Designing superior crosses in cassava using genomic mating to boost yield and genetic diversity

This study applied genomic mating and optimum contribution selection in cassava to improve cross selection efficiency and enhance yield and genetic diversity. Using data from 3,391 clones and models incorporating additive, dominance, and directional dominance effects, significant predictive gains were achieved for key traits such as dry matter content, root and shoot yield, and plant architecture. Directional dominance notably improved prediction accuracy, highlighting the relevance of non-additive effects. While inbreeding was effectively controlled, fewer crosses were selected, reflecting a balance between gain and diversity. The approach demonstrates the potential of genomic strategies to optimize breeding in clonally propagated crops.