## 5 Summary and Conclusions  
This study investigated the performances of the CenW ecosystem model to simulate carbon and water fluxes from paired eddy covariance towers of two managed grassland systems in France with different management practices: moving vs grazing. It showed that once parameterized, model/data agreement was very good for both sites and that CenW could adequately reproduce flux variability in response to management and climatic condition at daily, seasonal and interannual time scales. Model efficiencies for daily CO\({}_{2}\) fluxes were 0.65-0.80 and 0.73-0.85 for grazed and mowed paddocks, respectively. The mowed grassland ET, averaged SWC and harvested biomass were modeled with efficiencies of 0.82, 0.85, and 0.80, respectively. For the grazed paddock, model efficiency for daily ET was 0.80.  
Our study showed that management practices highly determined the temporal dynamics and seasonal and interannual variabilities of CO\({}_{2}\) fluxes and the C status of the grazed pasture. In addition, most of previous studies which derived annual carbon budgets of managed pastures from EC measurements assumed or showed that grazing animal' respiratory losses were satisfactorily captured in NEE fluxes at annual time scales but showed weaknesses at smaller time scales [22; 79].  
It also highlighted that large discrepancies existed between measured and modeled net carbon exchange and ecosystem respiration rates during grazing events and that it is likely that large losses of CO\({}_{2}\) to the atmosphere were not fully captured by the eddy covariance system. The model/data comparison showed that flux processing and interpretation needed to be done carefully in grazed systems to account for the presence of dairy cows in the paddock. So far, only few studies have used eddy covariance measurement in combination with process-based models in grazed pastures. Here our results clearly demonstrate that grazing animals' respiratory flux is most often not captured by EC systems, which could lead to substantial bias in NEE data taken during grazing events.  
In addition, because of the importance of CO\({}_{2}\) losses of carbon not accounted for in the annual C budget, large overestimations of the C status of the farm are likely to be made.  
The capture or not by EC of cows respiration is site specific, using detailed ecosystem models incorporating farming practices and their effects on vegetation and C dynamics could help to identify and correct possible issues with EC data in intensively managed grasslands. Model/data agreements for the mowed paddock were higher than those obtained for the grazed paddock, certainly because mowed systems are less complex and disturbed than grazed ones.  
Conceptualization, A.C., N.P., and N.S.; Methodology, N.P., A.C., and M.K.; Software, M.K.; Validation, N.P., A.C., and M.K.; Formal Analysis, N.P.; Investigation, N.P. and A.C.; Resources, A.C.; Writing--Original Draft Preparation, N.P.; Writing--Review and Editing, A.C., N.S., C.F., K.K., M.K.; Supervision, A.C.; Project Administration, A.C.; Funding Acquisition, A.C.  
The research leading to these results has received funding principally by the New Zealand Government to support the objectives of the \"Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases under grants SOW12-GPLER-LCR-PM (Proposal ID 16949-15 LCR)\", \"The AnaEE France (ANR-11-INBS-0001)\", \"AllEnvi\", and \"CNRS-INSU\".  
We would like to thank the National Research Infrastructure \"Agro-ecosystems, Cycles Biogeochimique et Biodiversite\" (ACBB) for their support in field experiment and the database. We are deeply indebted to [PERSON], [PERSON] and [PERSON] for their substantial technical assistance. Last but not least we would like to thanks Dr. [PERSON] (Department of Biological Sciences, University of Alabama, USA) for his technical support in refiltering the dataset.  
The authors declare no conflict of interest.