Detection of carryover in automated milk sampling equipment

Abstract

Equipment for sampling milk in automated milking systems may cause carryover problems if residues from one sample remain and are mixed with the subsequent sample. The degree of carryover can be estimated statistically by linear regression models. This study applied various regression analyses to several real and simulated data sets. The statistical power for detecting carryover milk improved considerably when information about cow identity was included and a mixed model was applied. Carryover may affect variation between animals, including genetic variation, and thereby have an impact on management decisions and diagnostic tools based on the milk content of somatic cells. An extended procedure is needed for approval of sampling equipment for automated milking with acceptable latitudes of carryover, and this could include the regression approach taken in this study. © American Dairy Science Association, 2006.