




































 

 

 
58 

† Corresponding author 
© 2015 Conscientia Beam. All Rights Reserved. 

 

CORRELATION BETWEEN MILK YIELD, SOMATIC CELL COUNT AND 

MILK QUALITY IN DAIRY FARMING 

 

Ferhan Kaygısız1* --- Gürhan Çiftçioğlu2 --- Gülhan H. Türkay3 --- Yavuz Cevger4 -

-- Ghassan Issa5 --- Hülya Yalçıntan6 --- Hasret Yardibi7 

 
1,6Istanbul University, Faculty of Veterinary Medicine, Animal Husbandry Department, Avcılar, İstanbul, Turkey 

2,5Istanbul University, Faculty of Veterinary Medicine, Food Hygiene and Technology Department ,Avcılar, İstanbul, Turkey 

3,7Istanbul University, Faculty of Veterinary Medicine, Biochemistry Department, Avcılar, İstanbul, Turkey 

4Ankara University, Faculty of Veterinary Medicine, Animal Health Economics and Management Department, Dışkapı, Ankara, 

Turkey 

 

ABSTRACT 

This study investigated between milk yield and Somatic Cell Count, sampled from 3 different dairy 

enterprises. Chemical and microbiological parameters analyzed for the relation with milk yield. Milk 

samples have been collected from 10 dairy cows, two times per month during 1 year period. Bacteriological 

analyzes have been employed for milks. Milk yield data for each cow have been recorded in every sampling 

day. Depending on the logistic regretion analyses on the data collected from all enterprises;  high total plate 

count  and E.coli counts have negative effects on milk yield, but has been found significally important 

(p<0.05) only for the data of E.coli. counts. In group 1; somatic cell count and E.coli counts have negative 

effect on milk yield and only the data of E.coli has been found statistically important (p<0.05).  In group 2; 

only the data related negative effect of E.coli counts on milk yield has been found as statistically important 

(p<0.05). The third group, total plate count has negative effect on milk yield.  Regarding to the chemical 

analyses of fat in milk, non-fat dry matter, density and protein values have been detected in the enterprises 1, 

2 and 3 as 2.88, 3.56, 4.34; 7.91, 7.83, 8.12; 1026.4, 1025.8, 1026; 3.04, 2.96, 3.07, respectively. 

According to the correlation analyses applied between milk yield and fat in milk, non fat-dry matter, protein 

and density in milk in all enterprises; there were significant correlation between non-fat dry matter and 

milk yield in enterprise 2 (p<0.05) and density and milk yield in enterprise 3 (p<0.01). 

Keywords: Cow, Raw milk, Milk yield, Milk quality, Somatic cell counts, Biochemical parameters. 

Received: 26 November 2014/ Revised: 3 April 2015/ Accepted: 7 April 2015/ Published: 10 April 2015 

 

 

Animal Review 
2015 Vol. 2, No. 2, pp. 58-67 
ISSN(e): 2409-6490 
ISSN(p): 2412-3382 
DOI: 10.18488/journal.ar/2015.2.2/101.2.58.67 
© 2015 Conscientia Beam. All Rights Reserved. 

 

 

http://crossmark.crossref.org/dialog/?doi=10.18488/journal.ar/2015.2.2/101.2.58.67


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59 

© 2015 Conscientia Beam. All Rights Reserved. 

Contribution/ Originality 

The study contributes the existing literature, which the correlation between SCC and the 

microbiological milk quality has been compared, in order to analize the possible effects on the 

production of dairy products and the reflection for dairy industry. 

 

1. INTRODUCTION 

Milk quality can have a significant impact on milk processing efficiency and product quality 

and encompasses factors relating to composition, udder health and hygiene [1]. 

Udder health and somatic cell count (SCC) are among the most important criteria in 

evaluating the quality of milk produced and herd management, in the countries where animal 

husbandry is developed [2, 3]. The negative relationship between SCC and milk yield is 

commonly used in estimating the financial losses related to mastitis in dairy cattle plants. These 

estimates guide for studies regarding the control and prevention of mastitis [4-7].  

