Soil dissolved organic carbon in terrestrial ecosystems: Global budget, spatial distribution and controls

Guo, Ziyu; Wang, Yihui; Wan, Zhongmei; Zuo, Yunjiang; He, Liyuan; Li, Dan; Yuan, Fenghui; Wang, Nannan; Liu, Jianzhao; Song, Yanyu; Song, Changchun; Xu, Xiaofeng

doi:10.1111/geb.13186

Cited by 70 publications

(52 citation statements)

References 136 publications

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“…We further found that red grapes of Eurasian species were associated with higher biomass levels relative to similarly aged white grapes of Eurasian species (Figure 1), indicating that vineyards growing red grape varieties exhibit a higher carbon storage capacity than do those growing white grape varieties (Table 4). We further found that soil active organic carbon fractions (MBC and DOC) were mainly concentrated in the surface soil of the grapevine rhizosphere (Figure 3), indirectly suggesting that soil microorganisms are present primarily within this surface soil layer where most nutrient conversion and circulation is likely to occur, consistent with previous reports for other soil types [48][49][50][51].…”

Section: Discussionsupporting

confidence: 91%

Carbon Storage Distribution Characteristics of Vineyard Ecosystems in Hongsibu, Ningxia

Zhang

Xue

Gao

et al. 2021

Plants

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Given that the global winegrape planting area is 7.2×106 hm2, the potential for winegrape crop-mediated carbon capture and storage as an approach to reducing greenhouse gas emissions warranted further research. Herein, we employed an allometric model of various winegrape organs to assess biomass distributions, and we evaluated the carbon storage distribution characteristics associated with vineyard ecosystems in the Hongsibu District of Ningxia. We found that the total carbon storage of the Vitis vinifera ‘Cabernet Sauvignon’ vineyard ecosystem was 55.35 t·hm−2, of which 43.12 t·hm−2 came from the soil, while the remaining 12.23 t·hm−2 was attributable to various vine components including leaves (1.85 t·hm−2), fruit (2.16 t·hm−2), canes (1.83 t·hm−2), perennial branches (2.62 t·hm−2), and roots (3.78 t·hm−2). Together, these results suggested that vineyards can serve as an effective carbon sink, with the majority of carbon being sequestered at the soil surface. Within the grapevines themselves, most carbon was stored in perennial organs including perennial branches and roots. Allometric equations based on simple and practical biomass and biometric measurements offer a means whereby grape-growers and government entities responsible for ecological management can better understand carbon distribution patterns associated with vineyards.

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Section: Discussionsupporting

confidence: 91%

Carbon Storage Distribution Characteristics of Vineyard Ecosystems in Hongsibu, Ningxia

Zhang

Xue

Gao

et al. 2021

Plants

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“…A standardized microbial data system that contains primary microbial variables with consistent measurement approach or after conversion is critical to reduce the bias associated with distinct methods (Xu et al, 2020). Last but not least, the observed data for bacterial and fungal biomass C commonly vary by more than five orders of magnitude (Guo et al, 2020;He et al, 2020;Sinsabaugh et al, 2016;Xu et al, 2013Xu et al, , 2017, while ecosystem-level variables commonly vary by less than three orders of magnitude. This large discrepancy makes the validation approach applied to ecosystem-level C pools and fluxes less robust in microbial models, as shown in this study of model validation (Section 3.1).…”

Section: Limitations and Improvementsmentioning

confidence: 99%

Dynamics of Fungal and Bacterial Biomass Carbon in Natural Ecosystems: Site‐Level Applications of the CLM‐Microbe Model

