1. Multi-model uncertainty analysis in predicting grain N for crop rotations in Europe
- Author
-
Yin, Xiaogang, Kersebaum, Kurt Christian, Kollas, Chris, Baby, Sanmohan, Beaudoin, Nicolas, Manevski, Kiril, Palosuo, Taru, Nendel, Claas, Wu, Lianhai, Hoffmann, Munir, Hoffmann, Holger, Sharif, Behzad, Armas-Herrera, Cecilia M., Bindi, Marco, Charfeddine, Monia, Conradt, Tobias, Constantin, Julie, Ewert, Frank, Ferrise, Roberto, Gaiser, Thomas, de Cortazar-Atauri, Iñaki Garcia, Giglio, Luisa, Hlavinka, Petr, Lana, Marcos, Launay, Marie, Louarn, Gaëtan, Manderscheid, Remy, Mary, Bruno, Mirschel, Wilfried, Moriondo, Marco, Öztürk, Isik, Pacholski, Andreas, Ripoche-Wachter, Dominique, Rötter, Reimund P., Ruget, Françoise, Trnka, Mirek, Ventrella, Domenico, Weigel, Hans Joachim, Olesen, Jørgen E., Unité d'Agronomie de Laon-Reims-Mons (AGRO-LRM), Institut National de la Recherche Agronomique (INRA), Department of Agroecology, Aarhus University [Aarhus], Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Natural Resources Institute Finland, Rothamsted Research, Crop Production Systems in the Tropics, Georg-August-Universität Göttingen, INRES, Rheinische Friedrich-Wilhelms-Universität Bonn, Department of Agri-food Production and Environmental Sciences, University of Florence (UNIFI), Unità di ricerca per i sistemi colturali degli ambienti caldo-aridi, Agricultural Research Council (CRA), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, UE Agroclim (UE AGROCLIM), Global Change Research Centre (CzechGlobe), Mendel University in Brno, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Thünen Institute of Biodiversity, Istituto di Biometeorologia [Firenze] (IBIMET), Consiglio Nazionale delle Ricerche (CNR), EurochemAgro, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), FACCE MACSUR 2812ERA147/CARBIOCIAL 01LL0902M/ KULUNDA 01LL0905L /NORFASYS 268277 292944/MACSUR D.M. 24064/7303/15/ QJ1310123, Agroressources et Impacts environnementaux (AgroImpact), Natural resources institute Finland, Georg-August-University [Göttingen], Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Agroclim (AGROCLIM), and Mendel University in Brno (MENDELU)
- Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences ,Irrigation ,010504 meteorology & atmospheric sciences ,Model calibration ,Soil Science ,Single Year simulation ,Plant Science ,01 natural sciences ,Model ensemble ,Crop ,continuous simulation ,model ensemble ,Grain N ,Uncertainty analysis ,0105 earth and related environmental sciences ,Mathematics ,2. Zero hunger ,model calibration ,grain n ,Crop yield ,Simulation modeling ,Continuous simulation ,Model inter-comparison ,Single year simulation ,04 agricultural and veterinary sciences ,Crop rotation ,model inter-comparison ,single year simulation ,Tillage ,Mean absolute percentage error ,Agronomy ,Continous simulation ,040103 agronomy & agriculture ,0401 agriculture, forestry, and fisheries ,Agronomy and Crop Science ,Model intercomparison - Abstract
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.
- Published
- 2017
- Full Text
- View/download PDF