J. Anim Sci., doi: 10.2527/jas.2006-677
©Copyright, 2007, The American Society of Animal Science
Analysis of beef cattle longitudinal data applying a non-linear model
S. Forni 1,
M. Piles 2,
A. Blasco 3,
L. Varona 4,
H. N. Oliveira 5,
R. B. Lôbo 6,
L. G. Albuquerque 1*
1 Faculdade de Ciências Agrárias e Veterinárias, UNESP, Jaboticabal, SP, 14884900, Brazil
2 IRTA, Unidad de Cunicultura, Caldes de Montbui, 68140, Spain
3 Departamento de Ciencia Animal, Universidad Politécnica de Valencia, 46071, Spain
4 Centre UdL-IRTA, Lleida, 25198, Spain
5 Faculdade de Medicina Veterinária e Zootecnia, UNESP, Botucatu, SP, 18618000, Brazil
6 Faculdade de Medicina, USP, Ribeirão Preto, SP, 14049900, Brazil
* To whom correspondence should be addressed. E-mail: lgalb{at}fcav.unesp.br.
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Abstract |
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The objective of this work was to evaluate the Nelore beef cattle growth curve parameters using the Von Bertalanffy function in a nested Bayesian procedure that allowed estimation of the joint posterior distribution of growth curve parameters, their (co)variance components, and the environmental and additive genetic components affecting them. A hierarchical model was applied; each individual had a growth trajectory described by the non-linear function, and each parameter of this function was considered to be affected by genetic and environmental effects that were described by an animal model. Random samples of the posterior distributions were drawn using Gibbs sampling and Metropolis-Hastings algorithms. The data set consisted of a total of 145,961 weights recorded from 15,386 animals. Even though the curve parameters were estimated for animals with few records, given that the information from related animals and the structure of systematic effects were considered in the curve fitting, all mature weights predicted were suitable. A large additive genetic variance for mature weight was observed. The parameter a of growth curves, which represents asymptotic adult weight, could be used as a selection criterion to control increases in adult weight when selecting for growth rate. The effect of maternal environment on growth was carried through to maturity and should be considered when evaluating adult weight. Other growth curve parameters showed small additive genetic and maternal effects. Mature weight and parameter k, related to the slope of the curve, presented a high positive genetic correlation. The results indicated that selection for growth rate would increase adult weight without substantially changing the shape of the growth curve. Selection to change the slope of the growth curve without modifying adult weight would be inefficient, because their genetic correlation is high. However, adult weight could be considered in a selection index with its corresponding economic weight in order to improve the overall efficiency of beef cattle production.
Key Words:
Bayesian analysis, beef cattle, growth curves, longitudinal data, selection, Von Bertalanffy
