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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 euro 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 euro 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.

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.

Dairy cows are negatively affected by the introduction of bovine viral diarrhea virus (BVDV), and consequently, produce less milk. Existing literature on potential milk production losses is based on relatively outdated data and hardly evaluates milk production loss in relation to a new BVDV infection in a surveillance system. This study determined the annual and quarterly loss in milk production of BVDV introduction in 3,126 dairy herds participating in the Dutch BVDV-free program between 2007 and 2017. Among these herds, 640 were "breakdown-herds" that obtained and subsequently lost their BVDV-free status during the study period, and 2,486 herds obtained and retained their BVDV-free status during the study period. Milk yields before and after BVDV introduction were compared through annual and quarterly linear mixed models. The fixed variables for both models included herd type (breakdown-herd or free-herd), bovine viral diarrhea status (on an annual and quarterly basis), year, season, and a random herd effect. The dependent variable was the average daily milk yield on the test day. To define the possible BVDV-introduction dates, 4 scenarios were developed. In the default scenario, the date of breakdown (i.e., loss of the BVDV-free status) was assumed as the BVDV-introduction date. For the other 3 scenarios, the BVDV-introduction dates were set at 4, 6, and 9 mo before the date of breakdown, based on the estimated birth date of a persistently infected calf. In the default scenario, the loss in milk yield due to BVDV introduction occurred mainly in the first year after breakdown, with a reduction in yield of 0.08 kg/cow per day compared with the last year before breakdown. For the other 3 scenarios, the greatest yield reduction occurred in the second year after BVDV introduction, with a loss of 0.09, 0.09, and 0.1 kg/cow per day, respectively. For the first 4 quarters after BVDV introduction in the default scenario, milk yield loss was 0.14, 0.09, 0.02, and 0.08 kg/cow per day, respectively. These quarterly results indicated that milk yield loss was greatest in the first quarter after BVDV introduction. Overall, BVDV introduction had a negative, but on average a relatively small, effect on milk yield for herds participating in the BVDV-free program. This study will enable dairy farmers and policymakers to have a clearer understanding of the quantitative milk production effect of BVDV on dairy farms in a control program.

