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

Mohd. Hafiz A. W., . and Mohamad Hifzan R., . and Izuan Bahtiar A. J., . and Nurulhuda M. O., . Evaluation of principal component analysis in animal breeding and selection in the breed improvement program of male Katjang-Boer crossbred. pp. 33-40. ISSN 2550-2123

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.

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Abstract

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.

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Item Type:	Article
AGROVOC Term:	Goats
AGROVOC Term:	Crossbreds
AGROVOC Term:	Animal breeding
AGROVOC Term:	Body measurements
AGROVOC Term:	Male animals
AGROVOC Term:	Breeds (animals)
AGROVOC Term:	Body parts
AGROVOC Term:	Body weight
AGROVOC Term:	Growth
Depositing User:	Mr. AFANDI ABDUL MALEK
Last Modified:	24 Apr 2025 00:55
URI:	http://webagris.upm.edu.my/id/eprint/10077

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