Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-24T21:38:21.467Z Has data issue: false hasContentIssue false
  • English
  • Français

Multiple-trait multiple-country genetic evaluation of Holstein bulls for female fertility and milk production traits

Published online by Cambridge University Press:  19 May 2014

M. A. Nilforooshan ,
J. H. Jakobsen ,
W. F. Fikse ,
B. Berglund  and
H. Jorjani
M. A. Nilforooshan*
Affiliation:
Interbull Centre, Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, 750 07 Uppsala, Sweden Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, 750 07 Uppsala, Sweden
J. H. Jakobsen
Affiliation:
Interbull Centre, Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, 750 07 Uppsala, Sweden Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, 750 07 Uppsala, Sweden
W. F. Fikse
Affiliation:
Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, 750 07 Uppsala, Sweden
B. Berglund
Affiliation:
Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, 750 07 Uppsala, Sweden
H. Jorjani
Affiliation:
Interbull Centre, Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, 750 07 Uppsala, Sweden Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, 750 07 Uppsala, Sweden
*
†E-mail: [email protected]
Get access

Abstract

The aim of this study was to investigate the effect of including milk yield data in the international genetic evaluation of female fertility traits to reduce or eliminate a possible bias because of across-country selection for milk yield. Data included two female fertility traits from Great Britain, Italy and the Netherlands, together with milk yield data from the same countries and from the United States, because the genetic trends in other countries may be influenced by selection decisions on bulls in the United States. Potentially, female fertility data had been corrected nationally for within-country selection and management biases for milk yield. Using a multiple-trait multiple across-country evaluation (MT-MACE) for the analysis of female fertility traits with milk yield, across-country selection patterns both for female fertility and milk yield can be considered simultaneously. Four analyses were performed; one single-trait multiple across-country evaluation analysis including only milk yield data, one MT-MACE analysis including only female fertility traits, and one MT-MACE analysis including both female fertility and milk yield traits. An additional MT-MACE analysis was performed including both female fertility and milk yield traits, but excluding the United States. By including milk yield traits to the analysis, female fertility reliabilities increased, but not for all bulls in all the countries by trait combinations. The presence of milk yield traits in the analysis did not considerably change the genetic correlations, genetic trends or bull rankings of female fertility traits. Even though the predicted genetic merits of female fertility traits hardly changed by including milk yield traits to the analysis, the change was not equally distributed to the whole data. The number of bulls in common between the two sets of Top 100 bulls for each trait in the two analyses of female fertility traits, with and without the four milk yield traits and their rank correlations were low, not necessarily because of the absence of the US milk yield data. The joint international genetic evaluation of female fertility traits with milk yield is recommended to make use of information on several female fertility traits from different countries simultaneously, to consider selection decisions for milk yield in the genetic evaluation of female fertility traits for obtaining more accurate estimating breeding values (EBV) and to acquire female fertility EBV for bulls evaluated for milk yield, but not for female fertility.

