Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions
| Autor: | Fiona Ehrhardt, Mark A. Liebig, Raphaël Martin, Jordi Doltra, Russel McAuliffe, Val Snow, Joël Léonard, Andrew D. Moore, Stephanie K. Jones, Lutz Merbold, Pete Smith, Lianhai Wu, Elizabeth A. Meier, Paul C. D. Newton, Arti Bhatia, Gianni Bellocchi, Massimiliano De Antoni Migliorati, Miko U. F. Kirschbaum, Ward Smith, Brian Grant, Renáta Sándor, Joanna Sharp, Lorenzo Brilli, Nuala Fitton, Jean-François Soussana, Elizabeth Pattey, Luca Doro, Katja Klumpp, Christopher D. Dorich, Bruno Basso, Raia Silvia Massad, Sylvie Recous, Patricia Laville, Qing Zhang, Matthew T. Harrison, Sandro José Giacomini, Susanne Rolinski, Mark Lieffering, Peter Grace, Vasileios Myrgiotis |
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| Přispěvatelé: | Collège de Direction (CODIR), Institut National de la Recherche Agronomique (INRA), Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS), Queensland University of Technology, Agresearch Ltd, Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de la Recherche Agronomique (INRA), University of Aberdeen, Michigan State University [East Lansing], Michigan State University System, Indian Agricultural Research Institute (IARI), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Catabrian Agricultural Research and Training Center (CIFA), Colorado State University [Fort Collins] (CSU), Università degli Studi di Sassari, Universidade Federal de Santa Maria = Federal University of Santa Maria [Santa Maria, RS, Brazil] (UFSM), Agriculture and Agri-Food [Ottawa] (AAFC), Tasmanian Institute of Agriculture, Scotland's Rural College (SRUC), Manaaki Whenua – Landcare Research [Lincoln], Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Agroressources et Impacts environnementaux (AgroImpact), USDA-ARS : Agricultural Research Service, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Potsdam Institute for Climate Impact Research (PIK), New Zealand Institute for Crop and Food Research, Partenaires INRAE, Rothamsted Research, Chinese Academy of Sciences (CAS), ANR, European Project: 277610,EC:FP7:KBBE,FP7-JPROG-2011-RTD,FACCE CSA(2011), Queensland University of Technology [Brisbane] (QUT), Università degli Studi di Firenze = University of Florence (UniFI), Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS), Agriculture and Agri-Food (AAFC), Biotechnology and Biological Sciences Research Council (BBSRC), Universidade Federal de Santa Maria (UFSM) |
| Rok vydání: | 2018 |
| Předmět: |
biogeochemical models
010504 meteorology & atmospheric sciences [SDV]Life Sciences [q-bio] Climate change Atmospheric sciences 01 natural sciences Grassland [SDV.IDA]Life Sciences [q-bio]/Food engineering Environmental Chemistry [SDV.BV]Life Sciences [q-bio]/Vegetal Biology [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering Agricultural productivity 0105 earth and related environmental sciences General Environmental Science agriculture 2. Zero hunger Global and Planetary Change geography geography.geographical_feature_category Ecology Ensemble forecasting nitrous oxide Crop yield Simulation modeling 04 agricultural and veterinary sciences benchmarking climate change greenhouse gases soil yield Climate change mitigation Agronomy 13. Climate action greenhouse gas Greenhouse gas [SDE]Environmental Sciences 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Environmental science |
| Zdroj: | Global Change Biology Global Change Biology, Wiley, 2018, 24 (2), pp.e603-e616. ⟨10.1111/gcb.13965⟩ Global Change Biology, 24 (2) Global Change Biology, 2018, 24 (2), pp.e603-e616. ⟨10.1111/gcb.13965⟩ |
| ISSN: | 1354-1013 1365-2486 |
| Popis: | International audience; 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 1SD 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 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. |
| Databáze: | OpenAIRE |
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