Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-11T13:48:38.453Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  07 February 2019

G. Abou Khadiga Open the ORCID record for G. Abou Khadiga [Opens in a new window] ,
B. Y. F. Mahmoud  and
E. A. El-Full
G. Abou Khadiga*
Affiliation:
Faculty of Desert and Environmental Agriculture, Fuka, Matrouh University, 51744 Matrouh, Egypt
B. Y. F. Mahmoud
Affiliation:
Faculty of Agriculture, Fayoum University, 63514 Fayoum, Egypt
E. A. El-Full
Affiliation:
Faculty of Agriculture, Fayoum University, 63514 Fayoum, Egypt
*
Author for correspondence: G. Abou Khadiga, E-mail: galal.aboukhadiga@mau.edu.eg
Get access Rights & Permissions [Opens in a new window]

Abstract

The current study investigated the influence of selection for body weight (BW) on growth curve parameters in two lines of Japanese quail through a mixed model approach. Live BWs of 1400 Japanese quail were recorded at 3-day intervals from hatching to 42 days of age. Birds were distributed equally across lines (selected and control) and sexes (male and female). The asymptotic weight parameter (β0) values were always higher in Gompertz than Richards models in both lines. The values of β0 were higher in the selected than control lines and in females than males across models. Differences were found in the inflection point for age and weight across lines and sexes. Values of the growth rate parameter (β2) ranged from 0.06 to 0.10 in both models, favouring males over females in both lines. Lower weights at the inflection point of both models were observed in the control line. Determination coefficient (R2) of both models in different genetic groups and sexes was similar. Mean square error (MSE) values of the Gompertz model were lower for females in selected v. control lines. In contrast, MSE values of the Richards model were lower for selected males v. control males. According to the criteria of choice (highest R2 and lowest MSE, Akaike information criterion and Bayesian information criterion), the Richards model was considered the best fitting model for the growth data of males, while the Gompertz model was the best for growth data of female quails in both lines.

