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Genetic parameters for lean meat yield, meat quality,

reproduction and feed efficiency traits for Australian pigs

1. Description of traits and heritability estimates

a ,

_*

b a

S. Hermesch

, B.G. Luxford , H.-U. Graser

a

Animal Genetics and Breeding Unit, joint Institute of NSW Agriculture and The University of New England, University of New England, Armidale, NSW, 2351, Australia

b

Bunge Meat Industries, Corowa, NSW, 2646, Australia

Received 15 September 1998; received in revised form 12 November 1999; accepted 9 December 1999

Abstract

Data from 1799 Large White and 1522 Landrace boars were used to obtain heritability and litter effect estimates for performance, carcass and meat quality traits applying a REML procedure fitting an animal model. Analysed traits included average daily gain from three to 18 weeks of age (ADG1) and average daily gain, feed intake and feed conversion ratio from 18 to 22 weeks of age. Carcass traits included two backfat measurements and one muscle depth measurement taken on the live animal using realtime ultrasound and on the carcass using Hennesy Chong equipment. Further carcass traits included weight of the whole back leg (BLW) and weight of the ham (LMW). Meat quality traits included pH recorded 45 min and 24 h after slaughter, colour of the m. longissimus dorsi and m. multifidus dorsi, drip loss percentage and intramuscular fat content. For ADG1 heritability and litter effect estimates were 0.10(6_{0.05) and 0.20(}6_{0.04) in Large White and}

0.48(6_{0.09) and 0.08(}6_{0.04) in Landrace. Data structure might have limited simultaneous estimation of these two effects.}

Heritability estimates ranged from 0.13 to 0.27(6_{0.04 to 0.05) for further performance traits and from 0.45 to 0.62(}6_{0.05 to}

0.07) for backfat measurements. Heritability estimates were lower for Hennesy Chong measurements in comparison to realtime ultrasound measurements. Heritability estimates for BLW and LMW were 0.22(6_{0.05) and 0.38(}6_{0.07). Estimates}

of heritabilities for meat quality traits ranged from 0.14 to 0.35(6_{0.04 to 0.06).}  _{2000 Elsevier Science B.V. All rights}

reserved.

Keywords: Pigs; Production; Carcass; Meat quality traits; Genetic parameters

1. Introduction

For successful pig production enterprise all econ-omically important aspects of producing pork have to *Corresponding author. Tel.: 161-267-73-3787; fax: 1

61-be considered. Besides efficient lean meat growth the 267-73-3266.

E-mail address: skahtenb@metz.une.edu.au (S. Hermesch). reproductive performance of the sow and marketing 0301-6226 / 00 / $ – see front matter  2000 Elsevier Science B.V. All rights reserved.

aspects of pork are of increasing importance. Histori- Performance records included the animal’s weight cally, reproductive performance and meat quality at 21 days of age, at 18 weeks of age when the characteristics were not considered as feasible breed- animal entered the test station and at 22 weeks of age ing objectives due to low heritabilities, difficulty of recorded shortly before slaughter. The trait recorded measurement and lack of economic importance before start of test was growth rate from three to 18 (McPhee, 1982). However, decline in pork quality weeks of age (ADG1). During this period of time and lack of improvement in reproductive traits animals were group penned in contrast to station suggests that these areas require greater considera- testing where animals were single penned. Traits tion (Ollivier et al., 1990). recorded in the test station included growth rate Meat quality has become a major concern in pig (ADG2), daily feed intake (FDINT) and feed conver-production. In Australia, genetic improvement for sion ratio (FCR). Pigs were fed ad libitum in both meat quality has been achieved by selection against housing systems.

stress susceptible pigs using the halothane test. Carcass traits included real time ultrasound mea-Studies in Europe showed that in the absence of the surements and measurements taken in the abattoir halothane gene meat quality characteristics still have with a Hennesy Chong grading machine. Real time a genetic variation of 20% which could be used in ultrasound measurements were recorded the day breeding programs (de Vries et al., 1994b). Meat before slaughter on the live animal and included fat quality can be described through a range of charac- depth at the P2 site which is located 6.5 cm of the teristics but the meat quality deficiency which is of mid line at the last rib (LFDP2) and fat and eye most concern to the consumer is pale, soft and muscle depth between the third and fourth last ribs exudative meat (PSE) (Jeremiah, 1994). Its signifi- (LFD3 / 4, LMD3 / 4). After a lairage time of 18 h cance to the Australian pork industry was shown by pigs were slaughtered and backfat and muscle depth a PRDC survey (PRDC, 1993). The genetic vari- were recorded on the same sites using a Hennesy ability of traits describing PSE meat is unknown for Chong grading machine (FDP2, FD3 / 4, MD3 / 4). In Australian pigs. The objective of this study was to addition, total weight of the left back leg (BLW) and obtain genetic parameters for meat quality, carcass lean meat weight of the back leg (LMW) were and performance traits as well as reproductive traits obtained on the day after slaughter as additional of the sow. Within this first paper heritability esti- carcass characteristics. To obtain lean meat weight, mates for production, carcass and meat quality traits the left back leg was derinded, defatted and slash obtained from a commercial breeding herd are boned, excluding the hock muscles.

presented. Meat quality traits consisted of pH measured 45

min after slaughter (pH45). Meat quality traits recorded 24 h after slaughter were taken on a chop 2. Material and methods including the loin eye area and the belly, which was taken from the anterior end of the left hand side of 2.1. Data recording and testing procedures the middle (between 5th and 6th thoracic vertebrae). Meat quality traits included pH (pH24) and colour of The study was undertaken in collaboration with the m. longissimus dorsi and m. multifidus dorsi Bunge Meat Industries where data were recorded (CLD, CMD). Colour measurements were taken with from July 1992 to June 1995 on 1799 Large White a Minolta Chromameter CR200b recording the L-and 1522 LL-andrace boars. Each week, two male value which describes the brightness of meat. A meat piglets per litter were randomly chosen at 21 days of sample was then cut from the m. longissimus dorsi age resulting in weekly batches of approximately 30 and suspended in an air filled plastic bag for a further pigs. The data set included two breeds, Large White 24 h in order to record drip loss percentage (DLP). and Landrace, with 71 and 77 sires, 695 and 608 Intramuscular fat content (IMF) was determined dams and 911 and 792 litters, respectively. Further from meat samples of m. longissimus dorsi with pedigree information was available from 3005 ani- either the Soxtec solvent extraction method (Foster mals comprising four generations. and Gonzales, 1992) or Near Infrared Spectroscopy

