Soil & Water Res., 2022, 17(2):69-79 | DOI: 10.17221/4/2022-SWR

Predictors for digital mapping of forest soil organic carbon stocks in different types of landscapeOriginal Paper

Luboš Borůvka ORCID...*,1, Radim Vašát ORCID...1, Vít Šrámek ORCID...2, Kateřina Neudertová Hellebrandová2, Věra Fadrhonsová2, Milan Sáňka ORCID...3, Lenka Pavlů ORCID...1, Ondřej Sáňka ORCID...3, Oldřich Vacek ORCID...1, Karel Němeček ORCID...1, Shahin Nozari1, Vincent Yaw Oppong Sarkodie ORCID...1
1 Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic
2 Forestry and Game Management Research Institute, Jíloviště-Strnady, Czech Republic
3 RECETOX, Masaryk University, Brno, Czech Republic

Forest soils have a high potential to store carbon and thus mitigate climate change. The information on spatial distribution of soil organic carbon (SOC) stocks is thus very important. This study aims to analyse the importance of environmental predictors for forest SOC stock prediction at the regional and national scale in the Czech Republic. A big database of forest soil data for more than 7 000 sites was compiled from several surveys. SOC stocks were calculated from SOC content and bulk density for the topsoil mineral layer 0-30 cm. Spatial prediction models were developed separately for individual natural forest areas and for four subsets with different altitude range, using random forest method. The importance of environmental predictors in the models strongly differs between regions and altitudes. At lower altitudes, forest edaphic series and soil classes are strong predictors, while at higher altitudes the predictors related to topography become more important. The importance of soil classes depends on the pedodiversity level and on the difference in SOC stock between the classes. The contribution of forest types as predictors is limited when one (mostly coniferous) type dominates. Better prediction results can be obtained in smaller, but consistent regions, like some natural forest areas.

Keywords: carbon stocks; digital soil mapping; environmental covariates; random forests; spatial distribution; terrain attributes

Published: March 4, 2022  Show citation

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Borůvka L, Vašát R, Šrámek V, Neudertová Hellebrandová K, Fadrhonsová V, Sáňka M, et al.. Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape. Soil & Water Res. 2022;17(2):69-79. doi: 10.17221/4/2022-SWR.
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