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ARTICLE |
1 IRTA – Unitat de Cunicultura, Torre Marimón s/n., 08140 Caldes de Montbuí, Barcelona, Spain
2 Area de Producció Animal, Centre UdL – IRTA, C/ Rovira Roure 177, 25198 Lleida, Spain
* To whom correspondence should be addressed. E-mail: miriam.piles{at}irta.es.
| Abstract |
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A Bayesian procedure, which allows consideration of the individual variation in the feed resource allocation pattern, is described and implemented in two sire lines of rabbit (Caldes and R). The procedure is based on a hierarchical Bayesian scheme, where the first stage of the model consists of a multiple regression model of food intake on metabolic body weight and body weight gain. In a second stage, an animal model was assumed including batch, parity order, litter size, and common environmental litter effects. Animals were reared during the fattening period (from weaning at 32 d of age to 60 d of age) in individual cages on an experimental farm, and were fed ad libitum with a commercial diet. Body weight (g) and cumulative food intake (g) were recorded weekly. Individual body weight gain (BWG, g) and average body weight (ABW, g) were calculated from these data for each 7-d period. Metabolic body weight (MBW, g0.75) was estimated as ABW0.75. The number of animals actually measured was 444 and 445 in the Caldes and R lines, respectively. Marginal posterior distributions of the genetic parameters were obtained by Gibbs sampling. Posterior means (posterior standard deviations) for heritabilities for partial coefficients of regression of food intake on metabolic body weight and food intake on body weight gain were estimated to be 0.35 (0.17) and 0.40 (0.17), respectively, in the Caldes line and 0.26 (0.19) and 0.27 (0.14), respectively, in line R. The estimated posterior means (posterior standard deviations) for the proportion of the phenotypic variance due to common litter environmental effects of the same coefficients of regression were respectively, 0.39 (0.14) and 0.28 (0.13) in the Caldes line and 0.44 (0.22) and 0.49 (0.14) in line R. These results suggest that efficiency of use of food resources could be improved by including these coefficients in an index of selection.
Key Words: Bayesian analysis, food efficiency, rabbits, selection
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