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* Departamento de Producción Animal, Facultad de Veterinaria, Universidad de León, 24071-León, Spain
Consorcio de Promoción del Ovino, 49630-Villalpando, Zamora, Spain
1 Corresponding author: dp2cga{at}unileon.es
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ABSTRACT |
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 TOP ABSTRACT ACKNOWLEDGEMENTSREFERENCES |
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Key Words: bulk tank milk • total bacterial count • milk quality • milking
The presence of microorganisms in milk and dairy products has important ramifications for safety, quality, regulations, and public health. Bulk tank total bacterial count (TBC) is the first and principal tool used by technicians and farmers to evaluate the efficiency of production processes, cleaning and sanitation practices, and to predict the keeping quality and shelf life of milk and dairy products. It is therefore a very useful variable for monitoring and improving flock milk quality in dairy cattle (Jayarao and Wolfgang, 2003). However, little information is available on TBC in dairy sheep (Muehlherr et al., 2003) and there are no known studies that empirically investigate the effect of variation factors on TBC under field conditions in this species. Indeed, TBC are affected by a number of sources of variation and an attempt should be made to identify them and assess some of their implications in hygiene practices or milk payment schemes. In addition, in dairy cattle there is evidence relating TBC to clinical and subclinical mastitis (Bramley, 1992; Jayarao and Wolfgang, 2003; Phuektes et al., 2003; Zadoks et al., 2004). This association has not been reported in dairy sheep so the relationship between TBC, subclinical mastitis (or SCC), and outbreaks of clinical mastitis (i.e., contagious agalactia in enzootic areas) must be studied in this species. Wider knowledge of TBC and its relationship with udder health would enable decisions to be made on improving milk quality, farm management practices, and flock udder health.
The purpose of this paper was to study factors affecting variation in TBC, in particular flock, sampling month, type and installation of milking, dry therapy practice, clinical outbreaks of contagious agalactia, and breed. Another objective was to study the relationship between TBC and bulk tank SCC in dairy sheep.
A total of 9,353 records for TBC were obtained throughout a complete year from 315 dairy ewe flocks (242 Spanish Assaf, 12 Awassi, 43 Churra, and 18 Castellana flocks) belonging to the Sheep Improvement Consortium (CPO) of Castilla-León (Spain). Castilla-León region has about 1.5 million milking ewes; flock characteristic features of this area have been described (Gonzalo et al., 2005). Teat washing was not done in any flocks before milking, as this is not usual practice in dairy sheep. All CPO flocks were enrolled in the Analysis Service of the Dairy Interprofessional Laboratory of Castilla-León. The mean number of repeated records per flock throughout the year was 30. Total bacterial count was determined by a Bactoscan 8000 (A/S N Foss Electric, Hillerød, Denmark), periodically checked by the reference method (IDF, 1991).
The information recorded by the CPO veterinary service included the following TBC variation factors: flock, breed, sampling month, dry therapy practice, milking type (hand and machine milking), and machine-milking installation (buckets, milking parlors with looped milk-line, and milking parlors with dead-ended milkline). Antibiotic dry therapy was given under veterinary supervision. In flocks where dry therapy was implemented, all animals were treated during the dry period (complete dry therapy). Clinical outbreaks of contagious agalactia were also reported and Mycoplasma agalactiae was isolated in bulk tank milk.
A mixed model, in which the flock and month within flock were random factors and the other effects fixed, was used. The PROC MIXED procedure of SAS (SAS Institute, 1998) was followed, according to the model below:
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where Yijklmno was the dependent variable logTBC, Bi was the fixed effect of breed, Hj(i) was the random effect of flock nested within breed, Tk was the fixed effect of type and installation of milking, Dl was the fixed effect of dry therapy, Mm(ij) was the random effect of month within flock, An was the fixed effect of clinical outbreak of contagious agalactia, b was the slope of regression corresponding to the covariable logSCC, and eijklmo was the random residual effect. Breed effect was divided into 4 levels: Spanish Assaf, Awassi, Churra, and Castellana. Effect of type and installation of milking was divided into 4 levels: hand milking, bucket, parlor with looped milkline, and parlor with dead-ended milkline. Dry therapy was divided into 2 levels depending on whether it was carried out in each flock during the previous drying-off or not. The contagious agalactia effect was divided into 2 levels: presence or absence of a clinical outbreak with M. agalactiae in bulk tank milk.
Estimation of variance components was made for random factors of the model according to REML methodology (VARCOMP procedure; SAS Institute, 1998).
To obtain TBC monthly variations throughout the year, a second statistical analysis was carried out on the same set of data, considering the same factors included in the prior model, although in this case, the effect of the month was considered as a fixed factor rather than a random factor within flock as before.
Mean logTBC was 5.13 ± 0.005 (geometric mean: 135 x 103 cfu/mL). This value was higher than that reported for cow bulk tank milk (Jayarao and Wolfgang, 2003), but was much lower than the limits established by the European Union (Directive 94/71/EEC) for sheep milk used in dairy products (1,500 x 103 cfu/mL in milk subjected to heat treatment and 500 x 103 cfu/mL for milk not subjected to heat treatment). Lower individual milk yields and the absence of teat-washing practice before milking might partially explain greater TBC values in dairy sheep compared with dairy cattle.
