- Email: [email protected]
Relationship of Somatic Cell Count with Milk Yield and Composition in Chinese Holstein Population
Agricultural Sciences in China
October 2010
2010, 9(10): 1492-1496
Relationship of Somatic Cell Count with Milk Yield and Composition in Chinese Holstein Population GUO Jia-zhong, LIU Xiao-lin, XU A-juan and XIA Zhi College of Animal Science and Technology, Shaanxi Key Laboratory of Molecular Biology for Agriculture, Northwest A&F University, Yangling 712100, P.R.China
Abstract The objective of this study was to analyze the relationship of somatic cell count (SCC) with milk yield, fat and protein percentage, fat and protein yield using analysis of variance and correlation analysis in Chinese Holstein population. The 10 524 test-day records of 568 Chinese Holstein Cattle were obtained from 2 commercial herds in Xi’an region of China during February 2002 to March 2009. Milk yield, fat percentage, fat and protein yield initially increased and then dropped down with parity, whereas protein percentage decreased and SCC increased.
Analysis of variance showed highly
significant effects of different subclasses SCC on milk yield and composition (P < 0.01). Compared with milk yield with SCC 200 000 cells mL-1, milk yield losses with SCC of 200 000-500 000 cells mL-1, 501 000-1 000 000 cells mL-1, 1 000 000 cells mL-1 were 0.387, 0.961 and 2.351 kg, respectively. The highly significant negative correlation coefficient between somatic cell score (SCS) and milk and protein yield, milk yield and fat and protein percentage, protein percentage and fat yield were -0.084, -0.037, -0.061, -0.168, and -0.088, respectively (P < 0.01). The highly significant positive correlation coefficients between SCS and fat yield and fat and protein percentage, milk yield and fat and protein yield, fat percentage and protein percentage and fat yield, protein yield and protein percentage and fat yield were 0.041, 0.177, 0.105, 0.771, 0.865, 0.122, 0.568, 0.318, and 0.695, respectively (P < 0.01). There was no significant relationship between fat percentage and protein yield (P > 0.05). The results of the present study first time provide the relevant base-line data for assessing milk production at Xi’an region of China. Key words: Holstein cattle, milk yield, milk composition, somatic cell count, somatic cell score
INTRODUCTION Mastitis, an inflammation of mammary gland mostly caused by microbial infection, is one of the most frequent, complex and costly diseases of dairy cattle. Once inflammation occurs, the synthetic activity of mammary gland reduces and milk constituents change. The inflammation of mammary gland results in an influx of somatic cell, predominantly polymorphonuclear neutrophils, which lead to the elevation of the somatic cell count (SCC) in milk. Hence, the SCC in raw milk Received 15 March, 2010
is generally considered to be an important index for mastitis detection and udder health in dairy farming. SCC in milk from healthy udders varies between 50 000 and 200 000 cells mL-1, depending on the age of cows (Smith 1996). While cows with subclinical mastitis can excrete up to a few million cells per milliliter sometimes, excretion is usually in the range from 200 000 to 500 000 cells mL-1 (Reneau 1986). In the past decades, the researches on variation of SCC and relationship of SCC and milk production traits have been paid much attention worldwide in dairy cattle. Several researches showed relationship of SCC with
Accepted 13 May, 2010
GUO Jia-zhong, Ph D, E-mail: [email protected]; Correspondence LIU Xiao-lin, Professor, Ph D, Tel: +86-29-87092158, Fax: +86-29-87092164, Email: [email protected] © 2010, CAAS. All rights reserved. Published by Elsevier Ltd. doi:10.1016/S1671-2927(09)60243-1
Relationship of Somatic Cell Count with Milk Yield and Composition in Chinese Holstein Population
milk yield and composition (Raubertas and Shook 1982; Jones et al. 1984; Ng-Kwai-Hang et al. 1984; Bartlett et al. 1990) of dairy cows using different analysis methods in some regions. The data used in these researches were from test day observations with dairy herd improvement (DHI) programs. It is noteworthy that the SCC needs to be transformed to SCS for the further statistical analysis, because the frequency distribution of SCC in milk is bias (Ali and Shook 1980). The rapid development of dairy cattle husbandry in China really began with the middle 1990s which reflected in genetic merit of dairy cows, herd size, and management. Since 1995, DHI programs were launched to be applied for management on some extensive dairy farms in some