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Cited by 979Soil Carbon and Nitrogen DynamicsSoil Geostatistics and MappingSoil erosion and sediment transport

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Global stocks and capacity of mineral-associated soil organic carbon

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Georgiou, Katerina, Jackson, Robert B., Vindušková, Olga, Abramoff, Rose Z., Ahlström, Anders, Feng, Wenting, Harden, Jennifer W., Pellegrini, Adam F. A., Polley, H. Wayne, Soong, Jennifer L., Riley, William J., Torn, Margaret S.·2022·Nature Communications
DOI 10.1038/s41467-022-31540-9PMC PMC9249731
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Abstract

Soil is the largest terrestrial reservoir of organic carbon and is central for climate change mitigation and carbon-climate feedbacks. Chemical and physical associations of soil carbon with minerals play a critical role in carbon storage, but the amount and global capacity for storage in this form remain unquantified. Here, we produce spatially-resolved global estimates of mineral-associated organic carbon stocks and carbon-storage capacity by analyzing 1144 globally-distributed soil profiles. We show that current stocks total 899 Pg C to a depth of 1 m in non-permafrost mineral soils. Although this constitutes 66% and 70% of soil carbon in surface and deeper layers, respectively, it is only 42% and 21% of the mineralogical capacity. Regions under agricultural management and deeper soil layers show the largest undersaturation of mineral-associated carbon. Critically, the degree of undersaturation indicates sequestration efficiency over years to decades. We show that, across 103 carbon-accrual measurements spanning management interventions globally, soils furthest from their mineralogical capacity are more effective at accruing carbon; sequestration rates average 3-times higher in soils at one tenth of their capacity compared to soils at one half of their capacity. Our findings provide insights into the world’s soils, their capacity to store carbon, and priority regions and actions for soil carbon management.

