Julie C. Dawson

In this study, we developed and evaluated genomic prediction strategies to identify valuable breeding material in crop germplasm collections, using ongoing efforts to breed carrot varieties with tall canopies and mild flavor as a model vegetable crop and breeding goal. Genomic prediction leverages high‐density genotype data to predict phenotypes or breeding values, potentially allowing for rapid identification of useful germplasm in a genotyped collection. Two different training populations were designed to approximate different potential breeding contexts in which genomic prediction could be applied. The genomic selection models developed using these phenotyped training populations had low‐to‐moderate (0.11–0.31) ability to predict the trait values of germplasm accessions evaluated in a separate environment, depending on the trait. Selected accessions were used as parents in crosses with two different elite inbred lines to initiate new breeding populations. In the target environment, these populations were evaluated for canopy height and flavor and compared to populations developed from phenotypically selected parents. At the F 2 stage, there were few differences between populations developed with genomic and phenotypic selection. While evaluation of future generations is required, these results suggest that genomic selection may allow for the identification of valuable germplasm accessions without the need for extensive field evaluation of potential parents.