Articles | Volume 19, issue 2
https://doi.org/10.5194/bg-19-375-2022
https://doi.org/10.5194/bg-19-375-2022
Research article
 | 
24 Jan 2022
Research article |  | 24 Jan 2022

A robust initialization method for accurate soil organic carbon simulations

Eva Kanari, Lauric Cécillon, François Baudin, Hugues Clivot, Fabien Ferchaud, Sabine Houot, Florent Levavasseur, Bruno Mary, Laure Soucémarianadin, Claire Chenu, and Pierre Barré

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Cited articles

Andriulo, A. E., Mary, B., and Guerif, J.: Modelling soil carbon dynamics with various cropping sequences on the rolling pampas, Agronomie, 19, 365–377, 1999. 
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Bailey, V. L., Bond-Lamberty, B., DeAngelis, K., Grandy, A. S., Hawkes, C. V., Heckman, K., Lajtha, K., Phillips, R. P., Sulman, B. N., Todd-Brown, K. E. O., and Wallenstein, M. D.: Soil carbon cycling proxies: Understanding their critical role in predicting climate change feedbacks, Glob. Chang. Biol., 24, 895–905, https://doi.org/10.1111/gcb.13926, 2018. 
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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.
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