Genetic variability for temperament indicators of Nellore cattle.

The aim of this study was to estimate genetic parameters of 4 temperament indicator traits for Nellore cattle and evaluate the possibility of using such traits as selection criteria in breeding programs. Temperament was assessed for 23,420 male and female animals at 550 d of age, which were born between 2002 and 2009. A temperament score (TS) was used, which is based on a scale from 1 to 5 and considers the reaction of animals after exiting the crush. Moreover, 9,150 individuals born in 2008 and 2009 were measured for these characteristics: movement score (MOV), where animals were scored from 1 to 5 according to their movement inside the crush; crush score (CS), which assigns scores from 1 to 4 for the general reactivity inside the crush; and flight speed (FS), which is a recording of the speed (m/s) at which animals exit the crush after being weighed. The weaning weight (WW) was included in the multitrait analysis to reduce the effect of the selection performed at weaning. Bayesian inference using Gibbs sampling was applied to estimate (co)variance components and breeding values of the animals. The model included random direct additive genetic and residual effects, fixed effects of contemporary groups, age of the dam (classes), and age of the animal as covariate (linear and quadratic effects for WW and TS, and only linear effects for the other traits). A linear model was applied to WW and FS, whereas a threshold model was used for TS, CS, and MOV. Heritability estimates for FS, TS, CS, and MOV were 0.35, 0.15, 0.19, and 0.18, respectively. The genetic correlation estimates of FS with TS (0.85), CS (0.85), and MOV (0.76) were high, although the phenotypic correlations were low (between 0.18 and 0.25). For CS and MOV, the genetic and phenotypic correlation estimates were high (0.99 and 0.71, respectively). We concluded that all of the temperament indicator traits addressed in this study presented enough genetic variability to respond to selection; however, the use of FS would result in a faster genetic gain. With regard to the practical applicability in breeding programs, the use of FS is also favorable because it produces data on a continuous scale, allows for recording of the data electronically, and requires low investment.