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<p>This study was designed for genetic analysis of lactation curve parameters and traits derived from wood’s model using Bayesian inference in first parity Murrah buffaloes. Data were collected from 657 buffaloes, with test day milk records over a period of 24 years spanning from 2000 to 2023. The lactation curve parameters, viz., ‘a’ (initial milk yield after calving), ‘b’ (ascending slope up to peak yield) and ‘c’ (descending slope after peak yield), along with lactation curve traits such as peak yield (PY), time of peak yield (PT), and persistency (PR) of individual buffaloes, were estimated using the incomplete gamma function (Wood’s model) through non-linear regression in SPSS software (Version 23). The studied parameters and traits were further analyzed by estimation of genetic parameters using animal model with Bayesian inference under GIBBS sampling. The heritability estimates for lactation curve parameters (a, b, and c) were low, ranging from 0.04 to 0.05, indicating limited genetic influence. However, moderate heritability was observed for PY (0.21), suggesting potential for genetic improvement. It was concluded that selection for higher peak yield can effectively enhance overall lactation performance, given its moderate heritability and favorable genetic correlations with peak time and persistency. These findings provide valuable insights for breeding strategies aimed at enhancing milk yield and lactation efficiency in Murrah buffaloes.</p>

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Genetic evaluation of lactation curve characteristics in Murrah buffaloes

  • 期刊论文

  • 发表日期：

    2026年2月12日

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  • Rakesh Nehra,
  • Yogesh C. Bangar,
  • Kamaldeep Dhundwal,
  • Sunil Kumar,
  • Poonam Sevda,
  • Pulkit Chugh

摘要

This study was designed for genetic analysis of lactation curve parameters and traits derived from wood’s model using Bayesian inference in first parity Murrah buffaloes. Data were collected from 657 buffaloes, with test day milk records over a period of 24 years spanning from 2000 to 2023. The lactation curve parameters, viz., ‘a’ (initial milk yield after calving), ‘b’ (ascending slope up to peak yield) and ‘c’ (descending slope after peak yield), along with lactation curve traits such as peak yield (PY), time of peak yield (PT), and persistency (PR) of individual buffaloes, were estimated using the incomplete gamma function (Wood’s model) through non-linear regression in SPSS software (Version 23). The studied parameters and traits were further analyzed by estimation of genetic parameters using animal model with Bayesian inference under GIBBS sampling. The heritability estimates for lactation curve parameters (a, b, and c) were low, ranging from 0.04 to 0.05, indicating limited genetic influence. However, moderate heritability was observed for PY (0.21), suggesting potential for genetic improvement. It was concluded that selection for higher peak yield can effectively enhance overall lactation performance, given its moderate heritability and favorable genetic correlations with peak time and persistency. These findings provide valuable insights for breeding strategies aimed at enhancing milk yield and lactation efficiency in Murrah buffaloes.