Udder health when drying off and at herd level supported by sensor systems

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Sensor systems currently are used to identify cows with signs of clinical mastitis. However, sensor systems have much more potential to support the farmers’ operational management of udder health.

Cows needing attention at drying off typically have an intramammary infection and need to be identified for appropriate treatment as part of a selective dry cow therapy program. Because there are disadvantages to both false positive and false negative alerts, the sensitivity and specificity of sensor systems should be equally high (over 90%). Alerts should be provided at an appropriate time (a few days before drying off) and detection performance should be reasonable.

Monitoring of udder health at the farm level can be done by combining sensor readings from all cows in the herd. Novel herd-level key performance indicators can be developed to monitor udder health daily. Disturbances at the group or herd level can be detected more quickly by utilizing sensor-based key performance indicators. Sensitivity should be reasonably high and because of the costs for further analysis of false positive outcomes, the specificity should be at least 99.5%. Moreover, sensor-based key performance indicators may be used to evaluate the effectiveness of dry cow and lactational therapy.

How can the management of clinical and subclinical mastitis be supported by sensor systems?

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Current sensor systems aim to detect cows with abnormal milk or mastitis. Although they may be less accurate than visual detection in detecting clinical mastitis, sensor systems have the advantage of multiple measurements per day. Mastitis detection, however, should be approached from a need to intervene (management support) perspective rather than based on clinical mastitis paradigms.

Cows with severe clinical mastitis need to be identified and treated properly as fast as possible. Sensor systems should have a very high sensitivity (at least 95%), combined with a high specificity (at least 99%) within a narrow time window (maximum 12 hours) to ensure that close to all cows with true cases are detected quickly. Since very sick animals may not visit a milking robot, detection algorithms need to take additional data into account, not only milk sensor data.

Cows that do not need immediate attention have a risk of progressing into severe clinical mastitis. However, they should get the chance to cure spontaneously under close monitoring. Intervention is needed for cows at risk of developing chronic mastitis, leading to production losses and increased risk of pathogen transmission. Sensor alerts should have a reasonable sensitivity (at least 80%) and a high specificity (at least 99.5%). The time window may be relatively long (around 7 days). Additional actions may contain further diagnostic testing.

Sensor technology and data monitoring in dairy cows

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Sensors that can measure physiological, behavioral and production indicators in dairy cows (milk yield, temperature, animal’s activity, etc.) may assist farmers to improve animal health and welfare and identify diseased cows earlier.

Currently there are different sensors available on the market, such as sensor systems for mastitis detection (e.g. electrical conductivity), oestrus detection for dairy cows, oestrus detection for youngstock, and other sensor systems (e.g. weighing platform, rumination time sensor, temperature sensor, milk temperature sensor, etc.).

These technologies and their adoption provide benefit to farmers by frequently monitoring dairy cattle without disturbing natural behavioral expression. The implementation of these tools via e.g. computer-controlled programs can become valuable instruments for improving detection rates, gaining insights into the fertility level of the herd, improving profitability of the farm, and reducing labor.

For example, clinical mastitis can be predicted by changes in the electrical conductivity of foremilk, enabling early treatment and significantly limiting the severity of the disease. In many cases, it may also prevent the appearance of any visible signs of infection.

On the other hand, a monitoring system based on feeding time of the individual cow can identify changes in feeding activity. It is expected that the farmer’s inspection of dairy cows that change their average feeding time in combination with other monitoring systems, will lead to earlier detection of mastitis and oestrus. Early detection and veterinary treatment of mastitis and oestrus is expected to be beneficial for both cow welfare and farm profitability.

Real-time health monitoring in pigs

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In order to improve sanitary control on a farm, the ideal would be, on the one hand, to prevent diseases (biosecurity and vaccination) and, on the other hand, to anticipate diseases. Work is currently underway on monitoring systems for early detection of diseases. By detecting them early, rapid, accurate and individualized treatment can be put in place, thus reducing the impact of the disease and reducing the consumption of antibiotics.

A monitoring system can obtain information from images and bisosensors every second and, through a computer system with predefined patterns, this information can be processed in real time to provide useful information to stockpersons.

This technology makes it possible to monitor the animal’s movement over several consecutive days through the use of accelerometers and artificial vision systems and establishes alerts when these reach relatively low levels.

After four to seven days of infection, an animal begins to make changes in its routines such as reducing movement or reducing playing and/or feeding time. With a real time monitoring system, pigs are monitored by video surveillance and movement patterns are established. When those patterns are altered, the computer system issues an alert of a possible case of animal with fever. The suspect animal is then individually tested to determine whether it does indeed have fever and needs to be treated with an antipyretic, or whether it does not have fever but moves to an individual surveillance system over the next few days.