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Divergent responses of soil organic carbon to afforestation

Nature Sustainability volume 3, pages 694–700 (2020)Cite this article

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Abstract

Large-scale afforestation is regarded as an effective natural climate solution. However, afforestation-induced changes in soil organic C (SOC) are poorly quantified due to the paucity of large-scale sampling data. Here, we provide the first comprehensive assessment of the afforestation impact on SOC stocks with a pairwise comparative study of samples from 619 control-and-afforested plot pairs in northern China. We found context-dependent effects of afforestation on SOC: afforestation increases SOC density (SOCD) in C-poor soils but decreases SOCD in C-rich soils, especially in deeper soil. Thus, the fixed biomass/SOC ratio assumed in previous studies could overestimate the SOC enhancement by afforestation. By extrapolating the sampling data to the entire region, we estimate that afforestation increased SOC stocks in northern China by only 234.9 ± 9.6 TgC over the last three decades. The study highlights the importance of including pre-afforestation soil properties in models of soil carbon dynamics and carbon sink projections.

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Fig. 1: The distributions of ΔSOCD.
Fig. 2: Controlling factors of ΔSOCD.
Fig. 3: Estimates of carbon change due to afforestation in northern China based on BRT.
Fig. 4: Comparison of carbon stocks and changes between soil and vegetation biomass.

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Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

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Acknowledgements

This study was supported by the National Key R&D Program of China grant no. 2017YFA0604702, the Strategic Priority Research Program (A) of the Chinese Academy of Sciences grant no. XDA20050101 and National Natural Science Foundation of China grant no. 41988101.

Author information

Author notes

  1. These authors contributed equally: Songbai Hong, Guodong Yin.

Authors and Affiliations

  1. Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Songbai Hong, Guodong Yin, Shilong Piao, Xiangyi Li & Kai Wang

  2. College of Water Sciences, Beijing Normal University, Beijing, China

    Guodong Yin

  3. Key Laboratory of Alpine Ecology, Center for Excellence in Tibetan Earth Science, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

    Shilong Piao

  4. Institute of Environmental Sustainability, Loyola University Chicago, Chicago, IL, USA

    Ray Dybzinski

  5. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Nan Cong

  6. CREAF, Barcelona, Spain

    Josep Peñuelas

  7. Global Ecology Unit CREAF-CSIC-UAB, Barcelona, Spain

    Josep Peñuelas

  8. Peking University Shenzhen Graduate School, Shenzhen, China

    Hui Zeng

  9. Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA

    Anping Chen

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Contributions

S.P. and A.C. designed the research. G.Y. and N.C. collected samples in the field. S.H. performed the analysis. S.H., A.C. and S.P. drafted the paper. All authors contributed to the interpretation of the results and to the text.

Corresponding authors

Correspondence to Shilong Piao or Anping Chen.

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Extended data

Extended Data Fig. 1 Robustness test of the negative correlation between ΔSOCD and SOCD_c.

The slope and p value are the results of 100 simulations (see Methods for details). The dotted line indicates the 95% CI of the simulated slopes. The observed error is the mean value in Extended Data Fig. 7. The observed slope is consistent with Fig. 2a.

Extended Data Fig. 2 Robustness test of the negative correlation between ΔSOCD and SOCD_c across six tree species groups.

The same methods are used with Extended Data Fig. 1.

Extended Data Fig. 3 Comparison of ∆SOCD across groups of the original vegetation types.

The central lines in the box-whisker plots indicate the medians, and the bottom and top edges of the boxes indicate the 25th and 75th percentiles, respectively. The maximum whisker lengths are specified as 1.5 times the interquartile range, and outliers are marked using+. Independent sample t-tests with false discovery rate correction were conducted to compare the data of each group with 0. p > 0.05 for all five groups. A one-way ANOVA (post hoc LSD test) was also used to test the difference between groups (p = 0.07).

Extended Data Fig. 4 Comparison between observed and BRT-predicted ΔSOCD.

80% of the samples are randomly selected to train the model and the remaining are used for test, which is repeated for 10 times to avoid contingency.

Extended Data Fig. 5 The uncertainty of the estimated SOCD based on BRT.

The uncertainty is from standard errors between BRT models run for 100 times.

Extended Data Fig. 6 The schematic diagram of the sampling design.

Three profiles were dug in each plot.

Extended Data Fig. 7 The variation between profiles.

The bars indicate the frequency distribution of the coefficient of variations for soil organic carbon densities between three profiles in each plot.

Extended Data Fig. 8 Uncertainties of the thresholds estimated from bootstrapping method across six tree species and all groups pooled together.

Error bars indicate the 95% confidence intervals.

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Supplementary Tables 1–5.

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Hong, S., Yin, G., Piao, S. et al. Divergent responses of soil organic carbon to afforestation. Nat Sustain 3, 694–700 (2020). https://doi.org/10.1038/s41893-020-0557-y

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