|May 22 - 26, 2017
Operations Research in Plant Breeding
Ames, IA, USA
Premise: Because efficiencies of genetic improvement projects can be measured there exist most efficient and less efficient designs. Further, there are methods to systematically design most efficient projects. Successful plant breeders in the 21st Century will be able to design genetic improvement projects by systematically addressing trade-offs among competing objectives using the same methods that have been used to optimize communication, energy, management and manufacturing systems.
Synopsis: Participants will learn rational design principles to translate project objectives into objective mathematical functions and use off-the-shelf software to design optimal breeding projects. The primary pedagogical approach will be team based learning using case studies.
- A mathematical framework for the simple challenge of introgressing a single allele while minimizing costs, time and maximizing probability of success.
- Apply the same framework to design projects for introgressing 30 alleles from one to multiple sources using Markov Decision Processes and the recently published Predicted Cross Value.
- Missing from evaluation of Genomic Prediction methods has been systematic assessment of conditions that affect accuracies of prediction. Response Surface Methods will be used to systematically determine conditions that maximize prediction accuracies for any GP methods.
- Design breeding systems that will optimize the trade-offs between maximizing genetic gain while minimizing loss of genetic diversity by employing the Genomic Mating approach.
- Online with access provided by Brenton Center, limited to 20 registrants
- On campus at Iowa State University in Ames, Iowa, limited to 20 registrants
Deniz Akdemir, Michigan State University
Bill Beavis, Iowa State University
John Cameron, Iowa State University
Ye Han, Iowa State University
Reka Howard, University of Nebraska Lincoln
Lizhi Wang, Iowa State University
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