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Research article - Peer-reviewed, 2017

Multi-model uncertainty analysis in predicting grain N for crop rotations in Europe

Yin, Xiaogang; Kersebaum, Kurt Christian; Kollas, Chris; Baby, Sanmohan; Beaudoin, Nicolas; Manevski, Kiril; Palosuo, Taru; Nendel, Claas; Wu, Lianhai; Hoffmann, Munir; Hoffmann, Holger; Sharif, Behzad; Armas-Herrera, Cecilia M.; Bindi, Marco; Charfeddine, Monia; Conradt, Tobias; Constantin, Julie; Ewert, Frank; Ferrise, Roberto; Gaiser, Thomas; de Cortazar-Atauri, Inaki Garcia; Giglio, Luisa; Hlavinka, Petr; Lana, Marcos; Launay, Marie; Louarn, Gaeetan; Manderscheid, Remy; Mary, Bruno; Mirschel, Wilfried; Moriondo, Marco; Oeztuerk, Isik; Pacholski, Andreas; Ripoche-Wachter, Dominique; Roetter, Reimund P.; Ruget, Francoise; Trnka, Mirek; Ventrella, Domenico; Weigel, Hans -Joachim; Olesen, Jurgen E.
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Realistic estimation of grain nitrogen (N; N in grain yield) is crucial for assessing N management in crop rotations, but there is little information on the performance of commonly used crop models for simulating grain N. Therefore, the objectives of the study were to (1) test if continuous simulation (multi-year) performs better than single year simulation, (2) assess if calibration improves model performance at different calibration levels, and (3) investigate if a multi-model ensemble can substantially reduce uncertainty in reproducing grain N. For this purpose, 12 models were applied simulating different treatments (catch crops, CO2 concentrations, irrigation, N application, residues and tillage) in four multi-year rotation experiments in Europe to assess modelling accuracy. Seven grain and seed crops in four rotation systems in Europe were included in the study, namely winter wheat, winter barley, spring barley, spring oat, winter rye, pea and winter oilseed rape. Our results indicate that the higher level of calibration significantly increased the quality of the simulation for grain N. In addition, models performed better in predicting grain N of winter wheat, winter barley and spring barley compared to spring oat, winter rye, pea and winter oilseed rape. For each crop, the use of the ensemble mean significantly reduced the mean absolute percentage error (MAPE) between simulations and observations to less than 15%, thus a multi-model ensemble can more precisely predict grain N than a random single model. Models correctly simulated the effects of enhanced N input on grain N of winter wheat and winter barley, whereas effects of tillage and irrigation were less well estimated. However, the use of continuous simulation did not improve the simulations as compared to single year simulation based on the multi-year performance, which suggests needs for further model improvements of crop rotation effects. (C) 2016 Elsevier B.V. All rights reserved.


Continuous simulation; Grain N; Model calibration; Model ensemble; Model inter-comparison; Single year simulation

Published in

European Journal of Agronomy
2017, Volume: 84, pages: 152-165

    UKÄ Subject classification

    Agricultural Science

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