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* Dipartimento S.En.Fi.Mi.Zo.–Sezione Produzioni Animali, Università degli Studi di Palermo, Viale delle Scienze–Parco d’Orleans, 90128 Palermo, Italy
Istituto Zooprofilattico Sperimentale della Sicilia A. Mirri, Via G. Marinuzzi 3, 90129 Palermo, Italy
Animal Breeding and Genetics Group, Wageningen Institute of Animal Sciences, Wageningen University, PO Box 338, 6700 AH Wageningen, the Netherlands
1 Corresponding author: vriggio{at}unipa.it
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Key Words: somatic cell count • milk production • genetic parameter • dairy sheep
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Genetic parameters for SCC are required to study possibilities of changing SCC by means of selection. Commonly, SCC is log-transformed to SCS. Genetic studies of SCS in dairy sheep are more recent and less frequent than in dairy cattle. The few available genetic studies are on a limited number of breeds; for example, on the Churra (Baro et al., 1994; El-Saied et al., 1998) and Lacaune (Barillet et al., 2001; Rupp et al., 2001, 2003) breeds and the estimates are usually based on average SCS during the lactation. Furthermore, information on the genetic relationship between SCS and milk yield and composition is lacking, and no references are reported in literature on dairy sheep reared in the south of Mediterranean area, where the husbandry system and the management are very different from those adopted for the breeds reared (i.e., Lacaune and Churra) in the north of the Mediterranean area.
The aim of this study was to evaluate genetic aspects of SCS and the relationships between SCS and milk yield, and fat and protein contents and yields in primiparous Valle del Belice ewes using a repeatability test-day animal model (Ptak and Schaeffer, 1993).
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Records were removed when ewes had an abortion and when both of the ewe’s parents were unknown. After editing, the data set consisted of 13,066 observations on 2,277 ewes. The pedigree file consisted of 4,369 animals; in addition to the 2,277 animals with records, 246 sires and 1,846 dams were included. On average, the sires served at least 2 of the 17 flocks under study, and they had 11.34 daughters.
The average number of milk production records per ewe was 5.74, and the average number of SCC test-days per ewe was 5.24. Test-day SCC were converted to SCS using a base 2 logarithmic function: SCS = log2 (SCC/100) + 3 (Ali and Shook, 1980).
The test-day traits analyzed as response variables were milk yield, fat and protein percentages and yields, and SCS. Variance components and genetic parameters for each trait were estimated using ASREML (Gilmour et al., 2002). Several models were tested to explore the fitted factors and to optimize the analysis; the repeatability test-day animal model reported below was the model with the highest coefficient of determination:
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where yijklm is the test-day trait’s measurement; µ is the population mean; FTDi is the random effect of flock by test-day interaction i (626 levels); YPSj is the fixed effect of year x season of lambing interaction j, where the season of lambing was equal to 1 if a ewe gave birth in the period January through June, otherwise it was equal to 2 (11 levels); LSk is the fixed effect of litter size class k (2 levels, single or multiple born lambs); DIMijklm and exp(–0.05 x DIMijklm) are 2 covariates used to model the shape of lactation curves (Wilmink, 1987); Al is the random additive genetic effect of the individual l (4,369 levels); PEl is the random permanent environmental effect on the individual l (2,277 levels); eijklm is the random residual effect.
All known relationships among individuals were considered in the animal model. Heritabilities (h2) and repeatabilities (r) were calculated as
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where 
A2 is the additive genetic variance, Ep2 is the permanent environmental variance, and Et>2 is the temporary environmental variance.
Bivariate analyses were used to estimate phenotypic and genetic correlations. The model was the same as for the univariate analyses. Estimated variance components from the univariate analyses were used as starting values for the bivariate analyses.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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. The daily average milk yield was 1,167 g, fat yield was 76.1 g, and protein yield was 62.6 g. The mean SCC was 1,484 (x 103 cells/mL), and the mean SCS was 6.89.
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The coefficient of variation for SCC was 246%, whereas the coefficient of variation for SCS was 31%.
Phenotypic variances after adjustment for fixed effects and flock x test-day interaction (FTD), the heritabilities, the repeatabilities, and the proportions of variance due to FTD are in Table 2. Heritability estimates for milk yield and milk composition traits were low and varied between 0.09 and 0.14. Standard errors of the heritability estimates were between 0.02 and 0.03. The heritability estimate for SCS was 0.14 with a standard error of 0.03.
