4.7 Article

The carbon balance of European croplands: A cross-site comparison of simulation models

Journal

AGRICULTURE ECOSYSTEMS & ENVIRONMENT
Volume 139, Issue 3, Pages 419-453

Publisher

ELSEVIER
DOI: 10.1016/j.agee.2010.08.004

Keywords

Cropland; Crop; Eddy flux; Carbon; CO2; Modelling

Funding

  1. Royal Society
  2. EU [505572, 017841]
  3. Consolider-Ingenio [Graccie]
  4. Balangeis [Sum2006-00030-C02]
  5. Carbored-ES [CGL2006-14195-C02-01]
  6. Natural Environment Research Council [earth010003] Funding Source: researchfish
  7. NERC [earth010003] Funding Source: UKRI

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Croplands cover approximately 45% of Europe and play an important role in the overall carbon budget of the continent. However, the estimation of their carbon balance remains uncertain due to the diversity of crops and cropping systems together with the strong influence of human management. Here, we present a multi-site model comparison for four cropland ecosystem models namely the DNDC, ORCHIDEE-STICS, CERES-EGC and SPA models. We compare the accuracy of the models in predicting net ecosystem exchange (NEE), gross primary production (GPP), ecosystem respiration (R-eco) as well as actual evapo-transpiration (ETa) for winter wheat (Triticum aestivum L) and maize (Zea mays L) derived from eddy covariance measurements on five sites along a gradient of climatic conditions from eastern to southwesterly Europe. The models are all able to simulate daily GPP. The simulation results for daily ETa and R-eco are, however, less accurate. The resulting simulation of daily NEE is adequate except in some cases where models fail due to a lack in phase and amplitude alignment. ORCHIDEE-STICS and SPA show the best performance. Nevertheless, they are not able to simulate full crop rotations or the multiple management practices used. CERES-EGC, and especially DNDC, although exhibiting a lower level of model accuracy, are able to simulate such conditions, resulting in more accurate simulation of annual cumulative NEE. (C) 2010 Elsevier B.V. All rights reserved.

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