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Digital mapping and spatial modeling of some soil physical and mechanical properties in a semi-arid region of Iran

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Abstract

The soil’s physical and mechanical (SPM) properties have significant impacts on soil processes, such as water flow, nutrient movement, aeration, microbial activity, erosion, and root growth. To digitally map some SPM properties at four global standard depths, three machine learning algorithms (MLA), namely, random forest, Cubist, and k-nearest neighbor, were employed. A total of 200-point observation was designed with the aim of a field survey across the Marvdasht Plain in Fars Province, Iran. After sampling from topsoil (0 to 30 cm) and subsoil depths (30 to 60 cm), the samples were transferred to the laboratory to determine the mean weight diameter (MWD) and geometric mean diameter (GMD) of aggregates in the laboratory. In addition, shear strength (SS) and penetration resistance (PR) were measured directly during the field survey. In parallel, 79 environmental factors were prepared from topographic and remote sensing data. Four soil variables were also included in the modeling process, as they were co-located with SPM properties based on expert opinion. For selecting the most influential covariates, the variance inflation factor (VIF) and Boruta methods were employed. Two covariate dataset scenarios were used to assess the impact of soil and environmental factors on the modeling of SPM properties including SPM and environmental covariates (scenario 1) and SPM, environmental covariates, and soil variables (scenario 2). From all covariates, nine soil and environmental factors were selected for modeling the SPM properties, of which four of them were the soil variables, three were related to remote sensing, and two factors had topographic sources. The results indicated that scenario 2 outperformed in all standard depths. The findings suggested that clay and SOM are key factors in predicting SPM, highlighting the importance of considering soil variables in addition to environmental covariates for enhancing the accuracy of machine learning prediction. The k-nearest neighbor algorithm was found to be highly effective in predicting SPM, while the random forest algorithm yielded the highest R2 value (0.92) for penetration resistance properties at 15–30 depth. Overall, the approach used in this research has the potential to be extended beyond the Marvdasht Plain of Fars Province, Iran, as well as to other regions worldwide with comparable soil-forming factors. Moreover, this study provides a valuable framework for the digital mapping of SPM properties, serving as a guide for future studies seeking to predict SPM properties. Globally, the output of this research has important significance for soil management and conservation efforts and can facilitate the development of sustainable agricultural practices.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We would like to thank Shiraz University for providing the needed facilities.

Funding

The research has been funded by Shiraz University.

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Authors and Affiliations

  1. Department of Soil Science, College of Agriculture, Shiraz University, Shiraz, Iran

    Pegah Khosravani, Majid Baghernejad & Ali Akbar Moosavi

  2. Soil and Water Research Institute (SWRI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

    Meisam Rezaei

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  1. Pegah Khosravani
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  2. Majid Baghernejad
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  3. Ali Akbar Moosavi
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  4. Meisam Rezaei
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Contributions

PK: investigation, methodology, modeling, and writing—original draft. MB: investigation, methodology, writing—review and editing, and funding acquisition. AAM: methodology, original draft, and editing. MR: methodology and modeling.

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Correspondence to Majid Baghernejad.

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Highlights

• Machine learning algorithms were used for mapping soil physical and mechanical properties at four soil depths in a semi-arid region of Iran.

• Among the three machine learning algorithms that were compared, a k-nearest neighbor was identified as the best algorithm.

• Soil variables were recognized as the most critical SCORPAN factor in comparing topographic and remote sensing indices.

• The Boruta algorithm was used to select the best features among the pool of environmental covariates.

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Khosravani, P., Baghernejad, M., Moosavi, A.A. et al. Digital mapping and spatial modeling of some soil physical and mechanical properties in a semi-arid region of Iran. Environ Monit Assess 195, 1367 (2023). https://doi.org/10.1007/s10661-023-11980-6

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