Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-17T01:53:13.707Z Has data issue: false hasContentIssue false
  • English
  • Français

Appropriate selection indices for functional traits in dairy cattle breeding schemes

Published online by Cambridge University Press:  06 December 2018

Arash Chegini ,
Navid Ghavi Hossein-Zadeh ,
Seyed Hossein Hosseini Moghaddam  and
Abdol Ahad Shadparvar
Arash Chegini
Affiliation:
Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, P.O.Box:41635-1314, Rasht, Iran
Navid Ghavi Hossein-Zadeh
Affiliation:
Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, P.O.Box:41635-1314, Rasht, Iran
Seyed Hossein Hosseini Moghaddam*
Affiliation:
Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, P.O.Box:41635-1314, Rasht, Iran
Abdol Ahad Shadparvar
Affiliation:
Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, P.O.Box:41635-1314, Rasht, Iran
*
Authors for correspondence: Seyed Hossein Hosseini Moghaddam, Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The objective of this study was to establish different single or multiple trait selection indices to calculate genetic and economic gains by combining some production, reproduction and udder health traits in a population similar to the overall practical situation in Iran, with and without imposing restrictions on genetic change for some traits. The SelAction software was used to perform the analyses based on selection index theory through a deterministic model. Results indicated that among established indices, the index that showed the highest genetic gain for milk yield did not maximize the total genetic and economic gains. Rather, the index that included all production, reproduction and udder health traits yielded the highest genetic and economic gains. When we placed restriction on the selection indices, the economic gain decreased and the amount of reduction depended on the heritability and the correlation of restricted trait(s) with other traits. Generally, regarding the economic genetic gain per generation, the indices based on records of 200 offspring were 4.819% more efficient than those that used information of 100 offspring.

Keywords

Udder healthselection indexeconomic gaingenetic gainresponse to selection
Type
Research Article
Information
Journal of Dairy Research , Volume 86 , Issue 1 , February 2019 , pp. 13 - 18
Copyright
Copyright © Hannah Dairy Research Foundation 2018 

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.)

Footnotes

†

This research is a part of Ph.D. thesis of the first author.

