Applying Spatial Geostatistical Analysis Models for Evaluating Variability of Soil Properties in Eastern Shiraz, Iran

The information on the spatial properties of soil is vital to improve soil management and to increase the crop productivity. Geostatistical analysis technique is one of the most important methods for determining the spatial properties of soil. The aim of this study was to investigate spatial variability of soil chemical and physical attributes for field management in eastern Shiraz, Iran, in 2010. In the study area, for applying geostatistical analysis, eighty soil samples were taken randomly. The variability of saturation percentage (SP), electrical conductivity (EC), soil pH, sand%, silt%, clay%, nitrogen (N), phosphorus (P) and potassium content (K) of the soil used to determine the spatial properties of soil by geostatistical analysis techniques. Soil properties were analyzed both geostatistically and statistically on the basis of the Semivariogram models. Thus, each soil parameter was used for different Semivariogram models such as spherical, circular and exponential because of their different spatial structures. The results showed that the best model to generate soil properties map was ordinary kriging with spherical and exponential Semivariogram models. The best model for soil pH, SP, K and N was the spherical model whereas for sand%, silt%, clay%, EC and P, the best model was the exponential model. Based on the models, the range of spatial dependency was found to vary within soil parameters. EC had the longest (134 meter) and pH had the shortest (19.1 meter) range of spatial dependency. Additionally, spatial patterns may vary among soil parameters in the study area. Therefore, Semivariogram models can be useful tools to determine spatial.