J. Dairy Sci. 89:4424���4432 �� American Dairy Science Association, 2006. Genetics of Body Condition Score in New Zealand Dairy Cows J. E. Pryce1 and B. L. Harris Livestock Improvement Corporation Ltd., Private Bag 3016, Hamilton, New Zealand ABSTRACT Body condition score (BCS) data were collected on 169,661 first-parity cows from herds participating in progeny testing schemes and linear type assessment. Genetic and residual variances for BCS estimated across time using a quadratic random regression model were found to be largest at the start of lactation. Herita- bility estimates ranged from 0.32 to 0.23 from d 1 to 200 of lactation, with a mean of 0.26. Genetic correlations between BCS and other traits were estimated using 2 approaches: 1) a multivariate analysis that included BCS and live weight, both adjusted for stage of lacta- tion 270-d cumulative yields of milk, fat, and protein average somatic cell score and 2 measures of fertility and 2) a bivariate random regression analysis in which BCS was considered to be a longitudinal trait across time, with the same measurements as in approach 1 for all other traits. Genetic correlations of BCS with the 2 fertility traits were 0.43 and 0.50 using the multi- variate analysis the corresponding random regression estimates between BCS as a longitudinal trait across time and 2 measures of fertility were 0.35 to 0.44 and 0.40 to 0.49, and tended to increase with stage of lacta- tion. Genetic correlations estimated using the random regression model fluctuated around the multivariate estimates for live weight and somatic cell score, which were 0.50 and ���0.12, respectively. Genetic correlations estimated using the multivariate analysis of BCS with fat and protein yields were close to zero. With the ran- dom regression model, genetic correlations between BCS and fat and protein yields were positive at d 1 of lactation (0.16 and 0.08, respectively) and were nega- tive by d 200 of lactation (���0.25 and ���0.20, respectively). In pastoral production systems, such as those typical in New Zealand, there appears to be an advantage in the total lactation yields of fat and protein for cows of higher BCS in early lactation, which is likely to be because these cows have body reserves that are avail- able to be mobilized in later lactation, when feed re- sources are sometimes limited. Received March 21, 2006. Accepted June 7, 2006. 1Corresponding author: jpryce@lic.co.nz 4424 Key words: random regression model, body condition score, fertility INTRODUCTION Body condition score is an approximate way of judg- ing the body lipid content of a cow (Garnsworthy, 1988). Although subjective, it is currently a favored practical method of evaluating body energy stores. It is assessed by either visual appraisal (Edmondson et al., 1989), or by feeling the spinous processes of the loin and around the tail area (Garnsworthy, 1988). Several authors have estimated covariance functions for BCS using random regression (RR) models (e.g., Koenen and Veerkamp, 1998 Jones et al., 1999 Veer- kamp and Thompson, 1999 Veerkamp et al., 2001 Berry et al., 2003 Dechow et al., 2004). Veerkamp et al. (2001) and Berry et al. (2003) extended the RR meth- odology to estimate genetic correlations between data that repeat across time, such as BCS, live weight (LWT), and milk yield, with measurements available at a single point in lactation, such as fertility. Veerkamp et al. (2001) suggested that traits related to energy balance, such as milk yield, BCS, and LWT, should be investigated as a function of lactation stage, because the duration and size of energy deficit vary across lacta- tion, which could affect genetic correlation estimates. In New Zealand, feed requirements for production are generally matched to pasture supply, and conse- quently an important management strategy is to have a single concentrated seasonal calving pattern so that feed usage is optimized. A period of slow grass growth and reduced pasture availability is sometimes experi- enced if there are dry conditions in the summer months (typically January to March), which corresponds to late lactation. During this time, mobilization of body tissue reserves is common to sustain lactation. Genetic corre- lations between BCS and milk production traits esti- mated using multivariate models have been reported to be close to zero using New Zealand data (Pryce and Harris, 2004 Harris et al., 2005). This contradicts re- search from the United States and Ireland (Dechow et al., 2001 Berry et al., 2003), where genetic correlations between BCS and milk production traits have been re- ported to be negative. This genetic correlation appears to be system dependent. Investigating the genetic rela-

