Brazil is the main coffee producer in the world. However, the impacts of climate change driven by greenhouse gas (GHG) emissions pose a major challenge for agriculture in tropical regions. This study established a GHG inventory of coffee production on farms in southern Minas Gerais, Brazil, over a two-year period, adopting a cradle-to-farm-gate approach. It considered scopes 1 and 2 emissions from on-farm activities. The emission inventories were based on Intergovernmental Panel on Climate Change (IPCC) methodologies adapted for Brazilian conditions. The emissions were categorized in direct and biogenic and by area (in hectares) and production (kg of coffee). Electricity consumption, fossil fuel use, wood burning and fertilizer application were considered. Direct total emissions ranged from 2617 to 6211 t CO₂e, 2.67 to 3.81 t CO₂e ha⁻¹, and from 1.52 to 4.59 kg CO₂e kg⁻¹ of coffee. Biogenic emissions ranged from 336 to 4955 t CO₂e, 0.28 to 2.95 t CO₂e ha⁻¹, and from 0.32 to 2.21 kg CO₂e kg⁻¹ of coffee. Urea-based nitrogen fertilizers were the main source of direct emission and wood burning was the main source of biogenic emission. Management practices such as applying non-urea-based fertilizers, adjusting nitrogen rates according to soil analyses and manual harvesting contributed to mitigating GHG emissions. The observed emission intensities were consistent with other reported values for Brazilian coffee production. Further reductions may be achieved by adopting agroforestry systems, increasing coffee straw retention in the soil and replacing urea with alternative nitrogen sources, including slow-release fertilizers and urease-inhibitor technologies.

This study evaluated the spatial and temporal variability of rainfall erosivity (R factor) and its implications for soil loss in the Velhas River Basin, Minas Gerais, Brazil. Rainfall erosivity was estimated from 49 rain gauge stations and CHIRPS precipitation data using empirical equations-based on monthly and annual precipitation totals. Soil loss was estimated using the RUSLE model for the years of minimum and maximum erosivity. Between 2014 and 2024, annual R values ranged from approximately 3900 to more than 9000 MJ mm ha⁻¹ h⁻¹ yr⁻¹, with the lowest values recorded in 2014 and the highest in 2022. Although 2020 had the highest annual rainfall, 2022 showed the highest erosivity, indicating that rainfall intensity and temporal concentration were more important than total rainfall volume. Furthermore, the comparison of erosivity was estimated from ANA stations and derived from CHIRPS agreement for paired station-year observations (r = 0.7196), although CHIRPS slightly underestimated erosivity values (mean bias −5.74%). Estimated soil loss ranged from 0.60 to 274.17 Mg ha⁻¹ yr⁻¹, with the highest values occurring mainly in exposed soil and agricultural areas. These findings highlight the importance of rainfall temporal distribution in erosion risk and support the use of satellite-derived precipitation products for regional-scale erosion assessments in data-scarce tropical basins.

This study assessed the spatial and temporal variability of rainfall erosivity (R factor) and its implications for potential soil loss in the Velhas River Basin, Minas Gerais, Brazil. Rainfall erosivity was estimated using data from 48 rain gauge stations and precipitation derived from CHIRPS product, processed in a cloud-based environment Google Earth Engine. Between 2014 and 2024, annual R values exhibited high variability, ranging from 3,900 to more than 9,000 MJ mm ha⁻¹ h⁻¹ yr⁻¹, with peak values recorded in the wettest year (2022) and the lowest values in 2014. Potential soil loss was estimated using the RUSLE model for the years of minimum and maximum erosivity, yielding values between 0.60 and 274.17 Mg ha⁻¹ yr⁻¹. The highest soil losses occurred in areas of exposed soil and agricultural land, whereas forest formations exhibited lower rates even under high rainfall erosivity conditions. The comparison between observed and estimated datasets revealed strong spatial and statistical agreement according to the Pearson correlation coefficient (r ≈ 0.999), although CHIRPS slightly underestimated extreme values. These results demonstrate the strong potential for integrating observed and remote sensing data in hydrosedimentological analyses at the basin scale.

