Novel ways to use sensor data to improve mastitis management.

Current sensor systems are used to detect cows with clinical mastitis. Although, the systems perform well enough to not negatively affect the adoption of automatic milking systems, the performance is far from perfect. An important advantage of sensor systems is the availability of multiple measurements per day. By clearly defining the need for detection of subclinical mastitis (SCM) and clinical mastitis (CM) from the farmers' management perspective, detection and management of SCM and CM may be improved. Sensor systems may also be used for other aspects of mastitis management. In this paper we have defined 4 mastitis situations that could be managed with the support of sensor systems. Because of differences in the associated management and the epidemiology of these specific mastitis situations, the required demands for performance of the sensor systems do differ. The 4 defined mastitis situations with the requirements of performance are the following: (1) Cows with severe CM needing immediate attention. Sensor systems should have a very high sensitivity (>95% and preferably close to 100%) and specificity (>99%) within a narrow time window (maximum 12 h) to ensure that close to all cows with true cases of severe CM are detected quickly. Although never studied, it is expected that because of the effects of severe CM, such a high detection performance is feasible. (2) Cows with mastitis that do not need immediate attention. Although these cows have a risk of progressing into severe CM or chronic mastitis, they should get the chance to cure spontaneously under close monitoring. Sensor alerts should have a reasonable sensitivity (>80%) and a high specificity (>99.5%). The time window may be around 7 d. (3) Cows needing attention at drying off. For selective dry cow treatment, the absence or presence of an intramammary infection at dry-off needs to be known. To avoid both false-positive and false-negative alerts, sensitivity and specificity can be equally high (>95%). (4) Herd-level udder health. By combining sensor readings from all cows in the herd, novel herd-level key performance indicators can be developed to monitor udder health status and development over time and raise alerts at significant deviances from predefined thresholds; sensitivity should be reasonably high, >80%, and because of the costs for further analysis of false-positive alerts, the specificity should be >99%. The development and validation of sensor-based algorithms specifically for these 4 mastitis situations will encourage situation-specific farmer interventions and operational udder health management.

Factors associated with milking-to-milking variability in somatic cell counts from healthy cows in an automatic milking system.

Fully automated on-line analysis equipment is available for analysis of somatic cell count (SCC) at every milking in automatic milking systems. In addition to results from on-line cell counters (OCC), an array of additional cow-level and quarter-level factors considered important for udder health are recorded in these systems. However, the amount of variability in SCC that can be explained by available data is unknown, and so is the proportion of the variability that may be due to physiological or normal variability. Our aim was to increase our knowledge on OCC as an indicator for disturbances in udder health by assessing the variability in OCC in cows free from clinical mastitis. The first objective was to evaluate how much of the variability in OCC could be explained by different potential sources of variability, including intramammary infection (IMI) status (assessed by bacterial culture of quarter milk samples). The second objective was to evaluate the repeatability of the OCC sensor used in our study and the agreement between OCC values and SCC measured in a dairy herd improvement (DHI) laboratory. A longitudinal study was performed in the research herd of the Norwegian University of Life Sciences from January 5th 2016 to May 22nd 2017. Data from 62,471 milkings from 173 lactations in 129 cows were analyzed. We used ln-transformed OCC values (in 1000 cells/ml) as the outcome (lnOCC) in linear mixed models, with random intercepts at cow-level and lactation-level within cow. We were able to explain 15.0% of the variability in lnOCC with the following fixed effects: lactation stage, parity, milk yield, OCC in residual milk from the previous milking, inter-quarter difference between the highest and lowest conductivity, season, IMI status, and genetic lineage. When including the random intercepts, the degree of explanation was 55.2%. The individual variables explained only a small part of the total variability in lnOCC. We concluded that physiological or normal variability is probably responsible for a large part of the overall variability in OCC in cows without clinical mastitis. This is important to consider when using OCC data for research purposes or in decision-support tools. Sensor repeatability was evaluated by analyzing milk from the same sample multiple times. The coefficient of variation was 0.11 at an OCC level relevant for detection of subclinical mastitis. The agreement study showed a concordance correlation coefficient of 0.82 when comparing results from the OCC with results from a DHI laboratory.

