72-4 Modelling Complex Crop Rotations and Treatments Across Sites in Europe with an Ensemble of Crop Models.

See more from this Division: ASA Section: Climatology & Modeling
See more from this Session: General Agroclimatology and Agronomic Modeling: I
Monday, November 3, 2014: 1:45 PM
Hyatt Regency Long Beach, Seaview A
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Kurt C. Kersebaum1, Chris Kollas1, Marco Bindi2, Claas Nendel3, Roberto Ferrise2, Marco Moriondo2, Jørgen Eivind Olesen4, Behzad Sharif4, Isik Ozturk5, Holger Hoffmann6, Mary Launay7, Dominique Ripoche7, Francoise Ruget7, Patrick Bertuzzi7, Iñaki Garcia De Cortazar7, Nicolas Beaudoin8, Cecilia Armas-Herrera9, Bruno Mary9, Christoph Müller10, Katharina Waha10, Domenico Ventrella11, Taru Palosuo12, Reimund P. Rötter13, Miroslav Trnka14, Petr Hlavinka14, Lianhai Wu15, Martin Wegehenkel1, Wilfried Mirschel1, Tobias Conradt10, Frank Wechsung16, Hans-Joachim Weigel17, Remy Manderscheid17 and Josef Eitzinger18, (1)Leibniz Centre for Agricultural Landscape Research, Muencheberg, Germany
(2)University of Florence, Florence, Italy
(3)ZALF - Leibniz Centre for Agricultural Landscape Research, Muencheberg, Germany
(4)Aarhus University, Tjele, Denmark
(5)Aarhus University, Tjele, (Non U.S.), DENMARK
(6)Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, Germany
(7)INRA, Avignon, France
(8)INRA, BARENTON-BUGNY, France
(9)INRA, PERONNE, France
(10)Potsdam Institute for Climate Impact Research, Potsdam, Germany
(11)C.R.A. Agricultural Research Council, Bari, Italy
(12)MTT Agrifood Research, Mikkeli, Finland
(13)Environmental Impacts Group, Natural Resources Institute Finland (Luke), Vantaa, Finland
(14)Mendel University Brno, Brno, Czech Republic
(15)Rothamsted Research, Okehampton, United Kingdom
(16)Potsdam Institute for Climate Impact Research, Berlin, GERMANY
(17)Thünen-Institute, Braunschweig, Germany
(18)University of Natural Resources and Life Sciences Vienna, Vienna, Austria
Crop modelling is widely used to assess agricultural production under current or future climate conditions. Recently, uncertainty of crop models became more and more a focus and ensemble modelling is seen as a suitable approach to overcome specific weaknesses of single models using a ensemble mean or median which has turned out to be a better predictor than any single model over a wide range of conditions. Studies on climate change impacts on crop productivity are mainly focussed on single crops. However, the position of crops within a rotation can have significantly influence crop production due to carry-over effects between two seasons. Therefore, the design and management of crop rotations provide an important measure to adapt to changing climatic conditions and to perform sustainable crop production. Within the European MACSUR project, we started a study on crop rotation effects using data from 5 sites across Europe with various treatments/variants which provide data for 201 cropping season at all. In a first step only few data for a rough calibration for each site where provided. Depending on the site different variants were investigated comprising irrigation, N fertilization level, atmospheric CO2 concentration, tillage, residue management, inserted catch crops and different soils. 14 models participated in this “low calibration run” providing results of single year simulations and/or continuous simulations over whole crop rotation. Comparing both ways of simulation, results were provided slightly better results with lower RMSE between observed and simulated crop yields for the continuous runs compared to the single year runs were achieved. However, distinct differences regarding model performance exist between the different crops showing that cereals are usually better simulated than tuber/beet crops. Regarding the treatment effects irrigation, nitrogen supply and catch crops as well as atmospheric CO2 concentration showed clearer effects than tillage and crop residue management.
See more from this Division: ASA Section: Climatology & Modeling
See more from this Session: General Agroclimatology and Agronomic Modeling: I