Inter-comparison of statistical models for projecting winter oilseed rape yield in Europe under climate change

Behzad Sharif


While intercomparison of process-based crop models for projections under climate change is being intensively studied at European as well as at the global scale, little effort has been made for comparing statistical models. In this study, several regression techniques (ordinary least squares, stepwise, shrinkage methods, principle components and partial least squares) were combined with different types of climate input variables (with different temporal resolution) in order to define a large range of statistical models. Each model was fitted to winter oilseed rape data collected in 689, 325 and 173 field experiments carried out in Denmark, Germany, and Czech Republic, respectively. The fitted models were then used to predict yield of winter oilseed rape in the field experiments during more than 20 years, up to 2013.  Interpretability of the estimated climate variable effects and accuracy of yield predictions were both analysed. Results suggest that recent statistical methods (e.g., shrinkage methods) may have considerable capabilities to complement traditional statistical methods in yield prediction. The selection of the most influential variables was strongly influenced by the statistical method used to analyse the data. Among the most recent statistical methods, the uncertainties in projecting yield of winter oilseed rape under climate change were mainly due to residual errors and uncertainty in estimated parameter values, and not to model choice.

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Authors: Behzad Sharif1, David Makowski2, Kurt Christian Kersebaum3, Mirek Trnka4,5, Kirsten Schelde1, Jørgen Eivind Olesen1

Affiliations: 1 Department of Agroecology, Aarhus University, Blichers Alle 20, 8830 Tjele, Denmark; 2 Institut National de Recherche Agronomique, Unité Mixte de Recherche 211 Agronomie, Thiverval-Grignon, France; 3 Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Str. 84,15374 Müncheberg, Germany; 4 Mendel University, Zemědělská 1665/1, 61300 Brno, Czech Republic; 5 Global Change Research Centre AS CR v.v.i., Bělidla 986/4b, Brno, Czech Republic

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