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Modeling the growth of four commercial broiler genotypes reared in the tropics

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Abstract

Genetic improvement in commercial broilers worldwide is heavily focused on selection for higher final body weight at a given age. Although commercial broilers are mostly sold by their final body weight, it is important to carefully consider how this weight is attained and at what cost. The cost of feeding broilers, which constitutes about 70% of the total cost of broiler production, varies considerably at different stages of the bird. Careful consideration of the growth curve of broilers and the parameters of the growth curve is critical to optimize profitability of commercial broiler production. The objective of this study was to model the variations of the growth curves of 4 commercial broiler genotypes reared in Ghana using the Gompertz and polynomial growth functions. Data on body weights at 1, 7, 14, 21, 28, 35, and 42 days for 4 unsexed commercial broiler genotypes were used to model both the Gompertz and polynomial growth functions. The 4 genotypes ranked differently for Gompertz predicted early (1-28 days), late growth (28–42 days), and body weight at 42 days. Gompertz function predicted growth better for broiler chicken than the polynomial as the parameters of the Gompertz function are biologically meaningful and heritable. Selection of broiler genotypes for production based on their growth curve (slower early growth and faster late growth) could minimize cost of production and thereby increase the profitability of commercial broiler production in the tropics.

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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, 1782-1788.

    Article  CAS  Google Scholar 

  • Anthony, N. B., Emmerson, D. A., Nector, K. A., Bacon, W. L., Siegel, P. B. and Dunnington, E. E., 1991. Comparison of the growth curves of weight selected populations of turkeys, quail and chickens. Poultry Science, 70, 13-19.

    Article  CAS  Google Scholar 

  • Anthony, N. B., 2007. A review of genetic practices in poultry: Efforts to improve meat quality. Journal of Muscle Food, 9 (1), 25-33.

    Article  Google Scholar 

  • Barbato, G. F., Siegel, P. B. and Cherry, J. A., 1983. Inheritance of body weight and associated traits in young chickens. Zeitschrift für Tierzüchtung und Züchtungsbiologie, 100, 350-360.

    Article  Google Scholar 

  • Barbato, G. F., 1991. Genetic architecture of growth curve parameters in chickens. Theoretical and Applied Genetics, 83, 24-32.

    Article  CAS  Google Scholar 

  • Barbato, G. F., 1999. Genetic relationships between selection for growth and reproductive effectiveness. Poultry Science, 78, 444-452.

    Article  CAS  Google Scholar 

  • Bashiru, H. A., Oseni, S. O. and Omadime, L. A., 2020. Assessment of spline functions and non-linear models for estimating growth curve parameters of Funaab-Alpha chickens. Slovak Journal of Animal Science, 53, 19-31.

    Google Scholar 

  • Chambers, J. R., 1990. Genetics of growth and meat production in chickens. In: R. D. Crawford (ed), Poultry breeding and genetics. Elsevier Science Publication, Armsterdam, 599-643.

    Google Scholar 

  • Dixon, L. M., 2020. Slow and steady wins the race: The behaviour and welfare of commercial faster growing broiler breeds compared to a commercial slower growing breed. PLoS ONE, 15(4), e0231006.

  • Dunnington, E. A. and Siegel, P. B., 1985. Long-term selection for eight-week body weight in chicken in chickens – direct and correlated responses. Theoretical and Applied Genetics, 71, 305-313.

    Article  CAS  Google Scholar 

  • Griffin, H. D., 1996. Understanding genetic variation in fatness in chickens. Annual Report, Roslin Institute, Edinburgh, UK.

  • Grossman, M. and Bohren, B. B., 1982. Comparison of proposed growth curve functions in chickens. Growth, 46, 259-274.

    Google Scholar 

  • Havenstein, G. B., Ferket, P. R. and Qureshi, M. A., 2003. Carcass composition and yield of 1957 versus 2001 broilers when fed representative 1957 and 2001 broiler diets. Poultry Science, 82, 1509-1518.

