Increasing dairy farm size and increase in automation in livestock production require that new methods are used to monitor animal health. In this study, a thermal camera was tested for its capacity to detect clinical mastitis. Mastitis was experimentally induced in 6 cows with 10 microg of Escherichia coli lipopolysaccharide (LPS). The LPS was infused into the left forequarter of each cow, and the right forequarters served as controls. Clinical examination for systemic and local signs and sampling for indicators of inflammation in milk were carried out before morning and evening milking throughout the 5-d experimental period and more frequently on the challenge day. Thermal images of experimental and control quarters were taken at each sampling time from lateral and medial angles. The first signs of clinical mastitis were noted in all cows 2 h postchallenge and included changes in general appearance of the cows and local clinical signs in the affected udder quarter. Rectal temperature, milk somatic cell count, and electrical conductivity were increased 4 h postchallenge and milk N-acetyl-beta-D-glucosaminidase activity 8 h postchallenge. The thermal camera was successful in detecting the 1 to 1.5 degrees C temperature change on udder skin associated with clinical mastitis in all cows because temperature of the udder skin of the experimental and control quarters increased in line with the rectal temperature. Yet, local signs on the udder were seen before the rise in udder skin and body temperature. The udder represents a sensitive site for detection of any febrile disease using a noninvasive method. A thermal camera mounted in a milking or feeding parlor could detect temperature changes associated with clinical mastitis or other diseases in a dairy herd.

No abstract

No abstract

Technical success and effectiveness of teat cleaning and the management factors associated with them were evaluated in 9 automatic milking herds. In total, 616 teats cleaned with a cleaning cup and 716 teats cleaned with rotating brushes were included. Technical success and the effectiveness of teat cleaning, including the location and nature of the dirt, were evaluated visually. On average, 79.9% of teat cleanings with a cleaning cup, and 85.0% of those cleaned with brushes succeeded technically; that is, the teat was correctly positioned in the cleaning device throughout the whole cleaning process. The difference between use of teat cups and brushes was significant. However, because technical success of teat cleaning is strongly dependent on herd characteristics, these results should be interpreted with caution. Factors associated with the technical success of teat cleaning with a cleaning cup were herd, days in milk, behavior of the cow, teat color, and teat location. For rotating brushes, behavior of the cow, teat location, udder and teat structure, and days in milk were associated with technical success. Excessive udder hair and technical failure of the automatic milking machine also caused a few technically unsuccessful teat cleanings with a cleaning cup. Teats with technically successful teat cleanings were evaluated for the effectiveness of teat cleaning. From originally dirty teats, the cleaning cup had a significant advantage over the brushes in the percentage of teats that became clean or almost clean during the cleaning process (79.8 vs. 72.9%). Teat orifices were least effectively cleaned compared with the teat barrel and apex. Bedding material (peat, sawdust, or straw) on the teat was cleaned almost completely. Factors associated with the effectiveness of teat cleaning were teat cleanliness before cleaning, herd, teat cleaning method, and teat condition. The variation among herds indicates the likelihood that herd management factors can be adjusted to improve milking hygiene. There is also a need to improve the precision and effectiveness of the teat cleaning mechanisms of automatic milking systems.

We have worked on automatically measuring the behavior of dairy cows during automatic milking. A milking robot offers a unique possibility for a dynamic measurement of physical data. Four strain gauge scales were installed into a milking robot in order to measure the weight of each leg separately, and a laser distance sensor was placed next to the robot in order to measure the radial movement of the cow's body surface. The data were collected into a PC. Three video cameras were installed to observe the system, and the data were recorded digitally. From the data, the dynamic weight or load of each leg and the respiration rate of a cow could be measured. Different stages of milking were observed, and the changes in behavior during milking were analyzed. The acquired information could be used to judge a cow's restlessness and welfare--for example, leg health and stress.