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Different climate sensitivity of particulate and mineral-associated soil organic matter

An Author Correction to this article was published on 03 May 2022

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Soil carbon sequestration is seen as an effective means to draw down atmospheric CO2, but at the same time warming may accelerate the loss of extant soil carbon, so an accurate estimation of soil carbon stocks and their vulnerability to climate change is required. Here we demonstrate how separating soil carbon into particulate and mineral-associated organic matter (POM and MAOM, respectively) aids in the understanding of its vulnerability to climate change and identification of carbon sequestration strategies. By coupling European-wide databases with soil organic matter physical fractionation, we assessed the current geographical distribution of mineral topsoil carbon in POM and MAOM by land cover using a machine-learning approach. Further, using observed climate relationships, we projected the vulnerability of carbon in POM and MAOM to future climate change. Arable and coniferous forest soils contain the largest and most vulnerable carbon stocks when cumulated at the European scale. Although we show a lower carbon loss from mineral topsoils with climate change (2.5 ± 1.2 PgC by 2080) than those in some previous predictions, we urge the implementation of coniferous forest management practices that increase plant inputs to soils to offset POM losses, and the adoption of best management practices to avert the loss of and to build up both POM and MAOM in arable soils.

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Fig. 1: Land cover distribution in Europe.
Fig. 2: Organic matter physical fraction distribution in mineral topsoils (0–20 cm) of the European Union and United Kingdom.
Fig. 3: SOC stocks partitioned in MAOM and POM, grouped by land cover.
Fig. 4: Response of SOM fractions to climate change.

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Data availability

Data from the LUCAS database can be accessed at The SOM fractionation database and the resulting spatial layers are available at ESDAC of the European Commission–Joint Research Centre (

Code availability

The most relevant R scripts regarding RF model training and validation are available at the ESDAC of the European Commission,

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This work was supported by the JRC (purchase order no. D.B720517) and through the OECD Co-operative Research Programme: Biological Resource Management for Sustainable Agricultural Systems fellowship. We thank M. G. Ravalli for providing statistical consultation.

Author information

Authors and Affiliations



E.L. and M.F.C. developed the research concepts and interpreted the data. M.F.C., E.L. and J.M.L. wrote the article. E.L. conducted the data and statistical analyses. M.L.H. conducted all the SOM fractionation, and contributed to the writing of the methods. P.P. contributed to revising the manuscript.

Corresponding author

Correspondence to Emanuele Lugato.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Geoscience thanks Lauric Cécillon and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Representativeness of LUCAS selected soil samples.

On the left, soil texture distribution of the 352 sub-samples (Fig. 1) over the total LUCAS points (n ~ 21000), when plotted in the texture triangle (left figure). On the right, histogram of total soil organic carbon (SOC) of the 352 sub-samples (red bars) in comparison to the whole LUCAS dataset (black bars).

Extended Data Fig. 2 Uncertainty on soil organic carbon predictions (g C kg−1 soil) in POM and MAOM fractions.

The figures present the standard deviation calculated from four data-model combinations used to predict soil organic carbon fractions (see Methods and Table S4).

Extended Data Fig. 3 Total soil organic carbon (g C kg−1 soil) distribution in mineral topsoil (0–20 cm) of the European Union and the United Kingdom.

The map was obtained by the sum of MAOM and POM estimated by the procedure described in the manuscript method section.

Extended Data Fig. 4 Texture distribution of soils from major land cover classes in the EU + UK, plotted as density in the texture triangle space.

The plot is made with the full LUCAS datasets (n ~ 20,000). CR= cropland, FR=forest, GR=grassland and SHR=shrubland.

Extended Data Fig. 5 Predicted mean annual temperature and precipitation changes under the RPC8.5 scenario.

The data are the average of HadGEM2-ES, CNRM-CM5 and IPSL-CM5A-LR global climate models at 30s resolution, for the average period 2061–2080. The data were downloaded from WorldClim repository (

Extended Data Table 1 Model summary of the multi-regression model for the particulate organic matter (POM) independent variable.

MAT = mean annual temperature; P = precipitation; SAND = sand content and LC = land cover classes (forest-FR, cropland-CR, grassland-GR and shrubland-SH).

Extended Data Table 2 Model summary of the multi-regression model for the particulate organic matter (MAOM) independent variable.

MAT = mean annual temperature; P = precipitation; SAND = sand content and LC = land cover classes (forest-FR, cropland-CR, grassland-GR and shrubland-SH).

Supplementary information

Supplementary Information

Supplementary Figs. 1–6 and Tables 1 and 2.

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Lugato, E., Lavallee, J.M., Haddix, M.L. et al. Different climate sensitivity of particulate and mineral-associated soil organic matter. Nat. Geosci. 14, 295–300 (2021).

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