Estimates of genetic parameters for carcass traits in Finnish Ayrshire and Holstein-Friesian

Livestock Production Science 64 (2000) 203–213 www.elsevier.com / locate / livprodsci

Estimates of genetic parameters for carcass traits in Finnish Ayrshire and Holstein-Friesian ¨ Parkkonen 1 , Anna-Elisa Liinamo*, Matti Ojala Paivi 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 In Finland and many other European countries, a major part of beef is produced as a by-product of dairy production. The breeding programs of dairy cattle seldom involve carcass quality, and beef *E-mail address: [email protected] (A.-E. Liinamo) 1 Present address: Animal Production Research, Agricultural Research Centre of Finland, FIN-31600 Jokioinen, Finland.

production traits are taken into account only indirectly (e.g., INTERBULL, 1996). In Finland, young bulls to be used in artificial insemination are evaluated based on their own yearling weight or growth at test station (Ojala, 1984). Previously, live weights of dairy cows were also reported in bull evaluation (Hietanen and Ojala, 1995). Growth and live weight are correlated with carcass traits but the correlation may not be strong enough to improve the poor beef producing ability of Finnish dairy breeds. Carcass quality evaluated at slaughter houses

0301-6226 / 00 / $ – see front matter  2000 Elsevier Science B.V. All rights reserved. PII: S0301-6226( 99 )00144-X

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predicts beef producing ability better than growth or live weight. In addition, using progeny results leads to improved reliability compared to individual measurements of young bulls. However, carcass data has not been available for animal breeding because slaughter houses have traditionally used their own identification system, and it has not been possible to combine this system with other cattle registers. In the new European cattle identification system introduced in Finland in the beginning of 1995 the same identity follows an animal from birth to carcass, thus enabling environmental and pedigree information to be merged with carcass data. Carcass traits of cattle, e.g., slaughter weight, fleshiness and fatness, have been studied considerably, and most of the traits have been found to be of high or moderate heritability (Wilson et al., 1976; Koch, 1978; Lamb et al., 1990; Robinson et al., 1990; Arnold et al., 1991; Gregory et al., 1994; Wheeler et al., 1996). However, the results can not be easily generalised into Finnish cattle population, because most of the studies have involved beef breeds which are only of marginal importance in Finland. Moreover, definitions of carcass traits and the models used in analyses differ in various countries (e.g., Jones et al., 1994; EU-ROP, 1995; Harris et al., 1995; United States Department of Agriculture, 1997). The prerequisites for incorporating carcass traits into Finnish dairy cattle breeding program are estimation of genetic parameters and development of an appropriate evaluation model for carcass traits in Finnish cattle population. The aim of this study was to investigate the factors affecting carcass traits in Finnish Ayrshire and Holstein-Friesian, and to estimate heritabilities in data sets divided by breed and sex. In addition, performance of animal model, sire model and sire maternal grandsire model were compared in order to find the model best suited for practical evaluation of carcass traits in Finnish dairy cattle breeding program.

2. Materials and methods Data for analyses was collected from Northern and Central Finland in two slaughter houses owned by Lihakunta Oyj, which began to identify slaughtered

animals according to the European cattle identification system as the first company in Finland. The period of data collection covered 20 months from the beginning of January 1996 to the end of August 1997. During that time more than 110 000 head of cattle were slaughtered in the two participating slaughter houses. Pedigrees were obtained from the database of Agricultural Data Processing Centre including parents and grandparents for the slaughtered animals registered within milk recording system. The pedigree data set included over 180 000 animals. Sixty-three percent of slaughtered animals were Finnish Ayrshires (Ay) and 26% Holstein-Friesians (HFr). Other breeds and their crosses were too rare in the data set for estimation of genetic parameters, and it was also considered important to study carcass traits in the most common breeds that produce the majority of beef. Thus, only purebred Ay or HFr carcasses were included in the analyses. Data was further limited to bulls and heifers that were slaughtered at the age of 300 through 899 days, with carcasses required to have slaughter weight of at least 130 kg. Cows were excluded from the analyses in this study because cow information will not be included in the possible carcass quality indices. The data was divided in subsets to study whether the factors affecting carcass traits and their genetic parameters differ in different breeds and sexes. The primary subsets were Ay bulls (AyB) with 22 231, HFr bulls (HFrB) with 8711, Ay heifers (AyH) with 5328 and HFr heifers (HFrH) with 1918 carcasses. These subsets were analysed with all the models and methods used in this study. Combinations within sexes and breeds were considered in combined subsets of Ay and HFr bulls (AyHFrB), Ay and HFr heifers (AyHFrH), Ay bulls and heifers (AyBH), and HFr bulls and heifers (HFrBH). Finally, all animals were analysed together (AyHFrBH). These combined data sets were analysed only with animal model using univariate analysis. The traits studied were slaughter weight, and carcass fleshiness and fatness. Slaughter weight is measured within 2 h from slaughter, and it is the weight of carcass without head, hide and abdominal organs, minus 2% of hot carcass deduction. Fleshiness and fatness are judged subjectively according to

