Asian-Australasian Journal of Animal Sciences

  • 제33권11호
  • /
  • Pages.1741-1754
  • /
  • 2020
  • /
  • 1011-2367(pISSN)
  • /
  • 1976-5517(eISSN)
아세아태평양축산학회 (Asian Australasian Association of Animal Production Societies)
권호 표지
DOI QR코드

DOI QR Code

Genetic parameters for milk yield in imported Jersey and Jersey-Friesian cows using daily milk records in Sri Lanka

  • Samaraweera, Amali Malshani (Animal Genetics & Breeding Unit, a joint venture between NSW Department of Agriculture and University of New England, University of New England) ;
  • Boerner, Vinzent (Animal Genetics & Breeding Unit, a joint venture between NSW Department of Agriculture and University of New England, University of New England) ;
  • Cyril, Hewa Waduge (National Livestock Development Board) ;
  • Werf, Julius van der (School of Environmental and Rural Science, University of New England) ;
  • Hermesch, Susanne (Animal Genetics & Breeding Unit, a joint venture between NSW Department of Agriculture and University of New England, University of New England)
  • 투고 : 2019.10.15
  • 심사 : 2020.02.06
  • 발행 : 2020.11.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Objective: This study was conducted to estimate genetic parameters for milk yield traits using daily milk yield records from parlour data generated in an intensively managed commercial dairy farm with Jersey and Jersey-Friesian cows in Sri Lanka. Methods: Genetic parameters were estimated for first and second lactation predicted and realized 305-day milk yield using univariate animal models. Genetic parameters were also estimated for total milk yield for each 30-day intervals of the first lactation using univariate animal models and for daily milk yield using random regression models fitting second-order Legendre polynomials and assuming heterogeneous residual variances. Breeding values for predicted 305-day milk yield were estimated using an animal model. Results: For the first lactation, the heritability of predicted 305-day milk yield in Jersey cows (0.08±0.03) was higher than that of Jersey-Friesian cows (0.02±0.01). The second lactation heritability estimates were similar to that of first lactation. The repeatability of the daily milk records was 0.28±0.01 and the heritability ranged from 0.002±0.05 to 0.19±0.02 depending on day of milk. Pearson product-moment correlations between the bull estimated breeding values (EBVs) in Australia and bull EBVs in Sri Lanka for 305-day milk yield were 0.39 in Jersey cows and -0.35 in Jersey-Friesian cows. Conclusion: The heritabilities estimated for milk yield in Jersey and Jersey-Friesian cows in Sri Lanka were low, and were associated with low additive genetic variances for the traits. Sire differences in Australia were not expressed in the tropical low-country of Sri Lanka. Therefore, genetic progress achieved by importing genetic material from Australia can be expected to be slow. This emphasizes the need for a within-country evaluation of bulls to produce locally adapted dairy cows.