(NIR). Near Infrared Reflectance spectra measure- shown in Table 1. In particular growth rate in the test ments were performed with a NIR scanning spec- station is characterised by a high phenotypic vari-trophotometer. This machine was calibrated on 300 ation which exceeds standard deviations presented in samples resulting in a correlation of above 0.95 other studies (i.e. van Steenbergen et al., 1990; between the two measurement techniques and was Mrode and Kennedy, 1993; von Felde et al., 1996). additionally used to allow for a quicker analysis of The average muscle depth was 37.8 mm recorded all samples. Samples were allocated randomly to the with real time ultrasound equipment and 46.6 mm two measurement techniques resulting in 656 sam- when muscle depth was measured with Hennesy ples for the Soxtec solvent extraction method and Chong machine. Besides this higher mean for the 1225 samples for the NIR method. No confounding Hennesy Chong measurement, the standard deviation existed with other systematic effects. was also increased. Gresham et al. (1992) also noted Means and number of observations per trait are these differences between measurement techniques. Table 1

a

Number of records, means and raw phenotypic standard deviations for analysed traits

Unit Number of Mean Standard

records deviation

Weight measurements

Weight at start of test kg 3353 73.3 9.3

Weight at end of test kg 3333 99.5 10.7

Performance traits

ADG1 g / d 3227 616.0 80.1

ADG2 g / d 3227 946.0 185.8

FDINT kg 3252 2.62 0.43

FCR 3221 2.85 0.58

Carcass traits

LFDP2 mm 3223 13.0 2.59

LFD3 / 4 mm 3203 13.1 2.61

LMD3 / 4 mm 2895 37.8 4.56

FDP2 mm 2303 12.9 3.13

FD3 / 4 mm 1383 13.2 3.00

MD3 / 4 mm 1369 46.5 9.57

BLW kg 2562 10.6 1.24

LMW kg 2563 5.7 0.73

Meat quality traits

CLD (1005white) 2535 53.16 4.91

CMD (1005white) 2581 45.78 4.10

pH45 2221 6.36 0.46

pH24 2479 5.74 0.26

DLP % 2705 1.98 1.87

IMF % 1881 1.69 0.67

a

Abbreviations for traits: ADG1: Average daily gain from three to 18 weeks; ADG2: Average daily gain in test station from 18 to 22 weeks; FDINT: Feed intake recorded in test station from 18 to 22 weeks; FCR: Feed conversion ratio defined as feed intake over growth rate (18 to 22 weeks); LFDP2: Backfat depth at P2 measured with real time ultrasound; LFD3 / 4: Backfat depth between the third and fourth last ribs measured with real time ultrasound; LMD3 / 4: Muscle depth of m. longissimus dorsi between the third and fourth last ribs on the live animal; FDP2: Backfat depth at P2 measured with Hennesy Chong grading machine; FD3 / 4: Backfat depth between third and fourth last ribs measured with Hennesy Chong grading machine; MD3 / 4: Muscle depth between third and fourth last ribs measured with Hennesy Chong grading machine; BLW: Weight of whole left back leg; LMW: Weight of slash boned left back leg; pH45: pH measured 45 min after slaughter; pH24: pH measured 24 h after slaughter; CLD: L-value of Minolta chromameter of m. longissimus dorsi; CMD: L-value of Minolta chromameter of m. multifidus dorsi; DLP: Drip loss percentage; IMF: Intramuscular fat content.

2.2. Analysis feed conversion ratio. Significant fixed effects for carcass traits included week of performance record-Analyses of traits were based on mixed models. ing, breed and parity of the sow. Liveweight at The fixed effect part of the model was derived using slaughter and hot carcass weight were included as PROC GLM (SAS, 1991). Relevant fixed effects for linear covariables for backfat and muscle depth production traits as well as the corresponding propor- measurements while age of the animal at slaughter

2

tion of variation explained by these effects (R ) are was fitted as a linear covariable for weight of the presented in Table 2. Fixed effects included week of back leg and ham weight.

performance recording, breed of the animal and The main fixed effect influencing meat quality parity number of the litter the animal was born in. characteristics was date of slaughter which was Week of recording was used to define management equivalent to recording week since all animals were groups since it also accounted for possible pen slaughtered on the same day each week (Table 2). effects the animal was housed in before and during This effect explained 38 and 45% of the total test. Breed was significant for average daily gain variation for pH45 and pH24. Slaughter day was of recorded before pigs entered the test. Parity number less importance for colour measurements, drip loss of the litter the animal originated from was signifi- percentage and intramuscular fat content explaining cant for both growth traits. Weight at test entry was 14 to 20% of the phenotypic variation for these included as a linear covariable for feed intake and traits. Further fixed effects fitted for meat quality

Table 2

2 a

Fixed and random effects for performance, carcass and meat quality traits and total variation explained by fixed effects (R )

Trait Fixed effects Random effects

2

R Week Breed Parity Weight Age Method Weight of sample animal litter Performance traits

ADG1 0.17 *** *** *** ✓ ✓

ADG2 0.18 *** *** ✓ ✓

b

FDINT 0.39 *** *** ✓

b

FCR 0.22 *** *** ✓

Carcass traits

c

LFDP2 0.35 *** *** *** ✓

c

LFD3 / 4 0.37 *** ** *** ✓

c

LMD3 / 4 0.31 *** *** *** ✓

d

FDP2 0.30 *** *** *** ✓

d

FD3 / 4 0.34 *** *** *** ✓

d

MD3 / 4 0.37 *** *** ✓

BLW 0.15 *** *** *** *** ✓ ✓

LMW 0.19 *** *** *** *** ✓ ✓

Meat quality traits

pH45 0.38 *** * ✓

pH24 0.45 *** ✓

CLD 0.18 *** *** ✓

CMD 0.14 *** ✓

DLP 0.18 *** *** * ✓

d

IMF 0.20 *** *** *** *** ✓

a

For further abbreviations see Table 1.

b

Animal weight at 18 weeks.

c

Animal weight at 22 weeks.

d

Hot carcass weight.

traits included breed and method of measurement for with estimates of 0.27 for model one and 0.10 for intramuscular fat content. Weight of meat sample and model two while litter effect had a larger importance

