Principal components regression of body measurements in five strains of locally adapted chickens in Nigeria
This study aimed at unfolding the interdependence among the linear body measurements in chickens and to predict body weight from their orthogonal body measurements using principal component regression. Body weight and seven biometric traits that are; body length (BL), breast girth (BG), wing length (WL), wing span (WS), thigh length (TL), shank length (SL), keel length (KL) were measured on eight week old chicks comprising 53 each of Marshal (M), Marshal x naked-neck (MNk), Marshal x normalfeathered (MNm), Naked-neck (Nk) and Normal-feathered (Nm). General linear model, factors and partial least squares procedures of statistical analysis system (S.A.S 9.1) were used to compute the variations among the five ecotypes. Pearson correlations between body weight and biometric traits were positive and highly related (r = 0.614-0.937, 0.518-0.929 and 0.496-0.943, 0.411-0.959 and 0.760-0.961 in M, MNk, MNm, Nk and Nm ecotypes respctively). Only the first principal component (PC) exhibited eigenvalues greater than 1. Observed communalities ranges from 0.787 to 0.946 in M, 0.784 to 0.957 in MNk, 0.685 to 0.928 in MNm, 0.818 to 0.959 in Nk and 0.930 to 0.998 in Nm. This offered credibility to the relevance of the principal component regression. In principal component regression models, TL alone accounted for 76.21%, 62.72%, and 75.52% of the variation in BW for M, MNm and Nk respectively. The best prediction equation (R2=85.61%) for BW was obtained when BG was included in the model for M. In Nk chickens, the best prediction equation (R2=85.52%) for BW was obtained when BG was included in the model. BG alone accounted for 91.66% and 69.05% of the variation in BW for Nm and MNk respectively. Principal component regression can be used to classify independent and informative variables thereby eliminating redundant information for the purpose of reducing costs of chicken genetic programmes.
Keywords: Body weight, Biometric traits, Principal component, Orthogonal, Eigenvalues and Linear measurement