in

Spatial distributions, driving factors, and future changes of soil organic carbon in China: arid regions vs. humid regions


Abstract

Soil carbon sequestration is of great significance for achieving China’s 2060 carbon neutrality goal. However, differences in carbon sequestration between arid and humid regions remain unclear. Here, based on the Chinese terrestrial ecosystems carbon density dataset, this study employed the random forest (RF) model to map soil organic carbon density (SOCD) (1 km × 1 km) in arid and humid regions, and assessed the spatial uncertainty. The results indicated: (1) The RF model can explain about 79%-88% of SOCD variation in 2020. (2) The SOCD in arid regions (4.03 kg C m−2 in 0–20 cm soil depth, and 9.84 kg C m−2 in 0–100 cm), were significantly lower than those in humid regions (5.54 kg C m−2, and 12.91 kg C m−2), and had greater spatial heterogeneity. (3) In arid regions, SOCD was mainly driven by mean annual precipitation (MAP), mean annual temperature (MAT), normalized difference vegetation index (NDVI), soil moisture (SM), and land use and land cover (LULC). In humid regions, it was driven by MAT, MAP, elevation, human footprint, and LULC. (4) SOCD exhibited a decreasing trend in arid regions, but exhibited an increasing trend in humid regions under different climate scenarios. The results of this study are of great significance for exploring the response of the SOC pool to climate change in arid and humid regions, identifying vulnerable areas and carbon sinks, and scientifically formulating regional carbon management policies to achieve the goal of carbon neutrality.

Data availability

Data collected and analyzed in this study are available from the corresponding author upon request.

References

  1. Batjes, N. H. Total carbon and nitrogen in the soils of the world. Eur. J. Soil. Sci. 47, 151–163. https://doi.org/10.1111/ejss.12115 (1996).

    Google Scholar 

  2. Jobbágy, E. G. & Jackson, R. B. The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecol. Appl. 10, 423–436 (2000).

    Google Scholar 

  3. Scharlemann, J. P., Tanner, E. V. & Hiederer, R. Global soil carbon: Understanding and managing the largest terrestrial carbon pool. Carbon Manag. 5, 81–91. https://doi.org/10.4155/cmt.13.77 (2014).

    Google Scholar 

  4. Plaza, C. et al. Soil resources and element stocks in drylands to face global issues. Sci. Rep. 8, 13788. https://doi.org/10.1038/s41598-018-32229-0 (2018).

    Google Scholar 

  5. Pütz, S. et al. Long-term carbon loss in fragmented Neotropical forests. Nat. Commun. 5, 5037. https://doi.org/10.1038/ncomms6037 (2014).

    Google Scholar 

  6. Lal, R. Digging deeper: A holistic perspective of factors affecting soil organic carbon sequestration in agroecosystems. Glob Chang. Biol. 24, 3285–3301. https://doi.org/10.1111/gcb.14054 (2018).

    Google Scholar 

  7. Jian, Y. U. et al. A review of the composition of soil carbon pool. Acta Ecol. Sin. 34, 4829–4838 (2014).

    Google Scholar 

  8. Li, X., Ding, J., Jie, L., Ge, X. & Zhang, J. Digital mapping of soil organic carbon using Sentinel series data: A case study of the ebinur lake watershed in Xinjiang. Remote Sens. 13, 769. https://doi.org/10.3390/rs13040769 (2021).

    Google Scholar 

  9. Griscom, B. W. et al. Natural climate solutions. Proc. Natl. Acad. Sci. 114, 11645–11650. https://doi.org/10.1073/pnas.1710465114 (2017).

    Google Scholar 

  10. Bonner, M. T. L. et al. Soil organic carbon recovery in tropical tree plantations May depend on restoration of soil microbial composition and function. Geoderma 353, 70–80. https://doi.org/10.1016/j.geoderma.2019.06.017 (2019).

    Google Scholar 

  11. Fu, C. et al. Climate and mineral accretion as drivers of mineral-associated and particulate organic matter accumulation in tidal wetland soils. Glob Chang. Biol. 30, e17070 (2024).

