Estimation of genetic parameters of milk and fat yields using different animal models in Iranian Holstein dairy cattle


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Authors

  • MOHAMMAD REZA BAHREINI BEHZADI Yasouj University, Yasouj 75918–74831 Iran
  • ZAHRA MEHRPOOR Yasouj University, Yasouj 75918–74831 Iran

https://doi.org/10.56093/ijans.v87i9.74320

Keywords:

Animal model, Dairy cattle, Fat yield, Genetic parameter, Milk yield

Abstract

Milk and fat yield records of the first 4 lactations from 58 herds of Holstein cows recorded from 1999 to 2015 were analyzed. Genetic parameters were estimated by using univariate, repeatability and multivariate animal models. The fixed effects of herd-year-season as contemporary group and age at calving as covariate were fitted in the model of analyses. Heritability estimates under univariate model for lactations 1 to 4 were 0.28, 0.20, 0.19 and 0.13 for milk yield and for fat yield were 0.15, 0.12, 0.12 and 0.05, respectively. The repeatability and heritability values under the repeatability model for milk and fat yield were 0.48 and 0.24, and 0.38 and 0.17, respectively. Estimated heritabilities using a bivariate model for lactations 1 to 4 were 0.29, 0.20, 0.20 and 0.15 for milk yield and 0.12, 0.11, 0.13 and 0.05 for fat yield, respectively. Heritability estimates for lactations 1 to 4 by multivariate model were 0.30, 0.23, 0.22 and 0.19 for milk yield, and 0.17, 0.16, 0.17 and 0.12 for fat yield, respectively. Genetic, phenotypic and environmental correlations of milk yield were 0.91, 0.50 and 0.36 for first and second lactation; and 0.87, 0.45 and 0.31 for first and third lactation; and 0.79, 0.39 and 0.26 for first and fourth lactation; and 0.97, 0.53 and 0.41 for second and third lactation; and 0.91, 0.50 and 0.39 for second and fourth lactation; and 0.96, 0.56 and 0.46 for third and fourth lactation, respectively. These parameters for fat yield were 0.87, 0.35, 0.25 and 0.91, 0.29, 0.16 and 0.83, 0.26, 0.17 and 0.96, 0.45, 0.35 and 0.90, 0.40, 0.32 and 0.98, 0.47, 0.38, respectively. Results from this study confirm that multivariate analysis is recommended for estimating the genetic parameter of milk and fat yield, because it considers the effects of selection bias of the first lactation.

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References

Abdallah J M and McDaniel B T. 2000. Genetic parameters and trends of milk, fat, days open, and body weight after calving in North Carolina experimental herds. Journal of Dairy Science 83: 1364–70. DOI: https://doi.org/10.3168/jds.S0022-0302(00)75004-1

Albuquerque L G, Keown J F and Van Vleck L D. 1996. Genetic parameters of milk, fat, and protein yield in the three lactations, using an animal model and restricted maximum likelihood. Brazilian Journal of Genetics 191: 79–86.

Alijani S, Jasouri M, Pirany N and Kia H D. 2012. Estimation of variance components for some production traits of Iranian Holstein dairy cattle using Bayesian and AI-REML methods. Pakistan Veterinary Journal 32: 562–66.

Al-Seaf A, Keown J F and Van Vleck L D. 2007. Genetic parameters for yield traits of cows treated or not treated with bovine somatotropin. Journal of Dairy Science 90: 501–06. DOI: https://doi.org/10.3168/jds.S0022-0302(07)72652-8

Bahreini Behzadi M R, Amini A, Aslaminejad A A and Tahmoorespour M. 2013. Estimation of genetic parameters for production traits of Iranian Holstein dairy cattle. Livestock Research for Rural Development 25(9): 1–7.

Banik S and Gandhi R S. 2006. Animal model versus conventional methods of sire evaluation in Sahiwal cattle. Asian Australasian Journal of Animal Science 19(9): 1225–28. DOI: https://doi.org/10.5713/ajas.2006.1225

Banik S and Gandhi R S. 2007. Effectiveness of DFREML versus conventional methods of Sahiwal sire evaluation. Indian Journal of Animal Sciences 77(11): 1143–47.

Banik S and Gandhi R S. 2010. Estimation of genetic parameters in Sahiwal cattle using single and multi-trait restricted maximum likelihood method. Indian Journal of Animal Sciences 80(3): 266 – 68.

Barash H, Silanikove N and Weller J I. 1996. Effect of season of birth on milk, fat, and protein production of Israeli Holsteins. Journal of Dairy Science 79: 1016–20. DOI: https://doi.org/10.3168/jds.S0022-0302(96)76453-6

Boujenane I. 2002. Estimates of genetic and phenotypic parameters for milk production in Moroccan Holstein-Friesian cows. Revue D’Elevage Et De Medecine Veterinaire Des Pays Tropicaux 55: 63–67. DOI: https://doi.org/10.19182/remvt.9848

Butcher D F and Freeman A E. 1968. Heritabilities and repeatabilities of milk and milk fat production by lactations. Journal of Dairy Science 51: 1387–91. DOI: https://doi.org/10.3168/jds.S0022-0302(68)87200-5

Dedkova L and Wolf J. 2001. Estimation of genetic parameters for milk production traits in Czech dairy cattle population. Czech Journal of Animal Science 46: 298–307.

