Adjustment of four growth models through Bayesian inference on weight and body nutrient depositions in laying quail

ABSTRACT An experiment was conducted to estimate the parameters of the Gompertz, Brody, Logistic, and Von Bertalanffy equations through Bayesian inference and evaluate the potential for growth in terms of weight and body composition of laying female quail (Coturnix coturnix japonica). The weights an...

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Veröffentlicht in:Revista brasileira de zootecnia 2016-12, Vol.45 (12), p.737-744
Hauptverfasser: Finco, Eline Maria, Marcato, Simara Márcia, Furlan, Antonio Claudio, Rossi, Robson Marcelo, Grieser, Daiane de Oliveira, Zancanela, Vittor, Oliveira, Taciana Maria Moraes de, Stanquevis, Caroline Espejo
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Sprache:eng
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Zusammenfassung:ABSTRACT An experiment was conducted to estimate the parameters of the Gompertz, Brody, Logistic, and Von Bertalanffy equations through Bayesian inference and evaluate the potential for growth in terms of weight and body composition of laying female quail (Coturnix coturnix japonica). The weights and body compositions of the birds were obtained weekly (1-119 days), allowing the adjustment of the four equations by Bayesian inference. The parameters mature weight (β1), integration constant (β2), maturity rate (β3), and their credibility intervals in four models on body weight and body components were properly estimated by Bayesian inference to describe the body growth in laying quail. The inflection point was determined by the 1st and 2nd derivatives of the Gompertz equation for body weight and body components (fat, protein, ash, and water). Based on Deviance Information Criterion (DIC) for the studied and analyzed variables, there is a model that fits best as a result of its better performance to achieve the DIC value. The Von Bertalanffy model proved to be very versatile, not obtaining good fit of data only for fat. The study shows that other models can also be used in several data sets as an alternative to Gompertz, which, due to its adequate biological interpretation and desirable characteristics in a curve growth, is generally the most used.
ISSN:1516-3598
1806-9290
1516-3598
DOI:10.1590/s1806-92902016001200002