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

Haas, Bettina; Baumberger, Maiken; Müller, Mona; Schweers, Julian; Hülsmann, Lisa; Lehndorff, Eva; Meyer, Hanna; Meyer, Nele

Research article (journal) | Peer reviewed

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 on SOC stocks. To this end, we took 96 soil cores in the Fichtelgebirge mountains, Germany, of 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 subsoil 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.

Details about the publication

JournalGeoderma Regional
Page rangee01102-e01102
StatusPublished
Release year2026
Language in which the publication is writtenEnglish
KeywordsSOC stock predictors, SOC stock patterns, Subsoil SOC, Regional scale, Interpretable machine learning

Authors from the University of Münster

Baumberger, Maiken
Meyer, Hanna