Abstract
BackgroundHaving a poor teat-end condition is associated with increased mastitis risk, hence avoiding milking machine settings that have a negative effect on teat-end condition is important for successful dairy production. Milking-time testing (MTT) can be used in the evaluation of vacuum conditions during milking, but the method is less suited for herds using automatic milking systems (AMS) and relationships with teat end condition is poorly described. This study aimed to increase knowledge on interpretation of MTT in AMS and to assess whether milk-flow data obtained routinely by an AMS can be useful for the management of teat-end health. A cross-sectional study, including 251 teats of 79 Norwegian Red cows milked by AMS was performed in the research herd of the Norwegian University of Life Sciences. The following MTT variables were obtained at teat level: Average vacuum level in the short milk tube during main milking (MTVAC), average vacuum in the mouthpiece chamber during main milking and overmilking, teat compression intensity (COMPR) and overmilking time. Average and peak milk flow rates were obtained at quarter level from the AMS software. Teat-end callosity thickness and roughness was registered, and teat dimensions; length, and width at apex and base, were measured. Interrelationships among variables obtained by MTT, quarter milk flow variables, and teat dimensions were described. Associations between these variables and teat-end callosity thickness and roughness, were investigated.ResultsPrincipal component analysis showed clusters of strongly related variables. There was a strong negative relationship between MTVAC and average milk flow rate. The variables MTVAC, COMPR and average and peak milk flow rate were associated with both thickness and roughness of the callosity ring.ConclusionsQuarter milk flow rate obtained directly from the AMS software was useful in assessing associations between milking machine function and teat-end condition; low average milk flow rates were associated with a higher likelihood of the teat having a thickened or roughened teat-end callosity ring. Since information on milk flow rate is readily available from the herd management system, this information might be used when evaluating causes for impaired teat-end condition in AMS.
Highlights
Having a poor teat-end condition is associated with increased mastitis risk, avoiding milking machine settings that have a negative effect on teat-end condition is important for successful dairy production
The overall aim of this study was to assess whether data obtained routinely by the automatic milking systems (AMS) can be used as a proxy for Milking-time testing (MTT) variables in the management of teat-end condition in AMS herds
In our application of Principal component analysis (PCA), we focused on the geometric interpretation of the relationships between variables, plotted as points in the component space using their loadings as coordinates on the “circle of correlations”
Summary
Having a poor teat-end condition is associated with increased mastitis risk, avoiding milking machine settings that have a negative effect on teat-end condition is important for successful dairy production. This study aimed to increase knowledge on interpretation of MTT in AMS and to assess whether milk-flow data obtained routinely by an AMS can be useful for the management of teat-end health. Interrelationships among variables obtained by MTT, quarter milk flow variables, and teat dimensions were described Associations between these variables and teat-end callosity thickness and roughness, were investigated. Changes in condition of the teat-end of dairy cattle, as evaluated by thickness and roughness of the callosity ring, have been associated with increased mastitis risk in previous studies from conventional milking systems (CMS) [1, 2]. AMS continuously records large amounts of data from the milking process Using such data in decision-support systems enabling the farmer to improve herd health has been subject to extensive research the last decade [8]. This approach has motivated us to explore whether improved utilization of data from the AMS might be useful when investigating causes for impaired teat end condition in AMS herds
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