[en] Test-day genetic evaluation models have many advantages compared with those based on 305-d lactations; however, the possible use of test-day model (TDM) results for herd management purposes has not been emphasized. The aim of this paper was to study the ability of a TDM to predict production for the next test day and for the entire lactation. Predictions of future production and detection of outliers are important factors for herd management (e. g., detection of health and management problems and compliance with quota). Because it is not possible to predict the herd-test-day (HTD) effect per se, the fixed HTD effect was split into 3 new effects: a fixed herd-test month-period effect, a fixed herd-year effect, and a random HTD effect. These new effects allow the prediction of future production for improvement of herd management. Predicted test-day yields were compared with observed yields, and the mean prediction error computed across herds was found to be close to zero. Predictions of performance records at the herd level were even more precise. Discarding herds enrolled in milk recording for <1 yr and animals with very few tests in the evaluation file improved correlations between predicted and observed yields at the next test day (correlation of 0.864 for milk in first-lactation cows as compared with a correlation of 0.821 with no records eliminated). Correlations with the observed 305-d production ranged from 0.575 to 1 for predictions based on 0 to 10 test-day records, respectively. Similar results were found for second and third lactation records for milk and milk components. These findings demonstrate the predictive ability of a TDM.
Disciplines :
Animal production & animal husbandry Genetics & genetic processes
Author, co-author :
Mayeres, P.
Stoll, J.
Bormann, J.
Reents, R.
Gengler, Nicolas ; Université de Liège - ULiège > Gembloux Agro-Bio Tech > Gembloux Agro-Bio Tech
Language :
English
Title :
Prediction of daily milk, fat, and protein production by a random regression test-day model
Publication date :
2004
Journal title :
Journal of Dairy Science
ISSN :
0022-0302
eISSN :
1525-3198
Publisher :
American Dairy Science Association, Champaign, United States - Illinois
Ali, T. E., and L. R. Schaeffer. 1987. Accounting for covariances among test-day milk yield in dairy cows. Can. J. Anim. Sci. 67:637-644.
Everett, R. W., F. Schmitz, and L. H. Wadell. 1994. A test day model for monitoring management and genetics in dairy cattle. J. Dairy Sci. 77(Suppl. 1):267. (Abstr.)
Gengler, N., A. Tijani, G. R. Wiggans, and I. Misztal. 1999. Estimation of (co)variance function coefficient for test day yield with expectation-maximization restricted maximum likelihood algorithm. J. Dairy Sci. 82:225. [Online.] Available: http://www.adsa.org/ manuscripts/8436e/.
Gengler, N., and G. R. Wiggans. 2001. Variance of effects of lactation stage within herd by herd yield. J. Dairy Sci. 84(Suppl. 1):216. (Abstr. 896)
INTERBULL. 2000. National genetic evaluations programs for dairy production traits practised in INTERBULL member countries 1999-2000. INTERBULL Bulletin 24. Upsala, Sweden. Available: http://www-interbull.slu.se/bulletins/ framesida-pub.htm.
Lidauer, M., E. A. Mäntysaary, I. Stranden, and J. Pösö. 2000. Multiple-trait random regression test-day model for all lactations. INTERBULL Bulletin 25:81-87. Available: http://www-interbull.-slu.se/bulletins/ framesida-pub.htm.
Mayeres, P., J. Stoll, R. Reents, and N. Gengler 2002. Alternative modeling of fixed effects in test day model to increase their usefulness for management decisions. INTERBULL Bulletin 29:128-132. Available: http://www-interbull.slu.se/bulletins/framesida-pub.htm.
Meyer, K., H.-U. Graser, and K. Hammond. 1989. Estimates of genetic parameters for first lactation test day production of Australian Black and White cows. Livest. Prod. Sci. 21:177-199.
Ptak, E., and L. R. Schaeffer. 1993. Use of test day yields for genetic evaluation of dairy sires and cows. Livest. Prod. Sci. 34:23-34.
Reents, R., J. C. M. Dekkers, and L. R. Schaeffer 1995. Genetic evaluation for somatic cell score with a test day model for multiple lactations. J. Dairy Sci. 78:2858-2870.
Reents, R., L. Dopp, M. Schmutz, and F. Reinhardt. 1998. Impact of application of a test-day model to dairy production traits on genetic evaluation of cows. INTERBULL Bulletin 17:49-54. Available: http://www-interbull.slu.se/ bulletins/framesida-pub.htm.
Schaeffer, L. R., and J. Jamrozik. 1996. Multiple-trait prediction of lactation yields for dairy cows. J. Dairy Sci. 79:2044-2055.
Swalve, H. H. 1995. The effect of test day models on the estimation of genetic parameters and breeding values for dairy yield traits. J. Dairy Sci. 78:929-938.
Van Bebber, J., N. Reinsch, W. Junge, and E. Kalm. 1997. Accounting for herd, year and season effects in genetic evaluations of dairy cattle: A review. Livest. Prod. Sci. 51:191-203.
Van Bebber, J., N. Reinsch, W. Junge, and E. Kalm. 1999. Monitoring daily milk yields with a recursive test day repeatability model (Kalman Filter). J. Dairy Sci. 82:2421-2429.
VanRaden, P. 1997. Lactation yields and accuracies computed from test day yields and (co)variances by best prediction . J. Dairy Sci. 80:3015-3022.
Van Vleck, L. D. 1987. Contemporary groups for genetic evaluations. J. Dairy Sci. 70:2456-2464.
Wiggans, G. R. 1985. Procedures for calculating lactation records. Natl Coop. Dairy Herd Improvement Handbook. Fact Sheet G-1, p.10. Ext. Serv., USDA, Washington, DC.