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.

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.

Corn grown as second-crop (safrinha) immediately after soybeans contributes to Brazil’s recent sustainable intensification of efforts to land spare and reduce deforestation. Brazil’s Mato Grosso state is very important for corn production with a large number of available hybrids for producers with different characteristics. Evaluating as many hybrids as possible increases the likelihood of identifying those that are more productive. Our experiment used a randomized block design to evaluate 20 corn hybrids for yield and quality in the western Cerrado savannah region of Mato Grosso state. There were significant differences in silage quality and grain yield between hybrids. Silage quality was correlated with higher grain yield. Corn grain yields for 10 higher-yielding hybrids (average = 11,425 kg/hectare) were significantly greater than the two lowest yielding hybrids (6974 and 8207 kg/hectare) and 64% more than the 2020 average in Mato Grosso. Precipitation was not limiting during our experiment but may be in the drier Cerrado area and during other years. Higher silage quality with lower lignin content and higher in vitro digestibility can improve beef productivity as Brazil transitions from extensive grazing to more intensified systems such as pasture supplementation. Increasing corn grain yields in Mato Grosso can hedge against lower crop yields elsewhere.

Brazil’s midwest has rapidly expanded large-scale commodity crops such as soybeans and maize. We surveyed both agricultural producers and agricultural professionals in the middle-north region of Mato Grosso state, Brazil. Agricultural professionals provide technical support to agricultural producers and both are served by Assistência Técnica e Extensão Rural (ATER) with nationally and internationally recognized extension outreach. Our objectives were to define and contrast agricultural producer and professional characteristics, especially source(s) relied upon for agricultural training. There were 94 agricultural producers and 89 agricultural professionals that responded to our surveys, which were summarized and contrasted using statistical software. There was a predominance of male farmers, married with a broad age range. Agricultural professionals who advise producers had a high educational level. Producers and professionals were most reliant on private sector agricultural companies and business support organizations for agricultural training, versus public institutions such as universities and state/federal agencies. In the state of Mato Grosso, extension outreach can involve joint efforts by public and private sector entities. However, more targeted efforts are needed to ensure that public sector research is more equally used by agricultural producers and professionals in the region, especially during field days and face-to-face technical lectures during the off-season.

Soil turning during pre-seeding preparation is meant to loosen and promote physical characteristics responsible for the development of crop roots in soil. In this study, we demonstrate effects of three methods of pre-seeding preparation at two depths of irrigation on carbon dioxide (CO₂) efflux as well as the temporal variability in the distribution of soil carbon. This experiment was conducted at the rural cooperative extension research station in Tangará da Serra, Mato Grosso, Brazil, for two wheat production years in 2011 and 2012. The experimental design was randomized in strips, with four repetitions. Wheat was irrigated during the June to early October dry season. The treatments include two methods of soil preparation: traditional preparation with one plowing and two disc harrowings and minimal preparation with one seed bed conditioner pass. A third treatment of direct seeding was also evaluated. Soil CO₂ efflux, soil temperature, air-filled soil porosity, and labile carbon were measured. We found soil preparation affects CO₂ efflux in irrigated cultivation systems within Brazil’s Cerrado savannah, with conventional tillage having greater CO₂ emissions than either reduced tillage or direct seeding. Soil CO₂ emissions were lower at 508 mm versus 698 mm irrigation depth, but wheat yields were significantly lower.

The GeoWEPP model has estimated water and soil losses caused by erosion at the watershed level in different parts of the world. However, this model was developed and its parameters have been adjusted for temperate climates, which are different from tropical climates such as those found in Brazil. Our study evaluated the performance of the GeoWEPP model in estimating soil erosion in three micro-watersheds in the Cerrado (i.e., savannah) of southeastern Mato Grosso state, Brazil. Major land uses modeled were soybean and corn cultivation, traditional pasture, and native vegetation. Input parameters for the GeoWEPP model involved climate, soil, land use and management, and topography. GeoWEPP was calibrated with input parameters for soil erodibility specified as interrill and rill soil erosion, soil critical shear stress, and saturated hydraulic conductivity obtained experimentally and estimated by internal routine equations of the GeoWEPP model. Soil losses observed in micro-watersheds with agriculture, pasture, and native vegetation were 0.11, 0.06, and 0.10 metric tons per hectare per year, respectively. GeoWEPP best modeled soil erosion for native vegetation and pasture, while over-estimating that for crops. Surface runoff was best modeled for crops versus native vegetation and pasture. The GeoWEPP model performed better when using soil erodibility input parameters.