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Soil organic carbon sequestration in agricultural long-term field experiments as derived from particulate and mineral-associated organic matter

  • Christopher Just
  • , Martin Armbruster
  • , Dietmar Barkusky
  • , Michael Baumecker
  • , Michael Diepolder
  • , Thomas F. Döring
  • , Lorenz Heigl
  • , Bernd Honermeier
  • , Melkamu Jate
  • , Ines Merbach
  • , Constanze Rusch
  • , David Schubert
  • , Franz Schulz
  • , Kathlin Schweitzer
  • , Sabine Seidel
  • , Michael Sommer
  • , Heide Spiegel
  • , Ulrich Thumm
  • , Peer Urbatzka
  • , Jörg Zimmer
  • Ingrid Kögel-Knabner, Martin Wiesmeier
  • Technical University of Munich
  • Agricultural Investigation and Research Centre (LUFA) Speyer
  • Leibniz Centre for Agricultural Landscape Research ZALF
  • Albrecht Daniel Thaer-Institut für Agrar- und Gartenbauwissenschaften
  • Institute for Organic Farming
  • Rheinische Friedrich-Wilhelms-Universität Bonn
  • Justus-Liebig-Universität Gießen
  • Yara International ASA
  • Helmholtz-Zentrum für Umweltforschung (UFZ)
  • Centre for Agronomy and Crop Production
  • Austrian Agency for Health and Food Safety
  • Hohenheim University
  • Brandenburg State Office of Rural Development

Research output: Contribution to journalArticlepeer-review

68 Scopus citations

Abstract

Soil organic matter (SOM) is indispensable for soil health and, in the context of climate change, is considered a significant CO2 sink. Improving agricultural management to increase long-term soil organic carbon (SOC) stocks for mitigating climate change requires tools that estimate short and long-cycling SOM pools. In this study, we analyzed changes in fast-cycling particulate organic matter (POM) and slow-cycling mineral-associated organic matter (MAOM) induced by common management practices, i.e., fertilization and crop rotation in topsoils from 25 Central European long-term field experiments. When relating MAOM-C contents to recent MAOM-C saturation levels, estimated sequestration potentials were only met in coarse-textured soils under appropriate agricultural management or fine-textured soils under extreme organic fertilization. Soil texture, organic fertilization, and below-ground OC inputs through root exudates and root biomass were decisive for estimating MAOM-C, allowing for calibration of a mixed-effects model (Nakagawa's: marginal R2m = 0.6, conditional R2c = 0.89). While the models containing soil texture and organic fertilization parameters can be validated and generalized (R2 = 0.43), the below-ground OC input predictor substantially decreases the generalizability of the validated models (R2 = 0.14). According to quantile regression models, we estimate the average difference in MAOM-C concentration between well-managed and control site (without organic fertilization) topsoils to 4.1 mg g−1 soil. In dependence on the soil bulk density, this amounts to 1.38 – 1.84 t ha−1 MAOM-C stocks or 5.06 – 10.1 t ha−1 CO2-equivalents. POM-C was difficult to predict (R2 = 0.28), presumably due to strong POM dynamics. The POM-C / MAOM-C ratio can inform on the effects of agricultural practices in before/after management change comparisons. Under increasing SOC concentration, an increasing POM-C / MAOM-C ratio indicates that the effects of organic fertilization do not transfer to real effects on long-term SOC sequestration. Because MAOM-C depends on soil texture, this ratio is also a covariate of soil texture, limiting it for comparisons between sites with different textures. However, our data indicate that agricultural long-term field experiment soils constantly approximate MAOM-C saturation when the POM-C/MAOM-C ratio is >0.35. This ratio might be used as a management goal to prevent organic over-fertilization and N loss, especially on coarse-textured soils. Thereby, the POM-C / MAOM-C ratio can help to optimize SOC management and sequestration on agricultural soils and support climate change mitigation strategies in Central Europe.

Original languageEnglish
Article number116472
JournalGeoderma
Volume434
DOIs
StatePublished - Jun 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 2 - Zero Hunger
    SDG 2 Zero Hunger
  2. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • Crop rotation
  • Fertilization
  • Fractionation
  • Mixed-effects modeling
  • POM-C / MAOM-C ratio indicator

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