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Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions |
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| Authors: | Fiona Ehrhardt Jean‐François Soussana Gianni Bellocchi Peter Grace Russel McAuliffe Sylvie Recous Renáta Sándor Pete Smith Val Snow Massimiliano de Antoni Migliorati Bruno Basso Arti Bhatia Lorenzo Brilli Jordi Doltra Christopher D. Dorich Luca Doro Nuala Fitton Sandro J. Giacomini Brian Grant Matthew T. Harrison Stephanie K. Jones Miko U. F. Kirschbaum Katja Klumpp Patricia Laville Joël Léonard Mark Liebig Mark Lieffering Raphaël Martin Raia S. Massad Elizabeth Meier Lutz Merbold Andrew D. Moore Vasileios Myrgiotis Paul Newton Elizabeth Pattey Susanne Rolinski Joanna Sharp Ward N. Smith Lianhai Wu Qing Zhang |
| Affiliation: | 1. INRA, Paris, France;2. UMR Ecosystème Prairial, INRA, Clermont‐Ferrand, France;3. Queensland University of Technology, Brisbane, Qld, Australia;4. Lincoln Research Centre, AgResearch, Lincoln, New Zealand;5. INRA, UMR FARE, Reims, France;6. HAS, CAR, Agricultural Institute, Martonvásár, Hungary;7. Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, UK;8. Department of Geological Sciences, Michigan State University, East Lansing, MI, USA;9. Indian Agricultural Research Institute, New Delhi, India;10. DISPAA, University of Florence, Florence, Italy;11. Cantabrian Agricultural Research and Training Center (CIFA), Muriedas, Spain;12. NREL, Colorado State University, Fort Collins, CO, USA;13. Desertification Research Centre, University of Sassari, Sassari, Italy;14. Soil Department, Federal University of Santa Maria (UFSM), Santa Maria, Brazil;15. Ottawa Research and Development Center, Agriculture and Agri‐Food Canada, Ottawa, ON, Canada;16. Tasmanian Institute of Agriculture, Burnie, Tas., Australia;17. SRUC, Edinburgh, UK;18. Landcare Research, Palmerston North, New Zealand;19. INRA, UMR ECOSYS, Université Paris‐Saclay, Thiverval‐Grignon, France;20. INRA, UR AgroImpact, Laon, France;21. USDA Agricultural Research Service, Mandan, ND, USA;22. AgResearch, Grasslands Research Centre, Palmerton North, New Zealand;23. CSIRO Agriculture and Food, St Lucia, Qld, Australia;24. ETH Zurich, Institute of Agricultural Sciences, Zurich, Switzerland;25. International Livestock Research Institute (ILRI), Mazingira Centre, Nairobi, Kenya;26. Agriculture & Food, Black Mountain Science and Innovation Precinct, CSIRO, Canberra, ACT, Australia;27. Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany;28. New Zealand Institute for Plant and Food Research, Christchurch, New Zealand;29. Sustainable Soils and Grassland Systems, Rothamsted Research, Devon, UK;30. LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China |
| 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 (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 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 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. |
| Keywords: | agriculture benchmarking biogeochemical models climate change greenhouse gases nitrous oxide soil yield |