Prediction of Lifetime Milk Yield using Principal Component Analysis in Gir Cattle

Abstract

The objective of the research was to investigate the relationship among production traits i.e., lactation milk yield, lactation length  and lactation peak milk yield of the first three lactations using principal component analysis and formulation of prediction equation  to predict lifetime milk production in Gir cattle. Data were from multiparous dairy cows of the University farm. Principal component  analysis with correlation matrix was used to find the relationship among lactation milk yield, lactation length and lactation peak milk  yield of first three lactation and other fixed effects, including the year of calving, season and parity with random effect of sire. The  principal components were fitted to identify the best-fitted model for predicting lifetime milk yield using all principal components as  a predictor in different combinations. The first six principal components (first lactation milk yield, lactation length and peak milk yield,  second lactation milk yield, lactation length and peak milk yield), explained 98% variation in the estimated values with adjusted R2=  59.85% variation in the estimated values. The curve estimation analysis revealed that the first six principal components as the predictor  was the most fitting model for predicting lifetime milk yield. The prediction equation found most fitted will be useful for the selection  of Gir cattle at an early stage of lactation.