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Fig. 1 Maximum mineral-associated carbon as a function of clay and silt content for high- and low-activity mineral soils estimated from a global observational synthesis. a Mineral-associated organic carbon (MOC; g C kg−1 soil) as a function of clay and silt content (CS; %) across sites. The maximum slope (fit as the 95th quantile) for each soil type represents the intrinsic capacity of minerals to store carbon and depends on mineral composition. High-activity minerals (HM) include soils dominated by illite, smectite, vermiculite, and chlorite (n = 1303) and low-activity minerals (LM) include soils dominated by kaolinite and gibbsite (n = 93). Shading depicts 90% confidence intervals on the slope. Soils near the upper quantiles are closer to MOC saturation. Within HM and LM soils, filled markers and crosses denote natural and managed ecosystems, respectively. b, c Distribution of soil texture (b) and climate (c) across sites. Black polygons depict Whittaker’s biomes119 according to mean annual temperature (MAT; °C) and mean annual precipitation (MAP; mm yr−1) values, following: (1) tropical rainforest; (2) tropical seasonal rainforest/savanna; (3) subtropical desert; (4) temperate rainforest; (5) temperate seasonal forest; (6) woodland/shrubland; (7) temperate grassland/desert; (8) boreal forest; and (9) tundra.
Fig. 1 Maximum mineral-associated carbon as a function of clay and silt content for high- and low-activity mineral soils estimated from a global observational synthesis. a Mineral-associated organic carbon (MOC; g C kg−1 soil) as a function of clay and silt content (CS; %) across sites. The maximum slope (fit as the 95th quantile) for each soil type represents the intrinsic capacity of minerals to store carbon and depends on mineral composition. High-activity minerals (HM) include soils dominated by illite, smectite, vermiculite, and chlorite (n = 1303) and low-activity minerals (LM) include soils dominated by kaolinite and gibbsite (n = 93). Shading depicts 90% confidence intervals on the slope. Soils near the upper quantiles are closer to MOC saturation. Within HM and LM soils, filled markers and crosses denote natural and managed ecosystems, respectively. b, c Distribution of soil texture (b) and climate (c) across sites. Black polygons depict Whittaker’s biomes119 according to mean annual temperature (MAT; °C) and mean annual precipitation (MAP; mm yr−1) values, following: (1) tropical rainforest; (2) tropical seasonal rainforest/savanna; (3) subtropical desert; (4) temperate rainforest; (5) temperate seasonal forest; (6) woodland/shrubland; (7) temperate grassland/desert; (8) boreal forest; and (9) tundra.CC BY · PubMed Central
Fig. 2 Percent mineralogical carbon saturation across ecosystems and soil depths. Percent mineralogical C saturation (%C saturation)—i.e., the proximity of a soil to its mineralogical carbon capacity (MOCmax)—was calculated for each measurement in our observational synthesis. a %C saturation grouped by managed (n = 573) and natural (n = 862) ecosystems across depths (see “Methods”). b %C saturation by ecosystem (cropland, forest, and grassland) and depth (surface < 30 cm and deep > 30 cm). Only categories with >5 observations for both surface (cropland n = 425; forest n = 242; grassland n = 355) and deep (cropland n = 21; forest n = 53; grassland n = 136) soils are shown. Box plots indicate the medians (horizontal lines), 1st and 3rd quartiles (boxes), 1.5× interquartile range (whiskers), and means (diamonds).
Fig. 2 Percent mineralogical carbon saturation across ecosystems and soil depths. Percent mineralogical C saturation (%C saturation)—i.e., the proximity of a soil to its mineralogical carbon capacity (MOCmax)—was calculated for each measurement in our observational synthesis. a %C saturation grouped by managed (n = 573) and natural (n = 862) ecosystems across depths (see “Methods”). b %C saturation by ecosystem (cropland, forest, and grassland) and depth (surface < 30 cm and deep > 30 cm). Only categories with >5 observations for both surface (cropland n = 425; forest n = 242; grassland n = 355) and deep (cropland n = 21; forest n = 53; grassland n = 136) soils are shown. Box plots indicate the medians (horizontal lines), 1st and 3rd quartiles (boxes), 1.5× interquartile range (whiskers), and means (diamonds).CC BY · PubMed Central
Fig. 3 Data-driven predictive relationships of mineral-associated organic carbon as a function of individual controlling factors and interactions. A random forest model was used to disentangle the significance and emergent relationships of individual covariates, using partial dependence relationships that describe the marginal effect of each variable on the predicted mineral-associated carbon (MOC) response (see “Methods”). a Predicted MOC as a function of particulate organic C (POC). For each plot, the points show random forest model predictions; best fit lines and shading show 99% confidence intervals on each trend. b Predicted MOC as a function of mean annual temperature (MAT), conditional on three distinct subsets of POC—namely, low (L), medium (M), and high (H) POC corresponding to unsaturated to saturated regimes. The breakpoints were selected as the median and third quartile of the POC distribution, which also correspond to approximate saturation thresholds in the partial dependence plot of POC alone.
Fig. 3 Data-driven predictive relationships of mineral-associated organic carbon as a function of individual controlling factors and interactions. A random forest model was used to disentangle the significance and emergent relationships of individual covariates, using partial dependence relationships that describe the marginal effect of each variable on the predicted mineral-associated carbon (MOC) response (see “Methods”). a Predicted MOC as a function of particulate organic C (POC). For each plot, the points show random forest model predictions; best fit lines and shading show 99% confidence intervals on each trend. b Predicted MOC as a function of mean annual temperature (MAT), conditional on three distinct subsets of POC—namely, low (L), medium (M), and high (H) POC corresponding to unsaturated to saturated regimes. The breakpoints were selected as the median and third quartile of the POC distribution, which also correspond to approximate saturation thresholds in the partial dependence plot of POC alone.CC BY · PubMed Central
Fig. 4 Global soil mineralogical carbon capacity, mineral-associated carbon stocks, and potential for carbon accrual. a Mineralogical carbon capacity (MOCmax; kg C m−2 soil), b mineral-associated organic carbon (MOC; kg C m−2 soil), and c percent mineralogical carbon saturation (%C saturation) in topsoil (0–30 cm) at 0.5° resolution, excluding tundra, peatlands, and deserts. MOCmax was estimated using our data-derived relationships with the amount and type of mineral. MOC was predicted using a random forest model trained on our observational dataset. %C saturation was calculated as MOC/MOCmax. Low %C saturation corresponds to a greater C deficit, and therefore highlights regions where targeted soil management could lead to higher C sequestration. d Carbon accrual in topsoil (t ha−1 yr−1; mean depth = 29 cm) as a function of %C saturation across sites, based on our global synthesis of studies that measured C accrual following management interventions (n = 103). Nonlinear fit depicts asymptotic regression model with shaded areas representing 10th and 90th quantiles.
Fig. 4 Global soil mineralogical carbon capacity, mineral-associated carbon stocks, and potential for carbon accrual. a Mineralogical carbon capacity (MOCmax; kg C m−2 soil), b mineral-associated organic carbon (MOC; kg C m−2 soil), and c percent mineralogical carbon saturation (%C saturation) in topsoil (0–30 cm) at 0.5° resolution, excluding tundra, peatlands, and deserts. MOCmax was estimated using our data-derived relationships with the amount and type of mineral. MOC was predicted using a random forest model trained on our observational dataset. %C saturation was calculated as MOC/MOCmax. Low %C saturation corresponds to a greater C deficit, and therefore highlights regions where targeted soil management could lead to higher C sequestration. d Carbon accrual in topsoil (t ha−1 yr−1; mean depth = 29 cm) as a function of %C saturation across sites, based on our global synthesis of studies that measured C accrual following management interventions (n = 103). Nonlinear fit depicts asymptotic regression model with shaded areas representing 10th and 90th quantiles.CC BY · PubMed Central

