Random regression model with heterogeneous residual variance reduces polynomial order fitted for permanent environmental effect but does not affect genetic parameters for milk production in Sahiwal cattle

• DOI: 10.1071/an15347
• Publication Date: 2016-08-07T20:04:27Z
• Authors: V. Prakash; A. K. Gupta; A. Gupta; R. S. Gandhi; A. Singh; A. K. Chakravarty
• Keywords: Additive genetic effects; Akaike information criterion; Brown Swiss

The random regression test-day models can accelerate the genetic improvement of Sahiwal cattle as milk yield offer a faster, accurate and economical approach evaluation. First three lactation monthly records cows calved between 1961 2012 at ICAR-National Dairy Research Institute, Karnal were used to fit model (RRM) with various order legendre polynomial, constant (RRM-HOM) or heterogeneous residual variance (RRM-HET). For both RRM-HOM RRM-HET third polynomial for modelling additive effects found best. There was reduction in permanent environmental due assumption variance, sixth fourth fifth be best, effect. two eigenvalues coefficient matrix explained more than 99% variation, whereas four ~98% environment variations. eigenfunction from did not show any large change along lactation, suggesting most variation by genes that act same way during lactation. heritability estimates generally low but moderate some yields, it ranged 0.007 0.088 first, 0.044 0.279 second, 0.089 0.129 RRM-HOM. values correlation yields 0.06 0.99 0.77 0.07 value 0.30 0.98 0.16 correlations adjacent test-days high (≥0.90). also gave similar estimates. rank sire breeding estimated using 0.98, 1.00, 0.99, respectively, indicating there no difference ranking animals models. Thus lower effect higher animal suitable evaluation prediction cattle.