2

hot carcass weight were fitted as linear covariables for this breed (c : 0.20). In contrast, growth rate was for drip loss percentage and intramuscular fat con- highly heritable in Landrace with estimates of 0.57

tent. and 0.48 for both models while litter effect was of

2

Estimation of variance components was based on less importance (c : 0.08). The design of this project an animal model applying a Restricted Maximum provided a similar data structure for both breeds in Likelihood procedure using the DFREML program regard to piglets per litter and management system. (Meyer, 1993). A variance of the likelihood function This excludes systematic effects as a possible reason

28

values of less than 10 was chosen as a conver- for these differences in heritabilities and litter ef-gence criterion. Approximate standard errors of fects. Results from Klassen (1992), who analysed heritability estimates were obtained as described in lifetime average daily gain for a number of Aus-Meyer (1989). Significance of random effects was tralian herds, confirm higher heritabilities for Aus-tested through a likelihood ratio test. Based on this tralian Landrace in comparison with Australian likelihood ratio test, litter effect was only significant Large White. Klassen (1992) who did not fit litter for growth rate, weight of the back leg and lean meat effect as an additional random effect estimated weight of the back leg as an additional random effect heritabilities ranging from 0.23 to 0.40 for Large

(Table 2). White and from 0.32 to 0.61 for Landrace pigs.

It is also possible that the size or structure of data sets in this study did not allow a reliable simulta-3. Results and discussion neous estimation of additive genetic effects and litter effects. To analyse the effect of size of the data set 3.1. Performance traits on estimated parameters, data were subdivided in a separate analysis using only data until a certain Heritability and litter effect estimates differed recording date with break off dates every six month. substantially between Large White and Landrace for For both breeds, genetic parameters did not change average daily gain from three to 18 weeks (Table 3). significantly by adding more data once the data set Heritability estimates were lower for Large White reached approximately 1000 animals. Therefore fur-Table 3

2 2

Heritabilities (h ), litter effects (c ) both with standard errors (s.e.) and variance components for growth rate from three to 18 weeks (ADG1) (full and reduced data set)

2 2 2 2 2

Data set Model h s.e. of h c s.e. of c s

p

a b

Full data LW Model 1 0.27 0.08 3959

c

Model 2 0.10 0.05 0.20 0.04 3921

c

LR Model 1 0.57 0.08 4569

Model 2 0.48 0.09 0.08 0.04 4604

combined Model 1 0.43 0.06 4307

c

Model 2 0.27 0.05 0.15 0.03 4210

Subset LW Model 1 0.39 0.10 3906

c

Model 2 0.17 0.07 0.16 0.04 3843

c

LR Model 1 0.60 0.09 4544

Model 2 0.48 0.10 0.08 0.05 4545

combined Model 1 0.47 0.07 4237

c

Model 2 0.27 0.06 0.14 0.03 3888

a

LW: Large White, LR: Landrace.

b

Model 1: random effects include animal effect and residual effect; Model 2: random effects include animal effect, litter effect and residual effect.

c

ther data collection lead to a decrease in standard genetic parameters for the reduced data set in errors for genetic parameters but did not greatly comparison to the full data set. For example, by not influence the estimates themselves. fitting litter effect a larger part of the variation due to The structure of the data set was based on a litter effect was picked up by the additive genetic random choice of two male piglets per litter. How- variance. These results are in agreement with results ever, it was not always possible to obtain two male from a simulation study by Meyer and Hill (1992) piglets and 14% of Large White pigs and 16% of who found that the sampling correlation does not Landrace pigs had no littermates recorded. To better appear to be dependent on the number of sire separate additive genetic effects and litter effects, families but is affected by their structure.

pigs with no littermates available were removed from For further illustration, the log likelihood was the data. Applying model one, the heritability esti- plotted for a range of heritability and litter effect mate was 0.39 in Large White (Table 4). When estimates for the full Large White data in Fig. 1. The model two was used in the reduced data set top area of the log likelihood represents combina-heritability increased to 0.17 while litter effect tions of heritability and litter effects estimates which decreased to 0.16. In contrast to Large White, are not significant different from the maximum log estimates of heritabilities and litter effects did not likelihood at a heritability of 0.10 and a litter effect change significantly between the full and the reduced of 0.20. This area covers a range of heritability data set in Landrace. estimates of 0.03 to 0.22 and a range of estimates of Meyer and Hill (1992) stated that parameters are litter effects of 0.04 to 0.22 illustrating that a correlated in models with multiple parameters imply- substantial cross-substitution can occur between ing that change in one parameter will lead to a these random effects until the log likelihood is corresponding change in the other parameter. Sam- changed significantly.

pling correlations between parameters are therefore Heritability estimates did not differ significantly often largely negative and in the situation of variance between breeds for further performance traits. There-components estimation, a considerable cross-substi- fore, heritability estimates are only presented from tution between parameters is possible before a the combined analysis. The second growth rate trait significant change in the log likelihood occurs. recorded in the test station (ADG2) was lowly

2

The analysis showed that sampling correlations heritable (h : 0.13) (Table 4). This trait was char-did not change significantly after the data set reached acterised by a high variation. This drastic increase in a size of approximately 1000 animals in both breeds. variation might be caused by random differences in However, for Large White the sampling correlation gut fill at test begin and test end. Given the short between heritability and litter effect estimates was testing period of four weeks these random differ-2_{0.43 for the total data set while it increased in ences have a large influence on recorded weight gain} magnitude (2_{0.54) for the reduced data set. The and therefore growth rate. It was not possible to} higher sampling correlation implies that cross-substi- account for these effects in the model and the tution between the two parameters was larger in the environmental variation was therefore increased. reduced data set. This helps to explain the change in Heritability estimates were moderate for feed intake

2 2

(h : 0.23) but low for feed conversion ratio (h : 0.13). These estimates of heritabilities were similar to estimates presented by Cameron and Curran Table 4

2 2

Heritabilities (h ), litter effects (c ) both with standard errors (s.e.) (1994) but generally lower than heritabilities

pre-a

and phenotypic variance for production traits (full data set) _{sented by Hofer and Schworer (1995), de Vries et al.¨}

2 2 2 2 2

Trait h s.e. of h c s.e. of c s (1994b) and Mrode and Kennedy (1993) indicating

p

that the testing period was too short to measure

ADG1 0.27 0.05 0.15 0.03 4210

growth rate and feed conversion ratio reliably. In

ADG2 0.13 0.04 0.08 0.03 29024

FDINT 0.23 0.04 0.140 addition, Archer et al. (1997) showed that a 70-day FCR 0.15 0.04 0.265 _{test was required to measure growth rate and feed}

a

Fig. 1. Log likelihood values for different estimates of heritability and litter effects for growth rate until 18 weeks in Large White (full data set).

