Articles | Volume 9, issue 1
https://doi.org/10.5194/soil-9-21-2023
© Author(s) 2023. 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-9-21-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
11 Jan 2023
Original research article |
| 11 Jan 2023
| 11 Jan 2023
Shapley values reveal the drivers of soil organic carbon stock prediction
Alexandre M. J.-C. Wadoux
CORRESPONDING AUTHOR
Sydney Institute of Agriculture & School of Life and Environmental Sciences, The University of Sydney, Sydney, Australia
Nicolas P. A. Saby
INRAE, Unité de Recherche Info&Sols, Orléans, France
Manuel P. Martin
INRAE, Unité de Recherche Info&Sols, Orléans, France
Related authors
Lei Zhang, Lin Yang, Thomas W. Crowther, Constantin M. Zohner, Sebastian Doetterl, Gerard B. M. Heuvelink, Alexandre M. J.-C. Wadoux, A.-Xing Zhu, Yue Pu, Feixue Shen, Haozhi Ma, Yibiao Zou, and Chenghu Zhou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-618, https://doi.org/10.5194/essd-2024-618, 2025
Revised manuscript accepted for ESSD
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Current understanding of depth-dependent variations and controls of soil organic carbon turnover time (τ) at global, biome, and local scales remain incomplete. We used the state-of-the-art soil and root profile databases and satellite observations to generate spatially-explicit new global maps of top- and subsoil τ, with quantified uncertainties for better user applications. The new insights from resulting maps facilitate modelling efforts of carbon cycle and support effective carbon management.
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.
Alexandre M. J.-C. Wadoux, José Padarian, and Budiman Minasny
SOIL, 5, 107–119, https://doi.org/10.5194/soil-5-107-2019, https://doi.org/10.5194/soil-5-107-2019, 2019
Benjamin Loubet, Nicolas P. Saby, Maryam Gebleh, Pauline Buysse, Jean-Philippe Chenu, Céline Ratie, Claudy Jolivet, Carmen Kalalian, Florent Levavasseur, Jose-Luis Munera-Echeverri, Sebastien Lafont, Denis Loustau, Dario Papale, Giacomo Nicolini, Bruna Winck, and Dominique Arrouays
EGUsphere, https://doi.org/10.5194/egusphere-2025-592, https://doi.org/10.5194/egusphere-2025-592, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
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Soil is a large pool of carbon storing globally from two to three times more carbon than the atmosphere and vegetation. We compute the soil stock evolution from 2005 to 2019 for a wheat-maize-barley-oilseed-rape crop rotation at a French crop site. The soil carbon stock decreased by around 70 ± 16 g C m-2 yr-1 over the period, leading to a total loss of around 8 % of the initial soil stock. This strong destocking is primarily explained by a decrease in the residue return to the site.
Lei Zhang, Lin Yang, Thomas W. Crowther, Constantin M. Zohner, Sebastian Doetterl, Gerard B. M. Heuvelink, Alexandre M. J.-C. Wadoux, A.-Xing Zhu, Yue Pu, Feixue Shen, Haozhi Ma, Yibiao Zou, and Chenghu Zhou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-618, https://doi.org/10.5194/essd-2024-618, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Current understanding of depth-dependent variations and controls of soil organic carbon turnover time (τ) at global, biome, and local scales remain incomplete. We used the state-of-the-art soil and root profile databases and satellite observations to generate spatially-explicit new global maps of top- and subsoil τ, with quantified uncertainties for better user applications. The new insights from resulting maps facilitate modelling efforts of carbon cycle and support effective carbon management.
Amicie A. Delahaie, Lauric Cécillon, Marija Stojanova, Samuel Abiven, Pierre Arbelet, Dominique Arrouays, François Baudin, Antonio Bispo, Line Boulonne, Claire Chenu, Jussi Heinonsalo, Claudy Jolivet, Kristiina Karhu, Manuel Martin, Lorenza Pacini, Christopher Poeplau, Céline Ratié, Pierre Roudier, Nicolas P. A. Saby, Florence Savignac, and Pierre Barré
SOIL, 10, 795–812, https://doi.org/10.5194/soil-10-795-2024, https://doi.org/10.5194/soil-10-795-2024, 2024
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This paper compares the soil organic carbon fractions obtained from a new thermal fractionation scheme and a well-known physical fractionation scheme on an unprecedented dataset of French topsoil samples. For each fraction, we use a machine learning model to determine its environmental drivers (pedology, climate, and land cover). Our results suggest that these two fractionation schemes provide different fractions, which means they provide complementary information.
Christophe Djemiel, Samuel Dequiedt, Walid Horrigue, Arthur Bailly, Mélanie Lelièvre, Julie Tripied, Charles Guilland, Solène Perrin, Gwendoline Comment, Nicolas P. A. Saby, Claudy Jolivet, Antonio Bispo, Line Boulonne, Antoine Pierart, Patrick Wincker, Corinne Cruaud, Pierre-Alain Maron, Sébastien Terrat, and Lionel Ranjard
SOIL, 10, 251–273, https://doi.org/10.5194/soil-10-251-2024, https://doi.org/10.5194/soil-10-251-2024, 2024
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The fungal kingdom has been diversifying for more than 800 million years by colonizing a large number of habitats on Earth. Based on a unique dataset (18S rDNA meta-barcoding), we described the spatial distribution of fungal diversity at the scale of France and the environmental drivers by tackling biogeographical patterns. We also explored the fungal network interactions across land uses and climate types.
