Introduction of milk, protein and fat yield, and milk protein and

Livestock Production Science 66 2000 251–261 www.elsevier.com locate livprodsci Estimation of genetic parameters for daily milk yield of primiparous Ayrshire cows by random regression test-day models ¨ ¨ ¨ Anne Kettunen, Esa A. Mantysaari , Jukka Poso Animal Production Research , Agricultural Research Centre of Finland-MTT, FIN-31600 Jokioinen, Finland Received 19 March 1999; received in revised form 13 January 2000; accepted 26 January 2000 Abstract Test-day data comprising of 63,331 test-day milk records of 6310 primiparous Finnish Ayrshire cows were used to estimate genetic parameters for daily milk production. Two alternative random regression RR sub-models were used to describe breeding values for the shape of lactation curves of individual cows: a five parameter logarithmic polynomial ASM or a normalised third order orthogonal polynomial. Permanent environment PE of a cow was described by either a common PE effect ASM and OPM or a normalised third order orthogonal polynomial OPM . Variance components PE1 PE4 were estimated with an animal model using EM-REML. A multitrait MT approach together with continuous covariance function CF was used to derive reference for RR estimates. Heritability estimates obtained by ASM 0.41–0.60 and OPM 0.28–0.53 were higher than those derived from CF analysis 0.20–0.28. Fitting the RR sub-model for PE effects PE1 strongly influenced the magnitude of heritability estimates 0.23–0.36. Estimates of heritability were found to be highest during early and late lactation when estimated by ASM and OPM models, while the converse was true for those derived PE1 by CF. Estimates obtained by the OPM model were highest at the beginning of lactation and between 183 and 256 days in PE4 milk. Genetic correlations were high between consecutive test days, but decreased when intervals between test days increased. Where models ASM and OPM indicated a negative correlation between distant test days, OPM estimates PE1 PE4 were consistent with those of CF. Due to the statistical complexity of RR test-day models use of MT is a more feasible approach for the estimation of covariance components for CF coefficients.  2000 Elsevier Science B.V. All rights reserved. Keywords : Dairy cattle; Test-day model; Random regression; Heritability; Genetic correlation

1. Introduction of milk, protein and fat yield, and milk protein and

fat content. Within the national milk recording In Finland estimates of dairy cattle breeding scheme, daily milk yields are measured monthly, values BV are based on 305-day lactation records while milk protein and fat content are assessed bimonthly. These recordings are subsequently aggre- gated into a measure of lactation yield. Since in- Corresponding author. Tel.: 1358-3-41-881; fax: 1358-3- dividual test-day TD records are weighted by the 4188-3618. ¨ E-mail address : esa.mantysaarimtt.fi E. Mantysaari duration of testing periods to give 305-day milk 0301-6226 00 – see front matter  2000 Elsevier Science B.V. All rights reserved. P I I : S 0 3 0 1 - 6 2 2 6 0 0 0 0 1 6 6 - 4 252 A . Kettunen et al. Livestock Production Science 66 2000 251 –261 ¨ yield, it approximates mean TD milk production to differ from 1.0 Kettunen and Mantysaari, 1996. throughout lactation. This finding supported the hypothesis that inclusion Attempts to improve the accuracy of BV estima- of RR function in BV estimation of dairy cattle from tion and a need to provide more comprehensive TD data is necessary. A random regression TD management information to farmers has stimulated approach has been recently used for both estimation an increased interest in the use of original TD of genetic parameters and genetic evaluation of measurements instead of aggregated lactation re- production traits Jamrozik and Schaeffer, 1997; cords. Use of the TD approach allows a more Kettunen et al., 1997; Jamrozik et al., 1997a. detailed statistical model to be developed, which can Use of RR test-day models in variance component account for genetic and environmental variation estimation has, however, turned out to be somewhat specific to individual TD yields. For the Finnish problematic. Firstly, very high estimates of heritabili- dairy cattle BV estimation the greatest advantage ty for daily milk yield have been reported Jamrozik afforded would be a more precise definition of the and Schaeffer, 1997; Kettunen et al., 1997, and the contemporary group CG. The current animal model pattern of estimates is contradictory to that estimated uses herd-calving year HY to describe CG. Further with multitrait models Meyer et al. 1989; Pander et partitioning of HY according to calving season is not al., 1992. Secondly, RR analysis has resulted in possible due to small herd sizes. Year of calving antagonistic relationships between early and late leads into an illogical grouping of records, since a lactation daily yields of protein Jamrozik and situation can arise, where cows produce for the Schaeffer, 1997 and milk Kettunen et al., 1997. majority of their lactation under the same environ- This is due to deficiencies in the definition of cow ment but are assigned to different classes according permanent environmental effects. Inclusion of RR to HY. Furthermore, HY characterises long-term function to describe PE effects can potentially im- effects of a particular calving year in a herd rather prove the properties of the statistical model. Thirdly, than short-term variation due to management effects when a logarithmic polynomial function was used, at the time of production. Since season of production RR coefficients were found to be highly correlated: accounts for more environmental variation than herd- additive term and second order polynomial 20.97, calving year-calving season HYS Swalve, 1995; and first and second order logarithms 20.98, in ¨ ¨ Poso et al., 1996, the use of the TD approach, where particular Kettunen et al., 1997. An orthogonal CG is defined as herd-test month HTM, improves polynomial function as RR sub-model could be used the properties of the statistical model. Furthermore, to overcome problems of dependency between vari- solutions of HTM effects can be utilised to improve ables. herd management. The objective of this study was to estimate genetic The genetic shape of the lactation curve can be parameters for first lactation TD milk yield of modelled by fitting regression coefficients within an Finnish Ayrshire cows. Two RR models with tradi- animal, commonly referred to as random regression tional consideration of PE effects were used: loga- RR coefficients Schaeffer and Dekkers, 1994. rithmic polynomial and normalised third order ortho- Additive genetic solutions are simply a set of BV gonal polynomial functions. In addition, the effect of estimates for the RR coefficients Jamrozik et al., modelling PE covariance structure with RR func- 1997a. The product of these estimates and the days tion on genetic parameters was assessed. Finally, in milk DIM dependent covariates give a BV of an results from RR analysis were evaluated by com- animal for each TD yield. This allows the genetic parison with estimates derived from a multitrait ranking of animals to vary at different stages of model MT by continuous covariance function. lactation. In addition, differences between actual and expected production can be calculated to monitor the management of individual herds and of individual

2. Material and methods