Articles | Volume 7, issue 1
https://doi.org/10.5194/soil-7-33-2021
© Author(s) 2021. 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-7-33-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Mapping soil slaking index and assessing the impact of management in a mixed agricultural landscape
Edward J. Jones
CORRESPONDING AUTHOR
School of Life and Environmental Sciences & Sydney Institute of Agriculture, Faculty of Science, The University of Sydney, New South Wales, Australia
Patrick Filippi
School of Life and Environmental Sciences & Sydney Institute of Agriculture, Faculty of Science, The University of Sydney, New South Wales, Australia
Rémi Wittig
École Nationale Supérieure d'Agronomie et des Industries Alimentaires (ENSAIA), University of Lorraine, Nancy, France
Mario Fajardo
School of Life and Environmental Sciences & Sydney Institute of Agriculture, Faculty of Science, The University of Sydney, New South Wales, Australia
Vanessa Pino
School of Life and Environmental Sciences & Sydney Institute of Agriculture, Faculty of Science, The University of Sydney, New South Wales, Australia
Alex B. McBratney
School of Life and Environmental Sciences & Sydney Institute of Agriculture, Faculty of Science, The University of Sydney, New South Wales, Australia
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Yin-Chung Huang, José Padarian, Budiman Minasny, and Alex B. McBratney
SOIL, 11, 553–563, https://doi.org/10.5194/soil-11-553-2025, https://doi.org/10.5194/soil-11-553-2025, 2025
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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.
Wartini Ng, Budiman Minasny, Alex McBratney, Patrice de Caritat, and John Wilford
Earth Syst. Sci. Data, 15, 2465–2482, https://doi.org/10.5194/essd-15-2465-2023, https://doi.org/10.5194/essd-15-2465-2023, 2023
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With a higher demand for lithium (Li), a better understanding of its concentration and spatial distribution is important to delineate potential anomalous areas. This study uses a framework that combines data from recent geochemical surveys and relevant environmental factors to predict and map Li content across Australia. The map shows high Li concentration around existing mines and other potentially anomalous Li areas. The same mapping principles can potentially be applied to other elements.
Mercedes Román Dobarco, Alexandre M. J-C. Wadoux, Brendan Malone, Budiman Minasny, Alex B. McBratney, and Ross Searle
Biogeosciences, 20, 1559–1586, https://doi.org/10.5194/bg-20-1559-2023, https://doi.org/10.5194/bg-20-1559-2023, 2023
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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.
José Padarian, Budiman Minasny, Alex B. McBratney, and Pete Smith
SOIL Discuss., https://doi.org/10.5194/soil-2021-73, https://doi.org/10.5194/soil-2021-73, 2021
Manuscript not accepted for further review
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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
Soil physical health is integral to maintaining functional agro-ecosystems. A novel method of assessing soil physical condition using a smartphone app has been developed – SLAKES. In this study the SLAKES app was used to investigate aggregate stability in a mixed agricultural landscape. Cropping areas were found to have significantly poorer physical health than similar soils under pasture. Results were mapped across the landscape to identify problem areas and pinpoint remediation efforts.
Soil physical health is integral to maintaining functional agro-ecosystems. A novel method of...