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The use of geostatistical methods and artificial neural network (ANN), which can predict the spatial variability of SOM is of outmost significance. The present research investigated the effects of different land use types at different altitudes on the spatial variability of SOM using the above-mentioned methods. A total of 249 combined samples from the depth of 0–15 cm on the basis of land use type and topography were collected from different parts of the research area (9545 km 2 ). Using cross validation, the methods were compared and the best fitted one was selected on the basis of mean error (ME) and root mean square error (RMSE). The best-fitted semivariogram models for SOM (R 2 = 0.894) and SOC (R 2 = 0.761) were spherical. The cross-validation method indicated ANN as the most accurate method for the prediction of SOM and SOC with the ME and RMSE of 0.37 and 0.36 for SOM, and 0.37 and 0.35 for SOC, respectively.

The interpolation methods and neural network to estimate the spatial variability of soil organic matter affected by land use type