Economic development, international food and feed demand, and government policies have converted Brazil’s natural ecosystems into agricultural land. The Integrated Farm System Model (IFSM) was evaluated using production, economic, and weather data collected on two cooperating farms in the Legal Amazon and Cerrado biomes in the Midwest state of Mato Grosso, Brazil. Three sustainable agricultural intensification strategies, namely grain supplementation, pasture re-seeding, and pasture fertilization were simulated in IFSM with double the beef cattle stocking density compared to extensive grazing. Livestock dry matter consumption simulated in IFSM was similar for pasture grazing estimates and actual feed consumed by beef cattle on the two collaborating farms. Grain supplementation best balanced beef production and profitability with lower carbon footprint compared to extensive grazing, followed by pasture fertilization and pasture re-seeding. However, pasture re-seeding and fertilization had greater use of water and energy and more nitrogen losses. Human edible livestock feed use was greatest for grain supplementation compared to other modeled systems. While grain supplementation appears more favorable economically and environmentally, greater use of human edible livestock feed may compete with future human food needs. Pasture intensification had greater human edible feed conversion efficiency, but its greater natural resource use may be challenging.

Since the dawn of Brazilian trade, extensive cattle farming has predominated. Brazil’s extensive pasture-based system uses pasture plants adapted to climate and soil conditions with limited use of purchased inputs. However, new technologies such as integrated crop and livestock systems have recently been adopted, with government support and public policies that are intended to encourage increased agricultural production in Brazil. Domestic and international stakeholders have prioritized sustainable agricultural development in Brazil’s beef sector to reduce deforestation and other natural-habitat conversions. This review provides an overview of beef production in Brazil, focusing particularly on (1) historical factors that have encouraged an extensive, low-intensity style of production and (2) how national public policies supporting agriculture have improved sustainability in Brazil’s beef industry. Since the beginning of the twenty-first century, specific public policies for rural areas began to implement changes that addressed environmental concerns. Programs aimed at protecting secondary forests and increasing their areas are needed to offset the 42% of Brazil’s greenhouse gas emissions that come from land-use change. To produce more beef with less environmental impact, cattle ranchers need to use their land more productively. Thus, public policy initiatives need to combat deforestation and preserve the environment and local communities, while sustainably intensifying Brazil’s beef production.

Crop cultivation on sandy soils is susceptible to water stress. Therefore, we determined the physical-hydric attributes of a Latossolo Vermelho distrófico (Oxisol) in northwestern Paraná state, Brazil. Soil samples were collected at depth ranges of 0 to 0.2 m, 0.2 to 0.4 m, and 0.4 to 0.6 m. We measured clay, silt, sand, fine and coarse sand contents, soil particle density, soil bulk density, total porosity, microporosity, and macroporosity. We also measured soil characteristics such as saturated and unsaturated soil hydraulic conductivities, pore distribution, water retention, available water capacity, and easily available water. We also estimated soil moisture, matric potential at field capacity, and time at field capacity. Validation of associations among these soil physical-hydric attributes was performed using principal component analysis. For the sandy soils analyzed, the distributions of coarse and fine sand fractions were measured for better evaluation of the soil’s physical and hydric attributes. Higher coarse sand contents increased soil hydraulic conductivities, maximum pore diameter, and macroporosity while reducing microporosity. Fine sand content reduced conductivity and increased soil water retention in subsurface layers. Simulated sugarcane yield increased with soil water storage. These results support improving crop simulation modeling of sugarcane to support sustainable intensification in regions with sandy soils.

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.

Brazil is the largest producer of sugarcane (Saccharum spp.) in the world, and this crop’s response to climate and soil water storage is essential for optimal management and genetic/yield improvements. The objective of our study was to build a multivariate model to estimate sugarcane yield in the subtropical conditions of the northwestern Paraná region using climatic and soil water storage variables. Observed yield data was used from experiments conducted at the Experimental Station of the Sugarcane Genetic Improvement Program of the Universidade Federal do Paraná. The sugarcane varieties RB72454, RB867515, RB966928, and RB036066 were analyzed in the 1998–2006, 2008, 2018 and 2019 harvest years. Stepwise multiple linear regression analysis with repeated cross-validation was developed to estimate sugarcane yield given climate and soil water storage variables for crop growth phases. The accumulated degree days in Phases I and II and soil water storage in Phase II of development significantly impacted sugarcane yield. The multiple linear regression model, with accumulated degree days and soil water storage in Phases I and II of development, successfully predicted sugarcane yield for analyzed varieties. Sugarcane production models like the one we developed can improve crop management for greater sustainability and climate change adaption in Brazil and other areas.