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Estimates of genetic parameters for carcass traits in
Finnish Ayrshire and Holstein-Friesian
1
*
¨
Paivi Parkkonen , Anna-Elisa Liinamo , Matti Ojala
University of Helsinki, Department of Animal Science, P.O. Box 28, FIN-00014 Helsinki University, Helsinki, Finland
Received 26 February 1999; received in revised form 23 August 1999; accepted 24 August 1999
Abstract
The aim of this study was to estimate genetic parameters for slaughter weight, and carcass fleshiness and fatness in Finnish Ayrshire and Holstein-Friesian bulls and heifers. Animal model, sire model and sire maternal grandsire model were tested for their suitability to evaluate young sires in progeny test. There were 38 188 records on animals slaughtered in two slaughter houses during a period of 20 months. Effects of year-month of slaughter, age at slaughter, sex and breed were statistically significant, and herd accounted for about 20–57% of the total variation in the data. Estimates of heritability in the different breed by sex data sets were in range of 0.07–0.14 for slaughter weight, 0.16–0.31 for fleshiness and 0.08–0.16 for fatness, whereas the corresponding within herd heritabilities varied from 0.15 to 0.29, 0.29 to 0.39 and 0.12 to 0.29, respectively. There was a positive genetic correlation of 0.38–0.66 between slaughter weight and fleshiness, whereas fatness was not genetically correlated with the other studied traits. All within herd correlations were high, from 0.55 to 0.93, and phenotypic and environmental correlations were also high or moderate. In the estimation of (co)variance components, sire model and sire maternal grandsire model were preferred to animal model due to computational requirements, and sire maternal grandsire model to sire model due to the possibility of including the sire path of maternal pedigree. 2000 Elsevier Science B.V. All rights reserved.
Keywords: Beef production; Carcass quality; Heritability; Genetic correlation
1. Introduction production traits are taken into account only
indirect-ly (e.g., INTERBULL, 1996). In Finland, young In Finland and many other European countries, a bulls to be used in artificial insemination are evalu-major part of beef is produced as a by-product of ated based on their own yearling weight or growth at dairy production. The breeding programs of dairy test station (Ojala, 1984). Previously, live weights of cattle seldom involve carcass quality, and beef dairy cows were also reported in bull evaluation (Hietanen and Ojala, 1995). Growth and live weight are correlated with carcass traits but the correlation *E-mail address: [email protected] (A.-E.
may not be strong enough to improve the poor beef Liinamo)
1 producing ability of Finnish dairy breeds.
Present address: Animal Production Research, Agricultural
Research Centre of Finland, FIN-31600 Jokioinen, Finland. Carcass quality evaluated at slaughter houses
predicts beef producing ability better than growth or animals according to the European cattle identifica-live weight. In addition, using progeny results leads tion system as the first company in Finland. The to improved reliability compared to individual mea- period of data collection covered 20 months from the surements of young bulls. However, carcass data has beginning of January 1996 to the end of August not been available for animal breeding because 1997. During that time more than 110 000 head of slaughter houses have traditionally used their own cattle were slaughtered in the two participating identification system, and it has not been possible to slaughter houses. Pedigrees were obtained from the combine this system with other cattle registers. In the database of Agricultural Data Processing Centre new European cattle identification system introduced including parents and grandparents for the slaug-in Fslaug-inland slaug-in the begslaug-innslaug-ing of 1995 the same identity htered animals registered within milk recording follows an animal from birth to carcass, thus en- system. The pedigree data set included over 180 000 abling environmental and pedigree information to be animals.
