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Total data for global pattern of organic carbon pools in forest soil

Cite this dataset

Zhang, Yuxue et al. (2024). Total data for global pattern of organic carbon pools in forest soil [Dataset]. Dryad.


Understanding the mechanisms of soil organic carbon (SOC) sequestration in forests is vital to ecosystem carbon budgeting, and helps gain insight in the functioning and sustainable management of world forests. An explicit knowledge of the mechanisms driving global SOC sequestration in forests is still lacking because of the complex interplays between climate, soil and forest type in influencing SOC pool size and stability. Based on a synthesis of 1179 observations from 292 studies across global forests, we quantified the relative importance of climate, soil property and forest type on total SOC content and the specific contents of physical (particulate vs. mineral-associated SOC) and chemical (labile vs. recalcitrant SOC) pools in upper 10 cm mineral soils, as well as SOC stock in the O horizons. The variability in the total SOC content of the mineral soils was better explained by climate (47~60%) and soil factors (26%~50%) than by NPP (10~20%). The total SOC content and contents of particulate (POC) and recalcitrant SOC (ROC) of the mineral soils all decreased with increasing mean annual temperature because SOC decomposition overrides the C replenishment under warmer climate. The content of mineral-associated organic carbon (MAOC) was influenced by temperature, which directly affected microbial activity. Additionally, the presence of clay and iron oxides physically protected SOC by forming MAOC. The SOC stock in the O horizons was larger in the temperate zone and Mediterranean regions than in the boreal and sub/tropical zones. Mixed forests had 64% larger SOC pools than either broadleaf or coniferous forests, because of i) higher productivity, and ii) litter input from different tree species resulting in diversification of molecular composition of SOC and microbial community. While climate, soil and forest type jointly determine the formation and stability of SOC, climate predominantly controls the global patterns of SOC pools in forest ecosystems.

README: Total_data

Description of the data and file structure

Abbreviations and the units of the variables in the dataset (Total_data.xlsx)

Filling these empty cells in the data file will interfere with a script used to analyze the data, it is necessary to leave these cells empty.

MAT: mean annual temperature(C);

MAP: mean annual precipitation(mm);

PET: potential evapotranspiration;

AI: Aridity Index,MAP/PET;

Soil_order: USDA;

NPP, gC/m²/year;

Elevation: Altitude,(m);

Forest type: Conifer, Broadleaf, mixed Tree; age, years

Soil type: Soil order according to USDA;

BD, bulk density,(;


SC(Silt+Clay) (%);

Fe,Al. Total Fe/Al,(g·kg-1soil);

Fed. Ald: free Fe and Al oxides.(g kg-1soil); dithionite extractable Fe;

Feo, Alo: amorphous Fe and Al oxides (g·kg-1soil); oxalate extractable Fe and Al;

Fed-Feo: crystalline iron (Fed-Feo);

Fep,Alp:organically-complexed Fe and Al (gkg-1soil), pyrophosphate extractable Fe and Al;

CEC.Cation exchange capacity (cmol kg-1 Soil) pH;

O-C/N/P, Organic layer,(g kg-1);


CN,ratio of SOC to TN;

POC, MOC(gkg-1soil);

MOC/SOC (%); POCf, MOCf(gkg-1 fraction) DOC,(mg kg-1);

LOC, Labile organic C,g/kg soil;

ROC, recalcitrant organic C.g/kg soil;

SOCt, measured values from various depth at < 30 cm down to 30 cm by using regression equations of the total SOC content with soil depth as described in Hansen et al. (2023);

Soil depth, topsoil, (cm),

TBF:Temperate broadleaf forests;TCF:Temperate conifer forests;TMF:Temperate mixed conifer-broadleaf forests SBF:Subtropical broadleaf forests;SCF:Subtropical conifer forests;SMF:Subtropical mixed conifer-broadleaf forests TrBF:Temperate broadleaf forests;

MBF:Mediterranean broadleaf forests;MCF:Mediterranean conifer forests;MMF:Mediterranean mixed conifer-broadleaf for

AGBC,The plant aboveground standing biomass C,Mg Cha-1; BGBC,belowground standing biomass C,Mg C ha-1 NPP,gC/m2/year

normalised difference vegetation index (NDVI) enhanced vegetationindex (EVD).


All statistical analyses were performed in R 4.2.3 software (R Development Core Team, 2022)

the R codes (R_code.txt) used to generate the results and figures reported in this study are available in supplementary materials of the paper.


We used the Google Scholar ( and China National Knowledge Infrastructure (CNKI, to search for published papers on studies of SOC in forest ecosystems globally. The searched keywords were “(soil organic matter OR soil carbon OR soil organic carbon physical fractions OR chemical fractions OR particulate OR mineral‐associated OR labile OR recalcitrant) AND (forest OR forest ecosystems)”. Specifically, the searched keywords were in Chinese terminology when using CNKI. The search yielded more than 1000 studies matching those keywords. To avoid bias in selection of publications and to increase the comparability of data, studies that satisfied the following criteria were incorporated into the final dataset for synthesis: (1) field studies conducted with natural and mature forests; (2) studies exclusively concerned with separation of SOC into physical (determined by density or particle size into POC and MAOC, respectively) or chemical (determined using the potassium permanganate [KMnO4] oxidation or acid [H2SO4] hydrolysis into LOC and ROC) pools (Table S1); and (3) information available on forest type (i.e., broadleaf, coniferous and mixed forest), total SOC content, and at least one of the variables related to forest and environmental conditions (Table S2). The resulting dataset contains the values of total SOC content and the contents of specific SOC pools in the mineral soil at varying depths down to 30 cm, as well as SOC content and stock for the O horizon.


National Natural Science Foundation of China, Award: 31870426

National Natural Science Foundation of China, Award: 31470623

National Natural Science Foundation of China, Award: 32271677

National Natural Science Foundation of China, Award: 32301384