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Since the 1980s, efforts have been made to develop sensors that measure a parameter from an individual cow. The development started with individual cow recognition and was followed by sensors that measure the electrical conductivity of milk and pedometers that measure activity. The aim of this review is to provide a structured overview of the published sensor systems for dairy health management. The development of sensor systems can be described by the following 4 levels: (I) techniques that measure something about the cow (e.g., activity); (II) interpretations that summarize changes in the sensor data (e.g., increase in activity) to produce information about the cow's status (e.g., estrus); (III) integration of information where sensor information is supplemented with other information (e.g., economic information) to produce advice (e.g., whether to inseminate a cow or not); and (IV) the farmer makes a decision or the sensor system makes the decision autonomously (e.g., the inseminator is called). This review has structured a total of 126 publications describing 139 sensor systems and compared them based on the 4 levels. The publications were published in the Thomson Reuters (formerly ISI) Web of Science database from January 2002 until June 2012 or in the proceedings of 3 conferences on precision (dairy) farming in 2009, 2010, and 2011. Most studies concerned the detection of mastitis (25%), fertility (33%), and locomotion problems (30%), with fewer studies (16%) related to the detection of metabolic problems. Many studies presented sensor systems at levels I and II, but none did so at levels III and IV. Most of the work for mastitis (92%) and fertility (75%) is done at level II. For locomotion (53%) and metabolism (69%), more than half of the work is done at level I. The performance of sensor systems varies based on the choice of gold standards, algorithms, and test sizes (number of farms and cows). Studies on sensor systems for mastitis and estrus have shown that sensor systems are brought to a higher level; however, the need to improve detection performance still exists. Studies on sensor systems for locomotion problems have shown that the search continues for the most appropriate indicators, sensor techniques, and gold standards. Studies on metabolic problems show that it is still unclear which indicator reflects best the metabolic problems that should be detected. No systems with integrated decision support models have been found.

Good udder health is not only important for the dairy farmer but, because of increasing interest of consumers in the way dairy products are produced, also for the dairy production chain as a whole. An important role of veterinarians is in advising on production diseases such as mastitis. A large part of this advice is given around the planning of management to maintain or improve the udder health status of a farm. Mastitis is a costly disease, due to losses (a reduction of output due to mastitis) and expenditure (additional inputs to reduce the level of mastitis). Worldwide, published estimates of the economic losses of clinical mastitis range from €61 to €97 per cow on a farm, with large differences between farms, e.g. in The Netherlands, losses due to clinical and subclinical mastitis varied between €17 and €198 per cow per year. Moreover, farmers tended to underestimate these costs. This indicates that for a large proportion of farms there are many avoidable losses. In order to provide good support to farmers' decision-making, it is important to describe the mastitis setting not only in terms of disease, e.g. incidence of clinical mastitis, but also in monetary terms; and to make good decisions, it is necessary to provide the dairy farmer with information on the additional expenditure and reduced losses associated with alternative decisions. Six out of 18 preventive measures were shown to have a positive nett benefit, viz blanket use of dry-cow therapy, keeping cows standing after milking, back-flushing of the milk cluster after milking a cow with clinical mastitis, application of a treatment protocol, washing dirty udders, and the use of milkers' gloves. For those measures that included a large amount of routine labour or investment, the reduced losses did not outweigh the additional expenditure. The advisor cannot expect that measures that are cost-effective are always implemented. Reasons for this are the objectives of the dairy farmer can be other than maximisation of profit, resources to improve the mastitis situation compete with other fields of management, risk involved with the decision, economic behaviour of the dairy farmer, and valuation of the cost factors by the dairy farmer. For all decision-makers this means that, although financial incentives do have an effect on the management of mastitis, it is not always sufficient to show the economic benefits of improved management to induce an improvement of management of mastitis.

