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The growth of the world population has led to the expansion of agricultural areas to produce food that meets world demand, making it necessary to increase productivity and maintain environmental sustainability in these areas. Seeking sustainable food production, the agricultural use of soil must be assessed in view of optimal use or land as natural resource, as well as minimize the effects of global warming related to land use and land cover (LULC). We hypothesize that different LULC affects Amazonian soil attributes. In this study, the effect of different LULC in the southern Brazilian Amazon, namely, native forest, pasture, and rice and soybean crops, on the spatial variability of soil fertility and texture was assessed, seeking to obtain information that will guide farmers in the near future to better exploit their areas and contribute to a more sustainable agriculture. Descriptive statistical analysis was performed for the pH, H + Al, Al, Ca, Mg, P, K, Cu, Fe, Mn, Zn, V, m, organic matter, clay, silt, and sand values from soil samples under different LULC. To verify the data normality, the Shapiro–Wilk test at 5% significance was performed. Outlier analysis using boxplot graphics, principal component analysis (PCA), and cluster analysis was performed. Data were submitted to geostatistical analysis to verify the spatial dependence degree of the variables through semivariograms for interpolated kriging maps. Except for silt, all variables were well represented in the factor map. PCA revealed that the data variability can be explained mainly by pH, V, Ca, K, and Zn values, which are inversely proportional to m, P, and sand. Through geostatistical analysis, spatial dependence ranging from moderate to strong was observed, generating reliability in the prediction of most attributes in pasture, rice, and soybean areas. Yet, a spatial dependence ranging from moderate to strong was found, generating reliability in the prediction of most attributes in pasture, rice, and soybean areas. Our findings reveal a lower fertility and higher acidity in forest areas, whereas crop areas presented the opposite result. [ABSTRACT FROM AUTHOR]

This paper evaluates compliance with environmental laws in the municipality of Sorriso and the impact of changing legislation on vegetation. To verify the size of the properties, the areas designated as legal reserves (LRs), permanent protection areas (APPs), and springs were studied. Details of compliance with the New Forest Code (NFC) were drawn from the Rural Environmental Register (CAR) database. The database provided by PRODES/CERRADO was used to gather data to monitor deforested areas. SojaMaps data were used to verify the areas used for soybean cultivation followed by the Perpendicular Crop Enhancement Index. The data were plotted and superimposed on the deforestation data provided by PRODES/CERRADO. The areas were calculated using QGiS software version 2.18.22. The results showed that only 20.04% of the original Cerrado vegetation cover remains in the municipality. The results also revealed environmental LR, APP, and spring deficits of 92,772.32, 1,656.28, and 322.86 ha, respectively. Measures such as the CAR in the New Forest Code are ineffective for inhibiting illegal deforestation, and new legislation authorized the loss of 75.22% of the APP areas due for recovery. The proposed changes to eliminate LRs will allow the suppression of 91,000 ha of vegetation in Sorriso. Expanding the Amazon Soy Moratorium to the Cerrado could bring immediate benefits to the maintenance of the last continuous forested areas in this biome. [ABSTRACT FROM AUTHOR]

In recent years, Brazil has become a major global contributor to the occurrence of national fires and greenhouse gas emissions. Therefore, this study aimed to evaluate the fire foci data of the past 20 years to determine their relationship with climatic variables in various Brazilian regions. The variables evaluated included fire foci, land surface temperature, rainfall, and standardized precipitation index, which were obtained via remote sensing from 2000 to 2019. The data were subjected to trend analyses (Mann–Kendall and Pettitt tests) and a multivariate analysis of canonical variables for evaluation. The results showed that the Midwest and North regions had the highest occurrence of fire foci throughout the study period, and that the North region had the highest accumulated annual rainfall. Thus, these regions require specific public policies to prevent future fires. Overall, the Midwest, Southeast, and South regions exhibit significant increasing fire foci tendencies. Our results reveal that this trend is related to the El Niño-Southern Oscillation (ENSO) phenomena, which alter climatic variables such as precipitation, land surface temperature, and the standardized precipitation index. Finally, the sugarcane growing area had a significant linear relationship with fire foci in the Southeast region, especially in the state of São Paulo, the major national sugarcane producer. [ABSTRACT FROM AUTHOR]