Keywords

female fertilitymilk yieldgenetic trendbiasdairy cattle
Type
Full Paper
Information
animal , Volume 8 , Issue 6 , June 2014 , pp. 887 - 894
Copyright
© The Animal Consortium 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Benhajali, H, Jakobsen, JH, Mattalia, S and Ducrocq, V 2013. Illustration of an international genetic evaluation robust to inconsistencies of genetic trends in national evaluations. In Proceedings of the 2013 Interbull Meeting, 23-25 August 2013, Nantes, France. Interbull Bulletin 47, 82–89.Google Scholar
Biffani, S and Canavesi, F 2007. International genetic evaluation for female fertility traits in dairy cattle. Italian Journal of Animal Science 6, 47–49.Google Scholar
Biffani, S, Marusi, M, Biscarini, F and Canavesi, F 2005. Developing a genetic evaluation for fertility using angularity and milk yield as correlated traits. In Proceedings of the 2005 Interbull Meeting, 2-4 June 2005, Uppsala, Sweden. Interbull Bulletin 33, 63–66.Google Scholar
de Jong, G 2005. Usage of predictors for fertility in the genetic evaluation, application in the Netherlands. In Proceedings of the 2005 Interbull Meeting, 2-4 June 2005, Uppsala, Sweden. Interbull Bulletin 33, 69–73.Google Scholar
Ducrocq, V, Delaunay, I, Boichard, D and Mattalia, S 2003. A general approach for international genetic evaluations robust to inconsistencies of genetic trends in national evaluations. In Proceedings of the 2003 Interbull Technical Workshop, 2-3 March 2003, Beltsville, MD, USA. Interbull Bulletin 30, 101–111.Google Scholar
Fikse, WF and Banos, G 2001. Weighting factors of sire daughter information in international genetic evaluations. Journal of Dairy Science 84, 1759–1767.Google Scholar
Henderson, CR 1975. Best linear unbiased estimation and prediction under a selection model. Biometrics 31, 423–447.Google Scholar
Interbull 2004. Interbull code of practice: weighting factors for the international genetic evaluation. Retrieved April 11, 2014, from https://wiki.interbull.org/public/CoP_AppendixIV?action=print&rev=25 Google Scholar
Interbull 2008. Interbull code of practice: method of international evaluation. Retrieved April 11, 2014, from https://wiki.interbull.org/public/CoP_chapter5?action=print&rev=16 Google Scholar
Interbull 2009. Description of national genetic evaluation systems as applied in different Interbull member countries. Retrieved April 11, 2014, from https://wiki.interbull.org/public/Nat_GE_Forms?action=print Google Scholar
Jairath, L, Dekkers, JCM, Schaeffer, LR, Liu, Z, Burnside, EB and Kolstad, B 1998. Genetic evaluation for herd life in Canada. Journal of Dairy Science 81, 550–562.Google Scholar
Jakobsen, JH and Hjerpe, E 2006. Interbull checks of incoming data. In Proceedings of the 2006 Interbull Technical Workshop, 2-3 March 2006, Wageningen, The Netherlands. Interbull Bulletin 34, 11–15.Google Scholar
Jorjani, H 2006. International genetic evaluation for female fertility traits. In Proceedings of the 2006 Interbull Technical Workshop, 2-3 March 2006, Wageningen, The Netherlands. Interbull Bulletin 34, 57–64.Google Scholar
Kadarmideen, HN, Thompson, R, Coffey, MP and Kossaibati, MA 2003. Genetic parameters and evaluations from single- and multiple-trait analysis of dairy cow fertility and milk production. Livestock Production Science 81, 183–195.Google Scholar
Klei, L 1998. Solving MACE equations. In Proceedings of the 1998 Interbull Meeting, 18-19 January 1998, Rotorua, New Zealand. Interbull Bulletin 17, 3–7.Google Scholar
Klei, L and Weigel, KA 1998. A method to estimate correlations among traits in different countries using data on all bulls. In Proceedings of the 1998 Interbull Meeting, 18-19 January 1998, Rotorua, New Zealand. Interbull Bulletin 17, 8–14.Google Scholar
Liu, Z, Reinhardt, F, Bünger, A and Reents, R 2004. Derivation and calculation of approximate reliabilities and daughter yield deviations of a random regression test-day model for genetic evaluation of dairy cattle. Journal of Dairy Science 87, 1896–1907.Google Scholar
Mark, T and Sullivan, PG 2006. Multiple-trait multiple-country genetic evaluations for udder health traits. Journal of Dairy Science 89, 4874–4885.CrossRefGoogle ScholarPubMed
Mrode, RA 2005. Linear models for the prediction of animal breeding values, 2nd edition. CABI Publishing, Wallingford, UK.Google Scholar
Mrode, RA and Coffey, MP 2009. Genetic analysis of evaluations for female fertility with production included. In Proceedings of the 2009 Interbull Meeting, 21-24 August 2009, Barcelona, Spain. Interbull Bulletin 40, 17–20.Google Scholar
Nilforooshan, MA 2011. Multiple-trait multiple country genetic evaluation of fertility traits in dairy cattle. PhD, Swedish University of Agricultural Sciences, Uppsala, Sweden.Google Scholar
Nilforooshan, MA, Jakobsen, JH, Fikse, WF, Berglund, B and Jorjani, H 2010. Application of a multiple-trait multiple-country genetic evaluation model for female fertility traits. Journal of Dairy Science 93, 5977–5986.Google Scholar
NRS (The Royal Dutch Cattle Syndicate) 2009. Breeding value estimation of female fertility. Chapter E-17, Handbook NRS. NRS, Arnhem, the Netherlands.Google Scholar
Pollak, EJ, van der Werf, J and Quaas, RL 1984. Selection bias and multiple trait evaluation. Journal of Dairy Science 67, 1590–1595.Google Scholar
Schaeffer, LR 1984. Sire and cow evaluation under multiple trait models. Journal of Dairy Science 67, 1567–1580.Google Scholar
Schaeffer, LR 1994. Multiple-country comparison of dairy sires. Journal of Dairy Science 77, 2671–2678.Google Scholar
Schaeffer, LR, Schenkel, FS and Fries, LA 1998. Selection bias on animal model evaluation. Proceedings of the 6th World Congress on Genetics Applied to Livestock Production, 11–16 January, Armidale, Australia, pp. 501–-508.Google Scholar
Sewalem, A and Kistemaker, G 2008. Including production in female fertility evaluations. In Proceedings of the 2008 Interbull Meeting, 16-19 June 2008, Niagara Falls, USA. Interbull Bulletin 38, 44–47.Google Scholar
Sullivan, PG and Wilton, JW 2001. Multiple-trait MACE with a variable number of traits per country. In Proceedings of the 2001 Interbull Meeting, 30-31 August 2001, Budapest, Hungary. Interbull Bulletin 27, 68–72.Google Scholar
Sullivan, PG, Wilton, JW, Schaeffer, LR, Jansen, GJ, Robinson, JAB and Allen, OB 2005. Genetic evaluation strategies for multiple traits and countries. Livestock Production Science 92, 195–205.Google Scholar
Sun, C, Madsen, P, Lund, MS, Zhang, Y, Nielsen, US and Su, G 2010. Improvement in genetic evaluation of female fertility in dairy cattle using multiple-trait models including milk production traits. Journal of Animal Science 88, 871–878.Google Scholar
Supplementary material: Image

Nilforooshan Supplementary Material

Figure S1

Download Nilforooshan Supplementary Material(Image)
Image 1.1 MB
Supplementary material: Image

Nilforooshan Supplementary Material

Figure S2

Download Nilforooshan Supplementary Material(Image)
Image 1.1 MB
Supplementary material: Image

Nilforooshan Supplementary Material

Figure S3

Download Nilforooshan Supplementary Material(Image)
Image 367.1 KB