Dynamics of somatic cell count patterns as a proxy for transmission of mastitis pathogens.

Management of udder health is particularly focused on preventing new infections. Data from the DeLaval Online Cell Counter (DeLaval, Tumba, Sweden) may be used in forecasting to improve decision support for improved udder health management. It provides online cell counts (OCC) as a proxy for somatic cell counts from every milking at the cow level. However, these values are typically too insensitive and nonspecific to indicate subclinical intramammary infection (IMI). Our aim was to describe and evaluate use of dynamic transmission models to forecast subclinical IMI episodes using milk cultures or changes in OCC patterns over time. The latter was expressed by an elevated mastitis risk variable. Data were obtained from the dairy herd of the Norwegian University of Life Sciences (Oslo, Norway). In total, 173 cows were sampled monthly for bacteriological milk culture during a 17-mo study period and 5,330 quarter milk samples were cultured. Mastitis pathogens identified were assigned to 1 of 2 groups, Pat 1 or Pat 2. Pathogens from which a high cell count would be expected during a subclinical IMI episode were assigned to the Pat 1 group. Pathogens not in the Pat 1 group were assigned to the Pat 2 group. Staphylococcus epidermidis, Staphylococcus aureus, and Streptococcus dysgalactiae were the most common Pat 1 pathogens. Corynebacterium bovis, Staphylococcus chromogenes, and Staphylococcus haemolyticus were the most common Pat 2 pathogens. The OCC were successfully recorded from 82,182 of 96,542 milkings. The current study included 324 subclinical IMI episodes. None of the mastitis pathogens demonstrated a basic reproduction number (R0) >1. Patterns of OCC change related to an episode of Pat 1 subclinical IMI at specificity levels of 80, 90, and 95% at sensitivity levels of 69, 59, and 48% respectively, demonstrated an R0 >1. An existing infection was significant for transmission for several Pat 2 pathogens, but only for Staphylococcus aureus and Staphylococcus epidermidis among Pat 1 pathogens. Dynamic transmission models showed that patterns of OCC change related to an episode of Pat 1 subclinical IMI were significantly related to the same pattern occurring in susceptible cows at specificity levels of 80, 90, and 99% at sensitivity levels of 69, 48, and 8%, respectively. We conclude that changes in herd prevalence of subclinical IMI can be predicted using dynamic transmission models based on patterns of OCC change. Choice of specificity level depends on management goals and tolerance for false-positive alerts.

The detection of intramammary infections using online somatic cell counts.

Timely and accurate identification of cows with intramammary infections is essential for optimal udder health management. Various sensor systems have been developed to provide udder health information that can be used as a decision support tool for the farmer. Among these sensors, the DeLaval Online Cell Counter (DeLaval, Tumba, Sweden) provides somatic cell counts from every milking at cow level. Our aim was to describe and evaluate diagnostic sensor properties of these online cell counts (OCC) for detecting an intramammary infection, defined as an episode of subclinical mastitis or a new case of clinical mastitis. The predictive abilities of a single OCC value, rolling averages of OCC values, and an elevated mastitis risk (EMR) variable were compared for their accuracy in identifying cows with episodes of subclinical mastitis or new cases of clinical mastitis. Detection of subclinical mastitis episodes by OCC was performed in 2 separate groups of different mastitis pathogens, Pat 1 and Pat 2, categorized by their known ability to increase somatic cell count. The data for this study were obtained in a field trial conducted in the dairy herd of the Norwegian University of Life Sciences. Altogether, 173 cows were sampled at least once during a 17-mo study period. The total number of quarter milk cultures was 5,330. The most common Pat 1 pathogens were Staphylococcus epidermidis, Staphylococcus aureus, and Streptococcus dysgalactiae. The most common Pat 2 pathogens were Corynebacterium bovis, Staphylococcus chromogenes, and Staphylococcus haemolyticus. The OCC were successfully recorded from 82,182 of 96,542 milkings during the study period. For episodes of subclinical mastitis the rolling 7-d average OCC and the EMR approach performed better than a single OCC value for detection of Pat 1 subclinical mastitis episodes. The EMR approach outperformed the OCC approaches for detection of Pat 2 subclinical mastitis episodes. For the 2 pathogen groups, the sensitivity of detection of subclinical mastitis episodes was 69% (Pat 1) and 31% (Pat 2), respectively, at a predefined specificity of 80% (EMR). All 3 approaches were equally good at detecting new cases of clinical mastitis, with an optimum sensitivity of 80% and specificity of 90% (single OCC value).

