Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates
Jacob D. Washburn, Jose Ignacio Varela,Alencar Xavier,Qiuyue Chen,David Ertl,Joseph L. Gage,James B. Holland,Dayane Cristina Lima,Maria Cinta Romay,Marco Lopez-Cruz,Gustavo de los Campos, Wesley Barber, Cristiano Zimmer, Ignacio Trucillo Silva, Fabiani Rocha,Renaud Rincent, Baber Ali,Haixiao Hu,Daniel E. Runcie, Kirill Gusev, Andrei Slabodkin, Phillip Bax,Julie Aubert,Hugo Gangloff,Tristan Mary-Huard, Theodore Vanrenterghem, Carles Quesada-Traver,Steven Yates,Daniel Ariza-Suarez, Argeo Ulrich,Michele Wyler,Daniel R. Kick,Emily S. Bellis,Jason L. Causey, Emilio Soriano Chavez, Yixing Wang,Ved Piyush, Gayara D. Fernando, Robert K. Hu,Rachit Kumar, Annan J. Timon,Rasika Venkatesh,Kenia Segura Aba, Huan Chen,Thilanka Ranaweera,Shin-Han Shiu, Peiran Wang, Max J. Gordon, B. Kirtley Amos,Sebastiano Busato, Daniel Perondi, Abhishek Gogna, Dennis Psaroudakis, Chun-Peng James Chen,Hawlader A. Al-Mamun,Monica F. Danilevicz,Shriprabha R. Upadhyaya,David Edwards,Natalia de Leon GENETICS（2024）
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genotype by environment,prediction,competition,maize,yield,phenotype
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