Research Article
Comparison of different mathematical functions for fitting growth curves of ascitic and healthy broiler chickens
Abstract
Ascites syndrome (AS) causes major economic losses in commercial meat-type chickens. The objectives of the current study were to select the best non-linear growth curve functions (GCFs) of the ascitic and healthy chickens, and to investigate the association of ascites incidence with the growth pattern. A total of 5584 body weight (BW) records belonging to 823 chickens (381 male and 442 female) from a paternal pure Arian broiler line were used. The birds were categorized into; healthy male and female, ascitic male and female. Five GCFs including Logistic, Gompertz, Lopez, Richards, and Von baretanalffy were fitted to the BW records of all groups, separately. After the estimation of growth curve parameters for all the chicks individually, the effect of sex and health status on the growth curve parameters were assessed. The results revealed that the Richards function is the best for all the groups. Comparison of the growth curves showed that the ascitic chickens reach the inflection point of the curve earlier than their healthy counterparts (P<0.05). The average growth rate of the healthy birds in the rearing period was significantly higher than that of the ascitic birds (P<0.05), thereby suggesting that there is no direct relationship between the rapid growth rate and the incidence of ascites. Therefore, genetic improvement of the used population for both rapid growth rate and reduced ascites incidence may be possible and the utilization of growth curve parameters in the selection index might be beneficial.
Keywords
Supporting Institution
This research was financially supported by the University of Tabriz grants (project no. 312, Master of Science thesis at University of Tabriz).
Project Number
project no. 312
Thanks
We cordially appreciated the Arian Farm staff for their technical assists and for providing the pedigreed chickens for the current study.
References
- Aggrey S (2002): Comparison of three nonlinear and spline regression models for describing chicken growth curves. Poult Sci, 81, 1782-1788.
- Aggrey S (2009): Logistic nonlinear mixed effects model for estimating growth parameters. Poult Sci, 88, 276-280.
- Arango J, Van Vleck LD (2002): Size of beef cows: early ideas, new developments. Faculty Papers and Publications in Animal Science, 237.
- Baghbanzadeh A, Decuypere E (2008): Ascites syndrome in broilers: physiological and nutritional perspectives. Avian Pathol, 37, 117-126.
- Balog J, Anthony N, Cooper M, et al (2000): Ascites syndrome and related pathologies in feed restricted broilers raised in a hypobaric chamber. Poult Sci, 79, 318-323.
- Currie RJ (1999): Ascites in poultry: recent investigations. Avian Pathol, 28, 313-326.
- Gupta A (2011): Ascites syndrome in poultry: a review. Worlds Poult Sci J, 67, 457-468.
- Hasanpur K, Nassiry M, Salekdeh GH, et al (2015): Influence of ascites syndrome on growth pattern of chickens reared at normal or cold ambient temperature. Ann Anim Sci, 15, 373-385.
- Hassanzadeh M, Buyse J, Toloei T, et al (2013): Ascites syndrome in broiler chickens: A review on the aspect of endogenous and exogenous factors interactions. J Poult Sci, 51, 229-241.
- Julian RJ (1993): Ascites in poultry. Avian Pathol, 22, 419-454.
- Kaplan S, Gürcan EK (2018): Comparison of growth curves using non-linear regression function in Japanese quail. J Appl Anim Res, 46, 112-117.
- Kaufmann KW (1981): Fitting and using growth curves. Oecologia, 49, 293-299.
- Kuhi HD, Kebreab E, Lopez S, et al (2003): An evaluation of different growth functions for describing the profile of live weight with time (age) in meat and egg strains of chicken. Poult Sci, 82, 1536-1543.
- Lee L, Atkinson D, Hirst AG, et al (2020): A new framework for growth curve fitting based on the von Bertalanffy Growth Function. Scientific Reports, 10, 1-12.
- Nurmeiliasari N (2010): Ascites Incidence in Broilers. J Sain Peternak Indones, 5, 59-64.
- Pavlidis H, Balog J, Stamps L, et al (2007): Divergent selection for ascites incidence in chickens. Poult Sci, 86, 2517-2529.
- Porter T, Kebreab E, Kuhi HD, et al (2010): Flexible alternatives to the Gompertz equation for describing growth with age in turkey hens. Poult Sci, 89, 371-378.
- Rabinovitch M (2008): Molecular pathogenesis of pulmonary arterial hypertension. J Clin Invest, 118, 2372-2379.
- Richards F (1959): A flexible growth function for empirical use. J Exp Bot, 10, 290-301.
- Riddell C, Springer R (1985): Cardiomyopathy and ascites in broiler chickens. Proceedings of the 34th Western Poultry Disease Conference, Austria.
- Sakomura N, Longo F, Oviedo-Rondon E, et al (2005): Modeling energy utilization and growth parameter description for broiler chickens. Poult Sci, 84, 1363-1369.
- SAS S (2009). STAT user's guide, version 9.2. Cary, NC, USA: SAS Inst, Inc.
- Segura-Correa J, Santos-Ricalde R, Palma-Avila I (2017): Non-Linear Model to Describe Growth Curves of Commercial Turkey in the Tropics of Mexico. Braz J Poult Sci, 19, 27-32.
