The Analysis of Milk Components and Pathogenic Bacteria Isolated from Bovine Raw Milk in Korea

Article Outline

• Abstract
• Introduction
• Materials and Methods
  • Milk Sampling and Analysis
  • Isolation and Identification of Bacteria
  • Determination of the Identified Bacteria by PCR
  • Statistical Analysis
• Results And Discussion
  • Changes in Bovine Raw Milk Components in Korea
  • Relationships Among Percentages of Milk Fat, Protein, Lactose, and MUN by SCCt
    • Differences in Milk Components and MUN by SCCt
    • Relationships Between SCCt and Milk Components
    • Relationships Between SCCt and Milk Components Categorized by the Presence of Bacterial Infection
  • Relationships Among the Percentages of Milk Fat, Protein, Lactose, and SCCt by MUN
    • Differences in Milk Components and SCCt by MUN
    • Relationships Between MUN and Milk Components
    • Relationships Between MUN and Milk Components Categorized by the Presence of Bacterial Infection in Milk
  • Relationships Between SCCt or MUN and Milk Components According to the Bacterial Species Isolated
• Conclusions
• Acknowledgments
• Supplementary data
• References
• Copyright

Abstract 

Bovine mastitis can be diagnosed by abnormalities in milk components and somatic cell count (SCC), as well as by clinical signs. We examined raw milk in Korea by analyzing SCC, milk urea nitrogen (MUN), and the percentages of milk components (milk fat, protein, and lactose). The associations between SCC or MUN and other milk components were investigated, as well as the relationships between the bacterial species isolated from milk. Somatic cell counts, MUN, and the percentages of milk fat, protein, and lactose were analyzed in 30,019 raw milk samples collected from 2003 to 2006. The regression coefficients of natural logarithmic-transformed SCC (SCCt) on milk fat (−0.0149), lactose (−0.8910), and MUN (−0.0096), and those of MUN on milk fat (−0.3125), protein (−0.8012), and SCCt (−0.0671) were negative, whereas the regression coefficient of SCCt on protein was positive (0.3023). When the data were categorized by the presence or absence of bacterial infection in raw milk, SCCt was negatively associated with milk fat (−0.0172), protein (−0.2693), and lactose (−0.4108). The SCCt values were significantly affected by bacterial species. In particular, 104 milk samples infected with Staphylococcus aureus had the highest SCCt (1.67) compared with milk containing other mastitis-causing bacteria: coagulase-negative staphylococci (n = 755, 1.50), coagulase-positive staphylococci (except Staphylococcus aureus; n = 77, 1.59), Streptococcus spp. (Streptococcus dysgalactiae, n = 37; Streptococcus uberis, n = 12, 0.83), Enterococcus spp. (n = 46, 1.04), Escherichia coli (n = 705, 1.56), Pseudomonas spp. (n = 456, 1.59), and yeast (n = 189, 1.52). These results show that high SCC and MUN negatively affect milk components and that a statistical approach associating SCC, MUN, and milk components by bacterial infection can explain the patterns among them. Bacterial species present in raw milk are an important influence on SCC in Korea.

Key words: bovine mastitis, somatic cell count, milk urea nitrogen, Staphylococcus aureus

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Introduction 

Mastitis is an inflammation of the mammary glands of dairy cows that can be caused by physical or chemical agents, with the majority of cases caused by bacterial infection. Mastitis is the most common and expensive disease affecting the dairy industry worldwide (Harmon, 1994; Quinn et al., 1994; Moussaoui et al., 2004). In Korea, there are approximately 8,000 dairy farms and 472,000 cows, yielding an average of 177,770,000kg of raw milk per year. The degree of self-sufficiency of milk produced in Korea is approximately 70% (Korea Dairy Committee, 2007), so managing and preventing bovine mastitis is an inevitable task.

One indicator of bacterial infection of the mammary glands is an increase in SCC, which has been used to monitor mastitis in dairy cows (O’Brien et al., 2001). Milk urea nitrogen is an indicator of protein utilization (Jonker et al., 1999) and can be increased by excessive feeding of protein (Broderick and Clayton, 1997). Therefore, SCC and MUN have become good management tools for predicting and diagnosing mastitis and for monitoring the use of protein and the improvement in milk quality (Harmon, 1994; Jonker et al., 2002).

