The reliance on soil acidity correctives and mineral fertilizers poses a threat to food production due to the finite nature of these resources and their susceptibility to price volatility from importation. Soil remineralizers have emerged as a sustainable alternative. This study assessed silicate agrominerals as soil remineralizers to replace limestone, gypsum, and conventional fertilizers in a no-tillage system. Conducted in a tropical climate on sandy/medium-textured Ultisol, twelve treatments (combinations of liming, gypsum, mineral fertilization, and remineralizer) were tested for their effects on soybean and sorghum agronomic traits. Applying a remineralizer at 2500 kg ha⁻¹ enhanced soybean productivity by 15% and sorghum by 35% in succession, along with increases in P, S, Ca, Mg, sum of bases (SB), cation exchange capacity (CEC), base saturation (V%) in the 0–0.20 m layer and organic matter in the 0–0.40 m layer, benefiting soil microbiological parameters, with the treatment combining all four products improving soil fertility; however, for better crop productivity, split applications appear to be an alternative to avoid nutrient imbalance. Due to the finer particle size of the remineralizer, which allows faster nutrient release, further research is recommended to investigate the long-term impacts on soil microbiota dynamics, optimal doses and combinations, and economic viability across various soil types and climates.

This study aimed to evaluate the performance of three spatial association models used in digital soil mapping and the effects of additional point sampling in a steep-slope watershed (1,200 ha). A soil survey was carried out and 74 soil profiles were analyzed. The tested models were: Multinomial logistic regression (MLR), C5 decision tree (C5-DT) and Random forest (RF). In order to reduce the effects of an imbalanced dataset on the accuracy of the tested models, additional sampling retrieved by photointerpretation was necessary. Accuracy assessment was based on aggregated data from a proportional 5-fold cross-validation procedure. Extrapolation assessment was based on the multivariate environmental similarity surface (MESS). The RF model including additional sampling (RF*) showed the best performance among the tested models (overall accuracy = 49%, kappa index = 0.33). The RF* allowed to link soil mapping units (SMU) and, in the case of less-common soil classes in the watershed, to set specific conditions of occurrence on the space of terrain-attributes. MESS analysis showed reliable outputs for 82.5% of the watershed. SMU distribution across the watershed was: Typic Rhodudult (56%), Typic Hapludult* (13%), Typic Dystrudept (10%), Typic Endoaquent + Fluventic Dystrudept (10%), Typic Hapludult (9.5%) and Rhodic Hapludox + Typic Hapludox (2%).

Hydro-sedimentological models might be useful tools for investigating the effectiveness of soil and water conservation practices. However, evaluating the usefulness of such models requires that predictions are tested against observational data and that uncertainty from model parameterization is addressed. Here we aimed to evaluate the capacity of the SWAT model to simulate monthly streamflow and sediment load in the Posses creek catchment (12 km2), Southeast Brazil. The SUFI-2 algorithm from SWAT-CUP was applied for calibration, testing, uncertainty, and sensitivity [...] ABSTRACT Hydro-sedimentological models might be useful tools for investigating the effectiveness of soil and water conservation practices. However, evaluating the usefulness of such models requires that predictions are tested against observational data and that uncertainty from model parameterization is addressed. Here we aimed to evaluate the capacity of the SWAT model to simulate monthly streamflow and sediment load in the Posses creek catchment (12 km2), Southeast Brazil. The SUFI-2 algorithm from SWAT-CUP was applied for calibration, testing, uncertainty, and sensitivity

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