regions (Xi’an, Shanghai and Hangzhou, China) by measuring and analyzing fat content, protein content, and somatic cell count per month regularly. It is available to use test-day data because of operation of DHI. To date, there are several papers of variability of test-day milk yield and composition or relationship between somatic cell count with milk yield and composition using analysis of variance and correlation analysis in different Chinese Holstein populations (Mao et al. 2002; Gao et al. 2007; Sun et al. 2009). However, the sample sizes in these researches are small and the information about milk production traits is not comprehensive, especially for fat and protein yield. The relevant researches on relationship between milk yield, composition and SCC should be paid more attention because of development of breeding programs involving mastitis resistance and growing intensification of dairy cattle production systems in China. The objective of this study was to investigate relationship between somatic cell count with milk yield and composition and to determine increased milk yield loss caused by SCC, in order to improve dairy industry in Chinese Holstein populations
MATERIALS AND METHODS Observations The 10 524 test-day observations on milk yield, fat, protein, and SCC were obtained from 568 Chinese Hol-
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stein cows in 2 commercial herds milked by machine at Shaanxi DHI program during February 2002 to March 2009. Fat and protein content was determined by Milkoscan 134 A/B. The SCC of milk samples was measured to almost thousand cells with FOSSOMATIC 90. The test-day sample number from 2002 to 2009 was 116, 311, 576, 909, 1 762, 2 730, 3 630, and 490, respectively. Dependent variables were sample day records of testday milk yield, fat and protein percentages, fat and protein yield, and SCC (the unit of SCC was cells mL-1). Moreover, SCC has been transformed to SCS for the correlation analysis, according to the formula: SCS = log2 (SCC/100000) + 3 (Shook 1982). According to the recommendation of International Dairy Foods Association and National Mastitis Council of America, the observations of milk yield and composition were divided into 4 classes with SCC varying ( 200 000, 200 000-500 000, 501 000-1 000 000, and 1 000 000 cells mL-1) in analysis of variance and correlation analysis.
Statistical analysis Analysis of variance and correlation analysis were performed with general linear model (GLM) and correlation analysis procedures of SPSS 16.0 for Windows. If the main effect from subclasses was significant, means were separated by Duncan’s multiple tests. Correlation coefficients were estimated directly from unclassified the observations and transformed SCS.
RESULTS Overall means and standard deviations for sample observations of daily milk yield, fat and protein percentage, fat and protein yield, and SCC are summarized in Table 1. The numbers of test day observations with the first, second, third, fourth and later parity for all the traits were 4 287, 3 103, 1 748 and 1 386, respectively. Milk yield, fat percentage, fat yield and protein yield increased initially and then dropped with parity, which peaked at second, third, second and second parity, respectively. Protein percentage decreased, but, SCC increased with parity. © 2010, CAAS. All rights reserved. Published by Elsevier Ltd.
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GUO Jia-zhong et al.
Changes in milk yield and composition with variation in SCC
highly significantly negative (P < 0.01) correlations between SCS and milk and protein yield, milk yield and fat and protein percentage, and protein percentage and fat yield. Fat percentage had no significant relationship with protein yield (P > 0.05). The highly significant positive correlations existed (P < 0.01) between SCS and fat yield, fat, protein percentage, milk yield and fat and protein yield, fat percentage and protein percentage, fat yield, protein yield and percentage, and protein yield.
The comparative results of different subclass of the test-day observations for milk yield and milk composition with SCC are shown in Table 2. The milk yield decreased (P < 0.05); but, fat and protein percentages increased (P < 0.05) with increase in SCC. Both fat and protein yield increased initially and then drop, and peaked at SCC of 200 000-500 000 cells mL -1 (P < 0.05). Compared with milk yield with SCC 200 000 cells mL -1, milk yield losses with SCC of 200 000-500 000, 501 000-1 000 000, and 1 000 000 cells mL-1 were 0.387, 0.961 and 2.351 kg, respectively.
