|Goddard, M - AGBU-UNE, AUSTRALIA|
Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: February 10, 1997
Publication Date: N/A
Interpretive Summary: The current evaluation system for determining genetic merit of dairy animals is based on estimates of yield during the first 305 days of lactation. These lactation yields typically are estimated from monthly measurements of milk volumes and analysis of milk samples for fat and protein percentages. Recent work with data collected for each test day has confirmed that genetic evaluations from a model based on test-day information are expected to be more accurate because of better accounting for environmental effects. These evaluations also should be more stable because of accounting for genetic differences in maturity rate and persistency. An approach for implementation of a multiple-trait test-day model for calculation of genetic evaluations for yield traits of U.S. dairy cattle was developed. This model has the capacity to provide breeders with more accurate evaluations on which to base selection decisions. In addition, because any number and distribution of test days as well as different frequencies of recording volume and components can be accommodated by an evaluation system based on a test-day model, data from a wide variety of milk recording and component sampling plans can be included, which will aid dairy producer efforts to reduce operational costs.
Technical Abstract: Possible benefits from use of a test-day model for genetic evaluation of yield traits include 1) more accurate estimation of environmental effects from including the influence of particular days of recording, 2) optimal use of information from all test days (especially for lactations with long intervals from calving to first test or between tests), 3) greater stability of bull evaluations from accounting for genetic differences among daughters in shape of lactation curve and maturity rate, and 4) improved accuracy of evaluations for component yields through contributions from information for milk yield. A multitrait analysis with 60 traits [3 yield traits (milk, fat, and protein), 2 parity groups (first and later) per trait, and 10 stages of lactation per parity] could provide these benefits. A computation approach is to estimate test-day effects within herd before analysis across herd, reduce rank of the genetic (co)variance matrix, use a canonical transformation with missing values replaced by their expectations, apply a repeatability model, and use 305-day records as historical data.