Table 1
shows the ANOVA of the variation factors of the TBC variable from the mixed model used. Breed, milking, and dry therapy contributed significantly (P < 0.05 to 0.001) to variation of TBC. The components of variance for the random factors of the mixed model and the percentages of variance explained are also shown in Table 1. Flock accounted for 22.0% of the total variance and was, therefore, an important source of variation in TBC. Thus, differences in management and hygiene practice in flocks would cause important TBC variations amongst flocks. Similarly, month within flock was also a relevant factor of variation in TBC (22.1% of total variance) probably due to different hygiene and management conditions throughout the year in each flock. In this sense, not only does hygiene vary among flocks, but also within flock. So, depending on the production level of the flock and availability of pastures and agricultural by-products, there are alternating periods of confinement and grazing throughout the year. These aspects are specific to each flock and they even vary across years within flock. Therefore, inclusion of confinement vs. grazing in the mathematical model was not possible. In addition, because of the reproductive singularity of dairy sheep flocks in Castilla-León (Gonzalo et al., 2005), month within flock could also explain, although only in part, differences in stage of lactation within flocks, due to lambing concentration. Nevertheless, this aspect could be debatable according to specific reproductive features in each flock. When month was considered as fixed effect and not within flock in the statistical model, the autumn months (log TBC: 5.24 to 5.40, and geometric means: 175 to 257 x 103 cfu/mL) showed higher values (P < 0.05) than the remaining months (5.05 to 5.22, and 113 to 169 x 103 cfu/mL).
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). Considering the value for both repeatabilities, the actual recording system of TBC is adequate for monitoring flock hygiene and for being used as a criterion for milk payment schemes in dairy sheep, though a larger number of samples per flock and year would improve the system.
For milking comparisons (Table 2), hand milking (logTBC: 5.31, and geometric mean: 202 x 103 cfu/mL) and bucket-milking machines (5.31 and 206 x 103 cfu/mL) had less desirable hygiene conditions (P < 0.05) than milking parlor systems. Within the latter, the systems with dead-ended milkline (5.10, and 125 x 103 cfu/mL) had higher TBC (P < 0.05) than the looped milkline systems (5.01, and 102 x 103 cfu/mL). These results reflect the inferior hygiene conditions of hand and bucket milking, systems that make it more difficult for farmers to reach milk quality standards as measured by TBC. Bucket-milking systems are not used in milking parlors where conditions are more suitable for hygiene milking. In addition, the greatest difficulties for cleaning in parlors with dead-ended milklines could explain the higher TBC values in comparison with looped milklines.
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Breed had a significant effect (P < 0.05) on TBC. The highest values (log TBC and geometric mean) were for Awassi (5.24 and 176 x 103 cfu/mL) characterized mainly by management system based on confinement housing, whereas the other breeds with more extensive systems, showed lower values (5.07 to 5.20, and 117 to 157 x 103 cfu/mL) than did Awassi. These results were consistent with greater TBC values found in confinement vs. grazing systems in dairy cattle (Goldberg et al., 1992).
Clinical outbreaks of contagious agalactia did not increase TBC (P > 0.05). This enzootic disease is associated with large increases in bulk tank SCC (Bergonier et al., 1996; Gonzalo et al., 2005), and is a serious hindrance for optimizing SCC and mastitis control strategies (Madanat et al., 2001). However, TBC was not a useful variable for diagnosing contagious agalactia. Mycoplasma agalactiae is an organism that does not have a bacterial wall; thus, it requires specific culture media and conditions for growth (Razin et al., 1998). The lack of cell wall could account for the emission of weak pulses not counted by the Bactoscan because of faint fluorescent staining of the mycoplasmal cells. Other authors (Phuektes et al., 2003) were not able to find a relationship between presence of Staphylococcus aureus, Streptococcus dysgalactiae, or Streptococcus uberis and TBC or SCC in bulk tank milk of dairy cattle; however, presence of Streptococcus agalactiae was associated with high SCC and TBC. Consequently, these associations should be specifically studied for each pathogen.
Parameter b (± SE) of the regression of logSCC on logTBC in the mixed model was significant (b = 0.119 ± 0.023; P < 0.001), and the correlation coefficient between these variables was r = 0.23 (P < 0.001), indicating that high SCC in bulk tank milk are associated with high TBC. The relationship between SCC and subclinical mastitis has been clearly demonstrated in dairy sheep (González-Rodríguez et al., 1995; Gonzalo et al., 2002; Marco, 1994), so high IMI prevalence would be related to high TBC values. In the same way, poor flock and milking hygiene could favor a higher frequency of mammary infections and high TBC values. Therefore, quality hygiene and quality health of bulk tank milk are complementary aspects, which should be studied at the same time.
In summary, total bacterial count can be used for monitoring hygiene in dairy ewe flocks and as a basis for milk payment schemes. The relatively low TBC recorded compared with European Union regulations suggests the presence of acceptable hygiene practices in dairy sheep flocks. Dry therapy practice and milking in parlor systems were associated with low TBC. Total bacterial count had a statistically significant correlation with bulk tank SCC, so the implementation of programs for improving hygiene and health quality in bulk tank milk would be more efficient if both variables are considered.
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ACKNOWLEDGEMENTS |
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TOPABSTRACTACKNOWLEDGEMENTSREFERENCES |
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Received for publication August 12, 2005. Accepted for publication October 13, 2005.
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