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Preliminary genetic correlations of milk production and milk composition with reproduction, growth, wool traits and worm resistance in crossbred ewes
Small Ruminant Research 82 (2009) 27–33
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Preliminary genetic correlations of milk production and milk composition with reproduction, growth, wool traits and worm resistance in crossbred ewes R.A. Afolayan a , N.M. Fogarty a,∗ , J.E. Morgan b , G.M. Gaunt c , L.J. Cummins d , A.R. Gilmour a a
The Cooperative Research Centre for Sheep Industry Innovation, NSW Department of Primary Industries, Orange Agricultural Institute, Orange, NSW 2800, Australia NSW Department of Primary Industries, Centre for Sheep Meat Development, Cowra, NSW 2794, Australia c Department of Primary Industries, Primary Industries Research, Rutherglen, Vic. 3685, Australia d Department of Primary Industries, Primary Industries Research, Hamilton, Vic. 3300, Australia b
a r t i c l e
i n f o
Article history: Received 7 October 2008 Received in revised form 2 January 2009 Accepted 8 January 2009 Available online 6 February 2009 Keywords: Genetic parameters Litter size Lambs weaned Wool traits Growth Worm resistance
a b s t r a c t Genetic correlations were estimated between ewe milk production and composition traits and other production traits, including early growth, wool, worm resistance and reproduction, among 944 crossbred ewes. Daily milk production of the ewes was estimated using the 4-h machine milking test procedure at approximately 3, 4 and 12 weeks of lactation, with milk composition assessed by sampling for fat %, protein % and lactose % at each milking. The ewes were the progeny of 74 maternal breed sires and mainly Merino dams and 77% of the ewes were milked on their first lactation. The production traits included pre-weaning and post-weaning growth rate of the ewes, their yearling wool production, clean yield and fibre diameter and worm egg count. The ewes were joined naturally to meat rams over 3 years, resulting in 2432 reproduction records. Ewe reproduction traits included: litter size (LS), rearing ability (RA) or lamb survival, number of lambs weaned (NLW) and average lamb weaning weight in the litter (AWW). Genetic correlations were estimated by bivariate mixed models using ASREML. The genetic correlations between milk yield and growth rates of the ewes were moderate and positive (0.38–0.49) with standard errors of about 0.3. The genetic correlations between the milk composition traits and growth traits were variable and generally similar to or smaller than their standard errors, except for lactose% and post-weaning gain (0.58). Estimates of the genetic correlations between milk yield and the wool traits and worm resistance were generally low and smaller than their standard errors. Lactose% was moderately positively correlated with the wool traits (0.19–0.51). The phenotypic correlations between all the milk traits and the ewe growth, wool and worm resistance traits were close to zero. The genetic correlations between milk yield and LS and NLW were moderate and positive (0.50–0.59), with the genetic correlation between milk yield and AWW being slightly lower (0.44) and RA close to zero. The phenotypic correlations between milk yield and the reproduction traits were all small and positive. The genetic correlations between the milk composition traits and reproduction traits were variable and all less than their standard errors. The corresponding phenotypic correlations were all close to zero. The genetic correlations in this study provide preliminary estimates of the parameters required for more accurate genetic evaluation and the development of breeding programs incorporating meat and wool objectives that include ewe reproduction and milk production. Crown Copyright © 2009 Published by Elsevier B.V. All rights reserved.
∗ Corresponding author. Tel.: +61 2 63913813; fax: +61 2 63913922. E-mail address: [email protected] (N.M. Fogarty). 0921-4488/$ – see front matter. Crown Copyright © 2009 Published by Elsevier B.V. All rights reserved. doi:10.1016/j.smallrumres.2009.01.006
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R.A. Afolayan et al. / Small Ruminant Research 82 (2009) 27–33
1. Introduction Many traits contribute genetically to productivity and economic performance of lamb production enterprises, including ewe reproduction and wool production, maternal performance and lamb growth (Fogarty et al., 2006). Milk production is an important component of the maternal ability of ewes (Morgan et al., 2006) as it affects lamb growth (Morgan et al., 2007), while there appears to be genetic variation among non-dairy sheep (Afolayan et al., 2009). Knowledge of the genetic relationships between milk production and milk composition traits and other production traits of economic importance are required to develop optimum breeding objectives and genetic evaluation programs for lamb production. The estimates of heritability for milk yield and composition traits in dairy sheep breeds reviewed by Hamann et al. (2004), with subsequent estimates cited by Afolayan et al. (2009), ranged from low to moderately high. However, our earlier study provides the only estimates of heritability for milk traits in non-dairy sheep used for lamb production (Afolayan et al., 2009) and there are no estimates of genetic correlations between milk traits and other lamb production traits. In Australia over 5 million crossbred ewes are mated annually to meat sire breed rams with their progeny comprising over 30% of the national lamb slaughter for meat production. The great majority of the crossbred ewes are first cross progeny of Border Leicester sires and Merino dams. The national maternal sire central progeny test (MCPT) was established to identify genetic variation and the potential for improvement in a range of production traits among maternal sires of crossbred ewes from several breeds available to the lamb industry (Fogarty et al., 2005a). Australian breeds used in the lamb industry have not been selected for milk production and rarely has any attempt been made to evaluate milk traits. This study provides estimates of genetic and phenotypic (co)variances between milk production and composition traits and ewe reproduction, wool production and growth traits. This provides parameters for development of breeding objectives and evaluation programs for lamb production that include milk traits and also provides information on the expected correlated responses in milk traits from selection on various production traits. 2. Materials and methods 2.1. Animals and management Data were available on milk production and milk composition from 944 lactations of crossbred ewes, of which 724 were first lactations, together with data on the early growth, wool production, worm resistance and reproductive performance of the ewes. The ewes were a subset of those evaluated for lamb production in the national Maternal sire Central Progeny Test (MCPT) which has been described previously (Fogarty et al., 2005a). The ewes were run at 3 sites (Cowra, Hamilton and Rutherglen) and were the progeny of 74 sires from several maternal crossing breeds and Merino dams, with also Corriedale dams at Hamilton, born in 3 cohort years at each site (Table 1). The sires were entered by breeders from several breeds including: Border Leicester (n = 18), East Friesian and crosses (n = 12), Finnsheep and crosses (n = 12), Coopworth (n = 9), White Suffolk (n = 7), Corriedale (n = 6), Booroola Leicester (n = 6) and Hyfer (n = 4). The MCPT also evaluated an additional 17 sires from various other divergent breeds with 1–2 sires per breed. Inclusion of these additional sires showed inflated sire variance for some reproduction traits in pre-
vious analyses (Afolayan et al., 2008) and hence the data from these 17 sires were excluded from these analyses. To provide genetic links for the evaluation, 3 of the sires had progeny at all sites and in all years, except that there were only 2 link sires of the 1998 cohort ewes at Rutherglen. The ewes at Cowra and Rutherglen were milked on 3 occasions during their primiparous lactation at approximately 3, 4 and 12 weeks after parturition. This provided 2 estimates of daily milk yield near the peak of lactation and a further estimate late in lactation and close to weaning. At Cowra, some additional ewes that were all progeny of link sires were also milked following their second (1998 cohort) or third (1997 cohort) joining. At Hamilton, the ewes were generally milked on 3 occasions at approximately 4, 6 and 12 weeks of lactation and the parity of the ewes varied (1997 cohort following their first and third joining; 1998 cohort, second and third joining; 1999 cohort, first and second joining). The 1999 cohort ewes at Hamilton were only milked on 2 occasions in 2001 (parity 2) at 4–6 and 12 weeks of lactation. The numbers of ewes in each cohort, their parity and their pre-joining live weight in the years in which they were milked are shown in Table 1. Data were available on the performance of the first cross ewes, which included their early growth rates between birth and weaning at about 12 weeks of age (ADGpre) and between weaning and postweaning weight at 4–6 months of age (ADGpost). Greasy fleece weight (GFW) was recorded for the ewes with approximately 12 months wool growth following a lamb shearing. A mid-side wool sample was taken from each fleece for yield (YLD) and average fibre diameter (FD) measurement in commercial laboratories and calculation of clean fleece weight (CFW). The ewes were monitored for parasite worm burden under natural challenge after weaning in their first year. When the mob level indicated sufficient worm burden (generally > 250 eggs/g) faecal samples were collected from individual ewes and total worm egg