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dc.creatorBruni, Elisaes_ES
dc.creatorChenu, Clairees_ES
dc.creatorAbramoff, Rose Z.es_ES
dc.creatorBaldoni, Guidoes_ES
dc.creatorBarkusky, Dietmares_ES
dc.creatorClivot, Hugueses_ES
dc.creatorHuang, Yuanyuanes_ES
dc.creatorKätterer, Thomases_ES
dc.creatorPikula, Dorotaes_ES
dc.creatorSpiegel, Heidees_ES
dc.creatorVirto Quecedo, Íñigoes_ES
dc.creatorGuenet, Bertrandes_ES
dc.identifier.citationBruni, E., Chenu, C., Abramoff, R. Z., Baldoni, G., Barkusky, D., Clivot, H., Huang, Y., Kätterer, T., Pikuła, D., Spiegel, H., Virto, I., & Guenet, B. (2022). Multi‐modelling predictions show high uncertainty of required carbon input changes to reach a 4‰ target. European Journal of Soil Science, 73(6).
dc.description.abstractSoils store vast amounts of carbon (C) on land, and increasing soil organic carbon (SOC) stocks in already managed soils such as croplands may be one way to remove C from the atmosphere, thereby limiting subsequent warming. The main objective of this study was to estimate the amount of additional C input needed to annually increase SOC stocks by 4‰ at 16 long-term agricultural experiments in Europe, including exogenous organic matter (EOM) additions. We used an ensemble of six SOC models and ran them under two configurations: (1) with default parametrization and (2) with parameters calibrated site-by-site to fit the evolution of SOC stocks in the control treatments (without EOM). We compared model simulations and analysed the factors generating variability across models. The calibrated ensemble was able to reproduce the SOC stock evolution in the unfertilised control treatments. We found that, on average, the experimental sites needed an additional 1.5 ± 1.2 Mg C ha−1 year−1 to increase SOC stocks by 4‰ per year over 30 years, compared to the C input in the control treatments (multi-model median ± median standard deviation across sites). That is, a 119% increase compared to the control. While mean annual temperature, initial SOC stocks and initial C input had a significant effect on the variability of the predicted C input in the default configuration (i.e., the relative standard deviation of the predicted C input from the mean), only water-related variables (i.e., mean annual precipitation and potential evapotranspiration) explained the divergence between models when calibrated. Our work highlights the challenge of increasing SOC stocks in agriculture and accentuates the need to increasingly lean on multi-model ensembles when predicting SOC stock trends and related processes. To increase the reliability of SOC models under future climate change, we suggest model developers to better constrain the effect of water-related variables on SOC decomposition.en
dc.description.sponsorshipThis work benefited from the French state aid managed by the ANR under the “Investissements d'avenir” programme with the reference ANR-16-CONV-0003 (CLAND project). EB, RZA and BG are supported by the European Union's Horizon 2020 research and innovation program under grant agreement No 101000289 (Holisoils project). RZA was also supported by the United States Department of Energy, Office of Science, and Office of Biological and Environmental Research. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the United States Department of Energy under contract DE-AC05-00OR22725.en
dc.relation.ispartofEuropean Journal of Soil Science 2022;73:e13330en
dc.rightsCreative Commons Attribution 4.0 International (CC BY 4.0)en
dc.subject4 per 1000 initiativeen
dc.subjectCarbon sequestrationen
dc.subjectClimate changeen
dc.subjectEuropean targetsen
dc.subjectSoil organic carbonen
dc.titleMulti-modelling predictions show high uncertainty of required carbon input changes to reach a 4‰ targeten
dc.typeArtículo / Artikuluaes
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.relation.projectIDinfo:eu-repo/grantAgreement/European Commission/Horizon 2020 Framework Programme/101000289en
dc.type.versionVersión publicada / Argitaratu den bertsioaes

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Creative Commons Attribution 4.0 International (CC BY 4.0)
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El Repositorio ha recibido la ayuda de la Fundación Española para la Ciencia y la Tecnología para la realización de actividades en el ámbito del fomento de la investigación científica de excelencia, en la Línea 2. Repositorios institucionales (convocatoria 2020-2021).
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