at Google ScholarTitle data
Haas, Bettina ; Baumberger, Maiken ; Müller, Mona ; Schweers, Julian ; Hülsmann, Lisa ; Lehndorff, Eva ; Meyer, Hanna ; Meyer, Nele:
Regional spatial and vertical patterns of SOC stocks in a low mountain landscape in Germany.
In: Geoderma Regional.
Vol. 46
(2026)
.
- e01102.
ISSN 2352-0094
DOI: https://doi.org/10.1016/j.geodrs.2026.e01102
Project information
| Project title: |
Project's official title Project's id Carbon4D: Ein landschaftsskaliges Modell der Mineralisation organischen Bodenkohlenstoffs in Raum, Tiefe und Zeit 455085607 |
|---|---|
| Project financing: |
Deutsche Forschungsgemeinschaft |
Abstract in another language
Despite growing attention on soil organic carbon (SOC) stocks and dynamics, uncertainties persist in our understanding of their regulating factors, especially in the subsoil. Here, we examined regional patterns of SOC stocks and their relation to land use and other potential predictors, including average soil temperature, average soil moisture, and topography. To this end, we took 96 soil cores in the Fichtelgebirge mountains, Germany, including three different land use types (cropland, coniferous forest, and meadow) up to a depth of one meter, and sliced them into 10 cm increments. The influence of land use was evident down to one meter but not across all soil depth increments. Coniferous forests exhibited the highest SOC stocks both in the topsoil (including the organic layer) and in total. On average, over 20 of SOC was stored below 30 cm in all land use types, however with a high variability. Land use was the relatively most important factor explaining SOC stock patterns in the top 20 cm of soil. In the subsoil, climatic factors and topography became more relevant to explain the SOC stocks. Soil temperature was positively associated with SOC stocks in the topsoil, but this relationship reversed and became negative in deeper soil increments. A similar but less-pronounced trend with depth was observed for soil moisture. The declining relative importance of all predictors with depth underscores the need for high-resolution, depth-resolved field measurements to disentangle and quantify interactions among SOC stock predictors, particularly in the subsoil.