    Article  CAS  Google Scholar 

  • Katanbaf, M. N., Dunnington, E. A. and Siegel, P. B., 1988. Allomorphic relationships from hatching to 56 days in parental lines and F1 crosses of chickens selected 27 generations for high or low body weight. Growth Development and Aging, 52, 11-22.

    CAS  Google Scholar 

  • Kaplan, S. and Gürcan, E. K., 2018. Comparison of growth curve using non-linear regression function in Japanese quail. Journal of Applied Animal Research, 46(1), 112-117.

    Article  Google Scholar 

  • Kestin, S. C., Gordon, S., Su, G. and Sorenson, P., 2001. Relationship in broiler chickens between lameness, liveweight, growth rate and age. Veterinary Record, 148, 195-197.

    Article  CAS  Google Scholar 

  • Lawrence, T. L. J. and Fowler, V. R., 2002. Growth of farm animals. CAB International, Wallingford, UK.

    Book  Google Scholar 

  • Microsoft Corporation, 2013. Microsoft Office Excel Macros. One Microsoft Way, Redmond, Washington, USA.

  • Mignon-Grasteau, S., Piles, M., Varona, L., de Rochambeau, H., Poivey, J. P., Blasco, A. and Beaumont, C., 2000. Genetic analysis of growth curve parameters for male and female chickens resulting from selection on shape of growth curve. Journal of Animal Science, 78, 2515-2524.

    Article  CAS  Google Scholar 

  • Narinç, D., Öksüz Narinç, N. and Aygün, A., 2017. Growth curve analyses in poultry science. World’s Poultry Science Journal, 73(2), 395-408.

    Article  Google Scholar 

  • Osei-Amponsah, R., Kayang, B. B., Naazie, A., Barchia, I. M. and Arthur, P. F., 2014. Iranian Journal of Applied Animal Science, 4(4), 855-861.

    Google Scholar 

  • Pakdel, A., Van Arendonk, J. A. M., Vereijken, A. L. J. and Boverhuis, H., 2005. Genetic correlations among ascitis-related traits and performance traits in broilers. British Poultry Science, 46, 35-42.

    Article  CAS  Google Scholar 

  • Pollock, D. L., 1999. A geneticist’s perspective from within a broiler primary breeder company. Poultry Science, 78, 414-418.

    Article  CAS  Google Scholar 

  • Praharaj, N. K., Gross, W. B., Dunnington, E. A. and Siegel, P. B., 1996. Feeding regimen by sire family interactions on growth, immunocompetence and disease resistance in chickens. Poultry Science, 75, 821-827.

    Article  CAS  Google Scholar 

  • Ricklefs, R. E., 1985. Modification of growth and development of muscles of poultry. Poultry Science, 64, 1653-1676.

    Article  Google Scholar 

  • SAS Institute Inc. 2013. SAS® 9.4 Statements: Reference. Cary, NC, USA.

  • Siegel, P. B. and Dunnington, E. A., 1987. Selection for growth in chickens. Critical Reviews in Poultry Biology, 1, 1-24.

    Google Scholar 

  • Siegel, P. B. and Wolford, J. H., 2003. A review of some results of selection for juvenile body weight in chickens. Journal of Poultry Science, 40, 81-91.

    Article  Google Scholar 

  • Walmsley, B. J., 2007. Modelling of growth and development for optimizing beef cattle production systems. PhD Thesis, University of New England, Australia.

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Acknowledgements

The authors are grateful to the technical staffs of the Animal Research Institute of the Council of Scientific and Industrial Research (CSIR-ARI) for the data collection and management of the experimental flock. We are also grateful to the reviewers for their helpful comments and suggestions.

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Authors

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Bernard Ato Hagan – conceived the idea for this work; analyzed the data; wrote the manuscript.

Christian Asumah – wrote the manuscript.

Ernest Darkwah Yeboah – wrote the manuscript.

Vida Korkor Lamptey – data collection; wrote the manuscript.

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Correspondence to Bernard Ato Hagan.

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Hagan, B.A., Asumah, C., Yeboah, E.D. et al. Modeling the growth of four commercial broiler genotypes reared in the tropics. Trop Anim Health Prod 54, 75 (2022). https://doi.org/10.1007/s11250-022-03082-1

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