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European Union SEUROP classification system (EUROP, 1995). In Finland, fleshiness is judged in 11 classes: P 2 , P, P 1 , O 2 , O, O 1 , R 2 , R, R 1 , U and E, from worst to best respectively. Fatness is judged in five classes numbered from 1 to 5, with class 1 being the leanest and class 5 the fattest. In this study, fleshiness was transformed to numbers so that the classes from P 2 to R 1 were replaced by numbers from 1 to 9. Due to the lack of subclasses in U and E, they were numbered as 11 and 14, respectively. Figs. 1 and 2 illustrate frequency distributions of fleshiness and fatness in the data, respectively. Data editing and preliminary analyses were done on WSYS and WSYS-L software (Vilva, 1992; 1997). For estimation of variance and covariance components two methods were used. Animal models and sire models were solved by VCE4.0 software (Groeneveld, 1997) using Restricted Maximum Likelihood (REML) method. Statistical significance of contrasts between different levels of fixed effects in mixed models was tested by F-test in PEST software (Groeneveld, 1990). Sire maternal grandsire models could not be solved using VCE4.0 due to the multiplier ]12 in the incidence matrix Z. Thus, estimates of variance and covariance components from sire maternal grandsire models were solved by Gibbs

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Sampling (GS) method with MTGSAM software (Van Tassell and Van Vleck, 1995). For comparison of methods also sire models were analysed with GS method. The following animal model was assumed in analysing the within breed and sex data subsets AyB, HFrB, AyH and HFrH: y ijklmn 5 m 1 slaughter house i 1 year-month j 1 age k 1 cl 1 a m 1 ´ijklmn where y ijklmn 5record of slaughter weight, fleshiness or fatness, m 5overall mean, slaughter house i 5fixed effect of ith slaughter house (i51,2), year-month j 5 fixed effect of jth month of slaughter ( j51–20), age k 5fixed effect of kth age class (k51–14), cl 5 random effect of lth herd, a m 5random additive genetic effect of mth animal, and ´ijklmn 5random residual effect. When analysing combined data sets AyHFrB and AyHFrH, also breedo 5fixed effect of oth breed (o51,2) was included, and in data sets AyBH and HFrBH sex p 5fixed effect of pth sex ( p51,2) was included. When analysing all animals, data set AyHFrBH, both breed and sex were included in the model in addition to the previous factors. There were two slaughter houses with 55% of

Fig. 1. Frequency distribution of fleshiness in bulls and heifers.

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Fig. 2. Frequency distribution of fatness in bulls and heifers.

carcasses coming from the bigger one. The original 20-month data collection period was retained in 20 classes for year-month of slaughter because there was no logical connection between the consecutive months or the same months in different years. Rather than using age at slaughter as covariate it was classified in 14 classes (1510, 11 and 12 mo, 2513 mo, 3514 mo, . . . , 12523 mo, 13524 and 25 mo and 14526 to 30 mo of age). Slaughtered animals originated from 6740 herds, with a quarter of herds having only one observation in the data. Dividing data in subsets further increased the proportion of small herds in the data subsets. The herds with few carcasses could not be left out without losing a considerable amount of information, so it was decided to keep all the herds in analyses as a random sample of herds in the area. The distributions of random effects were assumed multivariate normal with zero means and var(c) 5 Is 2c , var(a) 5 As a2 , and var(´) 5 Is ´2 . When using multitrait models, the expected values of random effects and the covariances between them were assumed zero. The variance of each random effect for the three traits (i51,2,3) was assumed to be 2 var(c i ) 5 Is 2ci,i , var(a i ) 5 As ai,i and var(´i ) 5 Is ´2 i,i . The covariances between the random effects in

