Improving the accuracy of predictions for cow survival by multivariate evaluation model
M. Khansefid
A C , M. Haile-Mariam A and J. E. Pryce A B
A Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Vic. 3083, Australia.
B School of Applied Systems Biology, La Trobe University, Bundoora, Vic. 3083, Australia.
C Corresponding author. Email: majid.khansefid@agriculture.vic.gov.au
Animal Production Science - https://doi.org/10.1071/AN21128
Submitted: 4 March 2021 Accepted: 12 May 2021 Published online: 7 July 2021
Journal compilation © CSIRO 2021 Open Access CC BY-NC-ND
Abstract
Context: Cow survival measures the ability of cows to survive from the current to subsequent lactation. In addition to economic gain, genetic selection for survival could improve animal welfare by increasing the adaptability and resilience of the cows to both environmental and health challenges. However, survival is a complex trait because it results from a diverse range of reasons for culling of cows from the herd. Consequently, the accuracy of genetic predictions of direct survival are often low.
Aims: Our aim was to increase the accuracy of predictions of survival in Holstein and Jersey sires by including important predictor traits in multi-trait evaluation models.
Methods: Phenotypic and genetic correlations between survival trait deviations (TDs) and 35 routinely measured traits (including milk yield, fertility and type traits) were estimated using bivariate sire models. Survival TDs for 538 394 Holstein and 63 839 Jersey cows were used in our study; these cows or their close relatives also had milk, fertility and type traits records between 2002 and 2019. These genetic parameters were required to assess the potential usefulness of predictor traits for the prediction of survival.
Key results: Survival was genetically correlated with milk, fat and protein yields, overall type, composite mammary system and fertility TDs in both Holstein and Jersey. Further, most of the type traits related to feet and legs, and rump, were also correlated with survival TDs in Jersey. For sires, the accuracy of predictions for survival increased by 0.05 for Holsteins (from 0.54 to 0.59) and for Jerseys (from 0.48 to 0.53) through the use of multivariate models compared with univariate models.
Conclusions: Survival was genetically associated with traits affecting voluntary and involuntary culling and when included in multi-trait genetic evaluation models, they moderately improved the accuracy of genetic prediction of survival.
Implications: Predictor traits can be used to increase the accuracy of predictions of survival through the use of multi-trait models. The inclusion of breed-specific predictor traits should be considered, especially for Jerseys in genetic evaluations of survival.
Additional keywords: longevity, functional traits, conformation traits, milk traits, dairy cattle.
References
Barkema HW, von Keyserlingk MAG, Kastelic JP, Lam TJGM, Luby C, Roy JP, LeBlanc SJ, Keefe GP, Kelton DF (2015) Invited review: changes in the dairy industry affecting dairy cattle health and welfare. Journal of Dairy Science 98, 7426–7445.| Invited review: changes in the dairy industry affecting dairy cattle health and welfare.Crossref | GoogleScholarGoogle Scholar | 26342982PubMed |
Boettcher PJ, Dekkers JCM, Warnick LD, Wells SJ (1998) Genetic analysis of clinical lameness in dairy cattle. Journal of Dairy Science 81, 1148–1156.
| Genetic analysis of clinical lameness in dairy cattle.Crossref | GoogleScholarGoogle Scholar | 9594404PubMed |
Byrne TJ, Santos BFS, Am PR, Martin-Collado D, Pryce JE, Axford M (2016) New breeding objectives and selection indices for the Australian dairy industry. Journal of Dairy Science 99, 8146–8167.
| New breeding objectives and selection indices for the Australian dairy industry.Crossref | GoogleScholarGoogle Scholar | 27522425PubMed |
Chamberlain T (2012) Understanding the economics of dairy farming Part 1: income, costs and profit. Livestock (London) 17, 30–33.
| Understanding the economics of dairy farming Part 1: income, costs and profit.Crossref | GoogleScholarGoogle Scholar |
DataGene (2021a) ‘Survival and Australian breeding indices: Technote 4.’ Available at https://datagene.com.au/sites/default/files/Upload%20Files/Technote%204%20Survival%20and%20Australian%20Breeding%20Indices.pdf [Verified 15 February 2021]
DataGene (2021b) ‘Type ABVs explained: Genetics Backgrounder # 2.’ Available at https://datagene.com.au/sites/default/files/Upload%20Files/02%20Type%20ABVs%20explained%20Mar%202020_0.pdf [Verified 15 March 2021]
Forabosco F, Jakobsen JH, Fikse WF (2009) International genetic evaluation for direct longevity in dairy bulls. Journal of Dairy Science 92, 2338–2347.
| International genetic evaluation for direct longevity in dairy bulls.Crossref | GoogleScholarGoogle Scholar | 19389992PubMed |
Fuerst-Waltl B, Sørensen MK (2010) Genetic analysis of calf and heifer losses in Danish Holstein. Journal of Dairy Science 93, 5436–5442.
| Genetic analysis of calf and heifer losses in Danish Holstein.Crossref | GoogleScholarGoogle Scholar | 20965359PubMed |
Garnsworthy PC (2004) The environmental impact of fertility in dairy cows: a modelling approach to predict methane and ammonia emissions. Animal Feed Science and Technology 112, 211–223.