키워드

참고문헌

  1. Economic and Social Statistics of Sri Lanka. Sri Lanka, Colombo: Central Bank of Sri Lanka;2018.
  2. Ducrocq V, Laloe D, Swaminathan M, Rognon X, Tixier-Boichard M, Zerjal T. Genomics for ruminants in developing countries: from principles to practice. Front Genet 2018;9:251. https://doi.org/10.3389/fgene.2018.00251
  3. Hoffmann I. Climate change and the characterization, breeding and conservation of animal genetic resources. Anim Genet 2010;41(Suppl 1):32-46. https://doi.org/10.1111/j.1365-2052. 2010.02043.x
  4. Vernooij AG, Houwers HWJ, Zijlstra J. Old friends- new trends: emerging business opportunities in the dairy sector of Sri Lanka. Wageningen, The Netherlands: Wageningen UR Livestock Research; 2015.
  5. Carlstrom C, Strandberg E, Johansson K, Pettersson G, Stalhammar H, Philipsson J. Genetic associations of in-line recorded milkability traits and udder conformation with udder health. Acta Agric Scand A Anim Sci 2016;66:84-91. https://doi.org/10.1080/09064702.2016.1260154
  6. Schaeffer LR, Jamrozik J. Multiple-trait prediction of lactation yields for dairy cows. J Dairy Sci 1996;79:2044-55. https://doi.org/10.3168/jds.S0022-0302(96)76578-5
  7. Elgersma GG, de Jong G, van der Linde R, Mulder HA. Fluctuations in milk yield are heritable and can be used as a resilience indicator to breed healthy cows. J Dairy Sci 2018;101:1240-50. https://doi.org/10.3168/jds.2017-13270
  8. Farm from Ambalangoda Ridiyagama Farm [Internet]. Colombo, Sri Lanka: National Livestock Development Board; c2019 [cited 2019 Jun 25]. Available from: http://www.nldb.gov.lk/RidiyagamaFarm.html
  9. Wood PDP. Algebraic model of the lactation curve in cattle. Nature 1967;216:164-5. https://doi.org/10.1038/216164a0
  10. Sneddona NW, Lopez-Villalobos N, Davisb SR, Hicksona RE, Shallooc L, Garricka DJ. Supply curves for yields of dairy products from first-lactation Holstein Friesian, Jersey and Holstein Friesian-Jersey crossbred cows accounting for seasonality of milk composition and production. In: Proceedings of the New Zealand Society of Animal Production; 2017.
  11. R Core Team. R: A language and environment for statistical computing. [Internet]. Vienna, Austria: R Foundation for Statistical Computing; c2017 [cited 2017 Jan 1]. Available from: https://www.R-project.org/
  12. Boerner V, Tier B. BESSiE: a software for linear model BLUP and Bayesian MCMC analysis of large-scale genomic data. Genet Sel Evol 2016;48:63. https://doi.org/10.1186/s12711-016-0241-x
  13. Sorensen D, Gianola D. Likelihood, Bayesian, and MCMC methods in quantitative genetics. New York, USA: Springer Science & Business Media; 2002. pp. 576-88.
  14. Meyer K. WOMBAT-A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). J Zhejiang Univ Sci B 2007;8:815-21. https://doi.org/10.1631/jzus.2007.B0815
  15. Akaike H. A new look at the statistical model identification. In: Parzen E, Tanabe K, Kitagawa G, editors. Selected papers of Hirotugu Akaike. New York, USA: Springer;1974. pp. 215-22.
  16. Schwarz G. Estimating the dimension of a model. Ann Stat 1978;6:461-4. https://doi.org/doi:10.1214/aos/1176344136
  17. Kollalpitiya KMPMB, Premaratne S, Peiris BL. Reproductive and productive performance of up-country exotic dairy cattle breeds of Sri Lanka. Trop Agric Res 2012;23:319-26. https://doi.org/10.4038/tar.v23i4.4867
  18. Smith DL, Smith T, Rude BJ, Ward SH. Short communication: comparison of the effects of heat stress on milk and component yields and somatic cell score in Holstein and Jersey cows. J Dairy Sci 2013;96:3028-33. https://doi.org/10.3168/jds.2012-5737
  19. Heins BJ, Hansen LB, Seykora AJ. Production of pure Holsteins versus crossbreds of Holstein with Normande, Montbeliarde, and Scandinavian red. J Dairy Sci 2006;89:2799-804. https://doi.org/10.3168/jds.S0022-0302(06)72356-6
  20. DairyNZ. More milk per cow, fewer cows: latest dairy stats [Internet]. c2017 [cited 2018 Oct 18]. Available from: https://www.dairynz.co.nz/news/latest-news/more-milk-per-cow-fewer-cows-latest-dairy-stats/
  21. Roche JR, Berry DP, Kolver ES. Holstein-Friesian strain and feed effects on milk production, body weight, and body condition score profiles in grazing dairy cows. J Dairy Sci 2006; 89:3532-43. https://doi.org/10.3168/jds.S0022-0302(06)72393-1
  22. Ojango JMK, Pollott GE. Genetics of milk yield and fertility traits in Holstein-Friesian cattle on large-scale Kenyan farms. J Anim Sci 2001;79:1742-50. https://doi.org/10.2527/2001.7971742x