different traits (i,i9 51,2,3 and i ± i9 ) were assumed to be cov(c i ,c i 0 ) 5 Isci,i 9 , var(a i , a i 9 ) 5 Asai, i 9 and cov(´i ,´i 9 ) 5 Is´ i,i 9 . Heritability was estimated as the proportion of the additive genetic variance of the total variance, h 2 5 s 2a /(s 2a 1 s 2c 1 s ´2 ). Within herd heritabilities (h 2w ) were estimated as h w2 5 s a2 /(s a2 1 s 2´ ). In sire model, the genetic effect of an animal was substituted by the genetic effect of a sire, and in sire maternal grandsire model the same section was substituted by the genetic effect of the sire and ]12 of the genetic effect of the maternal grandsire. All observations were kept in analyses when using the animal model. With the sire model, only sires with five or more progeny at the data set were included, and with sire maternal grandsire model a sire or a maternal grandsire was accepted only if it existed in a pedigree of at least two slaughtered animals. The restrictions decreased the number of observations but even more they decreased the number of sires thus increasing the number of progeny per sire (Table 1). The effect of restrictions on parameter estimates was studied by comparing the solutions obtained using animal model both for the unlimited data sets and the data sets limited for sire maternal grandsire model.

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Table 1 Number of slaughtered animals (N) and sires (S) in different models and data subsets Breed, sex a

AyB HFrB AyH HFrH a

Animal model

Sire model

Sire maternal grandsire model

N

S

N

S

N

S

22 231 8711 5328 1918

892 361 558 248

21 518 8440 4904 1688

366 180 310 126

21 273 8305 4815 1697

478 226 400 177

AyB–Ayrshire bulls; HFrB–Holstein-Friesian bulls; AyH–Ayrshire heifers; HFrH–Holstein-Friesian heifers.

The results from different methods were compared by solving sire models both by REML and GS methods. When using GS method, the slaughter house, the year-month of slaughter and the age at slaughter were given flat prior distributions. MTGSAM software provides inverted Wishart distribution for the prior distribution of variance and covariance components. Starting values were derived from the models solved by REML method. The convergence criterion of Gauss–Seidel iteration was 0.0001. GS algorithm was repeated for 30 000 rounds saving the solutions of every 30th round. Thus, the sample size in point estimation was 934.

3. Results The average slaughter weight for bulls was 273 kg and for heifers 203 kg (Table 2). Carcasses of HFr bulls were on average 10 kg heavier than carcasses of Ay bulls, while in heifers the difference between breeds was 8 kg. The average fleshiness of all carcasses was 4.3, i.e., between classes O2 and O. Thus an average carcass had profiles from straight to concave, and average muscle development (EU-ROP, 1995). Carcasses of bulls were classified on average one grade better than carcasses of heifers, and HFr 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 Trait / Breed, sex a

N

x

s

V%

Min

Max

Slaughter weight, kg AyB HFrB AyH HFrH

22 231 8711 5328 1918

270 280 201 209

40.8 41.6 35.8 37.5

15.1 14.9 17.8 17.9

130 131 130 130

466.0 490.5 412.5 354.5

Fleshiness AyB HFrB AyH HFrH

22 231 8711 5328 1918

4.43 4.75 3.50 3.85

0.99 1.02 0.93 1.03

22.4 21.4 26.6 26.8

0 0 0 0

11 11 8 14

Fatness AyB HFrB AyH HFrH

22 231 8711 5328 1918

2.15 2.19 2.69 2.81

0.41 0.45 0.79 0.89

18.8 20.5 29.3 31.7

0 1 0 0

5 5 5 5

a

AyB–Ayrshire bulls; HFrB–Holstein-Friesian bulls; AyH–Ayrshire heifers; HFrH–Holstein-Friesian heifers.

