Modelagem da variabilidade de atributos do solo em diferentes escalas espaciais usando geoestatística baseada em modelos lineares mistos e Wavelets bidimensionais

Abstract

Analyzing spatial variability, both in agricultural plots and in Watershed, requires robust statistical tools that consider the position and possible dependence between observations of variables in the field since the physical, chemical and biological attributes of the soil diversify widely in space. Therefore, the identification and quantification of spatial and temporal variability constitutes a valuable tool, as it allows defining the appropriate sampling spacing, reducing the costs of monitoring field data, especially in studies associated with hydrological simulation. In view of the above, this study's main objective is to analyze spatial variability on a sampling scale of agricultural areas and river basins, using linear mixed models and two dimensional Wavelet transform. For this, two agricultural areas located in the south of Rio Grande do Sul will be used, since these areas are representative of the use and management of soils adopted in the south of the state, that is, centered on the intensive use of soil, not associated with the use of conservation practices. Namely: the areas in question are described as Granja Bretania (Estancinha) and Systematized area of the Terras Baixas experimental station. It is noteworthy that the agricultural areas were described based on studies by Bitencourt et al. (2016) and Timm et al. (2020). The Sanga Ellert Watershed (SEW) will also be used as a study area, which has an area of 70 ha and has a sampling grid of 178 samples. Linear mixed models will be applied to these data from agricultural areas to analyze the effect of classes or land use in the spatial domain, under different variance and covariance matrix structures and incorporating the anisotropic effect. At SEW, we will seek to characterize and quantify multi-spatial correlations, within specific scales, between the physical, chemical and water attributes of the soil, in the Sanga Ellert watershed, using two-dimensional Wavelet transforms. Therefore, through this study, it is expected to understand the spatial variability of soil attributes, through the aforementioned statistical tools and to define the sampling scales according to the areas studied.