Articles | Volume 17, issue 20
https://doi.org/10.5194/bg-17-5025-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-17-5025-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Modelling dynamic interactions between soil structure and the storage and turnover of soil organic matter
Katharina Hildegard Elisabeth Meurer
CORRESPONDING AUTHOR
Department of Soil and Environment, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
Claire Chenu
AgroParisTech, UMR Ecosys INRA-AgroParisTech, Université
Paris-Saclay, 78850 Thiverval-Grignon, France
Elsa Coucheney
Department of Soil and Environment, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
Anke Marianne Herrmann
Department of Soil and Environment, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
Thomas Keller
Department of Soil and Environment, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
Department of Agroecology and Environment, Agroscope, 8046
Zürich, Switzerland
Thomas Kätterer
Department of Ecology, Swedish University of Agricultural Sciences,
750 05 Uppsala, Sweden
David Nimblad Svensson
Department of Soil and Environment, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
Nicholas Jarvis
Department of Soil and Environment, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
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Soil organic carbon (SOC) is crucial for climate regulation, soil quality, and food security. Predicting its evolution over the next decades is key for appropriate land management policies. However, SOC projections lack accuracy. Here we show for the first time that PARTYSOC, an approach combining thermal analysis and machine learning optimizes the accuracy of SOC model simulations at independent sites. This method can be applied at large scales, improving SOC projections on a continental scale.
Elisa Bruni, Bertrand Guenet, Yuanyuan Huang, Hugues Clivot, Iñigo Virto, Roberta Farina, Thomas Kätterer, Philippe Ciais, Manuel Martin, and Claire Chenu
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Increasing soil organic carbon (SOC) stocks is beneficial for climate change mitigation and food security. One way to enhance SOC stocks is to increase carbon input to the soil. We estimate the amount of carbon input required to reach a 4 % annual increase in SOC stocks in 14 long-term agricultural experiments around Europe. We found that annual carbon input should increase by 43 % under current temperature conditions, by 54 % for a 1 °C warming scenario and by 120 % for a 5 °C warming scenario.
Lauric Cécillon, François Baudin, Claire Chenu, Bent T. Christensen, Uwe Franko, Sabine Houot, Eva Kanari, Thomas Kätterer, Ines Merbach, Folkert van Oort, Christopher Poeplau, Juan Carlos Quezada, Florence Savignac, Laure N. Soucémarianadin, and Pierre Barré
Geosci. Model Dev., 14, 3879–3898, https://doi.org/10.5194/gmd-14-3879-2021, https://doi.org/10.5194/gmd-14-3879-2021, 2021
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Partitioning soil organic carbon (SOC) into fractions that are stable or active on a century scale is key for more accurate models of the carbon cycle. Here, we describe the second version of a machine-learning model, named PARTYsoc, which reliably predicts the proportion of the centennially stable SOC fraction at its northwestern European validation sites with Cambisols and Luvisols, the two dominant soil groups in this region, fostering modelling works of SOC dynamics.
Mathieu Chassé, Suzanne Lutfalla, Lauric Cécillon, François Baudin, Samuel Abiven, Claire Chenu, and Pierre Barré
Biogeosciences, 18, 1703–1718, https://doi.org/10.5194/bg-18-1703-2021, https://doi.org/10.5194/bg-18-1703-2021, 2021
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Evolution of organic carbon content in soils could be a major driver of atmospheric greenhouse gas concentrations over the next century. Understanding factors controlling carbon persistence in soil is a challenge. Our study of unique long-term bare-fallow samples, depleted in labile organic carbon, helps improve the separation, evaluation and characterization of carbon pools with distinct residence time in soils and gives insight into the mechanisms explaining soil organic carbon persistence.
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Short summary
We present a simple model that describes, for the first time, the dynamic two-way interactions between soil organic matter and soil physical properties (porosity, pore size distribution, bulk density and layer thickness). The model was able to accurately reproduce the changes in soil organic carbon, soil bulk density and surface elevation observed during 63 years in a field trial, as well as soil water retention curves measured at the end of the experimental period.
We present a simple model that describes, for the first time, the dynamic two-way interactions...
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