The changing socio-economic environment challenges dairy farmers to remain competitive and increase or maintain animal health. Making the optimal economic-epidemiological decision demands for an interdisciplinary approach in which the individual dairy farmer is the point of focus. Dairy farmers are the main decision makers in case of production diseases in dairy cattle, because in most cases production diseases are of management origin. However, production diseases are not always perceived as being important, because of hidden losses and gradual enter into the daily business of the farm. When controlling production diseases, the negative impact of the disease must therefore be clear, but also the economic benefit of possible disease control strategies. A range of publications and methods are available to determine the economic impact of an animal disease and/or disease control strategy. Existing applications, however, have some limitations hampering effective economic-epidemiological decision making. First, they often fail in taking into account some basic production-economic principles. They do not consider the production function and assume linear relations between inputs and outputs by using average figures. The challenge is to establish more accurate economic estimates considering the relationship between inputs and outputs of the farm. Second, existing studies often produce generic results, ignoring farm-specific differences. Due to the differences between farms, the published average economic impact of a disease or control strategy may be different from what individual farmers experience. Missing the farm specificity in economic analysis makes it more difficult to take the optimal decision at individual farm level. The objective of this dissertation is to explore how economic and epidemiological information can be combined within a production economic framework at individual farm level in order to allow farm-specific decision support on animal diseases. The case of gastrointestinal (GI) nematode infections is used to answer this objective. The conceptual framework that is presented in this dissertation, combines recent insights from veterinary science and farm economics. The framework introduces the use of the production function, which considers the relation between input(s) and output(s) of the production process. On dairy farms, examples of inputs are feed, labour and animal health costs, while outputs can be meat and milk. The advantage of using the production function is (1) that both input(s) and output(s) of the farm are taken into account and that the likely non-linear relation between production and the animal disease can be considered, (2) the unique position of the farms is considered in relation to the performance benchmark and the relation between this position and the level of infection, can be determined. This positioning and relating production to infection allows, at individual farm level, to optimize the level of infection and the economic farm performances. This framework is the starting point for empirical and analytical research to evaluate the economic impact of GI nematode infections and their control strategies. To make the framework operational, efficiency analysis, cluster analysis, partial budgeting and whole-farm simulations are combined in this dissertation. Efficiency analysis is used to determine the farms unique position in the input-output framework and to relate this position to the level of GI nematode infection and control strategies. Cluster analysis groups farms that are similar in their input-output transformation. This allows for analysing whether the relation between economic performance and the level of GI nematode infection depends on this input-output transformation. While efficiency analysis uses relative performance measures, partial budgeting allows for presenting the absolute effect of the level of infection and control strategies on conventional technical and economic key performance indicators (KPIs). And finally, a whole-farm simulation model is used to determine the effect of control strategies on the production parameters of the farm. Application of the methods requires farm-specific data on the farm´s infection level as well as on technical and economic performances. Therefore, in this dissertation, individual farm accountancy data are linked to individual farm GI nematode infection data. The various methods that are used incorporate multiple degrees of farm specificity in the evaluation of the relation between GI nematode infections and the economic farm performances. This dissertation shows that GI nematode infections reduce the technical efficiency of the farms. The size of the effect, however, differs from farm to farm. Low efficient farms can gain, by a similar reduction in infection, higher increases in milk production with the given input amounts, compared to high efficient farms. Although lowering the level of infection results in a higher increase of milk production in low efficient farms, for high efficient farms it can be the last bit to become completely efficient. When the unique position of farms in the input-output space is considered in relation to infection and grazing management, clear differences between farms are also observed. Three groups of farms can be distinguished based on their position in input-output space and for each group different economic-epidemiological improvement paths are derived. At the current price levels, improvement of the allocative efficiency (e.g., reflects the ability of a farm to use inputs in cost minimising proportions, given their respective prices) implies a higher level of GI nematode infection in two of the three groups. Only for the group with a high efficiency level and a high level of infection, reducing infection seems of economic interest. The high variation between farms within groups indicates that the epidemiological-economic relationship is even different between individual farms in each group. Overall, the results indicate a high degree of farm specificity is required when analysing the relationship between farm performances and infection. A decrease in GI nematode infection increases the technical efficiency of farms and results in an economic-epidemiological win-win situation. The increase in technical efficiency, due to a given infection reduction, becomes larger when relatively more concentrates and roughage are used. On the other hand, technical efficiency increases less when more pasture is used compared to other inputs. This dissertation also shows that the use of relatively more pasture per cow is associated with a higher level of infection. On the contrary, increasing pasture in combination with the use of relatively more concentrates and roughage is associated with better economic performances. Although an economic-epidemiological win-win situation is detected between improving TE and reducing infection, for some farms a trade-off exists between reducing the level of infection and optimizing AE. When grazing management to control GI nematode infections is implemented, the gross margin decreases. This is due to an increase of the feed costs, due to higher use of concentrates and higher costs for pasture, and a relatively low increase in milk production. Although grazing management can reduce the level of infection, they result in a lower economic performance. The application of the integrative conceptual framework allows to gain additional insights about the relationship between GI nematode infections and economic performance at farm level. Current advice on controlling infections are mainly based on mere parasitological findings on the farm. Considering also economic implications provides therefore added value to existing decision making. For the development of an economic-epidemiological decision support tool, farm-specific data on infection and the farm performances are needed. These data must be combined with the data of other farms, because the methods that are used in the framework define farm-specific relations based on a data set of several farms. For decision support in practice, the different methods considered in this dissertation need to be combined in a practical and user-friendly tool. This is not straightforward, because the farmer cannot be expected to be familiar with all methods. The results of efficiency analysis, concerning the possible improvement margins and the contribution of a lower level of infection and control strategies, may best be communicated with traditional KPIs the farmer is familiar with. For the development of a decision support tool and its successful implementation into practice, the challenge is to construct a tool that complies with a number of critical success factors. Critical success factors that are distinguished in literature are perceived usefulness, accessibility, flexibility, credibility, maintenance and adaptability. In addition, including stakeholders during the design, evaluation and implementation of decision support tools is very important. This dissertation shows that combining economic and epidemiological information for decision support is possible, but not self-evident. Insights are provided on the combined use of economic and epidemiological data and different methods on the translation of the results for decision support in practice. This dissertation shows the need for epidemiological and economic information from a representative set of farms, a combination of positive and normative methods and further research on a practical decision support system that is capable of embedding these methods. This decision support system should be focussed on the individual farm level, because the farm performances and also the relationship between GI nematode infections and farm performances are highly farm-specific.