Performance of process-based models for simulation of grain N in crop rotations across Europe
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.
| Item Type | Article |
|---|---|
| Open Access | Not Open Access |
| Additional information | MACSUR project (2812ERA147), funded by the German Federal Ministry of Food and Agriculture (BMEL). NB, JC, IGCA, ML, GL, BM, DRW, and FR were funded from the MACSUR project by INRA ACCAF Metaprogramme. CN received additional support by BMBF via the CARBIOCIAL research project (01LL0902 M). TC acknowledges financial support from the MACSUR project (031A103B), funded by the German Federal Ministry of Education and Research (BMBF). CM acknowledges financial support from the KULUNDA project (01LL0905L), funded by the BMBF. TP and RPR were supported by the NORFASYS project, funded by the Academy of Finland (decision nos. 268277 and 292944) and MACSUR, funded by the Finnish Ministry of Agriculture and Forestry. FE, HH and TG acknowledge financial support from the MACSUR project (2851ERA01), funded by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE). MC, LG, and DV acknowledge financial support from the MACSUR project funded by the Italian Ministry for Agricultural, Food and Forestry Policies (D.M. 24064/7303/15 of 16/Nov/2015). PH and MT acknowledge support from the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Program I (NPU I LO1415) and by the National Agency for Agricultural Research project no. QJ1310123. RM and HJW acknowledge financial support by BMEL |
| Keywords | calibration, crop model, crop rotation, Grain N content, model evaluation, model initialization |
| Project | 2812ERA147 |
| Date Deposited | 05 Dec 2025 09:10 |
| Last Modified | 19 Dec 2025 14:10 |
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