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Repeatability estimates for all milk production traits and SCS ranged between 0.29 and 0.47 and the standard errors were always around 0.01. The lowest estimate was found for fat percentage.
Estimates of genetic and phenotypic correlations are in Table 3. The standard errors of the estimated genetic correlations ranged from 0.02 to 0.18, and for the phenotypic correlations, the standard errors were around 0.01. No estimates of correlations are reported between milk production and fat and protein yields. In these cases, the analysis did not converge, probably because the estimates were very close to unity. The genetic correlation between fat and protein yields was strong and positive (0.95). The genetic and phenotypic correlations between milk yield and fat content were equal to 0.19 and to –0.13, whereas the genetic and phenotypic correlations between milk yield and protein content were –0.04 and –0.23, respectively. The genetic and phenotypic correlations between fat and protein content were 0.74 and 0.53, respectively.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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The coefficients of variation calculated for milk production traits were in agreement with the coefficients of variation found in other studies (Baro et al., 1994; El-Saied et al., 1998; Hamann et al., 2004). Coefficients of variation of 50% for fat yield, 47% for protein yield, 25% for fat percentage and 19% for protein percentage have been calculated, based on the results reported by Hamann et al. (2004).
The coefficient of variation for SCC obtained in this study was consistent with the value of 238.27% reported by Baro et al. (1994). Such a high value for the coefficient of variation is due to the skewed distribution of SCC. The coefficient of variation for SCS was 31% and similar to the one reported by Baro et al. (1994) in Churra sheep (28%) but lower than the value of 53% calculated based on the results reported by Hamann et al. (2004) in East Friesian sheep. Instead of calculating the coefficient of variation for SCS, it is more appropriate to calculate the coefficient of variation for the log-transformed SCC; that is, without adding the constant of 3 (Ali and Shook, 1980). The coefficient of variation for the log-transformed SCC is 56%, which is in the same order as the coefficients of variation for milk, fat, and protein yields.
The heritability estimate for test-day milk yield was lower than those reported for other sheep breeds, which are between 0.15 and 0.24 (El-Saied et al., 1998; Barillet et al., 2001; Othmane et al., 2002). In the literature, heritabilities for fat and protein percentage estimated with test-day models range from 0.06 to 0.39 (i.e., El-Saied et al., 1998; Barillet et al., 2001; Othmane et al., 2002). Hamann et al. (2004) reported heritability estimates of 0.15 for fat and protein yield, which are similar to the estimates found in the present study.
The heritability estimate for SCS falls within the range reported in the literature. Results based on repeatability test-day models for SCS, indicated heritability estimates ranging from 0.04 for the Churra breed (Baro et al., 1994) to 0.16 for the East Friesian breed (Hamann et al., 2004). Other studies reported higher heritability estimates for the average SCS during lactation, from 0.11 to 0.18 (Mavrogenis et al., 1999; Barillet et al., 2001; Rupp et al., 2001). Based on our estimated heritability and repeatability, we expect to find a heritability for the average SCS of 5 observations equal to 0.29 (Falconer and Mackay, 1996).
The low heritability estimates for milk production traits in the present study could be due to parentage errors (Van Vleck, 1970). In the typical Sicilian semi-extensive system, it is common practice to have a number of active rams in a flock for unrecorded natural mating from March until December. Therefore, it is often not known with certainty which ram is the sire of an animal. It was hypothesized that the pedigree is more accurate on the female side than on the male side. To test if there were any differences, 2 analyses were performed, one in which all the sires were assumed to be unknown and one in which all the dams were assumed to be unknown. However, we did not find any evidence for the fact that heritability estimates were affected by pedigree errors because the 2 analyses gave very similar heritability estimates that did not differ from the results reported in Table 2
. In addition, pedigree errors would also have affected the heritability estimate for SCS. However, our heritability estimate for SCS is relatively high, which conflicts with the hypothesis that estimates were lower due to pedigree errors.
Parameter estimates could also have been influenced by genetic differences between flocks, due to the lack of genetic connections between Valle del Belice flocks. The genetic exchange between flocks is indeed limited; if farmers sell ewes to other producers, this usually occurs after the first lactation (Finocchiaro et al., 2005). Limited genetic links between flocks might hinder the separation of the genetic effects from flock effects. These possible genetic differences can be accounted for by using genetic groups in the model. Therefore, an analysis was carried out in which the base animals within each flock were assigned to different genetic groups. However, this model did not have a big effect on the estimated genetic parameters.
