Performance of process-based models for simulation of grain N in crop rotations across Europe
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In: Agricultural Systems, Vol. 154, 01.06.2017, p. 63-77.
Research output: Journal contributions › Journal articles › Research › peer-review
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TY - JOUR
T1 - Performance of process-based models for simulation of grain N in crop rotations across Europe
AU - Yin, Xiaogang
AU - Kersebaum, Kurt Christian
AU - Kollas, Chris
AU - Manevski, Kiril
AU - Baby, Sanmohan
AU - Beaudoin, Nicolas
AU - Öztürk, Isik
AU - Gaiser, Thomas
AU - Wu, Lianhai
AU - Hoffmann, Munir
AU - Charfeddine, Monia
AU - Conradt, Tobias
AU - Constantin, Julie
AU - Ewert, Frank
AU - de Cortazar-Atauri, Iñaki Garcia
AU - Giglio, Luisa
AU - Hlavinka, Petr
AU - Hoffmann, Holger
AU - Launay, Marie
AU - Louarn, Gaëtan
AU - Manderscheid, Remy
AU - Mary, Bruno
AU - Mirschel, Wilfried
AU - Nendel, Claas
AU - Pacholski, Andreas
AU - Palosuo, Taru
AU - Ripoche-Wachter, Dominique
AU - P. Rötter, Reimund
AU - Ruget, Françoise
AU - Sharif, Behzad
AU - Trnka, Mirek
AU - Ventrella, Domenico
AU - Weigel, Hans Joachim
AU - E. Olesen, Jørgen
PY - 2017/6/1
Y1 - 2017/6/1
N2 - 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.
AB - 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.
KW - Ecosystems Research
KW - Calibration
KW - Crop model
KW - Crop rotation
KW - Grain N content
KW - Model evaluation
KW - Model initialization
KW - Sustainability Science
UR - http://www.scopus.com/inward/record.url?scp=85015720620&partnerID=8YFLogxK
U2 - 10.1016/j.agsy.2017.03.005
DO - 10.1016/j.agsy.2017.03.005
M3 - Journal articles
AN - SCOPUS:85015720620
VL - 154
SP - 63
EP - 77
JO - Agricultural Systems
JF - Agricultural Systems
SN - 0308-521X
ER -