Specialty coffee beans are those produced, processed, and characterized following the highest quality standards, toward delivering a superior final product. Environmental, climatic, genetic, and processing factors greatly influence the green beans' chemical profile, which reflects on the quality and pricing. The present study focuses on the assessment of eight major health-beneficial bioactive compounds in green coffee beans aiming to underscore the influence of the geographical origin and post-harvesting processing on the quality of the final beverage. For that, we examined the non-volatile chemical profile of specialty Coffea arabica beans from Minas Gerais state, Brazil. It included samples from Cerrado (Savannah), and Matas de Minas and Sul de Minas (Atlantic Forest) regions, produced by two post-harvesting processing practices. Trigonelline, theobromine, theophylline, chlorogenic acid derivatives, caffeine, caffeic acid, ferulic acid, and p-coumaric acid were quantified in the green beans by high-performance liquid chromatography with diode array detection. Additionally, all samples were roasted and subjected to sensory analysis for coffee grading. Principal component analysis suggested that Cerrado samples tended to set apart from the other geographical locations. Those samples also exhibited higher levels of trigonelline as confirmed by two-way ANOVA analysis. Samples subjected to de-pulping processing showed improved chemical composition and sensory score. Those pulped coffees displayed 5.8% more chlorogenic acid derivatives, with an enhancement of 1.5% in the sensory score compared to unprocessed counterparts. Multivariate logistic regression analysis pointed out altitude, ferulic acid, p-coumaric acid, sweetness, and acidity as predictors distinguishing specialty coffee beans obtained by the two post-harvest processing. These findings demonstrate the influence of regional growth conditions and post-harvest treatments on the chemical and sensory quality of coffee. In summary, the present study underscores the value of integrating target metabolite analysis with statistical tools to augment the characterization of specialty coffee beans, offering novel insights for quality assessment with a focus on their bioactive compounds.

Nitrogen is an essential element for coffee production. However, when fertilization do not consider the spatial variability of the agricultural parameters, it can generate economic losses, such as low productivity, and environmental impacts, such as pollution of air and eutrophication of water bodies. Thus, the monitoring of the nitrogen during different phases of the production is a key factor for the fertilization management, and remote sensing based on unmanned aerial vehicles imagery has been evaluated for this task. Thus, this work aimed to evaluate the potential of visible vegetation indices obtained from such images to monitor the spatial variability of the leaf nitrogen content in a coffee farm located in Divisa Nova Municipality, Minas Gerais. Therefore, we performed a leaf analysis using the Kjeldahl method to determine leaf nitrogen, and to process the images and produce the vegetation indices, we use Geographic Information Systems and photogrammetry software. As analyze methods, we used the Random Forest classification algorithm as an estimator and performed ordinary kriging to visualize the spatial variability as nitrogen content. Lastly, the Pearson correlation coefficient was employed to evaluate the relationship between the variables. However, the Random Forest models were unable to explain nitrogen variability, and we did not find any significant correlations between the tested vegetation indices and nitrogen content. Therefore, it is indicated the replication of the study in the vegetative phase of the coffee plants, with the establishment of different fertilization treatments, as well as the use of multispectral sensors and radiometric calibration techniques. Keys words: Vegetation indices; RGB; machine learning; Coffea arabica.

Since the mid-16th century, the Tietê River has been an important route for the territorial occupation and exploitation of natural resources in the interior of São Paulo and Brazil. Currently, the Tietê River is well known for environmental problems related to water pollution and contamination. However, little attention has been focused on water erosion, which is a serious issue that affects the soils and waters of the hydrographic basin. Thus, this work aimed to estimate soil loss caused by water erosion in this basin, which has an area of approximately 72,000 km², using the Revised Universal Soil Loss Equation (RUSLE). The RUSLE parameter survey and soil loss calculation were performed using geoprocessing techniques. The RUSLE estimated an average soil loss of 8.9 Mg ha-1 yr-1 and revealed that 18% of the basin's territory presents high erosion rates. These are priority zones for conservation practices to reduce water erosion and ensure long-term soil sustainability. The estimated sediment transport was 1.3 Mg ha-1 yr-1, whereas the observed sedimentation, which was calculated based on data from the fluviometric station, was 0.8 Mg ha-1 yr-1. Thus, the results were equivalent considering the large size of the study area and can be used to assist in managing the basin. Estimating soil losses can help in the planning of sustainable management of the Tietê River Hydrographic Basin and highlights the importance of minimizing water erosion, thus helping to prevent additional pollution and contamination with sediments, agrochemicals, and fertilizers.