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fatness of all carcasses was 2.27. In class 2, carcasses are slightly fat covered with flesh visible almost everywhere (EU-ROP, 1995). Carcasses of heifers were on average 0.5 grades fatter than carcasses of bulls. HFr heifers were also fatter than Ay heifers, but there was no difference between breeds in bulls. Slaughter weights as well as fleshiness and fatness grades tended to decrease during the 20 month observation period. However, the trend was neither linear nor similar in different data subsets. The differences between year-months were statistically significant (P.0.05) in all data subsets. The average ages at slaughter varied between months as well. On average, the animals were slaughtered at the age of 18.5 months. Heifers were 1.5 months older than bulls at slaughter, and HFr animals were slaughtered 0.5 months younger than Ay animals. Slaughter weight increased from the youngest to the oldest age class by 115 kg in bulls and by 90 kg in heifers. Fleshiness and fatness increased also with age. Improvement of fleshiness was faster in bulls but gain of fat was faster in heifers. In most data subsets, however, the heaviest carcasses with the highest fleshiness and fatness grades were not in the oldest age class. Estimates of heritability for slaughter weight were relatively low in all data subsets, varying from 0.07 to 0.14 (Table 3). Respective estimates of within herd heritability were somewhat higher, from 0.15 to 0.29. Heritabilities in HFr data sets were lower than heritabilities in Ay data sets. Variation between herds caused approximately one half of the total variance in slaughter weight. Fleshiness was estimated to have heritability of 0.16–0.31, within herd heritability of 0.29–0.39 and the fraction of total variance due to herds of 0.20– 0.26, depending on the data set used in the analyses (Table 3). Estimates of heritability for fatness were about the same magnitude as for slaughter weight, varying between 0.08 and 0.16 in different data sets. Estimated within herd heritabilities for the trait were from 0.12 to 0.29, and the herds caused about 23– 47% of the total variance of fatness. Estimates of genetic correlation between slaughter weight and fleshiness were from 0.65 to 0.66 in AyB, AyH and HFrH, and 0.38 in HFrB (Table 4). Genetic correlations between slaughter weight and fatness, and fleshiness and fatness, instead, were

estimated to be low, at most 0.21, in AyB, HFrH and AyH. Corresponding estimates from HFrH were outside this range; however, the structure of HFrH data set was poor due to the large number of herds and sires compared to the small number of records. All within herd correlations were high, especially the correlations between slaughter weight and fleshiness or fatness that were up to 0.73–0.93 depending on the data set (Table 5). There was little difference between phenotypic and environmental correlations, of which the correlation between slaughter weight and fleshiness was the highest (0.53–0.74), the exception being again the data set HFrH. Restrictions on data for sire and sire maternal grandsire models removed records with little or no connection with other records. Restrictions did not, however, affect estimates of heritability by more than 0.01 units when differently restricted HFrB and AyH datasets were analysed with animal model. Thus results from different models are comparable, although the data sets used in the analyses were not exactly the same. The results for sire models that were obtained either with REML or GS methods from the same data sets did not differ from each other either, showing that results by different methods agree with each other (Table 6). No matter what model or method was used, the estimated heritabilities and fractions of total variance due to herds in the three biggest data sets were almost the same, differences being in the bounds of standard errors of the estimates.

4. Discussion The data used in this study represented well the overall carcass quality of all the bulls and heifers slaughtered in Finnish slaughter houses in 1996 (TIKE, 1997). Only in fleshiness carcasses in this study were 0.20 units poorer than the average in the whole country. This difference was probably due to the exclusion of beef breeds from the data set in this study. Demand for beef in Finland varied somewhat during the data collection period. This caused uneven numbers of animals to be slaughtered in different months and the average age of animals at slaughter to fluctuate. At the highest month, 3254 animals

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Table 3 Number of records (N), number of herds (n), estimates of heritability (h 2 ) and their standard errors (se h 2 ), estimates of within herd heritability (h 2w ), and herd effects (c 2 ) and their standard errors (se c 2 ) for studied traits from univariate analyses with animal model Trait / Breed, sex a

N

n

h 2 6se h 2

h w2

c 2 6se c 2

Slaughter weight AyB HFrB AyHFrB AyH HFrH AyHFrH AyBH HFrBH AyHFrBH

22 231 8711 30 942 5328 1918 7246 27 559 10 629 38 188

4381 2957 5140 2903 1232 3597 5797 3570 6740

0.1360.01 0.0960.01 0.1160.01 0.1460.02 0.1060.04 0.1460.02 0.1260.01 0.0760.01 0.1160.01

0.26 0.19 0.24 0.29 0.23 0.28 0.23 0.15 0.21

0.5260.01 0.5360.01 0.5360.01 0.5260.01 0.5760.02 0.5160.01 0.4760.01 0.5060.01 0.4860.01

