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As herd sizes increase, the continuous monitoring becomes more important for dairy farmers and their veterinarians or consultants to maintain udder health. To avoid unnecessary expenditures for monitoring measures, recommendations are needed how to set up a monitoring system for a farm and which could be helpful indicators. Tools that simplify data analysis for monitoring purposes have to be developed. The overall objective of this thesis was to develop science-based recommendations for different aspects of the monitoring of the udder health on dairy farms on the product and the process level. On the product level, a tool to assess milk yield loss due to clinical mastitis in individual dairy herds was developed and prepared for implementation in a server-based software for monitoring dairy herds. On the process level, variables to monitor the teat condition and distinct negative energy balance as possible risk factors for mastitis were investigated. The procedure to establish a risk-based monitoring program for the udder health was summarized in a literature review. It consists of 1) setting goals, 2) a risk analysis, 3) planning of measures and the monitoring, 4) the implementation, and 5) the documentation and regular evaluation.
A longitudinal field study was conducted to assess the influence of short-term and long-term alterations of the teat tissue and of the infectious status of the udder quarter on its risk of naturally occurring new intramammary infections, inflammatory responses, and mastitis. Changes of the teat condition of right udder quarters of 135 cows on a commercial dairy farm in Saxony-Anhalt, Germany, were recorded monthly for 10 months using simple classification schemes. Quarter milk samples were collected from all examined quarters at each farm visit. Bacteriological culture results and SCC of the quarter milk samples were used to determine new inflammatory responses (increase from ≤100,000 cells/mL to >100,000 cells/mL between two samples), new infections (detection of a pathogen from a quarter that was free of this pathogen at the preceding sampling), and new mastitis (combination of a new inflammatory response and a new infection). Separate mixed Poisson regression models for new inflammatory responses, new infections, and new mastitis caused by specific pathogens or groups of pathogens (contagious, environmental, major, minor, or any) were used to estimate risk ratios and 95% confidence intervals. There was no effect of any variable describing the teat condition on the risk of new intramammary infections, inflammatory responses, or mastitis. Intramammary infections of the same udder quarter in the preceding month did not have an influence either.
In a retrospective cohort study, the association between the test day milk fat-protein ratio and the incidence rate of clinical mastitis was investigated in consideration of repeated cases of clinical mastitis. The objective of this study was to assess the validity of the test day milk fat-protein ratio as a monitoring variable for metabolic disorders as risk factors for the udder health. Herd records of 10 dairy herds of Holstein cows in Saxony, Germany, from September 2005 to September 2011 that compromised of 36,827 lactation periods of 17,657 cows were used for statistical analysis. A mixed Poisson regression model with the weekly incidence rate of clinical mastitis as the outcome variable was fitted that included repeated events of the outcome, updated measurements of independent variables, and multilevel clustering. Previous cases of clinical mastitis increased the incidence rate of clinical mastitis. However, given the assumptions that were made about the bias parameters and the methods used for the bias analysis, these conventional results were biased toward the null by the misclassification of clinical mastitis. Fat-protein ratios of <1.0 and >1.5 were associated with higher incidence rates of clinical mastitis depending on the week in milk. The effect of a fat-protein ratio >1.5 on the mastitis incidence rate increased considerably over the course of lactation whereas the effect of a fat-protein ratio <1.0 decreased. Fat-protein ratios <1.0 or >1.5 on the most recent test days of all cows irrespective of their time in milk seemed to be better predictors for clinical mastitis than the first test day results per lactation.
A linear mixed model was developed to estimate the reduced milk yield and the milk loss due to clinical mastitis for a specific herd based on individual cows’ mastitis and daily milk yield data. The short and long term decrease in the daily milk yield was described by expanding the lactation curve model of Ali and Schaeffer (1987). For calculating the short term drop, the model included the laps of time in days since the mastitis incident as a second-degree polynomial. The coefficients were estimated specifically for the first respectively the recurrent cases of mastitis per lactation. The long term decrease was also modeled separately for the first and the recurrent cases by estimating lactation curves without mastitis as well as for the first and the recurrent cases. The milk yield could be estimated for each day in milk according to the episode number and the time elapsed since the incident or with no mastitis incident, respectively, for a particular period of time in a specific dairy herd. The loss resulting from the reduced performance due to clinical mastitis would be specified by adding up the differences.
None of the variables that were evaluated in this thesis could be recommended to monitor possible risk factors for mastitis. Monitoring the teat condition to control the udder health seemed to be wasteful in most cases as the teat condition did not influence the risk of mastitis. The ability of test day milk fat-protein ratios <1.0 or >1.5 to predict clinical mastitis was very small and other measures to monitor metabolic disorders as risk factors for mastitis should be evaluated. However, a useful tool was developed to provide dairy farmers with herd-specific information about milk loss due to clinical mastitis as basis for management decisions.