The guidance on decision-making regarding deforestation in Amazonia has been efficient as a result of monitoring programs using remote sensing techniques. Thus, the objective of this study was to identify the expansion of soybean farming in disagreement with the Soy Moratorium (SoyM) in the Amazonia biome of Mato Grosso from 2008 to 2019. Deforestation data provided by two Amazonia monitoring programs were used: PRODES (Program for Calculating Deforestation in Amazonia) and ImazonGeo (Geoinformation Program on Amazonia). For the identification of soybean areas, the Perpendicular Crop Enhancement Index (PCEI) spectral model was calculated using a cloud platform. To verify areas (polygons) of largest converted forest-soybean occurrences, the Kernel Density (KD) estimator was applied. Mann–Kendall and Pettitt tests were used to identify trends over the time series. Our findings reveal that 1,387,288 ha were deforested from August 2008 to October 2019 according to PRODES data, of which 108,411 ha (7.81%) were converted into soybean. The ImazonGeo data showed 729,204 hectares deforested and 46,182 hectares (6.33%) converted into soybean areas. Based on the deforestation polygons of the two databases, the KD estimator indicated that the municipalities of Feliz Natal, Tabaporã, Nova Ubiratã, and União do Sul presented higher occurrences of soybean fields in disagreement with the SoyM. The results indicate that the PRODES system presents higher data variability and means statistically superior to ImazonGeo. [ABSTRACT FROM AUTHOR]

The collapse of mining tailing dams in Brumadinho, Minas Gerais, Brazil, that occurred in 2019 was one of the worst environmental and social disasters witnessed in the country. In this sense, monitoring any impacted areas both before and after the disaster is crucial to understand the actual scenario and problems of disaster management and environmental impact assessment. In order to find answers to that problem, the aim of this study was to identify and analyze the spatiality of the impacted area by rupture of the tailing dam of the Córrego do Feijão mine in Brumadinho, Minas Gerais, by using orbital remote sensing. Land use and land occupation, phytoplankton chlorophyll-a, water turbidity, total suspended solids on water, and carbon sequestration efficiency by vegetation (CO2Flux) were estimated by orbital imagery from the Landsat-8/OLI and MSI/Sentinel-2 sensors in order to assess the environmental impacts generated by the disaster. Data were extracted from spectral models in which the variables that best demonstrated the land use variation over the years were sought. Mean comparison by t-test was performed to compare the time series analyzed, that is, before and after the disaster. Through the analysis of water quality, it was observed that the environmental impact was calamitous to natural resources, especially water from Córrego do Feijão. [ABSTRACT FROM AUTHOR]

Brazil is one of the world's biggest emitters of greenhouse gases (GHGs). Fire foci across the country contributes to these emissions and compromises emission reduction targets pledged by Brazil under the Paris Agreement. In this paper, we quantify fire foci, burned areas, and carbon emissions in all Brazilian biomes (i.e., Amazon, Cerrado, Caatinga, Atlantic Forest, Pantanal and Pampa). We analyzed these variables using cluster analysis and non-parametric statistics to predict carbon and CO2 emissions for the next decade. Our results showed no increase in the number of fire foci and carbon emissions for the evaluated time series, whereby the highest emissions occur and will persist in the Amazon and Cerrado biomes. The Atlantic Forest, Pantanal, Caatinga and Pampa biomes had low emissions compared to the Amazon and Cerrado. Based on 2030 projections, the sum of emissions from fire foci in the six Brazilian biomes will exceed 5.7 Gt CO2, compromising the national GHG reduction targets. To reduce GHG emissions, Brazil will need to control deforestation induced by the expansion of the agricultural frontier in the Amazon and Cerrado biomes. This can only be achieved through significant political effort involving the government, entrepreneurs and society as a collective. [ABSTRACT FROM AUTHOR]