Directory UMM :Data Elmu:jurnal:L:Livestock Production Science:Vol65.Issue3.Aug2000:

(1)

www.elsevier.com / locate / livprodsci

Genetic parameters for lean meat yield, meat quality,

reproduction and feed efficiency traits for Australian pigs

3. Genetic parameters for reproduction traits and genetic

correlations with production, carcase and meat quality traits

a ,

_*

b a

S. Hermesch

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

Animal Genetics and Breeding Unit, Joint Institute of NSW Agriculture and The University of New England,

University of New England, Armidale, NSW 2351, Australia

Bunge Meat Industries, Corowa, NSW 2646, Australia

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

Abstract

Genetic parameters were obtained using REML procedures applied to a multiple trait animal model for number born alive (NBA), litter birth weight (LBW), average piglet weight at birth (ABW) recorded in the first, second and third parity (NBA_1,2,3, LBW_1,2,3, ABW_1,2,3) and 21-day litter weight (LW21 ) for 6050 Large White and Landrace sows. Heritability₁ estimates ranged from 0.06 to 0.22 for these reproductive traits, with lowest estimates for NBA1,2,3 and LW21 .1

Reproductive performance in the first parity should be regarded as a different trait than reproductive performance in later parities (range of genetic correlations (rg): 0.52–0.78, 6_{0.16–0.30). NBA} _{was unfavourably related with LBW ,}

1,2,3 1

ABW_1,2,3and LW21 . In addition, NBA₁ _1,2,3was negatively correlated with growth rate traits, feed intake and weight of the back leg and ham (BLW, LMW) (rg range: 2_{0.45 to} 2_0.01,6_{0.13–0.15). In contrast, genetic correlations were favourable}

between LBW , ABW and growth rate, BLW and LMW (rg values: 0.08–0.55,6_{0.12–0.25). NBA} _{and ABW}

1,2,3 1,2,3 1,2,3 1,2,3

were not genetically related with backfat measurements, while a low backfat was associated with a high LBW1,2,3 (rg:

2_{0.54 to} 2_0.08, 6_{0.09–0.36). Genetic correlations between reproduction traits and meat quality traits were inconsistent}

between traits and parities. A lower intramuscular fat content was associated with a higher LBW1,2,3 and ABW1,2,3 (rg:

2_{0.37 to} 2_0.12,6_{0.12–0.18). In summary, genetic correlations between reproduction traits and performance traits were}

only unfavourable between litter size and growth rate and feed intake. Genetic correlations between litter birth weight and average piglet weight at birth indicate that selection for leanness will also improve litter weight traits.  2000 Elsevier Science B.V. All rights reserved.

Keywords: Pigs; Reproduction; Production; Carcase; Meat quality; Genetic parameters

1. Introduction

*Corresponding author. Tel.: 161-267-73-3787; fax: 1

61-Genetic improvement of sow productivity has 267-73-3266.

E-mail address: [email protected] (S. Hermesch). mainly been focussing on litter size (i.e., de Vries and

(2)

Kanis, 1994). However, an increase in litter size The proportion of crossbred litters for the first three appears to be associated with decrease in piglet birth parities were 60.3, 64.4 and 74.2% for Large White weight and survival (Haley et al., 1988). Rydhmer et sows and 52.0, 56.2 and 69.1% for Landrace sows. al. (1992) found that a low average piglet weight at Each litter originated from the first mating only and birth increases piglet mortality. Therefore, in order to the proportion of litters from artificial insemination increase the number of piglets weaned per sow was 18.3, 23.0 and 24.8% for the first to third parity. through genetic improvement further reproductive The farrowing age for the first litter was restricted to traits of the sow including litter birth weight, average 270–500 days. Age of litter when litter weight was piglet weight at birth and 21-day litter weight should recorded ranged from 10 to 28 days with a mean of be considered in breeding programs. In addition, 19 days.

these reproductive traits might have stronger genetic Reproductive traits were analysed as a different relationships with other production traits as currently trait in the first, second and third parity and included assumed. Knowledge about genetic parameters be- number of piglets born alive (NBA1,2,3), litter birth tween reproduction traits and other performance weight (LBW1,2,3), average piglet weight at birth traits is mostly limited to growth rate, feed intake (ABW1,2,3) and litter weight at 21-days in the first and backfat (Short et al., 1994; Rydhmer et al., 1995; parity (LW21 ). Records available for 21-day litter1 Tholen et al., 1996; Crump et al., 1997) with varying weight recorded in the second and third parity were estimates between studies and data sets. Breeding not sufficient to analyse them as separate traits. programs consider a number of production, carcase Furthermore, recording procedures for litter birth and meat quality traits, and their genetic relationship weight and consequently average piglet birth weight with reproductive traits is required in order to changed in the third quarter of 1993 when litter establish whether reproductive traits should also be weight was recorded 3 days after birth. In total, analysed in a multitrait analysis. The objective of 13 518 litters were available for analysis.

this study was to obtain genetic parameters for

reproductive traits of the sow and to obtain genetic 2.2. Production, carcase and meat quality data correlations between reproduction traits and

product-ion, carcase and meat quality traits. Analysis of production, carcase and meat quality data was based on performance records from 1799 Large White and 1522 Landrace boars. Performance

