Comparison of Machine Learning Methods for Predicting Winter Wheat Yield in Germany
1 Comparison of Machine Learning Methods for Predicting Winter Wheat Yield in Germany Amit Kumar Srivastava Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, D-53115, Bonn, Germany. Tel: +49 228-73-2881; Email: amit.srivastava@uni-bonn.de Nima Safaei* Department of Business Analytics, Tippie College of Business, University of Iowa, Iowa City, IA, United States. Tel: +1 515 715 3804; Email: nima-safaei@uiowa.edu Saeed Khaki* Industrial and Manufacturing Systems Engineering Department, Iowa State University, Ames, IA, United States. Tel:+1 5157080815; Email: skhaki@iastate.edu Gina Lopez Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, D-53115, Bonn, Germany. Tel: +49 228-73-2870; Email: gina.lopez@uni-bonn.de Wenzhi Zeng State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China; Tel: +86 18571630103; Email: zengwenzhi1989@whu.edu.cn Frank Ewert Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, D-53115, Bonn, Germany. Email: frank.ewert@uni-bonn.de Thomas Gaiser Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, D-53115, Bonn, Germany. Tel: +49 228-73-2050; Email: tgaiser@uni-bonn.de Jaber Rahimi Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Germany; Email: Jaber.rahimi@kit.edu *These authors have made equal contribution to the paper. Abstract This study analyzed the performance of different machine learning methods for winter wheat yield prediction using extensive datasets of weather, soil, and crop phenology. To address the seasonality, weekly features were used that explicitly take soil moisture conditions and meteorological events into account.
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