Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions. Ehrhardt, F., Soussana, J., Bellocchi, G., Grace, P., McAuliffe, R., Recous, S., Sándor, R., Smith, P., Snow, V., de Antoni Migliorati, M., Basso, B., Bhatia, A., Brilli, L., Doltra, J., Dorich, C., D., Doro, L., Fitton, N., Giacomini, S., J., Grant, B., Harrison, M., T., Jones, S., K., Kirschbaum, M., U., F., Klumpp, K., Laville, P., Léonard, J., Liebig, M., Lieffering, M., Martin, R., Massad, R., S., Meier, E., Merbold, L., Moore, A., D., Myrgiotis, V., Newton, P., Pattey, E., Rolinski, S., Sharp, J., Smith, W., N., Wu, L., & Zhang, Q. Global Change Biology, 2017.
Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions [link]Website  doi  abstract   bibtex   
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 (N2 O) 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 N2 O 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 N2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) 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 N2 O emissions. Yield-scaled N2 O emissions (N2 O 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 N2 O emissions at field scale is discussed.
@article{
 title = {Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions},
 type = {article},
 year = {2017},
 pages = {603-616},
 websites = {http://doi.wiley.com/10.1111/gcb.13965},
 id = {8ea310de-031a-3d9b-9b43-071bbb8702fe},
 created = {2018-02-27T15:25:53.354Z},
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 profile_id = {5c1040db-25e3-36ea-a919-0994a44709e7},
 group_id = {c4af41cc-7e3c-3fd3-9982-bdb923596eee},
 last_modified = {2020-09-08T15:25:47.207Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Ehrhardt2017},
 private_publication = {false},
 abstract = {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 (N2 O) 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 N2 O 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 N2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) 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 N2 O emissions. Yield-scaled N2 O emissions (N2 O 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 N2 O emissions at field scale is discussed.},
 bibtype = {article},
 author = {Ehrhardt, Fiona and Soussana, Jean-François and Bellocchi, Gianni and Grace, Peter and McAuliffe, Russel and Recous, Sylvie and Sándor, Renáta and Smith, Pete and Snow, Val and de Antoni Migliorati, Massimiliano and Basso, Bruno and Bhatia, Arti and Brilli, Lorenzo and Doltra, Jordi and Dorich, Christopher D. and Doro, Luca and Fitton, Nuala and Giacomini, Sandro J. and Grant, Brian and Harrison, Matthew T. and Jones, Stephanie K. and Kirschbaum, Miko U. F. and Klumpp, Katja and Laville, Patricia and Léonard, Joël and Liebig, Mark and Lieffering, Mark and Martin, Raphaël and Massad, Raia S. and Meier, Elizabeth and Merbold, Lutz and Moore, Andrew D. and Myrgiotis, Vasileios and Newton, Paul and Pattey, Elizabeth and Rolinski, Susanne and Sharp, Joanna and Smith, Ward N. and Wu, Lianhai and Zhang, Qing},
 doi = {10.1111/gcb.13965},
 journal = {Global Change Biology},
 number = {October 2017},
 keywords = {FR_GRI,FR_LQ1}
}
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