2. Material and methods traits comprised growth rate from three to 18 weeks (ADG1). Animals were performance tested from 18 2.1. Reproductive data to 22 weeks providing information about growth rate during station testing (ADG2), feed intake (FDINT) Data from 3776 Large White and 2274 Landrace and feed conversion ratio (FCR). Animals were sows which farrowed between 1991 and March 1995 group penned until 18 weeks of age. The housing were used to obtain genetic parameters for reproduc- system in the test station consisted of single penning. tive traits of the sow recorded in the first, second and Animals were slaughtered at 22 weeks of age. third parity. The pedigree data set of these sows Carcase traits included backfat at P2 site and backfat included 150 Large White and 102 Landrace sires as and muscle depth between the third and fourth last well as 1284 Large White and 715 Landrace dams. ribs recorded on the live animal as well as on the Although animals were raised in the same unit, sows carcase (LFDP2, LFD3 / 4, LMD3 / 4, FDP2, FD3 / 4). farrowed in two different units. The proportion of Further carcase traits consisted of weight of the sows farrowing in unit two was 67.1, 78.7 and 88.0% whole back left leg and the slash boned ham (BLW, in the first, second and third parity. As another LMW). Meat quality traits included pH recorded 45 editing restriction, only purebred litters or crossbred min and 24 h after slaughter (pH45, pH24), colour of litters from Large White sows and Landrace service the m. longissimus dorsi and m. multifidus dorsi sires (LW*LR) or Landrace sows and Large White (CLD, CMD), drip loss percentage (DLP) and service sires (LR*LW) were included in the data set. intramuscular fat content (IMF). The fixed effect

(3)

models included date of recording (all traits), breed and quadratic covariable), and time period from (not significant for ADG2, FDINT, FCR, pH24 and farrowing to weighing (linear covariable). The num-CMD) and parity (ADG1, ADG2, BLW, LMW). ber of piglets weighed represented the sow’s own Weight of the animal at test beginning was fitted for piglets as well as piglets that were cross-fostered feed intake and feed conversion ratio. Backfat mea- from other sows. Breed of service sire was not surements, muscle depth and intramuscular fat con- significant for any reproductive trait.

tent were corrected for weight of the animal at Only a small proportion (1–3%) of the total slaughter. Litter was fitted for growth rate and back variation was explained by the fixed effect model for leg weight traits as an additional random effect. A number of piglets born alive. In contrast, the fixed detailed description of the data structure and the effect model explained 53–63% of the total variation analysed traits was given in Hermesch et al. (2000a) for litter birth weight and 30–48% for average piglet along with derivation of the appropriate model and weight at birth. These high coefficients of determi-heritability estimate for each trait. Furthermore, nation were mainly due to farrowing season and genetic correlations between these traits were pre- were caused by a change in performance recording sented in Hermesch et al. (2000b). of litter birth weight. Since October 1993, litter birth weight has been recorded 3 days after farrowing and 2.3. Analysis therefore includes piglets that were cross-fostered. In addition, comparing least-squares means of farrow-The significance of fixed effects fitted in the model ing seasons showed that litter birth weight increased for each trait was analysed using PROC GLM (SAS, by 3 kg from March 1992, but no explanation could 1991). Significant fixed effects included in the be given for this increase which was probably caused models to analyse reproductive traits of the sow were by a change in recording procedure.

farrowing season which was defined in 3-month Variance components were obtained from an ani-periods within year, breed of the sow, farrowing unit mal model with restricted maximum likelihood pro-and whether the sow was artificially inseminated or cedures (DFREML, Meyer, 1997) which provides naturally mated (Table 1). Covariables fitted for approximations of standard errors for heritabilities reproductive traits included the age at farrowing and genetic correlations. Whenever the approxima-(linear covariable), number of piglets weighed approxima-(linear tion of standard errors for genetic correlations failed

Table 1

2 a

Fixed and random effects for reproductive traits of the sow and total variation explained by fixed effects (R )

Fixed effects Random effect

R FS breed AI FU FA n Period Animal

NBA1 0.02 * *** * * *** ✓

NBA2 0.03 *** * *** *** ✓

NBA3 0.01 *** *** ✓

LBW1 0.43 *** *** *** ✓

LBW2 0.51 *** *** * * ✓

LBW3 0.44 *** *** ** ✓

ABW1 0.30 *** *** ✓

ABW2 0.45 *** *** *** ✓

ABW3 0.48 *** *** ✓

LW211 0.26 *** *** *** *** ✓

NBA1,2,3, litter size in the first to third parity; LBW1,2,3, litter weight in the first to third parity; ABW1,2,3, average piglet weight in the first to third parity; LW21 , litter weight at 21-days in the first parity; FS, farrowing season; AI, artificial insemination; FU, farrowing unit;1

FA, age at farrowing (linear covariable); n, number of weighed piglets for LW21 (linear and quadratic covariable); Period, period of time1

(4)

to converge, standard errors for genetic correlations weights in the second and third parity with estimates were obtained using the formula of Robertson of 0.22 and 0.20. A lower genetic variance rather (1959). than an increase in environmental variance is the Significant random effects were tested with a log cause for this reduced heritability for litter birth likelihood ratio test. For reproductive traits of the weight in the first parity. Gilts were farrowing at 337 sow only maternal effects were analysed as an days on average and their uterine capacity is smaller additional random effect. Only 19% of all sows with in comparison to multiparous sows. This might have records had at least one full sister available in the been a restriction on expression of their genetic data set and the data structure did not allow to fit potential in litter birth weight. Heritabilities were litter effect as an additional random effect. General- 0.15, 0.16, 0.15 for average piglet birth weight in the ly, maternal effects were not significant for reproduc- first three parities.