count recorded, with a cube root trans√ formation used for analysis (WEC, 3 eggs/g). The main species of worms were Trichostrongylus and Ostertagia. Further details of the generation and management of the ewes at each site, including genetic merit of the sires has been provided previously (Fogarty et al., 2005a,b). The crossbred ewes were mated naturally to groups of terminal sire rams at each site, generally within their age cohort, to evaluate their reproduction (Afolayan et al., 2008) and second cross lamb production (Afolayan et al., 2007). At Cowra, the crossbred ewes were randomly split after they were weaned into autumn and spring joining groups. The autumn joined ewes were joined in February/March and lambed in July/August and the spring joined ewes were joined in October/November and lambed in March/April. At Hamilton the ewes were joined in autumn (March/April) and lambs were born in August/September. At Rutherglen the ewes were joined in spring (November/December) and lambs were born in April/May. The autumn joined ewes at Cowra and Hamilton were first joined at approximately 7 months of age. The spring joined ewes at Cowra were first joined at 14 months of age and the ewes at Rutherglen were first joined at 17 months of age. The reproduction records of the ewes that lambed were cumulated over 3 years and included in the analysis, except at Hamilton. The ewes at Hamilton in 1999 experienced vibriosis pre-lambing which resulted in some abortion loss. These data were not included in the ewe reproduction records and these cohorts of ewes (1997 and 1998) were joined for an additional year. The reproduction traits included litter size (LS, number of lambs born including dead lambs per ewe lambing), ewe rearing ability (RA, ratio of lambs weaned to lambs born which is a measure of lamb survival as a trait of the ewe) and the composite trait of number of lambs weaned (NLW) per ewe lambing. The average weight of the lambs weaned in the litter (AWW) was also analysed. There were a total of 2432 ewe reproduction records, with the number of records, mean and standard deviation for all the traits analysed shown in Table 2. The project was conducted under approval of the Departmental Animal Ethics Committee at each site. 2.2. Milking procedures Milk production was measured using a 4-h milk test to estimate daily milk yield and collect samples for determining milk composition (Afolayan et al., 2009). On each occasion the ewes were initially milked out by machine followed by hand-stripping and the time recorded. The ewes were milked again (machine and hand-stripping) approximately 4 h later, with the time and weight of milk recorded and samples taken for analysis of composition. An intravenous injection of synthetic oxytocin (llium Syntocin, 10 IU/mL, Troy laboratories, Smithfield, NSW) was administered to the ewes prior to each milking. A dose of 5 IU of oxytocin
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Table 1 The number of sires and ewes that were milked and their pre-joining weight (S.D.) for the various production environments and cohorts of crossbred ewes at 3 parities. Environment and cohort
Sires
Parity 1 Ewes
Parity 2 Weight (kg)
Ewes
Parity3 Weight (kg)
Ewes
Weight (kg)
Cowra autumn 1997 1998 1999
12 10 9
97 64 60
40.1 (4.7) 35.8 (3.6) 41.8 (4.8)
– 21 –
– 62.6 (6.9) –
19 – –
69.9 (7.1) – –
Cowra spring 1997 1998 1999
12 10 9
120 70 59
51.2 (6.3) 57.1 (6.4) 56.0 (5.1)
– 21 –
– 70.4 (8.3) –
20 – –
73.2 (8.1) – –
Hamilton 1997 1998 1999
11 10 11
33 – 31
34.9 (4.1) – 32.5 (4.5)
– 31 44
– 48.9 (6.3) 53.7 (6.1)
33 31 –
57.8 (5.6) 63.6 (5.8) –
Rutherglen 1998 1999 2000
13 10 11
74 55 61
51.2 (5.9) 54.0 (4.6) 57.1 (6.8)
– – –
Total
74
724
117
was used following the results of an experiment in the first year and season at Cowra that examined the effects of varying dose rate (Morgan et al., 2000). The same operator milked all ewes within the sites at Cowra and Rutherglen. Daily milk yield was calculated by extrapolation from the milk yield at the second milking and the time interval from the initial milking. Milk samples were preserved, refrigerated at 4 ◦ C, and sent by overnight courier to commercial dairy laboratories. The laboratories were different for each site and the testing units were calibrated for analysis Table 2 The number of records and means (S.D.) for milk, growth, wool, worm resistance and reproduction traits of crossbred ewes. Trait
Records (n)
Mean (S.D.)
Milk Milk yield (kg/d) Fat (%) Protein (%) Lactose (%)
2679 2434 2433 2292
1.64 (0.90) 9.14 (2.21) 4.38 (0.89) 5.66 (0.47)
Growth Growth rate pre-weaning, ADGpre (g/d) Growth rate post-weaning, ADGpost (g/d) Wool Greasy fleece weight, GFW (kg) Clean fleece weight, CFW (kg) Yield, YLD (%) Fibre diameter, FD (m) Worm resistance Worm egg count, WEC √ (3 eggs/g) Reproduction Litter size, LS Rearing ability, RA Number of lambs weaned,a NLW Average lamb weaning weight, AWW (kg) a
Per ewe lambing.
885
208 (42)
877
116 (54)
849
4.5 (0.9)
848
3.4 (0.8)
848 848
75.2 (4.9) 26.9 (2.9)
647
6.1 (1.1)
2432 2432 2432
1.60 (0.64) 0.88 (0.26) 1.36 (0.61)
2314
28.4 (5.8)
– – –
– – –
– – –
103
of composition (fat%, protein%, lactose%) of sheep milk. Milk composition data were not available at Hamilton for the 1997 cohort ewes at parity 1. 2.3. Statistical analysis Mixed linear models applying a restricted maximum likelihood procedure using ASReml (Gilmour et al., 2006) were initially developed for each trait. Bivariate analyses were then used to estimate covariance components between the milk traits and the growth, wool production, worm resistance and reproduction traits. The model for milk traits included fixed effects for birth year cohort (1–3), production environment (Cowra autumn joining, Cowra spring joining, Hamilton autumn joining, Rutherglen spring joining), ewe parity (1–3 joining), sire breed (1–8), number of lambs born and suckled (3 levels) and all two- and three-way interactions. Days of lactation was fitted as a covariate as well as its interactions with the other fixed effects. The lactation curve was modelled using a cubic smoothing spline that allowed different shapes for the curve for each sire breed. The number of lambs born and suckled was defined as single born and reared (11), multiple born and single reared (21) and multiple born and reared (22), with most multiples being twins. Interactions that were not significant (P > 0.05) were removed from the final model. Results from the fixed effects analyses for these milk traits have been reported elsewhere (Afolayan et al., 2009) and are not presented here. The models for growth, wool and worm resistance traits of the ewes included fixed effects for site (Cowra, Hamilton, Rutherglen), birth year cohort (1–3), sire breed (1–8), type of birth and rearing (3 levels, single born and reared, multiple born and single reared, and multiple born and reared). Age of the ewe lamb was included within site and year as a linear covariate for the growth traits. All significant (P < 0.05) two-way interactions were included in the final model. Results from the fixed effects analyses for these production traits from the complete MCPT data have been reported elsewhere (Fogarty et al., 2005a,b) and are not presented here. The models for the reproduction traits included fixed effects for production environment (Cowra autumn joining, Cowra spring joining, Hamilton autumn joining, Rutherglen spring joining), parity (joining 1–3), birth year cohort (1–3), sire breed (1–8) and type of birth and rearing of the ewe (3 levels, as defined above for the ewe production traits). Prejoining weight was not fitted as a linear covariate to avoid bias due to differences between sire breeds, years and production environments. The second cross lamb weaning weights were adjusted for age and sex but not for the effects of type of birth and rearing to calculate AWW. For all traits analysed the two- and three-way interactions were included initially, but were subsequently removed from the final model if they were not significant (P > 0.05). Common significant interactions were environment × sire breed, environment × ewe type of birth and rearing. Sire was included as
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a random effect in the models for all traits which meant that sire breed was tested relative to the remaining between sire variance. Results from the fixed effects analyses for these reproduction traits from the complete MCPT data have been detailed elsewhere (Afolayan et al., 2008) and are not presented here. The random model for the milk traits in the bivariate analyses included sire, dam (of the ewe), dam × year, ewe, ewe × parity (as some ewes were milked over different parities) and residual which represents the repeated milkings at each lactation. The random model for the growth, wool and worm resistance traits included sire, dam, dam × year and residual, which represents the individual ewes as there is only one record per ewe for these traits. For the ewe reproduction traits the random model included sire, dam, dam × year, ewe and residual which represents the repeated lambings for ewes, i.e. at different parities. Additional bivariate analyses were also undertaken after exclusion of the lambing record of the ewe in the year of milking to examine the effect on the covariances due to any confounding between milk yield and the reproduction record at that lambing. The dam × year component was not significant for the milk composition, growth and wool traits and was not included in the final model. All bivariate analyses included the between sire covariance and a residual covariance which was a covariance at either the ewe or ewe × parity stratum depending on the traits involved. The phenotypic variances and covariances used for calculating genetic parameters were the sum of the appropriate variance components across all strata.