Lipson

Rodrigues

et al. 2021

J Adv Model Earth Syst

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Explicitly representing microbial processes has been recognized as a key improvement to Earth system models for the realistic projections of soil carbon (C) and climate dynamics. The CLM‐Microbe model builds upon the CLM4.5 and explicitly represents two major soil microbial groups, fungi and bacteria. Based on the compiled time‐series data of fungal (FBC) and bacterial (BBC) biomass C from nine biomes, we parameterized and validated the CLM‐Microbe model, and further conducted sensitivity and uncertainty analysis for simulating C cycling. The model performance was evaluated with mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) for relative change in FBC and BBC. The CLM‐Microbe model is able to reasonably capture the seasonal dynamics of FBC and BBC across biomes, particularly for tropical/subtropical forest, temperate broadleaf forest, and grassland, with MAE <0.49 for FBC and <0.36 for BBC and RMSE <0.52 FBC and <0.39 for BBC, while R2 values are relatively smaller in some biomes (e.g., shrub) due to small sample sizes. We found good consistencies between simulated and observed FBC (R2 = 0.70, P < 0.001) and BBC (R2 = 0.26, P < 0.05) on average across biomes, but the model is not able to fully capture the large variation in observed FBC and BBC. Sensitivity analysis shows that the most critical parameters are turnover rate and carbon‐to‐nitrogen ratio of fungi and bacteria and microbial assimilation efficiency. This study confirms that the explicit representation of soil microbial mechanisms enhances model performance in simulating C variables such as heterotrophic respiration and soil organic C density. The further application of the CLM‐Microbe model would deepen our understanding of microbial contributions to global C cycle.

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“…Climatic, edaphic, vegetation, and microbial variables were not fully reported in published studies, we extracted such variables from global datasets following our previous studies (Guo et al, 2020;He et al, 2020;Xu et al, 2013Xu et al, , 2017. For climatic variables, we extracted mean annual temperature (MAT) and mean annual precipitation (MAP) during 1970-2000 from the WorldClim database version 2 with the spatial resolution of 30 s (https://www.world clim.org/ data/world clim21.html).…”

Section: Climate Edaphic Vegetation and Microbial Datamentioning

confidence: 99%

“…The auxiliary datasets used included the global land area database and global vegetation distribution dataset. The global vegetation distribution dataset was obtained from a spatial map of 11 major biomes: boreal forest, temperate forest, tropical/subtropical forest, mixed forest, grassland, shrubland, tundra, desert, natural wetlands, cropland, and pasture, which have been used in our previous publications (Guo et al, 2020;He et al, 2020;Xu et al, 2013Xu et al, , 2017. The global land area database was from the surface data map of 0.5° × 0.5° generated for E3SM (https://web.lcrc.anl.gov/publi c/ e3sm/input data/lnd/clm2/surfd ata_map/).…”

Section: Climate Edaphic Vegetation and Microbial Datamentioning

confidence: 99%

Mapping soil microbial residence time at the global scale

2021

Global Change Biology

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Soil microbes are the fundamental engine for carbon (C) cycling. Microbial residence time (MRT) therefore determines the mineralization of soil organic C, releasing C as heterotrophic respiration and contributing substantially to the C efflux in terrestrial ecosystems. We took use of a comprehensive dataset (2627 data points) and calculated the MRT based on the basal respiration and microbial biomass C. Large variations in MRT were found among biomes, with the largest MRT in boreal forests and grasslands and smallest in natural wetlands. Biogeographic patterns of MRT were found along climate variables (temperature and precipitation), vegetation variables (root C density and net primary productivity), and edaphic factors (soil texture, pH, topsoil porosity, soil C, and total nitrogen). Among environmental factors, edaphic properties dominate the MRT variations. We further mapped the MRT at the global scale with an empirical model. The simulated and observed MRT were highly consistent at plot‐ (R2= .86), site‐ (R2 = .88), and biome‐ (R2 = .99) levels. The global average of MRT was estimated to be 38 (±5) days. A clear latitudinal biogeographic pattern was found for MRT with lower values in tropical regions and higher values in the Arctic. The biome‐ and global‐level estimates of MRT serve as valuable data for parameterizing and benchmarking microbial models.

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Soil dissolved organic carbon in terrestrial ecosystems: Global budget, spatial distribution and controls

Cited by 70 publications

References 136 publications

Carbon Storage Distribution Characteristics of Vineyard Ecosystems in Hongsibu, Ningxia

Carbon Storage Distribution Characteristics of Vineyard Ecosystems in Hongsibu, Ningxia

Dynamics of Fungal and Bacterial Biomass Carbon in Natural Ecosystems: Site‐Level Applications of the CLM‐Microbe Model

Mapping soil microbial residence time at the global scale

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