Application of the logistic model to describe the growth curve in dogs of different breeds

ABSTRACT: Objective. To model the growth in dogs of different size and breeds that during their development showed a relative body weight according to the standards of their racial group. Materials and methods. The data used were obtained from the Canine Research Center (CIC), property of Empresa So...

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Autores:
Posada Ochoa, Sandra Lucía
Gómez Osorio, Luis Miguel
Rosero Noguera, Jaime Ricardo
Tipo de recurso:
Article of investigation
Fecha de publicación:
2014
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
eng
spa
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/33934
Acceso en línea:
https://hdl.handle.net/10495/33934
Palabra clave:
Análisis de Regresión
Regression Analysis
Peso Corporal
Body Weight
Tamaño Corporal
Body Size
Perros
Dogs
Tasa de crecimiento
Growth rate
http://aims.fao.org/aos/agrovoc/c_16130
Rights
openAccess
License
https://creativecommons.org/licenses/by-nc-sa/4.0/
Description
Summary:ABSTRACT: Objective. To model the growth in dogs of different size and breeds that during their development showed a relative body weight according to the standards of their racial group. Materials and methods. The data used were obtained from the Canine Research Center (CIC), property of Empresa Solla S.A., located in the municipality of Rionegro (Antioquia, Colombia). The parameters of the growth curve were defined based on the logistic model using the procedure PROC NLIN of the SAS. Results. The adult weight (a) ranged from 2.12 Kg (York Shire Terrier) to 32.88 Kg (Weimaraner). For small, medium and large breeds, growth rates (1/b) during the exponential phase ranged between 9.91- 18.91%, 9.12-13.83% and, 8.17-14.38%, respectively, and the average age at which 50% of the adult weight was reached (x0) was 3.49±0.03, 4.21±0.42 y 5.27±0.86 months, correspondingly. Large dog breeds reached maturity (T99) later than smaller breeds, 14.37±1.79 vs. 9.46±1.63 mo. Conclusions. The logistic model was able to describe the growth in dogs of different size, however, a larger sample size will improve its predictive ability, given the individual variability that characterizes growth.