The European Union (EU) has banned the consumption of milk, of which bulk tank SCC 

(BTSCC) excesses the value of 400.000 cell/ml., since 1998. This limit is set in Turkish Food 

Codex as 500.000 cell/ml.  It became obligatory for the milk producers in Turkey which carries 

out EU integration procedures to take the necessary measures regarding mastitis in order to 

increase operational profitability and to ensure the milk hygiene.  

The aim of the present study is to determine the relationship between SCC and milk yield in 

three intensive plants and to explore the current situation in terms of recent quality of milk 

produced. 

 

2. MATERIALS AND METHODS 

30 cows in three different intensive plants were used as animal materials in the present study. 

Milk samples were taken from each udder lobe of 10 cows in each plant two times a month during 

one year. Charm Firefly, FF-FT-CARRY-CS model ATP Bioluminescence Somatic Cell count 

device and SCC kits were used for SCC determination. The milk composition analyses, fat, fat-free 

dry matter, density and protein analyses were performed by using the Milkana Ultrasonic Milk 

Analyser (Ekomilk EON TRADING LLÇ USA). After the SCC analysis of the milks obtained 

from dairy cattle, microbiological analyses were employed for milks, which were over the limit of 

500.000 cell/ml stated in the Turkish Food Codex [8] and therefore found to be unsuitable for 

consumption. The raw milk samples brought to the laboratory were subjected to microbiological 

analyses in terms of total number of aerobic mesophylic bacteria (TMC), generic Escherichia coli 

(E.coli) and Staphylococcus aureus (S.aureus) counts. For microbiological analyses, Serial decimal 

dilutions were prepared up to stage (1/10) 107 from main dilution of 10 ml milk sample / 90 ml 

phosphate buffer solution [9] and 1 ml from these dilutions were applied into appropriate 3M 

sterile petrifilms in duplicates. Petrifilm  aerobic count plates (3M, 06400) were used in order to 

determine Total Aerobic Mesophylic Bacteria Count (TMC). Petrifilms were inoculated and 

incubated at 321°C degree for 481 hours. The aerobic count plates were evaluated at the end of 

the incubation period [10]. Petrifilm E.coli/Coliform plates (3M, 06404) were used in order to 



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determine the number of coliform – E.coli bacteria. Petrifilms were inoculated and incubated at 

35°C degree for 242 hours. The coliform – E.coli bacteria count plates were evaluated at the end 

of the incubation period [11]. Petrifilm S. aureus count plates (3M, 06490) were used in order to 

determine the number of S. aureus. Petrifilms were inoculated and incubated at 371°C degree for 

481 hours. Biochemical identification tests were performed via the application of S.aureus discs 

on positive colonies at the end of the incubation period [12].  Additionally, the data about the 

daily milk efficiency, the number of lactation on the date when the milk samples were taken and 

about lactation period were recorded.   

In order to determine the relationship between SCC, milk yield, TMC, E.coli and S.aureus 

counts, the “logistic regression model” was used as a method of data analysis [13]. To this end, 

the variable milk yield (MY) was categorized into two groups based on intra-group average milk 

yield: “below the group average” and “above the group average”. It was included in the created 

regression equation under two categories as “low” and “high”.  Due to the heterogeneous 

structure of the independent variables, these variables were included in the equation being 

logarithmically transformed. Accordingly, 4 independent variables, which are logscc, logtmc, 

logecoli and logsaureus, were used.  A correlation analysis was performed in order to determine 

the relationship between milk yield and fat, fat-free dry matter, density and protein in milk.  

 

3. RESULTS 

3.1. Findings Related to the Relationship between Milk Yield and Milk Quality 

Analyze results regarding to all enterprises have been given in Table 1.  

