Abstract
Abstract: The objective of this work was to evaluate the potential of different threshold models to determine the genetic variability in Nellore cattle, with basis on the heritability estimates for the traits stayability (STA) and first calving probability at 36 months of age (CP36). Data came from the Nellore herds participating in the animal breeding program of the Embrapa-Geneplus partnership. Binomial and multi-threshold models were defined for the STA and CP36 traits. Heritability estimates were obtained following Bayesian procedures in the Multiple-trait Gibbs Sampler for Animal Models (MTGSAM) software, using a sire-maternal grandsire model. The heritability estimates, provided by the binary and alternative models, were, respectively, 0.08 and 0.12 for STA and 0.17 and 0.12 for CP36. The multi-threshold model can efficiently detect the genetic variability for stayability, but not for probability of calving for 36-month-old cows.
Highlights
Bovine traits associated with the reproduction are extremely important for ensuring an efficient beef cattle selection
The objective of this work was to evaluate the potential of different threshold models to determine the genetic variability in Nellore cattle, with basis on the heritability estimates for the traits stayability (STA) and first calving probability at 36 months of age (CP36)
The multi-threshold model can efficiently detect the genetic variability for stayability, but not for probability of calving for 36-month-old cows
Summary
Bovine traits associated with the reproduction are extremely important for ensuring an efficient beef cattle selection. High-birth and survival rates result in more animals for sale, in commercial herds, or for selection in the case of purebred herds. Except for age at first delivery, the interval between births and gestation period, which are continuous in nature, and other important reproductive traits, such as stayability and first calving probability, are categorical in nature. The procedures for defining the phenotypic expression of these traits, and analyzing the underlying genetic data vary according to the number of classes per variable (Santos et al, 2012; Malhado et al, 2013), and according to the complexity of the interactions between the environment, animal genetics, physiology, and animal management. The application and definition of these traits as selection criteria remain poorly explored
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