Modelling Lactation Curve for Genetic Evaluation of Crossbred Cattle

Abstract

The present study was conducted on first lactation 4415 monthly test-day milk yield records of 466 crossbred (CB) cattle sired by 89 bulls during 2000–2018 (19 years) maintained at Directorate of Livestock Farms, GADVASU, Ludhiana, Punjab, India. The crossbred cattle with lactation length of minimum 100 days were considered for the study. The aim was to develop the best lactation curve model and to compare the breeding values of sires based on actual and predicted first lactation 305-day milk yield (FL305DMY). The data were classified and coded according to different season and age at first calving groups for first lactation 305-day milk yield. Monthly test-day milk yields of first lactation were used to develop the best lactation curve model. The Polynomial Regression Function (PRF) model was the best model amongst all the models based on both R2 and RMSE values. The breeding values of 60 HF crossbred sires with two or more daughters were estimated from the actual and predicted FL305DMY using Polynomial Regression Function by applying two sire evaluation methods viz. least squares method (LSQ) and restricted maximum likelihood method (REML). The effectiveness of LSQ and REML methods of sire evaluation was compared on the basis of Spearman’s rank correlations, coefficients of determination (R2) and coefficients of variation (CV). LSQ was found most efficient and accurate method for sire evaluation using actual and predicted first lactation 305-day milk yield in HF crossbred cattle.