Performance of process-based models for simulation of grain N in crop rotations across Europe. (June 2017)

• Record Type: Journal Article
• Authors: Yin, Xiaogang; Kersebaum, Kurt Christian; Kollas, Chris; Manevski, Kiril; Baby, Sanmohan; Beaudoin, Nicolas; Öztürk, Isik; Gaiser, Thomas; Wu, Lianhai; Hoffmann, Munir; Charfeddine, Monia; Conradt, Tobias; Constantin, Julie; Ewert, Frank; de Cortazar-Atauri, Iñaki Garcia; Giglio, Luisa; Hlavinka, Petr; Hoffmann, Holger; Launay, Marie; Louarn, Gaëtan; Manderscheid, Remy; Mary, Bruno; Mirschel, Wilfried; Nendel, Claas; Pacholski, Andreas; Palosuo, Taru; Ripoche-Wachter, Dominique; P. Rötter, Reimund; Ruget, Françoise; Sharif, Behzad; Trnka, Mirek; Ventrella, Domenico; Weigel, Hans-Joachim; E. Olesen, Jørgen;
• Is Part Of: Agricultural systems. Volume 154(2017)
• Page Start: 63
• Page End: 77
• Publication Date: 2017-06
• Subjects: Calibration -- Crop model -- Crop rotation -- Grain N content -- Model evaluation -- Model initialization; Agricultural systems -- Periodicals; Agriculture -- Environmental aspects -- Periodicals; 338.16
• DOI: 10.1016/j.agsy.2017.03.005
• Languages: English
• ISSNs: 0308-521X

Abstract

Abstract: The accurate estimation of crop grain nitrogen (N; N in grain yield) is crucial for optimizing agricultural N management, especially in crop rotations. In the present study, 12 process-based models were applied to simulate the grain N of i ) seven crops in rotations, ii ) across various pedo-climatic and agro-management conditions in Europe, iii ) under both continuous simulation and single year simulation, and for iv ) two calibration levels, namely minimal and detailed calibration. Generally, the results showed that the accuracy of the simulations in predicting grain N increased under detailed calibration. The models performed better in predicting the grain N of winter wheat ( Triticum aestivum L.), winter barley ( Hordeum vulgare L.) and spring barley ( Hordeum vulgare L.) compared to spring oat ( Avena sativa L.), winter rye ( Secale cereale L.), pea ( Pisum sativum L.) and winter oilseed rape ( Brassica napus L.). These differences are linked to the intensity of parameterization with better parameterized crops showing lower prediction errors. The model performance was influenced by N fertilization and irrigation treatments, and a majority of the predictions were more accurate under low N and rainfed treatments. Moreover, the multi-model mean provided better predictions of grain N compared to any individual model. In regard to the Individual models, DAISY, FASSET, HERMES, MONICA and STICS are suitable for predicting grain N of the main crops in typical European crop rotations, which all performed well in both continuous simulation and single year simulation. Our results show that both the model initialization and the cover crop effects in crop rotations should be considered in order to achieve good performance of continuous simulation. Furthermore, the choice of either continuous simulation or single year simulation should be guided by the simulation objectives (e.g. grain yield, grain N content or N dynamics), the crop sequence (inclusion of legumes) and treatments (rate and type of N fertilizer) included in crop rotations and the model formalism. Highlights: The accuracy of simulations in predicting grain N increased under detailed calibration in most cases. Complex models of N cycling with well parameterized crops provide reasonably accurate simulations in predicting grain N. Most models performed better under low N and rainfed treatments. Multi-model ensemble mean provided more accurate prediction of grain N in crop rotations compared to any individual model. Emphasis should be given to model initialization and the cover crops effects in continuous simulation.