Mixed Models in Nonlinear Regression for Description of the Growth of Nelore Cattle
Raimundo Nonato Colares Camargo Júnior,
Cláudio Vieira de Araújo,
Welligton Conceição da Silva,
Simone Inoe de Araújo,
Raysildo Barbosa Lôbo,
Lílian Roberta Matimoto Nakabashi,
Letícia Mendes de Castro,
Flávio Luiz Menezes,
André Guimarães Maciel e Silva,
Lílian Kátia Ximenes Silva,
Jamile Andréa Rodrigues da Silva,
Antônio Vinicius Correa Barbosa,
José Ribamar Felipe Marques,
José de Brito Lourenço Júnior
Affiliations
Raimundo Nonato Colares Camargo Júnior
Postgraduate Program in Animal Science (PPGCAN), Institute of Veterinary Medicine, Federal University of Para (UFPA), Federal Rural University of the Amazon (UFRA), Brazilian Agricultural Research Corporation (EMBRAPA), Castanhal 68746-360, PA, Brazil
Cláudio Vieira de Araújo
Department of Agricultural and Environmental Sciences, Federal University of Mato Grosso (UFMT), Sinop 78550-728, MT, Brazil
Welligton Conceição da Silva
Postgraduate Program in Animal Science (PPGCAN), Institute of Veterinary Medicine, Federal University of Para (UFPA), Federal Rural University of the Amazon (UFRA), Brazilian Agricultural Research Corporation (EMBRAPA), Castanhal 68746-360, PA, Brazil
Simone Inoe de Araújo
Department of Agricultural and Environmental Sciences, Federal University of Mato Grosso (UFMT), Sinop 78550-728, MT, Brazil
Raysildo Barbosa Lôbo
National Association of Breeders and Researchers, Ribeirão Preto 14020-230, SP, Brazil
Lílian Roberta Matimoto Nakabashi
National Association of Breeders and Researchers, Ribeirão Preto 14020-230, SP, Brazil
Letícia Mendes de Castro
National Association of Breeders and Researchers, Ribeirão Preto 14020-230, SP, Brazil
Flávio Luiz Menezes
Department of Agricultural and Environmental Sciences, Federal University of Mato Grosso (UFMT), Sinop 78550-728, MT, Brazil
André Guimarães Maciel e Silva
Postgraduate Program in Animal Science (PPGCAN), Institute of Veterinary Medicine, Federal University of Para (UFPA), Federal Rural University of the Amazon (UFRA), Brazilian Agricultural Research Corporation (EMBRAPA), Castanhal 68746-360, PA, Brazil
Lílian Kátia Ximenes Silva
Department of Veterinary Medicine, Federal University of Para (UFPA), Castanhal 68746-360, PA, Brazil
Jamile Andréa Rodrigues da Silva
Institute of Animal Health and Production, Federal Rural University of the Amazônia (UFRA), Belém 66077-830, Brazil
Antônio Vinicius Correa Barbosa
Institute of Animal Health and Production, Federal Rural University of the Amazônia (UFRA), Belém 66077-830, Brazil
José Ribamar Felipe Marques
Brazilian Agricultural Research Corporation (EMBRAPA), Belém 70770-901, PA, Brazil
José de Brito Lourenço Júnior
Postgraduate Program in Animal Science (PPGCAN), Institute of Veterinary Medicine, Federal University of Para (UFPA), Federal Rural University of the Amazon (UFRA), Brazilian Agricultural Research Corporation (EMBRAPA), Castanhal 68746-360, PA, Brazil
Body weight records were used to characterize the growth curve of Nelore cattle. Body weight was regressed as a function of age, for both sexes, by using nonlinear models through the functions of Brody, Gompertz, Logistic, Richards, Meloun 1, Von Bertalanffy, and Von Bertalanffy. The quality of the model arrangements was evaluated by employing Akaike and Bayesian Schwarz information criteria. The Brody function provided the best adaptations by the evaluators and, considering the asymptotic weight and the maturation rate as random, a reduction in residual variance of 79% for males and 83% for females was obtained in relation to the models under fixed contexts. In males, the absolute and relative growth rates ranged from 0.921 to 0.261 kg/day and 2.39 to 0.08%, respectively. For the same rates, under another approach, females ranged from 0.922 to 0.198 kg/day and 2.55 to 0.06%, respectively. Males showed greater growth acceleration at the beginning of the growth trajectory, being equal to females at 397 days of age and from that age onward they presented lower estimates. The nonlinear regression model approach under the mixed-models context allows reduction of residual variance, increasing model accuracy.