GENETICALLY-INFORMED ENVIROTYPING TOOLS TO BETTER MATCH TEST AND TARGET ENVIRONMENTS

Better matching of test crop growth environments to target crop production environments is key for efficient crop breeding. We propose to optimize promising new envirotyping analysis and modeling methods and develop publicly accessible known-truth genotype-environment simulations to allow improved breeding schemes for better global crop yield. In conjunction with the development of simulations, we will improve our promising PreMiuM profile regression algorithm's run speed and develop breeder-relevant output plots and tables. We will combine PreMiuM profile regression covariate variable selection with standard linear model selection and fit methods to create a combined analysis workflow that will allow breeders to fit SNP and environment variates to their data. To illustrate these new analysis methods and inform our breeding program modeling, we will analyze real crop datasets with our improved PreMiuM and PreMiuM+model selection workflow and make spatial results maps to visualize the results in an easily interpretable field context.To leverage better envirotyping within breeding programs, we need modeling tools that allow exploration of program design constraints. We will develop breeding simulation models that incorporate realistic environment covariate features of test and target environments and flexible, extensible specifications of genetic gain within an open-source, widely used web-accessible modeling system that supports both student training and advanced breeder modeling. Modeling tools and better envirotyping tools will support crop breeders. Breeders will be able to design optimal germplasm exchange programs for maximum genetic gain by using PreMiuM results to inform setup of test and target environments.