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1 Department of Reproduction, Obstetrics and Herd Health, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
2 Coordination Centre for Veterinary Diagnostics, Veterinary and Agrochemical Research Centre, Brussels, Belgium
3 Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, Canada
Corresponding author: S. De Vliegher; e-mail: Sarne.Devliegher{at}UGent.be.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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27 mo. Heifers raised in herds with a higher bulk-milk somatic cell count had higher SCC. In herds in which heifers calved on slatted floors, heifers had lower SCC compared with herds in which heifers calved on nonslatted floors. A significant difference in SCC was observed between provinces. At the heifer level, SCC decreased with increasing DIM. On average, heifers calving in April to June had higher SCC, compared with those calving in the other months of the year. In the multilevel regression models, nearly all the variations of SCC resided at the heifer level, indicating that preventive measures against udder health problems in freshening dairy heifers should, in the short term, focus more on factors that vary between heifers than on factors that vary between herds. However, for the long term, the need to identify new, and to implement known, herd-level strategies is important.
Key Words: dairy heifer • multilevel • risk factor • somatic cell count
Abbreviation key: BMSCC = bulk milk SCC, HSCC = herd geometric mean SCC, LnSCC = log-transformed SCC
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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To reduce exposure to environmental pathogens, pregnant and peripartal heifers should be housed in a clean and dry environment (Shearer and Harmon, 1993). Poor hygiene of the calving area was associated with an increased prevalence of elevated SCC in heifers (Bareille et al., 2000). Supplementary preventive measures to control heifer mastitis include individual housing of preweaned calves to prevent suckling, fly control, supplementation of the diet with minerals, and segregation of pregnant heifers from dry cows (Shearer and Harmon, 1993). Frequent teat disinfection prior to parturition in primigravid dairy heifers did not improve udder health (Edinger et al., 2000; Waage et al., 2001). Vaccination of heifers was successful in reducing the incidence of Staphylococcus aureus IMI in one study (Giraudo et al., 1997) and not successful in another (Tenhagen et al., 2001). Treating pregnant heifers intramammarily with dry- or lactating-cow antibiotics reduced the prevalence of IMI at calving and increased milk production in their first lactation (Trinidad et al., 1990; Oliver et al., 1992; Owens et al., 1994; Owens et al., 2001) and is economically beneficial (Oliver et al., 2003). The use of antibiotics in heifers before calving should, however, be carefully promoted in the light of possible residue and resistance problems. Knowledge of more risk factors associated with high prevalence of IMI around parturition should be used to reach a more acceptable level of udder health in dairy heifers. Moreover, understanding at what level—herd versus individual heifer—preventive measures should be applied is essential in tackling the problem.
The objective of this study was to evaluate differences in herd-management practices and heifer characteristics associated with SCC of heifers in early lactation (between 5 and 14 DIM). The contribution of herd and heifer to the total variance of the SCC was assessed using multilevel, multivariable, linear-regression models.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Herd Selection and Management Practices
Herds were selected from the 3287 dairy herds in Flanders (Belgium) participating in year 2000 in the DHI program that had at least one heifer for which the first SCC was measured between 5 and 14 DIM. From these herds, only those that had at least 10 such SCC were selected. A total of 254 herds matched this criterion, and 20 of those were dropped from the study because inspection of their data for the years 1999 and 2000 indicated that their SCC status had changed. A questionnaire was sent to the 234 dairy herds during spring 2001.