Dematawewa C M B and Berger P J. 1998. Genetic and phenotypic parameters for 305–day yield, fertility and survival in Holsteins. Journal of Dairy Science 81: 2700–09. DOI: https://doi.org/10.3168/jds.S0022-0302(98)75827-8

De Vries M J, Van der Waaij E H and Van Arendonk J A M. 1998. Estimation of genetic parameters for litter size in sheep: A comparison of repeatability and a multivariate model. Animal Science 66: 685–88. DOI: https://doi.org/10.1017/S1357729800009255

Dong M C, Van Vleck L D and Wiggans G R. 1988. Effect of relationships on estimation of variance components with an animal model and restricted maximum likelihood. Journal of Dairy Science 71: 3047–52. DOI: https://doi.org/10.3168/jds.S0022-0302(88)79904-X

Ducrocq V, Boichard D, Bonaiti B, Barbat A and Briend M. 1990. A pseudo-absorption strategy for solving animal model equations for large data files. Journal of Dairy Science 73: 1945–55. DOI: https://doi.org/10.3168/jds.S0022-0302(90)78873-X

Gianola D and Sorensen D. 2004. Quantitative genetic models for describing simultaneous and recursive relationships between phenotypes. Genetics 167: 1407–24. DOI: https://doi.org/10.1534/genetics.103.025734

Jones L P and Goddard M E. 1990. Five years experience with the animal model for dairy cattle evaluations in Australia. 4th World Congress on Genetics Applied to Livestock Production, Edinburgh, Scotland, July 27, 13: 382–85.

Laben R C, Shanks R D, Berger P J and Preemaa A E. 1982. Factors affecting milk yield and reproductive performance. Journal of Dairy Science 65: 1005–15. DOI: https://doi.org/10.3168/jds.S0022-0302(82)82302-3

Meyer K. 1984. Estimates of genetic parameters for milk and fat yield for the first three lactations in British Friesian cows. Animal Production 38: 313–32. DOI: https://doi.org/10.1017/S0003356100041519

Meyer K. 1985. Genetic parameters for dairy production of Australian black and white cows. Livestock Production Science 12: 205–19. DOI: https://doi.org/10.1016/0301-6226(85)90051-X

Meyer K. 2007. WOMBAT – A tool for mixed model analyses in quantitative genetics by REML. Journal of Zhejiang University Science B 8: 815–21. DOI: https://doi.org/10.1631/jzus.2007.B0815

Mrode R A. 2005. Linear models for the prediction of animal breeding values (2nd ed). CAB International, Wallingford, UK. Palacios A, Espinoza L J, Pena G D, Iglesias G D, Rafael L P and Almeida R F. 2007. Estimation of covariance components for the first four lactations in Holstein cattle according to different models. Zootecnia Tropical 25: 9–18. DOI: https://doi.org/10.1079/9780851990002.0025

Raheja K L, Burnside E B and Schaeffer L R. 1989. Relationships between fertility and production in Holstein dairy cattle in different lactations. Journal of Dairy Science 72: 2670–78. DOI: https://doi.org/10.3168/jds.S0022-0302(89)79408-X

SAS, 2009. User’s guide, Release 9.2. SAS Institute Inc., Cary, NC, USA.

Schaeffer L R. 1999. Multiple traits animal models. Guelph University. http://www.aps.uoguelph.ca/lrs/Animalz/lesson15.

Tong A K W, Kennedy B W and Moxley J E. 1979. Heritabilities and genetic correlations for the first three lactations from records subject to culling. Journal of Dairy Science 62: 1784– 90. DOI: https://doi.org/10.3168/jds.S0022-0302(79)83497-9

Urioste J I, Rekaya R, Gianola D, Fikse W F and Weigel K A. 2003. Model comparison for genetic evaluation of milk yield in Uruguayan Holsteins. Livestock Production Science 84: 63– 73. DOI: https://doi.org/10.1016/S0301-6226(03)00051-4

Visscher P M and Thompson R. 1992. Univariate and multivariate parameter estimates for milk production traits using an animal model. I: Description and result of REML analyses. Genetic Selection Evolution 24: 415–30. DOI: https://doi.org/10.1186/1297-9686-24-5-415

Weller J I, Ron M and Bar-Anan R. 1987. Effects of persistency and production on the genetic parameters of milk and fat yield in Israeli-Holsteins. Journal of Dairy Science 70: 672–80. DOI: https://doi.org/10.3168/jds.S0022-0302(87)80057-7

Wiggans G R, Misztal I and Van Vleck L D. 1988a. Implementation of an animal model for genetic evaluation of dairy cattle in the United States. Journal of Dairy Science 71: (suppl 2): 54–69. DOI: https://doi.org/10.1016/S0022-0302(88)79979-8

Wiggans G R, Misztal I and Van Vleck L D. 1988b. Animal model evaluation of Ayrshire milk yield with all lactations, herd-sire interaction, and groups based on unknown parents. Journal of Dairy Science 71: 1319–29. DOI: https://doi.org/10.3168/jds.S0022-0302(88)79689-7

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2017-09-13

Published

2017-09-14

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How to Cite

BEHZADI, M. R. B., & MEHRPOOR, Z. (2017). Estimation of genetic parameters of milk and fat yields using different animal models in Iranian Holstein dairy cattle. The Indian Journal of Animal Sciences, 87(9), 1106–1110. https://doi.org/10.56093/ijans.v87i9.74320
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