Keywords

Gompertzgrowth curvesJapanese quailmixed modelRichards
Type
Animal Research Paper
Information
The Journal of Agricultural Science , Volume 156 , Issue 9 , November 2018 , pp. 1153 - 1159
Copyright
Copyright © Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abou Khadiga, G, Mahmoud, BYF, Farahat, GS, Emam, AM and El-Full, EA (2017) Genetic analysis of partial egg production records in Japanese quail using random regression models. Poultry Science 96, 2569–2575.Google Scholar
Aggrey, SE (2009) Logistic nonlinear mixed effects model for estimating growth parameters. Poultry Science 88, 276–280.Google Scholar
Aggrey, SE, Ankra-Badu, GA and Marks, HL (2003) Effect of long-term divergent selection on growth characteristics in Japanese quail. Poultry Science 82, 538–542.Google Scholar
Akaike, H (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716–723.Google Scholar
Akbaş, Y and Oğuz, I (1998) Growth curve parameters of lines of Japanese quail (Coturnix coturnix japonica), unselected and selected for four-week body weight. European Poultry Science (Archiv fur Geflugelkunde) 62, 104–109.Google Scholar
Alkan, S, Mendeş, M, Karabağ, K and Balcıoğlu, MS (2009) Effect of short-term divergent selection for 5-week body weight on growth characteristics of Japanese quail. European Poultry Science (Archiv fur Geflugelkunde) 73, 124–131.Google Scholar
Alkan, S, Narinç, D, Karslı, T, Karabağ, K and Balcıoğlu, MS (2012) Effects of thermal manipulations during early and late embryogenesis on growth characteristics in Japanese quails (Coturnix cot. japonica). European Poultry Science (Archiv fur Geflugelkunde) 76, 184–190.Google Scholar
Arnold, E (2017) easynls: Easy Nonlinear Model. R package version 5.0. Vienna, Austria: The R Foundation. Available at https://CRAN.R-project.org/package=easynls (Accessed 12 December 2018).Google Scholar
Beiki, H, Pakdel, A, Moradi-Shahrbabak, M and Mehrban, H (2013) Evaluation of growth functions on Japanese quail lines. Journal of Poultry Science 50, 20–27.Google Scholar
Del Garnero, AV, Marcondes, CR, Bezerra, LAF, Oliveira, HN and Lôbo, RB (2005) Parâmetros genéticos da taxa de maturação e do peso assintótico de fêmeas da raça Nelore. Arquivo Brasileiro de Medicina Veterinária e Zootecnia 57, 652–662.Google Scholar
Farahat, GS, Mahmoud, BY, El-Komy, EM and El-Full, EA (2018) Alterations in plasma constituents, growth and egg production traits due to selection in three genotypes of Japanese quail. Journal of Agricultural Science, Cambridge 156, 118–126.Google Scholar
Gürcan, EK, Çobanoğlu, Ö and Kaplan, S (2017) Flexible alternatives to models widely used for describing growth in Japanese quail. Journal of Animal and Plant Sciences 27, 48–56.Google Scholar
Hussen, SH, Abdulrahman Al-Khdri, AM and Hassan, AM (2016) Response to selection for body weight in Japanese quail (Coturnix coturnix japonica). Iranian Journal of Applied Animal Science 6, 453–459.Google Scholar
Hyánková, L and Knížetová, H (2009) Divergent selection for shape of growth curve in Japanese quail. 5. Growth pattern and low protein level in starter diet. British Poultry Science 50, 451–458.Google Scholar
Kaplan, S and Gürcan, EK (2018) Comparison of growth curves using non-linear regression function in Japanese quail. Journal of Applied Animal Research 46, 112–117.Google Scholar
Karadavut, U, Taskin, A and Genc, S (2017) Comparison of growth curve models in Japanese quail raised in cages enriched with different colored lights. Revista Brasileira de Zootecnia (Brazilian Journal of Animal Science) 46, 839–846.Google Scholar
Karaman, E, Narinc, D, Fırat, MZ and Aksoy, T (2013) Nonlinear mixed effects modeling of growth in Japanese quail. Poultry Science 92, 1942–1948.Google Scholar
Kızılkaya, K, Balcıoğlu, MS, Yolcu, Hİ and Karabağ, K (2005) The application of exponential method in the analysis of growth curve for Japanese quail. European Poultry Science (Archiv fur Geflugelkunde) 69, 193–198.Google Scholar
Kızılkaya, K, Balcıoğlu, MS, Yolcu, Hİ, Karabağ, K and Genc, İH (2006) Growth curve analysis using nonlinear mixed model in divergently selected Japanese quails. Archiv fur Geflugelkunde 70, 181–186.Google Scholar
Lenth, RV (2016) Least-squares means: the R package lsmeans. Journal of Statistical Software 69, 1–33.Google Scholar
Narinç, D and Aksoy, T (2014) Effects of multi-trait selection on phenotypic and genetic changes in a meat type dam line of Japanese quail. Kafkas Üniversitesi Veteriner Fakültesi Dergisi 20, 231–238.Google Scholar
Narinç, D, Aksoy, T and Karaman, E (2010 a) Genetic parameters of growth curve parameters and weekly body weights in Japanese quails (Coturnix coturnix japonica). Journal of Animal and Veterinary Advances 9, 501–507.Google Scholar
Narinç, D, Karaman, E, Firat, MZ and Aksoy, T (2010 b) Comparison of non-linear growth models to describe the growth in Japanese quail. Journal of Animal and Veterinary Advances 9, 1961–1966.Google Scholar
Ojedapo, LO and Amao, SR (2014) Sexual dimorphism on carcass characteristics of Japanese quail (Coturnix coturnix japonica) reared in derived savanna zone of Nigeria. International Journal of Science, Environment and Technology 3, 250–257.Google Scholar
R Development Core Team (2017) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Rossi, RM, de Grieser, DO, de Conselvan, VA and Marcato, SM (2017) Growth curves in meat-type and laying quail: a Bayesian perspective. Ciências Agrárias 38(suppl. 1), 2743–2754.Google Scholar
Schwarz, G (1978) Estimating the dimension of a model. Annals of Statistics 6, 461–464.Google Scholar
Sezer, M and Tarhan, S (2005) Model parameters of growth curves of three meat-type lines of Japanese quail. Czech Journal of Animal Science 50, 22–30.Google Scholar
Teleken, JT, Galvão, AC and da Robazza, WS (2017) Comparing non-linear mathematical models to describe growth of different animals. Acta Scientiarum (Animal Sciences) 39, 73–81.Google Scholar
Wang, Z and Zuidhof, MJ (2004) Estimation of growth parameters using a nonlinear mixed Gompertz model. Poultry Science 83, 847–852.Google Scholar