Table 2 shows that FTD effects explain a large proportion of the variation for milk production traits. Management of the Valle del Belice breed is indeed characterized by the enormous variability. Part of this variability is due to the fact that most of the farmers milk ewes by hand, but some of the farms use a milking machine. Furthermore, the lambing system is different from the one adopted in other Mediterranean regions (e.g., Carta et al., 1995; Ligda et al., 2000). The lambing season of the Valle del Belice breed is all year long, starting in July and finishing in the following June, with few lambings in May and June (Finocchiaro et al., 2005). The primiparous ewes usually give birth between December and March. Moreover, sheep are fed natural pastures and fodder crops; supplementation, consisting of hay and sometimes concentrates, is occasionally supplied, for example at the end of gestation (Cappio-Borlino et al., 1997). The grazing possibilities and the chemical and nutritional composition of the feed change annually and also differ among areas. It is interesting to highlight that, unlike the milk yield traits, FTD does not have a big effect on SCS (0.08). This result might be due to the fact that with production traits, FTD affects all ewes. Hence the effects of the flock means on that test-day are large. But with SCS, we are probably looking at just a few high SCC ewes each time, and consequently the FTD effects will remain small.
Repeatability estimates for milk composition traits were moderate and comparable with those reported for dairy ewes (i.e., El-Saied et al., 1998; Othmane et al., 2002; Serrano et al., 2003). The repeatability for SCS (0.36) was consistent with those reported by El-Saied et al. (1998) and Othmane et al. (2002) for the Churra breed (0.38 and 0.34, respectively) but higher than the ones reported by Serrano et al. (2003) for the Manchega breed (0.22) and by Hamann et al. (2004) for the East Friesian breed (0.23).
At present, no genetic and phenotypic correlations between milk and fat yield and between milk and protein yield could be estimated. Correlations estimated using unadjusted data were 0.90 between milk and fat yield and 0.96 between milk and protein yield. Sanna et al. (1997) reported genetic and phenotypic correlations equal to 0.89 and 0.93 between milk and fat yields and 0.94 and 0.97 between milk and protein yields. The genetic correlation between fat and protein yields was high and positive, indicating a strong association between these traits. This estimate was higher than the value of 0.68 reported by Hamann et al. (2004) and similar to the 0.93 reported by Sanna et al. (1997). Different genetic correlations from those obtained in this study were reported by Sanna et al. (1997) and by Othmane et al. (2002) between milk yield and fat content and between milk yield and protein content, whereas the phenotypic correlations between these traits obtained in the current study (–0.13 and –0.23) are similar to those reported by Sanna et al. (1997). The genetic and phenotypic correlations between fat and protein content were consistent with those reported by Sanna et al. (1997) and by Othmane et al. (2002). Hamann et al. (2004) reported higher genetic correlations between fat yield and fat content (0.53) and between protein yield and protein content (0.33) than those obtained in this study.
Estimated genetic correlations between SCS and milk production traits were all positive, indicating that selection for increased milk yield or fat and protein content will lead to higher SCS. The genetic correlations between production traits and SCS in cattle (for a review see Mrode and Swanson, 1996) resulted mostly in unfavorable genetic correlations (i.e., high milk associated with high level of SCC). In dairy sheep, estimated genetic correlations between milk yield and SCS are very different, ranging from antagonistic, i.e., from 0.04 to 0.18 (Barillet et al., 2001; Rupp et al., 2003), to favorable, i.e., from –0.15 to –0.37 (Baro et al., 1994; El-Saied et al., 1998, 1999). The phenotypic correlation between milk yield and SCS obtained in this study falls in the range (between –0.15 and –0.05) reported in the literature (i.e., Baro et al., 1994; El-Saied et al., 1998; Othmane et al., 2002). A genetic correlation equal to 0.31 has been estimated between SCS and fat and protein yields. These estimates are very different from those reported by Hamann et al. (2004) in East Friesian sheep (–0.04 and 0.06, respectively). Positive and low to moderate genetic correlations were estimated between SCS and fat content (0.16) and between SCS and protein content (0.24). These results were usually higher than those obtained in other studies (i.e., El-Saied et al., 1998; Othmane et al., 2002; Hamann et al., 2004), although Baro et al. (1994) reported a higher genetic correlation between SCS and protein content (0.37). Therefore, these results suggest that an increase in somatic cells occurs with an increase of fat and protein contents.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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ACKNOWLEDGEMENTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Received for publication May 22, 2006. Accepted for publication December 5, 2006.
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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), heritability (h2), repeatability (r), and flock-test-day (FTD) fraction (±SE) for test-day variables