DISCUSSION The accurate statistical results depend on the large-scale and reliable data sources. Compared with those in earlier studies (Bodoh et al. 1976; Ng-Kwai-Hang et al. 1982, 1984; Schutz et al. 1990), the sample size in our research was relatively smaller. However, compared to the reports on Chinese Holstein populations (Mao et al. 2002; Gao et al. 2007; Sun et al. 2009), our sample size was larger. The dataset of 10 524 test-day record was sufficient and representative. There are several reasons influencing the reliable dataset sources. To date,
Correlations between SCS and milk production traits Correlation analysis between SCS and milk yield and composition is summarized in Table 3. All the absolute values of correlation coefficients between SCS and milk production traits were not exceed to 0.2. There were
Table 1 Overall means and standard deviations for sample observations Traits Milk yield (kg) Fat percentage (%) Protein percentage (%) Fat yield (kg) Protein yield (kg) SCC (103 cells mL-1) SCS Sample number
Parity First 25.78 ± 6.09 3.68 ± 0.83 3.05 ± 0.50 0.94 ± 0.29 0.78 ± 0.21 292.36 ± 666.85 3.42 ± 1.67 4 287
Second 27.12 ± 8.13 3.74 ± 0.76 2.94 ± 0.41 1.01 ± 0.36 0.79 ± 0.25 687.27 ± 10 232.63 3.71 ± 1.78 3 103
Third 26.90 ± 8.34 3.75 ± 0.80 2.91 ± 0.37 1.00 ± 0.37 0.78 ± 0.24 420.50 ± 859.89 3.83 ± 1.86 1 748
Fourth and later
Total
25.09 ± 8.47 3.64 ± 1.01 2.93 ± 0.45 0.92 ± 0.42 0.73 ± 0.26 732.52 ± 6 814.10 3.77 ± 2.28 1 386
26.27 ± 7.49 3.71 ± 0.83 2.98 ± 0.45 0.97 ± 0.35 0.78 ± 0.23 488.19 ± 6 111.783 3.62±1.83 10 524
Fat yield (kg)
Protein yield (kg)
0.973b 1.007a 0.986a 0.952b
0.783 a 0.781 a 0.767 a 0.749 b
Protein percentage
Fat yield
Protein yield
0.105 ** -0.168 ** 0.122 **
0.041 ** 0.771 ** 0.568 ** -0.088 **
-0.037 ** 0.865 ** -0.011 0.318 ** 0.695 **
Table 2 The comparison of daily milk yield and milk composition with different SCC SCC (103 cells mL-1) 200 200-500 501-1 000 1 000
Milk yield (kg) 26.752 a 26.365 a 25.791 b 24.401 c
Fatpercentage (%) 3.648 c 3.832 b 3.837 b 3.917 a
Protein percentage (%) 2.945 c 2.981 b 2.998 b 3.112 a
Means with different superscript letters within columns differ significantly (P < 0.05).
Table 3 Correlation coefficients of SCS with milk yield and composition traits Traits SCS Milk yield Fat percentage Protein percentage Fat yield **
Milk yield -0.084 **
Fat percentage 0.177 ** -0.061 **
, highly significant correlations (P < 0.01).
© 2010, CAAS. All rights reserved. Published by Elsevier Ltd.
Relationship of Somatic Cell Count with Milk Yield and Composition in Chinese Holstein Population
there are a few extensive dairy farms that have advanced milking equipment and management software in China, including Xi’an region. Similarly, DHI program has not been carried out in every extensive farm, even though the program was launched since 1995 in China. The further reason is that the important roles of DHI program in management on dairy farms have not been recognized by most of the managers. Simple means with SD of test-day observations of milk yield and composition in this study reflect the situation in our region. Capper et al. (2009) reported averages of milk yield, fat and true protein percentages of American dairy herd, predominantly Holstein, were 29.3 kg d-1, 3.69 and 3.05%, respectively in 2007. Compared to this report, our observations of milk yield and protein percentages were lower, but fat percentage was a little higher. Even though compared to the results published in other Holstein populations about 20 yr ago, protein percentage was also lower (Ng-Kwai-Hang et al. 1982, 1984; Schutz et al. 1990). Our results suggest that more attention should be paid on protein percentage and given priority in genetic improvement for milk production traits in Chinese Holstein population. For fat and protein yield, most of our results were higher than report from Schutz et al. (1990), except for protein yield in the third parity. The statistical results of fat and protein yield have not been found in Chinese literatures. In principle, milk yield, fat, and protein percentage should be considered as a whole in breeding programs because of negative genetic correlation between milk yield, protein, and fat percentages, in order to gain the higher economic benefit. In this situation, undoubtedly, fat and protein yield are the good indexes to reflect changes of milk yield, fat, and protein percentage. Actually, protein and fat yield are the most important traits in the production traits in dairy cattle breeding program all around the world. The information for protein and fat yield should be analyzed further on Chinese Holstein populations. For SCC, our results are higher than previous reports in other countries (Raubertas and Shook 1982; Jones et al. 1984; Ng-Kwai-Hang et al. 1984; Bartlett et al. 1990). As an important parameter of mastitis detection, the higher SCC implies there is a high prevalence ratio of mastitis (Harmon 1994). Surprisingly, the value of standard deviation was much higher than
