A practical future-scenarios selection tool to breed for heat tolerance in Australian dairy cattle
Thuy T. T. Nguyen A D , Ben J. Hayes A B and Jennie E. Pryce A CA BioSciences Research Division, Department of Economic Development, Jobs, Transport and Resources, AgriBio Building, 5 Ring Road, Bundoora, Vic. 3083, Australia.
B Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, St Lucia, Qld 4072, Australia.
C School of Applied Systems Biology, La Trobe University, Bundoora, Vic. 3083, Australia.
D Corresponding author. Email: thuy.nguyen@ecodev.vic.gov.au
Animal Production Science 57(7) 1488-1493 https://doi.org/10.1071/AN16449
Submitted: 15 July 2016 Accepted: 7 October 2016 Published: 2 December 2016
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’.
Additional keywords: climate change, genomic selection, online application.
References
Barnier J, Russell K, Bostock M, Lu S, Kokenes S (2016) ‘scatterD3: D3 JavaScript scatterplot from R. R package version 0.6.2.’ Available at http://CRAN.R-project.org/package=scatterD3 [Verified 1 March 2016]Bivand R, Lewin-Koh N (2015) ‘maptools: Tools for reading and handling spatial objects. R package version 0.8-37.’ Available at http://CRAN.R-project.org/package=maptools [Verified 1 March 2016]
Bivand R, Keitt T, Rowlingson B (2015) ‘rgdal: bindings for the geospatial data abstraction library. R package version 1.1-1.’ Available at http://CRAN.R-project.org/package=rgdal [Verified 1 March 2016]
Byrne TJ, Santos BFS, Am PR, Martin-Collado D, Pryce JE, Axford M (2016) Breeding objectives and indexes for the Australian dairy industry. Journal of Dairy Science 99, 8146–8167.
| Breeding objectives and indexes for the Australian dairy industry.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC28Xhtlagt7bF&md5=2619b93b287c47f0de2d49b3abb8ebfaCAS |
Chang W, Cheng J, Allaire JJ, Xie Y, McPherson J (2016) ‘shiny: Web application framework for R. R package version 0.13.2.’ Available at http://CRAN.R-project.org/package=shiny [Verified 1 March 2016]
Cheng J, Xie Y (2016) ‘leaflet: create interactive web maps with the JavaScript ‘Leaflet’ library. R package version 1.0.1.’ Available at http://CRAN.R-project.org/package=leaflet [Verified 1 March 2016]
CSIRO and BoM (2015) Climate change in Australia. Information for Australia’s Natural Resource Management Regions. Technical report. (CSIRO and Bureau of Meteorology: Melbourne, Australia)
Folman Y, Rosenberg M, Ascarelli I, Kaim M, Herz Z (1983) The effect of dietary and climatic factors on fertility, and on plasma progesterone and oestradiol-17 beta levels in dairy cows. Journal of Steroid Biochemistry 19, 863–868.
| The effect of dietary and climatic factors on fertility, and on plasma progesterone and oestradiol-17 beta levels in dairy cows.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaL3sXltlCjsrg%3D&md5=42105f922db8257ee0dc2b8aaee41ebfCAS |
Garner JB, Douglas M, Williams RSO, Wales WJ, Nguyen TTT, Hayes BJ (2016) Validation of genomic selection for heat tolerance in lactating dairy cattle. Scientific Reports 6, 34114
| Validation of genomic selection for heat tolerance in lactating dairy cattle.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC28XhsF2qtrbO&md5=d1c55c6caf56bf1dfc72bb7abe45bd08CAS |
Hayes BJ, Carrick M, Bowman P, Goddard ME (2003) Genotype × environment interaction for milk production of daughters of Australian dairy sires from test-day records. Journal of Dairy Science 86, 3736–3744.
| Genotype × environment interaction for milk production of daughters of Australian dairy sires from test-day records.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXptFaisrs%3D&md5=9df2a927d0afe9bbfc23a1c71c325af0CAS |
Meuwissen TH, Hayes BJ, Goddard ME (2016) Genomic selection: a paradigm shift in animal breeding. Animal frontiers 6, 6–14.
| Genomic selection: a paradigm shift in animal breeding.Crossref | GoogleScholarGoogle Scholar |
Monty DEJ, Wolf LK (1974) Summer heat stress and reduced fertility in Holstein–Friesan cows in Arizona. American Journal of Veterinary Research 35, 1495–1500.
Nguyen TTT, Bowman P, Haile-Mariam M, Pryce JE, Hayes BJ (2016) Genomic selection for heat tolerance in Australian dairy cattle. Journal of Dairy Science 99, 2849–2862.
| Genomic selection for heat tolerance in Australian dairy cattle.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC28XlsVWgt7o%3D&md5=d59a8eba8f27175bd692d8978ffc8085CAS |
R Core Team (2015) ‘R: a language and environment for statistical computing.’ (R Foundation for Statistical Computing: Vienna) Available at http://www.R-project.org/ [Verified 1 March 2016]
RStudio Team (2015) ‘RStudio: integrated development for R.’ (RStudio, Inc.: Boston, MA) Available at http://www.rstudio.com/ [Verified 1 March 2016]
St-Pierre NR, Cobanov B, Schnitkey G (2003) Economic losses from heat stress by US livestock industries. Journal of Dairy Science 86, E52–E77.
| Economic losses from heat stress by US livestock industries.Crossref | GoogleScholarGoogle Scholar |
West JW (1994) Interactions of energy and bovine somatotropin with heat stress. Journal of Dairy Science 77, 2091–2102.
| Interactions of energy and bovine somatotropin with heat stress.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK2cXksFKhurg%3D&md5=6333ee489da1ba999038f8bb485e7f11CAS |
Xie Y (2015) ‘DT: a wrapper of the JavaScript library ‘DataTables’. R package version 0.1.’ Available at http://CRAN.R-project.org/package=DT [Verified 1 March 2016]
