ABSTRACT Metrics are fundamental to quantify and classify the spatial dependence of soil and agricultural attributes. This study aimed to propose and validate metrics based on two distinct approaches, one additive, which considers the arithmetic mean of the vertical and horizontal components, and the other multiplicative, which considers the geometric mean of the vertical and horizontal components of the semivariogram. Furthermore, we intend to propose the classification of spatial dependence based on the categorization of these metrics. Finally, a function […]

ABSTRACT The assessment of spatial variability of environmental variables such as soil properties is important for site-specific management. A geostatistical index that allows quantifying and characterizing the structure of spatial variability is fundamental in this context. Thus, this study aimed to develop a new spatial dependency index, called the Spatial Dependence Measure (SDM) for the spherical, exponential, Gaussian, cubic, pentaspherical, and wave semivariogram models; and comparing it with some of the indexes available in the literature. The SDM is also […]

Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plant-atmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil […]