Application of Principal Component Analysis on the Body Morphometric of Nigerian Indigenous Chickens reared intensively under Southern Guinea Savanna Condition of Nigeria

Authors

  • S. R. Amao Lecturer in the School of Vocational and Technical Education, Emmanuel Alayande College of Education, Oyo, Oyo State. Nigeria.

Keywords:

Morphometric, genetic stocks, principal component analysis, Nigerian local chicken, indigenous chicken

Abstract

The experiment employs the principal components analysis (PCA) on the body morphometric of three genetic stocks of Nigerian indigenous chickens reared intensively under southern guinea savanna condition of Nigeria. A total number of 300 birds comprises of 100 each of normal feathered, frizzled feathered and naked neck chickens are randomly selected from the pre-existing reared intensively birds in the farm. Data are collated on body weight (BDW), head length (HL), beak length (BKL), comb length (CL), neck length (NL), body length (BDL), wing length (WG), keel length (KL), thigh length (TL) and shank length (SL). The results from the morphometric measurements indicate that frizzled feather birds displayed superiority in terms of BDW, HL, BKL, CL, NL, TL and SL than naked neck and normal feathered chickens expect for BDL and KL which are favoured by normal feather birds. The pooled correlation matrix reveals that the values obtained highly positive significant correlation is noted between the BDW and HL, BLK, TL, WG, SL, CL and NL. For PCA, two principal components are extracted (PC1 and PC2). PC1 and PC2 contribute 83.14% of the total variance while PC1 account for 65.44% of the total variance. The screen plot indicates that only the first two components have eigenvalues greater than 1. This implies that only the first two components should be retained. The CL, SL, TL, BKL, HL and BDL contribute to the total variability of PC1 and these traits could use for selection in breeding programme to improve the body weight of the genetics stocks of Nigerian local birds.

References

Ajayi, O.O., Adeleke, M.A., Sanni, M.T., Yakubu, A., Peters, S.O., Immorin, I.G., Ozoje, M.O., Ikeobi, C.O.N. and Adebambo, O.A. (2012). Application of principal component and discriminant analyses to morpho-structural indices of indigenous and exotic chickens raised under intensive management system. Tropical Animal Health and Production, 44 (6):1247-1254.

Apuno, A. A. Mbap, S. T. and Ibrahim, T. (2011). Characterization of local chickens (Gallus gallus domesticus) in Shelleng and Song local government areas of Adamawa State, Nigeria. Agriculture and Biology Journal of North America, 2(1): 6-14

Amao, S. R. (2017). Estimation of body weight from linear body measurements in two commercial meat-type chickens raised in Southern Guinea Environment of Nigeria using principal component analysis approach. (In press)

Çamdeviren, H., Demir, N., Kanik, A. and Keskin, S. (2005). Use of principal component scores in multiple linear regression models for prediction of Cholophyll-a in reservoirs. Ecological Modelling, 181, 581-589.

Egena., S. S. A., Ijaiya, A. T., Ogah, D. M. and Aya, V. E. (2014). Principal component analysis of body measurements in a population of indigenous Nigerian chickens raised under extensive management system. Slovak Journal of Animal Science, 47(2): 77-82

Ekue F. N., Pone K. D., Mafeni M. J., Nfi A. N. and Njoya J. (2002). Survey of the traditional poultry production system in the Bamenda area, Cameroon. In Characteristics and Parameters of Family Poultry Production in Africa.

Everitt, B.S., Laudau, S. and Leese, M. (2001). Cluster analysis (4th ed). London: Arnold Publisher.

Eyduran E., Topal M. and Sonmez A. Y. (2010). Use of factor scores in multiple regression analysis for estimation of body weight by several body measurements in brown trouts (Salmo trutta fario). International Journal of Agriculture and Biology, 12, 611- 615.

Ibe S. N. (1989). Measures of size and conformation in commercial broilers, Journal of Animal Breeding and Genetic, 106, 461–469.

