A practical future-scenarios selection tool to breed for heat tolerance in Australian dairy cattle

Nguyen, Thuy T. T., Hayes, Ben J. and Pryce, Jennie E. (2017) A practical future-scenarios selection tool to breed for heat tolerance in Australian dairy cattle. Animal Production Science, 57 7: 1488-1493. doi:10.1071/AN16449


Author Nguyen, Thuy T. T.
Hayes, Ben J.
Pryce, Jennie E.
Title A practical future-scenarios selection tool to breed for heat tolerance in Australian dairy cattle
Journal name Animal Production Science   Check publisher's open access policy
ISSN 1836-5787
1836-0939
Publication date 2017-01-01
Sub-type Article (original research)
DOI 10.1071/AN16449
Open Access Status Not yet assessed
Volume 57
Issue 7
Start page 1488
End page 1493
Total pages 6
Place of publication Clayton, VIC, Australia
Publisher C S I R O Publishing
Language eng
Subject 1106 Food Science
1103 Animal Science and Zoology
Abstract Climate change will have an impact on dairy cow performance. When heat stressed, animals consume less feed, followed by a decline in milk yield. Previously, we have found that there is genetic variation in this decline. Selection for increased milk production, a major breeding objective, is expected to reduce heat tolerance (HT), as these traits are genetically unfavourably correlated. We aimed to develop a future-scenarios selection tool to assist farmers in making selection decisions, that combines the current national dairy selection index, known as the balanced performance index (BPI), with a proposed HT genomic estimated breeding value (GEBV). Heat-tolerance GEBV was estimated for 12 062 genotyped cows and 10 981 bulls, using an established genomic-prediction equation. Publicly available future daily average temperature and humidity data were used to estimate mean daily temperature–humidity index for each dairy herd. An economic estimate of an individual cow’s heat-tolerance breeding value (BV_HT) was calculated by multiplying head-tolerance GEBVs for milk, fat and protein by their respective economic values that are already used in the BPI. This was scaled for each region by multiplying BV_HT by the heat load, which is the temperature–humidity index units exceeding the threshold per year at a particular location. BV_HT were incorporated into the BPI as: BPI_HT = BPI + BV_HT; where BPI_HT is the ‘augmented BPI’ breeding value including HT. A web-based application was developed enabling farmers to predict the future heat load of a herd and take steps to aim at genetic improvement in future generations by selecting bulls and cows that rank high for the ‘augmented BPI’.
Keyword Climate change
Genomic selection
Online application
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Queensland Alliance for Agriculture and Food Innovation
 
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