The questionnaire was tested and fine-tuned on 6 dairy herds not participating in the study. It consisted of 117 main questions concerning general management, milking and dry cow management, and management of calves and heifers, all concerning the year 2000. A summary is presented in Table 1
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Statistical Analysis
Prior to statistical analysis, observations were explored and checked for unlikely values. No data were excluded for this reason. The regression-model building process involved several steps, as presented in a flow chart (Figure 1). Initially, unconditional associations were tested between the continuous, dependent variable on herd level, HSCC, and all herd-management practices (n = 117, Table 1
) as independent variables. An identical procedure was followed for LnSCC per heifer as dependent variable and 4 heifer characteristics—DIM, milk production, breed, and calving season—as independent variables. Independent samples t-tests, or one-way ANOVA for categorical independent variables and linear regression for continuous, independent variables were used. Statistical significance in this step was assessed at P < 0.15. If necessary, reclassification was done for categorical variables, with some low-frequency classes based on biological reasoning. Homogeneity of variances and deviances from normality were constantly checked using Levene statistics and Kolmogorov-Smirnoff test with Lilliefors correction. No problems were detected.
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0.6, only one was selected for further analysis, based on either the strongest association with the dependent variable or for biological reasons. Two herd-level variables, and no heifer-level variables, were discarded for this reason. The herd-level variable "calving season of heifers" was, although significant, not withheld for further analysis, as this variable was also available at heifer level (Tables 2, 3, 4).
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In the final step, a multilevel model was fit for the continuous dependent variable LnSCC per heifer using the restricted generalized iterative least-squares algorithm in MLwiN 1.1 (Goldstein et al., 1998). All previously selected independent variables on the herd level (n = 7) and on the heifer level (n = 3) were added as fixed effects. As the herd-level variable "average herd net gain" and the heifer-level variable "production at the day of SCC measurement" were highly correlated, it was decided to leave the latter out of the analysis, as it was also correlated with DIM. To evaluate the proportion of variance occurring at the 3 levels of the data hierarchy, a 3-level null model (intercept only) was fit with "province," "herd," and "heifer" as random effects. In addition, the variance distribution was calculated for the 2-level null model (intercept only) by including "herd" and "heifer" as random effects. For further modeling, "province" was included as a fixed effect. The continuous, independent variables were centered by subtracting their overall mean, providing better interpretations and better numerical stability of estimates (Dohoo et al., 2001). All first-order interactions were tested and removed when nonsignificant (Wald’s tests, P > 0.05) were followed by nonsignificant main effects. The adequacy of the final model was tested by examining normal probability plots of residuals and plots of residuals versus predicted values to check whether the assumptions of normality and homogeneity of variance had been fulfilled. No patterns indicating heteroscedasticity were revealed.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Geometric mean SCC for the 1912 heifers of the responding 159 herds was 92,000 cells/mL (range 1000 to 5,622,000 cells/ml). The interquartile range was 163,000 cells/mL. At the herd level, HSCC ranged from 24,000 to 256,000 cells/mL.
Of the 1912 heifers, 60% were Black Holstein-Friesian, 27% were Red Holstein, and 7% were Red and White Holstein crossbreds. Average milk quota on the 159 herds was 537,067 kg of milk (±19,406), and the average milking herd consisted of 75 cows (±2.5) (range 25 to 230). Average bulk milk SCC (BMSCC) for 2000 was 175,000 cells/mL (range 73,000 to 334,000 cells/mL). Of the 159 farms, only 50% had dairy cows, 38% also had beef cows, and 8% also farmed pigs. Nearly 80% of all farms had an AA-milk label, a label that was introduced in Belgium in the 1950s to stimulate milk consumption. In addition to the official analyses that are conducted for all producers, specific AA-milk regulations provide for additional counts of coliforms twice a month. A number of other criteria (bacteria count, BMSCC) are also more stringent. By meeting these higher quality requirements, farmers are paid a premium price for their milk.
Herd-Level Analysis
In the first step of the analysis, a first reduction of all herd data, based on unconditional associations and highly correlated variables, revealed 26 herd-management practices significantly (P < 0.15) associated with HSCC (Table 2, 3, and 4). Multivariable linear-regression analysis resulted in a final model with 7 significant herd-practice variables at P < 0.1 significance level (Table 5).