tive traits of the sow and were therefore not fitted in Literature estimates of heritabilities for litter birth the model. weight and average piglet weight at birth varied between research data and field data. Heritabilities for litter birth weight were 0.13 and 0.21 in the study

3. Results and discussion of Crump et al. (1997), which was based on field data. In contrast, high estimates of heritabilities with 3.1. Heritability estimates for reproductive traits of a range of 0.42–0.65 were obtained by Irvin and the sow Swiger (1984) and by Ferguson et al. (1985) for litter birth weight and average piglet weight at birth, Heritabilities and variance components for re- using data from research herds. Rydhmer et al. production traits of the sow are presented in Table 2 (1992) pointed out that litter birth weight is in-along with the number of records and means for each fluenced by the milk intake of piglets after birth trait. Litter size was lowly heritable with estimates of which depends on the milk performance of the sow. 0.08, 0.09 and 0.08 for the first three parities pooled An early weighing of the litter after birth is therefore over both breeds. These estimates are in agreement important, and delays in recording litter birth weight with literature estimates (Southwood and Kennedy, in field data might be the reason for lower 1990; Alfonso et al., 1994; Irgang et al., 1994; heritabilities. The recording policy for litter birth

Rydhmer et al., 1994; Rohe and Kennedy, 1995; weight changed in the third quarter of 1993 in this Ducos and Bidanel, 1996; Tholen et al., 1996). herd, when litter birth weight was recorded 3 days Litter birth weight in the first parity had a lower after farrowing and includes cross-fostered piglets. heritability (0.08) in comparison to litter birth This inconsistency in data recording and weighing after cross-fostering leads to an increase in environ-mental variation and therefore reduced heritability Table 2

2 _{(Tholen et al., 1996).}

Number of records (n), means, heritabilities (h ) with standard

errors (S.E.) and phenotypic variances (s _{) for reproduction traits} _{Litter weight at 21-days recorded in the first parity}

p a

of the sow _{(LW21 ) was lowly heritable (Table 2), which is in}

2 2 2

Trait n Mean h S.E. of h s agreement with estimates presented by Kaplon et al.

(1991), Siewerdt et al. (1995) and Tholen et al.

NBA1 5986 9.61 0.08 0.02 5.90

(1996). Litter weight at 21-days was strongly

in-NBA2 4113 10.06 0.09 0.02 5.94

NBA3 2965 10.79 0.08 0.03 6.11 fluenced by number of piglets weighed and the age LBW (Kg)1 4306 12.55 0.08 0.02 7.26 of the litter at weighing. Tholen et al. (1996) LBW (Kg)2 2084 13.41 0.22 0.05 8.44 _{compared regression coefficients for number of} LBW (Kg)3 1234 14.65 0.20 0.07 10.49

piglets after weighing. Linear and quadratic regres-ABW (g)1 4206 1342 0.15 0.03 124 129

sion coefficients varied considerably across parities ABW (g)2 2032 1390 0.16 0.04 98 286

ABW (g)3 1216 1419 0.15 0.06 79 504 and herds. This shows possible limitations in adjust-LW21 (Kg)1 1111 41.73 0.07 0.06 86.52 ing environmental variation caused by different

(5)

3.2. Estimation of genetic correlations first to third parity (rg5 2_0.15, 2_0.12, 2_0.20). However, litter birth weight in later parities was 3.2.1. Reproductive traits of the sow mainly positively correlated with number born alive Genetic and environmental correlations between (rg5 2_{0.04 to 0.43). In this study litter birth weight} reproduction traits of the sow are presented in Table was not corrected for number born alive. Using a 3. Estimates of genetic correlations between litter similar model, Bereskin (1984), Irvin and Swiger size in the first to third parity (NBA1,2,3) were (1984) and Siewerdt et al. (1995) found positive positive. Number born alive in the first parity was genetic correlations ranging from 0.42 to 0.92. The genetically a different trait than litter size in the negative genetic correlations found in this study second and third parity (genetic correlation (rg)5 _{between litter birth weight and number born alive} 0.62 for NBA , rg5_{0.61 for NBA ) supporting the} _{could be caused by the recording procedure of litter}

2 3

analysis of number born alive in the first parity as a birth weight. Piglet mortality is higher in larger separate trait to litter size in later parities. Although litters and in litters with a lower average birth weight Haley et al. (1988) suggested analysing litter size as (Fraser, 1990). Therefore, gilts with large litters are repeated records, results from more recent studies more likely to have a higher mortality rate,

especial-¨

(i.e., Alfonso et al., 1994; Rohe and Kennedy, 1995; ly within the first few days after farrowing. With the Tholen et al., 1996) confirm that number born alive delay of recording litter birth weight this will reduce in the first parity should be regarded as a different the total litter weight. In addition, the cross-fostering trait than litter size in later parities. The genetic practice of putting smaller piglets on to gilts and correlation between number born alive in the second taking their own bigger piglets away to older sows and third parity was high (0.95) and these traits can could also lead to this negative genetic correlation. It therefore be treated as repeated records in selection was not possible to incorporate these cross-fostering programs. Similar, genetic correlations between litter practices in the model.

