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SOYGEN

Science Optimized Yield Gains across ENvironments

Machinery in a soybean field.

Resources

Publicly available resources and tools for soybean breeders to implement cost-effective genomic prediction.

Variants

Identification of important structural variants that control seed yield.

Knowledge

Publicly available knowledge on genetic control of genotype-by-environment interactions in soybean, and improved models for prediction of breeding line performance in new environments

Germplasm

Enhanced germplasm and superior varieties developed through adoptions of genomics-assisted breeding techniques better adapted to future environmental conditions.

Collaborating Researchers

George Graef - University of Nebraska
Matthew Hudson - University of Illinois
David Hyten - University of Nebraska
Aaron Lorenz - University of Minnesota
Nicolas Federico Martin - University of Illinois
Leah McHale - Ohio State University
Carrie Miranda - North Dakota State University
Eliana Monteverde - University of Illinois
Katy Martin Rainey - Purdue University
Andrew Scaboo - University of Missouri
William Schapaugh - Kansas State University
Asheesh Singh - Iowa State University
Dechun Wang - Michigan State University

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For Students, Faculty, and Staff

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