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). Somatic cell counts in early lactation progressively declined from 133,000 at 5 DIM to 70,000 cells/mL at 14 DIM (Figure 3). Most of the 1912 SCC were measured in August to December (n = 1431 or 74.8%), with the highest proportion measured in September (n = 407 or 21.3%) and lowest in July (n = 21 or 1.1%).
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Multilevel Analysis
The final multilevel model for the continuously distributed LnSCC is presented in Table 6. Four management practices and two heifer characteristics were significantly associated with LnSCC of heifers between 5 and 14 DIM. In higher-producing herds and in herds with an average calving age of heifers >27 mo, SCC of heifers was lower than in less-producing herds or in herds with an average calving age 27 mo. Dairy heifers raised in herds with a higher BMSCC had higher LnSCC. In herds in which heifers were calved on slatted floors, LnSCC was lower than in herds in which heifers were calved on nonslatted floors. Province also remained a significant variable in the final model. At the heifer level, LnSCC decreased with increasing DIM. Heifers calving in April to June ran a higher risk of having elevated LnSCC opposed to the other 3 defined seasons of the year.
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. In the 3-level null model, 1.6, 1.5, and 96.8% of the variation occurred at the province, herd, and heifer level, respectively, whereas in the 2-level null model, 2.6 and 97.3% of the variation occurred at the herd and heifer level, respectively. These proportions changed slightly when adding the significant fixed effects. Of the total variance, 9.5% was explained by the fixed effects ([1.696-1.535/1.696] * 100).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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To identify management factors associated with elevated SCC, HSCC was calculated, giving an overall idea of udder health in periparturient heifers in a herd. A number of studies were able to find herd-management practices and heifer characteristics significantly associated with clinical mastitis (Waage et al., 1998, 2001; Myllys and Rautala, 1995). Not many studies, however, have focused on risk factors for subclinical mastitis in early postpartum heifers. Probably the first study to do so determined how the effect of location, season, stage of pregnancy, and herd influenced IMI in heifers (Fox et al., 1995). Two other studies focused on the issue using SCC of heifers (Østerås et al., 1997; Bareille et al., 2000).
The present study is, to our knowledge, the first one on a large number of dairy herds to study both management practices and some heifer characteristics that might influence the prevalence of mainly subclinical mastitis in recently freshened heifers. In addition, it was possible to estimate the distribution of variance on different levels (province, herd, and heifer), using multilevel modeling, a technique that takes into account the hierarchical nature of data. As a result, it was possible to identify the level at which interventions will have the greatest impact on the outcome. Approximately 97% of the variance of LnSCC occurred at the heifer level based on the null models, with very small variance estimates at the province and/or herd level. All variation at the herd level was explained by including the significant herd level fixed effects, with all variation left in SCC at the heifer level. Another study on variance components of SCC also reported that herd only explained a small proportion of the variation in LnSCC, compared with cows nested within herds (Schepers et al., 1997). The extreme distribution in the present study is most probably influenced by opting to work with SCC measured between 5 and 14 DIM, a period in which SCC vary more when compared to the rest of the lactation, and by opting for herds that had at least 10 SCC during 2000. The latter procedure resulted in selecting slightly larger and better-producing dairy herds with better udder health: geometric mean SCC of the 1912 heifers was lower when compared to the value obtained from the 14,766 heifers from 2000, of which they were a subset. This selection bias possibly influenced the variance distribution slightly, whereas if the herds would have been selected randomly, more variation at the herd level could have been expected. The selection procedure used in this present study was, however, necessary to get a reliable impression of udder health in heifers in these herds.
Hence, as the vast majority of the variation of SCC resides at the heifer level, focusing on heifers, rather than on the herds, seems to be necessary when dealing with udder health in the short term. Lack of substantial effects at the herd level, though, should not be considered as proof that the improvement of udder health of dairy heifers is beyond the control of the farmer. This also appears from the fact that the herd-level fixed effects incorporated in the final model completely reduced the herd-level variance. But, although management practices of herds are thought to be of a major importance, a large variation seems to exist among heifers in response to an identical management. A possible explanation for the lack of variation at the herd level may be that strategies with a known positive effect on udder health in freshening dairy heifers are not yet implemented in Belgian dairy herds. Another reason could be that important herd-level factors have not yet been identified and have consequently not been implemented. Therefore, if effective preventive measures leading to substantially different SCC levels among herds would be adopted by some herds and not by others, more variation could be expected at the herd level.
