Multi-model uncertainty analysis in predicting grain N for crop rotations in Europe
Xiaogang Yin
(1, 2)
,
Kurt Christian Kersebaum
(3)
,
Chris Kollas
(3, 4)
,
Sanmohan Baby
(2)
,
Nicolas Beaudoin
(1)
,
Kiril Manevski
(2)
,
Taru Palosuo
(5)
,
Claas Nendel
(6)
,
Lianhai Wu
(7)
,
Munir P. Hoffmann
(8)
,
Holger Hoffmann
(9)
,
Behzad Sharif
(2)
,
Cecilia Armas-Herrera
(1)
,
Marco Bindi
(10)
,
Monia Charfeddine
(11)
,
Tobias Conradt
(4)
,
Julie Constantin
(12)
,
Frank Ewert
(3, 9)
,
Roberto Ferrise
(10)
,
Thomas Gaiser
(13)
,
Inaki Garcia de Cortazar Atauri
(14)
,
Luisa Giglio
(11)
,
Petr Hlavinka
(15, 16)
,
Marcos Lana
(3)
,
Marie Launay
(14)
,
Gaëtan Louarn
(17)
,
Remy Manderscheid
(18)
,
Bruno Mary
(1)
,
Wilfried Mirschel
(3)
,
Marco Moriondo
(19)
,
Andreas Pacholski
(18, 20)
,
Dominique Ripoche
(14)
,
Reimund P. Rötter
(5, 8)
,
Francoise Ruget
(21)
,
Mirek Trnka
(15, 16)
,
Domenico Ventrella
(11)
,
Hans-Joachim Weigel
(18, 20)
,
Jørgen E. Olesen
(2)
1
AgroImpact -
Agroressources et Impacts environnementaux
2 Department of Agroecology
3 Institute of Landscape Systems Analysis
4 PIK - Potsdam Institute for Climate Impact Research
5 LUKE - Natural Resources Institute Finland
6 ZALF - Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research
7 Rothamsted Research
8 Crop Production Systems in the Tropics
9 INRES
10 Department of Agri-food Production and Environmental Sciences
11 Unità di ricerca per i sistemi colturali degli ambienti caldo-aridi
12 AGIR - AGroécologie, Innovations, teRritoires
13 Rheinische Friedrich-Wilhelms-Universität Bonn
14 AGROCLIM - Agroclim
15 CzechGlobe - Global Change Research Centre
16 MENDELU - Mendel University in Brno
17 P3F - Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères
18 Thünen Institute of Biodiversity
19 IBIMET - Istituto di Biometeorologia [Firenze]
20 EurochemAgro
21 EMMAH - Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes
2 Department of Agroecology
3 Institute of Landscape Systems Analysis
4 PIK - Potsdam Institute for Climate Impact Research
5 LUKE - Natural Resources Institute Finland
6 ZALF - Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research
7 Rothamsted Research
8 Crop Production Systems in the Tropics
9 INRES
10 Department of Agri-food Production and Environmental Sciences
11 Unità di ricerca per i sistemi colturali degli ambienti caldo-aridi
12 AGIR - AGroécologie, Innovations, teRritoires
13 Rheinische Friedrich-Wilhelms-Universität Bonn
14 AGROCLIM - Agroclim
15 CzechGlobe - Global Change Research Centre
16 MENDELU - Mendel University in Brno
17 P3F - Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères
18 Thünen Institute of Biodiversity
19 IBIMET - Istituto di Biometeorologia [Firenze]
20 EurochemAgro
21 EMMAH - Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes
Chris Kollas
- Fonction : Auteur
- PersonId : 977415
Nicolas Beaudoin
- Fonction : Auteur
- PersonId : 1206197
- IdRef : 120443007
Claas Nendel
- Fonction : Auteur
- PersonId : 772081
- ORCID : 0000-0001-7608-9097
Cecilia Armas-Herrera
- Fonction : Auteur
- PersonId : 981437
- ORCID : 0000-0001-6566-6944
Julie Constantin
- Fonction : Auteur
- PersonId : 736250
- IdHAL : julie-constantin
- ORCID : 0000-0001-9647-5374
Inaki Garcia de Cortazar Atauri
- Fonction : Auteur
- PersonId : 743512
- IdHAL : inaki-garcia-de-cortazar-atauri
- ORCID : 0000-0001-6941-9844
- IdRef : 126804362
Francoise Ruget
- Fonction : Auteur
- PersonId : 1203697
Résumé
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.