Indian Journal of Animal Research
SCOPUSWeb of Science
  • Year: 2026
  • Volume: 60
  • Issue: 3

Principal Component Analysis of Economic Traits in HD-K75 Pig, A Crossbred Pig Variety of India

  • Author:
  • Arundhati Phookan1*, Olympica Sarma2, Dimpi Khanikar3, T.C. Tolenkhomba4, Nipu Deka1, N.H. Mohan5, V.K. Gupta5
  • Total Page Count: 7
  • Page Number: 392 to 398

1ICAR-All India Coordinated Research Project on Pig, Assam Agricultural University, Khanapara-781 022, Guwahati, Assam, India.

2Department of Animal Genetics and Breeding, College of Veterinary and Animal Science, G.B. Pant University of Agriculture and Technology, Pantnagar-263 145, Uttarakhand, India.

3Department of Animal Genetics and Breeding, College of Veterinary Science, Assam Agricultural University, Khanapara-781 022, Guwahati, Assam, India.

4College of Veterinary Science and Animal Husbandry, Central Agricultural University, Selesih, Aizawl-796 014, Mizoram, India.

5ICAR-National Research Centre on Pig, Rani, Guwahati-781 015, Assam, India.

*Corresponding Author: Arundhati Phookan, ICAR-All India Coordinated Research Project on Pig, Assam Agricultural University, Khanapara-781 022, Guwahati, Assam, India. Email: arundhatiphookan@rediffmail.com.

Abstract

Principal component analysis (PCA) is a biometrical technique or a dimensionality reduction method where large number of measurements could be replaced by fewer measurements without significant loss of information. PCA is a multivariate methodology and helps to eliminate redundant traits and transforms original group of variables into another group known as principal components. It can be used with success when characteristics are correlated. PCA can help in accurate selection of superior animals.

The present study was carried out in HD-K75 pigs maintained at ICAR-All India Coordinated Research on Pig, AAU, Khanapara, Guwahati, Assam, India. A total of 13 economic traits viz. age at sexual maturity (ASM), age at first fertile service (AFFS), age at first farrowing (AFF), farrowing interval (FI), litter size at birth (LSB), litter weight at birth (LWB), litter size at weaning (LSW), litter weight at weaning (LWW), teats number (TN), body weight at Birth (BWB), body weight at weaning (BWW), body weight at 5 month (BM5) and body weight at 8 month (BM8) were considered. Data were collected from a total of 164 sows. Data belonged to 8 years from 2015 to 2023. The principal component analysis was performed using the factor module in SPSS 24.

Factor analysis with varimax rotation uncovered four principal components, collectively explaining 84.18% of the total variance. The first, second, third and fourth principal component accounted for 29.71%, 29.33%, 12.683% and 12.452% of the variance. High component loading was found for LSW and LWW in first component, LSB for second component, BW5 for third component and BW8 for fourth component respectively. The communality values ranged from 0.980 (ASM and AFFS) to 0.608 (BWW). These findings indicate that PCA can serve as a valuable tool in breeding programmes, allowing for a significant reduction in the number of economic traits to be used in selection procedure.

Keywords

Correlation, Economic traits, HD-K75, Principal component analysis