3. Results and discussion Mean estimates of variance components and heritability from the bivariate analyses between the milk traits and the growth, wool, worm resistance and reproduction traits are shown in Table 3. Earlier papers have reported the univariate estimates of variance components from these data for the milk traits (Afolayan et al., 2009) and from the complete MCPT data for wool and worm resistance (Ingham et al., 2007) and reproduction traits (Afolayan et al., 2008) and should be referred to for appropriate estimates of heritability and phenotypic variances. The mean estimates of
heritability from the bivariate analyses presented here are close to the corresponding univariate estimates for most milk production traits except for fat%, which was over 0.2 (Afolayan et al., 2009). The estimates are generally within the range reported for milk traits in dairy sheep breeds in Mediterranean environments (Othmane et al., 2002; Hamann et al., 2004; El-Saied et al., 2005; Marie-Etancelin et al., 2006; Gutierrez et al., 2007). However, the mean estimates for WEC and the wool traits were much higher than those reported in the earlier corresponding univariate results which had more data for sires (91 sires) and considerably more ewe records included (Ingham et al., 2007). The estimates of mean heritability for the other production traits were generally within the range of estimates of parameters reviewed by Fogarty (1995) and Safari et al. (2005). Estimates of the direct genetic and phenotypic correlations between the milk traits and growth, wool and worm resistance traits are shown in Table 4. The genetic correlations between milk yield and growth rate of the ewes pre-weaning (0.38) and post-weaning (0.49) were moderate and positive with standard errors about 0.3. The genetic correlations between the milk composition traits and growth traits were variable and generally similar to or smaller than their standard error, except for lactose% and post-weaning gain (0.58 ± 0.22). The phenotypic correlations between all the milk traits and the growth traits were close to zero. While there are no other estimates of these genetic correlations in comparable production systems in the literature, Pattie (1965) reported positive correlated responses in milk production following divergent selection for weaning weight in Merino sheep. Our genetic correlations with milk yield are of the order of 0.4 for pre-weaning
Table 3 Mean variance componentsa and heritability (h2 ) estimates from the bivariate analyses for milk, growth, wool, worm resistance and reproduction traits of crossbred ewes. Traitb Milk Milk yield (kg/d) Fat (%) Protein (%) Lactose (%)
p2
s2
d2
2 dy
2 w
2 wp
e2
h2
0.440 3.822 0.354 0.119
0.043 0.427 0.091 0.028
0.001 0.251 0.008 0.007
0.008 – – –
0.002 0.431 0.027 0.002
0.018 0.038 0.016 –
0.344 3.018 0.208 0.082
0.10 0.11 0.26 0.23
Growth ADGpre (g/d) ADGpost (g/d)
12.150 5.766
2.823 2.307
2.920 1.256
– –
– –
– –
6.949 2.545
0.23 0.40
Wool GFW (kg) CFW (kg) YLD (%) FD (m)
0.349 0.262 19.595 3.820
0.148 0.120 10.417 3.512
0.094 0.069 2.381 0.747
– – – –
– – – –
– – – –
0.122 0.084 7.360 −0.302
0.42 0.45 0.53 0.92
1.460
0.356
–
0.899
–
–
0.943
0.24
0.331 0.065 0.329 19.723
0.043 0.002 0.031 2.840
– – – –
– – – –
0.058 0.004 0.039 3.795
– – – –
0.230 0.060 0.258 13.090
0.13 0.03 0.09 0.14
Worm resistance √ WEC (3 eggs/g) Reproduction LS RA NLW AWW (kg) a
2 2 2 Phenotypic variance (p2 ), sire variance (s2 ), dam variance (d2 ), dam × year variance (dy ), ewe variance (w ) and ewe × parity variance (wp ) and residual
variance (e2 ). The residual variance for the growth, wool and worm resistance traits is at the ewe stratum and the residual variance for the reproduction traits is at the ewe × parity stratum. b See Table 2 for abbreviations of traits.
R.A. Afolayan et al. / Small Ruminant Research 82 (2009) 27–33
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Table 4 Estimates of genetic and phenotypic correlations (±S.E.) between milk yield and composition traits and growth, wool and worm resistance traits. Traita
Milk yield
Genetic ADGpre ADGpost GFW CFW YLD FD WEC
0.38 0.49 0.06 0.13 0.42 0.39 0.08
± ± ± ± ± ± ±
0.32 0.31 0.31 0.30 0.26 0.28 0.44
−0.01 −0.30 −0.22 −0.18 −0.10 −0.37 −0.06
± ± ± ± ± ± ±
0.42 0.35 0.31 0.30 0.32 0.27 0.45
−0.36 0.28 0.19 0.07 −0.33 −0.17 0.42
± ± ± ± ± ± ±
0.32 0.26 0.25 0.25 0.23 0.23 0.33
0.04 0.58 0.46 0.51 0.48 0.19 −0.22
± ± ± ± ± ± ±
0.33 0.22 0.26 0.23 0.21 0.23 0.37
Phenotypic ADGpre ADGpost GFW CFW YLD FD WEC
0.03 0.04 0.04 0.05 0.03 0.04 0.02
± ± ± ± ± ± ±
0.02 0.02 0.03 0.02 0.02 0.02 0.03
−0.02 0.00 0.00 −0.01 0.00 −0.06 0.00
± ± ± ± ± ± ±
0.03 0.03 0.03 0.03 0.03 0.03 0.03
−0.04 −0.02 −0.01 −0.01 −0.01 0.02 0.02
± ± ± ± ± ± ±
0.03 0.03 0.03 0.03 0.03 0.03 0.03
−0.01 0.07 0.04 0.04 0.03 0.01 −0.01
± ± ± ± ± ± ±
0.03 0.03 0.03 0.03 0.03 0.03 0.03
a
Fat%
Protein%
Lactose%
See Table 2 for abbreviations of traits.