of the measurement in cattle whereas a 35-day test were slightly lower (FDP2: 0.46, FD3 / 4: 0.45). was sufficient to record feed intake reliably. Similar high heritability estimates were obtained by Li and Kennedy (1994) analysing four breeds using

3.2. Carcass traits large data sets and by de Vries et al. (1994a) who

analysed Large White and Landrace data in an Backfat characteristics measured on the live ani- Australian herd. Overall these estimates are in agree-mal with real time ultrasound (LFDP2, LFD3 / 4) ment with average heritability estimates presented in

2

were highly heritable (h : 0.60 and 0.62) (Table 5). the reviews of Stewart and Schinckel (1989) and Heritability estimates for these two backfat measure- Ducos (1994) cited in Sellier (1998).

ments, recorded with the Hennesy Chong machine Heritability estimates were also higher for muscle depth recorded with real time ultrasound than for muscle depth recorded with Hennesy Chong equip-Table 5

2 2 2 2

Heritabilities (h ) and litter effects (c ; second row) with standard ment (h : 0.21 for LMD3 / 4; h : 0.02 for MD3 / 4).

a

errors (s.e.) and phenotypic variance for carcass traits _{The low heritability for muscle depth recorded with}

2 2 2 2 2

Trait h /c s.e. of h /c s Hennesy Chong was mainly due to a five times

p

larger residual variance and as a consequence use of

LFDP2 0.60 0.05 4.51

this trait in genetic evaluations is limited. No

esti-LFD3 / 4 0.62 0.05 4.49

LMD3 / 4 0.21 0.04 15.10 mates of heritabilities were found in the literature for

FDP2 0.46 0.06 7.20 _{muscle depth.}

FD3 / 4 0.45 0.07 6.34

Heritabilities differed substantially between both

MD3 / 4 0.02 0.05 60.87

breeds for weight of the whole back leg with

BLW 0.22 0.05 1.340

estimates of 0.08 for Large White and 0.46 for

0.14 0.03

LMW 0.38 0.07 0.450 Landrace. This weight measurement includes skin,

0.13 0.03 _{bones and fat of the back leg and is an indicator of}

a

heritabilities for this trait between breeds therefore comparison to Large White. Luxford (1995) ex-correspond to differences in heritability estimates for amined the effect of the halothane gene on meat growth rate. Heritabilities for lean meat of the whole quality characteristics in these two breeds. The back leg were 0.27 for Large White and 0.59 for halothane gene did not influence pH24 but influenced Landrace. Pooled estimates were 0.22 for back leg colour and drip loss percentage. This finding

sup-´

weight and 0.38 for lean meat weight (Table 5). ports results presented by Gueblez et al. (1995), These estimates not only correspond to results from McPhee and Trout (1995), Peschke et al. (1993) and

¨

Ollivier (1983) and Sonnichsen and Kalm (1984) Wittmann et al. (1993). Furthermore, McPhee and who analysed ham weight but also correspond to Trout (1995) found that the unfavourable effect of estimates from recent studies by Scholz and Triebler the halothane gene is of higher magnitude in a line (1992) and Hofer et al. (1992) who analysed the that had been selected for lean meat growth. Since weight of primary cuts. Landrace is a slightly leaner breed and has a higher incidence of the halothane gene, extreme cases of 3.3. Meat quality traits PSE meat characterised by a high drip loss

per-centage are anticipated.

Both pH measurements had low heritability esti- Heritability estimate for intramuscular fat content mates (0.15 for pH45 and 0.14 for pH24) (Table 6). of 0.35 was lower than estimates presented by However, in addition to these low heritabilities, Cameron (1990), Lo et al. (1992), de Vries et al. additive genetic variances were low for both pH (1994b) and Goodwin (1995). This low heritability measurements especially for pH24, which limits might be due to differences in the two measurement possible genetic progress in these traits. Heritability techniques applied in this study. However, no differ-estimates were 0.29 and 0.30 for the two colour ences were found in coefficient of determination for measurements and 0.23 for drip loss percentage. intramuscular fat content within measurement tech-Overall, these estimates are in agreement with esti- niques and variance components were not signifi-mates presented in recent studies by Lo et al. (1992), cantly different for the two techniques.

Bidanel et al. (1994a,b), de Vries et al. (1994b), ¨

Goodwin (1995), and Hofer and Schworer (1995) as

well as mean heritability estimates presented in the 4. Conclusions reviews by Hovenier et al. (1993) and Sellier (1998).

Among meat quality traits, only drip loss per- Heritabilities for growth rate differed substantially centage showed larger differences in heritability between Large White and Landrace pigs. Large estimates between breeds. Estimates were 0.20 for White pigs had low heritability estimates while litter Large White and 0.47 for Landrace. The frequency effects were of higher importance. In contrast, of the halothane gene has not been quantified in the heritabilities for growth rate were higher in Landrace Landrace breed but is expected to be higher in and litter effects were not significant for this breed. The structure of the data set might have not allowed a reliable simultaneous estimation of additive genetic effects and litter effects. A testing period of four Table 6

2 weeks might be too short to record average daily

Heritabilities (h ) with standard errors (s.e.) and phenotypic

a _{gain reliably which is indicated in low heritability}

variance for meat quality traits

2 2 2 estimates for growth rate recorded in the test station

Trait h s.e. of h s

p

and feed efficiency. Carcass traits were highly

herit-pH45 0.15 0.04 0.13

able with higher estimates for real time ultrasound

pH24 0.14 0.04 0.039

measurements taken on the live animal than Hennesy

CLD 0.29 0.06 20.25

Chong measurements recorded on the carcass.

CMD 0.30 0.05 13.85

DLP 0.23 0.05 3.07 Heritabilities were low for both pH measurements

IMF 0.35 0.06 0.339 _{and moderate for colour, drip loss percentage and}

a

´ ´ de qualite de la viande du porc charcutier. Journees Rech. Acknowledgements

Porcine en France 27, 155–164. ¨

Hofer, A., Hagger, C., Kunzi, N., 1992. Genetic evaluation of This work was funded by the Pig Research and _{on-farm tested pigs using an animal model. II: Prediction of} Development Corporation under project UNE17P. breeding values with a multiple trait model.

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ack-¨

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40, 277–289. 317–340.

` ´ ´ `

Ducos, A., 1994. Parametres genetiques des caracteres de pro- Meyer, K., 1993. DFREML User Notes V.2.1. Animal Genetics duction chez le porc. Mise au point bibliographique. Techni- and Breeding Unit, University of New England, Armidale,

porc 17 (3), 35–67. Australia.