References

Buttchereit, N, Stamer, E, Junge, W and Thaller, G (2011) Short communication: genetic relationships among daily energy balance, feed intake, body condition score, and fat to protein ratio of milk in dairy cows. Journal of Dairy Science 94, 1586–1591.Google Scholar
Chegini, A (2017) Genetic and economic aspects of mastitis in Holstein cows of Iran. PhD thesis, University of Guilan, Iran.Google Scholar
Christensen, LG (1998) Possibilities for genetic improvement of disease resistance, functional traits and animal welfare. Acta Agriculturae Scandinavica Section A–Animal Science 29, (Suppl.) 77–89.Google Scholar
Cunningham, EP, Moen, RA and Gjedrem, T (1970) Restriction of selection indexes. Biometrics 26, 67–74.Google Scholar
Dekkers, JCM (2003) Design and Economics of Animal Breeding Strategies. Iowa, USA: Iowa State University.Google Scholar
Fewson, D and Niebel, E (1986). Berücksichtigung indirekter Merkmale in Zuchtplänen für Zweinutzungsrinder. Züchtungskunde 58, 4–20.Google Scholar
Fuerst-Waltl, B, Fuerst, C, Obritzhauser, W and Egger-Danner, C (2016) Sustainable breeding objectives and possible selection response: finding the balance between economics and breeders’ preferences. Journal of Dairy Science 99, 1–14.Google Scholar
Ghiasi, H, Nejati-Javaremi, A, Pakdel, A and Gonzalez-Recio, O (2013) Selection strategies for fertility traits of Holstein cows in Iran. Livestock Science 152, 11–15.Google Scholar
Gonzalez-Recio, O, Alenda, R, Chang, Y, Weigel, K and Gianola, D (2006) Selection for female fertility using censored fertility traits and investigation of the relationship with milk production. Journal of Dairy Science 89, 4438–4444.Google Scholar
Hazel, LN (1943) The genetic basis for constructing selection indexes. Genetics 28, 476–490.Google Scholar
Kempthorne, O and Nordskog, AW (1959) Restricted selection indexes. Biometrics 15, 10–19.Google Scholar
Lassen, J, Sørensen, MK, Madsen, P and Ducrocq, V (2007) A stochastic simulation study on using different models for prediction of breeding values while changing the breeding goal. Animal: An International Journal of Animal Bioscience 1, 631–636.Google Scholar
Lindhe, B and Philipsson, J (1998) Genetic correlations between production with disease resistance and fertility in dairy cattle and consequences for total merit selection. Acta Agriculturae Scandinavica Section A–Animal Sience 48, 216–221.Google Scholar
Mathworks (2009a) MATLAB – The Language of Technical Computing – Getting Started with MATLAB Version 7.8.0. Natick, MA: The MathWorks.Google Scholar
Miglior, F, Muir, BL and Van Doormaal, BJ (2005) Selection indices in Holstein cattle of various countries. Journal of Dairy Science 88, 1255–1263.Google Scholar
Nielsen, HM, Christensen, LG and Groen, AF (2005) Derivation of sustainable breeding goals for dairy cattle using selection index theory. Journal of Dairy Science 88, 1882–1890.Google Scholar
Rutten, MJ and Bijma, P (2001) SelAction Manual. Wageningen, The Netherlands: University of Wageningen.Google Scholar
Sadeghi-Sefidmazgi, A, Moradi-Shahrbabak, M, Nejati-Javaremi, A and Shadparvar, A (2009) Estimation of economic values in three breeding perspectives for longevity and milk production traits in Holstein dairy cattle in Iran. Italian Journal of Animal Science 8, 359–375.Google Scholar
Sadeghi-Sefidmazgi, A, Moradi-Shahrbabak, M, Nejati-Javaremi, A, Miraei-Ashtiani, SR and Amer, PA (2011) Estimation of economic values and financial losses associated with clinical mastitis and somatic cell score in Holstein dairy cattle. Animal: An International Journal of Animal Bioscience 5, 33–42.Google Scholar
Sadeghi-Sefidmazgi, A, Moradi-Shahrbabak, M, Nejati-Javaremi, A, Miraei-Ashtiani, SR and Amer, PA (2012) Breeding objectives for Holstein dairy cattle in Iran. Journal of Dairy Science 95, 3406–3418.Google Scholar
Schaeffer, L (2014) Making covariance matrices positive definite. http://www.aps.uoguelph.ca/~Irs/ELARES/PDforce.pdf.Google Scholar
Sneddon, NW, Lopez-Villalobos, N, Davis, SR, Hickson, RE, Shalloo, L, Garrick, DJ and Geary, U (2016) Responses in lactose yield, lactose percentage and protein-to-protein plus-lactose ratio from index selection in New Zealand dairy cattle. New Zealand Journal of Agricultural Research 59, 90–105.Google Scholar
Sölkner, J, Miesenberger, J, Willam, A, Fürst, Ch and Baumung, R (2000) Total merit indices in dual purpose cattle. Archiv Tierzucht 43, 597–608.Google Scholar
Sørensen, MK, Berg, P, Jensen, J and Christensen, LG (1999) Stochastic simulation of breeding schemes for total merit in dairy cattle. GIFT seminar on genetic improvement of functional traits in cattle, Wageningen, The Netherlands, 7–9 November. Interbull Bulletin 23, 183–192.Google Scholar
Willam, A, Egger-Danner, C, Sölkner, J and Gierzinger, E (2002) Optimization of progeny testing schemes when functional traits play an important role in the total merit index. Livestock Production Science 77, 217–225.Google Scholar
Supplementary material: PDF

Chegini et al. supplementary material

Tables S1 and S2

Download Chegini et al. supplementary material(PDF)
PDF 20.5 KB