Site related δ13C of vegetation and soil organic carbon in a cool temperate region

Gautam, Mukesh Kumar; Lee, Kwang-Sik; Song, Byeong-Yeol; Bong, Yeon-Sik

doi:10.1007/s11104-017-3284-z

Cited by 14 publications

(16 citation statements)

References 52 publications

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“…Although in chestnut forest we observed the lowest C stock (Figure 7c,d), PGH approach allowed to observe a similar thickness of A and AB horizons (Figure 4). This fact would indicate that chestnut forest promoted organic carbon deepening (De Feudis et al, 2020b; Gautam et al, 2017), independently from soil organic matter turnover, likely due to the high appetibility of chestnut residues for soil fauna and microbes (Anderson, 1973). The deepening of organic matter in soil can be considered a positive aspect because it could prevent soil degradation due to the presence of stable SOC forms in depth (e.g.…”

Section: Discussionmentioning

confidence: 99%

Soil organic carbon stock assessment in forest ecosystems through pedogenic horizons and fixed depth layers sampling: What's the best one?

et al. 2022

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In soil organic carbon (SOC) survey, the role of sampling approach is crucial and should not be underestimated. In this sense, the appropriateness of soil sampling by pedogenic horizons (PGH) and fixed depth layers (FDL) in forest is still a field of debate. The present work aimed to: (a) study the spatial variability of SOC concentrations and C stocks through PGH and FDL samplings in uneven-aged sweet chestnut, European beech and Norway spruce forests of the Apennine chain (Italy); (b) clarify the major advantages and drawbacks of sampling by PGH and FDL. In a representative area (18 Â 18 m) of each forest, a soil profile was investigated and eight additional sampling points were selected. The sampling was performed both by PGH and FDL (0-15 and 15-30 cm). For each forest, no SOC content and C stock differences in 0-30 cm soil depth were found between PGH (58.8 ± 5.3 g kg À1 and 103 ± 7 g ha À1 , respectively) and FDL (52.7 ± 4.3 g kg À1 and 117 ± 9 Mg ha À1 , respectively) sampling. However, under Norway spruce, PGH sampling pointed-out that C was mainly stored in Oe and Oa horizons (51.1 vs. 146 Mg ha À1 in the 0-30 cm layer). The higher coefficient of variation obtained when soil horizons were considered (from 19.2% to 72.8% for PGH and from 16.5% to 25.7% for FDL) suggested that PGH sampling preserved the information about the spatial variability within an ecosystem. The information loss about SOC vertical and spatial distribution would indicate the inability of FDL sampling to support decision-making plans addressed for sustainable use of soil resource.

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Section: Discussionmentioning

confidence: 99%

Soil organic carbon stock assessment in forest ecosystems through pedogenic horizons and fixed depth layers sampling: What's the best one?

et al. 2022

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“…δ 13 C of SOC generally increases with soil depth (e.g., Brunn, Spielvogel, Sauer, & Oelmann, 2014; Guillaume et al, 2015; Wang, Wei, et al, 2017). δ 13 C changes with soil depth are related to four processes: (a) physical mixing (Bird et al, 2004; Brunn et al, 2014), showing that it increased promoted homogenization of soil column based on one process‐based modeling, and in return decreased the δ 13 C gradients with depth (Acton et al, 2013); (b) microbial decomposition (Gautam, Lee, Song, & Bong, 2017; Wang, Wei, et al, 2017), this is one commonly held cognition that isotopic fractionation often occurs during microbial decomposition (Guinina & Kuzyakov, 2014). Microbes degrade 12 C more readily than 13 C during SOC decomposition (Lerch, Nunan, Dignac, Chenu, & Mariotti, 2011), and thus the residual SOC is enriched more 13 C components in deeper soil layers (Diochon & Kellman, 2008; Guinina & Kuzyakov, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…showing that it increased promoted homogenization of soil column based on one process-based modeling, and in return decreased the δ 13 C gradients with depth (Acton et al, 2013); (b) microbial decomposition (Gautam, Lee, Song, & Bong, 2017;Wang, Wei, et al, 2017), this is one commonly held cognition that isotopic fractionation often occurs during microbial decomposition (Guinina & Kuzyakov, 2014).…”

Section: The Distribution Of Plant-derived Lignin Phenols In Soilsmentioning

confidence: 98%

Soil organic matter turnover depending on land use change: Coupling C/N ratios, δ¹³C, and lignin biomarkers

et al. 2020

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Soil organic carbon (SOC) stocks have been greatly depleted across the globe by conversion of wetlands to croplands, agroforestry, and urban areas. Here, we investigated SOC distribution and turnover in four land use types: a wetland, cropland, forestland, and construction land in the Baiyangdian Wetland, Northern China. The C : N ratios were up to 1.70‐times larger in cropland, forestland, and construction land than in the original wetland because of faster N losses compared to C following wetland conversion. The δ13C values of SOC increased with depth in wetland, and showed an overall depletion compared with the other three land use types. Acid‐to‐Aldehyde ratios of syringyl in wetland were 0.72–1.14‐, 0.72–1.72‐, and 1.18–1.43‐times, and cinnamyl/vanillyl ratios were 0.56–1.05‐, 0.22–0.48‐, and 0.40–0.76‐times those of cropland, forestland, and construction land, which reflects faster lignin decomposition rate in wetlands. The β value was defined by the slope of the linear regression between the logarithm of SOC and δ13SOC values, and decreased from cropland over construction land and forestland to wetland, reflecting the faster SOC turnover with the lower β values. However, SOC content and storage were up to 2.29‐ and 2.07‐times higher in wetlands than in soils of other land use types. The combination of C : N ratios, δ13C, and lignin monomer composition can explain the decrease of β value (corresponding to faster SOC turnover), and can be used as effective proxies to evaluate the sources and turnover of SOC in response to land use changes.