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that of the mean, which was partly due to the biased distribution of SCC. Another important reason causing relatively high SCC in our study was that the observations of dairy cows suffering clinical mastitis might not be culled. Hence, it is a significant and urgent issue how to standardize the operation of DHI program in practice. The SCC was divided into 4 subclasses according to the recommended criteria of International Dairy Foods Association (IDFA) and National Mastitis Council of America to performed analysis of variance in this study. The change in the patterns of milk yield and composition with variation in SCC were in line with previous researches (Ng-Kwai-Hang et al. 1984; Bartlett et al. 1990). Compared with milk yield with SCC of 200 000 cells mL -1, milk yield losses with SCC of 200 000-500 000, 501 000-1 000 000, and 1 000 000 cells mL-1 were 0.387, 0.961 and 2.351 kg, respectively. If multiplied by 568 (cows), the overall losses in every sample day would be 219.816, 545.848, and 1 335.368 kg, respectively. Furthermore, treatment cost should be added due to occurrence of clinical mastitis. Consequently, the total loss caused by mastitis was very high. Haenlein et al. (1973) and Sun et al. (2009) reported that there was no significant difference among total protein percentages with different SCC. But, our results suggested that total protein percentages increased with increase in SCC, which were similar with the results of Ng-Kwai-Hang et al. (1984) and Guo (2007). A reasonable explanation is increased protein percentage was mainly from the serum protein fraction when cows responded to infection. As a whole, fat and protein yield decreased with varying SCC in the present study. The changes in fat and protein yield with different SCC could not be found in other literatures. The absolute values of all the correlation coefficients were less than other studies (Ng-Kwai-Hang et al. 1984; Gao et al. 2007; Guo 2007). Our results were similar to those of Mao et al. (2002); but, this research could not illustrate whether SCC was transformed to SCS. As a whole, the correlation coefficients among production traits were higher than those between production traits and SCS, which were consistent with the previous study (Guo 2007). Therefore, genetic analysis should be carried out in order to get further information about the relationship of SCS with milk yield
© 2010, CAAS. All rights reserved. Published by Elsevier Ltd.
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and composition in Chinese Holstein populations in our region. Nevertheless, the data is not sufficient for genetic analysis with a test-day model now. As a consequence, it is necessary to collect the production data continuously and extend the DHI program.
CONCLUSION The results of the present study first provide the relevant base-line data for assessing milk production at Xi’an region of China. Phenotypic variation in SCC was high and more attention should be paid on protein percentage in dairy cattle breeding program. Milk yield losses due to increased SCC were very high in the Chinese Holstein population. The correlation coefficients among production traits were higher than those between production traits and SCS. As the dairy farming in China is scattered over a wide range of climate and topographic regions, the researches on SCC and milk production traits should be carried out separately.
Acknowledgements This study was supported by the National 863 Program of China (2008AA10Z144) and “13115” Sci-Tech Innovation Program of Shaanxi Province (2008ZDKG11). The authors are grateful to Mr. Song Ailong, Wang Jianhua, and Ru Caixia from Xi’an Modern Agriculture Development Corporation for their valuable help during the preparation of DHI records.
References
GUO Jia-zhong et al.
Gao S X, Wang G F, Shao Z W, Liu M Y, Ma Y, Wu H G, Zhao J W. 2007. Study on correlation between lacation month, the portion of milk components and bovine milk somatic cell count. China Dairy Industry, 35, 7-9. (in Chinese) Guo G. 2007. Genetic analysis of somatic cell score in Chinese Holstein cattle. Master thesis. China Agricultural University. Beijing, China. ( in Chinese) Harmon R J. 1994. Physiology of mastitis and factors affecting somatic cell counts. Journal of Dairy Science, 77, 2103-2112. Haenlein G F W, Schultz L H, Zikakis J P. 1973. Composition of proteins in milk with varying leukocyte contents. Journal of Dairy Science, 56, 1017-1024. Jones G M, Pearson R E, Clabaugh G A, Heald C W. 1984. Relationships between somatic cell counts and milk production. Journal of Dairy Science, 67, 1823-1831. Mao Y J, Yang Z P, Wang X L, Hua J D, Zhu J M, Zhang X H. 2002. The relationship among milk traits and udder traits SCC in milk for Chinese Holstein in South. Chinese Dairy Cattle, 20, 12-13. ( in Chinese) Ng-Kwai-Hang K F, Hayes J F, Moxley J E, Monardes H G. 1982. Environmental influences on protein content and composition of bovine milk. Journal of Dairy Science, 65, 1993-1998. Ng-Kwai-Hang K F, Hayes J F, Moxley J E, Monardes H G. 1984. Variability of test-day milk production and composition and relation of somatic cell counts with yield and compositional changes of bovine milk. Journal of Dairy Science, 67, 361-366. Raubertas R F, Shook G E. 1982. Relationship between lactation measures of somatic cell concentration and milk yield. Journal of Dairy Science, 65, 419-425. Reneau J K. 1986. Effective use of dairy herd improvement somatic cell couns in mastitis control. Journal of Dairy Science, 69, 1708-1720.