Amicie A. Delahaie, Pierre Barré, François Baudin, Dominique Arrouays, Antonio Bispo, Line Boulonne, Claire Chenu, Claudy Jolivet, Manuel P. Martin, Céline Ratié, Nicolas P. A. Saby, Florence Savignac, and Lauric Cécillon
SOIL, 9, 209–229, https://doi.org/10.5194/soil-9-209-2023, https://doi.org/10.5194/soil-9-209-2023, 2023
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We characterized organic matter in French soils by analysing samples from the French RMQS network using Rock-Eval thermal analysis. We found that thermal analysis is appropriate to characterize large set of samples (ca. 2000) and provides interpretation references for Rock-Eval parameter values. This shows that organic matter in managed soils is on average more oxidized and more thermally stable and that some Rock-Eval parameters are good proxies for organic matter biogeochemical stability.
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
Short summary
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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.
Elisa Bruni, Bertrand Guenet, Yuanyuan Huang, Hugues Clivot, Iñigo Virto, Roberta Farina, Thomas Kätterer, Philippe Ciais, Manuel Martin, and Claire Chenu
Biogeosciences, 18, 3981–4004, https://doi.org/10.5194/bg-18-3981-2021, https://doi.org/10.5194/bg-18-3981-2021, 2021
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Increasing soil organic carbon (SOC) stocks is beneficial for climate change mitigation and food security. One way to enhance SOC stocks is to increase carbon input to the soil. We estimate the amount of carbon input required to reach a 4 % annual increase in SOC stocks in 14 long-term agricultural experiments around Europe. We found that annual carbon input should increase by 43 % under current temperature conditions, by 54 % for a 1 °C warming scenario and by 120 % for a 5 °C warming scenario.
Claire Froger, Nicolas P. A. Saby, Claudy C. Jolivet, Line Boulonne, Giovanni Caria, Xavier Freulon, Chantal de Fouquet, Hélène Roussel, Franck Marot, and Antonio Bispo
SOIL, 7, 161–178, https://doi.org/10.5194/soil-7-161-2021, https://doi.org/10.5194/soil-7-161-2021, 2021
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Pollution of French soils by polycyclic aromatic hydrocarbons (PAHs), known as carcinogenic pollutants, was quantified in this work using an extended data set of 2154 soils sampled across France. The map of PAH concentrations in French soils revealed strong trends in regions with heavy industries and around cities. The PAH signatures indicated the influence of PAH emissions in Europe during the industrial revolution. Health risks posed by PAHs in soils were low but need to be considered.
Alexandre M. J.-C. Wadoux, José Padarian, and Budiman Minasny
SOIL, 5, 107–119, https://doi.org/10.5194/soil-5-107-2019, https://doi.org/10.5194/soil-5-107-2019, 2019
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Machine learning (ML) models have become key ingredients for digital soil mapping. To explain why the ML model is reliable, we apply a popular method from explainable artificial intelligence to the uncertainty prediction, with an application to the mapping of hydrocarbon pollutants on urban soil. We show the benefit of a joint analysis of the influence on the best estimate and the uncertainty to improve communication with end users and support decisions regarding covariates’ characterisation.
Ashenafi Ali, Teklu Erkossa, Kiflu Gudeta, Wuletawu Abera, Ephrem Mesfin, Terefe Mekete, Mitiku Haile, Wondwosen Haile, Assefa Abegaz, Demeke Tafesse, Gebeyhu Belay, Mekonen Getahun, Sheleme Beyene, Mohamed Assen, Alemayehu Regassa, Yihenew G. Selassie, Solomon Tadesse, Dawit Abebe, Yitbarek Wolde, Nesru Hussien, Abebe Yirdaw, Addisu Mera, Tesema Admas, Feyera Wakoya, Awgachew Legesse, Nigat Tessema, Ayele Abebe, Simret Gebremariam, Yismaw Aregaw, Bizuayehu Abebaw, Damtew Bekele, Eylachew Zewdie, Steffen Schulz, Lulseged Tamene, and Eyasu Elias
SOIL, 10, 189–209, https://doi.org/10.5194/soil-10-189-2024, https://doi.org/10.5194/soil-10-189-2024, 2024
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This paper focuses on collating legacy soil profile data and on the production of an updated national soil type map of Ethiopia, EthioSoilGrids version 1.0, using legacy data and a machine-learning approach. Given its quantitative digital representation, the map and the associated data make tremendous contributions to agricultural development planning and digital agricultural solutions, as well as improving the accuracy of global predictive soil mapping efforts.
Jonathan J. Maynard, Edward Yeboah, Stephen Owusu, Michaela Buenemann, Jason C. Neff, and Jeffrey E. Herrick
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Accurate information on soil properties is critical for identifying soil limitations and the management practices needed to improve crop yields on smallholder farms. This study evaluated the accuracy of soil map information for agronomic decision-making. Based on four publicly available soil maps in Ghana, we found that soil map data significantly overestimated crop suitability, potentially leading to ineffective agronomic investments by smallholder farmers.
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Maps of soil properties made by machine learning techniques are increasingly applied in Earth surface process modelling and agronomy. Maps of the same area made by different methods appear quite different and also differ from field-based polygon soil survey maps. We explore these differences both visually and numerically, using methods that quantify the spatial patterns. Readers can apply the methods to their areas of interest in the USA with the supplied R Markdown scripts.
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Short summary
We introduce Shapley values for machine learning model interpretation and reveal the local and global controlling factors of soil organic carbon (SOC) stocks. The method enables spatial analysis of the important variables. Vegetation and topography determine much of the SOC stock variation in mainland France. We conclude that SOC stock variation is complex and should be interpreted at multiple levels.
We introduce Shapley values for machine learning model interpretation and reveal the local and...
