C and N models Intercomparison – benchmark and ensemble model estimates for grassland production
Sandor, R and Ehrhardt, F and Basso, B and Bellocchi, G and Bhatia, A and Brilli, L and De Antoni Migliorati, M and Doltra, J and Dorich, C and Doro, L and Fitton, N and Giacomini, S and Grace, P and Grant, B and Harrison, MT and Jones, S and Kirschbaum, MUF and Klumpp, K and Laville, P and Leonard, J and Liebig, M and Lieffering, M and Martin, R and McAuliffe, R and Meier, E and Merbold, L and Moore, A and Myrgiotis, V and Newton, P and Pattey, E and Recous, S and Rolinski, S and Sharp, J and Massad, RS and Smith, P and Smith, W and Snow, V and Wu, L and Zhang, Q and Soussana, JF, C and N models Intercomparison - benchmark and ensemble model estimates for grassland production, Advances in Animal Biosciences, 7, (3) pp. 245-247. ISSN 2040-4700 (2016) [Contribution to Refereed Journal]
Much of the uncertainty in crop and grassland model predictions of how arable and grassland systems respond to changes in management and environmental drivers can be attributed to differences in the structure of these models. This has created an urgent need for international benchmarking of models, in which uncertainties are estimated by running several models that simulate the same physical and management conditions (ensemble modelling) to generate expanded envelopes of uncertainty in model predictions (Asseng et al., 2013). Simulations of C and N fluxes, in particular, are inherently uncertain because they are driven by complex interactions (Sándor et al., 2016) and complicated by considerable spatial and temporal variability in the measurements. In this context, the Integrative Research Group of the Global Research Alliance (GRA) on Agricultural Greenhouse Gases promotes a coordinated activity across multiple international projects (e.g. C and N Models Inter-comparison and Improvement to assess management options for GHG mitigation in agrosystems worldwide (C-N MIP) and Models4Pastures of the FACCE-JPI, https://www.faccejpi.com) to benchmark and compare simulation models that estimate C–N related outputs (including greenhouse gas emissions) from arable crop and grassland systems ( http://globalresearchalliance.org/e/model-intercomparison-on-agricultural-ghg-emissions). This study presents some preliminary results on the uncertainty of outputs from 12 grassland models, whereas exploring differences in model response when increasing data resources are used for model calibration.
Contribution to Refereed Journal
AgMIP, Global Research Alliance, greenhouse gases, crop, grassland, model, simulation, grass, cutting