Coffee production and trade are globally important, and Brazil is the world’s largest producer and second-largest consumer. However, climate change poses a major challenge to coffee production because the crop is highly sensitive to irregular rainfall distribution and temperature fluctuations. We evaluated temperature and precipitation fluctuations from 1984 to 2023 at two coffee-producing units in southern Minas Gerais, Brazil, to characterize regional climate dynamics and their associations with coffee yield and total production. We used the Mann–Kendall methodological approach, linear regression and Sen’s slope estimator to identify trends and estimate rates of change in maximum, minimum, and mean air temperature and in total, dry-season, and wet-season precipitation. We used Pearson’s and Spearman’s correlation coefficients to assess relationships between climatic and agronomic variables, selecting the appropriate coefficient based on prior Shapiro–Wilk normality tests. Most temperature variables showed warming trends in September, during the transition from the dry to the wet season. Minimum temperature showed more warming trends in the first months of wet season. Cooling trends were also detected in January, February, March, April, May, and December. Precipitation declined in May and increased in June but showed no trend in either the dry or wet season. Relationships between climatic and agronomic variables differed between production units: correlations were positive at one unit and negative at the other. Locally, these trends indicate vulnerability during critical phenological stages that coincide with these seasonal windows, resulting in pollen tube desiccation, greater consumption of photo assimilates during physiological rest, and poor bean development under reduced dry-season rainfall. The contrasting correlation patterns were attributed to local climate fluctuations and to the microclimatic, land-use, and land-cover characteristics of each production unit. Under this scenario, mitigating these adverse conditions is essential to minimize damage to coffee crops. Recommended strategies include the implementation of agroforestry systems, rigorous monitoring of pests favored by climate fluctuations, and the adoption of more climate-resilient cultivars. Ultimately, this study highlights that crop management and mitigation strategies designed to buffer these climate variations must be tailored to the distinct conditions of each production unit.

Coffee production and trade are globally important, and Brazil is the world's largest producer and second-largest consumer. However, climate change poses a major challenge to coffee production because the crop is highly sensitive to irregular rainfall distribution and temperature fluctuations. We evaluated temperature and precipitation fluctuations from 1984 to 2023 at two coffee-producing units in southern Minas Gerais, Brazil, to characterize regional climate dynamics and their impacts on coffee yield and total production. We used the Mann–Kendall test and Sen’s slope estimator to identify trends and estimate rates of change in maximum, minimum, and mean air temperature and in total, dry-season, and wet-season precipitation. We used Pearson’s and Spearman’s correlation coefficients to assess relationships between climatic and agronomic variables, selecting the appropriate coefficient based on prior Shapiro–Wilk normality tests. All temperature variables showed warming trends in September, during the transition from the dry to the wet season. Cooling trends were also detected in April, May, and December. At one production unit, dry-season rainfall declined over the historical series. At the other, precipitation declined in May but showed no trend in either the dry or wet season. Relationships between climatic and agronomic variables differed between production units: correlations were positive at one unit and negative at the other. Regionally, these trends indicate vulnerability during critical phenological stages that coincide with these seasonal windows, resulting in pollen tube desiccation, greater consumption of photoassimilates during physiological rest, and poor bean development under reduced dry-season rainfall. The contrasting correlation patterns were attributed to regional climate fluctuations and to the microclimatic, land-use, and land-cover characteristics of each production unit. Under this scenario, mitigating these impacts is essential to minimize damage to coffee crops. Recommended strategies include the implementation of agroforestry systems, rigorous monitoring of pests favored by climate fluctuations, and the adoption of more climate-resilient cultivars. Ultimately, this study highlights that crop management and mitigation strategies designed to buffer these climate impacts must be tailored to the distinct conditions of each production unit.