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“…Soil carbon turnover is a major determinant of the capacity of soil carbon sequestration (Luo et al, 2003), and a decrease in carbon turnover can sequestrate SOC without an increase in carbon input (Jastrow et al, 2006). Because it is difficult to detect the change in SOC stock over short periods due to the large pool size and huge spatial heterogeneity (Van Groenigen et al, 2014), the predicted β across the TP could provide a reliable method to evaluate the SOC turnover rate over a large spatial scale (Brunn et al, 2014;Gautam et al, 2017). Therefore, understanding the spatial variation of the β values is particularly important.…”

Section: Spatial Patterns Of the Data-driven β Across The Tpmentioning

confidence: 99%

“…The more negative the slope, the larger the decrease in the β value and the faster the turnover rate (Campbell et al, 2009). The method was widely used to study SOC turnover in the forest, grassland, and meadow ecosystems (Gautam et al, 2017;Peri et al, 2012;Zhao et al, 2019;Zhou et al, 2019). Acton et al (2013) noted that the β should be applied out in well-drained soils characterized by a gradual mixing of litter and root carbon inputs decomposing in the soil profile.…”

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confidence: 99%

A data-driven topsoil δ<sup>13</sup>C dataset and the drivers of spatial variability across the Tibetan Plateau

Lai

Shi

et al. 2021

Preprint

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Abstract. Soil carbon isotopes (δ13C) provide reliable insights at a long-term scale for studying soil carbon turnover. The Tibetan Plateau (TP), called “the third pole of the earth” is one of the most sensitive areas to global climate change and exhibits an early warning signal of global warming. Although many studies detected the variability of soil δ13C at site scales, a knowledge gap still exists in the spatial pattern of topsoil δ13C across the TP. To fill the substantial knowledge gap, we first compiled a database of topsoil δ13C with 396 observations from published literatures. Then we applied a Random Forest (RF) algorithm – a machine learning approach, to predict the spatial pattern of topsoil δ13C and β (indicating the decomposition rate of soil organic carbon (SOC), calculated by δ13C divided by logarithmically converted SOC). Finally, two datasets – topsoil δ13C and β with a fine spatial resolution of 1 km across the TP were developed. Results showed that topsoil δ13C varied significantly among different ecosystem types (p < 0.001). Topsoil δ13C was −26.3 ± 1.60 ‰ (mean ± standard deviation) for forests, 24.3 ± 2.00 ‰ for shrublands, −23.9 ± 1.84 ‰ for grasslands, −18.9 ± 2.37 ‰ for deserts, respectively. RF could well predict the spatial variability of topsoil δ13C with a model efficiency of 0.62 and root mean square error of 1.12 ‰, enabling to derive data-driven δ13C and β products. Data-driven topsoil δ13C varied from −28.26 ‰ to −16.95 ‰, with the highest topsoil δ13C in the north and northwest TP and the lowest δ13C in Southeast or South TP, indicating strong spatial variabilities in topsoil δ13C. Similarly, there were strong spatial variabilities in data-driven β, with the lowest β values at the east and middle TP, indicating a higher SOC turnover in the east and middle TP compared that of other regions in the TP. This study was the first attempt to develop a fine resolution product of topsoil δ13C and β across the TP, which could provide an independent data-driven benchmark for biogeochemical cycling models to study SOC turnover and terrestrial carbon-climate feedbacks over the TP under climate change. The data-driven δ13C and β datasets are public available at https://doi.org/10.6084/m9.figshare.16641292.v2 (Tang, 2021).

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Site related δ13C of vegetation and soil organic carbon in a cool temperate region

Cited by 14 publications

References 52 publications

Soil organic carbon stock assessment in forest ecosystems through pedogenic horizons and fixed depth layers sampling: What's the best one?

Soil organic carbon stock assessment in forest ecosystems through pedogenic horizons and fixed depth layers sampling: What's the best one?

Soil organic matter turnover depending on land use change: Coupling C/N ratios, δ¹³C, and lignin biomarkers

A data-driven topsoil δ<sup>13</sup>C dataset and the drivers of spatial variability across the Tibetan Plateau

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Site related δ13C of vegetation and soil organic carbon in a cool temperate region

Cited by 14 publications

References 52 publications

Soil organic carbon stock assessment in forest ecosystems through pedogenic horizons and fixed depth layers sampling: What's the best one?

Soil organic carbon stock assessment in forest ecosystems through pedogenic horizons and fixed depth layers sampling: What's the best one?

Soil organic matter turnover depending on land use change: Coupling C/N ratios, δ13C, and lignin biomarkers

A data-driven topsoil δ&lt;sup&gt;13&lt;/sup&gt;C dataset and the drivers of spatial variability across the Tibetan Plateau

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Product

Resources

About

Soil organic matter turnover depending on land use change: Coupling C/N ratios, δ¹³C, and lignin biomarkers

A data-driven topsoil δ<sup>13</sup>C dataset and the drivers of spatial variability across the Tibetan Plateau