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  Estimation of soil carbon input in France: An inverse modelling approach

Meersmans, J., Martin, M. P., Lacarce, E., Orton, T. G., Baets, S. D., Gourrat, M., et al. (2013). Estimation of soil carbon input in France: An inverse modelling approach. Pedosphere, 23(4), 422-436.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0014-4E45-5 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0014-75DF-E
Genre: Journal Article

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BPR025.pdf (Publisher version), 601KB
 
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Meersmans, J.1, Author
Martin, M. P., Author
Lacarce, E., Author
Orton, T. G., Author
Baets, S. DE, Author
Gourrat, M., Author
Saby, N. P. A., Author
Wetterlind, J., Author
Bispo, A., Author
Quine, T. A., Author
Arrouays, D., Author
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: Development of a quantitative understanding of soil organic carbon (SOC) dynamics is vital for management of soil to sequester carbon (C) and maintain fertility, thereby contributing to food security and climate change mitigation. There are well-established process-based models that can be used to simulate SOC stock evolution; however, there are few plant residue C input values and those that exist represent a limited range of environments. This limitation in a fundamental model component (i.e., C input) constrains the reliability of current SOC stock simulations. This study aimed to estimate crop-specific and environment-specific plant-derived soil C input values for agricultural sites in France based on data from 700 sites selected from a recently established French soil monitoring network (the RMQS database). Measured SOC stock values from this large scale soil database were used to constrain an inverse RothC modelling approach to derive estimated C input values consistent with the stocks. This approach allowed us to estimate significant crop-specific C input values (P <0.05) for 14 out of 17 crop types in the range from 1.84 ± 0.69 t C ha−1 year−1 (silage corn) to 5.15 ± 0.12 t C ha−1 year−1 (grassland/pasture). Furthermore, the incorporation of climate variables improved the predictions. C input of 4 crop types could be predicted as a function of temperature and 8 as a function of precipitation. This study offered an approach to meet the urgent need for crop-specific and environment-specific C input values in order to improve the reliability of SOC stock prediction.

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 Dates: 20132013
 Publication Status: Published online
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 Identifiers: Other: BPR025
ISSN: 1002-0160/CN 32-1315/P
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Title: Pedosphere
Source Genre: Journal
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Publ. Info: Beijing, China : Science Press
Pages: - Volume / Issue: 23 (4) Sequence Number: - Start / End Page: 422 - 436 Identifier: Other: 1002-0160
CoNE: https://pure.mpg.de/cone/journals/resource/1002-0160