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.
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.
Soil Loss Tolerance (T) reflects the maximum erosion rate that still allows a sustainable level of crop production. The T limit can be used to support the conservationist land-use planning and to propose erosion mitigation measures. In this context, we aim to determine the Soil Loss Tolerance limit to different soil classes located at the Coroado Stream Subbasin, southern Minas Gerais, Brazil. The soil classes of the subbasin area was classified as Dystrophic Red Latosols - LVd (90.0%), Eutrophic Red-Yellow Argisols - PVAe (5.4%), and Dystrophic Tb Haplic Cambisols - CXbd (1.9%). The following attributes were used to determine the T limits: texture, depth, density, permeability, and organic matter. To analyzing these parameters, we collect soil samples at 18 points distributed along the subbasin area. T values ranged from 4.75 to 7.40 Mg ha-1 year-1, with the lowest limit observed for CXbd (4.75 Mg ha-1 year-1). These results indicate that the Cambisol should be prioritized in the adoption of conservation practices to reduce water erosion and to maintain soil loss levels at acceptable rates. Latosols, Argisols, and Cambisols are the most common soils in the Brazilian territory. Thus, the results provided by the work can be used as a reference to monitoring the erosion process and evaluate the sustainability of agricultural activities in Brazil.
A erosão hídrica é uma das principais causas de degradação dos solos tropicais, cujo estudo requer a compreensão de algumas variáveis, como relevo, uso e ocupação do solo, classes de solo e presença de atividades antrópicas. A maioria dos estudos sobre erosão é baseada na utilização de modelos empíricos com técnicas de Geoprocessamento e Sensoriamento Remoto, que permitem identificar as regiões mais propensas à erosão. Esse estudo visa utilizar a análise multicritérios para identificar a suscetibilidade à erosão hídrica em duas sub-bacias hidrográficas localizadas na Serra da Mantiqueira, sul de Minas Gerais, Brasil. Para isso, foi elaborada uma análise multicritérios a partir de três elementos: relevo, uso e ocupação do solo e classes de solo. Os resultados demonstraram que as sub-bacias Ribeirão São Bento e Ribeirão José Lúcio apresentam susceptibilidade à erosão classificadas como média e baixa, respectivamente. Logo, o mapa de suscetibilidade à erosão hídrica pode ser utilizado como uma ferramenta eficiente no planejamento sustentável agrícola e ambiental da área.
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