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Spatial Prediction of Organic Matter Quality in German Agricultural Topsoils

Author

Listed:
  • Ali Sakhaee

    (Thünen Institute of Climate-Smart Agriculture, 38116 Braunschweig, Germany)

  • Thomas Scholten

    (Soil Science and Geomorphology, Institute of Geography, Eberhard Karls University Tübingen, 72070 Tübingen, Germany)

  • Ruhollah Taghizadeh-Mehrjardi

    (Soil Science and Geomorphology, Institute of Geography, Eberhard Karls University Tübingen, 72070 Tübingen, Germany)

  • Mareike Ließ

    (Department of Soil System Science, Helmholtz Centre for Environmental Research—UFZ, 06120 Halle (Saale), Germany
    Data Science Division, Department of Agriculture, Food, and Nutrition, University of Applied Sciences Weihenstephan-Triesdorf, 91746 Weidenbach, Germany)

  • Axel Don

    (Thünen Institute of Climate-Smart Agriculture, 38116 Braunschweig, Germany)

Abstract

Soil organic matter (SOM) and the ratio of soil organic carbon to total nitrogen (C/N ratio) are fundamental to the ecosystem services provided by soils. Therefore, understanding the spatial distribution and relationships between the SOM components mineral-associated organic matter (MAOM), particulate organic matter (POM), and C/N ratio is crucial. Three ensemble machine learning models were trained to obtain spatial predictions of the C/N ratio, MAOM, and POM in German agricultural topsoil (0–10 cm). Parameter optimization and model evaluation were performed using nested cross-validation. Additionally, a modification to the regressor chain was applied to capture and interpret the interactions among the C/N ratio, MAOM, and POM. The ensemble models yielded mean absolute percent errors (MAPEs) of 8.2% for the C/N ratio, 14.8% for MAOM, and 28.6% for POM. Soil type, pedo-climatic region, hydrological unit, and soilscapes were found to explain 75% of the variance in MAOM and POM, and 50% in the C/N ratio. The modified regressor chain indicated a nonlinear relationship between the C/N ratio and SOM due to the different decomposition rates of SOM as a result of variety in its nutrient quality. These spatial predictions enhance the understanding of soil properties’ distribution in Germany.

Suggested Citation

  • Ali Sakhaee & Thomas Scholten & Ruhollah Taghizadeh-Mehrjardi & Mareike Ließ & Axel Don, 2024. "Spatial Prediction of Organic Matter Quality in German Agricultural Topsoils," Agriculture, MDPI, vol. 14(8), pages 1-25, August.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:8:p:1298-:d:1450847
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    References listed on IDEAS

    as
    1. Frank Lehmkuhl & Stephan Pötter & Annika Pauligk & Janina Bösken, 2018. "Loess and other Quaternary sediments in Germany," Journal of Maps, Taylor & Francis Journals, vol. 14(2), pages 330-340, November.
    2. Odunayo David Adeniyi & Alexander Brenning & Alice Bernini & Stefano Brenna & Michael Maerker, 2023. "Digital Mapping of Soil Properties Using Ensemble Machine Learning Approaches in an Agricultural Lowland Area of Lombardy, Italy," Land, MDPI, vol. 12(2), pages 1-17, February.
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