| The environmental impact of fertility in dairy cows: a modelling approach to predict methane and ammonia emissions.Crossref | GoogleScholarGoogle Scholar |
Gilmour AR, Gogel BJ, Cullis BR, Welham S, Thompson R (2015) ‘ASReml user guide release 4.1 structural specification.’ (VSN International Ltd: Hemel Hempstead, UK) Available at www.vsni.co.uk
Giordano JO, Fricke PM, Wiltbank MC, Cabrera VE (2011) An economic decision-making support system for selection of reproductive management programs on dairy farms. Journal of Dairy Science 94, 6216–6232.
| An economic decision-making support system for selection of reproductive management programs on dairy farms.Crossref | GoogleScholarGoogle Scholar | 22118110PubMed |
Haile-Mariam M, Pryce JE (2015) Variances and correlations of milk production, fertility, longevity, and type traits over time in Australian Holstein cattle. Journal of Dairy Science 98, 7364–7379.
| Variances and correlations of milk production, fertility, longevity, and type traits over time in Australian Holstein cattle.Crossref | GoogleScholarGoogle Scholar | 26254520PubMed |
Haile-Mariam M, Pryce JE (2019) Genetic evaluation of gestation length and its use in managing calving patterns. Journal of Dairy Science 102, 476–487.
| Genetic evaluation of gestation length and its use in managing calving patterns.Crossref | GoogleScholarGoogle Scholar | 30343913PubMed |
Haile-Mariam M, Bowman PJ, Goddard ME (2003) Genetic and environmental relationship among calving interval, survival, persistency of milk yield and somatic cell count in dairy cattle. Livestock Production Science 80, 189–200.
| Genetic and environmental relationship among calving interval, survival, persistency of milk yield and somatic cell count in dairy cattle.Crossref | GoogleScholarGoogle Scholar |
Haile-Mariam M, Pryce J, Schrooten C, Hayes B (2015) Including overseas performance information in genomic evaluations of Australian dairy cattle. Journal of Dairy Science 98, 3443–3459.
| Including overseas performance information in genomic evaluations of Australian dairy cattle.Crossref | GoogleScholarGoogle Scholar | 25771052PubMed |
Hansen M, Madsen P, Jensen J, Pedersen J, Christensen LG (2003) Genetic parameters of postnatal mortality in Danish Holstein calves. Journal of Dairy Science 86, 1807–1817.
| Genetic parameters of postnatal mortality in Danish Holstein calves.Crossref | GoogleScholarGoogle Scholar | 12778591PubMed |
Ooi E, Stevenson MA, Beggs DS, Mansell PD, Pryce JE, Murray A, Pyman MF (2021) Herd manager attitudes and intentions regarding the selection of high-fertility EBV sires in Australia. Journal of Dairy Science 104, 4375–4389.
| Herd manager attitudes and intentions regarding the selection of high-fertility EBV sires in Australia.Crossref | GoogleScholarGoogle Scholar | 33485678PubMed |
Pritchard T, Coffey M, Mrode R, Wall E (2013) Understanding the genetics of survival in dairy cows. Journal of Dairy Science 96, 3296–3309.
| Understanding the genetics of survival in dairy cows.Crossref | GoogleScholarGoogle Scholar | 23477814PubMed |
Pryce JE, Bell MJ (2017) The impact of genetic selection on greenhouse-gas emissions in Australian dairy cattle. Animal Production Science 57, 1451–1456.
| The impact of genetic selection on greenhouse-gas emissions in Australian dairy cattle.Crossref | GoogleScholarGoogle Scholar |
Sasaki O (2013) Estimation of genetic parameters for longevity traits in dairy cattle: a review with focus on the characteristics of analytical models. Animal Science Journal 84, 449–460.
| Estimation of genetic parameters for longevity traits in dairy cattle: a review with focus on the characteristics of analytical models.Crossref | GoogleScholarGoogle Scholar | 23607602PubMed |
van der Heide EMM, Veerkamp RF, van Pelt ML, Kamphuis C, Ducro BJ (2020) Predicting survival in dairy cattle by combining genomic breeding values and phenotypic information. Journal of Dairy Science 103, 556–571.
| Predicting survival in dairy cattle by combining genomic breeding values and phenotypic information.Crossref | GoogleScholarGoogle Scholar | 31704017PubMed |
Wathes DC, Brickell JS, Bourne NE, Swali A, Cheng Z (2008) Factors influencing heifer survival and fertility on commercial dairy farms. animal 2, 1135–1143.
| Factors influencing heifer survival and fertility on commercial dairy farms.Crossref | GoogleScholarGoogle Scholar | 22443725PubMed |
Weigel KA, Palmer RW, Caraviello DZ (2003) Investigation of factors affecting voluntary and involuntary culling in expanding dairy herds in Wisconsin using survival Analysis. Journal of Dairy Science 86, 1482–1486.
| Investigation of factors affecting voluntary and involuntary culling in expanding dairy herds in Wisconsin using survival Analysis.Crossref | GoogleScholarGoogle Scholar | 12741574PubMed |
Workie ZW, Gibson JP, van der Werf JHJ (2021) Analysis of culling reasons and age at culling in Australian dairy cattle. Animal Production Science 61, 680–689.
| Analysis of culling reasons and age at culling in Australian dairy cattle.Crossref | GoogleScholarGoogle Scholar |
Zavadilová L, Němcová E, Štípková M (2011) Effect of type traits on functional longevity of Czech Holstein cows estimated from a Cox proportional hazards model. Journal of Dairy Science 94, 4090–4099.
| Effect of type traits on functional longevity of Czech Holstein cows estimated from a Cox proportional hazards model.Crossref | GoogleScholarGoogle Scholar | 21787944PubMed |