growth and increase to 0.5 with post-weaning growth. This suggests that selection to increase early growth of the ewes would result in a moderate increase (genetic improvement) in subsequent ewe milk yield. Our data are mainly from ewes in their first lactation with few multiparous ewes present. However, there is a moderately high repeatability of milk yield over lactations (Morrissey et al., 2007) as well as a high genetic correlation for milk scores between parities (Snowder et al., 2001). Selection to increase early growth of ewes should improve milk yield in primiparous and older ewes. Our earlier results also indicate that measurement of ewe milk yield at peak lactation or towards the end of lactation are closely related traits (Afolayan et al., 2009). The moderate positive genetic correlation between lactose% and post-weaning gain, together with the low correlations for fat% and protein% with both pre- and postweaning gain supports the assertion that milk volume is determined by lactose secretion, and in highly productive dairy animals the synthesis of fat and protein does not keep up with that of lactose when high rates of milk secretion are achieved (Holmes and Wilson, 1984). Estimates of genetic correlations between milk yield and the wool traits were generally low with high standard errors except for YLD which was moderate and favourable (0.42) and for FD which was moderate and unfavourable (0.39). Lactose% was also moderately positively correlated with the wool traits (0.19–0.51), while the genetic correlations with fat% and protein% were generally negative and smaller than their standard errors. The genetic correlations between WEC and the milk traits were generally close to zero and smaller than their standard errors, except for protein% (0.42 ± 0.33). The phenotypic correlations between the milk production traits and the wool and worm resistance traits were all close to zero. No other reports were found in the literature for genetic relationships between milk traits and wool traits. Our results suggest that selection for wool production may increase lactose% but is likely to have little impact on milk yield of ewes or other milk component traits. There may be some implications for sheep dairy products where milk composition is important (Bencini and Pulina, 1997), in contrast to the lamb industry,
where milk composition has little effect on lamb growth (Morgan et al., 2007). Estimates of genetic and phenotypic correlations from the bivariate analyses between the milk traits and ewe reproduction traits are shown in Table 5. The genetic correlations between milk yield and LS and NLW were moderate and positive (0.50–0.59), with AWW being slightly lower (0.44) and RA close to zero. Exclusion of the lambing record at milking had little effect on the genetic correlations, except to increase the magnitude of the standard errors. The phenotypic correlations between milk yield and the reproduction traits were all small and positive. The genetic correlations between milk composition and the reproduction traits were variable and all less than their standard errors. The corresponding phenotypic correlations were all close to zero. Improved milk production of ewes is important for increasing early growth of their lambs under grazing systems (Morgan et al., 2007). Divergent selection for weaning weight in Merino sheep has been shown to result in correlated responses in milk production of ewes (Pattie, 1965), which contributed to about half the differences in body weight of their crossbred lambs (Yates and Pattie, 1970). Our results have shown there are moderate genetic correlations between ewe milk production and the early growth of ewes and their subsequent litter size and lambing performance. There appears to be no genetic relationship between milk production and the fleece weight and worm resistance of the ewes, although there were moderate genetic correlations with wool yield (favourable) and fibre diameter (unfavourable). There also appears to be little genetic relationship between the milk composition traits and any of the ewe production traits, except perhaps for lactose% with growth and wool production. In seedstock or commercial sheep production systems, direct measurement of milk production is neither feasible nor practical and its improvement relies on indirect selection for genetically correlated traits. Australian sheep breeding programs have varying objectives that often include growth, wool weight and reproduction. Our study suggests that selection for objectives including growth and reproduction will result
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Table 5 Estimates of genetic and phenotypic correlations (±S.E.) between milk yield and composition traits and ewe reproduction traits. Traita
Milk yieldb
Genetic LS RA NLW AWW
0.44 0.12 0.53 0.47
± ± ± ±
0.42 0.63 0.37 0.34
0.50 0.11 0.59 0.44
± ± ± ±
0.34 0.52 0.32 0.34
−0.14 0.23 −0.03 −0.31
± ± ± ±
0.39 0.59 0.42 0.40
−0.03 0.34 0.08 −0.00
± ± ± ±
0.31 0.49 0.33 0.32
0.07 0.02 0.26 0.01
± ± ± ±
0.32 0.50 0.35 0.32
Phenotypic LS RA NLW AWW
0.06 0.04 0.07 0.02
± ± ± ±
0.02 0.02 0.02 0.02
0.04 0.05 0.08 0.08
± ± ± ±
0.02 0.02 0.02 0.02
−0.03 0.03 0.00 0.02
± ± ± ±
0.02 0.02 0.02 0.02
0.00 0.00 −0.00 −0.01
± ± ± ±
0.02 0.02 0.02 0.02
−0.01 0.00 −0.01 0.02
± ± ± ±
0.02 0.02 0.02 0.02
a b c
Milk yieldc
Fat%c
Protein%c
Lactose%c
See Table 2 for abbreviations of traits. Not including reproduction record at milking lambing. Includes all ewe reproduction records.
in small improvements in ewe milk production, with little effect from selection for wool weight. Selection for any of the production objectives would be expected to have little effect on milk composition. Genetic improvement in milk production would be expected to be achieved by selection that includes breeding values for maternal weaning weight, which is a trait evaluated in the national genetic evaluation (LAMBPLAN) program (Brown et al., 2007). A simple udder scoring system for ewes after lambing, which is moderately heritable and genetically correlated with weight of lamb weaned, has also been suggested as a practical way of improving milk production under commercial conditions (Snowder et al., 2001). 4. Conclusions This study adds to the very sparse literature on genetic correlations between milk production and composition traits and early growth, wool, worm resistance and reproduction traits of ewes. The results, together with the few other estimates in the dairy sheep literature, indicate moderately favourable genetic correlations of ewe milk production with early growth and reproduction. There was no relationship of ewe milk production with wool weight or with worm resistance and also of milk composition traits with ewe production traits. The genetic correlations in this study provide estimates of the parameters, albeit with high standard errors, that are required for more accurate genetic evaluation and the development of breeding programs with meat and wool objectives that include ewe reproduction and milk production. Acknowledgements The assistance of many staff members from the NSW Department of Primary Industries at Cowra and the Department of Primary Industries Vic at Hamilton and Rutherglen over several years of the study is greatly appreciated. In particular those who assisted with the milkings included: Kelly Lees, David Stanley, Phil Goodacre, Tony Markham, Darryl Hughes, Rob Urquhart, Ashley Radburn, Ken Masters, Taffy Phillips, Greg Seymour, Paul Curran, Peter Heazlewood, Kerry Groves, Murray Arnold and Brian Hurley. Dr Peter Holst and Dr Geoff Hinch are thanked for advice and support for the milking experiments. Dr Kevin Atkins is
thanked for discussions on the analyses. Meat and Livestock Australia provided financial support for the MCPT project and the Cooperative Research Centre for Sheep Industry Innovation provided financial support for the analysis. References Afolayan, R.A., Fogarty, N.M., Gilmour, A.R., Ingham, V.M., Gaunt, G.M., Cummins, L.J., 2008. Reproductive performance and genetic parameters in first cross ewes from different maternal genotypes. J. Anim. Sci. 86, 804–814. Afolayan, R.A., Fogarty, N.M., Ingham, V.M., Gilmour, A.R., Gaunt, G.M., Cummins, L.J., Pollard, T., 2007. Genetic evaluation of crossbred lamb production 3. Growth and carcass performance of second-cross lambs. Aust. J. Agric. Res. 58, 457–466. Afolayan, R.A., Fogarty, N.M., Morgan, J.E., Gaunt, G.M., Cummins, L.J., Gilmour, A.R., Nielsen, S., 2009. Genetic analysis of milk production and composition in crossbred ewes from different maternal genotypes. Anim. Prod. Sci. 49, 24–31. Bencini, R., Pulina, G., 1997. The quality of sheep milk: a review. Aust. J. Exp. Agric. 37, 485–504. Brown, D.J., Huisman, A.E., Swan, A.A., Graser, H.U., Woolaston, R.R., Ball, A.J., Atkins, K.D., Banks, R.G., 2007. Genetic evaluation for the Australian sheep industry. Proc. Assoc. Advmnt. Anim. Breed. Genet. 17, 187–194. El-Saied, U.M., De La Fuente, L.F., Carriedo, J.A., San Primitivo, F., 2005. Genetic and phenotypic parameter estimates of total and partial lifetime traits for dairy ewes. J. Dairy Sci. 88, 3265–3272. Fogarty, N.M., 1995. Genetic parameters for live weight, fat and muscle measurements, wool production and reproduction in sheep: a review. Anim. Breed. Abstr. 63, 101–143. Fogarty, N.M., Ingham, V.M., Gilmour, A.R., Cummins, L.J., Gaunt, G.M., Stafford, J., Hocking Edwards, J.E., Banks, R.G., 2005a. Genetic evaluation of crossbred lamb production. 1. Breed and fixed effects for birth and weaning weight of first-cross lambs, gestation length, and reproduction of base ewes. Aust. J. Agric. Res. 56, 443–453. Fogarty, N.M., Ingham, V.M., Gilmour, A.R., Cummins, L.J., Gaunt, G.M., Stafford, J., Hocking Edwards, J.E., Banks, R.G., 2005b. Genetic evaluation of crossbred lamb production. 2. Breed and fixed effects for post-weaning growth, carcass, and wool of first-cross lambs. Aust. J. Agric. Res. 56, 455–463. Fogarty, N.M., Ingham, V.M., McLeod, L., Gaunt, G.M., Cummins, L.J., 2006. Genetic resources to increase the profitability of crossbred lamb production. Aust. J. Exp. Agric. 46, 799–802. Gilmour, A.R., Gogel, B.J., Cullis, B.R., Thompson, R., 2006. ASReml User Guide Release 2.0. VSN International Ltd., Hemel Hempstead, UK. Gutierrez, J.P., Legaz, E., Goyache, F., 2007. Genetic parameters affecting 180-days standardised milk yield, test-day milk yield and lactation length in Spanish Assaf (Assaf E) dairy sheep. Small Rumin. Res. 70, 233–238. Hamann, H., Horstick, A., Wessels, A., Distl, O., 2004. Estimation of genetic parameters for test day milk production, somatic cell score and litter size at birth in East Friesian ewes. Livest. Prod. Sci. 87, 153–160. Holmes, C.W., Wilson, G.F., 1984. Milk Production From Pasture. Butterworths, Wellington, New Zealand. Ingham, V.M., Fogarty, N.M., Gilmour, A.R., Afolayan, R.A., Cummins, L.J., Gaunt, G.M., Stafford, J., Edwards, J.E.H., 2007. Genetic evaluation of
R.A. Afolayan et al. / Small Ruminant Research 82 (2009) 27–33 crossbred lamb production. 4. Genetic parameters for first-cross animal performance. Aust. J. Agric. Res. 58, 839–846. Marie-Etancelin, C., Manfredi, E., Aurel, M.R., Pailler, F., Arhainx, J., Ricard, E., Lagriffoul, G., Guillouet, P., Bibe, B., Barillet, F., 2006. Genetic analysis of milking ability in Lacaune dairy ewes. Genet. Seln. Evol. 38, 183–200. Morgan, J., Fogarty, N.M., Nicol, H., 2000. Oxytocin administration and its effect on ewe milk composition. Asian-Aust. J. Anim. Sci. 13 (Suppl. vol. C), 206–208. Morgan, J.E., Fogarty, N.M., Nielsen, S., Gilmour, A.R., 2006. Milk yield and milk composition from grazing primiparous non-dairy crossbred ewes. Aust. J. Agric. Res. 57, 377–387. Morgan, J.E., Fogarty, N.M., Nielsen, S., Gilmour, A.R., 2007. The relationship of lamb growth from birth to weaning and the milk production of their primiparous crossbred dams. Aust. J. Exp. Agric. 47, 899–904. Morrissey, A.D., Cameron, A.W.N., Caddy, D.J., Tilbrook, A.J., 2007. Predicting milk yield in sheep used for dairying in Australia. J. Dairy Sci. 90, 5056–5061.
