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
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Cited articles
Arrouays, D., Deslais, W., and Badeau, V.: The carbon content of topsoil and
its geographical distribution in France, Soil Use Manage., 17, 7–11,
2001. a
Arrouays, D., Jolivet, C., Boulonne, L., Bodineau, G., Saby, N. P. A., and
Grolleau, E.: A new projection in France: a multi-institutional soil
quality monitoring network, Comptes Rendus de l'Académie d'Agriculture de
France (France), 2002. a
Batjes, N. H.: Total carbon and nitrogen in the soils of the world, Europ.
J. Soil Sci., 47, 151–163, 1996. a
Beucher, A., Rasmussen, C. B., Moeslund, T. B., and Greve, M. H.:
Interpretation of convolutional neural networks for acid sulfate soil
classification, Front. Environ. Sci., 9, 679, https://doi.org/10.3389/fenvs.2021.809995, 2022. a, b, c
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a
Chen, S., Arrouays, D., Angers, D. A., Chenu, C., Barré, P., Martin, M. P.,
Saby, N. P. A., and Walter, C.: National estimation of soil organic carbon
storage potential for arable soils: A data-driven approach coupled with
carbon-landscape zones, Sci. Total Environ., 666, 355–367,
2019. a
Greenwell, B.: Package “fastshap”, R package
version 0.0.5,
https://CRAN.R-project.org/package=fastshap (last access: 10 April 2022), 2020. a
Guo, L., Sun, X., Fu, P., Shi, T., Dang, L., Chen, Y., Linderman, M., Zhang, G., Zhang, Y., Jiang, Q., Zhang, H., and Zeng, C.: Mapping soil organic carbon stock by
hyperspectral and time-series multispectral remote sensing images in
low-relief agricultural areas, Geoderma, 398, 115118, https://doi.org/10.1016/j.geoderma.2021.115118, 2021. a
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M.,
Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X.,
Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B.: SoilGrids250m: Global gridded soil
information based on machine learning, PLOS ONE, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017. a
ISO 10694: Soil quality – Determination of organic and total carbon
after dry combustion (elementary analysis), Standard, International
Organization for Standardization, Geneva, CH, 1995. a
Jones, A., Montanarella, L., and Jones, R.: Soil Atlas of Europe, European
Commission, 2005. a
Keenor, S. G., Rodrigues, A. F., Mao, L., Latawiec, A. E., Harwood, A. R., and
Reid, B. J.: Capturing a soil carbon economy, Roy. Soc. Open Sci., 8,
202305, https://doi.org/10.1098/rsos.202305, 2021. a
Kempen, B., Dalsgaard, S., Kaaya, A. K., Chamuya, N.,
Ruipérez-González, M., Pekkarinen, A., and Walsh, M. G.: Mapping
topsoil organic carbon concentrations and stocks for Tanzania, Geoderma,
337, 164–180, 2019. a
Lacoste, M., Martin, M. P., Saby, N. P. A., Paroissien, J.-B., Lehmann, S.,
Richer-De-Forges, A. C., and Arrouays, D.: Carbon content and stocks in the
O horizons of French forest soils, in: GlobalSoilMap: Basis of the Global
Spatial Soil Information System, edited by: Arrouays, D., McKenzie, N.,
Hempel, J., de Forges, A. R., and McBratney, A. B., CRC Press, Boca Raton,
FL, 2014. a
Laroche, B., Richer-De-Forges, A. C., Leménager, S., Arrouays, D.,
Schnebelen, N., Eimberck, M., Toutain, B., Lehmann, S., Nguenkam, M.-E. T.,
Héliès, F., Chenu, J.-P., Parot, S., Desbourdes, S., Girot, G., Voltz, M., and Bardy, M.: Le programme inventaire gestion conservation des
sols de France: volet référentiel régional pédologique,
Étude et Gestion des Sols, 21, 25–36, 2014. a
Lemenih, M. and Itanna, F.: Soil carbon stocks and turnovers in various
vegetation types and arable lands along an elevation gradient in southern
Ethiopia, Geoderma, 123, 177–188, 2004. a
Lugato, E., Panagos, P., Bampa, F., Jones, A., and Montanarella, L.: A new
baseline of organic carbon stock in European agricultural soils using a
modelling approach, Glob. Change Biol., 20, 313–326, 2014. a
Lundberg, S. M. and Lee, S.-I.: A unified approach to interpreting model
predictions, in: Proceedings of the 31st International Conference on Neural
Information Processing Systems, edited by: von Luxburg, U., Guyon, I., Bengio, S., Wallach, H., and Fergus, R., 4768–4777, Curran Associates Inc., Red Hook,
New York, 2017. a
Lyapustin, A., Wang, Y., Korkin, S., and Huang, D.: MODIS Collection 6 MAIAC algorithm, Atmos. Meas. Tech., 11, 5741–5765, https://doi.org/10.5194/amt-11-5741-2018, 2018. a
Martin, M. P., Orton, T. G., Lacarce, E., Meersmans, J., Saby, N. P. A.,
Paroissien, J. B., Jolivet, C., Boulonne, L., and Arrouays, D.: Evaluation of
modelling approaches for predicting the spatial distribution of soil organic
carbon stocks at the national scale, Geoderma, 223, 97–107, 2014. a, b, c
Martin, M. P., Dimassi, B., Román Dobarco, M., Guenet, B., Arrouays, D.,
Angers, D. A., Blache, F., Huard, F., Soussana, J.-F., and Pellerin, S.:
Feasibility of the 4 per 1000 aspirational target for soil carbon: A case
study for France, Glob. Change Biol., 27, 2458–2477, 2021. a
Meersmans, J., Martin, M. P., Lacarce, E., De Baets, S., Jolivet, C., Boulonne,
L., Lehmann, S., Saby, N. P. A., Bispo, A., and Arrouays, D.: A high
resolution map of French soil organic carbon, Agron. Sustain.
