Genetic parameters of milk yield using random regression in Jersey Crossbred cattle

Abstract

Random regression models (RRM) using Legendre polynomials (LP) of varying orders were used for genetic evaluation of test-day milk yield (TDMY) in Jersey crossbred cows. A total of 5,794 first lactation test-day records from 571 cows of ICAR-National Dairy Research Institute, Eastern Regional Station, West Bengal, India was used to estimate the genetic parameters and evaluate the performance of random regression models (RRM) employing LP functions for first-lactation test-day milk yields. Among the evaluated models, A3P4 provided the best overall fit according to AIC and BIC values. The pattern of additive genetic variance was relatively stable during early to mid-lactation and declined thereafter whereas permanent environmental variance peaked at the first test day, decreased sharply until mid-lactation then increased slightly towards the end, highlighting the influence of environmental factors during the initial and terminal stages of lactation. Heritability estimates were lowest at the onset and end of lactation and peaked during mid-lactation (0.10 to 0.36), reflecting the stronger influence of genetic factors during this stage. Genetic correlations between adjacent test days were high and gradually declined as intervals increased. Correlations were close to unity during mid-lactation, suggesting stable genetic expression and supporting selection based on early test-day records to improve overall lactation performance. The findings of this study suggested the use of differential orders of Legendre polynomials for additive genetic and permanent environmental effects as it provides a flexible and robust framework for modelling genetic variation in milk yield across lactation in Jersey crossbred cattle