    Google Scholar 

  12. Silan, G., Buosi, A., Bertolini, C. & Sfriso, A. Dynamics and drivers of carbon sequestration and storage capacity in phragmites australis-dominated wetlands. Estuar. Coast. Shelf Sci. 298, 108640. https://doi.org/10.1016/j.ecss.2024.108640 (2024).

    Google Scholar 

  13. Puissant, J. et al. Seasonality alters drivers of soil enzyme activity in subalpine grassland soil undergoing climate change. Soil Biol. Biochem. 124, 266–274. https://doi.org/10.1016/j.soilbio.2018.06.023 (2018).

    Google Scholar 

  14. Li, H. et al. Responses of soil organic carbon to climate change in the Qilian mountains and its future projection. J. Hydrol. 596, 126110. https://doi.org/10.1016/j.jhydrol.2021.126110 (2021).

    Google Scholar 

  15. Zhang, Z. et al. Historical and future variation of soil organic carbon in China. Geoderma 436, 116557. https://doi.org/10.1016/j.geoderma.2023.116557 (2023).

    Google Scholar 

  16. Wu, J. et al. Future soil organic carbon stocks in China under climate change. Cell. Rep. Sustain. 1, 100179. https://doi.org/10.1016/j.crsus.2024.100179 (2024).

    Google Scholar 

  17. Huang, X. et al. Land use change alters soil organic carbon: constrained global patterns and predictors. Earth’s Future 12. https://doi.org/10.1029/2023EF004254 (2024).

  18. Özkan, B., Dengiz, O. & Turan, İ. D. Site suitability analysis for potential agricultural land with Spatial fuzzy multi-criteria decision analysis in regional scale under semi-arid terrestrial ecosystem. Sci. Rep. 10, 22074. https://doi.org/10.1038/s41598-020-79105-4 (2020).

    Google Scholar 

  19. Schmidt, M. W. I. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56. https://doi.org/10.1038/nature10386 (2011).

    Google Scholar 

  20. Chakraborty, A., Sekhar, M., Bhanja, S. N. & Rao, L. Linking groundwater variability to ecosystem carbon and water use efficiencies across India. Ecol. Inf. 91, 103411. https://doi.org/10.1016/j.ecoinf.2025.103411 (2025).

    Google Scholar 

  21. Nakane, K. Dynamics of soil organic matier in different parts on a slope under evergreen oak forest. Jpn. J. Ecol. 25, 206–216. https://doi.org/10.18960/seitai.25.4_206 (1975).

    Google Scholar 

  22. Schlesinger, W. H. Carbon balance in terrestrial detritus. Annu. Rev. Ecol. Syst. 8, 51–81. https://doi.org/10.1146/annurev.es.08.110177.000411 (2003).

    Google Scholar 

  23. Oades, J. M. The retention of organic matter in soils. Biogeochemistry 5, 35–70. https://doi.org/10.1007/BF02180317 (1988).

    Google Scholar 

  24. Webb, W., Szarek, S., Lauenroth, W., Kinerson, R. & Smith, M. Primary productivity and water use in native Forest, Grassland, and desert ecosystems. Ecology 59, 1239–1247. https://doi.org/10.2307/1938237 (1978).

    Google Scholar 

  25. Sala, O. E., Parton, W. J., Joyce, L. A. & Lauenroth, W. K. Primary production of the central grassland region of the united States. Ecology 69, 40–45. https://doi.org/10.2307/1943158 (1988).

    Google Scholar 

  26. Amundson, R. G., Chadwick, O. A. & Sowers, J. M. A comparison of soil climate and biological activity along an elevation gradient in the Eastern Mojave desert. Oecologia 80, 395–400. https://doi.org/10.1007/BF00379042 (1989).

    Google Scholar 

  27. Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173. https://doi.org/10.1038/nature04514 (2006).

    Google Scholar 

  28. Rasmussen, C. et al. Beyond clay: towards an improved set of variables for predicting soil organic matter content. Biogeochemistry 137, 297–306. https://doi.org/10.1007/s10533-018-0424-3 (2018).

    Google Scholar 

  29. Stockmann, U. et al. The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agric. Ecosyst. Environ. 164, 80–99. https://doi.org/10.1016/j.agee.2012.10.001 (2013).

