Analysis of Test Day Milk Yield by Random Regression Models and Evaluation of Persistency in Iranian Dairy Cows

Subject Areas : Camel

M. Elahi Torshizi ¹ , A.A. Aslamenejad ² , M.R. Nassiri ³ , H. Farhangfar ⁴ , J. Solkner ⁵ , M. Kovac ⁶ , G. Meszaros ⁷ , S. Malovrh ⁸

1 - Department of Animal Science, Mashhad Branch, Islamic Azad University, Mashhad, Iran
2 - Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashahd, Iran
3 - Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashahd, Iran
4 - Department of Animal Science, Faculty of Agriculture, Birjand University, Birjand, Iran
5 - Department of Sustainable Agricultural Systems, University of Natural Resources and Applied Life Science, Vienna, Austria
6 - Department of Animal Science, Biotechnical Faculty, University of Ljubljana, 1230, Domžale, Slovenia
7 - Department of Sustainable Agricultural Systems, University of Natural Resources and Applied Life Science, Vienna, Austria
8 - Department of Animal Science, Biotechnical Faculty, University of Ljubljana, 1230, Domžale, Slovenia

Received: 2012-09-28 Accepted : 2012-05-01 Published : 2013-03-01

Keywords: legendre polynomial, random regression, persistency, estimation breeding value,

Abstract :

Variace / covariance components of 227118 first lactaiom test-day milk yield records belonged to 31258 Iranian Holstein cows were estimated using nine random regression models. Afterwards, different measures of persistency based on estimation breeding value were evaluated. Three functions were used to adjust fixed lactation curve: Ali and Schaeffer (AS), quadratic (LE3) and cubic (LE4) order of Legendre polynomial but for random effects, unequal order of Legendre polynomials (LE3, LE4, LE5 and Ali and Schaffer) functions were evaluated. Heterogeneous residual variance considered during days in milk and evaluation of models was based on eigenvalues and associated eigenvectors and residual variance. Model with Ali and Schaeffer function for fixed part and LE3 and LE4 for additive and permanent environmental effects was selected as the best model for random regression analysis in first parity dairy cows. The highest and lowest heritability were observed in the middle (0.29) and beginning (0.08) of lactation, respectively. Persistency measurement proposed by Cobuci (PSY1) (difference between estimation breeding value between 290 and 90 days) was preferential for using in further genetic evaluations for persistency in milk yield of Iranian Holstein cows.

References:

Ali T.E. and Schaeffer L.R. (1987). Accounting for covarainces among test day milk yields in dairy cows. Can. J. Anim. Sci. 67, 637-644.
Biassus I.D.O., Cobuci J.A., Costa J.A., Rorato P.R.N., Neto J.B. andCardoso L.L. (2010). Persistence in milk, fat and protein production of primiparous Holstein cows by random regression models. BrazilianJ. Zootec. 39(12), 2617-2624.
Cobuci J.A., Euclydes R.F., Costa C.N., Torres R.A.andCarmenn S.P. (2007). Genetic evaluation for persistency of lactation in Holstein cows using a random regression model. Gene. Mol. Biol. 30, 349-355.
Costa C.N., De Melo C., Packer I.U., Freitas A., Teixeira N.andCobuci J.A. (2008). Genetic parameters for test day milk yield of first lactation Holstein cows estimated by random regression using Legendre polynomials. R. Bras. Zootec. 4, 602-608.
Dekkers I.C.M., Jamrozik J., Hag J.H., Schaeffer L.R. andWeersink A. (1996). Genetic and economic evaluation of persistency in dairy cattle. Workshop on genetic improvement of functional traits in cattle. Gembloux, Belgium, Interbull Bulletin. 12, 97-102.
Druet T., Jaffrézic F. andDucrocq 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.
Dzomba E.F., Nephawe K.A., Maiwashe A.N., Cote S.W.P., Chimonyo M., Banga C.B. Muller C. andDzama K. (2010). Random regression test day model for analysis of dairy cattle production data in South Africa: creating the framewoerk. South African J. Anim. Sci. 40(4), 273-284.
Gengler N. (1996). Persistency of lactation yields: A review. Interbull Bulletin. 12, 97-102.
Groeneveld E., KovacM. and WangT. (2002). PEST User’s Guide and Reference Manual. Version 4.2.3. Department of Animal Science, University of Illinois.
Grossman M., Hartz S.M. andKoops W.J. (1999). Persistency of lactation yield: A novel approach. J. Dairy Sci. 82, 2192-2197.
Jakobsen J.H., Madsen P., Jensen J., Pedersen J., Christensen L.G.andSorensen D.A. (2002). Genetic parameters for milk production and persistency for Danish Holstein estimated in random regression models using REML. J. Dairy Sci. 85,1607-1616.
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.
Jensen J. (2001). Genetic evaluation of dairy cattle using test day models. J. Dairy. Sci. 84, 2803-2812.
Kirkpatrick M., Lofsvold D.andBulmer M. (1990). Analysis of the inheritance, selection and evolution of growth trajectories. Genetics. 124, 979-993.
Kirkpatrick M., Thompson R. and Hill W.G. (1994). Estimation of covariance structure of traits during growth and aging, illustrated with lactation in dairy cattle. Genet. Res. 64, 57-69.
Kovac M. andGroeneveld E. (2008). VCE-6 User’s Guide and Reference Manual. Version 6. Biotechnical Faculty, Department of Animal Science, University of Ljubljana, Slovenia.
Liu Y.X., Zhang J., Schaeffer L.R., Ynag R.Q. and Zhang W.L. (2006). Short communication: optimal random regression models for milk production in dairy cattle. J. Dairy Sci. 89, 2233-2235.
Olori V.E., Hill W.G., Mcguirk B.J. andBrotherstone 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. (2005). Statistics Analysis System User’s Guide, (Release 9.1). SAS Institute Inc., Cary, North Carolina, USA.
Schaeffer L.R. (2004). Application of random regression models in animal breeding. Livest. Prod. Sci. 86, 35-45.
Sölkner J. andFuchs W. (1987). A comparison of different measures of persistency with special respect to variation of test-day milk yields. Livest. Prod. Sci. 16, 305-319.
Strabel T., Kopacki W. andSzwaczkowski T. (2001). Genetic evaluation of persistency in random regression test day models. Interbull Bulletin. 27, 189-192.
Swalve H.H. (2000). Theoretical basis and computational methods for different test-day genetic evaluation methods. J. Dairy Sci. 83, 1115-1124.
Togashi K. andLin C.Y. (2006). Selection for milk production and persistency using eigenvectors of the random regression coefficient matrix. J. Dairy Sci. 89,4866-4873.
Togashi K., Lin C.Y., Atagi Y., Hagiya K. andNakanishi T. (2008). Genetic characteristics of Japanese Holstein cows based on multiple lactation random regression test day animal model. J. Dairy Sci. 114, 194-201.
Zimmermann E. andSommer H. (1973). Zum laktationsverlauf von kuhen in hocheistung-sherden und dessen beeinflussung durch nichterbliche faktoren. Zuchtungskunde. 45, 75-88.