An application of modified Logistic and Gompertz growth models in Japanese quail
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Keywords:
Gompertz, Logistic, Model Modification, Japanese QuailAbstract
Growth functions describe body weight changes over time, allowing information from longitudinal measurements to be combined into a few parameters with biological interpretation. The Gompertz and Logistic models, which have three parameters (A: asymptotic body weight, b: shape parameter,c: constant of average growth rate), have been used extensively in poultry species to describe the development of body weight. The first aim of this study was to gain new two parameters that are called hatching body weight (λ) and maximum growth rate (μ) these parameters which are important for animal breeding to the Logistic and Gompertz models respectively. Furthermore, the second aim of this study was to reveal similarities and differences of both models in growth data of Japanese quail by using various goodness of fit criteria and residual analysis.The growth data of 64 mixed sex Japanese quail consisted of individual live weights of 3-day intervals from hatching (day 0) to 42 days of age. The parameters λ, A and μ of the Gompertz and Logistic models were estimated as, 8.71, 242.10, 6.00 g and 14.71, 208.44, 6.50 g, respectively. As a result of the goodness of fit criteria and residuals analysis, the Gompertz model indicates a much better fit than the Logistic model to Japanese quail data set. According to the results, transformed Gompertz and Logistic models are not only more profitable for poultry species but also more useful for other livestock species such as goat, sheep and cattle.
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References
Aggrey S E. 2002. Comparison of three nonlinear and spline regression models for describing chicken growth curves. Poultry Science 81: 1782–88. DOI: https://doi.org/10.1093/ps/81.12.1782
Aggrey S E. 2009. Logistic nonlinear mixed effects model for estimating growth parameters. Poultry Science 88: 276–80. DOI: https://doi.org/10.3382/ps.2008-00317
Akbas Y and Yaylak E. 2000. Heritability estimates of growth curve parameters and genetic correlations between the growth curve parameters and weights at different age of Japanese quail. Archivfür Geflügelkunde 644: 141–46.
Akbas Y and Oguz I. 1998. Growth curve parameters of lines of Japanese quail (Coturnixcoturnix japonica), unselected and selected for four-week body weight. Archivfür Geflügelkunde 623: 104–09.
Alkan S, Mendes M, Karabag K and Balcioglu M S. 2009. Effects short term divergent selection for 5-week body weight on growth characteristics in Japanese quail. Arch Geflugelk 732: 124–31.
Anthony N B, Emmerson D A, Nestor K E, Bacon W L, Siegel P B and Dunnington E A. 1991. Comparison of growth curves of weight selected populations of turkeys, quail and chickens. Poultry Science 70: 13–19. DOI: https://doi.org/10.3382/ps.0700013
Anthony N B, Nestor K E and Bacon W L. 1986. Growth curves of Japanese quail as modified by divergent selection for 4- week body weight. Poultry Science 65: 1825–33. DOI: https://doi.org/10.3382/ps.0651825
Balcioglu M S, Kizilkaya K, Yolcu H I and Karabag K. 2005. Analysis of growth characteristics in short-term divergently selected Japanese quail. South African Journal of Animal Science 35: 83–89.
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. DOI: https://doi.org/10.2141/jpsa.0110142
Hyankova L, Knizetova H, Dedkova L and Hort J. 2001. Divergent selection shape of growth curve in Japanese quail 1. Responses in growth parameters and food conversion. British Poultry Science 42: 583–89. DOI: https://doi.org/10.1080/00071660120088371
Hyankova L, Novotna B and Darbas V M. 2008. Divergent selection for shape of growth curve in Japanese quail.Carcass composition and thyroid hormones. British Poultry Science 49: 96–102. DOI: https://doi.org/10.1080/00071660801949913
GraphPad 5. 2007. Prism 5 Statistics Guide. GraphPad Software Inc. San Diego. CA.
Gurcan E K, Cobanoglu O and Genc S. 2012. Determination of body weight-age relationship by non-linear models in Japanese quail. Journal of Animal and Veterinary Advances 113: 314– 17. DOI: https://doi.org/10.3923/javaa.2012.314.317
Karaman E, Narinc D, Firat M Z and Aksoy T. 2013. Nonlinear mixed effects modeling of growth in Japanese quail. Poultry Science 92: 1942–48. DOI: https://doi.org/10.3382/ps.2012-02896
Karkach A S. 2006. Trajectories and models of individual growth. Demographic Research 15: 347–400. DOI: https://doi.org/10.4054/DemRes.2006.15.12
Kizilkaya K, Balcioglu M S, Yolcu H I, Karabag K and Genc I H. 2006.Growth curve analysis using nonlinear mixed model in divergently selected Japanese quails. Archiv für Geflügelkunde 704: 181–86.
Korkmaz M and Uckardes F. 2013.Transformation to some growth models widely used in agriculture. Journal of Animal and Plant Science 23(3): 840–44.
Korkmaz M, Uckardes F and Kaygisiz A. 2011.Comparision of wood, gaines, parabolic, hayashi, dhanno, and polynomial, models for lactation season curve of simmental cows. Journal of Animal and Plant Science 21(3): 448–58.
Mignon-Grasteau S, Beaumont C and Ricard F H. 2001. Genetic analysis of a selection experiment on the growth curve of chickens.Poultry Science 80: 849–54. DOI: https://doi.org/10.1093/ps/80.7.849
Narinc D, Aksoy T and Karaman E. 2010a.Genetic parameters of growth curve parameters and weekly body weights in Japanese quail. Journal of Animal and Veterinary Advances 9: 501–07. DOI: https://doi.org/10.3923/javaa.2010.501.507
Narinc D, Karaman E, Firat M Z and Aksoy T. 2010b. Comparison of non-linear growth models to describe the growth in Japanese quail. Journal of Animal and Veterinary Advances 14: 1961–66. DOI: https://doi.org/10.3923/javaa.2010.1961.1966
Pineiro G, Perelman S, Guerschman J P and Paruelo J M. 2008. How to evaluate models: observed vs. predicted or predicted vs. observed. Ecological Modelling 216: 316–22. DOI: https://doi.org/10.1016/j.ecolmodel.2008.05.006
Ramos S B, Caetano S L, Savegnago R P, Nunes B N, Ramos A A and Munari D P. 2013. Growth curves for ostriches (Struthiocamelus) in a Brazilian population. Poultry Science 92(1): 277–82. DOI: https://doi.org/10.3382/ps.2012-02380
Rizzi C, Contiero B and Cassandro M. 2013.Growth patterns of Italian local chicken populations. Poultry Science 92(8): 2226– 35. DOI: https://doi.org/10.3382/ps.2012-02825
Sekeroglu A, Tahtali Y, Sarica M, Gulay S M, Abaci S H and Duman M. 2013. Comparison of growth curves of broiler under different stocking densities by Gompertz model. KafkasUniversitesi Veteriner Fakultesi Dergisi 19: 669–72. DOI: https://doi.org/10.9775/kvfd.2013.8635
Uckardes F, Korkmaz M and Ocal P. 2013. Comparison of models and estimation of missing parameters of some mathematical models related to in situ dry matter degradation. Journal of Animal and Plant Sciences 23(4): 999–1007.
Zwitering M H, Jongenburger I, Rombouts F M and Van’t R K. 1990. Modelling of the bacterial growth curve. Applied and Environmental Microbiology 56(6): 1875–981. DOI: https://doi.org/10.1128/aem.56.6.1875-1881.1990
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