期刊
POULTRY SCIENCE
卷 92, 期 8, 页码 2226-2235出版社
OXFORD UNIV PRESS
DOI: 10.3382/ps.2012-02825
关键词
body weight; growth model; nonlinear function; local population; chicken
Predictions of growth are important factors that contribute to the profitability of an operation in poultry production. Modern commercial hybrids have a higher body growth in comparison with the local purebreds. However a niche market for meat and egg poultry production needs to be established using local purebreds to promote biodiversity. The aim of this study was to model the growth response of male and female chickens belonging to 5 local Italian populations: a commercial slow-growing hybrid (Berlanda, B), the Padovana pure breed [2 plumage varieties: silver, argentata (PA) and chamois, camosciata (PC)], and their crosses PCxB and PCxPA. A total of 398 one-day-old birds were reared until 180 d of age under indoor conditions. The linear and 3 nonlinear models (logistic, Gompertz, and Richards) were compared to study the growth patterns of these chicken populations. Significant (P < 0.01) differences were observed among the genotypes for several curve parameters. In males, PCxB showed the lowest age at inflection point, B showed the highest age and BW, whereas PA showed the highest age and the lowest weight. In females, the age at the inflection point did not differ among the groups; B showed the highest weight. All the nonlinear models gave a good fit of male and female data with R-2 ranging from 0.992 and 0.999, but the logistic equation had higher value of root mean square error than the Gompertz and the Richards values. Based on residual sum of squares for both sexes, the Richards model was better (P < 0.05) than the logistic but not superior to the Gompertz. The logistic equation showed an overestimation of initial BW for all the groups and sex. For Italian local chicken populations, the Richards model requires a measure of BW recorded at 90 d or after to obtain a good fit of the asymptotic weight. However, the Gompertz model has the advantage that it requires one less parameter than the Richards model.
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