  23. Buvanendran V, Petersen PH. Genotype-environment interaction in milk production under Sri Lanka and Danish conditions. Acta Agric Scand https://doi.org/10.1080/000151 28009435283
  24. Musani SK, Mayer M. Genetic and environmental trends in a large commercial Jersey herd in the Central Rift Valley, Kenya. Trop Anim Health Prod 1997;29:108-16. https://doi.org/10.1007/BF02632330
  25. Haile-Mariam M, Bowman PJ, Goddard ME. Genetic and environmental relationship among calving interval, survival, persistency of milk yield and somatic cell count in dairy cattle. Livest Prod Sci 2003;80:189-200. https://doi.org/10.1016/S0301-6226(02)00188-4
  26. Prakash V, Gupta AP, Singh M, Ambhore GS, Singh A, Gandhi RS. Random regression test-day milk yield models as a suitable alternative to the traditional 305-day lactation model for genetic evaluation of Sahiwal cattle. Indian J Anim Sci 2017;87:340-4.
  27. Mackinnon MK, Thorpe W, Baker RL. Sources of genetic variation for milk production in a crossbred herd in the tropics. Anim Sci 1996;62:5-16. https://doi.org/10.1017/S1357729 800014259
  28. Prakash V, Gupta AK, Gupta A, Gandhi RS, Singh A, Chakravarty AK. Random regression model with heterogeneous residual variance reduces polynomial order fitted for permanent environmental effect but does not affect genetic parameters for milk production in Sahiwal cattle. Anim Prod Sci 2016;57:1022-30. https://doi.org/10.1071/AN15347
  29. Hill WG, Edwards MR, Ahmed MKA, Thompson R. Heritability of milk yield and composition at different levels and variability of production. Anim Sci 1983;36:59-68. https://doi.org/10.1017/S0003356100039933
  30. Dematawewa CMB, Berger PJ. Genetic and phenotypic parameters for 305-day yield, fertility, and survival in Holsteins. J Dairy Sci 1998;81:2700-9. https://doi.org/10.3168/jds.S0022- 0302(98)75827-8
  31. Singh A, Singh A, Singh M, et al. Estimation of genetic parameters for first lactation monthly test-day milk yields using random regression test day model in Karan fries cattle. Asian-Australas J Anim Sci 2016;29:775-81. https://doi.org/10.5713/ajas.15.0643
  32. Samaraweera AM, Boerner V, Cyril HW, van der Werf JHJ, Hermesch S. Genetic parameters for milk yield, persistency, conductivity and milking efficiency in first lactation Jersey cows in Sri Lanka. Auckland, New Zealand: Proceedings of the World Congress on Genetics Applied to Livestock Production; 2018
  33. Zwald NR, Weigel KA, Chang YM, Welper RD, Clay JS. Genetic evaluation of dairy sires for milking duration using electronically recorded milking times of their daughters. J Dairy Sci 2005;88:1192-8. https://doi.org/10.3168/jds.S0022-0302 (05)72785-5
  34. Povinelli M, Gallo L, Carnier P, Marcomin D, Zotto RD, Cassandro M. Genetic aspects of milk electrical conductivity in Italian Brown cattle. Ital J Anim Sci 2005;4:169-71. https://doi.org/10.4081/ijas.2005.3s.169
  35. Carlstrom C, Pettersson G, Johansson K, Strandberg E, Stalhammar H, Philipsson J. Feasibility of using automatic milking system data from commercial herds for genetic analysis of milkability. J Dairy Sci 2013;96:5324-32. https://doi.org/10.3168/jds.2012-6221
  36. Windig JJ, Calus MPL, Veerkamp RF. Influence of herd environment on health and fertility and their relationship with milk production. J Dairy Sci 2005;88:335-47. https://doi.org/10.3168/jds.S0022-0302(05)72693-X
  37. Bignardi AB, El Faro L, Cardoso VL, Machado PF, de Albuquerque LG. Random regression models to estimate test-day milk yield genetic parameters Holstein cows in Southeastern Brazil. Livest Sci 2009;123:1-7. https://doi.org/10.1016/j.livsci.2008.09.021
  38. Calus MPL. Estimation of genotype $\times$ environment interaction for yield, health and fertility in dairy cattle. Wageningen, The Netherlands: Wageningen University; 2006.
  39. Schaeffer LR. Random regression models. Random regression in animal breeding course notes. Ontario, Canada: University of Guelph;1997. pp. 1-15.
  40. Druet T, Jaffrezic F, Boichard D, Ducrocq V. Modeling lactation curves and estimation of genetic parameters for first lactation test-day records of French Holstein cows. J Dairy Sci 2003;86:2480-90. https://doi.org/10.3168/jds.S0022-0302(03)73842-9
  41. Canaza-Cayo AW, Lopes PS, da Silva MVGB, et al. Genetic parameters for milk yield and lactation persistency using random regression models in Girolando cattle. Asian-Australas J Anim Sci 2015;28:1407-18. https://doi.org/10.5713/ajas.14.0620
  42. Aliloo H, Pryce JE, Gonzalez-Recio O, Cocks BG, Hayes BJ. Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits. Genet Sel Evol 2016;48:8. https://doi.org/10.1186/s12711-016-0186-0