Predicting maize kernel number using QTL information

All traits showed significant variation, and both analyses detected several QTL for the studied traits. Associated QTL for EB and KNP per se did not localize with QTL detected for model parameters. This is the first report describing genomic regions for key physiological traits related to maize biomass partitioning around flowering and kernel set efficiency per unit of accumulated EB 15 days after anthesis. Quantitative trait loci information of model parameters helped to predict accumulated EB and KNP with higher accuracy (r2 = 0.13 and 0.12, p < 0.001, for EB and KNP, respectively) than trying to predict EB and KNP based on QTL detected on final traits per se (r2 < 0.01 and <0.01, p > 0.10, for EB and KNP, respectively). However, predictions using an average crop physiology model parameter across genotypes and individual RIL plant growth gave the highest accuracy (r2 = 0.46 and 0.37, p < 0.001, for EB and KNP, respectively). As such, we identified chromosome areas including potentially relevant genes involved in maize KNP determination, but this information helped to predict KNP at different environments only partially, suggesting other approaches might be needed.