- Şengül T, Kiraz S (2005): Non-linear models for growth curves in Large White turkeys. Turk J Vet Anim Sci, 29, 331-337.
- Winkel BJ (2012): Sourcing for parameter estimation and study of logistic differential equation. Int J Math Educ Sci Technol, 43, 67-83.
- Yang Y, Mekki D, Lv S, et al (2006): Analysis of fitting growth models in Jinghai mixed-sex yellow chicken. Int J Poult Sci, 5, 517-521.
- Zubair A, Leeson S (1996): Compensatory growth in the broiler chicken: a review. Worlds Poult Sci J, 52, 189-201.
Year 2022,
Volume: 69 Issue: 3, 289 - 295, 30.06.2022
Abstract
Project Number
project no. 312
References
- Aggrey S (2002): Comparison of three nonlinear and spline regression models for describing chicken growth curves. Poult Sci, 81, 1782-1788.
- Aggrey S (2009): Logistic nonlinear mixed effects model for estimating growth parameters. Poult Sci, 88, 276-280.
- Arango J, Van Vleck LD (2002): Size of beef cows: early ideas, new developments. Faculty Papers and Publications in Animal Science, 237.
- Baghbanzadeh A, Decuypere E (2008): Ascites syndrome in broilers: physiological and nutritional perspectives. Avian Pathol, 37, 117-126.
- Balog J, Anthony N, Cooper M, et al (2000): Ascites syndrome and related pathologies in feed restricted broilers raised in a hypobaric chamber. Poult Sci, 79, 318-323.
- Currie RJ (1999): Ascites in poultry: recent investigations. Avian Pathol, 28, 313-326.
- Gupta A (2011): Ascites syndrome in poultry: a review. Worlds Poult Sci J, 67, 457-468.
- Hasanpur K, Nassiry M, Salekdeh GH, et al (2015): Influence of ascites syndrome on growth pattern of chickens reared at normal or cold ambient temperature. Ann Anim Sci, 15, 373-385.
- Hassanzadeh M, Buyse J, Toloei T, et al (2013): Ascites syndrome in broiler chickens: A review on the aspect of endogenous and exogenous factors interactions. J Poult Sci, 51, 229-241.
- Julian RJ (1993): Ascites in poultry. Avian Pathol, 22, 419-454.
- Kaplan S, Gürcan EK (2018): Comparison of growth curves using non-linear regression function in Japanese quail. J Appl Anim Res, 46, 112-117.
- Kaufmann KW (1981): Fitting and using growth curves. Oecologia, 49, 293-299.
- Kuhi HD, Kebreab E, Lopez S, et al (2003): An evaluation of different growth functions for describing the profile of live weight with time (age) in meat and egg strains of chicken. Poult Sci, 82, 1536-1543.
- Lee L, Atkinson D, Hirst AG, et al (2020): A new framework for growth curve fitting based on the von Bertalanffy Growth Function. Scientific Reports, 10, 1-12.
- Nurmeiliasari N (2010): Ascites Incidence in Broilers. J Sain Peternak Indones, 5, 59-64.
- Pavlidis H, Balog J, Stamps L, et al (2007): Divergent selection for ascites incidence in chickens. Poult Sci, 86, 2517-2529.
- Porter T, Kebreab E, Kuhi HD, et al (2010): Flexible alternatives to the Gompertz equation for describing growth with age in turkey hens. Poult Sci, 89, 371-378.
- Rabinovitch M (2008): Molecular pathogenesis of pulmonary arterial hypertension. J Clin Invest, 118, 2372-2379.
- Richards F (1959): A flexible growth function for empirical use. J Exp Bot, 10, 290-301.
- Riddell C, Springer R (1985): Cardiomyopathy and ascites in broiler chickens. Proceedings of the 34th Western Poultry Disease Conference, Austria.
- Sakomura N, Longo F, Oviedo-Rondon E, et al (2005): Modeling energy utilization and growth parameter description for broiler chickens. Poult Sci, 84, 1363-1369.
- SAS S (2009). STAT user's guide, version 9.2. Cary, NC, USA: SAS Inst, Inc.
- Segura-Correa J, Santos-Ricalde R, Palma-Avila I (2017): Non-Linear Model to Describe Growth Curves of Commercial Turkey in the Tropics of Mexico. Braz J Poult Sci, 19, 27-32.
- Şengül T, Kiraz S (2005): Non-linear models for growth curves in Large White turkeys. Turk J Vet Anim Sci, 29, 331-337.
- Winkel BJ (2012): Sourcing for parameter estimation and study of logistic differential equation. Int J Math Educ Sci Technol, 43, 67-83.
- Yang Y, Mekki D, Lv S, et al (2006): Analysis of fitting growth models in Jinghai mixed-sex yellow chicken. Int J Poult Sci, 5, 517-521.
- Zubair A, Leeson S (1996): Compensatory growth in the broiler chicken: a review. Worlds Poult Sci J, 52, 189-201.
There are 27 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Veterinary Surgery |
| Journal Section | Research Article |
| Authors | |
| Project Number | project no. 312 |
| Publication Date | June 30, 2022 |
| Published in Issue | Year 2022Volume: 69 Issue: 3 |