    Google Scholar 

  30. Wiesmeier, M. et al. Soil organic carbon storage as a key function of soils – A review of drivers and indicators at various scales. Geoderma 333, 149–162. https://doi.org/10.1016/j.geoderma.2018.07.026 (2019).

    Google Scholar 

  31. Jenny, H. Factors of soil formation: A system of quantitative pedology / Hans Jenny. Soil Sci. 42, 415 (1941).

    Google Scholar 

  32. Carvalhais, N. et al. Global covariation of carbon turnover times with climate in terrestrial ecosystems. Nature 514, 213–217. https://doi.org/10.1038/nature13731 (2014).

    Google Scholar 

  33. Tenney, F. G. & Waksman, S. A. Composition of natural organic materials & and their decomposition in the soil: IV. The nature and rapidity of decomposition of the various organic complexes in different plant materials, under aerobic conditions. Soil Sci. 28 (1929).

  34. Soil Microbes. in Soil. Microbiol. 511–544 https://doi.org/10.1002/9781119114314.ch16 . (2020).

  35. Liang, C., Schimel, J. P. & Jastrow, J. D. The importance of anabolism in microbial control over soil carbon storage. Nat. Microbiol. 2, 17105. https://doi.org/10.1038/nmicrobiol.2017.105 (2017).

    Google Scholar 

  36. Liang, C., Amelung, W., Lehmann, J. & Kaestner, M. Quantitative assessment of microbial necromass contribution to soil organic matter. Glob. Change Biol. 25, 3578–3590. https://doi.org/10.1111/gcb.14781 (2019).

    Google Scholar 

  37. Zhu, X., Jackson, R. D., DeLucia, E. H., Tiedje, J. M. & Liang, C. The soil microbial carbon pump: from conceptual insights to empirical assessments. Glob. Change Biol. 26, 6032–6039. https://doi.org/10.1111/gcb.15319 (2020).

    Google Scholar 

  38. Paul, E. A. Dynamics of organic matter in soils. Plant. Soil. 76, 275–285. https://doi.org/10.1007/BF02205586 (1984).

    Google Scholar 

  39. Lugato, E., Lavallee, J. M., Haddix, M. L., Panagos, P. & Cotrufo, M. F. Different climate sensitivity of particulate and mineral-associated soil organic matter. Nat. Geosci. 14, 295–300. https://doi.org/10.1038/s41561-021-00744-x (2021).

    Google Scholar 

  40. Sokol, N. W. et al. Global distribution, formation and fate of mineral-associated soil organic matter under a changing climate: A trait-based perspective. Funct. Ecol. 36, 1411–1429. https://doi.org/10.1111/1365-2435.14040 (2022).

    Google Scholar 

  41. Luo, Z., Feng, W., Luo, Y., Baldock, J. & Wang, E. Soil organic carbon dynamics jointly controlled by climate, carbon inputs, soil properties and soil carbon fractions. Glob. Change Biol. 23, 4430–4439. https://doi.org/10.1111/gcb.13767 (2017).

    Google Scholar 

  42. Li, C. et al. Drivers and impacts of changes in china’s drylands. Nat. Reviews Earth Environ. 2, 858–873. https://doi.org/10.1038/s43017-021-00226-z (2021).

    Google Scholar 

  43. Cotrufo, M. F. & Lavallee, J. M. Incorporating aridity in soil carbon stewardship frameworks. Nat. Clim. Change. 15, 240–242. https://doi.org/10.1038/s41558-025-02270-9 (2025).

    Google Scholar 

  44. Jassal, R. S. et al. Effect of soil water stress on soil respiration and its temperature sensitivity in an 18-year-old temperate Douglas-fir stand. Glob. Change Biol. 14, 1305–1318. https://doi.org/10.1111/j.1365-2486.2008.01573.x (2008).

    Google Scholar 

  45. Stuart Chapin, F. III et al. The changing global carbon cycle: linking plant-soil carbon dynamics to global consequences. J. Ecol. 97, 840–850. https://doi.org/10.1111/j.1365-2745.2009.01529.x (2009).

    Google Scholar 

  46. Hobley, E. U., Baldock, J. & Wilson, B. Environmental and human influences on organic carbon fractions down the soil profile. Agric. Ecosyst. Environ. 223, 152–166. https://doi.org/10.1016/j.agee.2016.03.004 (2016).