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Othmane, M.H., Carriedo, J.A., San Primitivo, F., De la Fuente, L.F., 2002. Genetic parameters for lactation traits of milking ewes: protein content and composition, fat, somatic cells and individual laboratory cheese yield. Genet. Seln. Evol. 34, 581–596. Pattie, W.A., 1965. Selection for weaning weight in Merino sheep. 2. Correlated responses in other production characters. Aust. J. Exp. Agric. Anim. Husb. 5, 361–368. Safari, E., Fogarty, N.M., Gilmour, A.R., 2005. A review of genetic parameter estimates for wool, growth, meat and reproduction traits in sheep. Livest. Prod. Sci. 92, 271–289. Snowder, G.D., van Vleck, L.D., Knight, A.D., Kellom, T.R., Bromley, C.M., 2001. Usefulness of subjective ovine milk scores: II. Genetic parameter estimates. J. Anim. Sci. 79, 869–876. Yates, W.J., Pattie, W.A., 1970. The effect of genetic improvement within pure breeds on the performance of their crossbred lambs. Proc. Aust. Soc. Anim. Prod. 8, 154–158.
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Small Ruminant Research journal homepage: www.elsevier.com/locate/smallrumres
Preliminary genetic correlations of milk production and milk composition with reproduction, growth, wool traits and worm resistance in crossbred ewes R.A. Afolayan a , N.M. Fogarty a,∗ , J.E. Morgan b , G.M. Gaunt c , L.J. Cummins d , A.R. Gilmour a a
The Cooperative Research Centre for Sheep Industry Innovation, NSW Department of Primary Industries, Orange Agricultural Institute, Orange, NSW 2800, Australia NSW Department of Primary Industries, Centre for Sheep Meat Development, Cowra, NSW 2794, Australia c Department of Primary Industries, Primary Industries Research, Rutherglen, Vic. 3685, Australia d Department of Primary Industries, Primary Industries Research, Hamilton, Vic. 3300, Australia b
a r t i c l e
i n f o
Article history: Received 7 October 2008 Received in revised form 2 January 2009 Accepted 8 January 2009 Available online 6 February 2009 Keywords: Genetic parameters Litter size Lambs weaned Wool traits Growth Worm resistance
a b s t r a c t Genetic correlations were estimated between ewe milk production and composition traits and other production traits, including early growth, wool, worm resistance and reproduction, among 944 crossbred ewes. Daily milk production of the ewes was estimated using the 4-h machine milking test procedure at approximately 3, 4 and 12 weeks of lactation, with milk composition assessed by sampling for fat %, protein % and lactose % at each milking. The ewes were the progeny of 74 maternal breed sires and mainly Merino dams and 77% of the ewes were milked on their first lactation. The production traits included pre-weaning and post-weaning growth rate of the ewes, their yearling wool production, clean yield and fibre diameter and worm egg count. The ewes were joined naturally to meat rams over 3 years, resulting in 2432 reproduction records. Ewe reproduction traits included: litter size (LS), rearing ability (RA) or lamb survival, number of lambs weaned (NLW) and average lamb weaning weight in the litter (AWW). Genetic correlations were estimated by bivariate mixed models using ASREML. The genetic correlations between milk yield and growth rates of the ewes were moderate and positive (0.38–0.49) with standard errors of about 0.3. The genetic correlations between the milk composition traits and growth traits were variable and generally similar to or smaller than their standard errors, except for lactose% and post-weaning gain (0.58). Estimates of the genetic correlations between milk yield and the wool traits and worm resistance were generally low and smaller than their standard errors. Lactose% was moderately positively correlated with the wool traits (0.19–0.51). The phenotypic correlations between all the milk traits and the ewe growth, wool and worm resistance traits were close to zero. The genetic correlations between milk yield and LS and NLW were moderate and positive (0.50–0.59), with the genetic correlation between milk yield and AWW being slightly lower (0.44) and RA close to zero. The phenotypic correlations between milk yield and the reproduction traits were all small and positive. The genetic correlations between the milk composition traits and reproduction traits were variable and all less than their standard errors. The corresponding phenotypic correlations were all close to zero. The genetic correlations in this study provide preliminary estimates of the parameters required for more accurate genetic evaluation and the development of breeding programs incorporating meat and wool objectives that include ewe reproduction and milk production. Crown Copyright © 2009 Published by Elsevier B.V. All rights reserved.
