Dairy cattle

Random regression and Bayesian approaches for longitudinal modelling of lactation performance in dairy buffaloes

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Abstract

This study aimed to estimate genetic and phenotypic parameters for all lactation -related traits in Nili- Ravi buffaloes using both random regression models (RRM) and a Bayesian multivariate animal model (Bayes MAM). Data comprised 1,096 lactations from 345 buffaloes, each with 10 test-day milk yield records between 2001 and 2024, along with reproductive traits such as calving interval (CI), service period (SP), and age at first calving (AFC). The RRM incorporated second -order Legendre polynomials to model the trajectory of test-day yields across 10 time points. At the same time, Bayes MAM was implemented via Markov Chain Monte Carlo (MCMC) using weakly informative inverse -Wishart priors. Heritability estimates obtained through RRM for total milk yield (TMY), 305 -day milk yield (305-DMY), and average test-day yield ranged from 0.28 to 0.33, indicating moderate genetic control. Genetic correlations among test-day records increased with temporal proximity, ranging from 0.45 (TD1-TD10) to 0.92 (TD5-TD6). RRM demonstrated superior predictive performance, yielding a prediction correlation (r) of 0.359 and a lower root mean square error (RMSE = 142.6) compared to the Bayes MAM (r = 0.009; RMSE = 168.3). However, the Bayesian MAM model provided a better fit, as indicated by the deviance information criterion (DIC = 8042 vs. 9021). These results suggest that RRM is a powerful tool for modelling longitudinal milk production traits, providing more accurate predictions across lactation stages. In contrast, Bayesian MAM models offer stronger inference capabilities for estimating genetic (co)variances in multivariate contexts. These findings provide valuable insights for genetic improvement strategies in tropical dairy buffalo populations.

Keywords: 2026

How to Cite:

Nasir, K., Hyder, A. & Ahmad, S., (2026) “Random regression and Bayesian approaches for longitudinal modelling of lactation performance in dairy buffaloes”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2295701. doi: https://doi.org/10.31274/wcgalp.24360

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