Classical and genomic prediction of hybrid sweet corn performance in organic environments

Abstract Plant breeders need efficient systems to identify which inbreds combine create new hybrid cultivars. The North Carolina Design II (NC DII) is a useful mating design evaluate the potential of varieties and their inbred parents. Genomic best linear unbiased prediction (GBLUP) models, either with or without inclusion dominance term in model, have been found be an method for using rich marker sets prediction. This study used data phenotypic collected 11 organic trials across six locations on 40 sweet corn ( Zea mays L.) genotypes 100 progenies formed from four disconnected NC DII blocks predict performance untested hybrids. In 2017, validation 24 previously hybrids were grown five environments assess correlation between actual predicted by GBLUP general combining abilities (GCAs). Fivefold cross‐validation accuracy ranged 0.29 0.82 predictions based additive effects alone (GBLUP‐A) 0.70 0.91 combined (GBLUP‐AD). For all traits except flavor, addition model increased accuracy. Correlations values measured 2017 2015 2016 training 0.36 0.92 GCA‐based predictions, 0.34 0.94 0.38 plus model.