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Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem

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Abstract

Spatial prediction of soil organic matter is a global challenge and of particular importance for regions with intensive land use and where availability of soil data is limited. This study evaluated a Digital Soil Mapping (DSM) approach to model the spatial distribution of stocks of soil organic carbon (SOC), total carbon (Ctot), total nitrogen (Ntot) and total sulphur (Stot) for a data-sparse, semi-arid catchment in Inner Mongolia, Northern China. Random Forest (RF) was used as a new modeling tool for soil properties and Classification and Regression Trees (CART) as an additional method for the analysis of variable importance. At 120 locations soil profiles to 1 m depth were analyzed for soil texture, SOC, Ctot, Ntot, Stot, bulk density (BD) and pH. On the basis of a digital elevation model, the catchment was divided into pixels of 90 m × 90 m and for each cell, predictor variables were determined: land use unit, Reference Soil Group (RSG), geological unit and 12 topography-related variables. Prediction maps showed that the highest amounts of SOC, Ctot, Ntot and Stot stocks are stored under marshland, steppes and mountain meadows. River-like structures of very high elemental stocks in valleys within the steppes are partly responsible for the high amounts of SOC for grasslands (81–84% of total catchment stocks). Analysis of variable importance showed that land use, RSG and geology are the most important variables influencing SOC storage. Prediction accuracy of the RF modeling and the generated maps was acceptable and explained variances of 42 to 62% and 66 to 75%, respectively. A decline of up to 70% in elemental stocks was calculated after conversion of steppe to arable land confirming the risk of rapid soil degradation if steppes are cultivated. Thus their suitability for agricultural use is limited.

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Acknowledgements

The authors would like to thank Thomas Lendvaczky, Michael Schnabel, Sarah Schroeder, Hermann Autengruber, Peter Schad, Markus Steffens, Angelika Kölbl, Elfriede Schörk and Livia Wissing for their efforts in field sampling, laboratory work, helpful suggestions and for providing their knowledge. Qimei Lin and Yuandi Zhu are acknowledged for logistic handling. We also thank Xingguo Han, Yongfei Bai and the Institute of Botany (Chinese Academy of Sciences) for the opportunity to work at IMGERS. We are grateful to the Deutsche Forschungsgemeinschaft (DFG) for funding the MAGIM project (KO 1035/26-3, Forschergruppe 536 MAGIM—Matter fluxes in grasslands of Inner Mongolia as influenced by stocking rate).

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Authors and Affiliations

  1. Lehrstuhl für Bodenkunde, Department für Ökologie und Ökosystemmanagement, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Technische Universität München, 85350, Freising-Weihenstephan, Germany

    Martin Wiesmeier & Ingrid Kögel-Knabner

  2. Institute for Landscape Ecology and Resources Management, Justus-Liebig-University Giessen, Heinrich-Buff-Ring 26, 35392, Giessen, Germany

    Frauke Barthold & Benjamin Blank

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  1. Martin Wiesmeier
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  2. Frauke Barthold
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  4. Ingrid Kögel-Knabner
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Correspondence to Martin Wiesmeier.

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Responsible Editor: Elizabeth (Liz) A. Stockdale.

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Wiesmeier, M., Barthold, F., Blank, B. et al. Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem. Plant Soil 340, 7–24 (2011). https://doi.org/10.1007/s11104-010-0425-z

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