 
Table-1. Logistic regression analysis results between milk yield and microbiological tests for all enterprises 

Variables B Sx Wald df Sig Exp(B) 

logscc 0.100 0.298 0.112 1 0.738 1.105 
logtmc -0.190 0.214 0.790 1 0.374 0.827 

logecoli -0.336 0.162 4.310 1 0.038 0.714 
logsaureus 0.094 0.099 0.903 1 0.342 1.098 

Constant 0.754 1.076 0.491 1 0.484 2.126 
 

logscc: Logaritmic somatic cell counts, logtmc: Logaritmic total aerobic mesophylic bacteria, logecoli: Logaritmic generic Escherichia coli, 
logsaureus: Logaritmic Staphylococcus aureus 
B: Regression coefficient, Sx: Standart error, Wald: B/Sx, df: Degree of freedom, Sig: Significant, 
Exp: Odds Ratio 

 

When Table 1 is analyzed, where the results of the logistic regression performed on the data 

of all enterprises were shown, it was seen that TMC and E.coli counts in milk have a negative 

effect on milk yield; however only the value of E.coli was statistically significant (p<0.05). 

Accordingly, a logarithmic increase of 1 unit in E.coli counts in milk was caused a 0.34% decrease 

in the total milk yield. 

 

 

 

 
 



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Table-2. Logistic regression analysis results between milk yield and microbiological tests for Enterprise 1 
Variables B Sx Wald df Sig Exp(B) 

logscc -0.357 0.638 0.313 1 0.576 0.700 
logtmc 0.207 0.420 0.243 1 0.622 1.231 

logecoli -0.823 0.323 6.500 1 0.011 0.439 
logsaureus 0.083 0.184 0.202 1 0.653 1.086 

Constant 1.093 2.259 0.234 1 0.629 2.983 
 

logscc: Logaritmic somatic cell counts, logtmc: Logaritmic total aerobic mesophylic bacteria, logecoli: Logaritmic generic Escherichia coli, 
logsaureus: Logaritmic Staphylococcus aureus 
B: Regression coefficient, Sx: Standart error, Wald: B/Sx, df: Degree of freedom, Sig: Significant, 
Exp: Odds Ratio 

 

When Table 2, which shows the logistic regression analysis results of the data of the 1st 

enterprise, was examined, it was seen that number of somatic cells and E.coli counts have a 

negative effect on milk yield; however, only the value of E.coli was statistically significant 

(p<0.05). Accordingly, a logarithmic increase of 1 unit in E.coli counts in milk was caused an 

0.82% decrease in the total milk yield. 

 
Table-3. Logistic regression analysis results between milk yield and microbiological tests for Enterprise 2 

Variables B Sx Wald df Sig Exp(B) 

logscc 0.433 0.618 0.491 1 0.483 1.543 
logtmc 0.627 0.412 2.313 1 0.128 1.872 
logecoli -0.874 0.368 5.652 1 0.017 0.417 
logsaureus -0.041 0.224 0.033 1 0.856 0.960 
Constat -2.077 1.982 1.098 1 0.295 0.125 

 

logscc: Logaritmic somatic cell counts, logtmc: Logaritmic total aerobic mesophylic bacteria, logecoli: Logaritmic generic Escherichia coli,     
logsaureus: Logaritmic Staphylococcus aureus 
B: Regression coefficient, Sx: Standart error, Wald: B/Sx, df: Degree of freedom, Sig: Significant, 
Exp: Odds Ratio 

 

When Table 3, which shows the logistic regression analysis results of the data of the 2nd 

enterprise, was examined, it was seen that only the negative effect of E.coli counts on milk yield 

was  statistically significant (p<0.05). Accordingly, a logarithmic increase of 1 unit in E.coli 

counts in milk was caused an 87% decrease in the total milk yield.  

