Investigation of Growth in Turkeys Using a Multiphasic Model

Abstract

Growth and yield of modeling of poultry and eggs are commonly used by single-phase nonlinear regression equations. There are a limited number of studies modeled by multiphasic functions of the mentioned features. In this study, we put emphasis on multiphasic logistic growth model on the purpose of modelling growth of poultry and it was aimed to introduce that model. With this purpose, a breeding herd of turkeys’, both male and female, 60 week body weight growth was modeled using the multiphasic logistic growth function. The model fit was found relatively high, determining coefficients were obtained as 0.999 and 0.998 for female and male turkeys, respectively. The ‘a’ parameter represents half the weight of the asymptotic model, k parameter is the average growth rate, and c parameter represents the week where the highest growth rate takes place. The model parameters for female ɑ1, k1, c1, ɑ2, k1 and c2 were estimated to be respectively, 2475.9, 0.367, 11.64, 4035.4, 0.969 and 36.53. Parameters of male turkeys’ ɑ1, k1, c1, ɑ2, k1 and c2 were estimated to be, respectively, 3336.6, 0.399, 13.99, 5598.9, 0.467, and 31.26. As a result, fitting the two stage multiphasic model was found as very successfully for both female and male turkeys growth data according to the one stage model.

Keywords

• Multistage growth model
• Logistic function
• Turkey growth

Hindilerde Büyümenin Çok Evreli Bir Model Yardımıyla İncelenmesi

Abstract

Kanatlı hayvanlarda büyüme ve yumurta verimlerinin modellenmesinde çoğunlukla tek evreli doğrusal olmayan regresyon eşitlikleri kullanılmış, sınırlı sayıda çalışmada ise söz konusu özellikler çok evreli fonksiyonlarla modellenmiştir. Bu çalışmada kanatlı hayvanlarda büyümenin modellenmesi amacıyla çok evreli büyüme modeli üzerinde durulmuş ve modelin tanıtılması amaçlanmıştır. Bu amaçla bir damızlık hindi sürüsünde erkek ve dişi bireylere ait 60 haftalık canlı ağırlık verileri kullanılarak çok evreli lojistik fonksiyon ile büyüme modellenmiştir. Model uyumu oldukça yüksek bulunmuş, belirleme katsayıları dişilerde ve erkeklerde, sırasıyla 0,999 ve 0,998 olarak elde edilmiştir. Modelin a parametresi asimptotik ağırlığın yarısını, k parametresi ortalama büyüme hızını, c parametresi en yüksek büyüme hızının gerçekleştiği haftayı temsil etmektedir. İki evreli modelde; dişi hindiler için model parametreleri olan ɑ1, k1, c1, ɑ2, k1 ve c2 için tahmin edilen değerler sırası ile 2475,9, 0,367, 11,64, 4035,4, 0,969 ve 36,53 olarak bulunmuştur. Erkek hindilere ait ɑ1, k1, c1, ɑ2, k1 ve c2 parametrelerinin tahmin edilen değerleri ise sırası ile 3336,6, 0,399, 13,99, 5598,9, 0,467 ve 31,26 olarak elde edilmiştir. Sonuç olarak gerek belirleme katsayısı gerekse bilgi kriterlerine göre, dişi ve erkek hindilerin büyüme verilerine uydurulan iki evreli model tek evreli modele göre daha yüksek bir başarı göstermiştir.

Keywords

• Çok evreli model
• Lojistik fonksiyon
• Hindi büyümesi

References

1. Aggrey SE, Nichols CR, Cheng KM. 1993. Multiphasic analysis of egg production in Japanese quail. Poultry Sci, 72(12): 2185-2192.
2. Akaike H. 1973. A new look at the statistical model ıdentification. IEEE Trans Automat Cont, 19: 716-723.
3. Aslam M, Bastiaansen J, Crooijmans R, Ducro BS, Groenen M, Vereijken A. 2011. Genetic variences, heritabilities, and maternal effects on body weight, breast meat yield, meat quality traits and the shape of the growth curve in turkey birds. BMC Genet, 12: 1-9.
4. Bilgin OC, Esenbuga N. 2003. Parameter estimation in nonlinear growth models. Animal Prod, 44: 81–90.
5. Cebeci Z. 2020. R uygulamalı yeniden örnekleme teknikleri. Pegem Akademi, Ankara, Turkey, pp: 650.
6. Darmani-Kuhi H, France J, Kebreab E, Lopez S, Porter T, Strathe AB. 2010. Flexible alternatives to the gormpertz equation for describing growth with age in turkey hens. Poultry Sci, 89: 371-378.
7. Grossman M, Goosman TN, Koops WJ. 2000. A model for persistency of egg production. Poultry Sci, 79: 1715-1724.
8. Grossman M, Koops WJ. 1988a. Multiphasic analysis of growth curves in chickens. Poultry Sci, 67: 33-42.

9. Grossman M, Koops WJ. 1992. Characterization of poultry egg production using multiphasic approach. Poultry Sci, 71: 399-405.
10. Grossman M, Koops WJ. 2001. A model for ındividual egg production ın chickens. Poultry Sci, 80: 859-867.
11. Koops WJ. 1986. multiphasic growth curve analysis. Growth, 50: 169-177.
12. Koops WJ, Grossman M. 1991. Applications of a multiphasic growth function to body composition in pigs. J Animal Sci, 69: 3265-3273.
13. Minvielle F, Gourichon D, Inoue-Murayama M, Ito S, Kayang B, Miwa M, Monvoisin JL, Neau A, Vignal A. 2006. Search for QTL affecting the shape of the egg laying curve of the Japanese quail. BMC Genet, 7: 26.
14. Narinc D, Karaman E, Aksoy T, Fırat MZ. 2013. Investigation of non linear models to describe the long term egg production in Japanese quail. Poultry Sci, 92(6): 1676-1682.
15. Porter T, Kebreab E, Kuhi HD, Lopez S, Strathe AB, France J. 2010. Flexible alternatives to the gompertz equation for describing growth with age in turkey hens. Poultry Sci, 89: 371-378.
16. Ricklefs RE. 1985. Modification of growth and development of muscles of poultry. Poultry Sci, 64: 1563-1576.
17. SAS. 2009. SAS/STAT User’s Guide, Version 9.2. SAS Institute Inc., Cary, NC.
18. Schwartz G. 1978. Estimation the dimension of a model. Ann Stat, 6: 461-464.
19. Şengul T, Kiraz S. 2005. Non-Linear models for growth curves in large white turkeys. Turk J Vet Anim Sci, 29: 331-337.
20. Soltan M, El Kaschab S. 1997. Characterization of guail egg production by using a multiphasic analysis under selection for egg number. J King Saud Univ Agri Sci, 9: 189-196.
21. Yang X. 2013. A higher-order Levenberg-Marquardt method for nonlinear equations. App Math and Comput, 219(22): 10682-10694.
22. Yu T, Zhu H. 2020. Hyper-Parameter optimization: a review of algorithms and applications. URL: http://arxiv.org/pdf/2003.05689.pdf (erişim tarihi: 14 Haziran 2021).