Agriculture Commons^™

Utility Of Climatic Information Via Combining Ability Models To Improve Genomic Prediction For Yield Within The Genomes To Fields Maize Project, Diego Jarquin, Natalia De Leon, Cinta Romay, Martin Bohn, Edward S. Buckler, Ignacio Ciampitti, Jode Edwards, David Ertl, Sherry Flint-Garcia, Michael A. Gore, Christopher Graham, Candice N. Hirsch, James B. Holland, David Hooker, Shawn M. Kaeppler, Joseph Knoll, Elizabeth C. Lee, Carolyn J. Lawrence-Dill, Jonathan P. Lynch, Stephen P. Moose, Seth C. Murray, Rebecca Nelson, Torbert Rocheford, James C. Schnable, Patrick S. Schnable, Margaret Smith, Nathan Springer, Peter Thomison, Mitch Tuinstra, Randall J. Wisser, Wenwei Xu, Jianming Yu, Aaron Lorenz

Department of Agronomy and Horticulture: Faculty Publications

Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables …