Performance of process-based models for simulation of grain N in crop rotations across Europe

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

Standard

Performance of process-based models for simulation of grain N in crop rotations across Europe. / Yin, Xiaogang; Kersebaum, Kurt Christian; Kollas, Chris et al.

in: Agricultural Systems, Jahrgang 154, 01.06.2017, S. 63-77.

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

Harvard

Yin, X, Kersebaum, KC, Kollas, C, Manevski, K, Baby, S, Beaudoin, N, Öztürk, I, Gaiser, T, Wu, L, Hoffmann, M, Charfeddine, M, Conradt, T, Constantin, J, Ewert, F, de Cortazar-Atauri, IG, Giglio, L, Hlavinka, P, Hoffmann, H, Launay, M, Louarn, G, Manderscheid, R, Mary, B, Mirschel, W, Nendel, C, Pacholski, A, Palosuo, T, Ripoche-Wachter, D, P. Rötter, R, Ruget, F, Sharif, B, Trnka, M, Ventrella, D, Weigel, HJ & E. Olesen, J 2017, 'Performance of process-based models for simulation of grain N in crop rotations across Europe', Agricultural Systems, Jg. 154, S. 63-77. https://doi.org/10.1016/j.agsy.2017.03.005

APA

Yin, X., Kersebaum, K. C., Kollas, C., Manevski, K., Baby, S., Beaudoin, N., Öztürk, I., Gaiser, T., Wu, L., Hoffmann, M., Charfeddine, M., Conradt, T., Constantin, J., Ewert, F., de Cortazar-Atauri, I. G., Giglio, L., Hlavinka, P., Hoffmann, H., Launay, M., ... E. Olesen, J. (2017). Performance of process-based models for simulation of grain N in crop rotations across Europe. Agricultural Systems, 154, 63-77. https://doi.org/10.1016/j.agsy.2017.03.005

Vancouver

Yin X, Kersebaum KC, Kollas C, Manevski K, Baby S, Beaudoin N et al. Performance of process-based models for simulation of grain N in crop rotations across Europe. Agricultural Systems. 2017 Jun 1;154:63-77. doi: 10.1016/j.agsy.2017.03.005

Bibtex

@article{643a1222632e4eec96a4d1784ead8f78,
title = "Performance of process-based models for simulation of grain N in crop rotations across Europe",
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.",
keywords = "Ecosystems Research, Calibration, Crop model, Crop rotation, Grain N content, Model evaluation, Model initialization, Sustainability Science",
author = "Xiaogang Yin and Kersebaum, {Kurt Christian} and Chris Kollas and Kiril Manevski and Sanmohan Baby and Nicolas Beaudoin and Isik {\"O}zt{\"u}rk and Thomas Gaiser and Lianhai Wu and Munir Hoffmann and Monia Charfeddine and Tobias Conradt and Julie Constantin and Frank Ewert and {de Cortazar-Atauri}, {I{\~n}aki Garcia} and Luisa Giglio and Petr Hlavinka and Holger Hoffmann and Marie Launay and Ga{\"e}tan Louarn and Remy Manderscheid and Bruno Mary and Wilfried Mirschel and Claas Nendel and Andreas Pacholski and Taru Palosuo and Dominique Ripoche-Wachter and {P. R{\"o}tter}, Reimund and Fran{\c c}oise Ruget and Behzad Sharif and Mirek Trnka and Domenico Ventrella and Weigel, {Hans Joachim} and {E. Olesen}, J{\o}rgen",
year = "2017",
month = jun,
day = "1",
doi = "10.1016/j.agsy.2017.03.005",
language = "English",
volume = "154",
pages = "63--77",
journal = "Agricultural Systems",
issn = "0308-521X",
publisher = "Elsevier B.V.",

}

RIS

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 -

DOI