Soil organic matter (SOM) is one of the important soil parameters which directly and indirectly affects several soil physicochemical properties and environmental factors. The aim of this research was to predict soil organic matter (SOM) using kriging and cokriging methods using soil auxiliary data. Soil samples were gathered from an area of 63 km2 in Bonab plain in Iran and overall of 78 samples from depth 0-20 cm were collected. SOM and ten other soil physicochemical properties such as electrical conductivity (EC), soil texture, calcium carbonate equivalent (CCE), were measured. Later correlation between SOM and soil properties was determined and those properties with high correlation in 1% probability level with SOM were used to develop cross-semivariograms. Later SOM prediction was done on a grid of 100 m with kriging and cokriging methods using BMElib package developed for MATLAB software. Results showed that among studied soil properties, CCE, silt, sand and wet aggregate stability (WAS) had the highest correlations with SOM and therefore they were chosen as auxiliary data in cokriging of SOM. Spatial prediction of SOM with kriging method resulted in MSE and RMSE of 0.055 % and 0.234 % respectively. However, SOM prediction with developed cross-semivarigrams by using auxiliary data revealed that CCE and silt could improve SOM prediction with MSE and RMSE of 0.047%, 0.032% and 0.216%, 0.178 % respectively. The better performance of CCE and silt covariates in SOM prediction could be explained by their higher correlation with SOM and decreased nugget effect in developed cross-semivariograms (increased spatial dependency). As a conclusion, due to the nature of SOM which is controlled by some of the soil properties especially soil texture, CCE, aeration condition in soils, ets., selecting appropriate soil parameters with high correlation with SOM and high spatial dependency can improve spatial prediction of SOM and thus, a step forward in sustainable management of SO
