Nonlinear models of Brazilian sheep in adjustment of growth curves
C.C.P. Paz, G.C. Venturini, E. Contini, R.L.D. Costa, L.P. Lameirinha, C.R. Quirino
https://doi.org/10.17221/87/2017-CJASCitation:Paz C.C.P., Venturini G.C., Contini E., Costa R.L.D., Lameirinha L.P., Quirino C.R. (2018): Nonlinear models of Brazilian sheep in adjustment of growth curves . Czech J. Anim. Sci., 63: 331-338.Growth curves of the Morada Nova sheep males and females were described using nonlinear models and the relationships between body weight and thoracic circumference were evaluated. Altogether 1516 repeated measures of body weight and thoracic circumference of the Morada Nova sheep (668 males and 848 females) taken since birth till 730 days of age were used. The Brody, Richards, von Bertalanffy, Gompertz, and Logistic models have been tested. The Fisher’s test (F) was used to verify the differences (P < 0.05) in growth curves between males and females. The Gompertz model presented a significant difference (P < 0.001) for growth curve parameters between males (asymptotic weight (A) = 40.5 kg and maturing rate (k) = 0.0043 kg/day) and females (A = 36.44 kg and k = 0.0028 kg/day). The relationships between body weight and thoracic circumference presented R2 above 0.7 and a high significance (P < 0.0001) for all categories, showing that the thoracic circumference may be a good indicator of body weight. In addition, a significant effect (P < 0.05) of the relationship between thoracic circumference and prediction of animal’s body weight was verified using the models of linear, quadratic, and cubic regression. Among the models studied, the Gompertz model presented the best fit and biological interpretation. Furthermore, the Gompertz model indicated the need to separate animals by sex in order to properly meet nutritional requirements and determine adequate slaughter age. Thoracic circumference can be used to predict animal body weight with a high accuracy.
body weight; Gompertz; growth rate; mature weight; thoracic circumference
References:Impact Factor (Web of Science):
2020: 1.267
Q3 – Agriculture, Dairy and Animal Science
5-Year Impact Factor: 1.428
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