and propagation of fire. In the Amazon biome there is a fire risk, some possibly intentional, but can be regulated by elevated precipitation in the region. The identification of areas at high fire risk allows the implementation of proactive strategies for fire prevention for safeguarding Brazil's biomes and ecosystems, which are integral to the environment and biodiversity. [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]

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]

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]

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]