Fleshiness AyB HFrB AyHFrB AyH HFrH AyHFrH AyBH HFrBH AyHFrBH

22 225 8709 30 934 5328 1915 7242 27 552 10 624 38 176

4381 2956 5140 2903 1231 3596 5797 3569 6740

0.1760.01 0.2260.02 0.1860.01 0.1760.02 0.3160.05 0.2160.02 0.1660.01 0.2160.02 0.1760.01

0.22 0.28 0.24 0.23 0.39 0.27 0.20 0.26 0.21

0.2460.01 0.2260.01 0.2460.01 0.2660.01 0.2060.02 0.2560.01 0.2360.01 0.2060.01 0.2260.01

Fatness AyB HFrB AyHFrB AyH HFrH AyHFrH AyBH HFrBH AyHFrBH

22 225 8711 30 936 5328 1916 7243 27 552 10 627 38 179

4381 2957 5140 2903 1231 3596 5797 3570 6740

0.1260.01 0.0860.01 0.1060.01 0.1460.02 0.1660.04 0.1460.02 0.1260.01 0.1060.02 0.1060.01

0.16 0.12 0.14 0.23 0.29 0.23 0.18 0.15 0.15

0.2360.01 0.2960.01 0.2660.01 0.3760.01 0.4760.02 0.3860.01 0.3060.01 0.3560.01 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 houses, whereas at the lowest month the number was only 671. The maximum difference between monthly average ages at slaughter was 33 days. Since in some months it was not possible to get all animals slaughtered at the planned age, part of the animals grew over aimed finishing point and gained fat. A significant part of overaged animals may however have been put on restricted feeding, as they neither gained fat nor improved in fleshiness scores. Possibly this is why neither the heaviest carcasses nor the highest average fleshiness and fatness grades were in the oldest age class.

Traditionally the carcass quality has been better in Finnish Friesian than in Finnish Ayrshire, but importation of Holstein to the Finnish Friesian population has weakened carcass quality of Finnish Holstein-Friesian compared to Ayrshire (Liinamo, 1997). In this study, percentage of Holstein genes among the slaughtered animals was not taken into account, but in the HFr data, carcasses were still heavier and had higher grades in fleshiness and fatness than Ay carcasses. Breed differences in fleshiness and fatness were larger in heifers than in bulls. Heifers were more susceptible to gain fat with age, and differences in carcass quality between sexes

P. Parkkonen et al. / Livestock Production Science 64 (2000) 203 – 213

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Table 4 Estimates of genetic parameters for studied traits from multitrait analysis with animal model a Breed, sex b / Trait

Trait

AyB 1 Slaughter weight 2 Fleshiness 3 Fatness

1 0.1360.01 0.65 0.36

2 0.6660.03 0.1760.01 0.27

HFrB 1 Slaughter weight 2 Fleshiness 3 Fatness

1 0.0960.01 0.57 0.42

2 0.3860.06 0.2060.02 0.26

3 0.0560.09 0.1260.04 0.0860.01

AyH 1 Slaughter weight 2 Fleshiness 3 Fatness

1 0.1360.01 0.61 0.59

2 0.6660.06 0.1760.02 0.40

3 20.0160.11 0.1860.07 0.1360.01

HFrH 1 Slaughter weight 2 Fleshiness 3 Fatness

1 0.1160.03 0.54 0.68

2 0.6560.07 0.3160.04 0.39

3 0.7660.07 0.4460.10 0.2060.04

3 0.1660.01 0.2160.05 0.1260.01

a Heritabilities on diagonal with standard errors, genetic correlations above diagonal with standard errors, and phenotypic correlations below diagonal. b AyB–Ayrshire bulls; HFrB–Holstein-Friesian bulls; AyH– Ayrshire heifers; HFrH–Holstein-Friesian heifers.