birth weight and average piglet weight at birth in the Genetic correlations between litter size and aver-first parity and performance in these two traits in the age piglet weight at birth varied from 2_{0.86 to} second and third parity were significantly different 2_{0.27 (Table 3). Rydhmer et al. (1992) also found} from one ranging from 0.52 to 0.79. Litter birth an unfavourable relationship between number born weight and average piglet weight at birth in the first alive and average piglet weight at birth (rg5 litter should therefore be analysed as a separate trait 2_{0.34), while Irvin and Swiger (1984) found no} while performance in later parities should be re- relationship (rg5_{0.05) between these two traits.} garded as repeated records. Litter birth weight and average piglet weight at

Litter birth weight in the first parity had low birth were moderately correlated (rg5_{0.29–0.87).} negative genetic correlations with litter size in the Irvin and Swiger (1984) found a positive genetic Table 3

Genetic correlations (above diagonal) with standard errors (in brackets) and environmental correlations (below diagonal) between reproduction traits

NBA₁ NBA₂ NBA₃ LBW₁ LBW₂ LBW₃ ABW₁ ABW₂ ABW₃ LW21₁

NBA1 0.62(0.19) 0.61(0.30) 20.15(0.17) 0.13(0.22) 20.04(0.20) 20.74(0.10) 20.34(0.25) 20.27(0.32) 20.14(0.32)

NBA2 0.11 0.95(0.04) 20.12(0.17) 0.43(0.18) 0.28(0.29) 20.41(0.21) 20.69(0.16) 20.40(0.35) 20.15(0.30)

NBA3 0.09 0.12 20.20(0.30) 0.22(0.27) 0.25(0.29) 20.51(0.24) 20.86(0.36) 20.56(0.25) 20.75(0.17)

LBW1 0.42 0.06 0.10 0.78(0.16) 0.52(0.28) 0.78(0.13) 0.87(0.23) 0.55(0.36) 0.14(0.32)

LBW2 0.08 0.45 0.05 0.10 0.98(0.34) 0.47(0.17) 0.29(0.21) 0.41(0.32) 20.10(0.31)

LBW3 0.06 0.04 0.62 0.11 0.00 0.29(0.28) 0.48(0.29) 0.73(0.27) 0.43(0.31)

ABW1 20.66 20.09 0.00 0.29 20.02 20.01 0.79(0.19) 0.58(0.30) 20.11(0.29)

ABW2 20.05 20.67 20.07 0.07 0.27 0.03 0.07 0.78(0.33) 0.42(0.27)

ABW3 20.03 20.09 20.57 0.09 0.05 0.21 0.04 0.18 0.48(0.32)

LW211 20.07 0.05 0.10 0.14 0.07 0.00 0.28 20.04 0.00

(6)

correlation of 0.76 between these two traits. There- in the numerator relationship matrix. However, with fore, a high litter birth weight is associated with a such data it is not possible to estimate environmental high average piglet weight at birth. correlations between these traits and therefore it is Genetic correlations between litter weight at 21- also not possible to obtain phenotypic correlations. days and other reproduction traits of the sow were in Genetic correlations between reproductive traits of general not significantly different from zero. In the sow and performance traits recorded on boars are contrast, Siewerdt et al. (1995) found positive ge- presented in Table 4. Litter size was negatively netic correlations ranging from 0.37 to 0.89 between correlated with growth rate traits. This relationship 21-day litter weight and litter size and litter birth was stronger in the first parity (rg5 2_0.30, weight. Differing from the study of Siewerdt et al. 2_{0.42,) than in later parities (rg}5 2_{0.30 to 0.00).} (1995), litter weight at 21-days was adjusted for In comparison, genetic correlations between litter number of piglets after cross-fostering in this study size and growth rate ranged from 2_{0.15 to 0.23 in} which explains differences in estimates. Tholen et al. the studies of Rydhmer et al. (1992), Short et al. (1996) also adjusted 21-day litter weight for number (1994), Merks and Molendijk (1995), Tholen et al. of piglets after cross-fostering, and genetic correla- (1996) and Crump et al. (1997). In this study genetic tions between number born alive and 21-day litter correlations between reproduction traits of sows and weight averaged 2_{0.43 across parities, while aver-} _{performance traits of their sons were analysed.} age piglet weight at birth was positively correlated Therefore, the size of the litter might have directly with 21-day litter weight (average rg across parities, influenced performance traits and growth rate in 0.32). particular. For example, Dwyer et al. (1994) showed In order to optimise genetic improvement for that a higher level of nutrition increased the sec-number of piglets weaned per sow litter size should ondary to primary muscle fibre number ratio and be analysed in a multitrait analysis together with subsequently increased growth rate. In further analy-litter birth weight or average piglet weight at birth ses, genetic correlations between these two traits and 21-day litter weight. However, Tholen et al. were obtained dividing the total reproductive data set (1996) found that 21-day litter weight is mainly in half. In the first analysis reproductive records of influenced by the number of piglets after cross- dams of boars with performance data were excluded fostering and the length of the time period between from the reproductive data set. In contrast, the farrowing and weighing. For example, heavier litters second analysis was only based on reproductive tend to be weaned and therefore weighed earlier in information from dams of boars with growth rate order to make room for other sows that are due to records. Given the hypothesis that the negative farrow. These management factors cannot always be genetic correlation is caused by the direct influence explained well by the model which might lead to a of litter size on growth performance of the offspring decrease in heritabilities and might influence genetic an increase in genetic correlations is expected when correlations with other reproduction traits. In this dams of boars are excluded from the reproductive study litter birth weight and average piglet weight at data set. In addition, genetic correlations are ex-birth were also influenced by cross-fostering prac- pected to decrease when litter size is only available tices for part of the data which might have influenced from dams of boars with performance data. Among genetic parameters. In summary, these management all trait combinations between growth rate and litter practices reduce the value of litter weight traits for size traits, growth rate from 3 to 18 weeks (ADG1) genetic improvement of sow productivity and should and litter size in the first parity (NBA ) were the1 be minimised in nucleus herds. only trait combination where genetic correlations changed in this expected way. The genetic correla-3.2.2. Reproduction and production traits tion was 2_{0.05 for the data set when dams of boars} Reproduction traits are measurements of the sow were excluded from the reproduction data set and while performance, carcase and meat quality traits 2_{0.68 when the reproductive data set included only} were recorded on offspring of sows. Genetic correla- dams of boars with performance records. For the tions were therefore estimated through genetic links other trait combinations, genetic correlations did not

