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Genetic parameters for milkability from the first three lactations in Fleckvieh cows

Published online by Cambridge University Press:  01 March 2009

J. Dodenhoff  and
R. Emmerling
J. Dodenhoff*
Affiliation:
Bavarian State Research Center for Agriculture, Institute of Animal Breeding, 85586 Poing, Germany
R. Emmerling
Affiliation:
Bavarian State Research Center for Agriculture, Institute of Animal Breeding, 85586 Poing, Germany
*
E-mail: Joerg.Dodenhoff@LfL.bayern.de
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Abstract

Test-day records for average flow rate (AFR) from the routine dairy recording from Bavarian Fleckvieh cows were analysed. Two data sets with observations on approximately 20 000 cows each were sampled from the total data set. For the estimation of variance parameters, a two-step approach was applied. In a first step multiple-trait restricted maximum likelihood (REML) analyses were carried out. For each of the first three lactations, six time periods with up to 33 days were defined. An algorithm for iterative summing of expanded part matrices was applied in order to combine the estimates. In a second step covariance functions (CF) for additive-genetic variances and non-genetic animal variances were derived using second-order Legendre polynomials plus an exponential term. Estimates of test-day heritability for AFR ranged from 0.21 to 0.40, and were largest in lactation 1. For lactations 1 and 3, heritabilities decreased considerably towards the end of lactation. Genetic correlation estimates within lactation decreased as the distance between days in milk (DIM) increased. Genetic correlations between corresponding DIM in the three lactations were generally large, ranging from 0.80 to 0.99. The largest estimates were found between DIM from lactations 2 and 3. Results from this study suggest that including AFR data from second and third lactations in genetic evaluation systems could the improve accuracy of genetic selection.

Keywords

Fleckviehcattlegenetic parametersmilkability
Type
Full Paper
Information
animal , Volume 3 , Issue 3 , March 2009 , pp. 329 - 335
Copyright
Copyright © The Animal Consortium 2008