merged with carcass data. Sixty-three percent of slaughtered animals were Carcass traits of cattle, e.g., slaughter weight, Finnish Ayrshires (Ay) and 26% Holstein-Friesians fleshiness and fatness, have been studied considera- (HFr). Other breeds and their crosses were too rare bly, and most of the traits have been found to be of in the data set for estimation of genetic parameters, high or moderate heritability (Wilson et al., 1976; and it was also considered important to study carcass Koch, 1978; Lamb et al., 1990; Robinson et al., traits in the most common breeds that produce the 1990; Arnold et al., 1991; Gregory et al., 1994; majority of beef. Thus, only purebred Ay or HFr Wheeler et al., 1996). However, the results can not carcasses were included in the analyses.
be easily generalised into Finnish cattle population, Data was further limited to bulls and heifers that because most of the studies have involved beef were slaughtered at the age of 300 through 899 days, breeds which are only of marginal importance in with carcasses required to have slaughter weight of Finland. Moreover, definitions of carcass traits and at least 130 kg. Cows were excluded from the the models used in analyses differ in various coun- analyses in this study because cow information will tries (e.g., Jones et al., 1994; EU-ROP, 1995; Harris not be included in the possible carcass quality et al., 1995; United States Department of Agricul- indices.
ture, 1997). The prerequisites for incorporating The data was divided in subsets to study whether carcass traits into Finnish dairy cattle breeding the factors affecting carcass traits and their genetic program are estimation of genetic parameters and parameters differ in different breeds and sexes. The development of an appropriate evaluation model for primary subsets were Ay bulls (AyB) with 22 231, carcass traits in Finnish cattle population. HFr bulls (HFrB) with 8711, Ay heifers (AyH) with The aim of this study was to investigate the factors 5328 and HFr heifers (HFrH) with 1918 carcasses. affecting carcass traits in Finnish Ayrshire and These subsets were analysed with all the models and Holstein-Friesian, and to estimate heritabilities in methods used in this study. Combinations within data sets divided by breed and sex. In addition, sexes and breeds were considered in combined performance of animal model, sire model and sire subsets of Ay and HFr bulls (AyHFrB), Ay and HFr maternal grandsire model were compared in order to heifers (AyHFrH), Ay bulls and heifers (AyBH), and find the model best suited for practical evaluation of HFr bulls and heifers (HFrBH). Finally, all animals carcass traits in Finnish dairy cattle breeding pro- were analysed together (AyHFrBH). These combined
gram. data sets were analysed only with animal model
using univariate analysis.
The traits studied were slaughter weight, and
2. Materials and methods carcass fleshiness and fatness. Slaughter weight is
European Union SEUROP classification system (EU- Sampling (GS) method with MTGSAM software (Van ROP, 1995). In Finland, fleshiness is judged in 11 Tassell and Van Vleck, 1995). For comparison of classes: P2, P, P1, O2, O, O1, R2, R, R1, U methods also sire models were analysed with GS and E, from worst to best respectively. Fatness is method.
judged in five classes numbered from 1 to 5, with The following animal model was assumed in class 1 being the leanest and class 5 the fattest. In analysing the within breed and sex data subsets AyB, this study, fleshiness was transformed to numbers so HFrB, AyH and HFrH:
that the classes from P2 to R1 were replaced by
yijklmn5m 1slaughter housei1year-monthj
numbers from 1 to 9. Due to the lack of subclasses in
U and E, they were numbered as 11 and 14, 1agek1cl1am1´ijklmn
respectively. Figs. 1 and 2 illustrate frequency
distributions of fleshiness and fatness in the data, where yijklmn5record of slaughter weight, fleshiness
respectively. or fatness,m 5overall mean, slaughter housei5fixed
Data editing and preliminary analyses were done effect of ith slaughter house (i51,2), year-monthj5
on WSYS and WSYS-L software (Vilva, 1992; 1997). fixed effect of jth month of slaughter ( j51–20), For estimation of variance and covariance compo- agek5fixed effect of kth age class (k51–14), cl5
nents two methods were used. Animal models and random effect of lth herd, am5random additive sire models were solved by VCE4.0 software genetic effect of mth animal, and ´ijklmn5random (Groeneveld, 1997) using Restricted Maximum residual effect. When analysing combined data sets Likelihood (REML) method. Statistical significance AyHFrB and AyHFrH, also breedo5fixed effect of of contrasts between different levels of fixed effects oth breed (o51,2) was included, and in data sets in mixed models was tested by F-test in PEST AyBH and HFrBH sexp5fixed effect of pth sex software (Groeneveld, 1990). Sire maternal grandsire ( p51,2) was included. When analysing all animals, models could not be solved using VCE4.0 due to the data set AyHFrBH, both breed and sex were
in-1
]
multiplier 2 in the incidence matrix Z. Thus, esti- cluded in the model in addition to the previous mates of variance and covariance components from factors.
