Evaluating the minimum data set determination to assess saline-sodic soil quality

Document Type : Original Article

Authors

1 soil scince zanjan university

2 Department of Soil Science and Engineering, Faculty of Agriculture, University of Urmia

3 Department of Soil Science and Engineering, Faculty of Agriculture, University of Zanjan

4 Assistant Professor, Department of Soil Science, Faculty of Agriculture, University of Zanjan

10.30466/asr.2026.56064.1880

Abstract

Soil quality assessment is very important for studying the condition of all lands. Therefore, numerous methods have been proposed to achieve this objective. However, given the vast amount of data involved, there has long been a need for an approach that can effectively explain the desired outcome using a smaller dataset. The use of minimum data sets (MDS) is one of these methods. In this study, 24 different soil attributes, including physical, chemical, and heavy element properties, were analyzed in 80 soil samples from saline-sodic soils on the margins of Lake Urmia to determine the MDS approach using principal component analysis (PCA). The results revealed that eight indicators had eigenvalues greater than one. These indicators, identified as the MDS, were soil electrical conductivity, organic carbon, total Pb, bulk density, silt and clay, total Cd, and calcium carbonate equivalent, and together they accounted for over 78% of the total variance. Among the eight indicators, Pb and Cd had the lowest weights, suggesting the least influence on the soil quality index. Conversely, organic carbon and clay had the highest weights, highlighting their key role in determining the soil quality index for the study region. The coefficients of determination (R²) between the TDS and MDS datasets were 0.54 for the linear model and 0.64 for the nonlinear model, indicating that the MDS can reliably replace the TDS for soil quality assessment. Additionally, the higher R² value of the nonlinear model suggests its greater efficiency in evaluating soil quality in the study area.

Keywords

Main Subjects

References

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History

  • Receive Date: 17 March 2025
  • Revise Date: 29 September 2025
  • Accept Date: 06 September 2026

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