Soybean [(Glycine max L.) Merril] is the most important crop in world agribusiness nowadays. Brazil is the second soybean producer worldwide, with an estimated production in the 2018/2019 harvest of 115 million tons of grains grown in an area of approximately 36 million hectares (Conab, 2020). Most of the areas cultivated with soybean in Brazil are located in the Cerrado biome, where the crop represents 90% of the biome's agriculture. The 2013/2014 harvest, more than half (52%) of the soybean cultivated in Brazil was concentrated in the Cerrado (Carneiro Filho & Costa, 2016). Cerrado soils are quite acidic, with a pH ranging from less than

The search for high‐yielding genotypes and that are tolerant to abiotic stresses has been a major goal in plant breeding. Thus, the use of technologies such as precision agriculture associated with remote sensing tools for plant phenotyping has increased. The hypothesis of this research was that soya bean genotypes respond differently to low and adequate base saturation levels in the soil and that vegetation indexes can be efficient auxiliary tools in plant phenotyping for this purpose. The objective of this study was to evaluate the nutritional status and agronomic performance of soya bean genotypes grown in low and recommended base saturation conditions using high‐throughput phenotyping. The research was carried out in 2017/2018 and 2018/2019 crop seasons, in which two field experiments in each season were installed. In experiment I, genotypes (P1, P2, P3, P4, P5, P6, P7, P8, P9 and P10) were evaluated without soil correction (low saturation condition). In experiment II, liming was performed three months before sowing of the genotypes to raise the base saturation to 60% (recommended saturation). Canopy spectral behaviour at the following wavelengths was evaluated: green (550 nm), red (660 nm), red edge (735 nm) and near‐infrared (790 nm), and the vegetation indices (Vis) NDVI, SAVI, EVI and MSAVI were calculated. The variables evaluated were leaf calcium and magnesium contents and grain yield. The use of VI’s was efficient in assessing the performance of genotypes soya bean at different base saturation levels. The EVI showed moderate correlation with the nutritional and agronomic variables measured in each base saturation level. The approach used enabled both to identify genotypes tolerant to low base saturation soils and the ones with better response to liming.

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Spatial autocorrelation, which in this work was calculated using Moran's bivariate analysis, can be defined as the coincidence of similar values in nearby locations, or the absence of randomness of a variable due to its spatial distribution. Therefore, the objective of this study is to analyze the distribution and spatial autocorrelation of physical attributes of an Oxisol. The experiment was carried out in the irrigation and drainage area of the Universidade Federal de Viçosa, in Viçosa, Minas Gerais, Brazil. The soil in which the experimental meshes were installed was classified as a sandy clayey Oxisol. The attributes were determined: soil moisture on a dry basis, % (DB), soil moisture on a wet basis, % (WB), volumetric soil moisture, % (VS), particle density, g cm-1 (PD), sampled at different depths and within a grid of 90 georeferenced points. For spatial autocorrelation, the global Moran and local Moran indexes (LISA) were used as statistical tools. Bivariate analysis revealed that soil volumetric moisture is closely related to wet and dry basis moisture. It was also found that the surface particle density is related to the deeper layers of the soil, thus reinforcing that the solid fraction of a soil sample, without considering porosity, tends to remain constant. This happens because the predominant mineral constituents in soils are quartz, feldspars, and colloidal aluminum silicates, whose particle densities are around 2.65 g cm-3.