increased with age. The different development of sexes is in agreement with earlier reports (e.g., Berg and Butterfield, 1976). Estimated heritabilities, especially for slaughter weight and fatness, were relatively low. Estimated within herd heritabilities, however, were in range of heritabilities estimated in previous studies, in which the herd has been taken as a fixed effect. Main emphasis in studying carcass traits has been on beef breeds. In Hereford and some other beef breeds, heritability of carcass traits has been estimated to be moderate (Wilson et al., 1976; Koch, 1978; Lamb et al., 1990; Robinson et al., 1990; Arnold et al., 1991; Gregory et al., 1994; Wheeler et al., 1996). In dairy ¨ breeds, Kenttamies (1983) estimated heritabilities for slaughter weight in Finnish Ayrshire bulls as 0.2360.09 and in Friesian bulls as 0.6160.18. In the same study, estimates for fleshiness were 0.1460.08 in Ayrshire and 0.1560.13 in Friesian, and for fatness 0.0660.18 and 0.2660.15 in Ayrshire and Friesian, respectively. In larger data sets of Dutch Black and White and Dutch Red and White bulls,

Table 5 Fractions of total variance due to herds on diagonal with standard errors, within herd correlations above diagonal with standard errors, and environmental correlations below diagonal estimated by multitrait analysis with animal model Breed, sex a / Trait

Trait

AyB 1 Slaughter weight 2 Fleshiness 3 Fatness

1 0.5260.01 0.65 0.38

2 0.8660.01 0.2460.01 0.28

3 0.7460.01 0.6360.01 0.2360.01

HFrB 1 Slaughter weight 2 Fleshiness 3 Fatness

1 0.5460.01 0.61 0.46

2 0.8260.01 0.2460.01 0.29

3 0.7360.01 0.5560.02 0.3060.01

AyH 1 Slaughter weight 2 Fleshiness 3 Fatness

1 0.5260.01 0.74 0.56

2 0.8160.01 0.2660.01 0.44

3 0.8860.01 0.6960.02 0.3760.01

HFrH 1 Slaughter weight 2 Fleshiness 3 Fatness

1 0.5860.02 0.53 0.68

2 0.7360.04 0.2260.02 0.38

3 0.9360.01 0.6960.04 0.4860.02

a AyB–Ayrshire bulls; HFrB–Holstein-Friesian bulls; AyH– Ayrshire heifers; HFrH–Holstein-Friesian heifers.

estimated heritabilities for slaughter weight and fleshiness and fatness measured in SEUROP-scores have been 0.2260.03, 0.2360.03 and 0.2960.03, respectively (de Jong, 1997), and 0.25, 0.26 and 0.30, respectively (Van der Werf et al., 1998). Estimated correlations revealed a positive genetic connection between slaughter weight and fleshiness. Fatness was not genetically connected with other carcass traits. These correlations are favourable for work towards the breeding goal of carcasses with high fleshiness and low fatness that give the type of beef favoured by consumers at the present. Genetic correlation between fleshiness and slaughter weight may however be somewhat overestimated, for large carcasses appear more muscular than small carcasses and may thus unintentionally grade better than small carcasses of corresponding quality. Both the phenotypic and environmental correlations and especially the correlations within herds show that fatness tends to increase with slaughter weight and fleshiness. Hence management and feeding seem to have a key position in the production of animals of high carcass quality. Carcass quality can, however, be altered by

P. Parkkonen et al. / Livestock Production Science 64 (2000) 203 – 213 TABLE 6 Heritabilities of studied traits from different types of multitrait models a Trait / Breed, sex b

Model 1

Model 2a / 2b

Model 3

Slaughter weight AyB HFrB AyH HFrH

– 0.09 0.13 0.11

0.12 / 0.12 0.08 / 0.08 0.11 / 0.09 0.00 / 0.00

0.12 0.08 0.13 0.05

Fleshiness AyB HFrB AyH HFrH

– 0.20 0.17 0.31

0.20 / 0.21 0.25 / 0.25 0.17 / 0.16 0.21 / 0.20

0.20 0.23 0.15 0.27

Fatness AyB HFrB AyH HFrH

– 0.08 0.13 0.20

0.14 / 0.14 0.08 / 0.07 0.12 / 0.10 0.11 / 0.10

0.15 0.10 0.13 0.12

a Model 1: Animal model, REML method (three traits in AyB could not be solved by animal model due to limits in computational resources). Model 2a: Sire model, REML method. Model 2b: Sire model, GS method. Model 3: Sire–maternal grandsire model, GS method. b AyB–Ayrshire bulls; HFrB–Holstein-Friesian bulls; AyH– Ayrshire heifers; HFrH–Holstein-Friesian heifers.

