The costs of chronic mastitis: A simulation study of an automatic milking system farm.

Mastitis is a production disease in dairy farming that causes economic losses. Especially chronic mastitis (i.e., mastitis cases continuing longer than 28 days) can substantially affect the risk of transmission of intramammary infections (IMI) and total milk production losses. Insights into the impact of chronic mastitis on production and farm economics are needed to guide chronic mastitis decision-making. We aimed to estimate the costs of chronic mastitis with a Monte Carlo simulation model in which the costs of chronic mastitis were estimated as part of the total mastitis costs. The model simulated milk yields, IMI dynamics, somatic cell count (SCC), and pregnancy status on an average Dutch dairy farm with 100 cow places over 9 years. The model was parameterized using information from the literature and actual sensor data from automatic milking system (AMS) farms. The daily subclinical milk production losses were modeled using a generalized additive model and sensor data. Transmission of IMI was modeled as well. The model results indicated median total costs of mastitis of € 230 per generic IMI case (i.e., a weighted average of all pathogens). The most substantial cost factors were the extra mastitis cases due to transmission, culling, and milk production losses. Other significant costs originated from dry cow treatments and diverted milk. The model also indicated median total costs due to chronic mastitis of € 118 (51 % of the total mastitis costs). The share of chronic mastitis relative to the total mastitis costs was substantial. Transmission of contagious bacteria had the largest share among the chronic mastitis costs (51 % of the costs of chronic cases). The large share of chronic mastitis costs in the total mastitis costs indicates the economic importance of these mastitis cases. The results of the study point to the need for future research to focus on chronic mastitis and reducing its presence on the AMS dairy farm.

Estimating the nonlinear association of online somatic cell count, lactate dehydrogenase, and electrical conductivity with milk yield.

Reduction of milk yield is one of the principal components in the cost of mastitis. However, past research into the association between milk yield and mastitis indicators is limited. Past research has not been based on online or in-line daily measurements and has not fully explored nonlinearity and the thresholds at which milk yield starts to decrease. In dairy herds with automated milking systems equipped with sensors, mastitis indicators of individual cows are measured on an intraday frequency, which provides unprecedented avenues to explore such effects in detail. The aim of this observational study was primarily to investigate the nonlinear associations of lactate dehydrogenase (LDH), electrical conductivity (EC), and somatic cell count (SCC) with milk yield at various stages of lactation, parity, and mastitis chronicity status (i.e., whether the cow had SCC ≥200,000 SCC/mL for the last 28 d). We also investigated thresholds at which mastitis indicators (LDH, EC, and SCC) started to be negatively associated with milk yield. We used data from 21 automated milking system herds measuring EC and online SCC. Of these herds, 7 of the 21 additionally measured online LDH. We operationalized milk yield as milk synthesis rate in kilograms per hour. Applying a generalized additive model, we estimated the milk synthesis rate as a function of the 3 mastitis indicators for 3 different subgroups based on parity, stage of lactation, and mastitis chronicity. Partial dependence plots of the mastitis indicators were used to evaluate the milk synthesis rate to study if the milk synthesis rate was associated with mastitis indicators at a specific level. Results showed that milk synthesis rate decreased with increasing SCC, LDH, and EC, but in a nonlinear fashion. The thresholds at which milk synthesis rate started to decrease were 2.5 LnSCC (12,000 SCC/mL) to 3.75 LnSCC (43,000 SCC/mL), 0 to 1 LnLDH (1-2.7 U/L), and 5.0 to 6.0 mS/cm for EC. Additionally, another substantial decrease of milk synthesis rate was observed at thresholds of 5.625 LnSCC (277,000 SCC/mL) and 3 LnLDH (20 LDH U/L) but not for EC. Having chronic mastitis decreased milk synthesis rate in all models. The identified nonlinearities between mastitis indicators and milk synthesis rate should be incorporated in statistical models for more accurate estimations of milk loss due to mastitis.

Progression of different udder inflammation indicators and their episode length after onset of inflammation using automatic milking system sensor data.

