Water erosion is one of the main problems faced in coffee cultivation, as it promotes environmental degradation and crop yield decrease. Erosion estimates support the planning of conservation management practices and allowing determining the rates of soil losses. Thus, the objective of this paper was to estimate the soil loss by water erosion using the Erosion Potential Method in a sub-basin predominantly covered by coffee cultivation and then to compare the results with the Soil Loss Tolerance limits. The study area is the Coroado Stream Sub-basin, located at Alfenas Municipality, south of Minas Gerais, Brazil. The sub-basin presented an Erosion Coefficient of 0.272, indicating a predominance of low-intensity erosion. The total soil loss estimate was 1,772.01 Mg year-1 with an estimated average of 1.74 Mg ha-1 year-1. Soil Loss Tolerance limits range from 4.75 to 7.26 Mg ha-1 year-1 and, according to the Erosion Potential Method, only 1.0% of the sub-basin presented losses above the limits. The areas with the highest slopes and bare soil concentrated the highest losses rates and should be prioritized in the adoption of mitigation measures. The Erosion Potential Method estimated soil losses in tropical edaphoclimatic conditions in a fast, efficiently and at low cost, supporting the adoption of conservation management practices.

In the state of Rondônia, deforestation, and inadequate soil use and management have intensified the water erosion process, causing degradation of agricultural land. Modeling is a tool that can assist in the adoption of targeted and effective measures for soil and water conservation in the region. In this context, the objective of the research was to model soil losses due to water erosion in the state of Rondônia using the Revised Universal Soil Loss Equation (RUSLE). The parameters related to rain erosivity, relief, erodibility, and soil cover, as well as the conservation practices of the state of Rondônia, were considered. The modeling steps were performed with the aid of the Geographic Information System. Results were validated with data of total sediments transported with water discharge. The estimated total soil loss was about 605 million tons per year, corresponding to an average loss of 22.50 Mg ha-1 year-1. In 19% of the state, the erosion rate was higher than the soil loss tolerance(T), and these areas should be prioritized for adopting measures to mitigate the erosion process. The RUSLE underestimated the generation of sediments at 0.56 Mg ha-1 year-1, which corresponds to an error of 18.60%. Results obtained can assist in the development of different soil use and management scenarios and provide options for policymakers to encourage soil conservation in the state of Rondônia.

Water erosion is the principal degradation process of tropical soils, and its effects can be measured by modeling techniques. Erosion models provide a diagnosis of the soil loss intensity and can support the planning of soil conservation practices. Models with low data requirements, such as the Revised Universal Soil Loss Equation (RUSLE) and, more recently, the Erosion Potential Method (EPM), are mainly applied in Brazil. Thus, the objective of this work was to estimate water erosion soil-loss rates using the EPM and RUSLE models on a tropical subbasin, followed by a comparison of their outcomes. The models’ application considered soil physical parameters, edaphoclimatic conditions of the area, land use, and subbasin management practices. The accuracy of the methods was verified using total transported sediment and water discharge data. We compared the models using Pearson's correlation analyses, considering a 5% of significance. We found a predominance of moderate-intensity erosion with average soil loss of 1.17 and 1.46 Mg ha-1 year-1, measured by EPM and RUSLE, respectively. The EPM model underestimated soil losses by 15.27%, and RUSLE overestimated by 19.08%, indicating a higher percentage of areas with high erosion rates (4.60%). The models presented results with a different order of magnitude, but with significant correlations, indicating that both methods pointed out similar zones of intense and light-erosion rates.

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, and environmental impacts. Thus, the monitoring of nitrogen is essential to the fertilizing management, and remote sensing based on unmanned aerial vehicles imagery has been evaluated for this task. This work aimed to analyze the potential of vegetation indices of the visible range, obtained with such vehicles, to monitor the nitrogen content of coffee plants in southern Minas Gerais, Brazil. Therefore, we performed leaf analysis using the Kjeldahl method, and we processed the images to produce the vegetation indices using Geographic Information Systems and photogrammetry software. Moreover, the images were classified using the Color Index of Vegetation and the Maximum Likelihood Classifier. As estimator tool, we created Random Forest models of classification and regression. We also evaluated the Pearson correlation coefficient between the nitrogen and the vegetation indices, and we performed the analysis of variance and the Tukey-Kramer test to assess whether there is a significant difference between the averages of these indices in relation to nitrogen levels. However, the models were not able to predict the nitrogen. The regression model obtained a R 2 = 0.01. The classification model achieved an overall accuracy of 0.33 (33%), but it did not distinguish between the different levels of nitrogen. The correlation tests revealed that the vegetation indices are not correlated with the nitrogen, since the best index was the Green Leaf Index (R = 0.21). However, the image classification achieved a Kappa coefficient of 0.92, indicating that the tested index is efficient. Therefore, visible indices were not able to monitor the nitrogen in this case, but they should continue to be explored, since they could represent a less expensive alternative.