Agricultural soils provide ecosystem services, but the removal of natural vegetation reduces water infiltration capacity, increasing surface runoff. Thus, monitoring erosion is critical for sustainable agricultural management. Sediment losses and surface runoff were evaluated using a simulated rainfall of 75 mm/h in areas with crops and pastures in both the Caiabi River and Renato River sub-basins of the Teles Pires River watershed in Mato Grosso State, Brazil. In both the Caiabi and Renato sub-basins, data were collected from 156 observations in the upper, middle, and lower regions where (1) soybeans, (2) maize, and (3) pasture were grown alone, with another crop, or with soil that was scarified. Erosion occurred independent of soil texture and was closely related to the management and use of systems involving fewer crops and more soil scarification, regardless of sub-basin location. In uncovered, scarified soil, the soil losses from erosion were greater compared to covered soil, regardless of sub-basin and sub-basin region. In the Renato River sub-basin, soil losses in cultivated areas not planted with crops but with scarification were 66.01, 90.79, and 60.02 g/square meter in the upper, middle, and lower regions, respectively. Agricultural producers need to increase the planting of crops throughout the year and minimize soil disturbance, which will reduce soil erosion and improve sustainability.

Changes in land use can cause degradation of soil physical quality with negative effects on the environment and agricultural production. The effects of different land uses on soil physical-hydric attributes were studied in the Renato River and Caiabi River watersheds in the southern Brazilian Amazon. Three conditions of land use were evaluated: native forest, crops, and pasture in the headwater, middle, and mouth of each watershed. Particle size, particle density, bulk density, total porosity, macroporosity, microporosity, water contents at field capacity and permanent wilting point, and available water capacity in soil were evaluated in three soil layers down to 0.4 m. Data collected were subjected to the Kruskal–Wallis nonparametric test and Pearson’s correlations. Multivariate analyses were also performed using the principal component method. In the Renato watershed, in comparison with native forest, conventional management of pasture and crops caused soil physical degradation, increasing soil density in the surface layer and reducing macroporosity and total porosity. In the Caiabi watershed, converting native forest areas into pasture and crops altered water quality, influencing the water dynamics in the soil, by reducing soil water conductivity. Soil attributes varied by watershed, with texture variations between the headwater and mouth, indicating that changes in soil properties result from both management and the granulometric composition of the soil in different regions of the same watershed. Adoption of crop and pasture conservation practices can improve soil physical attributes in regions bordering agricultural areas in the southern Amazon.

The Teles Pires River basin in Brazil’s center-west has recently expanded agricultural economic development at the expense of both the Amazon rainforest and Cerrado savannah. We evaluated these changes occurring in this basin over the last 34 years. Maps were generated to determine changes in land use classifications between 1986, 1991, 1996, 2000, 2005, 2011, 2015, and 2020. The supervised classification of Landsat 5 and 8 images used the maximum likelihood algorithm. Satellite spatial data on land use downloaded from the United States Geological Survey were validated according to 1477 locations, where our research team categorized land use in the field during 2020. The growth in agricultural crops (+643%) and pasture (+250%) from 1986 to 2020 were detrimental to natural areas, such as the rainforest and savannah. The percentage increase in the agricultural areas between the evaluated years peaked around 1996 and stabilized in 2020 at 40% of the Teles Pires River basin’s land area. Land use change patterns were related to political/economic events in Brazil, forest/pasture conversions until 2011, and the change from pasture to crops from 2011 to 2020. There was greater intensity in the changes in the upper Teles Pires River basin toward the south, which expanded northward over time. Sustainable agricultural intensification is needed in such stabilized, frontier areas.

Improving environmental sustainability involves measuring indices that show responses to different production processes and management types. Suspended sediment concentration (SSC) in water bodies is a parameter of great importance, as it is related to watercourse morphology, land use and occupation in river basins, and sediment transport and accumulation. Although already established, the methods used for acquiring such data in the field are costly. This hinders extrapolations along water bodies and reservoirs. Remote sensing is a feasible alternative to remedy these obstacles, as changes in suspended sediment concentrations are detectable by satellite images. Therefore, satellite image reflectance can be used to estimate SSC spatially and temporally. We used Sentinel-2 A and B imagery to estimate SSC for the Teles Pires River in Brazil’s Amazon. Sensor images used were matched to the same days as field sampling. Google Earth Engine (GEE), a tool that allows agility and flexibility, was used for data processing. Access to several data sources and processing robustness show that GEE can accurately estimate water quality parameters via remote sensing. The best SSC estimator was the reflectance of the B4 band corresponding to the red range of the visible spectrum, with the exponential model showing the best fit and accuracy.

The soybean-corn succession in no-tillage system (DSS) is the most common cultivation practice in the state of Mato Grosso. Soils submitted to this type of system are prone to the formation of fertility gradients and surface compaction, restricting crop productivity. The objective of this work was to evaluate the physical-chemical attributes of an Oxisol after 11 years of continuous succession of soybean and corn in direct seeding, using geostatistical analysis techniques and main components. The experiment was conducted at the Chão Nativo farm located in the municipality of Sinop, Mato Grosso, Cerrado-Amazonian ecotone. For this, a rectangular mesh was installed with 103 sampling points distributed in an area of 7.02 ha, in which the grain yield of the soybean was determined as well as the physical and chemical attributes of the soil in the layers 0-10 and 0.10 - 0.20 m. Soybean showed a grain yield between 2.83 and 3.70 Mg ha-1, which was positively correlated with the spatial distribution of pH, phosphorus and potassium. The soil presented low physical quality due to high soil resistance to penetration and macroporosity less than 10%; however, no spatial correlation of physical attributes with soybean yield was observed.

Intensive rainfall is an important meteorological variable that is of technical interest in hydraulic projects. This study therefore generated Intensity-Duration-Frequency equations (IDF) for 14 weather stations in Mato Grosso State, based on pluviograph analysis. Annual maximum rainfall data regarding 10-to-1440-minute long rainfall events were collected from digitized daily pluviographs. Data adherence to the generalized extreme value distribution (GEV) was checked through the Kolmogorov-Smirnov test at a 20% significance level. Next, the maximum probable rainfall for return periods such as 2, 5, 10, 20, 30, 50 and 100 years was calculated and the IDF equations were adjusted. The performance of the IDF equations was evaluated based on mean absolute error (MAE), root mean square error (RMSE), bias, Willmott's concordance index and Nash-Sutcliffe efficiency index (ENS). Adjusting the IDF equations was only possible for rainfall durations ranging from 10 to 360 min at each station due to the low frequency of longer rainfalls. High variation was present in parameters of the IDF equation and in maximum rainfall intensity between stations. The satisfactory performance of the models, as attested to by statistical indices, allows using IDF equations adjusted for rainfall durations from 10 to 360 min, and return periods from 2 to 100 years, in the regions of the Mato Grosso weather stations.