In automatic milking systems (AMS), sensors can measure cow behavior and milk composition at every milking. The aim of this observational study of previously collected data was to gain insight into the differences in dynamics of udder inflammation indicators between cows that recover and those that do not recover after detection of an initial inflammation. Milk diversion (milk separated from the bulk tank and thus indicating farmer intervention), conductivity, and somatic cell count (SCC) data from 4 wk before the initial inflammation to 12 wk after the initial inflammation were used to analyze 2,584 cases of udder inflammation. An udder inflammation case was defined as an initial observation of SCC ≥200,000 cells/mL as well as 1 additional SCC measurement >200,000 cells/mL within 10 d after the initial case, among other requirements. The data originated from 15 AMS herds in 6 countries. Four subsets of cows were created based on whether milk was diverted after the initial inflammation and whether the udder inflammation cases recovered, using a 10-d rolling average SCC threshold of 200,000 cells/mL and checking whether this rolling mean was below the threshold within 90 d after the initial inflammation as the indication of recovery. This formed the following subsets of cow lactations: milk diverted-recovered, milk diverted-not recovered, no milk diverted-not recovered, no milk diverted-recovered. Thresholds of 100,000 SCC/mL and 300,000 SCC/mL for the definition of case and recovery were also applied in a sensitivity analysis but with no substantial difference in results. Linear mixed models were used for each subset to study the variation in SCC (natural logarithm of SCC divided by 1,000) and σ-conductivity (natural logarithm of standard deviation of quarter conductivities). When observing the fraction of cows with SCC <200,000 cells/mL in the recovery subsets, most cows recovered within 20 d after the initial inflammation. In the recovery subsets, both σ-conductivity and SCC stabilized, mostly within 3 to 4 wk after the initial inflammation. σ-Conductivity stabilized above the pre-onset level in all subsets and did not show a clear increase in the no-milk-diverted subgroups, whereas SCC stabilized closer to the pre-onset level. Overall, this study indicated a cutoff point between nonchronic and chronic changes in indicators 3 to 4 wk after the initial inflammation for SCC and σ-conductivity.

Antibiotics use versus profitability on sow farms in the Netherlands.

In 2009, the Dutch government provided policy objectives (i.e., targets) for a reduction in veterinary antibiotics use of -20 % in 2011, -50 % in 2013 and -70 % in 2015 relative to the use in 2009. The relationship between antibiotics use and performance of Dutch sow farms during this policy reform was analysed using the Farm Accountancy Data Network database comprising cross-sectional farm data from 2004 to 2016. The results show that there is a significant downward trend in antibiotics use of 57 %. Panel data analysis (n = 74 sow farms) revealed that the reduction in antibiotic use did not lead to negative effects on technical or economic farm results. A follow-up survey was conducted on measures taken to improve animal health, which made the reduction in antibiotic use feasible. Of the 79 sow farmers approached, 55 participated in this survey. Sow farmers used a variety of relatively easy and affordable measures, such as more attention to hygiene, use of pain killers and anti-inflammatory agents, or applied more preventive vaccinations. Multivariable linear regression models showed that the intention, perceived risk and uncertainty, and perceived capability (to keep or get the use of antibiotics under the target value) were the most important predictors for antibiotics use from 2014 to 2016. Sow farmers who used more antibiotics were more concerned that low antibiotics use compromises their farm results, perceived more risk and uncertainty, and thought to a lesser extent that they have enough knowledge and time. These results indicate that providing these farmers with knowledge and information on management practices to reduce the use of antibiotics may be helpful. Thereby, it would be useful to focus on continuous involvement of the veterinarian and possibly the feed supplier, preferably by means of individual advice, as the results showed that individual advice was the preferred way to gather knowledge for the interviewed sow farmers and the veterinarian appeared to be the most important source of information to the interviewed sow farmers, followed by the feed supplier. In summary, the study shows that decrease in antibiotics use can be quite successful without compromising on the economic or technical performance, and moreover taking into account farmers' attitudes, perceptions and preferences can be helpful to get a better understanding of farmers' decision making and is useful for the design of tailor-made interventions.