Ali A K A, Shook G E. 1980. An optimum transformation for somatic cell concentration in milk. Journal of Dairy Science,
Schutz M M, Hansen L B, Steuernagel G R, Kuck A L. 1990. Variation of milk, fat, protein, and somatic cells for dairy
63, 487-490. Bartlett P C, Miller G Y, Anderson C R, Kirk J H. 1990. Milk
cattle. Journal of Dairy Science, 73, 484-493. Shook G E. 1982. Approaches to summarizing somatic cell counts
production and somatic cell count in Michigan dairy herds. Journal of Dairy Science, 73, 2794-2800.
which improve interpretability In: Proceedings National Mastitis Council. Arlington VA. pp. 150-166.
Bodoh G W, Battista W J, Schultz L H, Johnston R P Jr. 1976. Variation in somatic cell counts in dairy herd improvement
Smith, K L. 1996. Standards for somatic cells in milk: physiological and regulatory. IDF Mastitis Newsletter, 21, 7-9.
milk samples. Journal of Dairy Science, 59, 1119-1123. Capper J L, Candy R A, Bauman D E. 2009. Environmental
Sun Y, Niu L X, Zhang Y L, Gao T Y. 2009. Change patterns of SCC in milk of dairy cows and its relationship with
impact of dairy production: 1994 compared with 2007. Journal of Animal Science, 87, 2160-2167.
composition. Animal Husbandry and Feed Science, 30, 133134. ( in Chinese) (Managing editor ZHANG Juan, SUN Lu-juan)
© 2010, CAAS. All rights reserved. Published by Elsevier Ltd.
October 2010
2010, 9(10): 1492-1496
Relationship of Somatic Cell Count with Milk Yield and Composition in Chinese Holstein Population GUO Jia-zhong, LIU Xiao-lin, XU A-juan and XIA Zhi College of Animal Science and Technology, Shaanxi Key Laboratory of Molecular Biology for Agriculture, Northwest A&F University, Yangling 712100, P.R.China
Abstract The objective of this study was to analyze the relationship of somatic cell count (SCC) with milk yield, fat and protein percentage, fat and protein yield using analysis of variance and correlation analysis in Chinese Holstein population. The 10 524 test-day records of 568 Chinese Holstein Cattle were obtained from 2 commercial herds in Xi’an region of China during February 2002 to March 2009. Milk yield, fat percentage, fat and protein yield initially increased and then dropped down with parity, whereas protein percentage decreased and SCC increased.
Analysis of variance showed highly
significant effects of different subclasses SCC on milk yield and composition (P < 0.01). Compared with milk yield with SCC 200 000 cells mL-1, milk yield losses with SCC of 200 000-500 000 cells mL-1, 501 000-1 000 000 cells mL-1, 1 000 000 cells mL-1 were 0.387, 0.961 and 2.351 kg, respectively. The highly significant negative correlation coefficient between somatic cell score (SCS) and milk and protein yield, milk yield and fat and protein percentage, protein percentage and fat yield were -0.084, -0.037, -0.061, -0.168, and -0.088, respectively (P < 0.01). The highly significant positive correlation coefficients between SCS and fat yield and fat and protein percentage, milk yield and fat and protein yield, fat percentage and protein percentage and fat yield, protein yield and protein percentage and fat yield were 0.041, 0.177, 0.105, 0.771, 0.865, 0.122, 0.568, 0.318, and 0.695, respectively (P < 0.01). There was no significant relationship between fat percentage and protein yield (P > 0.05). The results of the present study first time provide the relevant base-line data for assessing milk production at Xi’an region of China. Key words: Holstein cattle, milk yield, milk composition, somatic cell count, somatic cell score
INTRODUCTION Mastitis, an inflammation of mammary gland mostly caused by microbial infection, is one of the most frequent, complex and costly diseases of dairy cattle. Once inflammation occurs, the synthetic activity of mammary gland reduces and milk constituents change. The inflammation of mammary gland results in an influx of somatic cell, predominantly polymorphonuclear neutrophils, which lead to the elevation of the somatic cell count (SCC) in milk. Hence, the SCC in raw milk Received 15 March, 2010
is generally considered to be an important index for mastitis detection and udder health in dairy farming. SCC in milk from healthy udders varies between 50 000 and 200 000 cells mL-1, depending on the age of cows (Smith 1996). While cows with subclinical mastitis can excrete up to a few million cells per milliliter sometimes, excretion is usually in the range from 200 000 to 500 000 cells mL-1 (Reneau 1986). In the past decades, the researches on variation of SCC and relationship of SCC and milk production traits have been paid much attention worldwide in dairy cattle. Several researches showed relationship of SCC with