 
Table-4. Logistic regression analysis results between milk yield and microbiological tests for Enterprise 3 

Variables B Sx Wald df Sig Exp(B) 

logscc 0.546 0.470 1.347 1 0.246 1.726 
logtmc -0.787 0.427 3.394 1 0.065 0.455 
logecoli 0.204 0.268 0.576 1 0.448 1.226 
logsaureus 0.199 0.159 1.568 1 0.211 1.220 

Constant 0.562 2.238 0.063 1 0.802 1.754 
 

logscc: Logaritmic somatic cell counts, logtmc: Logaritmic total aerobic mesophylic bacteria, logecoli: Logaritmic generic Escherichia coli, 
logsaureus: Logaritmic Staphylococcus aureus 
B: Regression coefficient, Sx: Standart error, Wald: B/Sx, df: Degree of freedom, Sig: Significant, 
Exp: Odds Ratio 

 

When Table 4, which shows the logistic regression analysis results of the data of the 3rd 

enterprise, was examined, it was seen that TMC has a negative effect on milk yield for this group; 

however, this effect was not statistically significant (p<0.05). Accordingly, a logarithmic increase 



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62 

© 2015 Conscientia Beam. All Rights Reserved. 

of 1 unit in TMC in milk was caused a 79% decrease in the total milk yield although this value 

was not statistically significant. 

 

3.2. The Findings of the Relationship between Milk Efficiency and Milk Components  

The milk yield rates, the averages of some milk components and their standard deviations, 

belonging to the cattle in enterprises 1, 2 and 3, were given in Table 9. The lowest milk yield was 

observed in Enterprise 1, while the highest milk yield was observed in Enterprise 3. In parallel, 

the highest amount of fat and non-fat dry matter was observed in the milks of Enterprise 3. No 

statistically significant difference was found between the enterprises in terms of milk protein. 

 

Table-5. Milk yield and milk compositions in Enterprise 1, 2 and 3 

 Enterprise 1 Enterprise 2 Enterprise 3 

Parameters n X Sx n X Sx n X Sx 
Milk yield (kg/day) 103 16.00 ±4.00 79 18.70 ±2.95 97 22.64 ±8.17 
Fat  (%) 72 2.88 ±3.26 79 3.56 ±3.43 85 4.34 ±4.22 

Non-fat dry matter 
(%) 

86 7.91 ±1.07 84 7.83 ±1.02 87 8.12 ±0.96 

Dansity (g/ml) 88 1026.39 ±8.69 80 1025.84 ±6.20 99 1025.97 ±6.57 
Protein  (%) 92 3.04 ±0.41 92 2.96 ±0.35 100 3.07 ±0.43 

 

When Table 6, which shows the results of the correlation analysis performed between the 

milk yield rates and milk components of Enterprise 1, was examined, it was seen that there was 

no statistically significant relationship between milk yield and milk components. 

 

Table-6. Correlation analysis between milk yield and milk components belonging to Enterprise 1 

Enterprise 1  Milk 
Yield 

Fat Non-fat dry 
matter 

Dansity Protein 

Milk Yield   
kg/cow/day 

Correlation Coefficient 
Significancy 
Total (n) 

1.000 
- 
103 

.085 

.477 
72 

.32 

.774 
85 

.142 

.190 
87 

.022 

.839 
91 

Fat  (%) Correlation Coefficient 
Significancy 
Total (n) 

 1.000 
- 
72 

.101 

.399 
72 

.134 

.261 
72 

.135 

.257 
72 

Non-fat dry 
matter  (%) 

Correlation Coefficient 
Significancy 
Total (n) 

  1.000 
- 
86 

.262(a) 

.015 
86 

.326(b) 

.002 
86 

Dansity 
(g/ml) 

Correlation Coefficient 
Significancy 
Total (n) 

   1.000 
 
88 

.419(b) 

.000 
88 

Protein  (%) Correlation Coefficient 
Significancy 
Total (n) 

    1.000 
 
92 

(a)p<0.05  (b)p< 0.01    

 

 

 

 



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Table-7. Correlation analysis between milk yield and milk components belonging to Enterprise 2 

Enterprise 1  Milk 
Yield 

Fat Non-fat dry 
matter 

Dansity Protein 

Milk Yield   
kg/cow/day 

Correlation Coefficient 
Significancy 
Total (n) 

