PRINCIPAL COMPONENT ANALYSIS OF BODY WEIGHT AND BIOMETRIC TRAITS OF F1 CROSSBRED OF EXOTIC BROILERS X LOCAL CHICKENS

Abstract

Data on 228 F1 crossbred body weight and body measurement of birds were generated in south south Nigeria. The objective of the study was to define the body dimension of f1 cross bred chicks resulting from crosses of exotic broiler and local chickens. The biometric traits variables that contribute to body conformation by the use of principal component analysis were investigated. Variables measured include body weight, wing span, wing length, thigh length, shank length, kneel length and breast firth. Data were subjected to multivariate analysis using statistical package for social sciences (SPSS) 20 2007. The descriptive statistics observed that the mean body weight were 1913.33, 1338.29, 3399.63 and 3236.96g for male and female of main (Ex x Lc) and reciprocal crossbred chicks respectively. The reciprocal (Lc x Ex) crossbred chicks were significantly (P<0.05) superior to main (Ex x Lc) crossbred chicks in body weight and biometric traits. The coefficients of correlation range from -0.55 to 0.47 and -0.62 to 0.18 main crossbred male and female, while the reciprocal Lc x Ex range from -0.21 to 0.71 male and -0.12 to 0.39 female, respectively. The principal component analysis with variance maximizing orthogonal rotation was used to extract the components. Three principal components (PC) were extracted in the chickens explaining 62%and 54% main (Ex x Lc) crossbred, and 70 and 50% reciprocal (Lc x Ex) crossbred of the total variation in the original seven variables. The first principal component had the largest share of the total variance and correlated highly with knee length, wing span for main (Ex x Lc) crossbred male and the female body length. Only the reciprocal (Lc x Ex) PC1 loaded heavily on kneel length, wing length and thigh length male, while female was wing span only. Generally, PC1 out of all other PCs had the highest share of the total variance and it is regarded as generalized form of the birds. Prediction model based on principal component is more valid than the interdependent based models because it removed multicollinearity which might be present if interdependent variables are combined in a multiple regression model. This component could be used as selection criteria for improving body weight of Nigerian normal feathered local chickens.