Multi-Trait Genomic Models Improve Prediction of Organic Grain Yield in Oat

Summary

Overall, our results demonstrated that genomic prediction for organic grain yield in oat is feasible with moderate to high accuracy. Genetic correlation between organic and conventional grain yield was very high whereas the genetic correlation between organic grain yield and drone phenotypes was moderate. The best genomic prediction approach was found to be the combination of organic and conventional grain yield in a bivariate model. Especially when the validation line had conventional grain yield included in the training data set. These findings emphasize that integrating conventional trial data represents an efficient and effective strategy to accelerate genetic gain in organic breeding programs, particularly in the early stages of genomic selection implementation.