ASSESSMENT OF SPLINE FUNCTIONS AND NON-LINEAR MODELS FOR ESTIMATING GROWTH CURVE PARAMETERS OF FUNAAB-ALPHA CHICKENS
Keywords:
spline model, non-linear models, knots, growth parameters, regressionAbstract
The study assessed the growth of FUNAAB-Alpha chickens (FAC) using spline and non-linear functions in order to establish the most appropriate growth function(s) for FAC. Three hundred (300) day-old chickens of FAC were used for the study. They were raised intensively under a deep litter system for 20 weeks and body weight records were taken weekly with the aid of a digital scale. Spline models of different numbers of, and locations of, knots were fitted using the REG procedure of SAS® while four non-linear models (Gompertz, Logistic, Bertalanffy and Richards') were fitted using the NLIN procedure of SAS®. The estimated hatch weight (β0) for the male and female chickens ranged from 30.77 g to 74.71 g and from 15.56 g to 38.19 g, respectively. The regression coefficients ranged from -38.47 to 47.46 and -39.40 to 40.47 for the male and female, respectively. The highest magnitudes of these coefficients were estimated at early ages (3 to 10 weeks), implying that growth rate at early stage of life might be a key response to selection for later growth performance. For non-linear models, parameter A (or asymptotic weight) for all the models ranged from 3716 g to 2050 g and 1591 g to 3330 g for male and female, respectively. The parameter (B), the scaling parameter (constant of integration), ranged from 0.7541 to 15.441. Likewise, parameter K, which is the maturity index, ranged from 0.0463 to 0.2002. The age at inflection point for FUNAAB-Alpha chickens ranged between 13.30 and 17.63 weeks for male chickens and between 14.23 and 19.94 weeks for female chickens while the corresponding body weight at inflection point ranged between 754 and 1528 g and 586 and 1261 g for male and female chickens, respectively. Based on Akaike Information Criterion and Bayesian Information Criterion as best fit model selection criteria, it was concluded that the spline models of 3 and 4 knots were the best fit linear spline models while Bertalanffy and Gompertz models were selected as the best fit non-linear models.
References
<br>Adebambo, O. A, Ikeobi, C. O. N., Ozoje, M. O., Adebambo, A. O., Peters, S.O., Adeleke, M. A., Wheto, M. Y., Ojoawo, H. T., Osinbowale, D. A., Ogunpayimo, O., Bamidele, O., Sonaiya, E. B. & Dessie, T. (2018). Indigenous Chicken Breeding in Nigeria. Proceedings of 43rd Annual Conference of the Nigerian Society for Animal Production, March 18th – 22nd 2018, Federal University of Technology, Owerri, Nigeria.
<br>Adebambo, O. A. (2015). From PEARL project to ACGG in Nigeria. Paper presented at the First ACGG Nigeria Innovation Platform meeting Ibadan, Nigeria, 20 – 22 July. Retrieved from <br>https://cgspace.cgiar.org/bitstream/handle/10568/72670/acggpearlnigeriajul 2015. pdf?sequence=1
<br>Adedokun, S. A. & Sonaiya, E. B. (2001). Comparison of the performance of Nigerian Indigenous chickens from the three agro-ecological zones. Livestock Research for Rural Development, 13(2), 1–6.
<br>Aggrey, S. E. (2002). Comparison of Three Nonlinear and Spline Regression Models for Describing Chicken Growth Curves. Poultry Science, 81, 1782–1788.
<br>Al-Samarai, F. R. (2015). Growth Curve of Commercial Broiler as Predicted by Different Nonlinear Functions. American Journal of Applied Scientific Research, 1(2), 6–9.
<br>Aworetan, A. R. & Oseni, S. O. (2018). Modelling the Growth Curve of Nigerian Fulani Ecotype Chicken under Two Production Systems. Proceedings of 43rd Annual Conference of the Nigerian Society for Animal Production, March 18th – 22nd 2018, Federal Univeristy of Technology, Owerri, Nigeria.
<br>Eleroglu, H., Yildrim, A., Sekeroglu, A., Coksoyler, F. N. & Duman, M. (2014). Comparison of growth curves by growth models in slow-growing chicken genotypes raised in the organic system. International Journal of Agriculture and Biology, 16(3), 529–535.
