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Increased plant productivity exacerbates subsoil carbon losses under warming via nitrogen mining

Nature Geoscience volume 18, pages 510–517 (2025)Cite this article

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Abstract

Soils can be either a source or sink of atmospheric CO2 depending on how soil organic carbon (SOC) responds to climate warming and changes in plant productivity. Whereas warming typically accelerates SOC decomposition, the effect of plant productivity changes remains unclear. Here we use a space-for-change substitution approach to analyse a global dataset of SOC measurements down to 1 metre. We find that warming-induced SOC reduction in the 0–0.3-m topsoil is gradually offset by increasing plant productivity but exacerbated in the 0.3–1-m subsoil until plant productivity increase crosses a threshold of 30%. Consequently, entirely offsetting warming-induced SOC reduction in the top metre of soil requires an unrealistically high increase in plant productivity, albeit with substantial variance across ecosystems. Soil carbon-to-nitrogen ratio is the dominant predictor of the variance in SOC response, as this ratio determines whether nitrogen released during carbon loss meets the requirement for additional plant growth or whether additional nitrogen must be mined from soil organic matter. Such mining accelerates SOC losses, particularly in the subsoil, where the soil carbon-to-nitrogen ratio is lower than in topsoils. We conclude that globally, increased plant productivity may exacerbate SOC losses under climate warming, particularly in the subsoil.

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Fig. 1: The responses of SOC to changes in NPP under 1.5 °C warming.
Fig. 2: Drivers of the responses of SOC to plant productivity changes and warming.
Fig. 3: The effects of soil CNR and productivity changes on the response of SOC.
Fig. 4: Global patterns of SOC changes attributed to plant productivity changes.
Fig. 5: A nitrogen-driven framework for predicting SOC dynamics in response to warming and plant productivity increase.

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Data availability

The latest WoSIS dataset24 can be accessed at https://www.isric.org/news/wosis-new-set-standardized-soil-profiles-released-wosislatest-june-2022. The MODIS NPP data can be accessed at https://lpdaac.usgs.gov/products/mod17a3hgfv061/. The field-observation-derived NPP and its allocation in soil layers dataset can be gained from ref. 26. Other data used in this study are the same as those used in ref. 4, which are publicly accessible. The coastline data in all maps are publicly accessible and can be accessed at https://www.naturalearthdata.com/downloads/50m-physical-vectors/50m-coastline/. Source data are provided with this paper.

Code availability

Code (R scripts developed using R version 4.2.2) used to assess the data and generate the results are accessible via figshare at https://doi.org/10.6084/m9.figshare.25859092 (ref. 65).

Change history

  • 14 May 2025

    In the version of the article initially published, the editor recognition statement was incorrect and has been amended to “Primary Handling Editors: Xujia Jiang and Stefan Lachowycz, in collaboration with the Nature Geoscience team.” in the HTML and PDF versions of the article.

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Acknowledgements

This research has been financially supported by the National Natural Science Foundation of China (grant numbers 32241036, 32171639) and the Postdoctoral Fellowship Program and China Postdoctoral Science Foundation (grant number BX20240314).

Author information

Authors and Affiliations

  1. State Key Laboratory of Soil Pollution Control and Safety, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China

    Mingming Wang, Shuai Zhang, Baojing Gu, Zhou Shi & Zhongkui Luo

  2. Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, Zhejiang University, Hangzhou, China

    Mingming Wang, Shuai Zhang, Baojing Gu, Zhou Shi & Zhongkui Luo

  3. Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Guocheng Wang

  4. College of Agriculture, Nanjing Agricultural University, Nanjing, China

    Liujun Xiao

  5. Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China

    Mianhai Zheng

  6. Key Laboratory of Ecosystem Network Observation and Modelling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Shuli Niu

  7. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China

    Yuanhe Yang

  8. School of Integrative Plant Science, Cornell University, Ithaca, NY, USA

    Yiqi Luo

  9. Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China

    Ganlin Zhang

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Contributions

Z.L. conceived the study; M.W. compiled the data with the contribution of G.W., L.X. and Z.L.; M.W. and Z.L. designed the approach and data assessment procedure; M.W. conducted the data assessment and wrote the first draft; Z.L. revised the paper with the contributions of all authors.

