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Drivers of soil organic carbon storage and vertical distribution in Eastern Australia

Abstract

Aims

Drivers of soil organic carbon (SOC) storage are likely to vary in importance in different regions and at different depths due to local factors influencing SOC dynamics. This paper explores the factors influencing SOC to a depth of 30 cm in eastern Australia.

Methods

We used a machine learning approach to identify the key drivers of SOC storage and vertical distribution at 1401 sites from New South Wales, Australia. We then assessed the influence of the identified factors using traditional statistical approaches.

Results

Precipitation was important to and positively associated with SOC content, whereas temperature was important to and negatively associated with SOC vertical distribution. The importance of geology to SOC content increased with increasing soil depth. Land-use was important to both SOC content and its vertical distribution.

Conclusion

We attribute these results to the influence of precipitation on primary production controlling SOC content, and the stronger influence of temperature on microbial activity affecting SOC degradation and vertical distribution. Geology affects SOC retention below the surface. Land-use controls SOC via production, removal and vertical mixing. The factors driving SOC storage are not identical to those driving SOC vertical distribution. Changes to these drivers will have differential effects on SOC storage and depth distribution.

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Acknowledgments

This project is supported by funding from the Australian Government Department of Agriculture. Thank you to Matt Tighe at the University of New England for pre-review comments and to the reviewers who helped to improve this work.

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Correspondence to Eleanor Hobley.

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Responsible Editor: Klaus Butterbach-Bahl.

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Cite this article

Hobley, E., Wilson, B., Wilkie, A. et al. Drivers of soil organic carbon storage and vertical distribution in Eastern Australia. Plant Soil 390, 111–127 (2015). https://doi.org/10.1007/s11104-015-2380-1

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Keywords

  • Machine learning
  • Vertical distribution
  • Land-use
  • Climate
  • Geology