Ensemble modelling of carbon fluxes in grasslands and croplands

Renáta Sándor, Fiona Ehrhardt, Peter Grace, Sylvie Recous, Pete Smith, Valerie Snow, Jean-Francois Soussana, Bruno Basso, Arti Bhatia, Lorenzo Brilli, Jordi Doltra, Christopher Dorich, Luca Doro, Nuala Fitton, Brian Grant, Matthew Harrison, Ute Skiba, Miko Kirschbaum, Katja Klumpp, Patricia LavilleJoël Léonard, Raphaël Martin, Raia Silvia Massad, Andrew D Moore, Vasilis Myrgiotis, Elizabeth Pattey, Zhang Qing, Susanne Rolinski, Joanna Sharp, Ward N Smith, Lianhai Wu, Gianni Bellochi

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)


Croplands and grasslands are agricultural systems that contribute to land–atmosphere exchanges of carbon (C). We evaluated and compared gross primary production (GPP), ecosystem respiration (RECO), net ecosystem exchange (NEE) of CO2, and two derived outputs - C use efficiency (CUE=-NEE/GPP) and C emission intensity (IntC= -NEE/Offtake [grazed or harvested biomass]). The outputs came from 23 models (11 crop-specific, eight grassland-specific, and four models covering both systems) at three cropping sites over several rotations with spring and winter cereals, soybean and rapeseed in Canada, France and India, and two temperate permanent grasslands in France and the United Kingdom. The models were run independently over multi-year simulation periods in five stages (S), either blind with no calibration and initialization data (S1), using historical management and climate for initialization (S2), calibrated against plant data (S3), plant and soil data together (S4), or with the addition of C and N fluxes (S5). Here, we provide a framework to address methodological uncertainties and contextualize results. Most of the models overestimated or underestimated the C fluxes observed during the growing seasons (or the whole years for grasslands), with substantial differences between models. For each simulated variable, changes in the multi-model median (MMM) from S1 to S5 was used as a descriptor of the ensemble performance. Overall, the greatest improvements (MMM approaching the mean of observations) were achieved at S3 or higher calibration stages. For instance, grassland GPP MMM was equal to 1632 g C m−2 yr-1 (S5) while the observed mean was equal to 1763 m-2 yr-1 (average for two sites). Nash-Sutcliffe modelling efficiency coefficients indicated that MMM outperformed individual models in 92.3 % of cases. Our study suggests a cautious use of large-scale, multi-model ensembles to estimate C fluxes in agricultural sites if some site-specific plant and soil observations are available for model calibration. The further development of crop/grassland ensemble modelling will hinge upon the interpretation of results in light of the way models represent the processes underlying C fluxes in complex agricultural systems (grassland and crop rotations including fallow periods).

Original languageEnglish
Article number107791
Number of pages16
JournalField Crops Research
Early online date25 Apr 2020
Publication statusPublished - 1 Jul 2020

Bibliographical note

This study was coordinated by the Integrative Research Group of the Global Research Alliance (GRA) on agricultural GHGs and was supported by five research projects (CN‐MIP, Models4Pastures, MACSUR, COMET‐Global and MAGGNET), which received funding by a multi-partner call on agricultural greenhouse gas research of the Joint Programming Initiative ‘FACCE’ through its national financing bodies.

RS received mobility funding from the French Embassy in Budapest (Hungary) by way of “Make Our Planet Great Again” programme. SR (PIK) acknowledges funding from the MACMIT project (BMBF under grant 01LN1317A). US acknowledges funding for the data collection through the EU projects GREENGRASS (EC EVK2-CT2001-00105), CarboEurope (GOCE-CT-2003-505572) and the NitroEurope Integrated Project (017841), and SRUC’s contribution to compile the data (Stephanie K. Jones and Robert M. Rees). Giovanna Seddaiu from University of Sassari (Italy) is acknowledged for her support with EPIC simulations.


  • C fluxes
  • croplands
  • grasslands
  • multi-model ensemble
  • multi-model median (MMM)
  • Grasslands
  • Multi-model ensemble
  • Croplands
  • Multi-model median (MMM)


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