Simulation-based decision-making and implementation of tools in hybrid crop breeding pipelines

New technologies have been developed over the last few years aiming to support breeding pipeline optimization for long-term genetic gains. However, the implementation of these new tools and their impact on any breeding program's budget are not well studied. Here, we compare multiple breeding pipeline strategies accounting for genomic selection and high-throughput phenotyping (HTP) by means of hybrid gain and cost-effectiveness. We simulated a hybrid crop breeding program through coalescent theory. We compared two strategies for parental updates and four breeding pipelines: conventional breeding pipeline; conventional breeding pipeline with HTP; conventional breeding pipeline with genomic selection; conventional breeding pipeline with genomic selection and HTP. All analyses were implemented under three different levels of genotype-by-environment interaction (GxE) and two trait heritabilities (0.3 and 0.7). Overall, the results show that scenarios with early parental selection perform better than the others. In addition, the implementation of HTP delivered the highest hybrid gain in the long-term, whereas the implementation of genomic selection seems to be more cost-effective. We suggest, considering breeding programs with complex trait inheritance and accounting for higher levels of GxE, investing in breeding pipelines accounting for genomic selection as a strategy to create and maintain long-term hybrid gain. Moreover, considering an unconstrained budget, the investment in both, genomic selection and HTP, represents the best strategy. Hence, these results provide strategies that may aid breeders in optimizing self-pollination breeding programs. Early parental selection has the potential to increase long-term genetic performance.High-throughput phenotyping (HTP) can aid breeding program improvement.Breeding pipelines focusing on complex traits should consider the implementation of genomic selection and HTP.