    Google Scholar 

  47. Dai, L. et al. Positive asymmetric responses indicate larger carbon sink with increase in precipitation variability in global terrestrial ecosystems. TIG 2, 100060–100010 (2024).

    Google Scholar 

  48. Conant, R. T. et al. Temperature and soil organic matter decomposition rates – synthesis of current knowledge and a way forward. Glob. Change Biol. 17, 3392–3404. https://doi.org/10.1111/j.1365-2486.2011.02496.x (2011).

    Google Scholar 

  49. MEIER, I. C. & Leuschner, C. Variation of soil and biomass carbon pools in Beech forests across a precipitation gradient. Glob. Change Biol. 16, 1035–1045. https://doi.org/10.1111/j.1365-2486.2009.02074.x (2010).

    Google Scholar 

  50. Zhu, A. The review and outlook of digital soil mapping. Prog. Geogr. 37, 66–78. https://doi.org/10.18306/dlkxjz.2018.01.008 (2018).

    Google Scholar 

  51. Mei, L. et al. Characterization of spatial distribution of soil organic carbon in China based on environmental variables. Acta Pedol. Sin. 57, 48. https://doi.org/10.11766/trxb201812110454 (2020).

    Google Scholar 

  52. Shuai, M. et al. Advances in digital soil mapping based on machine learning. J. Agricultural Resour. Environ. 41, 744 (2024).

    Google Scholar 

  53. Mitsch, W. J. et al. Wetlands, carbon, and climate change. Landscape Ecol. 28, 583–597. https://doi.org/10.1007/s10980-012-9758-8 (2013).

    Google Scholar 

  54. Grinand, C. et al. Estimating Temporal changes in soil carbon stocks at ecoregional scale in Madagascar using remote-sensing. Int. J. Appl. Earth Obs. Geoinf. 54, 1–14. https://doi.org/10.1016/j.jag.2016.09.002 (2017).

    Google Scholar 

  55. Lamichhane, S., Kumar, L. & Wilson, B. Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review. Geoderma 352, 395–413. https://doi.org/10.1016/j.geoderma.2019.05.031 (2019).

    Google Scholar 

  56. Yang, R. M. et al. Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecol. Ind. 60, 870–878. https://doi.org/10.1016/j.ecolind.2015.08.036 (2016).

    Google Scholar 

  57. Li, X., Ding, J., Liu, J., Ge, X. & Zhang, J. Digital mapping of soil organic carbon using Sentinel series data: A case study of the ebinur lake watershed in Xinjiang. Remote Sens. 13. https://doi.org/10.3390/rs13040769 (2021).

  58. Keskin, H., Grunwald, S. & Harris, W. G. Digital mapping of soil carbon fractions with machine learning. Geoderma 339, 40–58. https://doi.org/10.1016/j.geoderma.2018.12.037 (2019).

    Google Scholar 

  59. McBratney, A. B., Santos, M. L. M. & Minasny, B. On digital soil mapping. Geoderma 117, 3–52. https://doi.org/10.1016/S0016-7061(03)00223-4 (2003).

    Google Scholar 

  60. Padarian, J., Minasny, B. & McBratney, A. B. Machine learning and soil sciences: a review aided by machine learning tools. SOIL 6, 35–52. https://doi.org/10.5194/soil-2019-57 (2020).

    Google Scholar 

  61. Song, X. D. et al. Pedoclimatic zone-based three-dimensional soil organic carbon mapping in China. Geoderma 363, 114145. https://doi.org/10.1016/j.geoderma.2019.114145 (2020).

    Google Scholar 

  62. Hobley, E., Wilson, B., Wilkie, A., Gray, J. & Koen, T. Drivers of soil organic carbon storage and vertical distribution in Eastern Australia. Plant. Soil. 390, 111–127. https://doi.org/10.1007/s11104-015-2380-1 (2015).

    Google Scholar 

  63. USA, C. et al. Climate change 2014: Impacts, adaptation, and vulnerability – IPCC WGII AR5 summary for policymakers 1–32 (2014).

