Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-24T09:42:58.043Z Has data issue: false hasContentIssue false
  • English
  • Français

Canonical discriminant analysis applied to broiler chicken performance

Published online by Cambridge University Press:  01 March 2008

M. F. Rosário ,
M. A. N. Silva ,
A. A. D. Coelho ,
V. J. M. Savino  and
C. T. S. Dias
M. F. Rosário*
Affiliation:
Department of Genetics, ‘Luiz de Queiroz’ College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, PO Box 83, 13400-970, Piracicaba, São Paulo, Brazil
M. A. N. Silva
Affiliation:
Department of Genetics, ‘Luiz de Queiroz’ College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, PO Box 83, 13400-970, Piracicaba, São Paulo, Brazil
A. A. D. Coelho
Affiliation:
Department of Genetics, ‘Luiz de Queiroz’ College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, PO Box 83, 13400-970, Piracicaba, São Paulo, Brazil
V. J. M. Savino
Affiliation:
Department of Genetics, ‘Luiz de Queiroz’ College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, PO Box 83, 13400-970, Piracicaba, São Paulo, Brazil
C. T. S. Dias
Affiliation:
Department of Exact Sciences, ‘Luiz de Queiroz’ College of Agriculture, University of São Paulo, 13400-970, Piracicaba, São Paulo, Brazil
*
E-mail: [email protected]
Get access

Abstract

The mechanisms involved in the control of growth in chickens are too complex to be explained only under univariate analysis because all related traits are biologically correlated. Therefore, we evaluated broiler chicken performance under a multivariate approach, using the canonical discriminant analysis. A total of 1920 chicks from eight treatments, defined as the combination of four broiler chicken strains (Arbor Acres, AgRoss 308, Cobb 500 and RX) from both sexes, were housed in 48 pens. Average feed intake, average live weight, feed conversion and carcass, breast and leg weights were obtained for days 1 to 42. Canonical discriminant analysis was implemented by SAS® CANDISC procedure and differences between treatments were obtained by the F-test (P < 0.05) over the squared Mahalanobis’ distances. Multivariate performance from all treatments could be easily visualised because one graph was obtained from two first canonical variables, which explained 96.49% of total variation, using a SAS® CONELIP macro. A clear distinction between sexes was found, where males were better than females. Also between strains, Arbor Acres, AgRoss 308 and Cobb 500 (commercial) were better than RX (experimental). Evaluation of broiler chicken performance was facilitated by the fact that the six original traits were reduced to only two canonical variables. Average live weight and carcass weight (first canonical variable) were the most important traits to discriminate treatments. The contrast between average feed intake and average live weight plus feed conversion (second canonical variable) were used to classify them. We suggest analysing performance data sets using canonical discriminant analysis.