Large-scale cross-sectional study of relationships between somatic cell count and milking-time test results in different milking systems.

Milking-time testing (MTT) is a method for evaluating the vacuum conditions in the teatcup during milking. The purpose is to evaluate the possible impact of the milking and milking equipment on udder health and milk quality. The method is commonly implemented by herd health advisory services, but results are interpreted empirically due to lack of scientific documentation on relationships between MTT result variables and objective measures of udder health. The current study was conducted to increase our understanding of associations between cow-level differences in composite milk somatic cell count (CMSCC) and MTT results in dairy cows milked in 3 different milking systems; automatic milking systems (AMS), milking parlors, and pipeline milking systems. Data from 7069 cows (predominantly Norwegian Red breed) in 1009 herds were used in a cross-sectional study. Multilevel linear regression models with a random intercept at herd level were used to describe relationships between CMSCC (on logarithmic scale) and the following MTT explanatory variables: average vacuum level in the short milk tube and mouthpiece chamber in the main milking and overmilking periods, the duration of these two periods, and vacuum stability, measured by sudden vacuum drops in the short milk tube. The models were corrected for the herd effect, mastitis history and differences in milk yield, lactation stage and parity between cows. Separate models were run for AMS, milking parlors, and pipeline milking systems, because this approach allowed for comparison between systems and for evaluation of the herd effect independently of milking system. The models described 8-10 % of the variation in CMSCC, indicating that MTT could only explain a relatively small proportion of a large total variation in CMSCC. In most observations, vacuum levels in the short milk tube during main milking were within the range recommended by the International Organization for Standardization. The results from our multivariable models showed decreasing CMSCC with increasing vacuum level in the short milk tube during the main milking period in AMS and milking parlors. Similarly, decreasing CMSCC was also associated with increasing duration of the main milking period in all 3 systems. These relationships are important for the interpretation of MTT results under practical conditions; finding high vacuum levels and long milking durations in a MTT is not associated with elevated CMSCC. In AMS herds, we also found indications that the relationships were different for cows where a case of mastitis had been treated before the MTT.

Milk-flow data collected routinely in an automatic milking system: an alternative to milking-time testing in the management of teat-end condition?

BACKGROUND: Having 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. RESULTS: Principal 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. CONCLUSIONS: Quarter 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.

Transmission dynamics of intramammary infections caused by Corynebacterium species.

The development of reliable models for transmission of intramammary infections (IMI) is the subject of extensive research. Such models are useful to enhance the identification and understanding of factors that affect pathogen-specific IMI dynamics. Longitudinal transmission models are valuable for predicting infection outbreak risks, quantifying the effectiveness of response tactics, and performing response planning. In this work, we focused on modeling Corynebacterium spp. by using a compartmental model. Previous investigations have considered modeling the transmission dynamics of several bacterial pathogens, but not Corynebacterium spp. We established a Corynebacterium spp. Susceptible-Infectious-Susceptible (SIS) model. We simulated the model numerically by using parameters that we estimated by a generalized linear model approach, using month of study as the time variable. The data, from which the parameters of the model were estimated, were obtained in a field trial conducted in 2 US dairy herds. Altogether, 786 cows were sampled at least once during the 13-mo study period. The total number of quarter milk cultures and cases of IMI caused by Corynebacterium spp. were 11,744 and 556, respectively, in farm A; the corresponding figures for farm B were 11,804 and 179. Our modeling study included only transmission from persistent IMI caused by Corynebacterium spp. within the lactation pens. The rate of new infections was significantly related to preexisting IMI in both farms, underscoring the importance of preexisting Corynebacterium spp. IMI for the transmission of Corynebacterium spp. within lactation pens. The estimated basic reproduction numbers (R0) in the 2 farms were 1.18 and 0.98, respectively. The nonsignificant disparity in R0 was associated with significant differences in cure rates between farms.