∗ Corresponding author. Tel.: +61 2 63913813; fax: +61 2 63913922. E-mail address: [email protected] (N.M. Fogarty). 0921-4488/$ – see front matter. Crown Copyright © 2009 Published by Elsevier B.V. All rights reserved. doi:10.1016/j.smallrumres.2009.01.006
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R.A. Afolayan et al. / Small Ruminant Research 82 (2009) 27–33
1. Introduction Many traits contribute genetically to productivity and economic performance of lamb production enterprises, including ewe reproduction and wool production, maternal performance and lamb growth (Fogarty et al., 2006). Milk production is an important component of the maternal ability of ewes (Morgan et al., 2006) as it affects lamb growth (Morgan et al., 2007), while there appears to be genetic variation among non-dairy sheep (Afolayan et al., 2009). Knowledge of the genetic relationships between milk production and milk composition traits and other production traits of economic importance are required to develop optimum breeding objectives and genetic evaluation programs for lamb production. The estimates of heritability for milk yield and composition traits in dairy sheep breeds reviewed by Hamann et al. (2004), with subsequent estimates cited by Afolayan et al. (2009), ranged from low to moderately high. However, our earlier study provides the only estimates of heritability for milk traits in non-dairy sheep used for lamb production (Afolayan et al., 2009) and there are no estimates of genetic correlations between milk traits and other lamb production traits. In Australia over 5 million crossbred ewes are mated annually to meat sire breed rams with their progeny comprising over 30% of the national lamb slaughter for meat production. The great majority of the crossbred ewes are first cross progeny of Border Leicester sires and Merino dams. The national maternal sire central progeny test (MCPT) was established to identify genetic variation and the potential for improvement in a range of production traits among maternal sires of crossbred ewes from several breeds available to the lamb industry (Fogarty et al., 2005a). Australian breeds used in the lamb industry have not been selected for milk production and rarely has any attempt been made to evaluate milk traits. This study provides estimates of genetic and phenotypic (co)variances between milk production and composition traits and ewe reproduction, wool production and growth traits. This provides parameters for development of breeding objectives and evaluation programs for lamb production that include milk traits and also provides information on the expected correlated responses in milk traits from selection on various production traits. 2. Materials and methods 2.1. Animals and management Data were available on milk production and milk composition from 944 lactations of crossbred ewes, of which 724 were first lactations, together with data on the early growth, wool production, worm resistance and reproductive performance of the ewes. The ewes were a subset of those evaluated for lamb production in the national Maternal sire Central Progeny Test (MCPT) which has been described previously (Fogarty et al., 2005a). The ewes were run at 3 sites (Cowra, Hamilton and Rutherglen) and were the progeny of 74 sires from several maternal crossing breeds and Merino dams, with also Corriedale dams at Hamilton, born in 3 cohort years at each site (Table 1). The sires were entered by breeders from several breeds including: Border Leicester (n = 18), East Friesian and crosses (n = 12), Finnsheep and crosses (n = 12), Coopworth (n = 9), White Suffolk (n = 7), Corriedale (n = 6), Booroola Leicester (n = 6) and Hyfer (n = 4). The MCPT also evaluated an additional 17 sires from various other divergent breeds with 1–2 sires per breed. Inclusion of these additional sires showed inflated sire variance for some reproduction traits in pre-
vious analyses (Afolayan et al., 2008) and hence the data from these 17 sires were excluded from these analyses. To provide genetic links for the evaluation, 3 of the sires had progeny at all sites and in all years, except that there were only 2 link sires of the 1998 cohort ewes at Rutherglen. The ewes at Cowra and Rutherglen were milked on 3 occasions during their primiparous lactation at approximately 3, 4 and 12 weeks after parturition. This provided 2 estimates of daily milk yield near the peak of lactation and a further estimate late in lactation and close to weaning. At Cowra, some additional ewes that were all progeny of link sires were also milked following their second (1998 cohort) or third (1997 cohort) joining. At Hamilton, the ewes were generally milked on 3 occasions at approximately 4, 6 and 12 weeks of lactation and the parity of the ewes varied (1997 cohort following their first and third joining; 1998 cohort, second and third joining; 1999 cohort, first and second joining). The 1999 cohort ewes at Hamilton were only milked on 2 occasions in 2001 (parity 2) at 4–6 and 12 weeks of lactation. The numbers of ewes in each cohort, their parity and their pre-joining live weight in the years in which they were milked are shown in Table 1. Data were available on the performance of the first cross ewes, which included their early growth rates between birth and weaning at about 12 weeks of age (ADGpre) and between weaning and postweaning weight at 4–6 months of age (ADGpost). Greasy fleece weight (GFW) was recorded for the ewes with approximately 12 months wool growth following a lamb shearing. A mid-side wool sample was taken from each fleece for yield (YLD) and average fibre diameter (FD) measurement in commercial laboratories and calculation of clean fleece weight (CFW). The ewes were monitored for parasite worm burden under natural challenge after weaning in their first year. When the mob level indicated sufficient worm burden (generally > 250 eggs/g) faecal samples were collected from individual ewes and total worm egg count recorded, with a cube root trans√ formation used for analysis (WEC, 3 eggs/g). The main species of worms were Trichostrongylus and Ostertagia. Further details of the generation and management of the ewes at each site, including genetic merit of the sires has been provided previously (Fogarty et al., 2005a,b). The crossbred ewes were mated naturally to groups of terminal sire rams at each site, generally within their age cohort, to evaluate their reproduction (Afolayan et al., 2008) and second cross lamb production (Afolayan et al., 2007). At Cowra, the crossbred ewes were randomly split after they were weaned into autumn and spring joining groups. The autumn joined ewes were joined in February/March and lambed in July/August and the spring joined ewes were joined in October/November and lambed in March/April. At Hamilton the ewes were joined in autumn (March/April) and lambs were born in August/September. At Rutherglen the ewes were joined in spring (November/December) and lambs were born in April/May. The autumn joined ewes at Cowra and Hamilton were first joined at approximately 7 months of age. The spring joined ewes at Cowra were first joined at 14 months of age and the ewes at Rutherglen were first joined at 17 months of age. The reproduction records of the ewes that lambed were cumulated over 3 years and included in the analysis, except at Hamilton. The ewes at Hamilton in 1999 experienced vibriosis pre-lambing which resulted in some abortion loss. These data were not included in the ewe reproduction records and these cohorts of ewes (1997 and 1998) were joined for an additional year. The reproduction traits included litter size (LS, number of lambs born including dead lambs per ewe lambing), ewe rearing ability (RA, ratio of lambs weaned to lambs born which is a measure of lamb survival as a trait of the ewe) and the composite trait of number of lambs weaned (NLW) per ewe lambing. The average weight of the lambs weaned in the litter (AWW) was also analysed. There were a total of 2432 ewe reproduction records, with the number of records, mean and standard deviation for all the traits analysed shown in Table 2. The project was conducted under approval of the Departmental Animal Ethics Committee at each site. 2.2. Milking procedures Milk production was measured using a 4-h milk test to estimate daily milk yield and collect samples for determining milk composition (Afolayan et al., 2009). On each occasion the ewes were initially milked out by machine followed by hand-stripping and the time recorded. The ewes were milked again (machine and hand-stripping) approximately 4 h later, with the time and weight of milk recorded and samples taken for analysis of composition. An intravenous injection of synthetic oxytocin (llium Syntocin, 10 IU/mL, Troy laboratories, Smithfield, NSW) was administered to the ewes prior to each milking. A dose of 5 IU of oxytocin
R.A. Afolayan et al. / Small Ruminant Research 82 (2009) 27–33
29
Table 1 The number of sires and ewes that were milked and their pre-joining weight (S.D.) for the various production environments and cohorts of crossbred ewes at 3 parities. Environment and cohort
Sires
Parity 1 Ewes
Parity 2 Weight (kg)
Ewes
Parity3 Weight (kg)
Ewes
Weight (kg)
Cowra autumn 1997 1998 1999
12 10 9
97 64 60
40.1 (4.7) 35.8 (3.6) 41.8 (4.8)
– 21 –
– 62.6 (6.9) –
19 – –
69.9 (7.1) – –
Cowra spring 1997 1998 1999
12 10 9
120 70 59
51.2 (6.3) 57.1 (6.4) 56.0 (5.1)
– 21 –
– 70.4 (8.3) –
20 – –
73.2 (8.1) – –
Hamilton 1997 1998 1999
11 10 11
33 – 31
34.9 (4.1) – 32.5 (4.5)
– 31 44
– 48.9 (6.3) 53.7 (6.1)
33 31 –
57.8 (5.6) 63.6 (5.8) –
Rutherglen 1998 1999 2000
13 10 11
74 55 61
51.2 (5.9) 54.0 (4.6) 57.1 (6.8)
– – –
Total
74
724
117
was used following the results of an experiment in the first year and season at Cowra that examined the effects of varying dose rate (Morgan et al., 2000). The same operator milked all ewes within the sites at Cowra and Rutherglen. Daily milk yield was calculated by extrapolation from the milk yield at the second milking and the time interval from the initial milking. Milk samples were preserved, refrigerated at 4 ◦ C, and sent by overnight courier to commercial dairy laboratories. The laboratories were different for each site and the testing units were calibrated for analysis Table 2 The number of records and means (S.D.) for milk, growth, wool, worm resistance and reproduction traits of crossbred ewes. Trait
Records (n)
Mean (S.D.)
Milk Milk yield (kg/d) Fat (%) Protein (%) Lactose (%)
2679 2434 2433 2292
1.64 (0.90) 9.14 (2.21) 4.38 (0.89) 5.66 (0.47)
Growth Growth rate pre-weaning, ADGpre (g/d) Growth rate post-weaning, ADGpost (g/d) Wool Greasy fleece weight, GFW (kg) Clean fleece weight, CFW (kg) Yield, YLD (%) Fibre diameter, FD (m) Worm resistance Worm egg count, WEC √ (3 eggs/g) Reproduction Litter size, LS Rearing ability, RA Number of lambs weaned,a NLW Average lamb weaning weight, AWW (kg) a
Per ewe lambing.