1.000 
- 
79 

.160 

.163 
78 

.237(a) 

.035 
79 

.031 

.784 
79 

.250 

.026 
79 

Fat  (%) Correlation Coefficient 
Significancy 
Total (n) 

 1.000 
- 
79 

.033 

.773 
79 

.161 

.158 
79 

.045 

.692 
79 

Non-fat dry 
matter  (%) 

Correlation Coefficient 
Significancy 
Total (n) 

  1.000 
- 
84 

.302(b) 

.006 
80 

.861(b) 

.000 
83 

Dansity 
(g/ml) 

Correlation Coefficient 
Significancy 
Total (n) 

   1.000 
 
80 

.326(b) 

.003 
80 

Protein  (%) Correlation Coefficient 
Significancy 
Total (n) 

    1.000 
- 
83 

(a)p<0.05  (b)p<0.01    

 

When Table 7, which shows the results of the correlation analysis performed between the 

milk yield rates and milk components of Enterprise 2, was examined, it was seen that there was a 

positive and statistically significant relationship between milk yield and the amount of dry matter 

and protein in milk (p<0.05).  

 

Table-8. Correlation analyze between milk yield and milk components belonging to Enterprise 3 

Enterprise 1  Milk 
Yield 

Fat Non-fat 
dry matter 

Dansity Protein 

Milk Yield 
kg/cow/day 

Correlation Coefficient 
Significancy 
Total (n) 

1.000 
- 
97 

.142 

.196 
85 

.136 

.209 
87 

.327(b) 

.001 
97 

.146 

.154 
97 

Fat  (%) Correlation Coefficient 
Significancy 
Total (n) 

 1.000 
- 
85 

.023 

.836 
85 

.130 

.237 
85 

.012 

.912 
85 

Non-fat dry 
matter  (%) 

Correlation Coefficient 
Significancy 
Total (n) 

  1.000 
- 
87 

.197 

.067 
87 

.283(b) 

.008 
87 

Dansity 
(g/ml) 

Correlation Coefficient 
Significancy 
Total (n) 

   1.000 
 
99 

.173 

.087 
99 

Protein  (%) Correlation Coefficient 
Significancy 
Total (n) 

    1.000 
 
100 

(a)p<0.05  (b)p<0.01    

 

When Table 8, which shows the results of the correlation analysis performed between the 

milk yield rates and milk components of Enterprise 3, was examined, it was seen that there was a 

negative and statistically significant relationship between milk yield and milk density (p<0.01). 

 



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4. DISCUSSION 

Regarding to the data obtained from 3 different enterprises and statistical analyzes during 

this research, it was not able to found a statistically significant relationship between milk yield 

and SCC. There are some studies in literature, in which the milk yield losses in cows with 

subclinical mastitis are determined based on the relationship between milk yield and SCC [14-

17].  The milk yield losses were reported between 5.6 and 11.9 kg at 3 million cell/ml SCC level, 

which is accepted as an advanced subclinical mastitis. In another study [18], it was stated that 

the decrease in milk yield up to SCC rate of 500.000 cell/ml was not statistically significant. In 

this present study, as a result of the analyses performed for each enterprises, TMC and E.coli 

counts were found to have a negative effect on milk yield; however, only the value of E.coli was 

statistically significant (p<0.05). Accordingly, 1 unit logarithmic increase of E.coli counts in milk 

was caused a 0.34% decrease in the total milk yield. It was statistically exposed that there would 

be a decrease in milk yield in subclinical mastitis cases which may be related to E.coli. Among the 

analysed 252 milk samples, E.coli was found to be higher than 10 cfu/ml. E.coli is one of the major 

pathogens causing subclinical mastitis and it causes SCC to increase. In a study [19] , it was 

reported that E.coli was isolated in 4.8% of the infected udder lobes in milk samples, while in 

another study [17]  reported that they isolated E.coli in 54% of the infected milk samples and 

SCC reached the value of 800.000 - 1.000.000 cell/ml in the milks where E.coli grew. The number 

of somatic cells in milk depends on many factors such as infection status of milk, the number of 

infected udder lobes, age of cattle, the number of lactation, seasonal conditions, techniques used 

and management conditions [20].  

