Prediction of random-regression coefficient for daily milk yield after 305 days in milk by using the regression-coefficient estimates from the first 305 days

• Published in: Animal bioscience Vol. 31; no. 10; pp. 1542 - 1549
• Main Authors: Yamazaki, Takeshi, Takeda, Hisato, Hagiya, Koichi, Yamaguchi, Satoshi, Sasaki, Osamu
• Format: Journal Article
• Language: English
• Published: Korea (South) Asian - Australasian Association of Animal Production Societies 01-10-2018; Asian-Australasian Association of Animal Production Societies (AAAP) and Korean Society of Animal Science and Technology (KSAST); Asian-Australasian Association of Animal Production Societies; 아세아·태평양축산학회
• Subjects: Dairy Cattle; Holstein Friesian cattle; Lactation; Lactation Curve; Milk production; Milk Yield; Random Regression Model; Veterinary research; 축산학; Lactation curve; Random regression model; Dairy cattle; Milk yield
• ISSN: 1011-2367; 2765-0189; 1976-5517; 2765-0235
• DOI: 10.5713/ajas.17.0861

Because lactation periods in dairy cows lengthen with increasing total milk production, it is important to predict individual productivities after 305 days in milk (DIM) to determine the optimal lactation period. We therefore examined whether the random regression (RR) coefficient from 306 to 450 DIM (M2) can be predicted from those during the first 305 DIM (M1) by using a random regression model.
We analyzed test-day milk records from 85690 Holstein cows in their first lactations and 131727 cows in their later (second to fifth) lactations. Data in M1 and M2 were analyzed separately by using different single-trait RR animal models. We then performed a multiple regression analysis of the RR coefficients of M2 on those of M1 during the first and later lactations.
The first-order Legendre polynomials were practical covariates of random regression for the milk yields of M2. All RR coefficients for the additive genetic (AG) effect and the intercept for the permanent environmental (PE) effect of M2 had moderate to strong correlations with the intercept for the AG effect of M1. The coefficients of determination for multiple regression of the combined intercepts for the AG and PE effects of M2 on the coefficients for the AG effect of M1 were moderate to high. The daily milk yields of M2 predicted by using the RR coefficients for the AG effect of M1 were highly correlated with those obtained by using the coefficients of M2.
Milk production after 305 DIM can be predicted by using the RR coefficient estimates of the AG effect during the first 305 DIM.