A new selection criteria to optimize growth in animal breeding programs

Abstract

Pedigree records and longitudinal measurements of live weight from 2628 buffaloes were analyzed. The aim of this research was to propose a new selection criteria, the Area Under the Growth Curve (AUGC), derived from a growth curve-based model. A hierarchical Bayesian approach with two levels was employed. In the first level, the growth trajectory was modeled using a fourth-degree polynomial, while in the second level, each parameter of the polynomial function was treated as a dependent variable influenced by environmental and genetic effects. The animal model included sex, dams’ parity and contemporary group (herd-year-season) as fixed effects, and relationships among animals as a random effect. Inference was conducted using Markov Chain Monte Carlo (MCMC) simulation algorithm. The proposed AUGC is interesting for use in selection programs because it allows breeders to identify heavier animals with lower risk in the production system. Additionally, that trait showed moderate to high heritabilities from weaning onwards, providing a useful new tool for cattle selection in the post-weaning phases.