Elevated SCC has been associated with a decrease in the percentages of lactose and fat in milk (Harmon, 1994). Mammary epithelial cells can be damaged by bacteria, resulting in a reduced ability to synthesize milk components. Moreover, MUN has been inversely associated with percentages of milk protein and fat and with SCC (Eicher et al., 1999; Godden et al., 2001; Johnson and Young, 2003).

More than 130 microorganisms are related to bovine mastitis, with mastitis-causing bacteria broadly classified as contagious or environmental pathogens (Watts, 1988; Quinn et al., 1994). Contagious pathogens such as Staphylococcus aureus and Streptococcus agalactiae can be transmitted from cow to cow (Bradley, 2002), whereas environmental pathogens, such as Streptococcus dysgalactiae, Streptococcus uberis, Enterococcus spp., CNS, and gram-negative enteric bacilli (Pseudomonas spp., Escherichia coli) can be transmitted during milking from the contaminated environment (Watts, 1988).

The objectives of this study were to examine and predict the quality of raw milk by analyzing SCC, MUN, and milk components of Holstein cows in Korea. We investigated the associations among SCC, MUN, and milk components based on the presence of bacteria and the bacterial species isolated from these raw milk samples.

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Materials and Methods 

Milk Sampling and Analysis 

From March 2003 to March 2006, a total of 30,019 bovine raw milk samples were randomly collected from 390 farms in 9 provinces in Korea. Teat ends were cleaned with 4% chlorhexidine before sampling. The first few milliliters of milk were discarded, and then quantities of 20 to 50mL of milk were collected aseptically into sterile vials. Milk samples were transported on ice to the laboratory and kept at 4°C until bacteriological assays and analysis of SCC, MUN, and milk components. Somatic cell counts and MUN were determined by Somacount 150 and Chemspec 150 instruments (Bentley Instruments Inc., Chaska, MN), respectively. The percentages of milk components, including milk fat, milk protein, and lactose, were analyzed by using a Bentley 150 instrument (Bentley Instruments Inc.).

Somatic cell count values were sorted into 5 categories: <200×10³cells/mL (grade 1); 200 to 349×10³cells/mL (grade 2); 350 to 499×10³cells/mL (grade 3); 500 to 750×10³cells/mL (grade 4); and >751×10³cells/mL (grade 5). Milk urea nitrogen was sorted in increments of 2 mg/dL; samples with MUN ≤6 mg/dL were grouped into one category, and those with MUN ≥24 mg/dL into another category (Johnson and Young, 2003).

Isolation and Identification of Bacteria 

Ten microliters of each milk sample was streaked onto 5% sheep blood agar plates (Promed, Sungnam, Gyeonggi, Korea) and incubated at 37°C for 24h. Colonies were initially assessed by their morphology and hemolysis patterns, followed by gram staining and motility tests. Biochemical tests, specifically, catalase, oxidase, coagulase, growth in 6.5 or 10% NaCl, esculin hydrolysis, carbohydrate (glucose, mannitol, ribose, sorbitol, and trehalose) fermentation tests, biochemical reaction on MacConkey agar (Becton Dickinson and Co., Sparks, MD), indole production, Lys decarboxylation, urease production, and citrate utilization tests, were performed as required.

Determination of the Identified Bacteria by PCR 

The identification of Staph. aureus, Strep. dysgalactiae, Strep. uberis, and Enterococcus spp. were further confirmed by PCR by using species-specific primers (Brakstad et al., 1992; Forsman et al., 1997; Martineau et al., 1998; Ke et al., 1999; Table 1). Chromosomal DNA was extracted by the guanidine thiocyanate method. Briefly, after culturing in 5% sheep blood agar, a single colony was inoculated into 3mL of trypticase soy broth (Becton Dickinson and Co.) and incubated at 37°C for 24h. The broth culture was centrifuged for 10min at 6,000×g in a 1.5-mL microcentrifuge tube, and the pellet was resuspended in 200μL of enzymatic lysis buffer [10mM Tris-HCl, 1mM EDTA, pH 8.0, 1.2% Triton X-100, and 18μg of lysozyme (Sigma-Aldrich, St. Louis, MO)]. In cases in which streptococcal isolates were suspected, 2.5 U/mL of mutanolysin (Sigma-Ald-rich) was added. For Staph. aureus, the pellet was re-suspended in 1mL of TE buffer (10mM Tris-HCl and 1mM EDTA, pH 8.0) containing 100 U/mL of lysostaphin (Sigma-Aldrich), to which 40μL of diatomaceous earth suspension [10g of diatomaceous earth (Sigma-Ald-rich), 50mL of tertiary distilled water, and 500μL of 32% HCl (wt/vol)] was added. The suspension was mixed by vortexing, incubated at room temperature for 10min, and centrifuged for 2min at 20,000×g. The pellet was washed with a second washing buffer [120g of guanidine thiocyanate (Amresco, Solon, OH) and 100mL of 0.1 M Tris HCl (pH 6.4)], serially washed with 70% ethanol and 100% acetone, and incubated at 56°C for 10min. The DNA was eluted in 200μL of Tris-EDTA buffer and incubated at 56°C for 10min. The supernatant was transferred to a new tube after centrifugation for 2min at 20,000×g.