  64. Xu, L. et al. A dataset of carbon density in Chinese terrestrial ecosystems (2010s). China Sci. Data. 4, 86. https://doi.org/10.11922/csdata.2018.0026.zh (2019). 

    Google Scholar 

  65. Minasny, B., McBratney, A. B., Malone, B. P. & Wheeler, I. Chapter One – Digital mapping of soil carbon. in Advances in Agronomy (ed Sparks, D. L.) vol. 118 1–47 (Academic, 2013).

  66. Peng, S., Ding, Y., Liu, W. & Li, Z. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth Syst. Sci. Data. 11, 1931–1946 (2019).

    Google Scholar 

  67. Daoud, J. I. Multicollinearity analysis. J. Phys.: Conf. Ser. 949, 012009 (2017).

    Google Scholar 

  68. Peters, J., Verhoest, N. E. C., Samson, R., Boeckx, P. & De Baets, B. Wetland vegetation distribution modelling for the identification of constraining environmental variables. Landscape Ecol. 23, 1049–1065. https://doi.org/10.1007/s10980-008-9261-4 (2008).

    Google Scholar 

  69. Breiman, L., Random & Forests Mach. Learn. 45, 5–32 https://doi.org/10.1023/A:1010933404324 (2001).

    Google Scholar 

  70. Cherkassky, V. & The Nature Of Statistical Learning Theory. IEEE Trans. Neural Networks 8, 1564–1564 https://doi.org/10.1109/TNN.1997.641482 (1997).

    Google Scholar 

  71. Jiao, S. et al. Soil microbiomes with distinct assemblies through vertical soil profiles drive the cycling of multiple nutrients in reforested ecosystems. Microbiome 6, 146 (2018).

    Google Scholar 

  72. Liu, F. et al. High-resolution and three-dimensional mapping of soil texture of China. Geoderma 361, 114061. https://doi.org/10.1016/j.geoderma.2019.114061 (2020).

    Google Scholar 

  73. Grace, J. B. & Keeley, J. E. A structural equation model analysis of postfire plant diversity in California shrublands. Ecol. Appl. 16, 503–514 (2006).

    Google Scholar 

  74. Jonsson, M. & Wardle, D. A. Structural equation modelling reveals plant-community drivers of carbon storage in boreal forest ecosystems. Biol. Lett. 6, 116–119. https://doi.org/10.1098/rsbl.2009.0613 (2010).

    Google Scholar 

  75. Ren, Z., Li, C., Fu, B., Wang, S. & Stringer, L. C. Effects of aridification on soil total carbon pools in china’s drylands. Glob. Change Biol. 30, e17091. https://doi.org/10.1111/gcb.17091 (2024).

    Google Scholar 

  76. Reichmann, L. G., Sala, O. E. & Peters, D. P. C. Precipitation legacies in desert grassland primary production occur through previous-year tiller density. Ecology 94 2, 435–443. https://doi.org/10.1890/12-1237.1 (2013).

    Google Scholar 

  77. Li, H. et al. Decipher soil organic carbon dynamics and driving forces across China using machine learning. Glob. Change Biol. 28, 3394–3410. https://doi.org/10.1111/gcb.16154 (2022).

    Google Scholar 

  78. Lal, R. Carbon sequestration in dryland ecosystems. Environ. Manage. 33, 528–544. https://doi.org/10.1007/s00267-003-9110-9 (2004).

    Google Scholar 

  79. Liu, S., Wang, H. & Luan, J. A review of research progress and future prospective of forest soil carbon stock and soil carbon process in China. Acta Ecol. Sin. 31, 5437–5448 (2011).

    Google Scholar 

  80. Wynn, J. G. et al. Continental-scale measurement of the soil organic carbon pool with climatic, edaphic, and biotic controls. Glob. Biogeochem. Cycles. 20. https://doi.org/10.1029/2005GB002576 (2006).

  81. Song, X. D. et al. Heuristic cellular automaton model for simulating soil organic carbon under land use and climate change: A case study in Eastern China. Agric. Ecosyst. Environ. 269, 156–166. https://doi.org/10.1016/j.agee.2018.09.034 (2019).

    Google Scholar 

  82. Piao, S. et al. Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai-Xizang plateau. Agric. For. Meteorol. 151, 1599–1608. https://doi.org/10.1016/j.agrformet.2011.06.016 (2011).