Foster, M.L., Gonzales, S.E., 1992. Soxtec Fat Analyzer for Meyer, K., Hill, W.G., 1992. Approximation of sampling variances determination of total fat in meat: collaborative study. J. Assoc. and confident estimates of variance components. J. Anim. Official Analytical Chemists International 75, 288–292. Breed. Genet. 109, 264–280.

Goodwin, R.N., 1995. Genetics of pork quality. In: National swine Mrode, R.A., Kennedy, B.W., 1993. Genetic variation in measures improvement federation conference and annual meeting. NSIF, of food efficiency in pigs and their genetic relationships with pp. 15–23, Des Moines, Iowa. growth rate and backfat. Anim. Prod. 56, 225–232.

´ ´

Gresham, J.D., McPeake, S.R., Bernard, J.K., Henderson, H.H., Ollivier, L., 1983. Dix ans d’une experience de selection

in-´ ´

1992. Commercial adaption of ultrasonography to predict pork diviuelle sur des verrats utilitses en insemination artificielle. II. ` ´ ´

carcass composition from live animal and carcass measure- Parametres geneticques. Genet. Sel. Evol. 15, 99–118. ´

ments. J. Anim. Sci. 70, 631–639. Ollivier, L., Gueblez, R., Webb, A.J., van der Steen, H.A.M., ´

Gueblez, R., Paboeuf, F., Sellier, P., Bouffaud, M., Boulard, J., 1990. Breeding goals for nationally and internationally pig ´

Brault, D., Tiran, M.H.L., Petit, G., 1995. Effet du genotype breeding organisations. In: 4th World Congr. on Genet. Appl. halothane sur les performances d’engraissement, de carcasse et to Livest. Prod., Edinburgh, pp. 383–394.

¨ ¨ Peschke, W., Forster, M., Wittmann, W., Dovc, P., Behringer, A., und Pietrain in Schleswig Holstein. 1. Mitteilung: Schatzung

¨ ¨

Odebrecht, J.S., 1993. Leistungsunterschiede MHS-reinerbig von Heritabilitaten. Zuchtungskunde 56, 238–248.

positiver und mischerbiger Pietrain-Schweine aus der Stewart, T.S., Schinckel, A.P., 1989. Genetic parameters for swine ¨

Stationsprufung, Vol. 4 / 93, Gruber INFO, pp. 29–33. growth and carcass traits. In: Young, L.D. (Ed.). Genetics of PRDC, 1993. The cost of pigmeat quality faults: with special Swine. USDA-ARS, Clay Center, Nebraska, pp. 77–79.

¨

reference to pale, soft, exudative meat, AACM International von Felde, A., Rohe, R., Kalm, E., 1996. Genetische Analyse von ¨

Consultance. Merkmalen der Mastleistung und des Schlachtkorperwertes ¨

SAS / STATS, 1991. User’s Guide, Release 6.03 Edition, 1991. stationsgeprufter Jungeber in Einzel- und Gruppenhaltung ¨

Scholz, A., Triebler, G., 1992. Zur Selektionswirkung der mittels Gibbs-Sampling. Zuchtungskunde 68, 305–318. ’biologischen’ Indizes Magerfleischfuttervertung bei zwei van Steenbergen, E.J., Kanis, E., Steen, H.A.M., 1990. Genetic

¨

Schweinerassen. Zuchtungskunde 64, 346–355. parameters of fattening performance and exterior traits of boars Sellier, P., 1998. Genetics of meat and carcass traits. In: tested in central stations. Livest. Prod. Sci. 24, 65–78.

Rothschild, M.F., Ruvinsky, A. (Eds.), The genetics of the pig. Wittmann, W., Peschke, W., Littmann, E., Behringer, J., Birken-¨

CAB International, pp. 463–510. Mayer, S., Dovs, P., Forster, M., 1993. Mast- und

Schlach-¨ ¨ ¨

Sonnichsen, C.M.L., Kalm, J.E., 1984. Parameterschatzung und tleistungen von DL-Kastraten in Abhangigkeit vom

MHS-¨ ¨

traits included breed and method of measurement for with estimates of 0.27 for model one and 0.10 for intramuscular fat content. Weight of meat sample and model two while litter effect had a larger importance

2

hot carcass weight were fitted as linear covariables for this breed (c : 0.20). In contrast, growth rate was for drip loss percentage and intramuscular fat con- highly heritable in Landrace with estimates of 0.57

tent. and 0.48 for both models while litter effect was of

2

Estimation of variance components was based on less importance (c : 0.08). The design of this project an animal model applying a Restricted Maximum provided a similar data structure for both breeds in Likelihood procedure using the DFREML program regard to piglets per litter and management system. (Meyer, 1993). A variance of the likelihood function This excludes systematic effects as a possible reason

28

values of less than 10 was chosen as a conver- for these differences in heritabilities and litter ef-gence criterion. Approximate standard errors of fects. Results from Klassen (1992), who analysed heritability estimates were obtained as described in lifetime average daily gain for a number of Aus-Meyer (1989). Significance of random effects was tralian herds, confirm higher heritabilities for Aus-tested through a likelihood ratio test. Based on this tralian Landrace in comparison with Australian likelihood ratio test, litter effect was only significant Large White. Klassen (1992) who did not fit litter for growth rate, weight of the back leg and lean meat effect as an additional random effect estimated weight of the back leg as an additional random effect heritabilities ranging from 0.23 to 0.40 for Large

(Table 2). White and from 0.32 to 0.61 for Landrace pigs.

