Abstract:
Soil chemical properties such as pH, electrical conductivity (EC), and calcium carbonate content (CaCO₃%) are important indicators of soil health and fertility, impacting nutrient availability and crop productivity. Accurate mapping of these properties is especially important in arid and semiarid regions, such as Libya, where soil salinity and alkalinity present challenges to agricultural practices. This study assesses the effectiveness of the Inverse Distance Weighting (IDW) method which is a type of spatial interpolation that estimates values at unsampled locations by weighting nearby sampled points based on their distance from the point being estimated. In IDW, closer points have a greater influence on the estimated value, while more distant points contribute less. IDW method is used for interpolating spatial distributions of soil pH, EC, and CaCO₃% in a 13,000 square meter area at the Faculty of Agriculture Farm, University of Tripoli, Libya. Using a total of 71 soil samples collected from a depth of 0–30 cm, the spatial variability of these properties through IDW was assessed, a method that estimates values at unsampled locations based on the weighted influence of nearby sampled points. The accuracy of IDW interpolations was evaluated through cross-validation, revealing that IDW provided reliable results for CaCO₃%, with R² values ranging from 63% to 75%. In contrast, the method demonstrated moderate effectiveness for pH (R² values between 41% and 50%) and lower accuracy for EC, with R² values as low as 6%. This suggests that soil pH and EC exhibit varying levels of spatial homogeneity, affecting the interpolation accuracy. Alternative methods like Kriging may be more appropriate for EC due to their capacity to account for spatial autocorrelation, a key factor in environmental variables such as soil properties. The findings underscore the importance of selecting appropriate interpolation techniques based on the specific characteristics of soil properties and their spatial distribution.
