Materials and methods carcass fleshiness and fatness. Slaughter weight is

204 P . Parkkonen et al. Livestock Production Science 64 2000 203 –213 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 Finland in the beginning 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

measured within 2 h from slaughter, and it is the Data for analyses was collected from Northern and weight of carcass without head, hide and abdominal Central Finland in two slaughter houses owned by organs, minus 2 of hot carcass deduction. Fleshi- Lihakunta Oyj, which began to identify slaughtered ness and fatness are judged subjectively according to P . Parkkonen et al. Livestock Production Science 64 2000 203 –213 205 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: P 2 , P, P 1 , O 2 , O, O 1 , R 2 , R, R 1 , 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 P 2 to R 1 were replaced by y 5 m 1 slaughter house 1 year-month ijklmn i j numbers from 1 to 9. Due to the lack of subclasses in U and E, they were numbered as 11 and 14, 1 age 1 c 1 a 1 ´ k l m ijklmn respectively. Figs. 1 and 2 illustrate frequency distributions of fleshiness and fatness in the data, where y 5record of slaughter weight, fleshiness ijklmn respectively. or fatness, m 5overall mean, slaughter house 5fixed i Data editing and preliminary analyses were done effect of ith slaughter house i 51,2, year-month 5 j on WSYS and WSYS - L software Vilva, 1992; 1997. fixed effect of jth month of slaughter j 51–20, For estimation of variance and covariance compo- age 5fixed effect of kth age class k51–14, c 5 k l nents two methods were used. Animal models and random effect of lth herd, a 5random additive m sire models were solved by VCE4.0 software genetic effect of mth animal, and ´ 5random ijklmn Groeneveld, 1997 using Restricted Maximum residual effect. When analysing combined data sets Likelihood REML method. Statistical significance AyHFrB and AyHFrH, also breed 5fixed effect of o of contrasts between different levels of fixed effects oth breed o 51,2 was included, and in data sets in mixed models was tested by F-test in PEST AyBH and HFrBH sex 5fixed effect of pth sex p software Groeneveld, 1990. Sire maternal grandsire p 51,2 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 in the incidence matrix Z. Thus, esti- cluded in the model in addition to the previous 2 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. 1. Frequency distribution of fleshiness in bulls and heifers. 206 P . Parkkonen et al. Livestock Production Science 64 2000 203 –213 Fig. 2. Frequency distribution of fatness in bulls and heifers. carcasses coming from the bigger one. The original different traits i,i 9 51,2,3 and i ± i9 were assumed 20-month data collection period was retained in 20 to be covc ,c 5 I s , vara , a 5 A s and i i 0 ci,i 9 i i 9 ai, i 9 classes for year-month of slaughter because there cov´ ,´ 5 I s . i i 9 ´i,i 9 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 s s 1 s 1 s . Within herd heritabilities h a a c ´ w 2 2 2 2 classified in 14 classes 1510, 11 and 12 mo, 2513 were estimated as h 5 s s 1 s . w a a ´ 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 varc 5 animals. The restrictions decreased the number of 2 2 2 I s , vara 5 As , and var´ 5 Is . 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 i 51,2,3 was assumed to be solutions obtained using animal model both for the 2 2 2 v arc 5 I s , vara 5 A s and var´ 5 I s . unlimited data sets and the data sets limited for sire i ci,i i ai,i i ´i,i The covariances between the random effects in maternal grandsire model. P . Parkkonen et al. Livestock Production Science 64 2000 203 –213 207 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 11 HFrB 8711 4.75 1.02 21.4 11 AyH 5328 3.50 0.93 26.6 8 HFrH 1918 3.85 1.03 26.8 14 Fatness AyB 22 231 2.15 0.41 18.8 5 HFrB 8711 2.19 0.45 20.5 1 5 AyH 5328 2.69 0.79 29.3 5 HFrH 1918 2.81 0.89 31.7 5 a AyB–Ayrshire bulls; HFrB–Holstein-Friesian bulls; AyH–Ayrshire heifers; HFrH–Holstein-Friesian heifers. 