It is also possible that the size or structure of data sets in this study did not allow a reliable

simulta-3. Results and discussion neous estimation of additive genetic effects and litter effects. To analyse the effect of size of the data set 3.1. Performance traits on estimated parameters, data were subdivided in a separate analysis using only data until a certain Heritability and litter effect estimates differed recording date with break off dates every six month. substantially between Large White and Landrace for For both breeds, genetic parameters did not change average daily gain from three to 18 weeks (Table 3). significantly by adding more data once the data set Heritability estimates were lower for Large White reached approximately 1000 animals. Therefore

fur-Table 3

2 2

Heritabilities (h ), litter effects (c ) both with standard errors (s.e.) and variance components for growth rate from three to 18 weeks (ADG1) (full and reduced data set)

2 2 2 2 2

Data set Model h s.e. of h c s.e. of c s

p

a b

Full data LW Model 1 0.27 0.08 3959

c

Model 2 0.10 0.05 0.20 0.04 3921

c

LR Model 1 0.57 0.08 4569

Model 2 0.48 0.09 0.08 0.04 4604

combined Model 1 0.43 0.06 4307

c

Model 2 0.27 0.05 0.15 0.03 4210

Subset LW Model 1 0.39 0.10 3906

c

Model 2 0.17 0.07 0.16 0.04 3843

c

LR Model 1 0.60 0.09 4544

Model 2 0.48 0.10 0.08 0.05 4545

combined Model 1 0.47 0.07 4237

c

Model 2 0.27 0.06 0.14 0.03 3888

a

LW: Large White, LR: Landrace.

b

Model 1: random effects include animal effect and residual effect; Model 2: random effects include animal effect, litter effect and residual effect.

c

244 S. Hermesch et al. / Livestock Production Science 65 (2000) 239 –248

ther data collection lead to a decrease in standard genetic parameters for the reduced data set in errors for genetic parameters but did not greatly comparison to the full data set. For example, by not influence the estimates themselves. fitting litter effect a larger part of the variation due to The structure of the data set was based on a litter effect was picked up by the additive genetic random choice of two male piglets per litter. How- variance. These results are in agreement with results ever, it was not always possible to obtain two male from a simulation study by Meyer and Hill (1992) piglets and 14% of Large White pigs and 16% of who found that the sampling correlation does not Landrace pigs had no littermates recorded. To better appear to be dependent on the number of sire separate additive genetic effects and litter effects, families but is affected by their structure.

pigs with no littermates available were removed from For further illustration, the log likelihood was the data. Applying model one, the heritability esti- plotted for a range of heritability and litter effect mate was 0.39 in Large White (Table 4). When estimates for the full Large White data in Fig. 1. The model two was used in the reduced data set top area of the log likelihood represents combina-heritability increased to 0.17 while litter effect tions of heritability and litter effects estimates which decreased to 0.16. In contrast to Large White, are not significant different from the maximum log estimates of heritabilities and litter effects did not likelihood at a heritability of 0.10 and a litter effect change significantly between the full and the reduced of 0.20. This area covers a range of heritability data set in Landrace. estimates of 0.03 to 0.22 and a range of estimates of Meyer and Hill (1992) stated that parameters are litter effects of 0.04 to 0.22 illustrating that a correlated in models with multiple parameters imply- substantial cross-substitution can occur between ing that change in one parameter will lead to a these random effects until the log likelihood is corresponding change in the other parameter. Sam- changed significantly.

pling correlations between parameters are therefore Heritability estimates did not differ significantly often largely negative and in the situation of variance between breeds for further performance traits. There-components estimation, a considerable cross-substi- fore, heritability estimates are only presented from tution between parameters is possible before a the combined analysis. The second growth rate trait significant change in the log likelihood occurs. recorded in the test station (ADG2) was lowly

2

The analysis showed that sampling correlations heritable (h : 0.13) (Table 4). This trait was char-did not change significantly after the data set reached acterised by a high variation. This drastic increase in a size of approximately 1000 animals in both breeds. variation might be caused by random differences in However, for Large White the sampling correlation gut fill at test begin and test end. Given the short between heritability and litter effect estimates was testing period of four weeks these random differ-2_{0.43 for the total data set while it increased in ences have a large influence on recorded weight gain} magnitude (2_{0.54) for the reduced data set. The and therefore growth rate. It was not possible to} higher sampling correlation implies that cross-substi- account for these effects in the model and the tution between the two parameters was larger in the environmental variation was therefore increased. reduced data set. This helps to explain the change in Heritability estimates were moderate for feed intake

2 2

(h : 0.23) but low for feed conversion ratio (h : 0.13). These estimates of heritabilities were similar to estimates presented by Cameron and Curran

Table 4

2 2

Heritabilities (h ), litter effects (c ) both with standard errors (s.e.) (1994) but generally lower than heritabilities pre-a

and phenotypic variance for production traits (full data set) _{sented by Hofer and Schworer (1995), de Vries et al.¨}

2 2 2 2 2

Trait h s.e. of h c s.e. of c s (1994b) and Mrode and Kennedy (1993) indicating

p

that the testing period was too short to measure

ADG1 0.27 0.05 0.15 0.03 4210

growth rate and feed conversion ratio reliably. In

ADG2 0.13 0.04 0.08 0.03 29024

FDINT 0.23 0.04 0.140 addition, Archer et al. (1997) showed that a 70-day

FCR 0.15 0.04 0.265 _{test was required to measure growth rate and feed}

a

Fig. 1. Log likelihood values for different estimates of heritability and litter effects for growth rate until 18 weeks in Large White (full data set).

of the measurement in cattle whereas a 35-day test were slightly lower (FDP2: 0.46, FD3 / 4: 0.45). was sufficient to record feed intake reliably. Similar high heritability estimates were obtained by Li and Kennedy (1994) analysing four breeds using 3.2. Carcass traits large data sets and by de Vries et al. (1994a) who analysed Large White and Landrace data in an Backfat characteristics measured on the live ani- Australian herd. Overall these estimates are in agree-mal with real time ultrasound (LFDP2, LFD3 / 4) ment with average heritability estimates presented in

2

were highly heritable (h : 0.60 and 0.62) (Table 5). the reviews of Stewart and Schinckel (1989) and Heritability estimates for these two backfat measure- Ducos (1994) cited in Sellier (1998).

ments, recorded with the Hennesy Chong machine Heritability estimates were also higher for muscle depth recorded with real time ultrasound than for muscle depth recorded with Hennesy Chong

equip-Table 5

2 2 2 2

Heritabilities (h ) and litter effects (c ; second row) with standard ment (h : 0.21 for LMD3 / 4; h : 0.02 for MD3 / 4). a

errors (s.e.) and phenotypic variance for carcass traits _{The low heritability for muscle depth recorded with}

2 2 2 2 2

Trait h /c s.e. of h /c s Hennesy Chong was mainly due to a five times

p

larger residual variance and as a consequence use of

LFDP2 0.60 0.05 4.51

this trait in genetic evaluations is limited. No

esti-LFD3 / 4 0.62 0.05 4.49

LMD3 / 4 0.21 0.04 15.10 mates of heritabilities were found in the literature for

