Principal component analysis approach in describing the biometric traits of Ostrich (Struthio camelus) eggs in Southern Guinea Savanna region of Nigeria

Abstract

The use of Principal Components Analysis (PCA) was recently common in the analysis of relationships between scores of traits in animals due to its merit over correlation and regression analyses that usually used for two traits at a time. Therefore, the study was carried out to examine the PCA approach in describing the biometric traits of Ostrich (Struthio camelus) eggs under Southern Guinea Savanna region of Nigeria. A total of eighty (80) eggs were sourced for this study and traits measured were egg weight (EW), egg length (EL), egg width (EWD), shell weight (SW), shell thickness (ST), shell index (SI), yolk weight (YW), yolk length (YL), yolk height (YH), albumen weight (AW), albumen length (AL), albumen height (AH), yolk index (YI), albumen index (AI) and haugh unit (HU). The descriptive statistics results revealed that the mean egg weight was 1742 g while other biometric measurements were 140.00 mm, 126.69 mm, 272.56 g, 25.10 mm, 0.4 %, 578.21 g, 214.62 mm, 23.03 mm, 884.36 g, 206.56 g, 39.10 mm, 33.35 %, 50.65 % and 89.65 for EL, EDW, SW, ST, SI, YW, YL, YH, AW, AL, AH, YI, AI and HU, respectively. The phenotypic correlations among traits were positive and highly significant (P<0.001, P<0.05) ranging from r = -0.31 to r = 0.92. The PCA with variance maximizing orthogonal rotation was used in extraction of the components. Three principal components were extracted for the eggs traits accounted for 66.17 % of the total variance while the first factor accounted for 51.94 % of the variation out of the total of 5 original statistic value traits extracted. It was concluded that four traits of higher communalities (EW=0.899, SW=0.904, AW=0.746 and YW=0.829) extracted components could be used as selection indices for improving the quality traits of Ostrich eggs with recommendations that information obtained could be useful in appropriate management, breeding programmes, selection and utilization for egg quality genetic resources.