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ARS Home » Plains Area » Mandan, North Dakota » Northern Great Plains Research Laboratory » Research » Publications at this Location » Publication #347292

Research Project: Sustainable Agricultural Systems for the Northern Great Plains

Location: Northern Great Plains Research Laboratory

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

Author
item Ehrhardt, Fiona - Institut National De La Recherche Agronomique (INRA)
item Soussana, Jean-francois - Institut National De La Recherche Agronomique (INRA)
item Bellocchi, Gianni - Vetagro Sup
item Grace, Peter - Queensland University Of Technology
item Mcauliffe, Russel - Agresearch New Zealand
item Recous, Sylvie - Institut National De La Recherche Agronomique (INRA)
item Sandor, Renata - Hungarian Academy Of Sciences
item Smith, Pete - University Of Aberdeen
item Snow, Val - Agresearch New Zealand
item Migliorati, Massimiliano - Queensland University Of Technology
item Basso, Bruno - Michigan State University
item Bhatia, Arti - Indian Agricultural Research Institute
item Brilli, Lorenzo - University Of Florence
item Doltra, Jordi - Center For Agricultural Research And Training, Cantabria Government (CIFA)
item Dorich, Christopher - Colorado State University
item Doro, Luca - University Of Sassari
item Fitton, Nuala - University Of Aberdeen
item Giacomini, Sandro - Universidade Federal De Santa Maria
item Grant, Brian - Agriculture And Agri-food Canada
item Harrison, Matthew - Tasmanian Institute Of Agricultural Research
item Jones, Stephanie - Sruc-scotland's Rural College
item Kirschbaum, Miko - Landcare Research
item Klumpp, Katja - Vetagro Sup
item Laville, Patricia - Institut National De La Recherche Agronomique (INRA)
item Leonard, Joel - Institut National De La Recherche Agronomique (INRA)
item Liebig, Mark
item Lieffering, Mark - Agresearch New Zealand
item Martin, Raphael - Vetagro Sup
item Massad, Raia Silvia - Institute National De La Recherche Agronomique De Tunisie (INRAT)
item Meier, Elizabeth - Commonwealth Scientific And Industrial Research Organisation (CSIRO)
item Merbold, Lutz - Eth Zurich
item Moore, Andrew - Commonwealth Scientific And Industrial Research Organisation (CSIRO)
item Myrgiotis, Vasileios - Sruc-scotland's Rural College
item Newton, Paul - Agresearch New Zealand
item Pattey, Elizabeth - Agriculture And Agri-food Canada
item Rolinski, Susanne - Potsdam Institute
item Sharp, Joanna - New Zealand Institute Of Plant & Food Research
item Smith, Ward - Agriculture And Agri-food Canada
item Wu, Lianhai - Rothamsted Research
item Zhang, Qing - Chinese Academy Of Sciences

Submitted to: Global Change Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/10/2017
Publication Date: 11/24/2017
Citation: Ehrhardt, F., Soussana, J., Bellocchi, G., Grace, P., McAuliffe, R., Recous, S., Sandor, R., Smith, P., Snow, V., Migliorati, M.D., 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., Klumpp, K., Laville, P., Leonard, J., Liebig, M.A., Lieffering, M., Martin, R., Massad, R., Meier, E., Merbold, L., Moore, A.D., Myrgiotis, V., Newton, P., Pattey, E., Rolinski, S., Sharp, J., Smith, W.N., Wu, L., Zhang, Q. 2017. Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions. Global Change Biology. 24(2):e603-e616. https://doi.org/10.1111/gcb.13965.

Interpretive Summary: The need to mitigate climate change requires the abatement of greenhouse gas (GHG) emissions and the sequestration of organic carbon (C) in cropland and grassland soils. However, this must be accomplished while increasing agricultural productivity under climate change to keep up with global increasing demand and improve food and nutritional security. Since the 1990s, the international scientific community has developed a number of simulation models that estimate GHG emissions and the dynamics of C and nitrogen (N) in agricultural (cropland and managed grassland) soils. It has recently been shown that an ensemble of models may reduce the uncertainties of crop yield simulations across contrasting soil and climate conditions in comparison with single models. In this study, we assess and report the results of 24 process-based integrated C&N models (16 cropland and 12 grassland models), by comparing multi-year (1 to 11 years) simulations to experimental data from nine sites (four temperate grasslands and five arable crop rotations with wheat, maize and rice) spanning four continents. The aim of the study was firstly to quantify the uncertainties of single models and model ensemble simulations; secondly, to assess for the first time, the potential of using model ensembles for predicting agricultural productivity and N2O emissions, jointly, at field scale.

Technical Abstract: Simulation models are extensively used to predict agricultural productivity and greenhouse gas (GHG) emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multispecies agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multimodel ensembles to predict productivity and nitrous oxide (N2O) emissions for wheat, maize, rice and temperate grasslands. Using a multistage 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 to 40% of the uncalibrated individual models were within two standard deviations (s.d.) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within one s.d. of observed N2O emissions. At stage 1, ensembles formed by the three lowest prediction model errors (RRMSE) predicted both yields and N2O emissions within experimental uncertainties for 44 and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2 to 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 N2O emissions. Yield-scaled N2O emissions (N2O emissions divided by crop yields) were ranked accurately by 3-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.