Dev., 32, 841–851, 2012. a
Minasny, B., McBratney, A. B., Malone, B. P., and Wheeler, I.: Digital mapping
of soil carbon, Adv. Agron., 118, 1–47, 2013. a
Mishra, U., Lal, R., Slater, B., Calhoun, F., Liu, D., and Van Meirvenne, M.:
Predicting soil organic carbon stock using profile depth distribution
functions and ordinary kriging, Soil Sci. Soc. Am. J., 73,
614–621, 2009. a
Mohammadifar, A., Gholami, H., Comino, J. R., and Collins, A. L.: Assessment of
the interpretability of data mining for the spatial modelling of water
erosion using game theory, Catena, 200, 105178, https://doi.org/10.1016/j.catena.2021.105178, 2021. a
Molnar, C.: Interpretable Machine Learning: A Guide for Making Black Box Models
Explainable, Lulu Press, Raleigh, 2020. a
Orchard, V. A. and Cook, F.: Relationship between soil respiration and soil
moisture, Soil Biology and Biochemistry, 15, 447–453, 1983. a
Padarian, J., McBratney, A. B., and Minasny, B.: Game theory interpretation of digital soil mapping convolutional neural networks, SOIL, 6, 389–397, https://doi.org/10.5194/soil-6-389-2020, 2020. a, b, c
Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu,
G.-Y., Williams, Z., Brunke, M. A., and Gochis, D.: A gridded global data set
of soil, intact regolith, and sedimentary deposit thicknesses for regional
and global land surface modeling, J. Adv. Model. Ea.
Syst., 8, 41–65, 2016. a
Plutzar, C., Kroisleitner, C., Haberl, H., Fetzel, T., Bulgheroni, C.,
Beringer, T., Hostert, P., Kastner, T., Kuemmerle, T., Lauk, C., Levers, C., Lindner, M., Moser, D., Müller, D., Niedertscheider, M., Paracchini, M., Schaphoff, S., Verburg, P., Verkerk, P. J., and Erb, K.-H:
Changes in the spatial patterns of human appropriation of net primary
production (HANPP) in Europe 1990–2006, Reg. Environ. Change, 16,
1225–1238, 2016. a
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, 2021. a, b
Probst, P., Wright, M. N., and Boulesteix, A.-L.: Hyperparameters and tuning
strategies for random forest, Wiley Interdisciplinary Reviews, Data Mining
and Knowledge Discovery, 9, e1301, https://doi.org/10.1002/widm.1301, 2019. a, b
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria,
https://www.R-project.org/, last access: 10 April 2022. a
Rabus, B., Eineder, M., Roth, A., and Bamler, R.: The shuttle radar topography
mission – a new class of digital elevation models acquired by spaceborne
radar, ISPRS J. Photogramm., 57, 241–262,
2003. a
Rahman, N., de Neergaard, A., Magid, J., van de Ven, G. W. J., Giller, K. E.,
and Bruun, T. B.: Changes in soil organic carbon stocks after conversion from
forest to oil palm plantations in Malaysian Borneo, Environ. Res.