    Google Scholar 

  83. Chen, R. et al. Effects of biotic and abiotic factors on forest biomass fractions. Natl. Sci. Rev. 8, nwab025 (2021).

    Google Scholar 

  84. Huang, H. et al. Water content quantitatively affects metabolic rates over the course of plant ontogeny. New. Phytol. 228, 1524–1534 (2020).

    Google Scholar 

  85. Deng, L. et al. Drought effects on soil carbon and nitrogen dynamics in global natural ecosystems. Earth Sci. Rev. 214, 103501. https://doi.org/10.1016/j.earscirev.2020.103501 (2021).

    Google Scholar 

  86. Zhao, M. & Running, S. W. Drought-Induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329, 940–943. https://doi.org/10.1126/science.1192666 (2010).

    Google Scholar 

  87. Yang, Y., Fang, J., Wenhong, M. A., Smith, P. & Wang, W. Soil carbon stock and its changes in Northern china’s grasslands from 1980s to 2000s. Glob. Change Biol. 16, 3036–3047. https://doi.org/10.1111/j.1365-2486.2009.02123.x (2010).

    Google Scholar 

  88. Huang, H. et al. A general model for seed and seedling respiratory metabolism. Am. Nat. 195, 534–546. https://doi.org/10.1086/707072 (2020).

    Google Scholar 

  89. Liu, Y., Li, S., Sun, X. & Yu, X. Variations of forest soil organic carbon and its influencing factors in East China. Ann. For. Sci. 73, 501–511. https://doi.org/10.1007/s13595-016-0543-8 (2016).

    Google Scholar 

  90. Ge, T. et al. Stability and dynamics of enzyme activity patterns in the rice rhizosphere: effects of plant growth and temperature. Soil Biol. Biochem. 113, 108–115. https://doi.org/10.1016/j.soilbio.2017.06.005 (2017).

    Google Scholar 

  91. von Lützow, M. & Kögel-Knabner, I. Temperature sensitivity of soil organic matter decomposition—what do we know? Biol. Fertil. Soils. 46, 1–15. https://doi.org/10.1007/s00374-009-0413-8 (2009).

    Google Scholar 

  92. Vogel, C. et al. Submicron structures provide Preferential spots for carbon and nitrogen sequestration in soils. Nat. Commun. 5, 2947. https://doi.org/10.1038/ncomms3947 (2014).

    Google Scholar 

  93. Saimun, M. S. R., Karim, M. R., Sultana, F. & Arfin-Khan, M. A. Multiple drivers of tree and soil carbon stock in the tropical forest ecosystems of Bangladesh. Trees Forests People. 5, 100108. https://doi.org/10.1016/j.tfp.2021.100108 (2021).

    Google Scholar 

  94. Kemmitt, S. J., Wright, D., Goulding, K. W. T. & Jones, D. L. pH regulation of carbon and nitrogen dynamics in two agricultural soils. Soil Biol. Biochem. 38, 898–911. https://doi.org/10.1016/j.soilbio.2005.08.006 (2006).

    Google Scholar 

  95. Han, L., Sun, K., Jin, J. & Xing, B. Some concepts of soil organic carbon characteristics and mineral interaction from a review of literature. Soil Biol. Biochem. 94, 107–121. https://doi.org/10.1016/j.soilbio.2015.11.023 (2016).

    Google Scholar 

  96. Schweizer, S. A., Mueller, C. W., Höschen, C., Ivanov, P. & Kögel-Knabner, I. The role of clay content and mineral surface area for soil organic carbon storage in an arable toposequence. Biogeochemistry 156, 401–420. https://doi.org/10.1007/s10533-021-00850-3 (2021).

    Google Scholar 

  97. Rousk, J., Brookes, P. C. & Bååth, E. Contrasting soil pH effects on fungal and bacterial growth suggest functional redundancy in carbon mineralization. Appl. Environ. Microbiol. 75, 1589–1596. https://doi.org/10.1128/AEM.02775-08 (2009).

