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Regional spatial and vertical patterns of SOC stocks in a low mountain landscape in Germany

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00009462
URN to cite this document: urn:nbn:de:bvb:703-epub-9462-0

Title 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 der Verlagsversion: https://doi.org/10.1016/j.geodrs.2026.e01102

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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

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.

Further data

Item Type: Article in a journal
Keywords: SOC stock predictors; SOC stock patterns; Subsoil SOC; Regional scale; Interpretable machine learning
DDC Subjects: 500 Science > 500 Natural sciences
Institutions of the University: Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Chair Soil Ecology > Chair Soil Ecology - Univ.-Prof. Dr. Eva Lehndorff
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Junior Professor Ecosystem Analysis and Simulation > Junior Professor Ecosystem Analysis and Simulation - Juniorprof. Dr. Lisa Hülsmann
Research Institutions > Central research institutes > Bayreuth Center of Ecology and Environmental Research- BayCEER
Faculties
Faculties > Faculty of Biology, Chemistry and Earth Sciences
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Chair Soil Ecology
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Junior Professor Ecosystem Analysis and Simulation
Research Institutions
Research Institutions > Central research institutes
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-9462-0
Date Deposited: 03 Jul 2026 11:42
Last Modified: 03 Jul 2026 11:43
URI: https://epub.uni-bayreuth.de/id/eprint/9462

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