sire maternal grandsire models were solved by Gibbs There were two slaughter houses with 55% of
Fig. 2. Frequency distribution of fatness in bulls and heifers.
carcasses coming from the bigger one. The original different traits (i,i9 51,2,3 and i ± i9) were assumed 20-month data collection period was retained in 20 to be cov(c ,c )5Is , var(a , a )5As and
i i0 ci,i9 i i9 ai, i9
classes for year-month of slaughter because there cov(´,´ )5Is .
i i9 ´i,i9
was no logical connection between the consecutive Heritability was estimated as the proportion of the
2
months or the same months in different years. Rather additive genetic variance of the total variance, h 5
2 2 2 2 2
than using age at slaughter as covariate it was sa/(s 1 s 1 sa c ´). Within herd heritabilities (h )w
2 2 2 2
classified in 14 classes (1510, 11 and 12 mo, 2513 were estimated as hw5sa/(s 1 sa ´).
mo, 3514 mo, . . . , 12523 mo, 13524 and 25 mo In sire model, the genetic effect of an animal was and 14526 to 30 mo of age). substituted by the genetic effect of a sire, and in sire Slaughtered animals originated from 6740 herds, maternal grandsire model the same section was
1
with a quarter of herds having only one observation substituted by the genetic effect of the sire and ] of
2
in the data. Dividing data in subsets further increased the genetic effect of the maternal grandsire.
the proportion of small herds in the data subsets. The All observations were kept in analyses when using herds with few carcasses could not be left out the animal model. With the sire model, only sires without losing a considerable amount of information, with five or more progeny at the data set were so it was decided to keep all the herds in analyses as included, and with sire maternal grandsire model a a random sample of herds in the area. sire or a maternal grandsire was accepted only if it The distributions of random effects were assumed existed in a pedigree of at least two slaughtered multivariate normal with zero means and var(c)5 animals. The restrictions decreased the number of
2 2 2
Is , var(a)5As , and var(´)5Is . When using observations but even more they decreased the
c a ´
multitrait models, the expected values of random number of sires thus increasing the number of effects and the covariances between them were progeny per sire (Table 1). The effect of restrictions assumed zero. The variance of each random effect on parameter estimates was studied by comparing the for the three traits (i51,2,3) was assumed to be solutions obtained using animal model both for the
2 2 2
var(c )5Is ,var(a )5As andvar(´)5Is . unlimited data sets and the data sets limited for sire
i ci,i i ai,i i ´i,i
Table 1
Number of slaughtered animals (N ) and sires (S ) in different models and data subsets
Breed, Animal Sire Sire maternal
a
sex model model grandsire model
N S N S N S
AyB 22 231 892 21 518 366 21 273 478
HFrB 8711 361 8440 180 8305 226
AyH 5328 558 4904 310 4815 400
HFrH 1918 248 1688 126 1697 177
a
AyB–Ayrshire bulls; HFrB–Holstein-Friesian bulls; AyH–Ayrshire heifers; HFrH–Holstein-Friesian heifers.