    Google Scholar 

  98. Terribile, F., Basile, A. & Moving Science Towards Operational Sustainability. The use of Geospatial decision support systems. Land. Degrad. Dev. 36, 1401–1404. https://doi.org/10.1002/ldr.5447 (2025).

    Google Scholar 

  99. Guo, G. et al. Diversity and distribution of autotrophic microbial community along environmental gradients in grassland soils on the Tibetan plateau. Appl. Microbiol. Biotechnol. 99, 8765–8776. https://doi.org/10.1007/s00253-015-6723-x (2015).

    Google Scholar 

  100. Guo, H. et al. The aggregate structure and organic carbon mineralization in forest soils along an elevation gradient in the Sygera mountains of the southeastern Tibetan plateau. Forests 16. https://doi.org/10.3390/f16020298 (2025).

  101. Zeng, Y., Fang, N. & Shi, Z. Effects of human activities on soil organic carbon redistribution at an agricultural watershed scale on the Chinese loess plateau. Agric. Ecosyst. Environ. 303, 107112. https://doi.org/10.1016/j.agee.2020.107112 (2020).

    Google Scholar 

  102. Wang, Q. et al. Distribution and storage of soil organic carbon in a coastal wetland under the pressure of human activities. J. Soils Sediments. 17, 11–22. https://doi.org/10.1007/s11368-016-1475-5 (2017).

    Google Scholar 

  103. Burke, I. C. et al. Texture, Climate, and cultivation effects on soil organic matter content in U.S. Grassland soils. Soil Sci. Soc. Am. J. 53, 800–805. https://doi.org/10.2136/sssaj1989.03615995005300030029x (1989).

    Google Scholar 

  104. Taylor, P. G. et al. Temperature and rainfall interact to control carbon cycling in tropical forests. Ecol. Lett. 20, 779–788. https://doi.org/10.1111/ele.12765 (2017).

    Google Scholar 

  105. Wieder, W. R., Bonan, G. B. & Allison, S. D. Global soil carbon projections are improved by modelling microbial processes. Nat. Clim. Change. 3, 909–912. https://doi.org/10.1038/nclimate1951 (2013).

    Google Scholar 

  106. Arias, P. et al. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Technical Summary (2021).

  107. Chen, N. et al. Contribution of multi-objective land use optimization to carbon neutrality: A case study of Northwest China. Ecol. Ind. 157, 111219. https://doi.org/10.1016/j.ecolind.2023.111219 (2023).

    Google Scholar 

  108. Baig, S., Medlyn, B. E., Mercado, L. M. & Zaehle, S. Does the growth response of Woody plants to elevated CO2 increase with temperature? A model-oriented meta-analysis. Glob Chang. Biol. 21, 4303–4319. https://doi.org/10.1111/gcb.12962 (2015).

    Google Scholar 

  109. Terrer, C. et al. Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass. Nat. Clim. Change. 9, 684–689. https://doi.org/10.1038/s41558-019-0545-2 (2019).

    Google Scholar 

  110. Xu, S. et al. Positive soil priming effects are the rule at a global scale. Glob. Change Biol. 30, e17502. https://doi.org/10.1111/gcb.17502 (2024).

    Google Scholar 

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Acknowledgements

The authors gratefully acknowledge the financial support provided by the National Natural Science Foundation of China (42171067), and the National Key Research and Development Program of China (2023YFD1901204).

Funding

Declaration.

This work was supported by the National Natural Science Foundation of China (42171067), and the National Key Research and Development Program of China (2023YFD1901204).

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B.Y.: Writing—review & editing, Writing—original draft, Visualization, Validation, Methodology, Supervision, Formal analysis, Conceptualization. S.Z.: Writing—review & editing, Writing—original draft, Validation, Supervision, Data curation, Conceptualization. X.W.: Writing—review & editing, Supervision, Funding acquisition.

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Correspondence to
Xiaoguo Wang.

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Yang, B., Zhang, S. & Wang, X. Spatial distributions, driving factors, and future changes of soil organic carbon in China: arid regions vs. humid regions.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-32482-0

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  • DOI: https://doi.org/10.1038/s41598-025-32482-0

Keywords

  • Soil organic carbon
  • Spatial distribution
  • Driving factors
  • Digital soil mapping
  • Arid region
  • Humid region


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