208 P . Parkkonen et al. Livestock Production Science 64 2000 203 –213 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. Estimates of genetic correlation between slaughter Demand for beef in Finland varied somewhat weight and fleshiness were from 0.65 to 0.66 in during the data collection period. This caused uneven AyB, AyH and HFrH, and 0.38 in HFrB Table 4. numbers of animals to be slaughtered in different Genetic correlations between slaughter weight and months and the average age of animals at slaughter fatness, and fleshiness and fatness, instead, were to fluctuate. At the highest month, 3254 animals P . Parkkonen et al. Livestock Production Science 64 2000 203 –213 209 Table 3 2 Number of records N , number of herds n, estimates of heritability h and their standard errors se , estimates of within herd h 2 2 2 heritability h , and herd effects c and their standard errors se for studied traits from univariate analyses with animal model w c 2 a 2 2 2 Trait Breed, sex N n h 6se h c 6se h 2 w c 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. were slaughtered in the two participating slaughter Traditionally the carcass quality has been better in houses, whereas at the lowest month the number was Finnish Friesian than in Finnish Ayrshire, but im- only 671. The maximum difference between monthly portation of Holstein to the Finnish Friesian popula- average ages at slaughter was 33 days. Since in some tion has weakened carcass quality of Finnish Hol- months it was not possible to get all animals stein-Friesian compared to Ayrshire Liinamo, slaughtered at the planned age, part of the animals 1997. In this study, percentage of Holstein genes grew over aimed finishing point and gained fat. A among the slaughtered animals was not taken into significant part of overaged animals may however account, but in the HFr data, carcasses were still have been put on restricted feeding, as they neither heavier and had higher grades in fleshiness and gained fat nor improved in fleshiness scores. Pos- fatness than Ay carcasses. Breed differences in sibly this is why neither the heaviest carcasses nor fleshiness and fatness were larger in heifers than in the highest average fleshiness and fatness grades bulls. Heifers were more susceptible to gain fat with were in the oldest age class. age, and differences in carcass quality between sexes 210 P . Parkkonen et al. Livestock Production Science 64 2000 203 –213 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 Breed, sex Trait Trait by multitrait analysis with animal model AyB 1 2 3 a Breed, sex Trait Trait 1 Slaughter weight 0.1360.01 0.6660.03 0.1660.01 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 ¨ breeds, Kenttamies 1983 estimated heritabilities for and may thus unintentionally grade better than small slaughter weight in Finnish Ayrshire bulls as carcasses of corresponding quality. Both the pheno- 0.2360.09 and in Friesian bulls as 0.6160.18. In the typic and environmental correlations and especially same study, estimates for fleshiness were 0.1460.08 the correlations within herds show that fatness tends in Ayrshire and 0.1560.13 in Friesian, and for to increase with slaughter weight and fleshiness. fatness 0.0660.18 and 0.2660.15 in Ayrshire and Hence management and feeding seem to have a key Friesian, respectively. In larger data sets of Dutch position in the production of animals of high carcass Black and White and Dutch Red and White bulls, quality. Carcass quality can, however, be altered by P . Parkkonen et al. Livestock Production Science 64 2000 203 –213 211 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. were combined, as the majority of herds represented Nevertheless, there were some inadequacies in the only one breed. On the other hand, heifers comprise data, one of them being the short time period of only 20 of carcasses, and carcass quality estima- observations. As Finnish dairy herds in milk record- tion could be solely based on bulls. Combining sexes ing average about 15 cows FABA, 1996, there was may, however, be of value in predicting fatness more consequently a large amount of herds with only one accurately, as heifers provide additional information or few carcasses in the data. An even more serious on fatness while most of the bulls were graded as 2. defect was that the data did not cover whole country. Animal model was fitted to the data first because Regional differences can somewhat be eliminated of its property to take all the relationships into with statistical model used, but data collected from account. Since there were problems due to the sizes an area covering the country more widely would 212 P . Parkkonen et al. Livestock Production Science 64 2000 203 –213 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 Hietanen, H., Ojala, M., 1995. Factors affecting body weight and regional data. In fact, the bulls with large numbers of its association with milk production traits in Finnish Ayrshire progeny were popular progeny tested bulls, and some and Friesian cows. Acta Agric. Scand. 45, 17–25. 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. more slaughter houses. Jones, S.D.M., Thorlakson, B., Robertson, W.M., 1994. The effect of breed type on beef carcass characteristics and Canadian carcass grade. Can. J. Anim. Sci. 74, 149–151. de Jong, G., 1997. Genetische parameters voor slachtkenmerken

5. Conclusions