(7)

Table 4

Genetic correlations with standard errors (in brackets) between reproduction and production, carcase and meat quality traits

NBA1 NBA2 NBA3 LBW1 LBW2 LBW3 ABW1 ABW2 ABW3

b b b b b b b b b

ADG1 20.30 (0.14) 20.01 (0.14) 20.26 (0.17) 0.39 (0.13) 0.38 (0.12) 0.35 (0.18) 0.33 (0.12) 0.27 (0.14) 0.33 (0.17)

b b b b b b b b b

ADG2 20.42 (0.16) 20.30 (0.17) 0.00 (0.20) 0.18 (0.20) 0.08 (0.19) 0.12 (0.22) 0.45 (0.14) 0.35 (0.17) 0.09 (0.25) FDINT 20.19 (0.22) 20.24 (0.25) 20.05 (0.29) 20.11 (0.22) 20.20 (0.22) 20.22 (0.31) 0.16 (0.20) 0.03 (0.26) 0.10 (0.35)

FCR 0.09 (0.30) 0.00 (0.30) 0.08 (0.22) 20.53 (0.27) 20.60 (0.28) 20.57 (0.45) 20.38 (0.25) 20.43 (0.33) 20.13 (0.46)

b b

LFDP2 0.10 (0.17) 0.17 (0.19) 20.07 (0.23) 20.39 (0.19) 20.43 (0.21) 20.30 (0.25) 20.07 (0.11) 20.33 (0.09) 20.14 (0.13)

b b

LFD3 / 4 0.09 (0.17) 0.16 (0.19) 20.05 (0.23) 20.35 (0.09) * 20.23 (0.11) 20.06 (0.09) * *

LMD3 / 4 20.13 (0.23) 0.25 (0.25) 0.00 (0.30) 20.16 (0.24) 0.31 (0.29) 0.15 (0.33) 20.18 (0.16) 20.09 (0.15) 0.18 (0.34)

FDP2 20.15 (0.21) 0.16 (0.23) 20.07 (0.27) 20.23 (0.22) 20.44 (0.25) 20.08 (0.31) * * 0.05 (0.32)

FD3 / 4 20.28 (0.23) 20.02 (0.26) 20.28 (0.28) 20.26 (0.25) 20.54 (0.27) 20.22 (0.36) 0.12 (0.17) 20.13 (0.26) 0.13 (0.37)

b b b b b b b b

BLW 20.45 (0.13) 20.23 (0.15) 20.24 (0.19) 0.46 (0.13) 0.42 (0.13) 0.42 (0.16) 0.29 (0.14) 0.27 (0.16) *

b b b b b b b b b

LMW 20.31 (0.14) 20.26 (0.13) 20.08 (0.18) 0.52 (0.11) 0.55 (0.10) 0.61 (0.11) 0.13 (0.13) 0.32 (0.14) 0.37 (0.17)

b b b b b b b b b

pH45 20.34 (0.16) 20.19 (0.17) 20.26 (0.21) 20.30 (0.17) 20.22 (0.14) 20.40 (0.17) 0.13 (0.16) 0.05 (0.18) 0.30 (0.21)

b b b b b b b b b

pH24 20.26 (0.18) 0.10 (0.18) 20.25 (0.22) 0.08 (0.19) 0.17 (0.17) 20.23 (0.20) 0.19 (0.16) 20.03 (0.19) 0.08 (0.24)

b b b b b b b b b

CLD 20.11 (0.16) 20.53 (0.11) 20.27 (0.18) 20.11 (0.16) 20.28 (0.14) 0.41 (0.12) 0.00 (0.15) 0.36 (0.14) 0.31 (0.18)

b b b b b b b b b

CMD 20.42 (0.12) 20.45 (0.11) 20.34 (0.16) 0.00 (0.14) 20.27 (0.13) 20.09 (0.16) 0.21 (0.12) 0.00 (0.14) 0.25 (0.19)

b b b b b b b b b

DLP 0.17 (0.16) 0.34 (0.14) 20.08 (0.20) 0.05 (0.16) 0.10 (0.15) 0.42 (0.15) 20.23 (0.14) 0.34 (0.14) 0.25 (0.21)

b b b b b b b b b

IMF 20.11 (0.14) 0.08 (0.14) 0.11 (0.18) 20.37 (0.13) 20.32 (0.12) 20.26 (0.15) 20.15 (0.13) 20.19 (0.14) 20.12 (0.18)

ADG1, average daily gain from 3 to 18 weeks; ADG2, average daily gain during station testing from 18 to 22 weeks; FDINT, feed intake recorded during station testing from 18 to 22 weeks; FCR, feed conversion ratio defined as feed intake over growth rate (18–22 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. longissimusdorsi 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; 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.