Table 1. Species-specific PCR primers for Staphylococcus aureus, Streptococcus dysgalactiae, Streptococcus uberis, and Enterococcus spp.

Polymerase chain reaction was performed in a Mastercycler Gradient Thermal Cycler (Eppendorf Mastercycler Gradient, Hamburg, Germany), and all reagents were purchased from Takara Bio Inc. (Otsu, Shiga, Japan). Each PCR reaction mixture was made up to a final volume of 20μL, consisting of 13.7μL of distilled water, 0.4μL of each primer (10 pmol/μL; Table 1), 1.6μL of 2.5mM dNTP, 1.2μL of 25mM MgCl₂, 2μL of 10× buffer, 5 U of Taq polymerase (0.1μL), and 1μL of template DNA. The amplification protocol consisted of an initial denaturation at 94°C for 5min, followed by 30 cycles of denaturation at 94°C for 30s, annealing at 52 or 55°C for 30s, and extension at 72°C for 1min, followed by a final extension at 72°C for 7min (Table 1). The PCR products were electrophoresed on 1.5% agarose gels, made with 0.5× TBE (0.89 M Tris, 0.89 M boric acid, 0.02 M EDTA, pH 8.0), at 110V for 1h. The gels were stained with ethidium bromide (0.5μg/mL) and visualized under a UV transilluminator.

Statistical Analysis 

Statistical analysis was performed with the SPSS program (version 11.5.0, SPSS Inc., Chicago, IL). For the analysis of SCC, values were naturally logarithmically transformed [SCCt = ln(SCC)]. Multilinear regression models in SPSS were used to investigate the associations between SCCt or MUN and the percentages of milk fat, milk protein, and lactose. The model was:

and

where y_scct is SCCt; μ is the intercept; pf is the test-day milk fat percentage; pp is the test-day milk protein percentage; pl is the test-day lactose percentage; mun is MUN; ɛ is the random residual; y_mun is MUN; scct is SCCt; and α, β, γ, and δ are coefficients.

In addition, the associations among all variables and the differences among milk samples were analyzed according to the presence or absence of bacterial infection in milk. The general effects were estimated (Hojman et al., 2004) by univariate GLM, presented as:

and

where y_scctij is SCCt; y_munij is MUN; μ is the intercept; md_i is the month of milk recording (i = 1 to 12); id_j are isolates (j = 0 or 1); pf is the test-day milk fat percentage; pp is the test-day milk protein percentage; pl is the test-day lactose percentage; and ɛ_ij is the random residual.

For analysis of the relationship between SCCt or MUN and milk components according to the bacterial species isolated, the milk samples from which >2 bacterial species were isolated were not included in the analysis. The level of significance was set at P<0.05.

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Results And Discussion 

Changes in Bovine Raw Milk Components in Korea 

The levels of SCC and MUN in bovine raw milk are summarized in Table 2. There was a decrease in SCC (SCCt) and MUN (P<0.001) during the study period. Grade 1 milk (SCC <200×10³ cells/mL) proportions did not change over time and ranged from 64% in 2003 to 70% in 2006 (Table 2).

Table 2. Yearly patterns of milk components and SCC of bovine raw milk on Korean dairy farms

a–dMeans within a column with different superscripts differ (one-way ANOVA, P<0.001).

1SCCt = natural logarithmic-transformed SCC.

2Grade 1 = milk with SCC <200×103 cells/mL.

