Comparison of Interpolation Techniques for Assessment of Spatial Variability of Soil Chemical Properties for Oil Palm Plantation Zonal Management

Abstract

Abstract Spatial variability map of the soil properties is highly dependent on availability of data either in detailed or semi-detailed resolution. Variation on the data resolution affects the interpolation technique, thus the zonal management zone. However, there are many interpolation techniques offers, thus raises the issues of the most suitable method in zone classification. The study aim is to evaluate the best interpolation method in assessing the spatial variability of the soil chemical properties from highly weathered tropical mineral inland 108.5 ha oil palm plantation. These soil samples were brought back for further lab analysis. The spatial variability of the soil chemical properties was done by using interpolation techniques were utilized for geospatial statistical and deterministic analysis. Due to a variety of algorithms embedded in the interpolation techniques, the strength of Pearson correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and RMSE-observations standard deviation ratio (RSR) were applied for accurate quantification of interpolation method. On average, EBK and OK were the suitable interpolation methods with the least average model validation statistical value i.e. error. Therefore, the evaluation and comparison of r, NSE, PBIAS, and RSR were appropriate approaches in determining the accuracy of the interpolation method for the zonal management practices.