885
208 (42)
877
116 (54)
849
4.5 (0.9)
848
3.4 (0.8)
848 848
75.2 (4.9) 26.9 (2.9)
647
6.1 (1.1)
2432 2432 2432
1.60 (0.64) 0.88 (0.26) 1.36 (0.61)
2314
28.4 (5.8)
– – –
– – –
– – –
103
of composition (fat%, protein%, lactose%) of sheep milk. Milk composition data were not available at Hamilton for the 1997 cohort ewes at parity 1. 2.3. Statistical analysis Mixed linear models applying a restricted maximum likelihood procedure using ASReml (Gilmour et al., 2006) were initially developed for each trait. Bivariate analyses were then used to estimate covariance components between the milk traits and the growth, wool production, worm resistance and reproduction traits. The model for milk traits included fixed effects for birth year cohort (1–3), production environment (Cowra autumn joining, Cowra spring joining, Hamilton autumn joining, Rutherglen spring joining), ewe parity (1–3 joining), sire breed (1–8), number of lambs born and suckled (3 levels) and all two- and three-way interactions. Days of lactation was fitted as a covariate as well as its interactions with the other fixed effects. The lactation curve was modelled using a cubic smoothing spline that allowed different shapes for the curve for each sire breed. The number of lambs born and suckled was defined as single born and reared (11), multiple born and single reared (21) and multiple born and reared (22), with most multiples being twins. Interactions that were not significant (P > 0.05) were removed from the final model. Results from the fixed effects analyses for these milk traits have been reported elsewhere (Afolayan et al., 2009) and are not presented here. The models for growth, wool and worm resistance traits of the ewes included fixed effects for site (Cowra, Hamilton, Rutherglen), birth year cohort (1–3), sire breed (1–8), type of birth and rearing (3 levels, single born and reared, multiple born and single reared, and multiple born and reared). Age of the ewe lamb was included within site and year as a linear covariate for the growth traits. All significant (P < 0.05) two-way interactions were included in the final model. Results from the fixed effects analyses for these production traits from the complete MCPT data have been reported elsewhere (Fogarty et al., 2005a,b) and are not presented here. The models for the reproduction traits included fixed effects for production environment (Cowra autumn joining, Cowra spring joining, Hamilton autumn joining, Rutherglen spring joining), parity (joining 1–3), birth year cohort (1–3), sire breed (1–8) and type of birth and rearing of the ewe (3 levels, as defined above for the ewe production traits). Prejoining weight was not fitted as a linear covariate to avoid bias due to differences between sire breeds, years and production environments. The second cross lamb weaning weights were adjusted for age and sex but not for the effects of type of birth and rearing to calculate AWW. For all traits analysed the two- and three-way interactions were included initially, but were subsequently removed from the final model if they were not significant (P > 0.05). Common significant interactions were environment × sire breed, environment × ewe type of birth and rearing. Sire was included as
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R.A. Afolayan et al. / Small Ruminant Research 82 (2009) 27–33
a random effect in the models for all traits which meant that sire breed was tested relative to the remaining between sire variance. Results from the fixed effects analyses for these reproduction traits from the complete MCPT data have been detailed elsewhere (Afolayan et al., 2008) and are not presented here. The random model for the milk traits in the bivariate analyses included sire, dam (of the ewe), dam × year, ewe, ewe × parity (as some ewes were milked over different parities) and residual which represents the repeated milkings at each lactation. The random model for the growth, wool and worm resistance traits included sire, dam, dam × year and residual, which represents the individual ewes as there is only one record per ewe for these traits. For the ewe reproduction traits the random model included sire, dam, dam × year, ewe and residual which represents the repeated lambings for ewes, i.e. at different parities. Additional bivariate analyses were also undertaken after exclusion of the lambing record of the ewe in the year of milking to examine the effect on the covariances due to any confounding between milk yield and the reproduction record at that lambing. The dam × year component was not significant for the milk composition, growth and wool traits and was not included in the final model. All bivariate analyses included the between sire covariance and a residual covariance which was a covariance at either the ewe or ewe × parity stratum depending on the traits involved. The phenotypic variances and covariances used for calculating genetic parameters were the sum of the appropriate variance components across all strata.
3. Results and discussion Mean estimates of variance components and heritability from the bivariate analyses between the milk traits and the growth, wool, worm resistance and reproduction traits are shown in Table 3. Earlier papers have reported the univariate estimates of variance components from these data for the milk traits (Afolayan et al., 2009) and from the complete MCPT data for wool and worm resistance (Ingham et al., 2007) and reproduction traits (Afolayan et al., 2008) and should be referred to for appropriate estimates of heritability and phenotypic variances. The mean estimates of
heritability from the bivariate analyses presented here are close to the corresponding univariate estimates for most milk production traits except for fat%, which was over 0.2 (Afolayan et al., 2009). The estimates are generally within the range reported for milk traits in dairy sheep breeds in Mediterranean environments (Othmane et al., 2002; Hamann et al., 2004; El-Saied et al., 2005; Marie-Etancelin et al., 2006; Gutierrez et al., 2007). However, the mean estimates for WEC and the wool traits were much higher than those reported in the earlier corresponding univariate results which had more data for sires (91 sires) and considerably more ewe records included (Ingham et al., 2007). The estimates of mean heritability for the other production traits were generally within the range of estimates of parameters reviewed by Fogarty (1995) and Safari et al. (2005). Estimates of the direct genetic and phenotypic correlations between the milk traits and growth, wool and worm resistance traits are shown in Table 4. The genetic correlations between milk yield and growth rate of the ewes pre-weaning (0.38) and post-weaning (0.49) were moderate and positive with standard errors about 0.3. The genetic correlations between the milk composition traits and growth traits were variable and generally similar to or smaller than their standard error, except for lactose% and post-weaning gain (0.58 ± 0.22). The phenotypic correlations between all the milk traits and the growth traits were close to zero. While there are no other estimates of these genetic correlations in comparable production systems in the literature, Pattie (1965) reported positive correlated responses in milk production following divergent selection for weaning weight in Merino sheep. Our genetic correlations with milk yield are of the order of 0.4 for pre-weaning
Table 3 Mean variance componentsa and heritability (h2 ) estimates from the bivariate analyses for milk, growth, wool, worm resistance and reproduction traits of crossbred ewes. Traitb Milk Milk yield (kg/d) Fat (%) Protein (%) Lactose (%)
p2
s2
d2
2 dy
2 w
2 wp
e2
h2
0.440 3.822 0.354 0.119
0.043 0.427 0.091 0.028
0.001 0.251 0.008 0.007
0.008 – – –
0.002 0.431 0.027 0.002
0.018 0.038 0.016 –
0.344 3.018 0.208 0.082
0.10 0.11 0.26 0.23
Growth ADGpre (g/d) ADGpost (g/d)
12.150 5.766
2.823 2.307
2.920 1.256
– –
– –
– –
6.949 2.545
0.23 0.40
Wool GFW (kg) CFW (kg) YLD (%) FD (m)
0.349 0.262 19.595 3.820
0.148 0.120 10.417 3.512
0.094 0.069 2.381 0.747
– – – –
– – – –
– – – –
0.122 0.084 7.360 −0.302
0.42 0.45 0.53 0.92
1.460
0.356
–
0.899
–
–
0.943
0.24
0.331 0.065 0.329 19.723
0.043 0.002 0.031 2.840
– – – –
– – – –
0.058 0.004 0.039 3.795
– – – –
0.230 0.060 0.258 13.090
0.13 0.03 0.09 0.14
Worm resistance √ WEC (3 eggs/g) Reproduction LS RA NLW AWW (kg) a
2 2 2 Phenotypic variance (p2 ), sire variance (s2 ), dam variance (d2 ), dam × year variance (dy ), ewe variance (w ) and ewe × parity variance (wp ) and residual
variance (e2 ). The residual variance for the growth, wool and worm resistance traits is at the ewe stratum and the residual variance for the reproduction traits is at the ewe × parity stratum. b See Table 2 for abbreviations of traits.