Estimates of standard errors obtained from approximation of Robertson (1959). *Estimate did not converge.

differ in accordance with expectations. Therefore, Litter birth weight and average piglet weight at these analyses indicate that the negative genetic birth were both positively correlated with growth rate correlation between growth rate measured earlier in (rg5_{0.08–0.45). Positive genetic correlations} be-life (ADG1) and litter size in the first parity may tween growth rate and litter birth weight and average have been influenced by the direct effect of litter size piglet weight at birth have also been reported by on growth rate. However, genetic correlations be- Vangen (1980), Hutchens et al. (1981), Rydhmer et tween other trait combinations seem not to be al. (1992), Tholen et al. (1996) and Crump et al. influenced by the direct effect of litter size. (1997).

Considering that a high growth rate was associated Low negative genetic correlations were found with a high feed intake (Hermesch et al., 2000b), the between litter birth weight and feed intake. Litter lowly negative genetic correlations (rg5 2_0.19, _{birth weight and average piglet weight at birth were} 2_0.24, 2_{0.05) between litter size and feed intake} _{both negatively correlated with feed conversion ratio.} are in agreement with relationships between litter This favourable relationship was stronger for litter size and growth rate. The average of genetic correla- birth weight, with estimates of 2_0.53, 2_{0.60 and} tions between litter size and feed intake presented by 2_{0.57 for the three parities, than for average piglet} Short et al. (1994) was 2_{0.12, thus supporting these} _{weight at birth, with genetic correlations with feed} results. In contrast, Crump et al. (1997) presented efficiency of 2_0.38, 2_{0.43 and} 2_{0.13 for the first} slightly positive genetic correlations between litter three parities. The strong genetic correlations be-size and feed intake and feed conversion ratio of tween litter birth weight and feed conversion ratio 0.20 and 0.23, respectively. result from positive genetic correlations between

(8)

litter birth weight and growth rate and lowly negative apparent between pH45 and litter birth weight (rg genetic correlations between litter birth weight and range, 2_{0.40 to} 2_{0.22). A darker colour is related} feed intake. Furthermore, this favourable genetic to a larger litter size (rg range, 2_{0.53 to} 2_0.11) relationship is supported by Kerr and Cameron and a higher litter birth weight (rg range, 2_{0.28 to} (1995) who found a reduced litter weight in the 0.00). Genetic correlations ranged from 2_{0.08 to} selection line selected for high lean feed conversion 0.34 for litter size in the first to third parity and drip ratio. loss percentage. Overall, genetic correlations be-tween reproduction traits and meat quality traits were inconsistent and mostly of low magnitude indicating 3.2.3. Reproduction and carcase traits

no clear genetic relationships between these trait Genetic correlations between number born alive

groups. Estimates of genetic correlations between and backfat measured with real time ultrasound

reproduction traits and meat quality traits were not ranged from 2_{0.07 to 0.17 (Table 4). Estimates of}

available in the literature. genetic correlations between litter size and carcase

Finally, intramuscular fat content had no genetic backfat measurements varied from 2_{0.28 to 0.16.}

relationship with litter size. However, a higher This indicates no genetic relationship between

lean-intramuscular fat content was associated with a lower ness and litter size which is also apparent in genetic

litter birth weight with genetic correlations ranging correlations of muscle depth with litter size.

Litera-from 2_{0.37 to} 2_{0.26 and a lower average piglet} ture estimates between litter size and leanness were

weight at birth. Genetic correlations were 2_0.15, generally close to zero (Johansson and Kennedy, ₂

0.19 and 2_{0.12 for the first three parities.} In-1983; Short et al., 1994; Ducos and Bidanel, 1996;

tramuscular fat content was reduced for leaner pigs Tholen et al., 1996).

(Hermesch et al., 2000b). Leanness was not ge-Weight of the back leg and lean meat weight of

netically related to litter size (Table 4), while litter the back leg were closely related to average daily

birth weight and average piglet weight at birth were gain (Hermesch et al., 2000b). Negative genetic

favourably related to leanness. These genetic correla-correlations in the range of 2_{0.45 to} 2_0.08

tions between reproduction traits and intramuscular between these two weight measurements and litter

fat content are therefore in agreement with results size are therefore in agreement with genetic

correla-between leanness and reproduction traits. tions found between average daily gain traits and

litter size.

