Evaluation of Models to Describe Temporal Growth in Local Chickens of Ghana

Document Type: Research Article

Authors

1 Department of Animal Science University of Ghana, Legon, Ghana

2 Livestock and Poultry Research Centre, University of Ghana, Legon, Ghana

3 Department of Primary Industries, Elizabeth Macarthur Agricultural Institute, Camden, Australia

Abstract

The logistic, Gompertz, Richards and asymmetric logistic growth curve models were fitted to body weight data of local Ghanaian chickens and French SASSO T44 chickens. All four growth models provided good fit for each sex by genotype growth data with R2 values ranging from 86.7% to 96.7%. The rate constant parameter, k, ranged between 0.137 and 0.271 and were significantly different from zero for all genotype by sex groups. Predicted mature weight from the four models ranged from 2840 g to 3020 g for SASSO T44 female, 3225 g to 3448 g for SASSO T44 male, 1170 g to 1332 g for Ghanaian female and 1607 g to 1777 g for Ghanaian male chickens. For the Richards and asymmetric logistic functions, the shape parameter (n) which influences the point of inflection ranged from -0.126 to 0.713, indicating that the shape of each of the genotype by sex sigmoid function is negatively asymmetric. Between the two simpler models, with fixed inflection point, the logistic function was characterised by a younger age at start of the growth acceleration phase, older age at the point of inflection, younger age at the end of the growth deceleration phase and lower mature body weights relative to the Gompertz function. Based on the Bayesian information criterion (BIC), the Gompertz function was preferred to the logistic function. The R2, BIC values and predicted body weights for the asymmetric logistic function were similar to those of the Richards function. These complex models with flexible inflection point provided better goodness of fit relative to the Gompertz model. Therefore it is concluded that, where data structure and availability of adequate computing power permit, models with flexible inflection point such as the Richards function can be used to provide accurate parameter estimates for the characterization of growth of indigenous chickens.

Keywords


Ali K.O., Katule A.M. and Brenøe U.T. (2002). Comparing genotypes of different body sizes for growth-related traits in chickens: live weight and growth performance under intensive and feed-restricted extensive systems. Acta Agric. Scand. A Anim. Sci. 52,1-10.
Dana N.E.H. vander Waaij E.H. and van Arendonk J.A.M. (2011). Genetic and phenotypic parameter estimates for body weights and egg production in Horro chicken of Ethiopia. Trop. Anim. Health Prod. 43(1), 21-28.
Darmani Kuhi H., Porter T., Lopez S., Kebreab E., Strathe A.B., Dumas A., Dijkstra J. and France J. (2010). A review of mathematical functions for the analysis of growth in poultry. World’s Poult. Sci. J. 66, 227-239.
Goliomytis M., Panopoulou E. and Rogdakis E. (2003). Growth curves for body weight and major component parts, feed consumption, and male broiler chickens raised to maturity. Poult. Sci. 82, 1061-1068.
Kebreab E., Schulin-Zeuthen M., Lopez S., Soler J., Dias R.S., de Lange C.F.M. and France J. (2007). Comparative evaluation of mathematical functions to describe growth and efficiency of phosphorus utilisation in growing pigs. J. Anim. Sci. 85,2498-2507.
Kusec G., Baulain U., Kallweit E. and Glodeck P. (2007). Influence of MHS genotype and feeding regime on allometric and temporal growth of pigs assessed by magnetic resonance imaging. Livest. Sci. 110, 89-100.
Mohammed D.M., Abdalsalam Y.I., Khein A.M., Jin-Yu W. and Hussein M.H. (2005). Comparison of egg characteristics of different Sudanese indigenous chicken types. Int. J. Poult. Sci. 4(7), 455-457.
Narinc D., Askoy T., Karaman E. and Ilaslan Curek D. (2010). Analysis of fitting growth models in medium growing chicken raised indoor system. Trend. Anim. Vet. Sci. J. 1(1), 12-18.
Olawoyin O.O. (2007). Evaluation of the growth parameters of four strains of cockerels. African J. Anim. Biomed. Sci. 2(2), 17-25.
Orheruata A.M., Nwokoro S.O., Oyekpen E. and Ojo A.E. (2010). Modelling the growth of rabbits raised in humid climate for improved breeding. African J. Agric. Res. 5(22),3126-3129.
OseiAmponsah R. (2010). Genetic and phenotypic characterisation of local chicken ecotypes of Ghana. Ph D. Thesis. Univ., Ghana, Legon.
OseiAmponsah R., Kayang B.B. and Naazie A. (2013). Phenotypic and genetic parameters for production traits of local chickens in Ghana. Anim. Genet. Res. 53, 45-50.
Payne R.W., Harding S.A., Murray D.A., Soutar D.M., Baird D.B., Welham S.J., Kane A.F., Gilmour A.R., Thompson R., Webster R. and Unnicliffe Wilson G. (2007). Gen Stat Release 10 Reference Manual., Part 2 Directives, VSN International, Hemel Hempstead, UK.
Roush W.B. and Branton S.L. (2005). A comparison of fitting growth models with a genetic algorithm and nonlinear regression. Poult. Sci. 84,494-502.
SASSO. (2010). The world’s largest choice in coloured breeders. Availabl at: http://sasso.fr/coloured-breeder.php?lg=en&produit=light-chickens&ref_coq=T44. Accessed Apr. 2011.
Schwarz G. (1978). Estimating dimensions of a model. Ann. Stats. 6,461-464.
Wellock I.L., Emmans G.C. and Kyriazakis I. (2004). Describing and predicting potential growth in the pig. Anim. Sci. 78, 379-388.
Yang Y., Mekki D.M., Li S.J., Wang L.Y. Yu J.H. and Wang J.Y. (2006). Analysis of fitting growth models in Jinghai mixed-sex Yellow chicken. Int. J. Poult. Sci. 5(6), 517-521.