Indirect selection decision making for production and reproduction traits using multi-variate Markov chain Monte Carlo (MCMC) algorithm in Sahiwal cattle


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Authors

  • Nistha Yadav ICAR-National Dairy Research Institute
  • Sabyasachi Mukherjee ICAR NDRI Karnal
  • Anupama Mukherjee ICAR-National Dairy Research Institute

https://doi.org/10.33785/IJDS.2026.v79i02.006

Abstract

The prosperity in dairy traits of livestock productivity can be achieved by properly emphasizing both the analytical approach and traits of economic importance that affect present and future generations in a herd viz. reproduction and production. The study was so planned to explore genetic parameters and correlation components for reproduction traits (AFC: Age at First Calving, FSP: First Service Period), milk production traits (FLMY: First Lactation Milk Yield) and milk composition traits (FLSNFY: First Lactation SNF Yield, FLFY: First Lactation Fat Yield) in Sahiwal cattle of Livestock farm unit from ICAR-NDRI Karnal, Haryana, India. A comparative analytic approach (LSML v/s Bayesian) was considered by using LSML Harvey and Gibbs sampler Animal model approach of Bayesian application with a mixed model equation and estimated breeding values (EBVs) for production traits. Marginal posterior means for heritability varied between 0.17±0.0148-0.51±0.0114 by univariate, bivariate, and trivariate analysis while between 0.12±0.003-0.49±0.002 by multi-trait analysis. Heritability estimate was higher for FLSNFY (0.51±0.01) followed by FLFY, AFC, FLMY (0.45±0.01, 0.29±0.04, 0.20±0.01) and lowest for FSP (0.17±0.01). High and positive genetic and phenotypic correlation (0.99±0.0001 and 0.98±0.004) was observed for FLFY-FLSNFY by multi-trait analysis. AFC has a moderate positive correlation with considered traits, while FSP has a negative genetic correlation (FSP-FLMY: -0.15±0.02; FSP-FLSNFY: -0.11±0.02; FSP-FLFY: -0.04±0.02). High correlations of reproduction traits suggest earlier age selection and directly reflect the production potential of the herd. Gibbs sampling was advantageous for genetic analysis with heterogeneous (reproduction traits) and homogenous (production traits) factors. Our work revealed improvement in the genetic gain of dairy cattle by indirect selection through AFC for FSP, FLMY, FLSNFY, and FLFY. High correlations of reproduction traits suggested earlier age selection could bring better production potential of the herd through a balanced selection decision.

Author Biographies

  • Nistha Yadav, ICAR-National Dairy Research Institute

    Animal Genetics and Breeding Division

  • Anupama Mukherjee, ICAR-National Dairy Research Institute

    Animal Genetics and Breeding Division

    Principal Scientist

Submitted

2025-02-19

Published

2026-05-10

Issue

Section

ANIMAL PRODUCTION & REPRODUCTION

How to Cite

Yadav, N., Mukherjee, S., & Mukherjee, A. (2026). Indirect selection decision making for production and reproduction traits using multi-variate Markov chain Monte Carlo (MCMC) algorithm in Sahiwal cattle. Indian Journal of Dairy Science, 79(2). https://doi.org/10.33785/IJDS.2026.v79i02.006