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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.

Cotton is the most widely consumed natural fiber globally and emits fewer greenhouse gases compared to synthetic alternatives. Brazil is currently the largest cotton exporter, and understanding its potential for sustainable expansion is crucial. This study developed agroclimatic zoning maps for cotton (Gossypium hirsutum L.) across Brazil under current and future climate conditions using data from the World-Clim and MapBiomas platforms. Four climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) were assessed over multiple time periods. Results showed that rising temperatures and reduced rainfall will likely reduce cotton suitability in traditional producing regions such as Bahia. However, areas with potential for cotton cultivation, especially in Mato Grosso, which currently accounts for 90% of national production, remain extensive, with agroclimatic conditions indicating a theoretical expansion potential of up to 40 times the current cultivated area. This projection must be interpreted with caution, as it does not account for economic, logistical, or social constraints. Notably, Brazilian cotton is cultivated with minimal irrigation, low fertilizer input, and high adoption of no-till systems, making it one of the least carbon-intensive globally.

This study investigates the impact of climate change on the agroclimatic zoning of castor bean ( Ricinus communis L. ) cultivation in South America, emphasizing its significance for biofuel production. Historical climate data and future projections from the CMIP6 model were utilized, examining various climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-6.0, and SSP5-8.5) for the period 2021–2100. The analysis focused on temperature and rainfall as primary parameters, with an integrated approach to zoning to ensure comprehensive coverage. Seasonal zoning variations were illustrated using graphs, while Pearson’s correlation assessed the relationship between historical climate conditions and castor bean yield in South America, highlighting the effects of climate change. A graph showing the percentage variation in agroclimatic zoning classes under different climate scenarios revealed significant changes in areas suitable for cultivation. Under severe scenarios, such as SSP5-8.5, “Optimal” areas decreased from 14.1% (2021–2040) to 4.9% (2081–2100) due to rising temperatures. Simultaneously, areas classified as “Marginal due to High Temperatures” increased from 11.2 to 29.4% over the same period. Castor bean yield exhibited strong correlations with ideal climate conditions, with coefficients of 0.99 in “Optimal” areas and 0.88 in “Very Suitable” areas. Conversely, regions impacted by high temperatures showed a negative correlation of approximately − 1.00, indicating severe yield declines as temperatures rise. The study concludes that without adaptive strategies, such as efficient irrigation and heat-tolerant cultivars, castor bean production will face critical challenges, particularly in regions currently deemed most suitable for cultivation. This highlights the urgent need for mitigation and adaptation measures to sustain castor bean production under changing climatic conditions.

Coffee production has a large share in Brazilian agribusiness and a cultural and social importance in the country. Worldwide, Brazil is the largest producer of arabica coffee and the second largest of canephora species. In 2020, national production was 14.3 million bags of canephora coffee. Few studies on canephora coffee adaptation to climate changes can be found in the literature. Thus, our goal was to identify areas suitable for Coffea canephora cultivation in Brazil under CMIP-5 climate change framework. The study was carried out for the entire country using data on average air temperature data for the entire year, November, and the coldest month, as well as average annual accumulated water deficit for the period of 1960–2020. These data were gathered from the Meteorological Database for Teaching and Research (BDMEP) of the National Institute of Meteorology of Brazil-INMET (Brazil 1992). Furthermore, BCC-CSM1.1 climate model was used at 125 × 125 km resolution to simulate future climate using WorldClim 2 data for 2041–2080, in the Representative Concentration Pathway (RCP) scenarios 2.6, 4.5, 6.0, and 8.5. Potential climate changes can negatively impact canephora coffee plantations in all CMIP5 RCP scenarios studied. The BCC-CSM1.1 scenarios showed a 65% reduction in total areas suitable for coffee cultivation in Brazil. Rondônia and Bahia were states with the greatest impact of climate change since they had the largest reduction in areas suitable for canephora coffee growth. Currently, both states are major C. canephora producers and can therefore directly compromise regional economy. Thermal excess was the most common class for future scenarios, averaging 56.76% of the entire country.

Barley (Hordeum vulgare) is a globally significant cereal crop, widely used in both food production and brewing. However, it is particularly vulnerable to climate change, especially extreme temperature fluctuations, which can severely reduce yields. To address this challenge, a detailed climate zoning study was conducted to assess the suitability of barley production areas across South America, considering both current conditions and future climate scenarios from the Intergovernmental Panel on Climate Change (IPCC). The study utilized historical climate data along with projections from the CMIP6 IPSL-CM6A-LR model for the period 2021–2100. Several indices, such as evapotranspiration, were calculated, and factors like soil composition and topography were integrated into the classification of regions based on their agricultural potential. Critical variables in this assessment included temperature, precipitation, and water or thermal excess. The results showed that 6.59% of South America's territory is currently suitable for barley cultivation without additional irrigation, with these regions concentrated primarily in temperate southern areas. In contrast, 18.62% of the region is already unsuitable due to excessive heat. Projections under future climate scenarios indicate a shrinking of suitable areas, alongside an expansion of unsuitable regions. In the worst-case scenario, only 1.48% of the territory would remain viable for barley farming. These findings emphasize the crop's vulnerability to climate change, underscoring the urgency of developing agricultural adaptation strategies. The predicted contraction in suitable barley cultivation areas demonstrates the profound impact of climate change on agriculture and highlights the need for proactive measures to ensure sustainable barley production in South America.

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.

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.

Coffee is an important crop in the global market, being produced in several countries, such as Brazil, Vietnam, Colombia, Ethiopia, and India. Brazil is the world’s largest producer (1.5 million ha), playing an important role in generating jobs and income, especially for family farmers. Coffee is very susceptible to climate and may have high or low yields depending on air temperature and rainfall during the production cycle. Thus, this study aimed to carry out climate zoning for the cultivation of Arabian coffee under different climate change scenarios recommended by IPCC to measure the future impact of climate on Brazilian coffee. The study was carried out for the entire Brazilian territory, using data on annual mean air temperature, mean air temperature of November, mean air temperature of the coldest month, and cumulative annual mean water deficit obtained from the Meteorological Database for Teaching and Research (BDMEP) of the National Institute of Meteorology of Brazil—INMET, covering the period 1960–2020. Moreover, the BCC–CSM 1.1 climate model, with a resolution of 125 × 125 km, collected from the WorldClim 2 platform for 2041 to 2080, using the Representative Concentration Pathway (RCP) 2.6, 4.5, 6.0, and 8.5 scenarios, was employed to obtain future climate data. Brazil has well-defined regional seasons, normally with a hot, humid summer and a cold, dry winter. The country showed great climate variability among regions, with the Northeast region showing the highest values for air temperature and water deficit, the North region concentrating the lowest values of water deficit, and the South region showing the lowest air temperatures. All future climate change scenarios showed a reduction in the total areas suitable for coffee cultivation in Brazil, with a mean reduction of 50%. Furthermore, areas with restrictions due to thermal excess and water deficiency were the most common throughout the country in future scenarios, with a mean of 63% of the entire territory. The most affected regions were Minas Gerais, São Paulo, and Paraná. Future climate changes may negatively affect coffee cultivation in all the studied RCP scenarios.

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.

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 m 2  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.