<br>FAO. (2007). The State of the World's Animal Genetic Resources for Food and Agriculture, edited by Barbara Rischkowsky and Dafydd Pilling. Rome. <br>(available at http://www.fao.org/docrep/010/a1250e/a1250e00.htm
<br>FAO. (2012). Phenotypic characterization of animal genetic resources. FAO Animal Production and Health Guidelines No. 11. Rome.
<br>Galeano-Vasco, L. F., Cerón-Muñoz, M. F. & Narváez-Solarte, W. (2014). Ability of non-linear Mixed models to predict growth in laying hens. Revista Brasileira de Zootecnia, 43(11), 573–578.
<br>Grossman, M., Bohren, B. B. & Anderson, V. L. (1985). Logistic growth of chicken: a comparison of techniques to estimate parameters. Journal of Heredity, 76(5), 397–399.
<br>Harrell, F. E. (2004). Regression Modeling Strategies: with applications to linear models, logistic regression, and survival analysis. Springer-Verlag New York, Inc. New York, USA.
<br>Ikanni, E. I. & Annatte, A. I. (2000). Improving the performance of local chickens. Extension Bulletin No. 92, Poultry series No. 6, National Agriculture Extension and Research Liaison Services, Ahmadu Bello University Zaria, Zaria, Nigeria.
<br>Kaps, M. & Lamberson, W. R. (2004). Biostatistics for Animal Science. CABI Publishing International, pp. 282.
<br>Laird, A. K., Tyler, S. A. & Barton, A. D. (1965). Dynamics of normal growth. Growth, 29, 233–248.
<br>Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 11(2), 431–441.
<br>Meng, F. R., Meng C. H., Tang, S. Z. & Arp, P. A. (1997). A new height growth model for dominant and co-dominant trees. Forest Science, 43, 348–354.
<br>Meyer, K. (2005). Random regression analyses using B-splines to model growth of Australian Angus cattle. Genetics Selection Evolution, 37, 473–500.
<br>Ngeno, K., Bebe, B. O. & Kahi, A. K. (2010). Estimation of Growth Parameters of Indigenous Chicken Populations Intensively reared in Kenya. Egerton Journal of Science and Technology, 11, 13-28.
<br>Nwosu, C. C. & Asuquo, B. O. (1985). Body weight improvement in the local chicken. Proceedings of the 10th Annual Conference of the Nigerian Society for Animal Production, Ile-Ife. March 24-28, pp. 23–29.
<br>Oleforuh-Okoleh, V. U., Kurutsi, R. F. & Ideozu, H. M. (2017). Phenotypic Evaluation of Growth Traits in Two Nigerian Local Chicken Genotypes. Animal Research International, 14(1), 2611–2618.
<br>Osei-Amponsah, R., Kayan, B. B., Naazie, A., Barchiaand, I. M. & Arthur, P. F. (2014). Evaluation of models to describe temporal growth in local chickens of Ghana. Iranian Journal of Applied Animal Science, 4(4), 811–855.
<br>Peters, S. O., Ikeobi, C. O. N., Ozoje, M. O., Famakinwa, O. A., Oshodi, Y. S. & Adebambo, O. A. (2007). Egg quality of the Nigerian local chicken as influenced by some major genes. Nigerian Journal of Animal Production, 37(1), 25–31.
<br>Rizzi, C., Contiero, B. & Cassandro, M. (2013). Growth Patterns of Italian Local Chicken Populations. Poultry Science, 92, 2226–2235.
<br>SAS. (2003). SAS Help and documentation. Version 9.0 SAS Institute Inc., Cary, NC. 27513, USA.
<br>Sonaiya, E. B. & Swan, S. E. J. (2004). Small scale poultry production: Technical Guide. FAO Publications. Rome Italy, pp. 7–25.
<br>Stone, C. J. (1986). Generalized Additive Models: Comments. Statistical Science, 1(3), 312–314.
<br>Zhao, Z. H., Li, S. F., Huang, H. Y., Li, C. M., Wang, Q. B. & Xue, L. G. (2015). Comparative Study on Growth and Developmental Model of Indigenous Chicken Breeds in China. Open Journal of Animal Sciences, 5, 219–223.</div>