Although statistically significant associations between SCC and both management practices and heifer characteristics were found in the present study, this does not prove causality. When studying large data sets with many variables, as in the present study, the probability of finding associations by chance increase substantially (Dohoo et al, 1997). In addition, the management ability of the farmer, influencing both udder health and other management practices, could be a confounder (Barkema et al., 1998b).
Significant differences in SCC between the 5 provinces in Flanders (Belgium) were seen in the present study. It was, however, not possible to explain this by the differences in management between herds. Surprisingly, the average BMSCC per province were not reflecting the same order in udder health, with the province Vlaams-Brabant having the lowest BMSCC (155,000 cells/mL) and the province West-Vlaanderen having the highest BMSCC (183,000 cells/mL).
Better-producing dairy herds had an overall better udder health in heifers in early lactation. To reach higher levels of milk production, these herds have to be well managed and, therefore, it is likely that they are more aware of the importance of heifers’ udder health. Nevertheless, it seems that the herd-management practices associated with this higher production were not captured in the present study.
Higher BMSCC, reflecting the prevalence of subclinical mastitis in the lactating cows, was associated with a higher risk for individual heifers in a herd to have an elevated SCC. This indicates that heifers raised in herds with a higher prevalence of subclinical mastitis run higher risks to calve with IMI or to develop IMI within 1 to 2 wk postpartum, which agrees with the findings of another study (Bareille et al., 2000). On the other hand, high SCC of heifers could theoretically have increased the BMSCC of the particular herd, although only one SCC per heifer will not have had a huge impact on the BMSCC, as this was calculated as the geometric mean of all months in 2000.
Calving of heifers on slatted floors, as opposed to calving on nonslatted floors, reduced the odds of heifers having a high SCC. Calving areas that have slatted floors will have drier bedding, as opposed to areas with completely closed floors. The same reasoning is behind the finding in another study in which incomplete slatted floors and alleys increased the incidence rate for Escherichia colii mastitis versus complete slatted floors and alleys (Barkema et al., 1999b). Providing a dry and clean environment to heifers and cows that are calving is important. A poor hygiene of the calving area was associated with an increased prevalence of elevated SCC in heifers (Bareille et al., 2000). Mucking out the calving area less often than once a month was a significant risk for clinical mastitis (Peeler et al., 2000). An increased incidence rate of E. coli mastitis, but not S. aureus mastitis, was found in herds in which there was no disinfection of the calving area after parturition (Elbers et al., 1998).
In herds in which heifers calved at an older age, heifers were at a reduced risk of a high SCC. This was a bit surprising, as older heifers will have been at risk of contracting IMI during a longer period. The current data were measured at the herd level, which makes conclusions on the individual heifer-level invalid. No conclusions can, therefore, be drawn regarding a possible dilution effect, as it is known that heifers calving at an older age tend to produce a bit more compared with younger heifers.
On the heifer level, 2 variables were significantly associated with SCC. Days in milk, as was expected in early lactation, was negatively correlated with SCC. A similar pattern was seen in other studies looking at composite milk samples (Dohoo, 1993; Laevens et al., 1997). Dilution due to an increasing production will most probably have contributed to this effect (Schepers et al., 1997), but this finding also suggests a spontaneous cure of IMI caused by CNS, and possibly by treatment of some heifers.
