Growth curve evaluation for Indonesian indigenous Red Kedu chicken by using non-linear models

Document Type : Research Article (Regular Paper)

Authors

1 Department of Animal Science, Faculty of Animal and Agricultural Sciences, Universitas Diponegoro, Tembalang Campus, Semarang, 50275 Central Java, Indonesia

2 Faculty of Veterinary and Animal Sciences, MNS University of Agriculture, Multan, 66000, Pakistan

Abstract

This study analyzed growth patterns in Red Kedu chickens using nonlinear models to assess their development over time and to identify the model which describe their growth best. In controlled conditions, 129 chickens (54 males and 75 females) were raised, and body weights were recorded weekly until 21 weeks of age. The models used for this study were Gompertz, Logistic, Von Bertalanffy and Brody.  Mean squared error (MSE), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Coefficient of determination (R2), and correlation coefficient (r) were used to assess the model fit. The Gompertz model demonstrated the best fit for females, while the Von Bertalanffy model performed optimally for males. Results revealed distinct growth dynamics between sexes. Males consistently exhibited higher asymptotic weights (A) thus growth rates (C) become slower compared to females. Asymptotic weight estimations ranged from 1,484.15±28.26 g (Logistic) to 3,425.81±66.69 g (Brody) for females and from 2,339.96±49.74 g (Von Bertalanffy) and 3,660.64±51.92 g (Gompertz) for males, respectively. The weight at the inflection point (Wi) was estimated from 494.22 g (Von Bertalanffy) to 742.01 g (Logistic) and from 1,346.68 g (Gompertz) to 1,473.80 g (Von Bertalanffy) for females and males, respectively. The Gompertz model was the best for female chickens, while the Von Bertalanffy model performed best for males. The Brody model had the worst performance in both sexes based on value of MSE, AIC, BIC and R2