FDP2 0.46 0.06 7.20 _{muscle depth.}

FD3 / 4 0.45 0.07 6.34

Heritabilities differed substantially between both

MD3 / 4 0.02 0.05 60.87

breeds for weight of the whole back leg with

BLW 0.22 0.05 1.340

estimates of 0.08 for Large White and 0.46 for

0.14 0.03

LMW 0.38 0.07 0.450 Landrace. This weight measurement includes skin,

0.13 0.03 _{bones and fat of the back leg and is an indicator of}

a

246 S. Hermesch et al. / Livestock Production Science 65 (2000) 239 –248

heritabilities for this trait between breeds therefore comparison to Large White. Luxford (1995) ex-correspond to differences in heritability estimates for amined the effect of the halothane gene on meat growth rate. Heritabilities for lean meat of the whole quality characteristics in these two breeds. The back leg were 0.27 for Large White and 0.59 for halothane gene did not influence pH24 but influenced Landrace. Pooled estimates were 0.22 for back leg colour and drip loss percentage. This finding

sup-´

weight and 0.38 for lean meat weight (Table 5). ports results presented by Gueblez et al. (1995), These estimates not only correspond to results from McPhee and Trout (1995), Peschke et al. (1993) and

¨

Ollivier (1983) and Sonnichsen and Kalm (1984) Wittmann et al. (1993). Furthermore, McPhee and who analysed ham weight but also correspond to Trout (1995) found that the unfavourable effect of estimates from recent studies by Scholz and Triebler the halothane gene is of higher magnitude in a line (1992) and Hofer et al. (1992) who analysed the that had been selected for lean meat growth. Since weight of primary cuts. Landrace is a slightly leaner breed and has a higher incidence of the halothane gene, extreme cases of 3.3. Meat quality traits PSE meat characterised by a high drip loss

per-centage are anticipated.

Both pH measurements had low heritability esti- Heritability estimate for intramuscular fat content mates (0.15 for pH45 and 0.14 for pH24) (Table 6). of 0.35 was lower than estimates presented by However, in addition to these low heritabilities, Cameron (1990), Lo et al. (1992), de Vries et al. additive genetic variances were low for both pH (1994b) and Goodwin (1995). This low heritability measurements especially for pH24, which limits might be due to differences in the two measurement possible genetic progress in these traits. Heritability techniques applied in this study. However, no differ-estimates were 0.29 and 0.30 for the two colour ences were found in coefficient of determination for measurements and 0.23 for drip loss percentage. intramuscular fat content within measurement tech-Overall, these estimates are in agreement with esti- niques and variance components were not signifi-mates presented in recent studies by Lo et al. (1992), cantly different for the two techniques.

Bidanel et al. (1994a,b), de Vries et al. (1994b), ¨

Goodwin (1995), and Hofer and Schworer (1995) as

well as mean heritability estimates presented in the 4. Conclusions

reviews by Hovenier et al. (1993) and Sellier (1998).

Among meat quality traits, only drip loss per- Heritabilities for growth rate differed substantially centage showed larger differences in heritability between Large White and Landrace pigs. Large estimates between breeds. Estimates were 0.20 for White pigs had low heritability estimates while litter Large White and 0.47 for Landrace. The frequency effects were of higher importance. In contrast, of the halothane gene has not been quantified in the heritabilities for growth rate were higher in Landrace Landrace breed but is expected to be higher in and litter effects were not significant for this breed. The structure of the data set might have not allowed a reliable simultaneous estimation of additive genetic effects and litter effects. A testing period of four

Table 6

2 weeks might be too short to record average daily

Heritabilities (h ) with standard errors (s.e.) and phenotypic

a _{gain reliably which is indicated in low heritability}

variance for meat quality traits

2 2 2 estimates for growth rate recorded in the test station

Trait h s.e. of h s

p

and feed efficiency. Carcass traits were highly

herit-pH45 0.15 0.04 0.13

able with higher estimates for real time ultrasound

pH24 0.14 0.04 0.039

measurements taken on the live animal than Hennesy

CLD 0.29 0.06 20.25

Chong measurements recorded on the carcass.

CMD 0.30 0.05 13.85

DLP 0.23 0.05 3.07 Heritabilities were low for both pH measurements

IMF 0.35 0.06 0.339 _{and moderate for colour, drip loss percentage and}

a

´ ´ de qualite de la viande du porc charcutier. Journees Rech. Acknowledgements

Porcine en France 27, 155–164. ¨

Hofer, A., Hagger, C., Kunzi, N., 1992. Genetic evaluation of

This work was funded by the Pig Research and _{on-farm tested pigs using an animal model. II: Prediction of} Development Corporation under project UNE17P. breeding values with a multiple trait model.

Landwirtschaf-tliches Jahrbuch 30, 83–98.

Staff of Bunge Meat Industries are gratefully

ack-¨

Hofer, A., Schworer, D., 1995. Genetic parameters of production

nowledged for data collection. Constructive

com-and meat quality traits in station tested Swiss Large White pigs.

ments from both anonymous referees are greatly _{In: van Arendonk, J.A.M. (Ed.), 46th Annual Meeting of}

appreciated. EAAP, Prague.

Hovenier, R., Kanis, E., Asseldonk, T.V., Westrink, N.G., 1993. Breeding for pig meat quality in halothane negative populations-a review. Pig News and Information 14, 17N–25N. Jeremiah, L.E., 1994. Consumer responses to pork loin chops with References _{different degrees of muscle quality in 2 Western Canadian}

cities. Can. J. Anim. Sci. 74, 425–432.

Archer, J.A., Arthur, P.F., Herd, R.M., Parnell, P.F., Pitchford, Klassen, D., 1992. A simulation-base algorithm for mixed model W.S., 1997. Optimum postweaning test for measurement of estimation of genetic parameters with an application to the growth rate, feed intake, and feed efficiency in British breed Australian pig industry. PhD thesis. University of New Eng-cattle. J. Anim. Sci. 75, 2024–2032. land, Armidale, Australia.

´

Bidanel, J.P., Ducos, A., Gueblez, R., Labroue, F., 1994a. Genetic Li, X., Kennedy, B.W., 1994. Genetic parameters for growth rate parameters of backfat thickness, age at 100 kg and ultimate pH and backfat in Canadian Yorkshire, Landrace, Duroc and in on-farm tested French Landrace and Large White pigs. Hampshire pigs. J. Anim. Sci. 72, 1450–1454.

