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Can We Derive Climate‐Forest‐Soil Dependent Proxies for Litter Carbon?

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Title: Can We Derive Climate‐Forest‐Soil Dependent Proxies for Litter Carbon?
Authors: Neumann, Mathias1 (AUTHOR) mathias.neumann@boku.ac.at, Herzberger, Edwin2 (AUTHOR), Englisch, Michael2 (AUTHOR), Hasenauer, Hubert1 (AUTHOR)
Source: European Journal of Soil Science. May/Jun2025, Vol. 76 Issue 3, p1-12. 12p.
Subjects: Standard deviations, Forest density, Soil mineralogy, Carbon in soils, Forest surveys
Abstract: Organic soil layers, including litter, fermentation and humus layers depending on humus form, are a large carbon pool in forests. Soil carbon in organic and mineral layers can be quantified using (i) direct field observations using profiles or cores, (ii) pedo‐transfer functions using simple‐to‐measure proxies for soil carbon, or (iii) biogeochemical modelling considering soil carbon input and output. Despite large amounts of soil data available for researchers, there is little knowledge available on suitable proxies and estimation concepts for carbon in soil layers of predominantly organic origin (here called litter carbon), compared to carbon in mineral soil layers. Here, we test models using litter carbon measurements from Austria. We consider forest and site information as well as litter depth measurements as input data in a machine learning approach for covariate selection and fit multivariate models with remaining significant covariates. We validate the developed models versus independent validation data sets. Our results show a clear relationship between litter carbon and litter depth, with the latter being linked to different humus forms. Models using forest and site parameters in addition to litter depth reach explained variation up to +60%, while models solely using forest and site parameters were clearly inferior in estimating litter carbon (< 30% explained variation). Validation with German, Swedish and Austrian data confirms that litter depth, key forest and site parameters (i.e., air temperature, soil pH, share of broadleaves, soil carbon) are needed for predicting litter carbon with bias < 1 tC/ha and root mean square error < 15 tC/ha. A model estimating litter carbon by first estimating litter bulk density and then multiplying litter bulk density with measured litter depth best explained the observed increase in litter carbon of Austrian forests, with lowest bias, plausible results, and 64% explained variation. Measured litter depth is thus a potent proxy for litter carbon without invasive, time‐demanding measurements. We discuss potential research topics (including soil fauna, role of pH in litter decay, using large‐scale litter depth surveys such as National Forest Inventories) to explore the still large unexplained variation of litter carbon. [ABSTRACT FROM AUTHOR]
Copyright of European Journal of Soil Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Can We Derive Climate‐Forest‐Soil Dependent Proxies for&#160;Litter Carbon?
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– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Organic soil layers, including litter, fermentation and humus layers depending on humus form, are a large carbon pool in forests. Soil carbon in organic and mineral layers can be quantified using (i) direct field observations using profiles or cores, (ii) pedo‐transfer functions using simple‐to‐measure proxies for soil carbon, or (iii) biogeochemical modelling considering soil carbon input and output. Despite large amounts of soil data available for researchers, there is little knowledge available on suitable proxies and estimation concepts for carbon in soil layers of predominantly organic origin (here called litter carbon), compared to carbon in mineral soil layers. Here, we test models using litter carbon measurements from Austria. We consider forest and site information as well as litter depth measurements as input data in a machine learning approach for covariate selection and fit multivariate models with remaining significant covariates. We validate the developed models versus independent validation data sets. Our results show a clear relationship between litter carbon and litter depth, with the latter being linked to different humus forms. Models using forest and site parameters in addition to litter depth reach explained variation up to +60%, while models solely using forest and site parameters were clearly inferior in estimating litter carbon (&lt; 30% explained variation). Validation with German, Swedish and Austrian data confirms that litter depth, key forest and site parameters (i.e., air temperature, soil pH, share of broadleaves, soil carbon) are needed for predicting litter carbon with bias &lt; 1 tC/ha and root mean square error &lt; 15 tC/ha. A model estimating litter carbon by first estimating litter bulk density and then multiplying litter bulk density with measured litter depth best explained the observed increase in litter carbon of Austrian forests, with lowest bias, plausible results, and 64% explained variation. Measured litter depth is thus a potent proxy for litter carbon without invasive, time‐demanding measurements. We discuss potential research topics (including soil fauna, role of pH in litter decay, using large‐scale litter depth surveys such as National Forest Inventories) to explore the still large unexplained variation of litter carbon. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: &lt;i&gt;Copyright of European Journal of Soil Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/ejss.70135
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 1
    Subjects:
      – SubjectFull: Standard deviations
        Type: general
      – SubjectFull: Forest density
        Type: general
      – SubjectFull: Soil mineralogy
        Type: general
      – SubjectFull: Carbon in soils
        Type: general
      – SubjectFull: Forest surveys
        Type: general
    Titles:
      – TitleFull: Can We Derive Climate‐Forest‐Soil Dependent Proxies for Litter Carbon?
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Neumann, Mathias
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            NameFull: Herzberger, Edwin
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            NameFull: Englisch, Michael
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            NameFull: Hasenauer, Hubert
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          Dates:
            – D: 01
              M: 05
              Text: May/Jun2025
              Type: published
              Y: 2025
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              Value: 13510754
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              Value: 76
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              Value: 3
          Titles:
            – TitleFull: European Journal of Soil Science
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