The results from different methods were compared 3. Results
by solving sire models both by REML and GS
methods. When using GS method, the slaughter The average slaughter weight for bulls was 273 kg house, the year-month of slaughter and the age at and for heifers 203 kg (Table 2). Carcasses of HFr slaughter were given flat prior distributions. MTGSAM bulls were on average 10 kg heavier than carcasses software provides inverted Wishart distribution for of Ay bulls, while in heifers the difference between the prior distribution of variance and covariance breeds was 8 kg. The average fleshiness of all components. Starting values were derived from the carcasses was 4.3, i.e., between classes O2 and O. models solved by REML method. The convergence Thus an average carcass had profiles from straight to criterion of Gauss–Seidel iteration was 0.0001. GS concave, and average muscle development (EU-ROP, algorithm was repeated for 30 000 rounds saving the 1995). Carcasses of bulls were classified on average solutions of every 30th round. Thus, the sample size one grade better than carcasses of heifers, and HFr in point estimation was 934. was classified 0.3 grades better than Ay. The average
Table 2
Number of observations (N ), means (x), standard deviations (s), coefficients of variation (V %), and minimum (Min) and maximum (Max) values of studied traits in different data subsets
a
Trait / Breed, sex N x s V % Min Max
Slaughter weight, kg
AyB 22 231 270 40.8 15.1 130 466.0
HFrB 8711 280 41.6 14.9 131 490.5
AyH 5328 201 35.8 17.8 130 412.5
HFrH 1918 209 37.5 17.9 130 354.5
Fleshiness
AyB 22 231 4.43 0.99 22.4 0 11
HFrB 8711 4.75 1.02 21.4 0 11
AyH 5328 3.50 0.93 26.6 0 8
HFrH 1918 3.85 1.03 26.8 0 14
Fatness
AyB 22 231 2.15 0.41 18.8 0 5
HFrB 8711 2.19 0.45 20.5 1 5
AyH 5328 2.69 0.79 29.3 0 5
HFrH 1918 2.81 0.89 31.7 0 5
a
fatness of all carcasses was 2.27. In class 2, carcas- estimated to be low, at most 0.21, in AyB, HFrH and ses are slightly fat covered with flesh visible almost AyH. Corresponding estimates from HFrH were everywhere (EU-ROP, 1995). Carcasses of heifers outside this range; however, the structure of HFrH were on average 0.5 grades fatter than carcasses of data set was poor due to the large number of herds bulls. HFr heifers were also fatter than Ay heifers, and sires compared to the small number of records. but there was no difference between breeds in bulls. All within herd correlations were high, especially Slaughter weights as well as fleshiness and fatness the correlations between slaughter weight and fleshi-grades tended to decrease during the 20 month ness or fatness that were up to 0.73–0.93 depending observation period. However, the trend was neither on the data set (Table 5). There was little difference linear nor similar in different data subsets. The between phenotypic and environmental correlations, differences between year-months were statistically of which the correlation between slaughter weight significant (P.0.05) in all data subsets. The average and fleshiness was the highest (0.53–0.74), the ages at slaughter varied between months as well. exception being again the data set HFrH.
On average, the animals were slaughtered at the Restrictions on data for sire and sire maternal age of 18.5 months. Heifers were 1.5 months older grandsire models removed records with little or no than bulls at slaughter, and HFr animals were connection with other records. Restrictions did not, slaughtered 0.5 months younger than Ay animals. however, affect estimates of heritability by more Slaughter weight increased from the youngest to the than 0.01 units when differently restricted HFrB and oldest age class by 115 kg in bulls and by 90 kg in AyH datasets were analysed with animal model. heifers. Fleshiness and fatness increased also with Thus results from different models are comparable, age. Improvement of fleshiness was faster in bulls although the data sets used in the analyses were not but gain of fat was faster in heifers. In most data exactly the same. The results for sire models that subsets, however, the heaviest carcasses with the were obtained either with REML or GS methods highest fleshiness and fatness grades were not in the from the same data sets did not differ from each
oldest age class. other either, showing that results by different
meth-Estimates of heritability for slaughter weight were ods agree with each other (Table 6). No matter what relatively low in all data subsets, varying from 0.07 model or method was used, the estimated to 0.14 (Table 3). Respective estimates of within heritabilities and fractions of total variance due to herd heritability were somewhat higher, from 0.15 to herds in the three biggest data sets were almost the 0.29. Heritabilities in HFr data sets were lower than same, differences being in the bounds of standard heritabilities in Ay data sets. Variation between herds errors of the estimates.
caused approximately one half of the total variance in slaughter weight.
