Comparison of growth curve models in Japanese quail raised in cages enriched with different colored lights

Abstract

-This study compared the growth curve models for the live weight and body length of Japanese quail raised under lights of various colors. The Brody, Gompertz, and von Bertalanffy growth models were used to investigate the effect of different colored lights on Japanese quail growth over a period of six weeks (1-42 days). Four lights of different colors, comprising yellow, red, blue, and white, were used in the study. According to the different light colors, the mean and standard error for the live weight and body length on day 42 were calculated as 196.09 and 3.87 g and 29.48 and 0.192 cm, respectively. Furthermore, while the differences in live weight according to the color of the light being used were statistically significant on days 14, 21, and 28, there were significant differences in body length on days 7, 28, 35, and 42, depending on the color of the light used. The highest values of R 2 for body length and live weight were 0.9935 and 0.9988; the lowest sum of square error values for body length and live weight were 9.6588 and 10.6623 according to the Gompertz model. Test results did not reveal autocorrelation among serial data except for those grown under red colored lights.

Quail growth curve model identity

“…We observed a strong inverse relationship (r = -0.94) between adult weight (β 1 ) and maturation rate (β 3 ) only in Strain 1, which was expected, given that the higher the adult weight, the lower is the rate of maturation (Grieser et al, 2015). Biologically, this correlation can be interpreted as indicating that animals with higher growth rates are less likely to reach higher weights at maturity than those that grow more slowly in early life; that is, birds that are heavier at maturity tend to have a lower growth rate (Karadavut et al, 2017). It should be noted that this relationship is biologically the most important (Kaplan and Gürcan, 2018).…”

“…Quail production at the national level has grown in recent years, mainly due to the lower costs of poultry production, and the inherent characteristics of bird physiology, as quails are precocious birds that have a high growth rate (Karadavut et al, 2017). However, despite the intense interest, there is little information available regarding the characteristic's growth curves of European quails (Couturnix couturnix sp.)…”

Modelling of growth alteration in Japanese quail after a selection experiment for body weight at 4 weeks of age

“…In the current study, high R 2 values of both growth models (close to 1) indicate the suitability of the models for describing growth curves of Japanese quail. Similar high values have been reported in previous studies (Narinç et al ., 2010 a ; Karadavut et al ., 2017; Kaplan and Gürcan, 2018). Significant differences between the models for most of the growth curve parameters indicated the peculiarity of each data set and the different potentiality of each model to describe the growth data of each group.…”

“…The most common growth models used in the studies on Japanese quail are Gompertz and Richards. Both models are considered the best to fit data in terms of goodness-of-fit criteria and their biologically interpretable model parameters (Karadavut et al ., 2017; Rossi et al ., 2017; Kaplan and Gürcan, 2018). Inflection point weight in these models is mostly identified as 35–40% of the asymptotic weight (Teleken et al ., 2017; Kaplan and Gürcan, 2018).…”

The Use of Contrasts in Multivariate Nonlinear Mixed Models to Compare Treatments in Longitudinal Factorial Experiments

“…In addition, when there are many individuals, each one with growth measurements over time, the nonlinear mixed effects models are more appropriate than fixed effects because it is possible to evaluate growth at both the individual unit level, as well as the population level (HARRING and BLOZIS, 2014;LI and JIANG, 2013;REGADAS FILHO et al, 2014). Nonlinear models are widely employed not only to quantify growth, but also to make comparisons between the parameters of the model, to verify the influence of treatment groups in planned experiments (REGADAS FILHO et al, 2014;STRATHE et al, 2010;KARADAVUT et al, 2017). The purpose of this study was to establish contrasts in multivariate nonlinear mixed models to verify the effects of treatments in longitudinal factorial experiments with multiple responses.…”