Spatial Prediction of Soil Physical Properties Using IDW, Kriging , and Spline Interpolation Techniques: A Case Study from Baqubah city, Iraq

Abstract

This research assesses the comparative efficiency of three spatial interpolation methods—Inverse Distance Weighting (IDW), Ordinary Kriging, and Spline—for forecasting important soil physical attributes in the semiarid area of Baqubah, central Iraq.  A total of 54 georeferenced soil samples were examined to measure water content, Atterberg limits, specific gravity, and soil texture fractions (sand, silt, and clay).  The findings demonstrate that Ordinary Kriging consistently gave the lowest root mean square error (RMSE) and maximum prediction accuracy for most parameters, including water content (RMSE = 1.85), liquid limit (RMSE = 3.99), specific gravity (RMSE = 0.04), and sand content (RMSE = 1.10).  IDW exhibited higher performance for silt and clay fractions, whereas Spline interpolation provided the greatest RMSE values across all variables, suggesting limited usefulness in varied terrains.  These results underline the durability of geostatistical approaches, notably Kriging, in capturing spatial variability and delivering solid forecasts under data-sparse settings.  The research supports for the incorporation of Kriging into digital soil mapping initiatives, particularly in arid and semiarid locations where precise soil information is crucial for sustainable land management and agricultural planning.