Show simple item record

dc.contributor.authorDunne, F. L.
dc.contributor.authorMcParland, Sinead
dc.contributor.authorKelleher, Margaret M.
dc.contributor.authorWalsh, S.W.
dc.contributor.authorBerry, Donagh
dc.date.accessioned2019-10-14T15:40:50Z
dc.date.available2019-10-14T15:40:50Z
dc.date.issued2019-04-10
dc.identifier.citation1. Dunne FL, McParland S, Kelleher MM, Walsh SW, Berry DP. How herd best linear unbiased estimates affect the progress achievable from gains in additive and nonadditive genetic merit. Journal of Dairy Science 2019;102(6):5295-5304; doi https://doi.org/10.3168/jds.2018-16119.en_US
dc.identifier.urihttp://hdl.handle.net/11019/1795
dc.descriptionpeer-revieweden_US
dc.description.abstractSustainable 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.en_US
dc.description.sponsorshipThis publication emanated from research supported in part by a research grant from Science Foundation Ireland (Dublin, Ireland) and the Department of Agriculture, Food and Marine on behalf of the Government of Ireland (Dublin, Ireland) under the Grant 16/RC/3835 (VistaMilk) as well as funding from the Irish Department of Agriculture, Food and the Marine STIMULUS research grant MultiRepro (Dublin, Ireland).
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofseriesJournal of Dairy Science;Vol. 102 (6)
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectbest linear unbiased estimatesen_US
dc.subjectdairyen_US
dc.subjectmanagementen_US
dc.subjectgenotype by environmenten_US
dc.titleHow herd best linear unbiased estimates affect the progress achievable from gains in additive and nonadditive genetic meriten_US
dc.typeArticleen_US
dc.embargo.terms2020-04-10en_US
dc.identifier.doihttps://doi.org/10.3168/jds.2018-16119
dc.contributor.sponsorScience Foundation Irelanden_US
dc.contributor.sponsorDepartment of Agriculture, Food and the Marineen_US
dc.contributor.sponsorGrantNumber16/RC/3835en_US


Files in this item

Thumbnail
Name:
How-herd-best-linear-unbiased- ...
Size:
325.2Kb
Format:
PDF
Description:
main article

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-ShareAlike 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 3.0 United States