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Assessment Quantitative of Soil Quality in Different Uses of Shahrekord Plain Using the Analytical Hierarchical Process (AHP)
A. Hematifard, M. Naderi, A. Karimi, J. Mohammadi,
Volume 23, Issue 1 (6-2019)
Abstract
Assessment of soil quality helps to make a balance between soil function and soil resources, improving soil quality and achieving the sustainable agriculture. For the quantitative evaluation of soil quality in the Shahrekord plain, Chaharmahal va Bakhtiari province, 106 compound surficial soil samples (0-25 cm) were collected. After the pre-treatments of soil samples, 11 physico-chemical soil characteristics/indicators as the total data set (TDS) were measured using the standard methods. Statistical analysis showed the usefulness of Principle Component Analysis (PCA) transformation. The minimum data set (MDS) was selected using PCA. Analytical Hierarchy Process (AHP) was carried out for the quantitative determination of indicator priorities and weights. Soil quality of the samples was calculated by introducing TDS and MDS into Integrate Quality Index (IQI) and Nemero Quality Index (NQI). The results showed the soil quality of the land uses was as Rangelands> Drylands<Irrigated croplands. The correlation coefficient between IQI-TDS and IQI-MDS was 0.97, while this value for NQI-TDS and NQI-MDS was 0.98. The correlation coefficient between IQI-TDS and NQI-TDS was 0.87 and that between IQI-MDS and NQI-MDS was 0.91. Classification of the resulted soil quality map IQI-TDS revealed that 12.5 % and 15.5 % of the plain were in very high and low quality conditions, respectively.

Spatial Distribution of Soil Quality Index in Saline and Sodic Soils of Urmia Lake Using Kriging and IDW Methods
H. Rezazadeh, P. Alamdari, S. Rezapour, M. S. Askari,
Volume 29, Issue 3 (10-2025)
Abstract
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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