management changes only within the limits of the genetic potential of the animal which, in turn, can be further improved by breeding. When estimating breeding values for carcass traits of Ay and HFr young bulls it might be feasible to analyse the carcass data of their offspring for both breeds and sexes together, even though there were some differences in genetic parameters between breeds and sexes. Combining the two data sets might reduce the number of herds with one or few carcasses, and thus improve the structure of data. However, in this study this effect was not strong when breeds were combined, as the majority of herds represented only one breed. On the other hand, heifers comprise only 20% of carcasses, and carcass quality estimation could be solely based on bulls. Combining sexes may, however, be of value in predicting fatness more accurately, as heifers provide additional information on fatness while most of the bulls were graded as 2. Animal model was fitted to the data first because of its property to take all the relationships into account. Since there were problems due to the sizes

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of data sets in multitrait analyses, other alternatives were also considered. Sire model is the simplest model for estimating breeding values for sires, if the only group of animals for which the breeding values for carcass traits need to be estimated is sires themselves. However, there are also relationships on maternal side through common grandsires, and those can not be taken into account by sire model. In that aspect, a better option could be the use of a sire maternal grandsire model. However, in this study there were only minor differences in estimated variance and covariance components or their proportions between different models. For the sake of computational resources, sire or sire maternal grandsire models are often preferred to animal models, and sire maternal grandsire models, in turn, are preferred to sire models as they include the sire path of maternal pedigree. Nevertheless, estimated breeding values for carcass traits in cows may also be of interest in herd level. For that reason animal model, whenever it is computationally feasible, might be the most suitable model for practical evaluation of carcass traits. The type and size of data in this study gave reliable estimates for genetic parameters, and might therefore be suitable also for the estimation of the breeding values. Obtaining estimates of breeding value for carcass quality traits for young AI-bulls does not lengthen generation interval, because the beef producing progeny are slaughtered already before the milk producing daughters complete their first lactation. Moreover, Liinamo and van Arendonk (1998) have shown that genetic improvement in carcass traits does not retard genetic response in milk production traits. Thus breeding for carcass quality in dairy cattle seems a quite feasible option for improving the overall economy of cattle producing sector. Nevertheless, there were some inadequacies in the data, one of them being the short time period of observations. As Finnish dairy herds in milk recording average about 15 cows (FABA, 1996), there was consequently a large amount of herds with only one or few carcasses in the data. An even more serious defect was that the data did not cover whole country. Regional differences can somewhat be eliminated with statistical model used, but data collected from an area covering the country more widely would

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represent all the young AI-bulls used better, and provide them more slaughtered progeny than a regional data. In fact, the bulls with large numbers of progeny were popular progeny tested bulls, and some of the young bulls had only few progeny in the data. Thus it is not feasible to estimate breeding values for young AI-bulls until there is data available from more slaughter houses.

5. Conclusions Carcasses of Finnish Ayrshire and HolsteinFriesian bulls classify typically in fleshiness in grades O and O2 and in fatness in grade 2, carcasses of heifers being of poorer quality. However, there is variation in carcass quality due to both genetic and environmental effects. As dairy breeds provide most of the beef in Finland, main concern should be on improving management in beef production and introducing a carcass quality index in breeding program of dairy breeds. The latter is possible when the national cattle identification system is in use in all major slaughter houses in the country.

References Arnold, J.W., Bertrand, J.K., Benyshek, L.L., Ludvig, C., 1991. Estimates of genetic parameters for live ultrasound, actual carcass data, and growth traits in beef cattle. J. Anim. Sci. 69, 985–992. Berg, R.T., Butterfield, R.M., 1976. In: New Concepts in Cattle Growth, Sydney University Press, p. 240. EU-ROP, 1995. Community scale for the classification of carcasses of adult bovine animals. Office for Official Publications of the European Communities (EU-ROP), Luxembourg. ¨ FABA, 1996. Kotielainjalostuksen tilastokirja (Statistics of animal breeding). Finn. Anim. Breed. Assoc., Vantaa, Finland, 61 pp. Gregory, K.E., Cundiff, L.V., Koch, R.M., Dikeman, M.E., Koohmaraie, M., 1994. Breed effects, retained heterosis, and estimates of genetic and phenotypic parameters for carcass and meat traits of beef cattle. J. Anim. Sci. 72, 1174–1183. Groeneveld, E., 1990. In: PEST User’s Manual, Institute of Animal Husbandry and Animal Behaviour, Federal Agricultural Research Centre, Germany, p. 73. Groeneveld, E., 1997. In: VCE4 User’s Guide and Reference Manual, Institute of Animal Husbandry and Animal Behaviour, Federal Agricultural Research Centre, Germany, p. 49. Harris, J.J., Lunt, D.K., Savell, J.W., Hawkins, E.W., Orme, L.E.,

