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Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions


Ehrhardt, F and Soussana, J-F and Bellocchi, G and Grace, P and McAuliffe, R and Recous, S and Sandor, R and Smith, P and Snow, V and Migliorati, MDA and Basso, B and Bhatia, A and Brilli, L and Doltra, J and Dorich, CD and Doro, L and Fitton, N and Giacomini, SJ and Grant, B and Harrison, MT and Jones, SK and Kirschbaum, MUF and Klumpp, K and Laville, P and Leonard, J and Liebig, M and Lieffering, M and Martin, R and Massad, RS and Meier, E and Merbold, L and Moore, AD and Myrgiotis, V and Newton, P and Pattey, E and Rolinski, S and Sharp, J and Smith, WN and Wu, L and Zhang, Q, Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions, Global Change Biology, 24, (2) pp. 603-616. ISSN 1354-1013 (2017) [Refereed Article]

Copyright Statement

Copyright 2017 John Wiley & Sons Ltd.

DOI: doi:10.1111/gcb.13965


Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N2O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2 4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents.Comparisons were performed by reference to the experimental uncertainties of observed yields and N2O emissions. Results showed that across sites and crop/grassland types, 23%40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N2O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2O emissions within experimental uncertainties for 44% and 33% of the crop and grass-land growth cycles, respectively. Partial model calibration (stages 24) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44%to 27%) and to a lesser and more variable extent for N2O emissions. Yield-scaled N2O emissions (N2O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N2O emissions at field scale is discussed.

Item Details

Item Type:Refereed Article
Keywords:agriculture, benchmarking, biogeochemical models, climate change, greenhouse gases, nitrous oxide, soil, yield
Research Division:Agricultural, Veterinary and Food Sciences
Research Group:Agriculture, land and farm management
Research Field:Agricultural systems analysis and modelling
Objective Division:Environmental Policy, Climate Change and Natural Hazards
Objective Group:Mitigation of climate change
Objective Field:Management of greenhouse gas emissions from animal production
UTAS Author:Harrison, MT (Associate Professor Matthew Harrison)
ID Code:122740
Year Published:2017
Web of Science® Times Cited:75
Deposited By:TIA - Research Institute
Deposited On:2017-11-24
Last Modified:2022-07-01

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