Improving spatial prediction of soil organic matter using soil auxiliary data as secondary information

Document Type : Research Paper

Authors

1 Department of soil science and engineering, Faculty of Agriculture, University of Maragheh.

2 Department of Soil Science and Engineering, Faculty of Agriculture, University of Maragheh, Maragheh, Iran

Abstract

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 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. 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 SOM as a key soil quality index, especially in areas with salinization and desertification danger.

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Environmental Resources Research
Volume 11, Issue 1
July 2023
Pages 141-154

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