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References

Bagnato, A, Rossoni, A, Maltecca, C, Vigo, D, Ghiroldi, S 2003. Milkability traits recorded with flowmeters in Italian Brown Swiss. Conference at the 54th Annual Meeting of the European Association for Animal Production, Rome, Italy, 2pp.Google Scholar
Boettcher, PJ, Dekkers, JCM, Kolstad, BW 1998. Development of an udder health index for sire selection based on somatic cell score, udder conformation, and milking speed. Journal of Dairy Science 81, 1157–1168.CrossRefGoogle ScholarPubMed
Bruckmaier, R 2001. Milk ejection during machine milking in dairy cows. Livestock Production Science 70, 121–124.CrossRefGoogle Scholar
Dodenhoff, J, Sprengel, D, Duda, J, Dempfle, L 1999. Potential use of parameters of the milk flow curve for genetic evaluation of milkability. Interbull Bulletin 23, 131–141.Google Scholar
Druet, T, Jaffrézic, F, Ducrocq, V 2005. Estimation of genetic parameters for test day records of dairy traits in the first three lactations. Genetics Selection Evolution 37, 257–271.CrossRefGoogle ScholarPubMed
Emmerling, R, Lidauer, M, Mäntysaari, EA 2002a. Multiple lactation random regression test-day model for Simmental and Brown Swiss in Germany and Austria. Interbull Bulletin 29, 111–117.Google Scholar
Emmerling, R, Mäntysaari, EA, Lidauer, M 2002b. Reduced rank covariance functions for a multi-lactation test-day model. Proceedings of the 7th World Congress of Genetics Applied to Livestock Production, Montpellier, France, communication no. 17-03.Google Scholar
Gäde, S, Stamer, E, Junge, W, Kalm, E 2006. Estimates of genetic parameters for milkability from automatic milking. Livestock Science 104, 135–146.CrossRefGoogle Scholar
Ilahi, H, Kadarmideen, HN 2004. Bayesian segregation analysis of milk flow in Swiss dairy cattle using Gibbs sampling. Genetics Selection Evolution 36, 563–576.CrossRefGoogle ScholarPubMed
Interbull (The International Bull Evaluation Service) 2007. Description of National genetic evaluation systems for dairy cattle traits as applied in different Interbull member countries. Retrieved August 1, 2007, from http://www-interbull.slu.se/national_ges_info2/framesida-ges.htmGoogle Scholar
Jensen, J 2001. Genetic evaluation of dairy cattle using test-day models. Journal of Dairy Science 84, 2803–2812.CrossRefGoogle ScholarPubMed
Kirkpatrick, M, Lofsvold, D, Bulmer, M 1990. Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124, 979–993.CrossRefGoogle ScholarPubMed
Krogmeier, D, Luntz, B, Goetz, K-U 2006. Investigations on the economical value of type traits on the basis of auction sales of first lactation Brown Swiss and Simmental cows. Züchtungskunde 78, 464–478.Google Scholar
Liu, Z, Reinhardt, R, Reents, R 2000. Estimating parameters of a random regression test day model for first three lactation milk production traits using the covariance function approach. Interbull Bulletin 25, 74–80.Google Scholar
Liu, Z, Reinhardt, R, Reents, R 2001. Parameter estimates of a random regression test day model for first three lactation somatic cell scores. Interbull Bulletin 26, 61–65.Google Scholar
Madsen, P, Jensen, J 2000. A user’s guide to DMU. A package for analyzing multivariate mixed models. National Institute for Animal Science, Tjele, Denmark.Google Scholar
Mäntysaari, EA 1999. Derivation of multiple trait reduced rank random regression (RR) model for the first lactation test day records of milk, protein and fat. Conference at the 50th Annual Meeting of the European Association for Animal Production, Zurich, Switzerland, 8pp.Google Scholar
Mein, GA 1998. Design of milk harvesting systems for cows producing 100 pounds of milk daily. National Mastitis Council. Retrieved August 1, 2007, from http://www.nmconline.org/articles/100lbcow.htmGoogle Scholar
Norberg, E, Rasmussen, MD 2007. Genetic parameters for automatic recorded milk flow rates in Danish Cattle. In Book of abstracts of the 58th annual meeting of the European Association for Animal Production (ed. Y van der Honing), p. 346. Wageningen Academic Publishers, Wageningen, The Netherlands.Google Scholar
Rupp, R, Boichard, D 1999. Genetic parameters for clinical mastitis, somatic cell score, production, udder type traits, and milking ease in first lactation Holsteins. Journal of Dairy Science 82, 2198–2204.CrossRefGoogle ScholarPubMed
Sprengel, D, Dodenhoff, J, Duda, J, Dempfle, L 2000. Genetic parameters for milkability traits in Fleckvieh. Conference at the 51st Annual Meeting of the European Association for Animal Production, Den Haag, The Netherlands, 6pp.Google Scholar
Sprengel, D, Dodenhoff, J, Götz, K-U, Duda, J, Dempfle, L 2001. International genetic evaluation for milkability. Interbull Bulletin 27, 35–40.Google Scholar
Steidle, E, Goeft, H, Immler, S, Rosenberger, E, Korndoerfer, R, Duda, J, Troeger, F, Bruckmaier, R, Worstorff, H, Model, I, Harch, M, Deneke, J 2000. Lactation consulting with milk flow curves. Bayerische Landesanstalt fuer Tierzucht, Grub, Germany.Google Scholar
Swalve, HH 2000. Theoretical basis and computational methods for different test-day genetic evaluation methods. Journal of Dairy Science 83, 1115–1124.CrossRefGoogle ScholarPubMed
Wiggans, GR, Thornton, LLM, Neitzel, RR, Gengler, N 2007. Genetic evaluation of milking speed for Brown Swiss dairy cattle in the United States. Journal of Dairy Science 90, 1021–1023.CrossRefGoogle ScholarPubMed
Zwald, NR, Weigel, KA, Chang, YM, Welper, RD, Clay, JS 2005. Genetic evaluation of dairy sires for milking duration using electronically recorded milking times of their daughters. Journal of Dairy Science 88, 1192–1198.CrossRefGoogle ScholarPubMed