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Research works printed but not linked: 2

  1. Torn, M. S., Trumbore, S. E., Chadwick, O. A., Vitousek, P. M. & Hendricks, D. M. Mineral control of soil organic carbon storage and turnover. Nature 3603, 3601–3603 (1997).
  2. Liaw, A. & Wiener, M. Classification and Regression by randomForest. R. N. 2(3), 18–22 (2002).

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Metadata

Authors
Georgiou, K., Jackson, R. B., Vindušková, O., Abramoff, R. Z., Ahlström, A., Feng, W., Harden, J. W., Pellegrini, A. F. A., Polley, H. W., Soong, J. L., Riley, W. J., & Torn, M. S.
Affiliations
  • Lawrence Livermore National Laboratory (US)
  • Stanford University (US)
  • University of Antwerp (BE)
  • Oak Ridge National Laboratory (US)
  • Lund University (SE)
  • Chinese Academy of Agricultural Sciences (CN)
  • United States Geological Survey (US)
  • University of Cambridge (GB)
  • Agricultural Research Service (US)
  • Colorado State University (US)
  • Lawrence Berkeley National Laboratory (US)
Corresponding author
Georgiou, Katerina
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2022-07-01
Volume
13
Issue
1
Pages
3797-3797
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-022-31540-9
PMC
PMC9249731
OpenAlex
W4283743401
Cited by
979
References
119
Citations by year
2026: 322 · 2025: 299 · 2024: 213 · 2023: 125 · 2022: 20
FWCI
88.54
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, cc-by)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Lund University Publications (Lund University) (submittedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
  • Apollo (University of Cambridge) (submittedVersion, cc-by)
  • Apollo
  • Apollo (cc-by)
  • Greater South Information System
Funders
DOE | LDRD | Lawrence Livermore National Laboratory (21-ERD-045)
Topics
  • Life Sciences › Agricultural and Biological Sciences › Soil Science › Soil Carbon and Nitrogen Dynamics
  • Life Sciences › Agricultural and Biological Sciences › Soil Science › Soil Geostatistics and Mapping
  • Life Sciences › Agricultural and Biological Sciences › Soil Science › Soil erosion and sediment transport
Keywords
mineral-associated organic carbon, soil organic carbon stocks, carbon storage capacity, carbon sequestration, soil carbon management, agricultural soils, soil depth, carbon-climate feedback
MeSH terms
Agriculture, Carbon, Minerals, Soil, Carbon Sequestration

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