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? |
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| 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] |
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| Database: | Engineering Source |
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