When the results of the analysis performed on the data of Enterprise 1 are analyzed, SCC was 

found to have a negative effect on milk yield but this effect was not statistically significant.  No 

statistically significant effect of SCC on milk yield was found in Enterprises 2 and 3. According to 

the data analyses, it was agreed that the number of animals used in the present study was not 

sufficient to work with statistical data; therefore, it would be appropriate to use a higher number 

of experimental animals in the future studies.   

The average values of milk yield, milk fat, non-fat dry matter, density and protein of the milk 

samples taken from three different enterprises are given in Table 9. The parameters of milk 

components analyzed in the present research were within the change interval developing with 

various effects. These limits were reported to be 2.5-6% for milk fat, 8-9% for non-fat dry matter 

and 2.9-5% for protein21.  When the milk fat values given in the table were analyzed, the rate of 

milk fat was found to be within the change interval in Enterprise 1, while this rate was found to 

be lower compared to Enterprises 2 and 3. It is known that the rate of fat in milk varies 

depending on the race of cattle, the content and the amount of the consumed feed and also 

seasonal changes are observed [21, 22]. It is suggested that mastitis affects the content of milk 

and changes the rate of fat in milk [23]. A lower rate of fat in milk in Enterprise 1 shows that the 

number of cows with mastitis is higher in this enterprise.  

When the amount of non-fat dry matter in the milk samples taken from the plants was 

analysed, it was seen that this amount was below the change limits in Enterprises 1 and 2, while it 



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© 2015 Conscientia Beam. All Rights Reserved. 

was within usual limits in Enterprise 3.  It is also known that the amount of non-fat dry matter 

varies depending on seasons and feed and it is related to milk amount and decreases due to 

mastitis [24]. The average milk densities in the three plants from which the milk samples were 

taken were found to be lower than the density values reported in literature [21].  It is reported 

that milk density changes depending on all the matters comprising milk [25].  When the milk 

samples taken from the plants were evaluated in terms of protein values, no statistically 

significant difference was found among the enterprises.  It is known that changes in the rate of 

milk protein are not as common as the changes in milk fat [22] and milk protein was observed to 

be at lower limits in each enterprise. As the mastitis bacteria cause damages on udder gland cells 

and therefore cause a decrease in protein, fat and milk sugar synthesis, they negatively affect milk 

yield and quality [26]. Although no statistically significant differences were observed among the 

amounts of fat, non-fat dry matter, density and protein in the milk samples analyzed in the 

present research, the parameters were found to be at the lower limits of the usual values.  

A correlation analysis was performed in order to determine the relationship between milk 

efficiency and fat, non-fat dry matter, density and protein in milk in each enterprise. A positive 

and statistically significant relationship was found between milk yield and the amount of dry 

matter and protein in milk (p<0.05) in Enterprise 2, while there was a statistically significant 

relationship between milk yield and density (p<0.01) in Enterprise 3.  

 

5. CONCLUSIONS 

The findings of the present study show that the subclinical mastitis cases, which may occur in 

dairy plants, may cause not only losses in milk yield but also decreases the chemical quality of 

milk. This strengthens the idea that the financial losses encountered especially in bacterial 

mastitis are related to the reduced technological quality of milk as well as reduced milk yield. 

Given that a high rate of microbiological load to be observed in milk pose a risk for public health 

as well, early diagnosis of mastitis in dairy plants and steps to prevent mastitis are of great 

importance. 

  

Funding: This study received no specific financial support. 
 

Competing Interests: The authors declare that they have no competing interests. 
 

Contributors/Acknowledgement: This study was supported by Research Fund of Istanbul University. 

Project number: 1199. The authors also would like to thank Kaan Göksal and İlker Çoban for their 
assistance. 

 

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