Livest. Prod. Sci. 40, 291–301. Lo, L.L., McLaren, D.G., McKeith, F.K., Fernando, R.L., ´

Bidanel, J.P., Ducos, A., Labroue, F., Gueblez, R., Gasnier, C., Novakofski, J., 1992. Genetic analyses of growth, real-time 1994b. Genetic parameters of backfat thickness, age at 100 kg ultrasound, carcass and pork quality traits in Duroc and and meat quality traits in Pietrain pigs. Annales de Zootechnie Landrace pigs. I. Breed effects. J. Anim. Sci. 70, 2373–2386.

43, 141–149. Luxford, B.G., 1995. Evaluation of the halothane gene in several

Cameron, N.D., 1990. Genetic and phenotypic parameters for nucleus lines in a temperate environment, In: Proc. of the 11th carcass traits, meat and eating quality traits in pigs. Livest. Conf. of the Austr. Assoc. of Anim. Breed. and Genet., pp.

Prod. Sci. 26, 119–135. 639–642.

Cameron, N.D., Curran, M.K., 1994. Selection for components of McPhee, C.P., 1982. Biological influences on the genetic improve-efficient lean growth rate in pigs 4. Genetic and phenotypic ment of pigs, In: Proc. of 3rd Conf. of Austr. Assoc. of Anim. parameter estimates and correlated responses in performance Breed. and Genet., pp. 177–180.

test traits with ad-libitum feeding. Anim. Prod. 59, 281–291. McPhee, C.P., Trout, G.R., 1995. The effects of selection for lean de Vries, A.G., Kerr, R., Tier, B., Long, T., 1994a. Gametic growth and the halothane allele on carcass and meat quality of imprinting effects on rate and composition of pig growth. pigs transported long and short distances to slaughter. Livest.

Theor. Appl. Genet. 88, 1037–1042. Prod. Sci. 42, 55–62.

de Vries, A.G., van der Wal, P.G., Long, T., Eikelenboom, G., Meyer, K., 1989. Restricted Maximum Likelihood to estimate Merks, J.M.W., 1994b. Genetic parameters for pork quality and variance components for animal models with several random production traits in Yorkshire populations. Livest. Prod. Sci. effects using a derivative-free algorithm. Genet. Sel. Evol. 21,

40, 277–289. 317–340.

` ´ ´ `

Ducos, A., 1994. Parametres genetiques des caracteres de pro- Meyer, K., 1993. DFREML User Notes V.2.1. Animal Genetics duction chez le porc. Mise au point bibliographique. Techni- and Breeding Unit, University of New England, Armidale,

porc 17 (3), 35–67. Australia.

Foster, M.L., Gonzales, S.E., 1992. Soxtec Fat Analyzer for Meyer, K., Hill, W.G., 1992. Approximation of sampling variances determination of total fat in meat: collaborative study. J. Assoc. and confident estimates of variance components. J. Anim. Official Analytical Chemists International 75, 288–292. Breed. Genet. 109, 264–280.

Goodwin, R.N., 1995. Genetics of pork quality. In: National swine Mrode, R.A., Kennedy, B.W., 1993. Genetic variation in measures improvement federation conference and annual meeting. NSIF, of food efficiency in pigs and their genetic relationships with pp. 15–23, Des Moines, Iowa. growth rate and backfat. Anim. Prod. 56, 225–232.

´ ´

Gresham, J.D., McPeake, S.R., Bernard, J.K., Henderson, H.H., Ollivier, L., 1983. Dix ans d’une experience de selection

in-´ ´

1992. Commercial adaption of ultrasonography to predict pork diviuelle sur des verrats utilitses en insemination artificielle. II.

` ´ ´

carcass composition from live animal and carcass measure- Parametres geneticques. Genet. Sel. Evol. 15, 99–118. ´

ments. J. Anim. Sci. 70, 631–639. Ollivier, L., Gueblez, R., Webb, A.J., van der Steen, H.A.M., ´

Gueblez, R., Paboeuf, F., Sellier, P., Bouffaud, M., Boulard, J., 1990. Breeding goals for nationally and internationally pig ´

Brault, D., Tiran, M.H.L., Petit, G., 1995. Effet du genotype breeding organisations. In: 4th World Congr. on Genet. Appl. halothane sur les performances d’engraissement, de carcasse et to Livest. Prod., Edinburgh, pp. 383–394.

248 S. Hermesch et al. / Livestock Production Science 65 (2000) 239 –248

¨ ¨

Peschke, W., Forster, M., Wittmann, W., Dovc, P., Behringer, A., und Pietrain in Schleswig Holstein. 1. Mitteilung: Schatzung

¨ ¨

Odebrecht, J.S., 1993. Leistungsunterschiede MHS-reinerbig von Heritabilitaten. Zuchtungskunde 56, 238–248.

positiver und mischerbiger Pietrain-Schweine aus der Stewart, T.S., Schinckel, A.P., 1989. Genetic parameters for swine ¨

Stationsprufung, Vol. 4 / 93, Gruber INFO, pp. 29–33. growth and carcass traits. In: Young, L.D. (Ed.). Genetics of PRDC, 1993. The cost of pigmeat quality faults: with special Swine. USDA-ARS, Clay Center, Nebraska, pp. 77–79.

¨

reference to pale, soft, exudative meat, AACM International von Felde, A., Rohe, R., Kalm, E., 1996. Genetische Analyse von ¨

Consultance. Merkmalen der Mastleistung und des Schlachtkorperwertes

¨

SAS / STATS, 1991. User’s Guide, Release 6.03 Edition, 1991. stationsgeprufter Jungeber in Einzel- und Gruppenhaltung ¨

Scholz, A., Triebler, G., 1992. Zur Selektionswirkung der mittels Gibbs-Sampling. Zuchtungskunde 68, 305–318. ’biologischen’ Indizes Magerfleischfuttervertung bei zwei van Steenbergen, E.J., Kanis, E., Steen, H.A.M., 1990. Genetic

¨

Schweinerassen. Zuchtungskunde 64, 346–355. parameters of fattening performance and exterior traits of boars Sellier, P., 1998. Genetics of meat and carcass traits. In: tested in central stations. Livest. Prod. Sci. 24, 65–78.

Rothschild, M.F., Ruvinsky, A. (Eds.), The genetics of the pig. Wittmann, W., Peschke, W., Littmann, E., Behringer, J., Birken-¨

CAB International, pp. 463–510. Mayer, S., Dovs, P., Forster, M., 1993. Mast- und

Schlach-¨ ¨ ¨

Sonnichsen, C.M.L., Kalm, J.E., 1984. Parameterschatzung und tleistungen von DL-Kastraten in Abhangigkeit vom

MHS-¨ ¨