Corresponding author

Correspondence to Zhongkui Luo.

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Nature Geoscience thanks Ben Bond-Lamberty, Emanuele Lugato and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Xujia Jiang and Stefan Lachowycz, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 The global distribution of soil profiles among biomes.

The number behind biomes is the sample size of soil profiles in the biome. This data contains the latest WoSIS24. TS forests, tropical/subtropical forests; Med/Mon shrublands, Mediterranean/montane shrublands; TS grasslands/savannas, tropical/ subtropical grasslands/savannas. Coastline data from Natural Earth (https://www.naturalearthdata.com/downloads/50m-physical-vectors/50m-coastline).

Extended Data Fig. 2 Schematic representation of the approach used to quantify the response of soil organic carbon (SOC) to plant productivity changes and warming.

Each dot represents one SOC observation. Dots in the ith plate share the same mean annual temperature (MATA, i) and plant productivity (NPPA, i), while dots in the same color plates indicate a group sharing the same mean annual precipitation (MAP), precipitation seasonality, landform, and soil type. ΔNPP (shown in Supplementary Table 1) and ΔT (1.5 °C in this study) are changes in NPPA and MATA of interest, respectively. SOC values in the warming (W), and warming plus NPP changes (W+NPP) plates are compared to the values in the ambient (A) plates in the same group to calculate a weighted average effect size (i.e., the response of SOC to ΔT, and ΔT + ΔNPP) by the inverse of the sum of within- and between-group variances, using meta-analytic techniques (Methods). Figure adapted with permission from ref. 4, Springer Nature Limited.

Extended Data Fig. 3 Changes in belowground net primary productivity (BNPP) allocation.

a, the changes [(BNPP0.3-1 m/BNPP-Treatment – BNPP0.3-1 m/BNPP-Control) / (BNPP0.3-1 m/BNPP-Control)] in BNPP allocated into subsoil (0.3–1 m) on a global scale. b, the changes in BNPP allocated into subsoil among ecosystems. Dashed line shows the 1:1 line. The shading envelope represents the 95% confidence interval, with the corresponding similar colour solid line shows the average. TS forests, tropical/subtropical forests; Med/Mon shrublands, Mediterranean/montane shrublands; TS grasslands/savannas, tropical/ subtropical grasslands/savannas.

Source data

Extended Data Fig. 4 Changes in belowground net primary productivity (BNPP) under warming and NPP change scenarios.

a, BNPP changes coincide total NPP changes in all three soil depth layers on a global scale. b, BNPP changes in three soil depth layers among ecosystems. Dashed line shows the 1:1 line. The shading envelope represents the 95% confidence interval, with the corresponding similar colour solid line shows the average. TS forests, tropical/subtropical forests; Med/Mon shrublands, Mediterranean/montane shrublands; TS grasslands/savannas, tropical/ subtropical grasslands/savannas.

Source data

Extended Data Fig. 5 The performance of random forest model for predicting global soil organic carbon changes induced by 20% NPP increase.

The colour solid line is the regression line. The grey dotted line represents 1:1 line.

Source data

Extended Data Fig. 6 The difference of soil carbon:nitrogen ratio between two soil depth layers.

Boxplots show the median and interquartile range (IQR), with whiskers extending to the most extreme values within the 1.5 times of IQR. n represents the sample size. P values are for two-tailed tests.

Source data

Extended Data Fig. 7 Percentage shift of ecosystem types induced by plant productivity changes under warming.

The percentage shift is calculated as the fraction of ecosystem types at each profile location in the W+NPP group that are different from the ecosystem type in the A group. Boxplots show the median and interquartile range (IQR), with whiskers extending to the most extreme values within the 1.5 times of IQR. The red point represents the average. The numbers at the top of the box are the sample size.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–4 and Tables 1 and 2.

Supplementary Data 1

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Supplementary Data 2

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Source Data Fig. 1

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Source Data Extended Data Fig. 7

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Wang, M., Zhang, S., Wang, G. et al. Increased plant productivity exacerbates subsoil carbon losses under warming via nitrogen mining. Nat. Geosci. 18, 510–517 (2025). https://doi.org/10.1038/s41561-025-01697-1

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