Spatial variability of soil physical properties in longitudinal profiles

Abstract

The spatial variability of physical properties, such as bulk density, penetration resistance and gravimetric moisture, obtained by applying geostatistics in precision agriculture, can effectively indicate the physical behavior of agricultural soils in longitudinal profi les. In this way, the spatial dependence of physical properties in streets of coffee plantations with different lengths was evaluated in the southern Minas Gerais, Brazil. For this purpose, fi ve longitudinal profi les were measured in streets, each one with depths ranging from 0 to 0.60 m, in six layers of 0.10 m, being the database composed of 432 property, 144 by property, submitted to the ordinary kriging geostatistical method in order to obtain spatial variability maps using the R software. They were evaluated by the lower mean cross-validation error of theoretical models fi tted by ordinary least squares (OLS), being detected in higher superfi cial layers, from 0 to 0.30 m, lower bulk density and lower penetration resistance, with variable gravimetric moisture in the length direction of some streets of coffee plantations, being that these properties presented different structures of spatial dependence for each street.

Spatial Variations and Distribution Patterns of Soil Salinity at the Canal Scale in the Hetao Irrigation District

“…The fitting accuracy of the semi-variance function was characterized using the determination coefficient (R 2 ) and residual sum of squares (RSS), in which R 2 was between 0 and 1; the larger the value, the better the fitting effect. The smaller the RSS value, the better the simulation effect [32].…”

“…It can effectively estimate the attribute value of an unsampled area. Some scholars at home and abroad have used inverse-distance-weighted interpolation to study soil spatial variation [23,24,32,44]. However, when the variation coefficient of the predicted object is large, Kriging interpolation has higher accuracy.…”

“…Some scholars have used the inverse-distance-weighted interpolation method to study soil spatial variation. However, some studies have indicated that the accuracy of Kriging is higher when the coefficient of variation of the predicted object is large [23,24,32,44,[46][47][48]. The combination of ArcGIS and geostatistics can be used to effectively monitor spatial variations in regional soil salt factors [2,20,[46][47][48][49][50].…”