Volume 29, Issue 3 (Fall 2025)
jwss 2025, 29(3): 113-129 |
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Rezazadeh H, Alamdari P, Rezapour S, Askari M S. Spatial Distribution of Soil Quality Index in Saline and Sodic Soils of Urmia Lake Using Kriging and IDW Methods. jwss 2025; 29 (3) :113-129
URL: http://jstnar.iut.ac.ir/article-1-4497-en.html
URL: http://jstnar.iut.ac.ir/article-1-4497-en.html
Department of Soil Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, Iran. , rezazade269@yahoo.com
Abstract: (90 Views)
Soil quality assessment plays a crucial role in sustainable land management, particularly in degraded areas such as saline and sodic soils. This study aimed to determine the spatial distribution of the Soil Quality Index (SQI) in saline and sodic soils around Lake Urmia using two geostatistical interpolation methods: Kriging and Inverse Distance Weighting (IDW). A total of 82 soil samples were collected from a depth of 0–30 cm, and 24 physical, chemical, and heavy metal properties were analyzed. The Soil Quality Index was calculated based on both linear and non-linear approaches. Principal Component Analysis (PCA) was used to identify a Minimum Data Set (MDS), including: calcium carbonate equivalent, EC, clay percentage, BD, silt percentage, organic carbon, Pb, and cadmium, which explained more than 78% of the total variance. The results indicated that the SQI showed moderate spatial variability across the study area, with a decreasing trend from west to east. Comparison of the interpolation methods revealed that Kriging performed better in the linear model, while IDW showed higher accuracy in the non-linear approach. The best-fitted theoretical model was spherical, with a range of influence varying between 6,130 and 20,610 meters. Overall, integrating the Soil Quality Index with geostatistical methods provides a powerful tool for understanding spatial variability and supporting effective planning in saline and sodic soils.
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