Fleshiness was estimated to have heritability of 4. Discussion
0.16–0.31, within herd heritability of 0.29–0.39 and
the fraction of total variance due to herds of 0.20– The data used in this study represented well the 0.26, depending on the data set used in the analyses overall carcass quality of all the bulls and heifers (Table 3). Estimates of heritability for fatness were slaughtered in Finnish slaughter houses in 1996 about the same magnitude as for slaughter weight, (TIKE, 1997). Only in fleshiness carcasses in this varying between 0.08 and 0.16 in different data sets. study were 0.20 units poorer than the average in the Estimated within herd heritabilities for the trait were whole country. This difference was probably due to from 0.12 to 0.29, and the herds caused about 23– the exclusion of beef breeds from the data set in this 47% of the total variance of fatness. study.
Table 3
2
Number of records (N ), number of herds (n), estimates of heritability (h ) and their standard errors (se ), estimates of within herdh 2
2 2
heritability (h ), and herd effects (c ) and their standard errors (se ) for studied traits from univariate analyses with animal modelw c 2
a 2 2 2
Trait / Breed, sex N n h6seh 2 hw c6sec 2
Slaughter weight
AyB 22 231 4381 0.1360.01 0.26 0.5260.01
HFrB 8711 2957 0.0960.01 0.19 0.5360.01
AyHFrB 30 942 5140 0.1160.01 0.24 0.5360.01
AyH 5328 2903 0.1460.02 0.29 0.5260.01
HFrH 1918 1232 0.1060.04 0.23 0.5760.02
AyHFrH 7246 3597 0.1460.02 0.28 0.5160.01
AyBH 27 559 5797 0.1260.01 0.23 0.4760.01
HFrBH 10 629 3570 0.0760.01 0.15 0.5060.01
AyHFrBH 38 188 6740 0.1160.01 0.21 0.4860.01
Fleshiness
AyB 22 225 4381 0.1760.01 0.22 0.2460.01
HFrB 8709 2956 0.2260.02 0.28 0.2260.01
AyHFrB 30 934 5140 0.1860.01 0.24 0.2460.01
AyH 5328 2903 0.1760.02 0.23 0.2660.01
HFrH 1915 1231 0.3160.05 0.39 0.2060.02
AyHFrH 7242 3596 0.2160.02 0.27 0.2560.01
AyBH 27 552 5797 0.1660.01 0.20 0.2360.01
HFrBH 10 624 3569 0.2160.02 0.26 0.2060.01
AyHFrBH 38 176 6740 0.1760.01 0.21 0.2260.01
Fatness
AyB 22 225 4381 0.1260.01 0.16 0.2360.01
HFrB 8711 2957 0.0860.01 0.12 0.2960.01
AyHFrB 30 936 5140 0.1060.01 0.14 0.2660.01
AyH 5328 2903 0.1460.02 0.23 0.3760.01
HFrH 1916 1231 0.1660.04 0.29 0.4760.02
AyHFrH 7243 3596 0.1460.02 0.23 0.3860.01
AyBH 27 552 5797 0.1260.01 0.18 0.3060.01
HFrBH 10 627 3570 0.1060.02 0.15 0.3560.01
AyHFrBH 38 179 6740 0.1060.01 0.15 0.3360.01
a
AyB–Ayrshire bulls; HFrB–Holstein-Friesian bulls; AyHFrB–Ayrshire and Holstein-Friesian bulls; AyH–Ayrshire heifers; HFrH– Holstein-Friesian heifers; AyHFrH–Ayrshire and Holstein-Friesian heifers; AyBH–Ayrshire bulls and heifers; HFrBH–Holstein-Friesian bulls and heifers; AyHFrBH–Ayrshire and Holstein-Friesian bulls and heifers.
