IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i8p1298-d1450847.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/8/1298/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/14/8/1298/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Odunayo David Adeniyi & Hauwa Bature & Michael Mearker, 2024. "A Systematic Review on Digital Soil Mapping Approaches in Lowland Areas," Land, MDPI, vol. 13(3), pages 1-22, March.
    2. Giuseppe Lo Papa & Calogero Schillaci & Maria Fantappiè & Giuliano Langella, 2024. "Editorial of the Special Issue Digital Soil Mapping, Decision Support Tools and Soil Monitoring Systems in the Mediterranean," Land, MDPI, vol. 13(6), pages 1-4, June.
    3. Dorijan Radočaj & Mateo Gašparović & Mladen Jurišić, 2024. "Open Remote Sensing Data in Digital Soil Organic Carbon Mapping: A Review," Agriculture, MDPI, vol. 14(7), pages 1-19, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:14:y:2024:i:8:p:1298-:d:1450847. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.