Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-10T13:33:39.082Z Has data issue: false hasContentIssue false
  • English
  • Français

Efficiency of part lactation test day records for genetic evaluations using fixed and random regression models

Published online by Cambridge University Press:  18 August 2016

R. A. Mrode ,
G. J. T. Swanson  and
C. M. Lindberg
R. A. Mrode*
Affiliation:
Animal Data Centre Limited, Fox Talbot House, Greenways Business Park, Bellinger Close, Chippenham, Wiltshire SN15 1BN, UK
G. J. T. Swanson
Affiliation:
Animal Data Centre Limited, Fox Talbot House, Greenways Business Park, Bellinger Close, Chippenham, Wiltshire SN15 1BN, UK
C. M. Lindberg
Affiliation:
Animal Data Centre Limited, Fox Talbot House, Greenways Business Park, Bellinger Close, Chippenham, Wiltshire SN15 1BN, UK
*
† E-mail: raphm@animaldata.co.uk
Get access

Abstract

The efficiency of part lactation test day (TD) records in first parity for the genetic evaluation of bulls and cows using a random regression model (RRM) and a fixed regression model (FRM) was studied, modelling the random and fixed lactation curves by Legendre polynomials. The data set consisted of 9 242 783 TD records for first lactation milk yield of 1 134 042 Holstein Friesian heifers. The efficiency of both models with part lactation TD records was examined by comparing predicted transmitting abilities (PTAs) for 305-day milk yield for 114 bulls and their 4697 daughters, from analyses where the maximum number of TD records of these daughters was restricted to the initial 2, 4 or 6 TDs with those estimated from 10 TDs. The correlations of PTAs estimated from 2, 4 or 6 TDs with those from 10 TDs computed for cows and bulls within each model were very similar. A rank correlation of 0·91 (0·92 FRM) was obtained for cows between PTAs based on 2 TDs and those from 10 TDs. The correlation increased to 0·96 with 4 TDs and 0·98 with 6 TDs. For bulls, correlations between PTAs estimated from 4 or 6 TDs with those estimated from 10 TDs were high at 0·98 and 0·99 respectively. With 2 TDs, the correlation was 0·95. The average under-prediction of PTAs with 2, 4 or 6 TDs relative to 10 TDs was generally higher and more variable with a FRM compared with a RRM for highly persistent cows and bulls. A similar trend was observed for mean over-prediction of PTAs, except for the initial predictions based on 2 TDs when the RRM gave a higher mean over-prediction for bulls and their daughters with low persistency but high initial TD records. The range of over and under-predictions were large (up to 200 kg milk) for some bulls when only 2 TDs were included in both models. A moderate correlation of 0·64 was obtained between persistency evaluations estimated from 10 TDs with those estimated from 2 TDs. The correlation increased to 0·71 with 4 TDs included and 0·85 with 6 TDs. The moderately high correlation between 6 TDs and 10 TDs of 0·85 was unexpected given the high correlation of 0·99 between PTAs for yield estimated from 6TDs with those estimated from 10 TDs.

Keywords

genetic effectsmilk recordingpersistenceregression analysis
Type
Breeding and genetics
Information
Animal Science , Volume 74 , Issue 2 , April 2002 , pp. 189 - 197
Copyright
Copyright © British Society of Animal Science 2002

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bauman, D. E., Everett, R. W., Weiland, W. H. and Collier, R. J. 1999. Production responses to bovine somatotropin in Northeast dairy herds. Journal of Dairy Science 82: 2564–2573.CrossRefGoogle ScholarPubMed
Brotherstone, S., White, I. M. S. and Meyer, K. 2000. Genetic modelling of dairy milk yield using orthogonal polynomials and parametric curves. Animal Science 70: 407–415.CrossRefGoogle Scholar
Gilmour, A. R., Cullis, B. R. and Welham, S. J. 2000. Asreml reference manual. NSW Agriculture, Orange, 2800 Australia. Web site: ftp.res.bbsrc.ac.uk/pub/aar/asreml.ps.ZGoogle Scholar
Jamrozik, J., Dekkers, J. C. M. and Schaeffer, L. R. 1997. Genetic evaluation of dairy cattle using test day yields and random regression model. Journal of Dairy Science 80: 1217–1226.Google Scholar
Kistemaker, G. J. 2000. Partitioning estimated breeding values in animal models. Proceedings of the 2000 Interbull meeting, Bled, Slovenia, bulletin no. 25, pp. 65–69.Google Scholar
Liu, Z., Jamrozik, J. and Jansen, G. 1998. A comparison of fixed and random regression models applied to dairy test day production data. Proceedings of the 1998 Interbull Meeting, Rotorua, New Zealand, bulletin no. 17, pp. 60–63.Google Scholar
Ptak, E. and Schaeffer, L. R. 1993. Use of test day yields for genetic evaluations of dairy sires and cows. Livestock Production Science 34: 23–34.CrossRefGoogle Scholar
Schaeffer, L. R. and Dekkers, J. C. M. 1994. Random regressions in animal models for test-day production in dairy cattle. Proceedings of the fifth world congress on genetics applied to livestock production, Guelph, Ontario, Canada, vol. 18, pp. 443–446.Google Scholar
Schaeffer, L. R., Jamrozik, J., Kistemaker, G. J. and Doormaal, B. J.Van. 2000. Experience with a test day model. Journal of Dairy Science 83: 1135–1144.Google Scholar