LAUSR

Abstract Soil organic carbon (SOC) is the regulatory soil property for soil fertility. The SOC status and its variability pattern can be studied through spatial interpolation techniques. In the current study we compared deterministic (Inverse Distance Weightage, IDW), geostatistical (spherical and exponential kriging (OK) and Empirical Bayesian Kriging, EBK) and Machine Learning (Random Forest, RF, Support Vector Machine, SVM) method for samples collected at four grid spacings (20, 40, 60 and 80 m) to find out the combination of best interpolation method and sample spacing to produce a variability map for SOC. Geostatistical models with corresponding highest R2 (31.9%), Lin CCC (0.49) and Pearson Correlation Coefficient (0.57) performed better than deterministic and machine learning models. A more precise prediction technique of geostatistical methods than deterministic interpolation may be due to differences in weightage calculation technique and a higher biasness in terms of Mean Error associated with IDW as compared to IDW. The presence of higher local uncertainty and lack of auxiliary supporting parameters e.g., crop and nutrient management) made ML (R2 19.4%, 21.7% in case of RF and SVM respectively) to be a poor predictor than EBK. Among the six interpolation methods, EBK was found to be the best interpolation method in each sample grid spacing [31.9% (p