1995. Relationship between USDA and Japanese beef grades. Meat Sci. 39, 87–95. Hietanen, H., Ojala, M., 1995. Factors affecting body weight and its association with milk production traits in Finnish Ayrshire and Friesian cows. Acta Agric. Scand. 45, 17–25. INTERBULL, 1996. Sire evaluation procedures for non-dairyproduction and growth and beef production traits practiced in various countries. Bull. no. 13, Int. Bull Eval. Serv., Uppsala, Sweden, 201 pp. 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 bij melkkoeien, blanke kalveren, rose´ kalveren en vleesstieren. NRS 97-0755, NRS, Arnhem, The Netherlands. 21 pp. ¨ Kenttamies, H., 1983. Genetical and environmental factors affecting slaughter traits in beef production experiments in Finland. Journal of the Scientific Agricultural Society of Finland 55, 555–570. Koch, R.M., 1978. Selection in beef cattle. III. Correlated response of carcass traits to selection for weaning weight, yearling weight and muscling score in cattle. J. Anim. Sci. 47, 142–150. Lamb, M.A., Robinson, O.W., Tess, M.W., 1990. Genetic parameters for carcass traits in Hereford bulls. J. Anim. Sci. 68, 64–69. Liinamo, A.-E., 1997. Effect of Holsteinization on carcass quality traits of Finnish Friesians. Book of Abstracts 48th EAAP, Vienna, Austria. p. 81. Liinamo, A.-E., van Arendonk, J.A.M., 1998. Combining selection for carcass quality and milk traits in dairy cattle. Proc. 6th World Congr. Genet. Appl. Livest. Prod., Armidale, Australia. 25, 387–390. Ojala, M., 1984. Factors influencing growth of Ayrshire bulls tested at station. Acta Agric. Scand. 34, 167–176. Robinson, D.L., Schneeberger, M., Sivarajasingam, S., Ukkonen, M., Barwick, S.A., McDonald, C.A., Tier, B., Hammond, K., ¨ Sundstrom, B., 1990. Genetic evaluation for carcass traits in Australian beef cattle. In: Proc. of 4th World Congr, Gen. Appl. Livest. Prod, Edinburgh, pp. 461–464. Van Tassell, C.P., Van Vleck, L.D., 1995. A manual for use MTGSAM. A set of fortran programs to apply Gibbs sampling to animal models for variance component estimation. United States Department of Agriculture, Agricultural Research Service. URL5http: / / chuch.agsci.colostate.edu / cvantass / mtgsam.html. 85 pp. TIKE, 1997. Carcass classification statistics of bovine animals. Information Centre of the Ministry of Agriculture and Forestry of Finland (TIKE). United States Department of Agriculture, 1997. Standards for beef carcasses. URL5http: / / www.ams.usda.gov / standards / beefcar.pdf. Vilva, V., 1992. WSYS-D. Dept. of Anim. Sci., Univ. of Helsinki, Finland. Vilva, V., 1997. WSYS-L. Dept. of Anim. Sci, Univ. of Helsinki, Finland. URL5http: / / xiron.pc.helsinki.fi / guide / wsysl / wxnew.html Van der Werf, J.H.J., Van der Waaij, L., Groen, A.F., de Jong, G.,

P. Parkkonen et al. / Livestock Production Science 64 (2000) 203 – 213 1998. Constructing an index for beef characteristics in dairy cattle based on carcass traits. Livest. Prod. Sci 54, 11–20. Wheeler, T.L., Cundiff, L.V., Koch, R.M., Crouse, J.D., 1996. Characterization of biological types of cattle (Cycle IV): Carcass traits and longissimus palatability. J. Anim. Sci. 74, 1023–1035.

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Wilson, L.L., McCurley, J.R., Ziegler, J.H., Watkins, J.L., 1976. Genetic parameters of live and carcass characters from progeny of Polled Hereford sires and Angus-Holstein cows. J. Anim. Sci. 43, 569–576.