Lett., 13, 105001, https://doi.org/10.1088/1748-9326/aade0f, 2018. a
Reichstein, M., Bahn, M., Ciais, P., Frank, D., Mahecha, M. D., Seneviratne,
S. I., Zscheischler, J., Beer, C., Buchmann, N., Frank, D. C., Papale, D., Rammig, A., Smith, P., Thonicke, K., van der Velde, M., Vicca, S., Walz, A., and Wattenbach, M.:
Climate extremes and the carbon cycle, Nature, 500, 287–295, 2013. a
Rovira, P., Sauras-Yera, T., and Romanyà, J.: Equivalent-mass versus
fixed-depth as criteria for quantifying soil carbon sequestration: How
relevant is the difference?, Catena, 214, 106283, https://doi.org/10.1016/j.catena.2022.106283, 2022. a
Running, S. and Zhao, M.: MOD17A3HGF MODIS/Terra net primary production
gap-filled yearly L4 global 500 m SIN grid V006, NASA EOSDIS land processes
DAAC, 2019. a
Saby, N. P. A., Arrouays, D., Antoni, V., Lemercier, B., Follain, S., Walter,
C., and Schvartz, C.: Changes in soil organic carbon in a mountainous
French region, 1990–2004, Soil Use Manage., 24, 254–262, 2008. a
Saby, N. P. A., Chenu, J.-P., Szergi, T., Csorba, A., Bertuzzi, P., Toutain, B., Picaud, C., Gay, L., and Creamer, R.: French RMQS soil profile and monitoring dataset with related management practices data, Recherche Data Gouv, V1 [data set], https://doi.org/10.15454/AIQ9WS, 2020. a
Shapley, L. S.: A Value for n-Person Games, in: Contributions to the Theory of
Games, edited by: Harold William, K. and Albert William, T., Vol. 28,
Annals of Mathematics Studies, chap. 17, 31–40, Princeton University
Press, Princeton, 1953. a
Stewart, C. E., Plante, A. F., Paustian, K., Conant, R. T., and Six, J.: Soil
carbon saturation: linking concept and measurable carbon pools, Soil Sci.
Soc. Am. J., 72, 379–392, 2008. a
Thierion, V., Ghaith, A., Billecocq, P., Gaudé, C., Mesona, L., Laurent,
B., Bertrand, M., and Bigot, S.: D’OSO à la cartographie de
végétation par télédétection multi-temporelle–Exemples
d’utilisation des images Sentinel-2, in: Colloque de Bilan et de
Prospective du PNTS, 2018. a
Trabucco, A., Zomer, R. J., Bossio, D. A., van Straaten, O., and Verchot,
L. V.: Climate change mitigation through afforestation/reforestation: a
global analysis of hydrologic impacts with four case studies, Agr.
Ecosyst. Environ., 126, 81–97, 2008. a
Tuanmu, M.-N. and Jetz, W.: A global, remote sensing-based characterization of
terrestrial habitat heterogeneity for biodiversity and ecosystem modelling,
Global Ecol. Biogeogr., 24, 1329–1339, 2015. a
Van Wesemael, B., Paustian, K., Meersmans, J., Goidts, E., Barancikova, G., and
Easter, M.: Agricultural management explains historic changes in regional
soil carbon stocks, P. Natl. Acad. Sci. USA, 107,
14926–14930, 2010. a
Vos, C., Don, A., Hobley, E. U., Prietz, R., Heidkamp, A., and Freibauer, A.:
Factors controlling the variation in organic carbon stocks in agricultural
soils of Germany, Europ. J. Soil Sci., 70, 550–564, 2019. a
Wadoux, A. M. J.-C. and Molnar, C.: Beyond prediction: methods for interpreting
complex models of soil variation, Geoderma, 422, 115953, https://doi.org/10.1016/j.geoderma.2022.115953, 2022. a, b
Wadoux, A. M. J.-C., Samuel-Rosa, A., Poggio, L., and Mulder, V. L.: A note on
knowledge discovery and machine learning in digital soil mapping, Europ.
J. Soil Sci., 71, 133–136, 2020. a
Wadoux, A. M. J.-C., Heuvelink, G. B. M., Lark, R. M., Lagacherie, P., Bouma,
J., Mulder, V. L., Libohova, Z., Yang, L., and McBratney, A. B.: Ten
challenges for the future of pedometrics, Geoderma, 401, 115155, https://doi.org/10.1016/j.geoderma.2021.115155,
2021a. a
Wadoux, A. M. J.-C., Román-Dobarco, M., and McBratney, A. B.: Perspectives
on data-driven soil research, Europ. J. Soil Sci., 72,
1675–1689, 2021b. a
Wang, B., Gray, J. M., Waters, C. M., Anwar, M. R., Orgill, S. E., Cowie,
A. L., Feng, P., and Li Liu, D.: Modelling and mapping soil organic carbon
stocks under future climate change in south-eastern Australia, Geoderma,
405, 115442, https://doi.org/10.1016/j.geoderma.2021.115442, 2022. a
Wright, M. N. and Ziegler, A.: ranger: A Fast Implementation of Random
Forests for High Dimensional Data in C
Zomer, R. J., Trabucco, A., Bossio, D. A., and Verchot, L. V.: Climate change
mitigation: A spatial analysis of global land suitability for clean
development mechanism afforestation and reforestation, Agr.
Ecosyst. Environ., 126, 67–80, 2008. a
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...
