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:
[email protected] Nima Safaei* Department of Business Analytics, Tippie College of Business, University of Iowa, Iowa City, IA, United States. Tel: +1 515 715 3804; Email:
[email protected] Saeed Khaki* Industrial and Manufacturing Systems Engineering Department, Iowa State University, Ames, IA, United States. Tel:+1 5157080815; Email:
[email protected] Gina Lopez Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, D-53115, Bonn, Germany. Tel: +49 228-73-2870; Email:
[email protected] Wenzhi Zeng State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China; Tel: +86 18571630103; Email:
[email protected] Frank Ewert Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, D-53115, Bonn, Germany. Email:
[email protected] Thomas Gaiser Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, D-53115, Bonn, Germany. Tel: +49 228-73-2050; Email:
[email protected] Jaber Rahimi Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Germany; Email:
[email protected] *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.