Use of Random regression model for modeling growth trajectory in Jamunapari goat in the semi-arid region of India

Dige, Mahesh Shivanand; Rout, Pramod Kumar; Singh, Manoj Kumar; Bhusan, Saket; Kaushik, Rakesh; Mahala, Sudarshan; Gowane, G. R.

doi:10.1016/j.livsci.2021.104713

Cited by 4 publications

(1 citation statement)

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“…Researchers worldwide have evaluated lactation traits in goats using random regression test-day model (Amin, 2018;Andonov et al, 2013;Arnal et al, 2019Arnal et al, , 2020Brito et al, 2018;Liu et al, 2000;Menéndez-Buxadera et al, 2010;Menezes et al, 2011;Mucha et al, 2014;Nogueira et al, 2020;Silva et al, 2013;Thepparat et al, 2015;Zumbach et al, 2008). In India, few studies have applied random regression models to small ruminants, primarily for modelling growth curves (Dhakad et al, 2022;Dige et al, 2021;Mahala et al, 2020;Venkataramanan, 2016). Dige et al (2022) conducted a genetic evaluation of the lactation curve using RRM in Jamunapari goats.…”

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confidence: 99%

Modelling the lactation curve in Alpine × Beetal crossbred dairy goats using random regression models fitted with Legendre polynomial and B‐spline functions

Upadhyay,

Alex,

Dige

et al. 2024

J Animal Breeding Genetics

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The current study sought to genetically assess the lactation curve of Alpine × Beetal crossbred goats through the application of random regression models (RRM). The objective was to estimate genetic parameters of the first lactation test‐day milk yield (TDMY) for devising a practical breeding strategy within the nucleus breeding programme. In order to model variations in lactation curves, 25,998 TDMY records were used in this study. For the purpose of estimating genetic parameters, orthogonal Legendre polynomials (LEG) and B‐splines (BS) were examined in order to generate suitable and parsimonious models. A single‐trait RRM technique was used for the analysis. The average first lactation TDMY was 1.22 ± 0.03 kg and peak yield (1.35 ± 0.02 kg) was achieved around the 7th test day (TD). The present investigation has demonstrated the superiority of the B‐spline model for the genetic evaluation of Alpine × Beetal dairy goats. The optimal random regression model was identified as a quadratic B‐spline function, characterized by six knots to represent the central trend. This model effectively captured the patterns of additive genetic influences, animal‐specific permanent environmental effects (c2) and 22 distinct classes of (heterogeneous) residual variance. Additive variances and heritability (h2) estimates were lower in the early lactation, however, moderate across most parts of the lactation studied, ranging from 0.09 ± 0.04 to 0.33 ± 0.06. The moderate heritability estimates indicate the potential for selection using favourable combinations of test days throughout the lactation period. It was also observed that a high proportion of total variance was attributed to the animal's permanent environment. Positive genetic correlations were observed for adjacent TDMY values, while the correlations became less pronounced for more distant TDMY values. Considering better fitting of the lactation curve, the use of B‐spline functions for genetic evaluation of Alpine × Beetal goats using RRM is recommended.

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