Using Environmental Covariates and Soil Digital Mapping Technique in Predicting Soil Crusting Index of East Azerbaijan Province

Document Type : Original Article

Authors

1 Department of Soil Science and Engineering, Faculty of Agriculture, University of Tabriz, Tabriz- Iran

2 Professor, Soil Science and Engineering Department, Faculty of Agriculture, University of Tabriz- Iran.

3 Associated Professor, Department of Range and Watershed Management, Faculty of Natural Resources, Urmia University

4 Ph.D Student, Department of Soil Science and Engineering, Faculty of Agriculture, University of Tabriz, Tabriz

5 M.Sc. Graduate, Department of Soil Science and Engineering, Faculty of Agriculture, University of Tabriz, Tabriz

Abstract

Soil crusting is one of the degradation features which causes to decrease the land quality. To fix the crises due to soil crusting, it is therefore needed to identify the degraded areas and improve soil resource management. Since the soil properties have a spatial continuity, providing the digital maps using environmental covariates could be an interesting issue to study the spatial distribution. For this, a total of 107 soil samples were randomly taken over the East Azerbaijan Province, subsequently soil crusting index was calculated based on FAO method. To predict the soil crusting index across the study area, two models i.e., random forests (RF) and multiple linear regression (MLR) within the R programming environment using the data derived from digital elevation model (DEM) (18 indices) as well as remote sensing (eight indices) were evaluated. Results showed that the calculated soil crusting index for the entire study area varied from 0.07 to 2.25. Based on the results, RF was superior to MLR when using DEM-derived data, while MLR was distinguished as a parsimonious model when using RS data. It is concluded that selection of the best-fit model mainly depends on the available soil and covariates data used in modelling. Despite somewhat differences in pixel values between provided maps by the relevant models, the final maps demonstrated a similar trend. Generally, based on the results, the highest soil crusting index was found for west and central part of province, followed by south-eastern and north-eastern areas. The provided maps show that the forest and pasture areas have low value of crusting index, while the cultivated and miscellaneous lands were in the following orders which was consistent with field observations. This research further supports the importance of the digital soil mapping (DSM) technique in soil resources management.

Keywords

Main Subjects

References

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Applied Soil Research
Volume 12, Issue 2 - Serial Number 2
September 2024
Pages 31-45

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History

  • Receive Date: 16 March 2023
  • Revise Date: 30 September 2023
  • Accept Date: 08 October 2023

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