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

• Published in: Global change biology Vol. 24; no. 2; pp. e603 - e616
• Main Authors: Ehrhardt, Fiona, Soussana, Jean‐François, Bellocchi, Gianni, Grace, Peter, McAuliffe, Russel, Recous, Sylvie, Sándor, Renáta, Smith, Pete, Snow, Val, de Antoni Migliorati, Massimiliano, Basso, Bruno, Bhatia, Arti, Brilli, Lorenzo, Doltra, Jordi, Dorich, Christopher D., Doro, Luca, Fitton, Nuala, Giacomini, Sandro J., Grant, Brian, Harrison, Matthew T., Jones, Stephanie K., Kirschbaum, Miko U. F., Klumpp, Katja, Laville, Patricia, Léonard, Joël, Liebig, Mark, Lieffering, Mark, Martin, Raphaël, Massad, Raia S., Meier, Elizabeth, Merbold, Lutz, Moore, Andrew D., Myrgiotis, Vasileios, Newton, Paul, Pattey, Elizabeth, Rolinski, Susanne, Sharp, Joanna, Smith, Ward N., Wu, Lianhai, Zhang, Qing
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01-02-2018; Wiley
• Subjects: Agricultural practices; Agricultural production; agriculture; Arable land; benchmarking; biogeochemical models; Biogeochemistry; Calibration; Chemical and Process Engineering; Climate change; Climate change mitigation; Computer simulation; Continents; Crop rotation; Crop yield; Crops; Emissions; Engineering Sciences; Environmental Sciences; Errors; Farm buildings; Food engineering; Food security; Grain; Grasslands; Greenhouse effect; Greenhouse gases; Life Sciences; Mitigation; Modelling; Nitrous oxide; Pasture; Prediction models; Productivity; Rice; Rotation; Security; soil; Uncertainty; Vegetal Biology; Wheat; yield; agriculture; nitrous oxide; biogeochemical models; climate change; greenhouse gas
• ISSN: 1354-1013; 1365-2486
• DOI: 10.1111/gcb.13965

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.
The potential of simulation models used to predict variables affecting food security and climate change mitigation has not been systematically assessed. We report an international intercomparison of 24 process‐based models for the estimation of agricultural productivity and N2O emissions (individually or as ensembles) against nine long‐term experimental datasets (rotational crops and grasslands) using a five‐stage modelling protocol. Uncalibrated multi‐model medians were within the range of observed uncertainties for grain yields (wheat, maize and rice) and N2O emissions, while were poor predictor for grasslands ANPP. N2O emissions intensities ranked accurately with reduced ensembles (three models) across stages, crop species and sites.