Keywords

Main Subjects


Aggrey, S. 2002. Comparison of three nonlinear and spline regression models for describing chicken growth curves. Poultry Science 81, 1782-1788. https://doi.org/10.1093/ps/81.12.1782
Akaike, H. 1974. A new look at the statistical model identification. IEEE Trans. Auto. Control 19, 716-723. DOI: 10.1109/TAC.1974.1100705
Al-Ali, M.R., Razuki W.M., Al-Anbari, E.H., 2022. Characterization of growth curve patterns for Iraqi indigenous chickens through nonlinear growth models. Indian Journal of Ecology 49, 324-331.
Asmara, I.Y., 2014. Risk status of selected indigenous chicken breeds in Java, Indonesia: Challenges and opportunities for conservation. Charles Darwin University (Doctoral Thesis). Australia.
Barrett, J.P. 1974. The coefficient of determination-some limitations. American Statistical Association 28, 19-20. https://doi.org/10.1080/00031305.1974.10479056
Bett, R.C., Bhuiyan A.K.F.H., Khan, M.S., Silva, G.L.L.P., Thuy, L.T., Sarker, S.C., ... Ibrahim, M.N.M., 2014. Indigenous chicken production in the South and South East Asia. Livestock Research for Rural Development 26, 12. http://www.lrrd.org/lrrd26/12/bett26229.html
Bridges, T., Turner, L., Stahly, T., Usry, J., Loewer, O., 1992. Modeling the physiological growth of swine part I: Model logic and growth concepts. Transactions of the ASAE 35, 1019-1028. https://doi.org/10.13031/2013.28696
Cahyadi, M., Park, H.B., Seo, D.W., Jin, S., Choi, N., Heo, K.N., Kang, B.S., Jo, C., Lee, J.H., 2015. Genetic parameters for growth-related traits in Korean native chicken. Korean Journal of Poultry Science 42, 285-289. https://doi.org/10.5536/KJPS.2015.42.4.285.
Faraji-Arough, H., Rokouei, M., Maghsoudi, A., Mehri, M., 2019. Evaluation of non-linear growth curves models for native slow-growing Khazak chickens. Poultry Science Journal 7, 25-32. https://doi.org/10.22069/psj.2019.15535.1355
Gautam, L., 2024. Assessment of growth pattern in indigenous Kadaknath chickens by non-linear models. Journal of Animal and Plant Sciences 34, 1012-1019 https://doi.org/10.36899/JAPS.2024.4.078
Gompertz, B., 1825. XXIV. On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. In a letter to Francis Baily, Esq. FRS &c. Philosophical transactions of the Royal Society of London 115, 513-583.
Harville, D.A., and D.R. Jeske. 1992. Mean Squared Error of Estimation or Prediction under a General Linear Model. Journal of American Statistical  Association 87, 724-731. https://doi.org/10.1080/01621459.1992.10475274
Hollings, T., A. Robinson, M. van Andel, C. Jewell, M. Burgman. 2017. Species distribution models: A comparison of statistical approaches for livestock and disease epidemics. PloS one, 12(8), e0183626. https://doi.org/10.1371/journal.pone.0183626
Kpomasse, C.C., Kouame, Y.A.E., N’nanle, O., Houndonougbo, F.M., Tona, K., Oke, O.E., 2023. The productivity and resilience of the indigenous chickens in the tropical environments: improvement and future perspectives. Journal of Applied Animal Research 51, 456-469. https://doi.org/10.1080/09712119.2023.2228374
Magothe, T.M., Muhuyi, W.B., Kahi, A.K., 2010. Influence of major genes for crested-head, frizzle-feather, and naked-neck on body weights and growth patterns of indigenous chickens reared intensively in Kenya. Tropical Animal Health and Production 42, 173-183. https://doi.org/10.1007/s11250-009-9403-y
Mengesha, M. 2012. Indigenous chicken production and the innate characteristics. Asian Journal of Poultry Science 6, 56-64. https://doi.org/10.3923/ajpsaj.2012.56.64
Manjula, P., Park, H.B., Seo, D., Choi, N., Jin, S., Ahn, S.J., ... Lee, J.H., 2018. Estimation of heritability and genetic correlation of body weight gain and growth curve parameters in Korean native chicken. Asian-Australasian Journal of Animal Sciences 31, 26-31. https://doi.org/10.5713/ajas.17.0179
Mata-Estrada, A., González-Cerón, F., Pro-Martínez, A., Torres Hernández, G., Bautista-Ortega, J., Becerril Pérez, C.M., VargasGalicia, A.J., Sosa-Montes, E., 2020. Comparison of four nonlinear growth models in creole chickens of Mexico. Poultry Science 99, 1995-2000. https://doi.org/10.1016/j.psj.2019.11.031
Miguel, J., Ciria, J., Asenjo, B., Calvo, J., 2008. Effect of caponisation on growth and on carcass and meat characteristics in Castellana Negra native Spanish chickens. Animal 2, 305-311. https://doi.org/10.1017/S1751731107001127
Narinç, D., Narinç, N.O., Aygün, A., 2017. Growth curve analyses in poultry science. World's Poultry Science Journal 73, 395-408. https://doi.org/10.1017/S0043933916001082
Nguyen Hoang, T., Do, H.T., Bui, D.H., Pham, D.K., Hoang, T.A., Do, D.N., 2021. Evaluation of non-linear growth curve models in the Vietnamese indigenous Mia chicken. Animal Science Journal 92, 1-7. https://doi.org/10.1111/asj.13483
Nguyen, T.H., Nguyen, C.X., Luu, M.Q., Nguyen, A.T., Bui, D.H., Pham, DK., Do, D.N., 2021. Mathematical models to describe the growth curves of Vietnamese Ri chicken. Brazilian Journal of Biology 83, 1-7. https://doi.org/10.1590/1519-6984.249756
Norris, D., Ngambi, J., Benyi, K., Makgahlele, M., Shimelis, H., Nesamvuni, E., 2007. Analysis of growth curves of indigenous male Venda and Naked Neck chickens. South African Journal of Animal Science 37, 21-26. doi:10.4314/sajas.v37i1.4021
Pearl, R., 1929. The biology of population growth. American Journal of Sociology 35, 403-410.
Plaengkaew, S., Khumpeerawat, P., Stalder, K.J., 2021. Using non-linear models to describe growth curves for Thai black-bone chickens. Agriculture and Natural Resources 55, 1049-1056. https://doi.org/10.34044/j.anres.2021.55.6.15
Podisi, B.K., Knott, S.A., Burt, D.W., Hocking, P.M., 2013. Comparative analysis of quantitative trait loci for body weight, growth rate, and growth curve parameters from 3 to 72 weeks of age in female chickens of a broiler–layer cross. BMC Genetics 14, 1-11. https://doi.org/10.1186/1471-2156-14-22
Richards, O.W., Kavanagh, A.J., 1945. The analysis of growing form. Oxford University. pp.188-229
Rizzi, C., Contiero, B., Cassandro, M., 2013. Growth patterns of Italian local chicken populations. Poultry Science 92, 2226-2235. https://doi.org/10.3382/ps.2012-02825
SAS, SAS/STAT. 2021.  SAS OnDemand for Academics. https://www.sas.com/id_id/software/on-demand-for-academics.html
Schwarz, G., 1978. Estimating the dimension of a model. The Annals of Statistics 6, 461-464. https://www.jstor.org/stable/2958889
Selvaggi, M., Laudadio, V., Dario, C., Tufarelli, V., 2015. Modelling growth curves in a nondescript Italian chicken breed: An opportunity to improve genetic and feeding strategies. Poultry Science 52, 288-294. https://doi.org.10.2141/ jpsa.0150048
Setiaji, A., Lestari, D.A., Ma'rifah, B., Krismiyanto, L., Agusetyaningsih, I., Sugiharto, S., 2023. Gompertz non-linear model for predicting growth performance of commercial broiler chickens. Journal of the Indonesian Tropical Animal Agriculture 48, 143-149. https://doi.org/10.14710/jitaa.48.2.143-149
Sutopo, S., Lestari, D.A., Kurnianto, E., Setiaji, A., 2022. Egg weight, sex, and variety effects on body weights and growth ability of Kedu chickens. Advances in Animal and Veterinary Sciences 10, 1017-1022. http://dx.doi.org/10.17582/journal.aavs/2022/10.5.1017.1022
Von Bertalanffy, L., 1957. Quantitative laws in metabolism and growth. The Quarterly Review of Biology 32, 217-231.
Xie, W.Y., Pan, N.X., Zeng, H.R., Yan, H.C., Wang, X.Q., Gao, C.Q., 2020. Comparison of nonlinear models to describe the feather growth and development curve in yellow-feathered chickens. Animal 14, 1005-1013. https://doi.org/10.1017/S1751731119003082
Yang, Y., Mekki, D.M., Lv, S.J., Wang, L.Y., Yu, J.H., Wang, J.Y., 2006. Analysis of fitting growth models in Jinghai mixed-sex yellow chicken. International Journal of Poultry Science 5, 517-521. https://doi.org/10.3923/ijps.2006.517.52
Zhao, Z., Li, S., Huang, H., Li, C., Wang, Q., Xue, L., 2015. Comparative study on growth and developmental model of Indigenous chicken breeds in China. Open Journal of Animal Sciences 5, 219-223. https://doi.org/10.4236/ojas.2015.52024 
Zuidhof, M.J., 2020. Multiphasic poultry growth models: method and application. Poultry Science 99, 5607-5614. https://doi.org/10.1016/j.psj.2020.08.049