Iqbal, A., Akram M., Sahota A. W., Javed K., Hussain J., Sarfraz Z. and Mehmood S. (2012). Laying characteristics and egg geometry of four varieties of Indigenous Aseel chicken in Pakistan. Journal of Animal Plant Science, 22 (4), 848-852.

Ikpeme E. V., Kooffreh M. E., Udensi O. U., Ekerette E. E., Ashishie L. A. and Ozoje M. O. (2016). Multivariate-based genetic diversity analysis of three genotypes of Nigerian local chickens (Gallus domestica). International Journal of Science and Research Methodology, 5 (2), 1-12

Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20: 141-151

Kor A., Baspinar E., Karaca S. and Keskin S. (2006). The Determination Of Growth in Akkeci (White goat) female kids by various growth models. Czech Journal of Animal Science, 51 (3), 110-116.ea

Latshaw, J. D. and Bishop, B. L. (2001). Estimating body weight and body composition of chickens by using noninvasive measurements. Poultry Science, 80, 868-873.

Mendes, M. (2009). Multiple linear regression models based on principal component scores to predict slaughter weight of broiler, Arch. Geflügelk, 73 (2), 139–144,

Mendes, M. andAkkartal, E. (2007). Canonical correlation analysis for studying the relationships between pre - and post- slaughter traits of Ross 308 broiler chicken. Arch Geflugelk,71:267-271

Morrison D. F. (1976.) Multivariate Statistical Methods, McGraw-Hill Company, New York.

Oguntunji, A. O. and Ayorinde, K. L. (2014). Multivariate analysis of morphological traits of the Nigerian Muscovy duck (Cuirina moschata), Animal Science and Fisheries Management,63(243);483-493.

Osaiyuwu, O. H., Salako, A. E. andAdurogbangba, O. (2010). Body dimension of Fulani and Yoruba ecotype chicken under intensive systems of management. Proceedings of 35th Annual Conference of Nigerian Society for Animal Production, 14-17 March, 2010. University of Ibadan, Nigeria, 55-59

Raick C., Beckers J. M., Soetaert K. and Gregoire M. (2006). Can principal component analysis be used to predict the dynamics of a strongly non-linear marine biogeochemical model? Ecological Modelling, 196, 345-364.

Rosario M.F., Silva M. A. N., Coelho A. A. D., Savino V. J. M. and Dias C. T. S. (2008). Canonical discriminant analysis applied to broiler chicken performance. Animal, 2 (3), 419-424.Length Res

SAS (2009). Statistical Analysis System. Version 9.2, Carry, North Carolina, SAS Institute Corporated.

Sharma, S. (1996). Applied multivariate techniques. John Wiley and Sons, Inc., Canada.

Sousa S. I. V., Martins F. G., Alvim-Ferraz M. C. M., and Pereira M. C. (2007). Multiple linear regression and artificial neural Networks based on principal components to predict ozone concentrations. Environmental Modelling and Software , 22, 97-103.

SPSS (2013). Statistical package for the social sciences. SPSS Inc., 444 Michigan Avenue, Chicago, IL60611, USA.

Udeh, I. and Ogbu, C. I. (2011). Principal component analysis of body measurements in three strains of broiler chicken. Science World Journal, 6 (2), 11-14. A

Yakubu A., Kuje D. and Okpeku M. (2009). Principal components as measure of size and shape in Nigerian indigenous chickens. Thai Journal of Agricultural Science, 42 (3), 167-176.

Downloads

Published

2023-11-24

How to Cite

Amao, S. R. (2023). Application of Principal Component Analysis on the Body Morphometric of Nigerian Indigenous Chickens reared intensively under Southern Guinea Savanna Condition of Nigeria. Journal of Environmental Issues and Agriculture in Developing Countries (JEIADC), 10(1), 1–12. Retrieved from http://icidr.org.ng/index.php/jeiadc/article/view/298

Issue

Section

Articles