Water erosion has severe impacts on soil and the carbon cycle. In tropical regions, it is significantly influenced by rainfall, soil erodibility, rapid changes in land use and land cover (LULC), and agricultural management practices. Understanding the dynamics of water erosion is essential for implementing precise land degradation control. This study aimed to estimate soil and soil organic carbon (SOC) losses due to water erosion over five years in a coffee-producing area in Brazil using the revised universal soil loss equation (RUSLE). The results revealed that average soil losses in coffee plantation areas ranged from 1.77 to 1.80 Mg ha⁻¹ yr⁻¹, classified as very low. Total and potential soil loss ranged from 2184.60 to 6657.14 Mg ha⁻¹, a 305% difference, demonstrating the efficiency of vegetative cover (C factor) and conservation practices (P factor) in reducing soil loss rates. SOC losses were less than 200 kg ha⁻¹ yr⁻¹, with averages of 17.67 and 13.00 kg ha⁻¹ yr⁻¹ in coffee areas. In conclusion, agricultural management practices, such as the presence of native vegetation, maintaining vegetative cover in coffee rows, contour planting, and improving agronomic techniques, are essential for reducing soil and SOC losses, even in scenarios of biennial alternation in coffee production. Thus, sustainable agricultural management plays a crucial role in mitigating water erosion, maintaining productivity, and addressing climate change.

The adverse impacts of soil degradation and nutrient loss resulting from water erosion are significant environmental concerns that have profound implications for both water quality and biodiversity. This study aims to evaluate the impact of forest fires on soil loss through water erosion in the Serra da Canastra National Park, in Minas Gerais state, Brazil. Using the Revised Universal Soil Loss Equation (RUSLE), which considers rainfall erosivity, soil erodibility, slope length and slope and vegetation cover, the C factor (vegetation cover) values were obtained from data from literature and methods based on the Normalized Differential Vegetation Index (NDVI). Validation was carried out using data on total sediment, water flow and daily runoff from the hydrosedimentological station and the InVEST software. The results highlight the significant impact of wildfires on soil loss through water erosion and indicate that areas recently affected by wildfires, especially on steep slopes and with more erodible soils, are subject to the highest rates of soil loss. Soil loss rates varied from 0.75 to 12.55 Mg ha^-1 yr^-1, in part due to the different ways of obtaining factor C. The research emphasizes the need to conserve vegetation cover to prevent soil erosion, particularly in regions impacted by forest fires. This study offers valuable insights that can contribute to enhancing the sustainable environmental management of the Serra da Canastra National Park.

This research analyzed the morphological/morphometric parameters of the relief and drainage network of the Formiga River subbasin, southern Minas Gerais State, Brazil. This study identified and examined the influences of control mechanisms in the development of current configurations. The methodology was based on the application of indices and parameters: (1) slope length (Hack's Sl); (2) normalized slope index (ksn); (3) river hierarchy; (4) drainage density; (5) hydrographic density; (6) sinuosity index; (7) density of structural lineaments; (8) transverse topographic symmetry factor; (9) asymmetry factor of drainage basins; (10) topographic profiles transverse to the valleys; (11) relief ratio; (12) roughness index; (13) roughness concentration index; (14) compactness coefficient; (15) circularity index and (16) form factor. The results indicated 3 sectors of the subbasin with distinct geomorphic behaviors, which resulted in peculiar surface structures and divergent current dynamics. The subbasin presents physical evidence that demonstrates and corroborates strong tectonic and structural control in the development of the current drainage network and relief. Human action has also affected, from the construction of dams, the dynamics of the lower course of the river, drowned by the reservoir of the hydroelectric plant of Furnas.