Abstract

Cation exchange capacity (CEC) is one of the most important soil attributes which control some basic properties of soil such as acidity, water and nutrient retaining capacity. However, the measurement of cation exchange capacity in large areas is time consuming and requires high expenses. One way to save time and expenses is to use simple soil covariates and geostatistical methods in mapping CEC. Therefore, the aim of the present research was to investigate the role of soil covariates in the improvement of spatial variability of CEC. The study area is located in southwest Iran on the Aghili plain, Gotvand, Khuzestan province. In this study, ordinary kriging and cokriging methods were used to predict CEC. 107 soil samples were gathered on a random grid of 200-700 m. 74 samples were used for training and 33 samples for testing the results. A principle component analysis was performed for covariate selection. Clay was selected as a covariate in cokriging due to high correlation between clay and CEC[FE1] in the first principle component analysis. Based on the cross validation result of predicted dataset, RMSE and ME for cokriging were 2.16 and 0.03 cmol (+)/kg respectively, and 3.36 and 0.09 cmol (+)/kg for kriging, respectively. Based on these results, cokriging performed better than kriging for predition of cation exchange capacity since it used a covariate such as clay, for the improvement of CEC spatial prediction.

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