Mega-Environment Analysis To Assess Adaptability, Stability, And Genomic Predictions In Grain Sorghum Hybrids

Abstract Multi-environment trials (MET) are fundamental for assessing genotype-by-environment interaction (GxE) effects, adaptability and stability of genotypes and provide valuable information about target regions. As such, a MET involving grain sorghum hybrid combinations derived from elite inbred lines adapted to diverse sorghum production regions was developed to assess agronomic performance, stability, and genomic-enabled prediction accuracies within mega-environments (ME). Ten females and ten males from the Texas A&M and Kansas State sorghum breeding programs were crossed following a factorial mating scheme to generate 100 hybrids. Grain yield, plant height, and days to anthesis were assessed in a MET consisting of ten environments across Texas and Kansas over two years. Genotype plus Genotype-by-block-of-environment biplot (GGB) assessed ME, while the "mean-vs-stability" view of the biplot and the Bayesian Finlay-Wilkinson regression evaluated hybrid adaptability and stability. A genomic prediction model including the GxE effect was applied within ME to assess prediction accuracy. Results suggest that grain sorghum hybrid combinations involving lines adapted to different target regions can produce superior hybrids. GGB confirmed distinct regions of sorghum adaption in the U.S. Further, genomic predictions within ME reported inconsistent results, suggesting that additional effects rather than the correlations between environments are influencing genomic prediction of grain sorghum hybrids.