Milk urea nitrogen is a quick and accurate reflection of nitrogen absorption by cattle, suggesting that this trait may serve as a guide for identifying the diets provided to these animals. Monthly variations between SCCt and lactose percentage, and between MUN and milk protein percentage are shown in Figure 1. The monthly variation in SCCt during the year, except for July, was inversely related to the percentage of lactose (Figure 1A), and the variation between SCCt and milk fat percentage had patterns similar to those between SCCt and lactose percentage (data not shown). Milk urea nitrogen during the year was a mirror image of the increases and decreases in percentage of milk protein (Figure 1B). The same pattern was observed between MUN and percentage of milk fat (data not shown). A seasonal trend of SCCt in Korea was distinct (Figure 1A). The SCCt increased with increases in temperature and humidity from late summer to fall (from August to October), whereas it decreased from winter to spring (from December to May, except in March; P<0.001). This seasonal trend is similar to a report from Canada (Sargeant et al., 1998) and supports literature dealing with heat stress and its detrimental effects on milk production and udder health; SCC in milk was higher in heat-stressed cows (Elvinger et al., 1991). Milk urea nitrogen was low during the fall and high during the other seasons, except in February, and milk protein percentages decreased from spring to summer and then continuously increased until winter. We observed decreased MUN within its optimal range (Moon et al., 2000; Figure 2).

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• Figure 1. 

  Monthly variation between natural logarithmic-transformed SCC (SCCt, ▵) and concentration of lactose (■, A), and variation between MUN (▵) and concentration of protein (■, B).

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• Figure 2. 

  The relationship between MUN and the natural logarithmic-transformed SCC (SCCt). The quadratic function approximating the observed values on the SCCt and MUN axes is shown with its equation. The dotted rectangle shows the optimal MUN concentrations (12 to 18 mg/dL).

Regionally, there were statistical differences in SCC, MUN, milk fat, milk protein, and lactose (Table 3). Jeju Island, located in the southern part of Korea, showed the lowest SCC (215×10³cells/mL) and MUN (14.29 mg/dL) values compared with the other regions. Jeju Island has a mild climate and low humidity, and cattle grazing is more common compared with the other regions. For MUN, similar values were observed across neighboring regions (Jeonbuk and Jeonnam, Gyeongbuk and Gyeongnam, and Gangwon and Gyeonggi), except for the Chungbuk and Chungnam regions. Lactose percentages among regions were not different. Percentages of milk fat in Chungnam, Gyeongbuk, and Jeonnam and milk protein in Gangwon, Gyeongbuk, and Gyeonggi were higher compared with those in the other regions.

Table 3. Regional patterns of milk components and SCC of bovine raw milk on Korean dairy farms

¹SCCt = natural logarithmic-transformed SCC.

a–hMeans within a column with different superscripts differ (one-way ANOVA, P<0.001).

Relationships Among Percentages of Milk Fat, Protein, Lactose, and MUN by SCCt 

Differences in Milk Components and MUN by SCCt 

The values for milk fat, milk protein, lactose, MUN, and SCC (SCCt) over a 4-yr period are shown in Table 2. Milk with SCC <500×10³cells/mL had greater percentages of milk components and MUN (P<0.001) compared with milk containing >500×10³cells/mL (Table 4). As the SCC grade increased, lactose percentage and MUN decreased (P<0.001; Table 4). In contrast, the percentages of milk fat and protein increased between grades 1 and 2, but decreased between grades 3 and 5. In mastitis, the increased permeability of the blood-milk mammary epithelial barrier associated with initiation of inflammation leads to an influx of serum proteins, including lactoferrin, BSA, and IgG, into milk (Moussaoui et al., 2004). According to Urech et al. (1999), this increased proportion of serum proteins compensates for the significantly lower proportion of CN in total protein in the milk from quarters with subclinical mastitis.

Table 4. Means of milk components and transformed SCC (SCCt) grouped according to SCC level and grade on Korean dairy farms

a–eMeans within a column with different superscripts differ (one-way ANOVA, P<0.001).

1SCCt = the natural logarithmic-transformed SCC.

Relationships Between SCCt and Milk Components 

In our regression model, all of the variables (i.e., percentages of milk fat, milk protein, and lactose and MUN) influenced SCCt (P<0.001). There were negative relationships between SCCt and milk fat (b = −0.0149), lactose (b = −0.8910), and MUN (b = −0.0096), similar to those shown previously (Welper and Freeman, 1992; Harmon, 1994), yet we found a positive relationship between SCCt and milk protein percentage (b = 0.3023). Although we did not analyze each milk protein, such as CN, whey, and nonprotein nitrogen, the positive relationship observed between them may be due to an increase in whey protein in raw milk. According to Leitner et al. (2004), milk protein concentrations, especially concentrations of whey protein, were higher in infected than in uninfected glands, and there were no differences in CN concentrations.