R.A. Afolayan et al. / Small Ruminant Research 82 (2009) 27–33
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Table 4 Estimates of genetic and phenotypic correlations (±S.E.) between milk yield and composition traits and growth, wool and worm resistance traits. Traita
Milk yield
Genetic ADGpre ADGpost GFW CFW YLD FD WEC
0.38 0.49 0.06 0.13 0.42 0.39 0.08
± ± ± ± ± ± ±
0.32 0.31 0.31 0.30 0.26 0.28 0.44
−0.01 −0.30 −0.22 −0.18 −0.10 −0.37 −0.06
± ± ± ± ± ± ±
0.42 0.35 0.31 0.30 0.32 0.27 0.45
−0.36 0.28 0.19 0.07 −0.33 −0.17 0.42
± ± ± ± ± ± ±
0.32 0.26 0.25 0.25 0.23 0.23 0.33
0.04 0.58 0.46 0.51 0.48 0.19 −0.22
± ± ± ± ± ± ±
0.33 0.22 0.26 0.23 0.21 0.23 0.37
Phenotypic ADGpre ADGpost GFW CFW YLD FD WEC
0.03 0.04 0.04 0.05 0.03 0.04 0.02
± ± ± ± ± ± ±
0.02 0.02 0.03 0.02 0.02 0.02 0.03
−0.02 0.00 0.00 −0.01 0.00 −0.06 0.00
± ± ± ± ± ± ±
0.03 0.03 0.03 0.03 0.03 0.03 0.03
−0.04 −0.02 −0.01 −0.01 −0.01 0.02 0.02
± ± ± ± ± ± ±
0.03 0.03 0.03 0.03 0.03 0.03 0.03
−0.01 0.07 0.04 0.04 0.03 0.01 −0.01
± ± ± ± ± ± ±
0.03 0.03 0.03 0.03 0.03 0.03 0.03
a
Fat%
Protein%
Lactose%
See Table 2 for abbreviations of traits.
growth and increase to 0.5 with post-weaning growth. This suggests that selection to increase early growth of the ewes would result in a moderate increase (genetic improvement) in subsequent ewe milk yield. Our data are mainly from ewes in their first lactation with few multiparous ewes present. However, there is a moderately high repeatability of milk yield over lactations (Morrissey et al., 2007) as well as a high genetic correlation for milk scores between parities (Snowder et al., 2001). Selection to increase early growth of ewes should improve milk yield in primiparous and older ewes. Our earlier results also indicate that measurement of ewe milk yield at peak lactation or towards the end of lactation are closely related traits (Afolayan et al., 2009). The moderate positive genetic correlation between lactose% and post-weaning gain, together with the low correlations for fat% and protein% with both pre- and postweaning gain supports the assertion that milk volume is determined by lactose secretion, and in highly productive dairy animals the synthesis of fat and protein does not keep up with that of lactose when high rates of milk secretion are achieved (Holmes and Wilson, 1984). Estimates of genetic correlations between milk yield and the wool traits were generally low with high standard errors except for YLD which was moderate and favourable (0.42) and for FD which was moderate and unfavourable (0.39). Lactose% was also moderately positively correlated with the wool traits (0.19–0.51), while the genetic correlations with fat% and protein% were generally negative and smaller than their standard errors. The genetic correlations between WEC and the milk traits were generally close to zero and smaller than their standard errors, except for protein% (0.42 ± 0.33). The phenotypic correlations between the milk production traits and the wool and worm resistance traits were all close to zero. No other reports were found in the literature for genetic relationships between milk traits and wool traits. Our results suggest that selection for wool production may increase lactose% but is likely to have little impact on milk yield of ewes or other milk component traits. There may be some implications for sheep dairy products where milk composition is important (Bencini and Pulina, 1997), in contrast to the lamb industry,
where milk composition has little effect on lamb growth (Morgan et al., 2007). Estimates of genetic and phenotypic correlations from the bivariate analyses between the milk traits and ewe reproduction traits are shown in Table 5. The genetic correlations between milk yield and LS and NLW were moderate and positive (0.50–0.59), with AWW being slightly lower (0.44) and RA close to zero. Exclusion of the lambing record at milking had little effect on the genetic correlations, except to increase the magnitude of the standard errors. The phenotypic correlations between milk yield and the reproduction traits were all small and positive. The genetic correlations between milk composition and the reproduction traits were variable and all less than their standard errors. The corresponding phenotypic correlations were all close to zero. Improved milk production of ewes is important for increasing early growth of their lambs under grazing systems (Morgan et al., 2007). Divergent selection for weaning weight in Merino sheep has been shown to result in correlated responses in milk production of ewes (Pattie, 1965), which contributed to about half the differences in body weight of their crossbred lambs (Yates and Pattie, 1970). Our results have shown there are moderate genetic correlations between ewe milk production and the early growth of ewes and their subsequent litter size and lambing performance. There appears to be no genetic relationship between milk production and the fleece weight and worm resistance of the ewes, although there were moderate genetic correlations with wool yield (favourable) and fibre diameter (unfavourable). There also appears to be little genetic relationship between the milk composition traits and any of the ewe production traits, except perhaps for lactose% with growth and wool production. In seedstock or commercial sheep production systems, direct measurement of milk production is neither feasible nor practical and its improvement relies on indirect selection for genetically correlated traits. Australian sheep breeding programs have varying objectives that often include growth, wool weight and reproduction. Our study suggests that selection for objectives including growth and reproduction will result
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R.A. Afolayan et al. / Small Ruminant Research 82 (2009) 27–33
Table 5 Estimates of genetic and phenotypic correlations (±S.E.) between milk yield and composition traits and ewe reproduction traits. Traita
Milk yieldb
Genetic LS RA NLW AWW
0.44 0.12 0.53 0.47
± ± ± ±
0.42 0.63 0.37 0.34
0.50 0.11 0.59 0.44
± ± ± ±
0.34 0.52 0.32 0.34
−0.14 0.23 −0.03 −0.31
± ± ± ±
0.39 0.59 0.42 0.40
−0.03 0.34 0.08 −0.00
± ± ± ±
0.31 0.49 0.33 0.32
0.07 0.02 0.26 0.01
± ± ± ±
0.32 0.50 0.35 0.32
Phenotypic LS RA NLW AWW
0.06 0.04 0.07 0.02
± ± ± ±
0.02 0.02 0.02 0.02
0.04 0.05 0.08 0.08
± ± ± ±
0.02 0.02 0.02 0.02
−0.03 0.03 0.00 0.02
± ± ± ±
0.02 0.02 0.02 0.02
0.00 0.00 −0.00 −0.01
± ± ± ±
0.02 0.02 0.02 0.02
−0.01 0.00 −0.01 0.02
± ± ± ±
0.02 0.02 0.02 0.02
a b c
Milk yieldc
Fat%c
Protein%c
Lactose%c
See Table 2 for abbreviations of traits. Not including reproduction record at milking lambing. Includes all ewe reproduction records.
in small improvements in ewe milk production, with little effect from selection for wool weight. Selection for any of the production objectives would be expected to have little effect on milk composition. Genetic improvement in milk production would be expected to be achieved by selection that includes breeding values for maternal weaning weight, which is a trait evaluated in the national genetic evaluation (LAMBPLAN) program (Brown et al., 2007). A simple udder scoring system for ewes after lambing, which is moderately heritable and genetically correlated with weight of lamb weaned, has also been suggested as a practical way of improving milk production under commercial conditions (Snowder et al., 2001). 4. Conclusions This study adds to the very sparse literature on genetic correlations between milk production and composition traits and early growth, wool, worm resistance and reproduction traits of ewes. The results, together with the few other estimates in the dairy sheep literature, indicate moderately favourable genetic correlations of ewe milk production with early growth and reproduction. There was no relationship of ewe milk production with wool weight or with worm resistance and also of milk composition traits with ewe production traits. The genetic correlations in this study provide estimates of the parameters, albeit with high standard errors, that are required for more accurate genetic evaluation and the development of breeding programs with meat and wool objectives that include ewe reproduction and milk production. Acknowledgements The assistance of many staff members from the NSW Department of Primary Industries at Cowra and the Department of Primary Industries Vic at Hamilton and Rutherglen over several years of the study is greatly appreciated. In particular those who assisted with the milkings included: Kelly Lees, David Stanley, Phil Goodacre, Tony Markham, Darryl Hughes, Rob Urquhart, Ashley Radburn, Ken Masters, Taffy Phillips, Greg Seymour, Paul Curran, Peter Heazlewood, Kerry Groves, Murray Arnold and Brian Hurley. Dr Peter Holst and Dr Geoff Hinch are thanked for advice and support for the milking experiments. Dr Kevin Atkins is
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