Genetic Analysis of Milk Yield in Iranian Holstein Cattle by the Test Day Model
محورهای موضوعی : Camel
ی. نادری
1
(
Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
)
N. امام جمعه کاشان
2
(
Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
)
ر. واعظ ترشیزی
3
(
Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
)
م. امین افشار
4
(
Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
)
کلید واژه: milk yield, random regression, legendre polynomials,
چکیده مقاله :
Using monthly test day records the genetic parameters of Iranian Holstein cattle in first lactation were studied. Data of 277400 test-day milk records from 65320 cows and 2210 sires were analyzed by an animal random regression model using restricted maximum likelihood methodology. The model included herd-test-date, interaction between year-season of calving, days in milk (linear and quadratic) and dam age (linear and quadratic) as fixed effects and random regression coefficients for additive genetic and permanent environmental effects. The average of 305 days milk yield was 9760 (±1324) kilogram. Differences of milk yield among provinces were significant (P<0.05). The average of heritability estimates of milk was 0.50. The genetic correlations between adjacent test-day records were high and decreased with increase in interval between tests.
Biassus I.D.O., Cobuci J.A., Costa J.A., Rorato P.R.N., Neto J.B. and Cardoso L.L. (2010). Persistence in milk, fat and protein production of primiparousHolstein cows by random regression models. Brazilian J. Zootec. 39(12), 2617-2624.
Burnham P.K. and Anderson R.D. (2002). Model Selection and Inference: A Practical Information Theoretic Approach. Springer Verlag, New York, NY.
Druet T., Jaffrezic F. and Ducrocq V. (2003). Modeling of lactation curves and estimation of genetic parameters for first lactation test-day records of French Holstein cows. J. Dairy Sci. 86, 2480-2490.
Hammami H., Rekik B., Soyeurt H., Ben Gara A. and Gengler N. (2008). Genetic parameters for Tunisian Holsteins using a test-day random regression model.J. Dairy Sci. 91, 2118-2126.
Jamrozik J. and Schaeffer L. (1997). Estimates of genetic parameters for a test day model with random regression for yield traits of first lactation Holsteins. J. Dairy Sci. 80, 762-770.
Kirkpatrick M., Lofsvold D. and Bulmer M. (1990). Analysis of the inheritance, selection and evolution of growth trajectories. Genetics. 124, 979-993.
Lidauer M., Mantisaari E.A. and Stranden I. (2003). Comparison of test-day models for genetic evaluation of production traits in dairy cattle. Livest. Prod. Sci. 79, 73-86.
Mayeres P., Stool J., Boormann J., Reents R. and Gengler N. (2004). Prediction of daily milk, fat and protein production by a random regression test-day model.J. Dairy Sci. 87, 1925-1933.
Melo C., Packer I.U., Costa C.N. and Machado P.F. (2007). Genetic parameters for test day milk yields of first lactation Holstein cows by random regression models. Animal. 1, 325-334.
Meyer K. (2000). Random regressions to model phenotypic variation in monthly weights of Australian beef cows. Livest. Prod. Sci. 65(1), 19-38.
Meyer K. (2005). Advances in methodology for random regression analyses.Australian J. Exp. Agric. 45, 847-858.
Meyer K. (2007). WOMBAT - a tool for mixed model analysis in quantitative genetics by REML. J. Zhejiang Univ. Sci. 8, 815-821.
Misztal I. (2006). Properties of random regression models using linear splines. J. Anim. Breed. Genet. 123, 74-80.
Mostert B.E., Theron H.E., Kanfer F.H.J. and Van Marle-Koster E. (2006). Adjustment for heterogeneous variances and a calving year effect in test-day models for national genetic evaluation of dairy cattle in South Africa. South African J. Anim. Sci. 36, 165-174.
Mrode R. and Coffey M. (2008). Understanding cow evaluations in univariate and multivariate animal and random regression models. J. Dairy Sci. 91, 794-801.
Olori V.E., Hill W.G., McGuirk B.J. and Brotherstone S. (1999). Estimating variance components for test day milk records by restricted maximum likelihood with a random regression animal model. Livest. Prod. Sci. 61, 53-63.
SAS Institute. (2005). SAS®/STAT Software, Release 9.1. SAS Institute, Inc., Cary, NC. USA.
Schaeffer L.R. and Dekkers J.C.M. (1994). Random regressions in animal models for test-day production in dairy cattle. Pp. 443-446 in Proc. 5th World Congr. Genet. Appl. Livest. Prod., Guelph, Canada.
Schaeffer L.R., Jamrozik J., Kistemaker G.J. and Van Doormaal B.J. (2000). Experience with a test-day model. J. Dairy Sci. 83, 1135-1144.
Schaeffer L.R. and Jamrozik J. (2008). Random regression models: a longitudinal perspective. J. Anim. Breed. Genet. 125, 145-146.
Strabel T. and Misztal I. (1999). Genetic parameters for first and second lactation milk yield of Polish black and white cattle with random regression test-day models. J. Dairy Sci. 82, 2805-2810.
Swalve H.H. and Guo Z. (1999). An illustration of lactation curves stratified by lactation yields within herd. Arch. Tierz. 42, 515-525.
Van der Werf M. (1997). Random Regressions in Animal Breeding.aCourseanotesaavailableaat:
http://www.personal.une.Edu.au/~jvanderw/CFcoursenotes.pdf. Accessed Dec. 2009.
Zavandilova L., Jamrozik J. and Schaeffer L.R. (2005). Genetic parameters for test day model with random regression for production traits of Czech Holstein cattle.Czech. J. Anim. Sci. 50, 142-154.
