Statistical and geostatistical techniques for geospatial modeling of soil cation exchange capacity

• Published in: Communications in Soil Science and Plant Analysis Vol. 49; no. 18; pp. 2301 - 2314
• Main Authors: Seyedmohammadi, Javad, Matinfar, Hamid Reza
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 11.10.2018; Taylor & Francis Ltd
• Subjects: Cation exchange; Cation exchanging; CEC; Chemical properties; cokriging; Correlation; Dependent variables; Geostatistics; kriging; Kriging interpolation; Organic chemistry; PCA; Pollutants; Sediment pollution; Soil chemistry; Soil pollution; Soil properties; Soil quality; Soil testing; Spatial variability interpolation; Statistical analysis; Statistical methods
• ISSN: 0010-3624; 1532-2416; 1532-4133
• DOI: 10.1080/00103624.2018.1499765

Soil cation exchange capacity (CEC) is a main criterion of soil quality and pollutant sequestration capacity. This research was carried out to evaluate cokriging prediction map of soil CEC spatial variability with the principal components derived from soil physical and chemical properties. Two hundred and forty-seven soil samples were collected that 75% of them were used for training soil CEC and 25% for testing of prediction. The first principal component (PC1) was highly correlated with soil CEC (r = 0.81, P < 0.01), whiles there was no significant correlation between CEC and PC2 (r = -0.19). Then, the PC1 was used as an auxiliary variable for the prediction of soil CEC in cokriging method. The determination coefficient (R 2 ) of cross-validation for the test dataset was 0.47 for kriging and 0.71 for cokriging. Therefore, according to the results, principal components that have the highest positive and significant correlation with the dependent variable have the most potential for cokriging prediction.