TY - JOUR

T1 - Characterization of distributions of somatic cell counts

AU - Ten Napel, J.

AU - De Haas, Y.

AU - De Jong, G.

AU - Lam, T. J G M

AU - Ouweltjes, W.

AU - Windig, J. J.

PY - 2009/3/1

Y1 - 2009/3/1

N2 - There is more useful information in distributions of somatic cell count (SCC) than is currently used in practice. Analysis of SCC of individual quarters (n = 450,834 quarter records of 133,102 cows) showed that the presence of pathogens did not change the peak of the SCC distribution. Instead, the percentages of observations in the tail changed. Probability density functions of specified sets of up to 5 standard distributions were then fitted on the number of records per class, using a maximum likelihood procedure. Analysis of cow SCC (2 data sets: n = 335,135 test-day records of 41,567 cows on 407 farms and n = 1,665,431 test-day records) showed that a mixture of a normal, a log-normal and an exponential density function (N+LN+E) best described the distribution of SCC. A mixture of 4 normal and an exponential distribution (4N+E) was also a good approximation. For this last mixture, each distribution could be associated with presence or absence of pathogens. The first 2 normal distributions appear to consist of uninfected cows and cows recovering from an infection, the third normal distribution may be associated with minor pathogens, and the fourth normal and the exponential distribution with major pathogens and persistent infections. Estimated percentages of records in each underlying distribution differed between parities, between stages of lactation, and between records with previous records being above or below 100,000 cells/mL. The categorical nature of cow-SCC can be utilized by deriving new traits such as the fraction of cow-SCC records in a lactation that are associated with an infection with a major pathogen.

AB - There is more useful information in distributions of somatic cell count (SCC) than is currently used in practice. Analysis of SCC of individual quarters (n = 450,834 quarter records of 133,102 cows) showed that the presence of pathogens did not change the peak of the SCC distribution. Instead, the percentages of observations in the tail changed. Probability density functions of specified sets of up to 5 standard distributions were then fitted on the number of records per class, using a maximum likelihood procedure. Analysis of cow SCC (2 data sets: n = 335,135 test-day records of 41,567 cows on 407 farms and n = 1,665,431 test-day records) showed that a mixture of a normal, a log-normal and an exponential density function (N+LN+E) best described the distribution of SCC. A mixture of 4 normal and an exponential distribution (4N+E) was also a good approximation. For this last mixture, each distribution could be associated with presence or absence of pathogens. The first 2 normal distributions appear to consist of uninfected cows and cows recovering from an infection, the third normal distribution may be associated with minor pathogens, and the fourth normal and the exponential distribution with major pathogens and persistent infections. Estimated percentages of records in each underlying distribution differed between parities, between stages of lactation, and between records with previous records being above or below 100,000 cells/mL. The categorical nature of cow-SCC can be utilized by deriving new traits such as the fraction of cow-SCC records in a lactation that are associated with an infection with a major pathogen.

KW - Distribution

KW - Mastitis

KW - Somatic cell count

UR - http://www.scopus.com/inward/record.url?scp=61449171156&partnerID=8YFLogxK

U2 - 10.3168/jds.2007-0824

DO - 10.3168/jds.2007-0824

M3 - Article

C2 - 19233818

AN - SCOPUS:61449171156

SN - 0022-0302

VL - 92

SP - 1253

EP - 1264

JO - Journal of Dairy Science

JF - Journal of Dairy Science

IS - 3

ER -