Relationships Between SCCt and Milk Components Categorized by the Presence of Bacterial Infection 

Using a univariate GLM, we analyzed the relationships between SCCt and the percentages of milk fat, milk protein, and lactose and MUN after categorizing them into 2 groups based on the presence or absence of bacterial infection in milk samples (Table 5). Most of the variables included in the model significantly influenced SCCt (P<0.001, R² = 0.281). When bacteria were not present in milk, there were positive associations between SCCt and percentages of milk fat (b = 0.0177) and protein (b = 0.4275) and a negative association between SCCt and the percentage of lactose (b =−0.6886; P<0.001). The relationship between SCCt and MUN was not significant. When bacteria were present in milk, the relationships between SCCt and the percentages of milk protein (b = −0.2693) and lactose (b =−0.4108) were negative (P<0.001). The SCCt values were significantly higher in May (b = −0.2257), June (b = 0.0851), July (b = 0.2238), August (b = 0.3593), September (b = 0.2838), October (b = 0.2180), and November (b = −0.0875) than in the other months.

Table 5. The relationship between the natural logarithmic-transformed SCC (SCCt) and milk components on Korean dairy farms categorized by the presence or absence of bacteria and the month of milk sampling and analyzed by univariate GLM

1Means of the explained variable (SCCt).

2Raw milk samples of cattle with no bacterial infection.

3Raw milk samples of cattle with bacterial infection.

4Relationship of each milk component with the explained variable (SCCt) is presented as a slope.

Relationships Among the Percentages of Milk Fat, Protein, Lactose, and SCCt by MUN 

Differences in Milk Components and SCCt by MUN 

As MUN increased, the percentages of milk fat and protein decreased (P<0.001) regardless of bacterial infection (Table 6). Negative nonlinear associations between MUN and the percentages of milk fat and protein have been reported (Godden et al., 2001). There was no relationship between MUN and lactose.

Table 6. Means of milk components and transformed SCC (SCCt) grouped according to MUN level on Korean dairy farms

a–fMeans within a column with different superscripts differ (one-way ANOVA, P<0.001).

1SCCt = the natural logarithmic-transformed SCC.

The relationship between SCCt and MUN could be separated into 2 phases (Figure 2). Up to a MUN concentration of 16 mg/dL, SCCt was inversely related to MUN. Afterward, SCCt increased as MUN concentrations increased. Various relationships between SCC and MUN have been reported: either a significant linear association (Johnson and Young, 2003), a slightly negative relationship (Godden et al., 2001), or no relationship at all (Eicher et al., 1999). At optimal MUN (12 to 18 mg/dL), SCCt was lowest (Moon et al., 2000). This association may be due to a relationship between nutrition and the immune response of cattle. The SCC, a marker for immune status, increased as MUN deviated from optimal concentrations, reflecting the nutritional status of cattle.

Relationships Between MUN and Milk Components 

All of the variables included in the regression model influenced MUN (P<0.001), except for percentage of lactose (P>0.05). There were significant negative associations between MUN and milk fat (b = −0.3125), milk protein (b = −0.8012), and SCCt (b = −0.0671), as shown previously (Godden et al., 2001; Johnson and Young, 2003).

Relationships Between MUN and Milk Components Categorized by the Presence of Bacterial Infection in Milk 

The relationships among MUN; percentages of milk fat, milk protein, and lactose; and SCCt were analyzed by univariate GLM after categorizing them into 2 groups based on the presence or absence of bacterial infection in milk samples (Table 7). Most of the variables included in the model influenced MUN (P<0.001, R² = 0.077). Regardless of the presence of bacterial infection in milk, the association between MUN and milk fat or protein was negative (P<0.001), whereas the associations between MUN and lactose (both in the presence and absence of bacteria) and SCCt (in the absence of bacteria) were not significant (P>0.05). Milk urea nitrogen was correlated with the month of the year (P<0.001), being positive in April (b = 0.6259) and May (b = 0.2195) and negative in the other months.

Table 7. The relationship between MUN and milk components categorized by presence or absence of bacteria and month of milk sampling on Korean dairy farms analyzed by univariate GLM

1Means of the explained variable (MUN).

2Raw milk samples of cattle with no bacterial infection.

3Raw milk samples of cattle with bacterial infection.