Table 4 Table 5
Estimates of genetic parameters for studied traits from multitrait Fractions of total variance due to herds on diagonal with standard
a
analysis with animal model errors, within herd correlations above diagonal with standard errors, and environmental correlations below diagonal estimated
b
2 Fleshiness 0.65 0.1760.01 0.2160.05 AyB 1 2 3
3 Fatness 0.36 0.27 0.1260.01 1 Slaughter weight 0.5260.01 0.8660.01 0.7460.01 2 Fleshiness 0.65 0.2460.01 0.6360.01
HFrB 1 2 3 3 Fatness 0.38 0.28 0.2360.01
1 Slaughter weight 0.0960.01 0.3860.06 0.0560.09
2 Fleshiness 0.57 0.2060.02 0.1260.04 HFrB 1 2 3
3 Fatness 0.42 0.26 0.0860.01 1 Slaughter weight 0.5460.01 0.8260.01 0.7360.01 2 Fleshiness 0.61 0.2460.01 0.5560.02
AyH 1 2 3 3 Fatness 0.46 0.29 0.3060.01
1 Slaughter weight 0.1360.01 0.6660.06 20.0160.11
2 Fleshiness 0.61 0.1760.02 0.1860.07 AyH 1 2 3
3 Fatness 0.59 0.40 0.1360.01 1 Slaughter weight 0.5260.01 0.8160.01 0.8860.01 2 Fleshiness 0.74 0.2660.01 0.6960.02
HFrH 1 2 3 3 Fatness 0.56 0.44 0.3760.01
1 Slaughter weight 0.1160.03 0.6560.07 0.7660.07
2 Fleshiness 0.54 0.3160.04 0.4460.10 HFrH 1 2 3
3 Fatness 0.68 0.39 0.2060.04 1 Slaughter weight 0.5860.02 0.7360.04 0.9360.01 2 Fleshiness 0.53 0.2260.02 0.6960.04
a
Heritabilities on diagonal with standard errors, genetic
correla-3 Fatness 0.68 0.38 0.4860.02
tions above diagonal with standard errors, and phenotypic
correla-a
tions below diagonal. AyB–Ayrshire bulls; HFrB–Holstein-Friesian bulls; AyH–
b
AyB–Ayrshire bulls; HFrB–Holstein-Friesian bulls; AyH– Ayrshire heifers; HFrH–Holstein-Friesian heifers. Ayrshire heifers; HFrH–Holstein-Friesian heifers.
