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Modeling the spatial variation of calcium carbonate equivalent to depth using machine learning techniques

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Abstract

Inorganic carbon is the largest source of carbon in terrestrial surface, particularly in arid and semiarid regions, including the Chahardowli Plain in western Iran. Inorganic carbon plays an equal or greater role than organic soil carbon in these areas, although less attention has been paid in quantifying their variability. The objective of this study was to model and map calcium carbonate equivalent (CCE) presenting inorganic carbon in soil using machine learning and digital soil mapping techniques. Chahardowli Plain in foothills of the Zagros Mountains in the southeast of Kurdistan Province in Iran was taken as a case study area. CCE was measured at 0–5, 5–15, 15–30, 30–60, and 60–100 cm depths following GloalSoilMap.net project specifications. A total of 145 samples were collected from 30 soil profiles using the conditional Latin hypercube (cLHS) method of sampling. Relationships between CCE and environmental predictors were modeled using random forest (RF) and decision tree (DT) models. In general, the RF model performed slightly superior than the DT model. The mean value of CCE increased with soil depth, from 3.5% (0–5 cm) to 63.8% (30–60 cm). Remote sensing (RS) variables and terrestrial variables were equally important. The importance of RS variables was higher at the surface than terrestrial variables, and vice versa. The most significant variables were Channel Network Base Level (CNBL) variable and Difference Vegetation Index (DVI) with the same variable importance value (21.1%). In areas affected by river activities, the use of the CNBL and vertical distance to channel networks (VDCN) as variables in digital soil mapping (DSM) could increase the accuracy of soil property prediction maps. The VDCN played a principal role in soil distribution in the study area by affecting the rate of discharge and, thus, erosion and sedimentation. A high percentage of carbonate in parts of the region could exacerbate nutrient deficiencies for most crops and provide information for sustainably managing agricultural activity.

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Acknowledgements

We express our sincere thanks to Dr. David G. Rossiter, for reviewing the results of the analysis, and his valuable guidance, comments, and suggestions which have improved the scientific quality of this paper. We also wish to extend our deep gratitude to Prof. Thomas Scholten for reviewing the results of the analysis and his valuable guidance.

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Authors and Affiliations

  1. Department of Soil Science, University of Zanjan, Zanjan, Iran

    Leila Lotfollahi & Mohammad Amir Delavar

  2. School of Environmental Sciences, University of Guelph, 50 Stone Road East, Guelph, N1G 2W1, Canada

    Asim Biswas

  3. Scientific Staff of Soil and Water Research Institute (SWRI), Agricultural Research, Education and Extension Organization (AREEO), Karadj, Iran

    Mohammad Jamshidi

  4. Ardakan University, Ardakan, Iran

    Ruhollah Taghizadeh-Mehrjardi

  5. Department of Geosciences, University of Tübingen, Rümelinstr. 19-23, Tübingen, Germany

    Ruhollah Taghizadeh-Mehrjardi

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Lotfollahi, L., Delavar, M.A., Biswas, A. et al. Modeling the spatial variation of calcium carbonate equivalent to depth using machine learning techniques. Environ Monit Assess 195, 607 (2023). https://doi.org/10.1007/s10661-023-11126-8

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