In contrast to litter size, litter weight at birth had a

4. Conclusions

favourable genetic relationship with backfat mea-surements and leanness. Genetic correlations for

Reproductive performance of the sow is lowly litter birth weight and backfat measurements ranged

heritable. Reproductive performance in the first from 2_{0.54 to} 2_{0.08. These estimates are of}

parity should be regarded as a different trait to higher magnitude than the genetic correlation of

reproductive performance in later parities in the 2_{0.05 presented by Young et al. (1978) for backfat}

populations investigated. Litter size was unfavourab-and litter birth weight. Genetic correlations between

ly correlated with litter birth weight in the first average piglet weight at birth and backfat

measure-parity, average piglet weight at birth and 21-day ments ranged from 2_{0.33 to 0.13, which was not}

litter weight and these traits should be analysed in a significantly different from zero in most cases.

multitrait analysis in genetic evaluations. For part of the data litter weight traits were recorded 3 days after 3.2.4. Reproduction and meat quality traits birth and cross-fostering practices might have in-Genetic correlations between pH45 and litter size fluenced genetic parameters. In summary, genetic ranged from 2_{0.34 to} 2_{0.19 (Table 4), indicating} _{correlations were only unfavourable between litter} that an increasing litter size is associated with a size and growth rate and feed intake. Genetic corre-lower pH at 45 min (pH45). In regard to PSE meat, lations between reproductive traits of the sow and this is an unfavourable relationship which was also carcase traits indicate that selection for lean meat

(9)

Hutchens, L.K., Hintz, R.L., Johnson, R.K., 1981. Genetic and growth does also lead to an increase in litter birth

phenotypic relationships between pubertal and growth charac-weight and average piglet charac-weight at birth.

teristics of gilts. J. Anim. Sci. 53, 946–951.

Irgang, R., Favero, J.A., Kennedy, B.W., 1994. Genetic parameters for litter size of different parities in Duroc, Landrace and Large White sows. J. Anim. Sci. 72, 2237–2246.

Acknowledgements

Irvin, K.M., Swiger, L.A., 1984. Genetic and phenotypic parame-ters for sow productivity. J. Anim. Sci. 58, 1144–1150. This work was funded by the Pig Research and _{Johansson, K., Kennedy, B.W., 1983. Genetic and phenotypic} Development Corporation under project UNE17P. relationships of performance test measurements with fertility in Swedish Landrace and Yorkshire sows. Acta Agric. Scand. 33, Staff of Bunge Meat Industries are gratefully

ack-195–199. nowledged for data collection. Constructive

com-Kaplon, M.J., Rothschild, M.F., Berger, P.J., Healey, M., 1991. ments from the anonymous referees are greatly _{Population parameter estimates for performance and} reproduc-appreciated. tive traits in polish large white nucleus herds. J. Anim. Sci. 69,

91–98.

Kerr, J.C., Cameron, N.D., 1995. Reproductive performance of pigs selected for components of efficient lean growth. Anim.

References _{Sci. 60, 281–290.}

Merks, J.W.M., Molendijk, R.J.F., 1995. Genetic Correlations Alfonso, L., Noquera, J.L., Babot, D., Estany, J., 1994. Selection Between Production Traits and First Parity Traits. In: 46th

for litter size in swine using a multivariate animal model. In: Annual Meeting of the EAAP, Prague, 4.11.

5th World Congr. Genet. Appl. Livest. Prod. Guelph, pp. Meyer, K., 1997. An ‘average information’ Restricted Maximum

347–350. Likelihood algorithm for estimating reduced rank genetic

Bereskin, B., 1984. A genetic analysis of sow productivity traits. covariance matrices or covariance functions for animal models J. Anim. Sci. 59, 1149–1163. with equal design matrices. Gen. Sel. Evol. 29, 97–116. Crump, R.E., Thompson, R., Haley, C.S., Mercer, J., 1997. Robertson, A., 1959. The sampling variance of the genetic

Individual animal model estimates of genetic correlations correlation coefficient. Biometrics 15, 459–485. ¨

between performance test and reproduction traits of Landrace Rohe, R., Kennedy, B.W., 1995. Estimation of genetic parameters pigs performance tested in a commercial nucleus herd. Anim. for litter size in Canadian Yorkshire and Landrace swine with Sci. 65, 291–298. each parity of farrowing treated as a different trait. J. Anim. de Vries, A.G., Kanis, E., 1994. Selection for efficiency of lean Sci. 73, 2959–2970.

tissue deposition in pigs. In: Principles of Pig Science. Nottin- Rydhmer, L., Johansson, K., Stern, S., Eliasson-Selling, L., 1992. gham University Press, Nottingham, pp. 23–42. A genetic study of pubertal age, litter traits, weight loss during Ducos, A., Bidanel, J.P., 1996. Genetic correlations between lactation and relations to growth and leanness in gilts. Acta

production and reproductive traits measured on the farm, in the Agric. Scand. Sect. A Anim. Sci. 42, 211–219.

Large White and French Landrace pig breeds. Anim. Breed. Rydhmer, L., Eliasson-Selling, L., Johannsson, K., Stern, S., Genet. 113, 493–504. Andersson, K., 1994. A genetic study of estrus symptoms at Dwyer, C.M., Stickland, N.C., Fletcher, J.M., 1994. The influence puberty and their relationship to growth and leanness in gilts. J.

of maternal nutrition on muscle fiber number development in Anim. Sci. 72, 1964–1970.

the porcine fetus and on subsequent postnatal growth. J. Anim. Rydhmer, L., Lundeheim, N., Johannsson, K., 1995. Genetic Sci. 72, 911–917. parameters for reproduction traits in sows and relations to Ferguson, P.W., Harvey, W.R., Irvin, K.M., 1985. Genetic, pheno- performance-test measurements. J. Anim. Breed. Genet. 112,

typic and environmental relationships between sow body 33–42.

weight and sow productivity traits. J. Anim. Sci. 60, 375–384. SAS / STATS, 1991. User’s Guide. Release 6.03 edition, SAS Fraser, D., 1990. Behavioural perspectives on piglet survival. J. Institute Inc.