Keywords

animal breedingCANDISC procedureMANOVApoultry industry
Type
Full Paper
Information
animal , Volume 2 , Issue 3 , March 2008 , pp. 419 - 424
Copyright
Copyright © The Animal Consortium 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barbosa, L, Regazzi, AJ, Lopes, PS, Breda, FC, Sarmento, JLR, Torres, RA, Torres Filho, TA 2005. Evaluation of genetic divergence among lines of laying hens using cluster analysis. Brazilian Journal of Poultry Science 7, 79–83.CrossRefGoogle Scholar
Berri, C, Wacrenier, N, Millet, N, Le Bihan-Duval, E 2001. Effect of selection for improved body composition on muscle and meat characteristics of broilers from experimental and commercial lines. Poultry Science 80, 833–838.CrossRefGoogle ScholarPubMed
Canadian Council on Animal Care 1993. Guide to the use of experimental animals, vol. 1 . CCAC, Ottawa, Ont., Canada.Google Scholar
Carneiro, PLS, Fonseca, R, Pires, AV, Torres Filho, RA, Torres, RA, Peixoto, JO, Lopes, PS, Euclydes, RF 2002. Estudo da divergência genética entre linhagens de matrizes de frangos de corte por meio de análise multivariada. Arquivo Brasileiro de Medicina Veterinária e Zootecnia 54, 75–83.CrossRefGoogle Scholar
Dobek, A, Moliński, K, Szydłowski, M, Szwaczkowski, T 2000. Analysis of single gene multitrait effects in livestock by the use of Gibbs sampling. Journal of Applied Genetics 41, 275–283.Google ScholarPubMed
Fonseca, R, Torres Filho, RA, Torres, RA, Peixoto, JO, Pires, AV, Carneiro, PLS, Souza, GH, Bueno, RS, Lopes, PS, Euclydes, RF 2002. Avaliação de frangos de corte utilizando técnicas de análise multivariada: I – Características de carcaça. Arquivo Brasileiro de Medicina Veterinária e Zootecnia 54, 525–529.CrossRefGoogle Scholar
Food and Agriculture Organization of the United Nations 2006. Retrieved September 2, 2006, from http://www.fao.org.Google Scholar
Harris, RJ 1975. A primer of multivariate statistics. Academic Press, New York, NY, USA.Google Scholar
Hoffmann, I 2005. Research and investment in poultry genetic resources – challenges and options for sustainable use. World Poultry Science Journal 61, 57–70.CrossRefGoogle Scholar
Johnson, RA, Wichern, DW 1992. Applied multivariate statistical analysis. Prentice Hall, NJ, USA.Google Scholar
Lahav, T, Atzmon, G, Blum, S, Ben-Ari, G, Weigend, S, Cahaner, A, Lavi, U, Hillel, J 2006. Marker-assisted selection based on a multi-trait economic index in chicken: experimental results and simulation. Animal Genetics 37, 482–488.CrossRefGoogle ScholarPubMed
Le Bihan-Duval, E 2004. Genetic variability within and between breeds of poultry technological meat quality. World Poultry Science Journal 60, 331–340.CrossRefGoogle Scholar
Liu, Y, Lyon, BG, Windham, WR, Lyon, CE, Savage, EM 2004. Principal component analysis of physical, color, and sensory characteristics of chicken breasts deboned at two, four, six, and twenty-four hours postmortem. Poultry Science 83, 101–108.CrossRefGoogle ScholarPubMed
Mardia, KV 1974. Applications of some measures of multivariate skewness and kurtosis in testing normality and robustness studies. Sankhya B 36, 115–128.Google Scholar
Pinto, LFB, Packer, IU, Melo, CMR, Ledur, MC, Coutinho, LL 2006. Principal components analysis applied to performance and carcass traits in the chicken. Animal Research 55, 419–425.CrossRefGoogle Scholar
Pires, AV, Carneiro, PLS, Torres Filho, RA, Fonseca, R, Torres, RA, Euclydes, RF, Lopes, PS, Barbosa, L 2002. Estudo da divergência genética entre seis linhas de aves Legorne utilizando técnicas de análise multivariada. Arquivo Brasileiro de Medicina Veterinária e Zootecnia 54, 314–319.CrossRefGoogle Scholar
Rosário, MF, Silva, MAN, Savino, VJM, Coelho, AAD, Moraes, MC 2005. Avaliação do desempenho zootécnico de genótipos de frangos de corte utilizando-se a análise de medidas repetidas. Revista Brasileira de Zootecnia 34 (suppl. 2), 2253–2261.CrossRefGoogle Scholar
Statistical Analysis Systems Institute 2006. SAS OnlineDoc®, version 8.02. SAS Institute Inc., Cary, NC, USA. Retrieved September 5, 2006 from http://www.id.unizh.ch/software/unix/statmath/sas/sasdoc/stat/index.htm.Google Scholar
Szwaczkowski, T, Szydłlowski, M, Moliński, K, Dobek, A 2001. Multivariate analysis of mixed inheritance model of performance traits in layers using Gibbs sampling. Journal of Animal Breeding and Genetics 118, 205–211.CrossRefGoogle Scholar
Szydłowski, M, Szwaczkowski, T 2001. Bayesian segregation analysis of production traits in two strains of laying hens. Poultry Science 80, 125–131.CrossRefGoogle Scholar
Viana, CFA, Almeida e Silva, M, Pires, AV, Lopes, PS, Piassi, M 2000. Estudo da divergência genética entre quatro linhagens de matrizes de frangos de corte utilizando técnicas de análise multivariada. Revista Brasileira de Zootecnia 29, 1074–1081.CrossRefGoogle Scholar
Yang, N, Jiang, RS 2005. Recent advances in breeding for quality chickens. World Poultry Science Journal 61, 373–381.CrossRefGoogle Scholar