Submitted to: Interbull Annual Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 8/26/1999
Publication Date: N/A
Citation: Interpretive Summary: Test day models provide more accurate estimation of genetic effects in dairy cattle because they better account for the environmental effects on test day, and allow for genetic differences in the shape of the lactation curve. In order to do test day model evaluations, we must know the genetic and phenotypic relationships among the test day yields. Test days close to one another are closely related. (Co)variance functions are a compact way to represent these relationships with a reduced number of parameters. This study estimated these parameters across lactation thereby providing estimates of the relationships among test day yields both within and across lactation. These estimates will be used in a test day model which will provide estimates of genetic differences in persistency, a measure of the distribution of a cows milk yield within lactation, and rate of maturity, an indication of the genetic difference between first and later lactation yield. It is computationally demanding to estimate so many relationships simultaneously. This study included milk, fat, and protein yields. This study should assist in implementation of a full test day model which will improve accuracy of genetic evaluations of milk yield.
Technical Abstract: (Co)variance components for milk, fat, and protein yields during first and second, representing later, lactations were estimated from data for test days from 23,029 Holstein cows from 37 herds in Pennsylvania and Wisconsin. Four lactation stages of 75 d were defined in each lactation, and the test day nearest the center of each interval was used. A total of 9110 observations were available for the final analysis of lactations with test days in all four lactation stages. Data were preadjusted for lactation curves within lactation stages using all available records. (Co)variance functions were used to describe the (co)variance structure within and across yield trait and parity. (Co)variance components of biological functions (305-d yields, persistency, defined as difference between yields on days 280 and 60, and maturity rate, defined as difference between second and first lactation yields) were developed from (co)variance functions. Results provide only a first indication of the (co)variance structure within and across lactations. Procedures that can accommodate more data are needed.