Articles | Volume 6, issue 1
https://doi.org/10.5194/soil-6-35-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-6-35-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| Highlight paper
| 
06 Feb 2020
Review article | Highlight paper |
| 06 Feb 2020
| 06 Feb 2020
Machine learning and soil sciences: a review aided by machine learning tools
José Padarian
CORRESPONDING AUTHOR
Sydney Institute of Agriculture & School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia
Budiman Minasny
Sydney Institute of Agriculture & School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia
Alex B. McBratney
Sydney Institute of Agriculture & School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia
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Uncertainty quantification plays a crucial role in reporting machine learning models in soil spectroscopy. This study introduces Monte Carlo conformal prediction (MC-CP), a novel method for uncertainty quantification in deep-learning soil spectral models. MC-CP outperformed two established methods, providing the most reliable results. Its efficiency and robustness make it a practical choice for implementing soil spectral models in decision making.
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Soil organic carbon (SOC) is of a heterogeneous nature and varies in chemistry, stabilisation mechanisms, and persistence in soil. In this study we mapped the stocks of SOC fractions with different characteristics and turnover rates (presumably PyOC >= MAOC > POC) across Australia, combining spectroscopy and digital soil mapping. The SOC stocks (0–30 cm) were estimated as 13 Pg MAOC, 2 Pg POC, and 5 Pg PyOC.
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Soil organic carbon sequestration is considered an attractive technology to partially mitigate climate change. Here, we show how the SOC storage potential varies globally. The estimated additional SOC storage potential in the topsoil of global croplands (29–67 Pg C) equates to only 2 to 5 years of emissions offsetting and 32 % of agriculture's 92 Pg historical carbon debt. Since SOC is temperature-dependent, this potential is likely to reduce by 18 % by 2040 due to climate change.
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Short summary
The application of machine learning (ML) has shown an accelerated adoption in soil sciences. It is a difficult task to manually review all papers on the application of ML. This paper aims to provide a review of the application of ML aided by topic modelling in order to find patterns in a large collection of publications. The objective is to gain insight into the applications and to discuss research gaps. We found 12 main topics and that ML methods usually perform better than traditional ones.
The application of machine learning (ML) has shown an accelerated adoption in soil sciences. It...