4Relationship of each milk component with the explained variable (MUN) was presented as a slope.

5SCCt = natural logarithmic-transformed SCC.

Relationships Between SCCt or MUN and Milk Components According to the Bacterial Species Isolated 

Milk samples containing Staph. aureus, CNS spp., coagulase-positive staphylococci (CPS), Streptococcus spp., Enterococcus spp., E. coli, Pseudomonas spp., and yeast had higher SCCt and lower percentages of milk components and MUN than did milk samples without bacterial infection (P<0.001; Table 8). Average SCC of infected and uninfected milk samples were 1,180,000±1.75cells/mL and 168,000±21.98cells/mL, respectively. Milk samples of cows infected with contagious pathogens (Staph. aureus and CPS) had higher SCCt and lower percentages of milk fat and lactose than milk samples infected with environmental pathogens (Pseudomonas spp., E. coli, yeast, CNS spp., Enterococcus spp., and Streptococcus spp.) or those without bacterial infection (Table 8). In addition, each bacterial species differently influenced the SCCt of bovine raw milk (P<0.001; Table 9), in the order Staph. aureus, CPS, Pseudomonas spp., E. coli, yeast, CNS spp., Enterococcus spp., and Streptococcus spp.

Table 8. Means of milk components of the milk samples from Korean dairy farms grouped according to the presence of bacterial infection and the kinds of infected bacteria (environmental or contagious bacteria)

a–cMeans within a column with different superscripts differ (one-way ANOVA, P<0.001).

1SCCt = natural logarithmic-transformed SCC.

2Environmental bacteria = Pseudomonas spp., Escherichia coli, yeast, CNS, Enterococcus spp., Streptococcus spp.

3Contagious bacteria = coagulase-positive staphylococci and Staphylococcus aureus.

Table 9. The transformed SCC (SCCt) according to bovine infection with the different bacterial species analyzed by linear regression and bacterial isolation frequencies on Korean dairy farms

1Extent of the influence to transformed SCC (SCCt).

2Streptococcus dysgalactiae and Streptococcus uberis.

3Coagulase-positive staphylococci.

Although the incidence of infection with contagious pathogens was much lower than that of infection with environmental pathogens, contagious pathogens had a greater effect on SCCt than did environmental pathogens (P<0.001). Bovine mastitis caused by Staph. aureus is very difficult to treat and can be transmitted easily to other cows in the herd (Moroni et al., 2006). Supporting earlier findings (Moroni et al., 2006), we found that CNS spp. were the most commonly identified bacteria (32%) in milk samples that were bacteriologically positive. Approximately 70 to 80% of E. coli infections had clinical outcomes (abnormal milk, udder swelling, or systemic symptoms; Harmon, 1994). We isolated E. coli (30%) and Pseudomonas spp. (19%) in milk samples with bacterial infection. These pathogens can cause chronic infections that respond inadequately to antimicrobial therapy (Erskine et al., 2002). The influence of these organisms on SCCt was followed by Staph. aureus (Table 9). Our results indicate that controlling Staph. aureus is critical for reducing SCCt in milk, and careful control of CNS spp., E. coli, and Pseudomonas spp. in raw milk in Korea is required to reduce or prevent mastitis.

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Conclusions 

We statistically analyzed the relationship between SCCt or MUN and milk components. The SCCt was negatively associated with the percentages of milk fat and lactose, and MUN was negatively associated with the percentages of milk fat and protein; however, we observed a positive relationship between SCCt and milk protein percentage by using a linear regression model. In infected milk samples, there were negative relationships between SCCt and the percentages of milk fat, milk protein, and lactose. The bacterial species was an important factor influencing SCC, with Staph. aureus being the most responsible for the increase in SCC, and the high incidence of CNS, E. coli, and Pseudomonas spp. could not be disregarded. Because there is no simple solution for controlling bovine mastitis (its prevention and early detection) by maintaining good milking hygiene, low SCC and optimal MUN may be an effective way not only of controlling this disease, but also of improving milk quality.

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Acknowledgments 

This work was supported by a grant from the Agribrands Purina Korea Inc., Sungnam, Gyeonggi, Republic of Korea. Additional support was provided by the Korean Research Foundation Grant (KRF-2006-005-J02903, KRF-2007-331-E00254), Research Institute of Veterinary Science, College of Veterinary Medicine, BK21 Program for Veterinary Science, Seoul National University.

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Supplementary data 

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