GENETICS OF BODY CONDITION SCORE 4425 tionship between BCS and milk production across time using data from cows fed pasture may help our under- standing of this complex relationship. If genetic correlations between BCS and other traits of importance in the New Zealand breeding goal vary across lactation, this may have further implications. For example, it may be useful to provide genetic evalua- tions of BCS at particular stages of lactation, especially for fertility, where BCS is already used as a genetic predictor (Harris et al., 2005). The main aim of the present study was to estimate genetic variation of BCS as a function of time and to investigate the genetic relationship between BCS and other traits under selection in New Zealand. The na- tional breeding index is Breeding Worth and currently includes 270-d yields of milk, fat, and protein LWT fertility residual survival and SCS. Residual survival is survival after all other traits in Breeding Worth are excluded, so genetic correlations between this trait and other traits in the index are zero. The analyses under- taken were 1) to estimate genetic (co)variances of BCS longitudinally across first-lactation data using RR mod- els and 2) to estimate genetic correlations of BCS with other traits under selection in New Zealand using a) multivariate and b) RR models, where BCS was consid- ered to be a repeated trait across lactation and other traits were single measurements. MATERIALS AND METHODS Data The BCS data were collected on 169,661 first-parity cows in herds that participated in the progeny testing (PT) schemes of either Livestock Improvement or Ambreed (Hamilton, New Zealand), or that participated in linear type classification, known in New Zealand as the Traits Other than Production (TOP) inspection, from the 2000���2001 through the 2003���2004 seasons. Approximately half of the cows were from PT herds the rest of the cows were from pedigree herds. Both Holstein-Friesian and Jersey breeds and their crosses were represented in the data. At least 2 BCS measurements were taken in the PT herds. The first coincided with recording of LWT and the second was at the time of TOP inspection. In the herds not participating in PT, one BCS measurement was taken at the time of TOP inspection. Inspectors received training to evaluate BCS. Scoring was done visually on a 1 to 9 scale. A single LWT record per cow was available for animals that participated in PT and was recorded on the same day as the PT BCS mea- surement. Additional data on fertility milk, fat, and protein yields SCS breed and pedigree were extracted from Journal of Dairy Science Vol. 89 No. 11, 2006 the New Zealand Dairy core database and the Livestock Improvement national database. Breed proportions of the animal and its sire and dam were available in 16ths, which allowed the estimation of both breed and hetero- sis effects (Koch et al., 1985). Proportions of Jersey (J), Holstein-Friesian of New Zealand ancestry (NZHF), and Holstein-Friesian of North American and Euro- pean ancestry (NAHF) were calculated. A more detailed description is provided by Harris and Kolver (2001). Yield deviations, for cumulative first-lactation milk (MY270), fat (FY270), and protein (PY270) expressed to a standardized lactation length of 270 d were calcu- lated using the method of Johnson (1996). This method adjusts for the test-day environment, takes account of culling, and weights test-day records according to the number of tests, stage of lactation, and interval between tests. Yield deviations were expressed with respect to herd-year-season-age contemporary groups. Test-day records on SCS were available on 61,134 animals of the animals that had BCS records (these were Livestock Improvement���s PT animals). Following Harris and Winkelman (2004), a logarithmic transfor- mation to the base 2 was used to transform SCC and the lactation mean per animal was calculated this value was deviated from the mean logarithmic-trans- formed SCC of contemporaries within the same herd- year. The fertility measures used in the analyses were pre- sented for mating within the first 21 d of the mating season (PM21), scored as a binomial trait to allow for cows in different stages of their reproductive cycle. The second fertility measure was calving within 42 d from the planned start of calving (CR42), also scored as a binomial trait. If the culling reason was recorded as infertility, then the CR42 record was set to be zero. Cows culled for other reasons were treated as missing data. Full details of the methodology used to calculate the fertility measures is given in Harris et al. (2005). RR Analysis Legendre polynomials were used to model the addi- tive genetic covariance function. Starting values of ad- ditive genetic variances of Legendre polynomials for the longitudinal analysis of BCS were obtained from (co)variances estimated from a 4-trait multivariate analysis of BCS derived by dividing data into approxi- mately equal-sized blocks according to the interval from calving to observation date following the procedure de- scribed by Kirkpatrick et al. (1990). The cut-off points for the blocks were 21 to 62 d 62 to 90 d 90 to 111 d and 111 to 200 d. The software used to do the analysis was the average information REML algorithm (AI-REML) of Johnson and Thompson (1995) that