Structure of Breeding Programs to Capitalize on Reproductive Technology for Genetic Improvement

Open Access
Publisher Website
Google Scholar
Abstract
Potential rates of genetic progress are limited by biological constraints, which along with genetic parameters determine the structure of breeding programs to be employed for maximum genetic improvement. The objective here is to determine whether current progeny-testing programs in dairy cattle, which have been dictated and constrained by low female reproductive rates, need to be changed to capitalize on new reproductive technologies and how these changes should be implemented. Many differences between breeding programs diminish when selection on animal model genetic evaluations across all age and population groups is adopted as a strategy. Progeny-testing schemes then evolve toward dispersed open nucleus breeding schemes when multiple ovulation and embryo transfer is used on bull-dams. Nucleus breeding schemes have been advocated to capitalize on embryo transfer technology. In nucleus breeding schemes utilizing high reproductive rates, inbreeding, rather than reproductive rate, poses a limit to genetic progress, and strategies that maximize response to selection while limiting inbreeding need to be employed. One strategy is mating each dam to several sires rather than only one sire. In vitro embryo production techniques can be used to facilitate such mating strategies. Large-scale in vitro embryo production programs, in which large numbers of embryos per female are tested in the commercial population, offer the greatest potential for genetic gain with low rates of inbreeding. Cloning has an impact mainly on methods for dissemination of genetic improvement. Breeding herds, genetically inferior to marketed clones, are needed for continuous genetic gain. Reproductive technologies offer the potential for genetic improvement. Whether new breeding programs require changes in population structure, e.g., by creation of nucleus breeding herds, depends mainly on logistics and on quantity and quality of field information.