Adjustment of growth curves in cutting quails by Bayesian inference

Authors

DOI:

https://doi.org/10.33448/rsd-v10i6.15530

Keywords:

Ponderal development; Metabolizable energy; Bayesian inference; Gompertz model; Absolute growth rate.

Abstract

The aim was to compare growth curves in meat quails (Coturnix coturnix coturnix), between gender and diets with different levels of metabolizable energy (ME), using non-linear regression models under Bayesian inference. Body weight records were used for 579 males and 581 females, weighed at birth, at 7, 14, 21, 35 and 42 days of age. The diets were composed by three different levels of metabolizable energy, being 2750; 2950 and 3150 kcal of EM / kg. The models used the functions of Gompertz, Logística, Richards and Von Bertalanffy, considering the parameter related to the maturity rate (K) as random. Markov chains were generated via Gibbs sampler, adopting the Deviance Criterion in choosing the most appropriate model. In both genders. The Gompertz function showed a significantly higher adjustment according to the evaluated criterion, and a lower residual variance estimate, indicating greater precision in the prediction of the animals' body weight. Different absolute growth rates were observed between the genders, so the description of growth curves must be carried by gender. The analyzes indicated that the level of 2750 kcal / kg of ME in the diet promotes growth in a similar way to the other levels in the phase from 1 to 14 days of age in males and from 1 to 21 days of age in females. After these ages, a better performance was observed using the level of 3150 kcal / kg of EM for males and levels of 2950 and 3150 kcal / kg for females.

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Published

21/05/2021

How to Cite

TON, A. P. S. .; LAUREANO, M. M. M.; ARAÚJO, S. I.; MENEZES, F. L. de; ARAÚJO, C. V. de. Adjustment of growth curves in cutting quails by Bayesian inference. Research, Society and Development, [S. l.], v. 10, n. 6, p. e8910615530, 2021. DOI: 10.33448/rsd-v10i6.15530. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/15530. Acesso em: 15 jun. 2021.

Issue

Section

Agrarian and Biological Sciences