Evidence map›Paper›PMID 39172788›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2024

Reducing the uncertainty in estimating soil microbial-derived carbon storage.

Han Hu, Chao Qian, Ke Xue, Rainer Georg Jörgensen, Marco Keiluweit, Chao Liang, Xuefeng Zhu, Ji Chen, Yishen Sun, Haowei Ni and 9 more

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. Article
  7. Microbial necromass carbon enhances arsenic methylation in paddy soils.Proceedings of the National Academy of Sciences of the United States of America · 2025
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  11. Frontiers in microbiology · 2025
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Han Hu *State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.
Chao Qian *National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China.ORCID 0000-0001-6011-2512
Ke Xue *National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China.ORCID 0000-0001-6789-2670
Rainer Georg JörgensenDepartment of Soil Biology and Plant Nutrition, University of Kassel, Kassel 34117, Germany.
Marco KeiluweitInstitute of Earth Surface Dynamics, University of Lausanne, Lausanne CH-1015, Switzerland.ORCID 0000-0002-7061-8346
Chao LiangInstitute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China.ORCID 0000-0002-9089-6546
Xuefeng ZhuInstitute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China.
Ji ChenDepartment of Agroecology, Aarhus University, Tjele 8830, Denmark.ORCID 0000-0001-7026-6312
Yishen SunState Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.
Haowei NiState Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.
Jixian DingState Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.
Weigen HuangState Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.
Jingdong MaoDepartment of Chemistry and Biochemistry, Old Dominion University, Norfolk, VA 23529.
Rong-Xi TanNational Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China.ORCID 0009-0005-1547-051X
Jizhong ZhouSchool of Biological Sciences, University of Oklahoma, Norman, OK 73069.ORCID 0000-0003-2014-0564
Thomas W CrowtherDepartment of Environmental Systems Science, Institute of Integrative Biology, ETH Zurich 8092, Switzerland.ORCID 0000-0001-5674-8913
Zhi-Hua ZhouNational Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China.
Jiabao ZhangState Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.
Yuting LiangState Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.ORCID 0000-0001-5443-4486

Funding

MOST | National Key Research and Development Program of China (NKPs) 2021YFD1900400National Natural Scientific Foundation of China 42377121
6 · The paper itself

Abstract

Soil organic carbon (SOC) is the largest carbon pool in terrestrial ecosystems and plays a crucial role in mitigating climate change and enhancing soil productivity. Microbial-derived carbon (MDC) is the main component of the persistent SOC pool. However, current formulas used to estimate the proportional contribution of MDC are plagued by uncertainties due to limited sample sizes and the neglect of bacterial group composition effects. Here, we compiled the comprehensive global dataset and employed machine learning approaches to refine our quantitative understanding of MDC contributions to total carbon storage. Our efforts resulted in a reduction in the relative standard errors in prevailing estimations by an average of 71% and minimized the effect of global variations in bacterial group compositions on estimating MDC. Our estimation indicates that MDC contributes approximately 758 Pg, representing approximately 40% of the global soil carbon stock. Our study updated the formulas of MDC estimation with improving the accuracy and preserving simplicity and practicality. Given the unique biochemistry and functioning of the MDC pool, our study has direct implications for modeling efforts and predicting the land-atmosphere carbon balance under current and future climate scenarios.

Indexed as

CarbonSoilSoil MicrobiologyBacteriaCarbon CycleCarbon SequestrationClimate ChangeEcosystemMachine LearningUncertaintyCarbonSoilmethodologymicrobial derived carbonsoil carbon cycle

Identifiers

PMID39172788
PMCPMC11363314

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.