Accepted 13 May, 2010
GUO Jia-zhong, Ph D, E-mail: [email protected]; Correspondence LIU Xiao-lin, Professor, Ph D, Tel: +86-29-87092158, Fax: +86-29-87092164, Email: [email protected] © 2010, CAAS. All rights reserved. Published by Elsevier Ltd. doi:10.1016/S1671-2927(09)60243-1
Relationship of Somatic Cell Count with Milk Yield and Composition in Chinese Holstein Population
milk yield and composition (Raubertas and Shook 1982; Jones et al. 1984; Ng-Kwai-Hang et al. 1984; Bartlett et al. 1990) of dairy cows using different analysis methods in some regions. The data used in these researches were from test day observations with dairy herd improvement (DHI) programs. It is noteworthy that the SCC needs to be transformed to SCS for the further statistical analysis, because the frequency distribution of SCC in milk is bias (Ali and Shook 1980). The rapid development of dairy cattle husbandry in China really began with the middle 1990s which reflected in genetic merit of dairy cows, herd size, and management. Since 1995, DHI programs were launched to be applied for management on some extensive dairy farms in some regions (Xi’an, Shanghai and Hangzhou, China) by measuring and analyzing fat content, protein content, and somatic cell count per month regularly. It is available to use test-day data because of operation of DHI. To date, there are several papers of variability of test-day milk yield and composition or relationship between somatic cell count with milk yield and composition using analysis of variance and correlation analysis in different Chinese Holstein populations (Mao et al. 2002; Gao et al. 2007; Sun et al. 2009). However, the sample sizes in these researches are small and the information about milk production traits is not comprehensive, especially for fat and protein yield. The relevant researches on relationship between milk yield, composition and SCC should be paid more attention because of development of breeding programs involving mastitis resistance and growing intensification of dairy cattle production systems in China. The objective of this study was to investigate relationship between somatic cell count with milk yield and composition and to determine increased milk yield loss caused by SCC, in order to improve dairy industry in Chinese Holstein populations
MATERIALS AND METHODS Observations The 10 524 test-day observations on milk yield, fat, protein, and SCC were obtained from 568 Chinese Hol-
1493
stein cows in 2 commercial herds milked by machine at Shaanxi DHI program during February 2002 to March 2009. Fat and protein content was determined by Milkoscan 134 A/B. The SCC of milk samples was measured to almost thousand cells with FOSSOMATIC 90. The test-day sample number from 2002 to 2009 was 116, 311, 576, 909, 1 762, 2 730, 3 630, and 490, respectively. Dependent variables were sample day records of testday milk yield, fat and protein percentages, fat and protein yield, and SCC (the unit of SCC was cells mL-1). Moreover, SCC has been transformed to SCS for the correlation analysis, according to the formula: SCS = log2 (SCC/100000) + 3 (Shook 1982). According to the recommendation of International Dairy Foods Association and National Mastitis Council of America, the observations of milk yield and composition were divided into 4 classes with SCC varying ( 200 000, 200 000-500 000, 501 000-1 000 000, and 1 000 000 cells mL-1) in analysis of variance and correlation analysis.
Statistical analysis Analysis of variance and correlation analysis were performed with general linear model (GLM) and correlation analysis procedures of SPSS 16.0 for Windows. If the main effect from subclasses was significant, means were separated by Duncan’s multiple tests. Correlation coefficients were estimated directly from unclassified the observations and transformed SCS.
RESULTS Overall means and standard deviations for sample observations of daily milk yield, fat and protein percentage, fat and protein yield, and SCC are summarized in Table 1. The numbers of test day observations with the first, second, third, fourth and later parity for all the traits were 4 287, 3 103, 1 748 and 1 386, respectively. Milk yield, fat percentage, fat yield and protein yield increased initially and then dropped with parity, which peaked at second, third, second and second parity, respectively. Protein percentage decreased, but, SCC increased with parity. © 2010, CAAS. All rights reserved. Published by Elsevier Ltd.
