An automated in-line clinical mastitis detection system using measurement of conductivity from foremilk of individual udder quarters

Abstract

AIM: To assess a novel method for automatic in-line detection of clinical mastitis. METHODS: For a brief period at the start of milking for each cow, electrical conductivity of foremilk was measured for each quarter in turn, using a single sensor installed in the long milk tube (LMT) about 1.5 m downstream from the milking-machine claw. Sequential separation of flow between udder quarters was achieved by control of pulsation to individual teatcups within a conventional cluster. The ratio of conductivity values between quarters was used as an indicator of mastitis status. The concept was evaluated initially in a pilot trial in a 200-cow herd milked in a 23-stall swing-over herringbone milking parlour. It was then tested rigorously in a field trial in a 640-cow herd milked in a 50-stall rotary milking parlour. Both trials were conducted in the Waikato region of New Zealand. In the latter trial, sensor results were compared with visual inspection of a commercial in-line mastitis filter fitted to each milking unit. These filters were inspected for clots immediately after every cow's milking, for 3 weeks. The dataset of approximately 27,000 individual milkings was tested against several published or potential alter- native ‘gold standards’ for diagnosing clinical mastitis. RESULTS: In the pilot trial, 12–14 clinical events were detected out of 19 true clinical quarters, with a false-alert rate of between three and five false electrical-conductivity alerts per 1,000 individual milkings. In the more rigorous field trial, sensitivity ranged from 68 to 88%, and the false-alert rate (false-alert episodes per 1,000 individual milkings) ranged from 2.3 to 7.0. CONCLUSION: The novel clinical mastitis detection system, based on separation of the flow and measurement of electrical conductivity from foremilk of individual udder quarters, has the potential to provide a new tool for helping farmers to monitor clinical mastitis in herds milked with conventional clusters.