Calving season was also associated with SCC. Heifers calving in April to June had a higher risk of having elevated SCC, opposed to heifers calving in the other 3 defined seasons of the year. The fact that heifers have been in confined housing for a long period puts them at an increased risk of getting IMI, which is understandable. Myllys and Rautala (1995) identified 2 peak periods during which more heifers were treated for clinical mastitis, which is in accordance with the one peak in the SCC in our data. It should, however, be noted that the peak during July in our data for the 1912 heifers was only based on 21 values. In other studies, calving in late spring or summer was associated with an increased risk for clinical mastitis (Waage et al., 1998), and there was a significant association between the number of heifers with SCC >200,000 cells/mL and calving month, with May holding the highest, and December the lowest, risk versus September as reference (Østerås et al., 1997).
The significant heifer-level variables in the present study are limited in improving udder health in dairy heifers. Changing the calving season to the most favorable months is possible. As most of the variation was present at the heifer level, it would have been interesting to have more details on that level. Variables like calving problems of individual heifers, prepartum milk leakage, age at calving, and so on would have reduced the variance at that level. The present study was, however, designed mainly to focus on management practices, and although only 4 of these practices were significantly associated with SCC, they completely explained the variation at the herd level.
Some variables expected to be of importance and included in the questionnaire were not significant in the present study. From literature, for instance, it is known that fly control in heifers is very important in prevention of summer mastitis. Horn flies, in addition, are capable of spreading S. aureus-induced IMI to heifers (Owens et al., 1998). Another study revealed that heifers from herds using fly control had a lower prevalence of IMI than herds without fly control (Nickerson et al., 1995). In the present study, fly control resulted in lower SCC in the univariable analysis, but this variable disappeared in further multivariable analyses. Supplementing heifers with minerals was a managerial practice with an expected protective influence (Barkema et al., 1998b) on HSCC, and although a univariable association was present in our study, it was not present in the final model.
Although analyzing the data on a dichotomous scale based on a threshold reflecting a heifer to either be infected or not infected, was an option, it was decided to work on a continuous scale for several reasons. Dichotomizing the data would have reduced the power of the study to find significant variables. In addition, the discussion of which threshold was avoided and the use of a threshold could have resulted in biological fallacy—e.g., when using a cutoff of 200,000 cells/mL, classifying heifers with a SCC of 201,000 and 2,500,000 in the same group is debatable. Additionally, one of the aims of this study was to calculate the variance components, which is less straightforward in binary models (Dohoo et al., 2001). Finally, we believe that the actual SCC reflects better whether a heifer is infected in 0, 1, 2, 3, or 4 quarters, where a binary model would classify a heifer as either infected or not.
Based on the data used in the present study, it was not possible to decide whether a heifer suffered from an elevated SCC because of a prepartum infection (most likely to be subclinical) or because of an infection occurring during the first 2 wk after calving (most likely to be clinical). Yet, it was possible to relate the significant management factors to pre- or postpartum infections. It is known that prevalence of IMI, mainly caused by minor pathogens and S. aureus at calving, is very high (Oliver and Mitchell, 1983; Trinidad et al., 1990; Pankey et al., 1991; Myllys, 1995; Fox et al., 1995; Nickerson et al., 1995), indicating that heifers were infected prior to calving. Prepartum management factors will therefore have influenced this phenomenon, and we believe that the variables "calving season" or "BMSCC" are most probably related to the aforementioned prepartum IMI. On the other hand, some of the heifers will have been infected in the first couple of days after calving, as we know from previous studies that incidence rate of mainly clinical mastitis is high during that period (Barkema et al., 1998a). We trust, therefore, that the variable "hygiene in the calving pen" is related to new infections in early lactation.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Conducting more studies combining bacteriological culture, SCC recording, registration of clinical cases, and recording of detailed heifer information and data on management practices on a large number of herds, are essential in tackling udder health problems in dairy heifers. In addition, the effect of an elevated SCC on production, udder health, and culling risk throughout the whole first lactation should be studied.
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ACKNOWLEDGEMENTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Received for publication January 31, 2003. Accepted for publication September 15, 2003.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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