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Changes in milk yield and composition with variation in SCC
highly significantly negative (P < 0.01) correlations between SCS and milk and protein yield, milk yield and fat and protein percentage, and protein percentage and fat yield. Fat percentage had no significant relationship with protein yield (P > 0.05). The highly significant positive correlations existed (P < 0.01) between SCS and fat yield, fat, protein percentage, milk yield and fat and protein yield, fat percentage and protein percentage, fat yield, protein yield and percentage, and protein yield.
The comparative results of different subclass of the test-day observations for milk yield and milk composition with SCC are shown in Table 2. The milk yield decreased (P < 0.05); but, fat and protein percentages increased (P < 0.05) with increase in SCC. Both fat and protein yield increased initially and then drop, and peaked at SCC of 200 000-500 000 cells mL -1 (P < 0.05). Compared with milk yield with SCC 200 000 cells mL -1, milk yield losses with SCC of 200 000-500 000, 501 000-1 000 000, and 1 000 000 cells mL-1 were 0.387, 0.961 and 2.351 kg, respectively.
DISCUSSION The accurate statistical results depend on the large-scale and reliable data sources. Compared with those in earlier studies (Bodoh et al. 1976; Ng-Kwai-Hang et al. 1982, 1984; Schutz et al. 1990), the sample size in our research was relatively smaller. However, compared to the reports on Chinese Holstein populations (Mao et al. 2002; Gao et al. 2007; Sun et al. 2009), our sample size was larger. The dataset of 10 524 test-day record was sufficient and representative. There are several reasons influencing the reliable dataset sources. To date,
Correlations between SCS and milk production traits Correlation analysis between SCS and milk yield and composition is summarized in Table 3. All the absolute values of correlation coefficients between SCS and milk production traits were not exceed to 0.2. There were
Table 1 Overall means and standard deviations for sample observations Traits Milk yield (kg) Fat percentage (%) Protein percentage (%) Fat yield (kg) Protein yield (kg) SCC (103 cells mL-1) SCS Sample number
Parity First 25.78 ± 6.09 3.68 ± 0.83 3.05 ± 0.50 0.94 ± 0.29 0.78 ± 0.21 292.36 ± 666.85 3.42 ± 1.67 4 287
Second 27.12 ± 8.13 3.74 ± 0.76 2.94 ± 0.41 1.01 ± 0.36 0.79 ± 0.25 687.27 ± 10 232.63 3.71 ± 1.78 3 103
Third 26.90 ± 8.34 3.75 ± 0.80 2.91 ± 0.37 1.00 ± 0.37 0.78 ± 0.24 420.50 ± 859.89 3.83 ± 1.86 1 748
Fourth and later
Total
25.09 ± 8.47 3.64 ± 1.01 2.93 ± 0.45 0.92 ± 0.42 0.73 ± 0.26 732.52 ± 6 814.10 3.77 ± 2.28 1 386
26.27 ± 7.49 3.71 ± 0.83 2.98 ± 0.45 0.97 ± 0.35 0.78 ± 0.23 488.19 ± 6 111.783 3.62±1.83 10 524
Fat yield (kg)
Protein yield (kg)
0.973b 1.007a 0.986a 0.952b
0.783 a 0.781 a 0.767 a 0.749 b
Protein percentage
Fat yield
Protein yield
0.105 ** -0.168 ** 0.122 **
0.041 ** 0.771 ** 0.568 ** -0.088 **
-0.037 ** 0.865 ** -0.011 0.318 ** 0.695 **
Table 2 The comparison of daily milk yield and milk composition with different SCC SCC (103 cells mL-1) 200 200-500 501-1 000 1 000
Milk yield (kg) 26.752 a 26.365 a 25.791 b 24.401 c
Fatpercentage (%) 3.648 c 3.832 b 3.837 b 3.917 a
Protein percentage (%) 2.945 c 2.981 b 2.998 b 3.112 a
Means with different superscript letters within columns differ significantly (P < 0.05).
Table 3 Correlation coefficients of SCS with milk yield and composition traits Traits SCS Milk yield Fat percentage Protein percentage Fat yield **
Milk yield -0.084 **
Fat percentage 0.177 ** -0.061 **
, highly significant correlations (P < 0.01).
© 2010, CAAS. All rights reserved. Published by Elsevier Ltd.
