Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1111/gcb.13965 |
Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions | |
Ehrhardt, Fiona1; Soussana, Jean-Francois1; Bellocchi, Gianni2; Grace, Peter3; McAuliffe, Russel4; Recous, Sylvie5; Sandor, Renata2,6; Smith, Pete7; Snow, Val4; Migliorati, Massimiliano de Antoni; Basso, Bruno8; Bhatia, Arti9; Brilli, Lorenzo10; Doltra, Jordi11; Dorich, Christopher D.12; Doro, Luca13; Fitton, Nuala7; Giacomini, Sandro J.14; Grant, Brian15; Harrison, Matthew T.16; Jones, Stephanie K.17; Kirschbaum, Miko U. F.18; Klumpp, Katja2; Laville, Patricia19; Leonard, Joel20; Liebig, Mark21; Lieffering, Mark22; Martin, Raphael2; Massad, Raia S.19; Meier, Elizabeth23; Merbold, Lutz24,25; Moore, Andrew D.26; Myrgiotis, Vasileios17; Newton, Paul22; Pattey, Elizabeth15; Rolinski, Susanne27; Sharp, Joanna28; Smith, Ward N.15; Wu, Lianhai29; Zhang, Qing30 | |
2018-02-01 | |
发表期刊 | GLOBAL CHANGE BIOLOGY
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ISSN | 1354-1013 |
EISSN | 1365-2486 |
出版年 | 2018 |
卷号 | 24期号:2页码:E603-E616 |
文章类型 | Article |
语种 | 英语 |
国家 | France; Australia; New Zealand; Hungary; Scotland; USA; India; Italy; Spain; Brazil; Canada; Switzerland; Kenya; Germany; England; Peoples R China |
英文摘要 | 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. |
英文关键词 | agriculture benchmarking biogeochemical models climate change greenhouse gases nitrous oxide soil yield |
领域 | 气候变化 ; 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000423994700019 |
WOS关键词 | GREENHOUSE-GAS MITIGATION ; NITROUS-OXIDE EMISSIONS ; GRAZING MANAGEMENT ; CLIMATE ; WHEAT ; SYSTEMS ; CARBON ; YIELD ; GRASSLAND ; BUDGET |
WOS类目 | Biodiversity Conservation ; Ecology ; Environmental Sciences |
WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/16978 |
专题 | 气候变化 资源环境科学 |
作者单位 | 1.INRA, Paris, France; 2.INRA, UMR Ecosyst Prairial, Clermont Ferrand, France; 3.Queensland Univ Technol, Brisbane, Qld, Australia; 4.Lincoln Res Ctr, AgRes, Lincoln, New Zealand; 5.INRA, UMR FARE, Reims, France; 6.Inst Agr, CAR, HAS, Martonvasar, Hungary; 7.Univ Aberdeen, Inst Biol & Environm Sci, Aberdeen, Scotland; 8.Michigan State Univ, Dept Geol Sci, E Lansing, MI 48824 USA; 9.Indian Agr Res Inst, New Delhi, India; 10.Univ Florence, DISPAA, Florence, Italy; 11.Cantabrian Agr Res & Training Ctr CIFA, Muriedas, Spain; 12.Colorado State Univ, NREL, Ft Collins, CO USA; 13.Univ Sassari, Desertificat Res Ctr, Sassari, Italy; 14.Fed Univ Santa Maria UFSM, Soil Dept, Santa Maria, RS, Brazil; 15.Agr & Agri Food Canada, Ottawa Res & Dev Ctr, Ottawa, ON, Canada; 16.Tasmanian Inst Agr, Burnie, Tas, Australia; 17.SRUC, Edinburgh, Midlothian, Scotland; 18.Landcare Res, Palmerston North, New Zealand; 19.Univ Paris Saclay, INRA, UMR ECOSYS, Thiverval Grignon, France; 20.INRA, UR AgroImpact, Laon, France; 21.USDA ARS, Mandan, ND USA; 22.Grasslands Res Ctr, AgRes, Palmerston North, New Zealand; 23.CSIRO, Agr & Food, St Lucia, Qld, Australia; 24.Inst Agr Sci, ETH Zurich, Zurich, Switzerland; 25.Mazingira Ctr, ILRI, Nairobi, Kenya; 26.CSIRO, Black Mt Sci & Innovat Precinct, Agr & Food, Canberra, ACT, Australia; 27.Potsdam Inst Climate Impact Res PIK, Potsdam, Germany; 28.New Zealand Inst Plant & Food Res, Christchurch, New Zealand; 29.Rothamsted Res, Sustainable Soils & Grassland Syst, Harpenden, Devon, England; 30.Chinese Acad Sci, Inst Atmospher Phys, LAPC, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Ehrhardt, Fiona,Soussana, Jean-Francois,Bellocchi, Gianni,et al. Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions[J]. GLOBAL CHANGE BIOLOGY,2018,24(2):E603-E616. |
APA | Ehrhardt, Fiona.,Soussana, Jean-Francois.,Bellocchi, Gianni.,Grace, Peter.,McAuliffe, Russel.,...&Zhang, Qing.(2018).Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions.GLOBAL CHANGE BIOLOGY,24(2),E603-E616. |
MLA | Ehrhardt, Fiona,et al."Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions".GLOBAL CHANGE BIOLOGY 24.2(2018):E603-E616. |
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