Reprod. Fertil. 40, 335–370. Short, T.H., Wilson, E.R., McLaren, D.G., 1994. Relationships Haley, C.S., Avalos, E., Smith, C., 1988. Selection for litter size in between growth and litter traits in pig dam lines. In: 5th World

the pig. Anim. Breed. Abstr. 56, 317–332. Congr. Genet. Appl. Livest. Prod. Guelph, pp. 413–416. Hermesch, S., Luxford, B.G., Graser, H.-U., 2000a. Genetic Siewerdt, F., Cardellino, R.A., Costa da Rosa, V., 1995. Genetic

parameters for lean meat yield, meat quality, reproduction and parameters for litter traits in three pig breeds in southern feed efficiency traits for Australian pigs. 1. Description of traits Brazil. Braz. J. Genet. 18, 199–205.

and heritability estimates. Livest. Prod. Sci. 65, 239–248. Southwood, O.I., Kennedy, B.W., 1990. Estimation of direct and Hermesch, S., Luxford, B.G., Graser, H.-U., 2000b. Genetic maternal genetic variance for litter size in Canadian Yorkshire parameters for lean meat yield, meat quality, reproduction and and Landrace swine using an animal model. J. Anim. Sci. 68, feed efficiency traits for Australian pigs. 2. Genetic relation- 1884–1887.

ships between production, carcase and meat quality traits. Tholen, E., Bunter, K.L., Hermesch, S., Graser, H.-U., 1996. The Livest. Prod. Sci. 65, 249–259. genetic foundation of fitness and reproduction traits in

(10)

Aus-tralian pig populations: II. Relationships between weaning to Young, L.D., Pumfrey, R.A., Cunningham, P.J., Zimmermann, conception interval, farrowing interval, stayability and other D.R., 1978. Heritabilities and genetic and phenotypic correla-common reproduction and production traits. Austr. J. Agric. tions for prebreeding traits, reproductive traits and principal Res. 47, 1275–1290. components. J. Anim. Sci. 46, 937–949.

Vangen, O., 1980. Studies on two trait selection experiment in pigs. III. Correlated responses in daily feed intake, feed conversion and carcass traits. Acta Agric. Scand. 30, 125–141.

(1)

3.2. Estimation of genetic correlations first to third parity (rg5 2_0.15, 2_0.12, 2_0.20). However, litter birth weight in later parities was

3.2.1. Reproductive traits of the sow mainly positively correlated with number born alive

Genetic and environmental correlations between (rg5 2_{0.04 to 0.43). In this study litter birth weight}

reproduction traits of the sow are presented in Table was not corrected for number born alive. Using a

3. Estimates of genetic correlations between litter similar model, Bereskin (1984), Irvin and Swiger

size in the first to third parity (NBA1,2,3) were (1984) and Siewerdt et al. (1995) found positive

positive. Number born alive in the first parity was genetic correlations ranging from 0.42 to 0.92. The

genetically a different trait than litter size in the negative genetic correlations found in this study

second and third parity (genetic correlation (rg)5 _{between litter birth weight and number born alive}

0.62 for NBA , rg5_{0.61 for NBA ) supporting the} _{could be caused by the recording procedure of litter}

2 3

analysis of number born alive in the first parity as a birth weight. Piglet mortality is higher in larger

separate trait to litter size in later parities. Although litters and in litters with a lower average birth weight

Haley et al. (1988) suggested analysing litter size as (Fraser, 1990). Therefore, gilts with large litters are

repeated records, results from more recent studies more likely to have a higher mortality rate,

especial-¨

(i.e., Alfonso et al., 1994; Rohe and Kennedy, 1995; ly within the first few days after farrowing. With the

Tholen et al., 1996) confirm that number born alive delay of recording litter birth weight this will reduce

in the first parity should be regarded as a different the total litter weight. In addition, the cross-fostering

trait than litter size in later parities. The genetic practice of putting smaller piglets on to gilts and

correlation between number born alive in the second taking their own bigger piglets away to older sows

and third parity was high (0.95) and these traits can could also lead to this negative genetic correlation. It

therefore be treated as repeated records in selection was not possible to incorporate these cross-fostering

programs. Similar, genetic correlations between litter practices in the model.

birth weight and average piglet weight at birth in the Genetic correlations between litter size and

aver-first parity and performance in these two traits in the age piglet weight at birth varied from 2_{0.86 to}

second and third parity were significantly different 2_{0.27 (Table 3). Rydhmer et al. (1992) also found}

from one ranging from 0.52 to 0.79. Litter birth an unfavourable relationship between number born

weight and average piglet weight at birth in the first alive and average piglet weight at birth (rg5

litter should therefore be analysed as a separate trait 2_{0.34), while Irvin and Swiger (1984) found no}

while performance in later parities should be re- relationship (rg5_{0.05) between these two traits.}

garded as repeated records. Litter birth weight and average piglet weight at

Litter birth weight in the first parity had low birth were moderately correlated (rg5_{0.29–0.87).}

negative genetic correlations with litter size in the Irvin and Swiger (1984) found a positive genetic

Table 3