Comparative Analysis of Individual Growth Curves in Broiler Chickens
DOI:
https://doi.org/10.5281/zenodo.18234746Keywords:
Growth curves, Broiler chickens, SASAbstract
In this study, growth curves for 62 individual broiler chickens were obtained by evaluating weekly live weight data collected over a six-week period, using several growth models, including Gompertz, Gamma, Logistic, Richards, Bertalanffy, Cubic, Cubic Piecewise, Wilmink, Wood, Exponential, Monomolecular, and McNally. In the comparison of these growth curves, various statistical criteria, such as mean square error, adjusted coefficient of determination, accuracy factor, bias factor, Durbin-Watson statistic, Akaike information criterion, adjusted Akaike information criterion, and Bayesian information criterion, were employed. The Gompertz model provided the best fit across all criteria, accurately representing the growth curve of broiler chickens (R2 =0.99, AIC= 62.42, CAIC: 76.42, BIC: 57.50). Similar to other studies in the literature, this model has been shown to produce reliable results under varying environmental and genetic conditions. The obtained data provide significant contributions to decision support systems in growth monitoring, genetic analysis, and the development of production strategies.
References
Akinsola, O., Sonaiya, E. B., Bamidele, O., Hassan, W. A. A., Yakubu, A., Asayi, F., Uduak, O., Olayınka, A., & Adebambo, O. A. (2021). Comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken. Journal of Applied Animal Research, 49(1), 158–166. https://doi.org/10.1080/09712119.2021.1915792
Anthony, N. B., Emmerson, D. A., & Nestor, K. E. (1991). Research note: Influence of body weight selection on the growth curve of turkeys. Poultry Science, 70(1), 192–194. https://doi.org/10.3382/ps.0700192
Anthony, N. B., Nestor, K. E., & Bacon, W. L. (1986). Growth curves of Japanese quail as modified by divergent selection for 4-week body weight. Poultry Science, 65(10), 1825–1833. https://doi.org/10.3382/ps.0651825
Barbato, G. F. (1991). Genetic architecture of growth curve parameters in chickens. Theoretical and Applied Genetics, 83(1), 24–32.
Bessei, W. (2006). Welfare of broilers: A review. World's Poultry Science Journal, 62(3), 455–466.
Buzala, M., & Janicki, B. (2016). Review: Effects of different growth rates in broiler breeder and layer hens on some productive traits. Poultry Science, 95(9), 2151–2159. https://doi.org/10.3382/ps/pew173
Çetenak, T., Gök, İ., Yavuz, E., & Şahin, M. (2024). Statistical models and evaluation criteria used in poultry farming. Black Sea Journal of Agriculture, 7(6), 710–719. https://doi.org/10.47115/bsagriculture.1532659
Demuner, L. F., Suckeveris, D., Muñoz, J. A., Caetano, V. C., Lima, C. G., de Faria Filho, D. E., & de Faria, D. E. (2017). Adjustment of growth models in broiler chickens. Pesquisa Agropecuária Brasileira, 52(12), 1241–1252. https://doi.org/10.1590/S0100-204X2017001200013
Knízetová, H., Hyánek, J., Kníze, B., & Roubícek, J. (1991). Analysis of growth curves of fowl. I. Chickens. British Poultry Science, 32(5), 1027–1038.
Maruyama, K., Potts, W. J. E., Bacon, W. L., & Nestor, K. E. (1998). Modeling turkey growth with the relative growth rate. Growth, Development & Aging, 62(4), 123–139.
Norris, D., Ngambi, J. W., Benyi, K., Makgahlela, M. L., Shimelis, H. A., & Nesamvuni, E. A. (2007). Analysis of growth curves of indigenous male Venda and Naked Neck chickens. South African Journal of Animal Science, 37(1), 21–26. https://doi.org/10.4314/sajas.v37i1.4021
Ricklefs, R. E. (1967). Relative growth, body constituents, and energy content of nestling barn swallows and red-winged blackbirds. The Auk, 84(4), 560–570. https://doi.org/10.2307/4083336
Şahin, M., & Efe, E. (2010). Use of cubic spline regressions in modeling lactation curves in dairy cattle. Kahramanmaraş Sütçü İmam Üniversitesi Journal of Natural Sciences, 13(2), 17–22.
Şengül, T., Çelik, Ş., Şengül, A. Y., İnci, H., & Şengül, Ö. (2024). Investigation of growth curves with different nonlinear models and MARS algorithm in broiler chickens. PLoS ONE, 19(11), e0307037. https://doi.org/10.1371/journal.pone.0307037
Şengul, T., & Kiraz, S. (2005). Non-linear models of growth curves in large white turkeys. Turkish Journal of Veterinary and Animal Sciences, 29(2), 331–337.
Silambarasan, P., Samanta, R., & Das, T. K. (2012). Production performance of broiler chickens influenced by feed restriction systems. Indian Journal of Animal Sciences, 82(11), 1451–1455.
Tolun, T., Gök, İ., & Şahin, M. (2024). Modeling of some egg characteristics in henna partridges. Black Sea Journal of Agriculture, 7(6), 729–742. https://doi.org/10.47115/bsagriculture.1555738
Tolun, T., Yavuz, E., Şahin, M., & Gök, İ. (2023). Modeling egg curves in partridges. Black Sea Journal of Agriculture, 6(1), 21–25. https://doi.org/10.47115/bsagriculture.1139272
Tzeng, R. Y., & Becker, W. A. (1981). Growth patterns of body and abdominal fat weight in male broiler chickens. Poultry Science, 60(6), 1101–1106. https://doi.org/10.3382/ps.0601101
Yalçınöz, E., & Şahin, M. (2020). Modeling of egg production curves in laying hens. Kahramanmaraş Sütçü İmam Üniversitesi Journal of Agriculture and Natural Sciences, 23(5), 1373–1378. https://doi.org/10.18016/ksutarimdoga.vi.691069
Yavuz, E., Önem, A. B., Kaya, F., Çanga, D., & Şahin, M. (2019). Modeling of individual growth curves in Japanese quails. Black Sea Journal of Engineering Science, 2(1), 11–15.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Black Sea Journal of Statistics
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