A model to calculate the economic losses of mastitis on an average Dutch dairy farm was developed and used as base for a tool for farmers and advisors to calculate farm-specific economic losses of mastitis. The economic losses of a clinical case in a default situation were calculated as euro210, varying from euro164 to euro235 depending on the month of lactation. The total economic losses of mastitis (subclinical and clinical) per cow present in a default situation varied between euro65 and euro182/cow per year depending on the bulk tank somatic cell count. The tool was used to measure perception of the total economic losses of mastitis on the farm and the farmers' assessment of the cost factors of mastitis on 78 dairy farms, of which 64 were used for further analyses. Most farmers (72%) expected their economic losses to be lower than those revealed by our calculation made with their farm information. Underestimating the economic losses of mastitis can be regarded as a general problem in the dairy sector. The average economic losses assessed by the farmers were euro78/cow per year, but a large variation was given, euro17-198/cow per year. Although the average assessment of the farmers of the different cost factors is close to the default value, there is much variation. To improve the adoption rate of advice and lower the incidence of mastitis, it is important to show the farmers the economic losses of mastitis on their farm. The tool described in this paper can play a role in that process.

Foot disorders are an important health problem in dairy cattle, in terms of economics and animal welfare. The incidence, severity, and duration of foot disorders account for their importance. Prevalence of both subclinical and clinical foot disorders is high. More insight into the economic consequences could increase awareness among dairy farmers and could be an incentive for them to take action on this problem of animal welfare. The objective of this research was to estimate the economic consequences of different types of foot disorders, both clinical and subclinical. A dynamic stochastic Monte Carlo simulation model was used, taking into account the different types of foot disorders. The economic consequences of the foot disorders modeled were costs due to milk production losses, culling, prolonged calving interval, labor of the dairy farmer and the foot trimmer, visits of a veterinarian, treatment, and discarded milk. Under the milk quota system in The Netherlands, costs due to foot disorders for a default farm with 65 cows averaged $4,899 per year (ranging from $3,217 to $7,001), an annual loss of $75 per cow. This calculation implies that the costs due to foot disorders are more substantial than farmers might think. The costs of subclinical foot disorders account for 32% of all costs due to foot disorders. The costs due to foot disorders that are present without treatment or detection by the farmer are considerable. This finding implies that farmers might underestimate the benefits of taking action earlier and more thoroughly. A clinical foot disorder costs, on average, $95, and a subclinical foot disorder $18. The highest costs classified by foot disorder were those due to digital dermatitis, which has a high incidence and relatively high clinical prevalence. The highest costs classified by cost factor were those due to milk production losses and culling. Sensitivity analysis showed that variables regarding milk production were important for economic costs due to foot disorders. Furthermore, the probability of getting a foot disorder and probability of cure were important for estimating the costs due to foot disorders. Farmer awareness concerning dairy cow foot health and taking action more thoroughly, therefore, could reduce the economic consequences and improve welfare simultaneously.

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To examine the development of teat end callosity thickness and roughness in early lactation and to quantify cow factors of interest, a system to classify teat end condition was developed. A distinction was made between rough and smooth rings around the teat orifice. In addition, a classification of the degree of callosity was developed. Kappa coefficients for the repeatability of scoring by this classification system by different workers were 0.71 for teat end callosity thickness and 0.86 for teat end callosity roughness. The teat end callosity classification system was used for a longitudinal study with 40 cows during the first 14 wk of lactation. Models were built to predict teat end callosity thickness and roughness, machine-on time, and milk yield. For the response variables, teat end callosity thickness, machine-on time, and milk yield, the consecutive measurements appeared to follow a lactation curve model with a subject-specific general slope and intercept. Teat end callosity increased rapidly the first 8 wk. Cow factors such as days in milk, parity, machine-on time, and teat end shape were associated with the degree of teat end callosity, and the probability of the callosity ring to become rough. Teat end callosity thickness did not decrease within the 14-wk trial period for most teats. Pointed or round teat ends showed more callus than inverted teat ends. Longer machine-on time resulted in a higher probability of the callosity ring to become rough. Rear teats showed less callosity than front teats in this study.