Descriptive Analysis of Somatic Cell Count Using Statistical Tools
Keywords:
clustering, correlation analysis, mastitis, similarity measures, somatic cell countAbstract
Somatic cell count (SCC) can be used as an indicator of subclinical mastitis and its analysis in relation with the milk composition can provide useful information on the existence of some correlations or patterns. Based on milk production data recorded during 5 years (2012-2015, 2017) at the Research and Development Station for Bovine Arad we conducted a statistical analysis aiming to identify correlations between SCC and milk characteristics (protein and fat content, lactose, non-fat solids, milk quantity, pH, casein) and to find potential profiles of SCC evolution. The correlation analysis was based on 226 lactating cows for which at least 20 measurements were available. Both classical correlation coefficient (i.e. Pearson) and correlation coefficient for repeated measurements (i.e. Bland-Altman) have been computed. In both cases, a moderate negative correlation between SCC and the lactose level has been identified while no significant correlation between SCC and the other milk characteristics has been detected. However, a more accurate description of the relation between SCC and lactose was obtained using a linear mixed model. Aiming to analyse SCC profiles, an additional attribute has been added to the data based on the following encoding rule: the attribute has value 0 if SCC is smaller than 2x105 cells/ml, 1 if it is larger than 2x105 cells/mL and 2 if the value is missing. In this way, data vectors containing 13 values per year have been constructed for 175 cows and a dissimilarity matrix has been constructed as a first step for cluster analysis. Overall, the results have shown that lactose and SCC were negatively correlated.
References
Halasa, T., Huijps, K., Osteras, O., Hogeveen, H., Economic effects of bovine mastitis and mastitis management: A review, Vet Q, 2007, 29, 18–31
Malek dos Reis C. B., Barreiro, J. R., Mestieri, L., Porcionato, M. A., dos Santos M. V., Effect of somatic cell count and mastitis pathogens on milk composition in Gyr cows, BMC Vet Res, 2013, 9:67, 1-7
Cha, E., Bar, D., Hertl, J. A., Tauer, L. W., Bennett, G., González, R. N., Schukken, Y. H., Welcome, F. L., Gröhn, Y. T., The cost and management of different types of clinical mastitis in dairy cows estimated by dynamic programming, J Dairy Sci, 2011, 94, 9, 4476-4487
Bar, D., Tauer, L. W., Bennett, G., Gonzalez, R. N., Hertl, J. A., Schukken, Y. H., Schulte, H. F., Welcome, F. L., Grohn, Y. T., The Cost of Generic Clinical Mastitis in Dairy Cows as Estimated by Using Dynamic Programming, J Dairy Sci, 2008, 91, 6, 2205-2214
Adkins, P. R. F., Middleton, J. R., Methods for Diagnosing Mastitis, Veterinary Clinics of North America: Food Animal Practice, 2018, 34, 3, 479–491
Schukken, Y. H., Wilson, D. J., Welcome, F., Garrison-Tinofsky, L., Gonzales, R. N., Monitoring udder health and milk quality using somatic cell counts, Vet Res, 2003, 34, 579–596
Alhussien, M. N, Dang, A. K., Milk somatic cells, factors influencing their release, future prospects, and practical utility in dairy animals: An overview, Vet World, 2018, 11, 5, 562-577
Sharma, N., Singh, N. K., Bhadwal, M. S., Relationship of Somatic Cell Count and Mastitis: An Overview, Asian-Aust. J. Anim. Sci, 2011, 24, 3, 429-438
International Dairy Federation, Recommendations for presenting of mastitis related data, IDF Bulletin 321, 1997, Brussels, Belgium
Ballou, L. U., Pasquini, M., Bremel, R. D., Everson, T., Sommer D., Factors affecting herd milk composition and milk plasmin at four levels of somatic cell counts, J Dairy Sci, 1995, 78, 2186–2195
Bland, J. M., Altman, D. G., Calculating correlation coefficients with repeated observations: part 1 - Correlation within subjects, BMJ, 1995, 310, 446
Bland, J. M., Altman, D. G., Calculating correlation coefficients with repeated observations: part 2 - Correlation between subjects, BMJ, 1995, 310, 633
Bakdash, J. Z., Marusich, L. R., Repeated Measures Correlation, Frontiers in Psychology, 2017, 8, Article 456, 1-13
Pinheiro, J. C., Bates, D. M., Mixed-Effects Models in S and S-PLUS, Springer, 2000
Raudenbush, S. W., Bryk, A. S., Hierarchical linear models, Applications and data analysis methods (2nd Ed.), Sage Publications, 2002
Bakdash, J. Z., Marusich, L. R., rmcorr: Repeated Measures Correlation, R package version 0.3.0, 2018, https://CRAN.R-project.org/package=rmcorr
Bliese, P., multilevel: Multilevel Functions, R package version 2.6, 2016, https://CRAN.R-project.org/package=multilevel
Flynt, A., Dean, N., A Survey of Popular R Packages for Cluster Analysis, Journal of Educational and Behavioral Statistics, 2016, 41, 2, 205-225
Mori, U., Mendiburu, A., Lozano J. A., Distance Measures for Time Series in R: the TSdist Package, The R Journal, 2016, 8, 2, 451-459
Cinar, M., Serbester, U., Ceyhan, A., Gorgulu, M., Effect of Somatic Cell Count on Milk Yield and Composition of First and Second Lactation Dairy Cows, Italian Journal of Animal Science, 2015, 14:1, 3646, 105-108
Fernandes A. M., Oliveira C. A. F., Tavolaro P., Relationship between somatic cell counts and composition of milk from individual Holstein cows. Arq. Inst. Biol. Sao Paulo, 2004, 71:163-166
Rajčević, M., Potočnik, K., Levstek, J., Correlations between somatic cells count and milk composition with regard to the season. Agriculturae Conspectus Scientificus, 2003, 68 (3): 221-226.
