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Evaluation of principal component analysis in animal breeding and selection in the breed improvement program of male Katjang-Boer crossbred

Mohd. Hafiz, A.W.^1*, Mohamad Hifzan, R. ², Izuan Bahtiar, A.J.¹ and Nurulhuda, M.O.³

¹Livestock Science Research Centre, MARDI Muadzam Shah, 26700 Muadzam Shah, Pahang
²Livestock Science Research Centre, MARDI Headquarters, P/O Box 12301, General Post Office, 50774 Kuala Lumpur
³Livestock Science Research Centre, MARDI Kluang, 86009 Kluang, Johor

Malaysian J. Anim. Sci. 2021 24(1): 33-40

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The evaluation of body composition and growth performance is important to assess the animals? 
potential. Body measurements of animals have been widely used to assess skeletal growth and 
changes in animal conformation against age. Principal component analysis could be used to 
determine the factors that explain the highest variation in the dataset over the dependent variable. The objective of this study was to evaluate the principal component of the body measurements namely body length (BL), chest depth (CD), chest girth (CG), withers height (WH), width at withers, (WW), hip height (HH) and rump width (RW) and body indices namely length index (LI), depth index (DI), body index (BI), conformation index (CI), Proportionality (Pr), Relative depth of thorax (RDT), thoracic development (TD) and area index (AI) as selection criteria of 56 male Katjang-Boer goat. Two factors were extracted with a total variance was 86.87% where the first 
and second factor contributed to 48.05% and 38.82% after varimax rotation. The BL, CD, CG, HH 
and HW contributed the most for the first factor while the second factor mainly influenced by RW 
and WW. Three factors were extracted from body indices that accounted for 96.41% of total 
variance where the first, second third factor contributed to 42.70%, 27.55% and 26.16% of total 
variance, respectively. The LI, BI, Pr and AI contributed the most for the first factor while the 
second and third factor was influenced by CI, TD and DI, RDT, respectively. It is concluded that 
principal component analysis could be employed in animal breeding and selection as it can reduce 
the number of parameters to be considered in the breed improvement program.