Relationship of Somatic Cell Count with Milk Yield and Composition in Chinese Holstein Population
there are a few extensive dairy farms that have advanced milking equipment and management software in China, including Xi’an region. Similarly, DHI program has not been carried out in every extensive farm, even though the program was launched since 1995 in China. The further reason is that the important roles of DHI program in management on dairy farms have not been recognized by most of the managers. Simple means with SD of test-day observations of milk yield and composition in this study reflect the situation in our region. Capper et al. (2009) reported averages of milk yield, fat and true protein percentages of American dairy herd, predominantly Holstein, were 29.3 kg d-1, 3.69 and 3.05%, respectively in 2007. Compared to this report, our observations of milk yield and protein percentages were lower, but fat percentage was a little higher. Even though compared to the results published in other Holstein populations about 20 yr ago, protein percentage was also lower (Ng-Kwai-Hang et al. 1982, 1984; Schutz et al. 1990). Our results suggest that more attention should be paid on protein percentage and given priority in genetic improvement for milk production traits in Chinese Holstein population. For fat and protein yield, most of our results were higher than report from Schutz et al. (1990), except for protein yield in the third parity. The statistical results of fat and protein yield have not been found in Chinese literatures. In principle, milk yield, fat, and protein percentage should be considered as a whole in breeding programs because of negative genetic correlation between milk yield, protein, and fat percentages, in order to gain the higher economic benefit. In this situation, undoubtedly, fat and protein yield are the good indexes to reflect changes of milk yield, fat, and protein percentage. Actually, protein and fat yield are the most important traits in the production traits in dairy cattle breeding program all around the world. The information for protein and fat yield should be analyzed further on Chinese Holstein populations. For SCC, our results are higher than previous reports in other countries (Raubertas and Shook 1982; Jones et al. 1984; Ng-Kwai-Hang et al. 1984; Bartlett et al. 1990). As an important parameter of mastitis detection, the higher SCC implies there is a high prevalence ratio of mastitis (Harmon 1994). Surprisingly, the value of standard deviation was much higher than
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that of the mean, which was partly due to the biased distribution of SCC. Another important reason causing relatively high SCC in our study was that the observations of dairy cows suffering clinical mastitis might not be culled. Hence, it is a significant and urgent issue how to standardize the operation of DHI program in practice. The SCC was divided into 4 subclasses according to the recommended criteria of International Dairy Foods Association (IDFA) and National Mastitis Council of America to performed analysis of variance in this study. The change in the patterns of milk yield and composition with variation in SCC were in line with previous researches (Ng-Kwai-Hang et al. 1984; Bartlett et al. 1990). Compared with milk yield with SCC of 200 000 cells mL -1, milk yield losses with SCC of 200 000-500 000, 501 000-1 000 000, and 1 000 000 cells mL-1 were 0.387, 0.961 and 2.351 kg, respectively. If multiplied by 568 (cows), the overall losses in every sample day would be 219.816, 545.848, and 1 335.368 kg, respectively. Furthermore, treatment cost should be added due to occurrence of clinical mastitis. Consequently, the total loss caused by mastitis was very high. Haenlein et al. (1973) and Sun et al. (2009) reported that there was no significant difference among total protein percentages with different SCC. But, our results suggested that total protein percentages increased with increase in SCC, which were similar with the results of Ng-Kwai-Hang et al. (1984) and Guo (2007). A reasonable explanation is increased protein percentage was mainly from the serum protein fraction when cows responded to infection. As a whole, fat and protein yield decreased with varying SCC in the present study. The changes in fat and protein yield with different SCC could not be found in other literatures. The absolute values of all the correlation coefficients were less than other studies (Ng-Kwai-Hang et al. 1984; Gao et al. 2007; Guo 2007). Our results were similar to those of Mao et al. (2002); but, this research could not illustrate whether SCC was transformed to SCS. As a whole, the correlation coefficients among production traits were higher than those between production traits and SCS, which were consistent with the previous study (Guo 2007). Therefore, genetic analysis should be carried out in order to get further information about the relationship of SCS with milk yield
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and composition in Chinese Holstein populations in our region. Nevertheless, the data is not sufficient for genetic analysis with a test-day model now. As a consequence, it is necessary to collect the production data continuously and extend the DHI program.
CONCLUSION The results of the present study first provide the relevant base-line data for assessing milk production at Xi’an region of China. Phenotypic variation in SCC was high and more attention should be paid on protein percentage in dairy cattle breeding program. Milk yield losses due to increased SCC were very high in the Chinese Holstein population. The correlation coefficients among production traits were higher than those between production traits and SCS. As the dairy farming in China is scattered over a wide range of climate and topographic regions, the researches on SCC and milk production traits should be carried out separately.
Acknowledgements This study was supported by the National 863 Program of China (2008AA10Z144) and “13115” Sci-Tech Innovation Program of Shaanxi Province (2008ZDKG11). The authors are grateful to Mr. Song Ailong, Wang Jianhua, and Ru Caixia from Xi’an Modern Agriculture Development Corporation for their valuable help during the preparation of DHI records.
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