Type

Journal Article

Authors

Donagh P. Berry
S W Walsh
Margaret M. Kelleher
Sinead McParland
F. L. Dunne

Subjects

Agriculture & Food Science

Topics
performance metrics dairy best linear unbiased estimates milk yield genetic merit genotype by environment management estimated breeding value

How herd best linear unbiased estimates affect the progress achievable from gains in additive and nonadditive genetic merit (2019)

Abstract Sustainable dairy cow performance relies on coevolution in the development of breeding and management strategies. Tailoring breeding programs to herd performance metrics facilitates improved responses to breeding decisions. Although herd-level raw metrics on performance are useful, implicitly included within such statistics is the mean herd genetic merit. The objective of the present study was to quantify the expected response from selection decisions on additive and nonadditive merit by herd performance metrics independent of herd mean genetic merit. Performance traits considered in the present study were age at first calving, milk yield, calving to first service, number of services, calving interval, and survival. Herd-level best linear unbiased estimates (BLUE) for each performance trait were available on a maximum of 1,059 herds, stratified as best, average, and worst for each performance trait separately. The analyses performed included (1) the estimation of (co)variance for each trait in the 3 BLUE environments and (2) the regression of cow-level phenotypic performance on either the respective estimated breeding value (EBV) or the heterosis coefficient of the cow. A fundamental assumption of genetic evaluations is that 1 unit change in EBV equates to a 1 unit change in the respective phenotype; results from the present study, however, suggest that the realization of the change in phenotypic performance is largely dependent on the herd BLUE for that trait. Herds achieving more yield, on average, than expected from their mean genetic merit, had a 20% greater response to changes in EBV as well as 43% greater genetic standard deviation relative to herds within the worst BLUE for milk yield. Conversely, phenotypic performance in fertility traits (with the exception of calving to first service) tended to have a greater response to selection as well as a greater additive genetic standard deviation within the respective worst herd BLUE environments; this is suggested to be due to animals performing under more challenging environments leading to larger achievable gains. The attempts to exploit nonadditive genetic effects such as heterosis are often the basis of promoting cross-breeding, yet the results from the present study suggest that improvements in phenotypic performance is largely dependent on the environment. The largest gains due to heterotic effects tended to be within the most stressful (i.e., worst) BLUE environment for all traits, thus suggesting the heterosis effects can be beneficial in mitigating against poorer environments.
Collections Ireland -> Teagasc -> Livestock Systems
Ireland -> Teagasc -> Animal & Bioscience
Ireland -> Teagasc -> Animal & Grassland Research & Innovation Programme

Full list of authors on original publication

Donagh P. Berry, S W Walsh, Margaret M. Kelleher, Sinead McParland, F. L. Dunne

Experts in our system

1
D P Berry
Teagasc
Total Publications: 243
 
2
S. W. Walsh
Teagasc
Total Publications: 21
 
 
4
S McParland
Teagasc
Total Publications: 44