estimated heritabilities for slaughter weight and increased with age. The different development of fleshiness and fatness measured in SEUROP-scores sexes is in agreement with earlier reports (e.g., Berg have been 0.2260.03, 0.2360.03 and 0.2960.03, and Butterfield, 1976). respectively (de Jong, 1997), and 0.25, 0.26 and
Estimated heritabilities, especially for slaughter 0.30, respectively (Van der Werf et al., 1998). weight and fatness, were relatively low. Estimated Estimated correlations revealed a positive genetic within herd heritabilities, however, were in range of connection between slaughter weight and fleshiness. heritabilities estimated in previous studies, in which Fatness was not genetically connected with other the herd has been taken as a fixed effect. Main carcass traits. These correlations are favourable for emphasis in studying carcass traits has been on beef work towards the breeding goal of carcasses with breeds. In Hereford and some other beef breeds, high fleshiness and low fatness that give the type of heritability of carcass traits has been estimated to be beef favoured by consumers at the present. Genetic moderate (Wilson et al., 1976; Koch, 1978; Lamb et correlation between fleshiness and slaughter weight al., 1990; Robinson et al., 1990; Arnold et al., 1991; may however be somewhat overestimated, for large Gregory et al., 1994; Wheeler et al., 1996). In dairy carcasses appear more muscular than small carcasses
¨
TABLE 6 of data sets in multitrait analyses, other alternatives Heritabilities of studied traits from different types of multitrait were also considered. Sire model is the simplest
a
models
model for estimating breeding values for sires, if the
b
Trait / Breed, sex Model 1 Model 2a / 2b Model 3 only group of animals for which the breeding values
Slaughter weight for carcass traits need to be estimated is sires AyB – 0.12 / 0.12 0.12 themselves. However, there are also relationships on HFrB 0.09 0.08 / 0.08 0.08 maternal side through common grandsires, and those
AyH 0.13 0.11 / 0.09 0.13
can not be taken into account by sire model. In that
HFrH 0.11 0.00 / 0.00 0.05
aspect, a better option could be the use of a sire
Fleshiness maternal grandsire model. However, in this study AyB – 0.20 / 0.21 0.20 there were only minor differences in estimated HFrB 0.20 0.25 / 0.25 0.23 variance and covariance components or their
pro-AyH 0.17 0.17 / 0.16 0.15
portions between different models. For the sake of
HFrH 0.31 0.21 / 0.20 0.27
computational resources, sire or sire maternal
gran-Fatness dsire models are often preferred to animal models,
AyB – 0.14 / 0.14 0.15 and sire maternal grandsire models, in turn, are HFrB 0.08 0.08 / 0.07 0.10 preferred to sire models as they include the sire path
AyH 0.13 0.12 / 0.10 0.13
of maternal pedigree. Nevertheless, estimated
breed-HFrH 0.20 0.11 / 0.10 0.12
ing values for carcass traits in cows may also be of
a
Model 1: Animal model, REML method (three traits in AyB
interest in herd level. For that reason animal model, could not be solved by animal model due to limits in
computation-whenever it is computationally feasible, might be the al resources). Model 2a: Sire model, REML method. Model 2b:
most suitable model for practical evaluation of Sire model, GS method. Model 3: Sire–maternal grandsire model,
GS method. carcass traits.
b
AyB–Ayrshire bulls; HFrB–Holstein-Friesian bulls; AyH– The type and size of data in this study gave Ayrshire heifers; HFrH–Holstein-Friesian heifers.
reliable estimates for genetic parameters, and might therefore be suitable also for the estimation of the management changes only within the limits of the breeding values. Obtaining estimates of breeding genetic potential of the animal which, in turn, can be value for carcass quality traits for young AI-bulls further improved by breeding. does not lengthen generation interval, because the When estimating breeding values for carcass traits beef producing progeny are slaughtered already of Ay and HFr young bulls it might be feasible to before the milk producing daughters complete their analyse the carcass data of their offspring for both first lactation. Moreover, Liinamo and van Arendonk breeds and sexes together, even though there were (1998) have shown that genetic improvement in some differences in genetic parameters between carcass traits does not retard genetic response in milk breeds and sexes. Combining the two data sets might production traits. Thus breeding for carcass quality reduce the number of herds with one or few carcas- in dairy cattle seems a quite feasible option for ses, and thus improve the structure of data. However, improving the overall economy of cattle producing in this study this effect was not strong when breeds sector.
1995. Relationship between USDA and Japanese beef grades. represent all the young AI-bulls used better, and
Meat Sci. 39, 87–95. provide them more slaughtered progeny than a
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INTERBULL, 1996. Sire evaluation procedures for non-dairy-of the young bulls had only few progeny in the data.
production and growth and beef production traits practiced in Thus it is not feasible to estimate breeding values for
various countries. Bull. no. 13, Int. Bull Eval. Serv., Uppsala, young AI-bulls until there is data available from Sweden, 201 pp.
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