Performance of frequently used interpolation methods to predict spatial distribution of selected soil properties in an agricultural watershed in Ethiopia

Abstract

Soil maps of an agricultural watershed provide a wealth of knowledge and can be a vital tool for implementing site specific soil managements. Hence, watershed based soil assessment was conducted to select an optimum spatial interpolation method, while aiming for sustainable soil managements. Thus, intensive soil sampling was undertaken to investigate the performance of ordinary kriging (OK), inverse distance weighting (IDW) and radial basis functions (RBF) for predicting the spatial distribution of soil texture, pH, soil organic carbon (SOC) and available phosphorus (AP). The 72ha study area was divided into a 100m by 100m grids and approximately at the center of each grid, topsoil (10-15cm) samples were collected over 75 locations across the entire study area. The exponential and Gaussian models were best fitted in the semivariogram of the measured soil variables. The performance of each interpolation method was assessed quantitatively in terms of Nash-Sutcliffe efficiency (E), coefficient of determination (R2) and index of agreement (d). The interpolated maps generated based on the highest value of E displayed OK was best performed for SOC and sand. RBF was most suitable for mapping of AP and clay, while IDW gave better result when applied to pH. The highest value of R2, E and d (0.51, 0.51, and 0.83, respectively) resulted from the spatial interpolation of AP. Generally, the methodology used in this study was adequate for spatial interpolation and evaluation of measured soil properties and can serve as a general method for surface map generation in future studies of similar regions.