Modeling Spatial Variability of Soil Chemical Parameters for Site-Specific Farming Using Stochastic Methods

Spatial variability of soil properties affects nutrient transport in the field. The purpose of this study was to examine the extent of spatial variability in the soil chemical parameters, and to develop stochastic models to represent these variations. The parameters selected include concentration levels of Ca, Mg, Mn, NO3-N, P, K and Zn, and OM and pH of soil. Data were collected from 55 grids of 20 × 20 m size from a field within the Coastal Plains of Virginia. Analyses were performed based on the deterministic and stochastic components of the chemical parameters. All the parameters had different degrees of variability in the spatial domain. NO3-N, P, K and Zn exhibited greater degrees of variability compared to other parameters. Among the nine parameters, NO3-N and Zn had the greatest spatial variation with coefficient of variation (CV) of 40 and 49%, respectively, while pH had the lowest variation with a CV of only 4%. The spatial variations of each parameter were not random, but were mutually correlated with their values at the adjoining grids. The analysis showed that the deterministic component of these parameters could be represented by a Fourier series containing sine and cosine functions, while different types of models were required to describe their stochastic component. A second-order autoregressive model, AR(2), for Ca, Mg, Mn and OM; a first-order autoregressive model, AR(1), for pH; and a mixed autoregressive-moving average, ARMA(2,1), for NO3-N, P, K and Zn parameters were found suitable. These models were capable of describing the spatial structure of chemical parameters, and hence can be used to determine their values at any unsampled locations to develop site-specific nutrient management plans for the study site.