Using activity-based monitoring systems to detect dairy cows in oestrus: a field evaluation

Authors: Kamphuis C, Jago JG, Burke CR, Dela Rue BT
Publication: New Zealand Veterinary Journal, Volume 62, Issue 2, pp 57-62, Mar 2014
Publisher: Taylor and Francis

Animal type: Cattle - dairy
Subject Terms: Diagnostic procedures, Grazing, Oestrus/oestrous, Pasture/crop
Article class: Scientific Article

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Abstract:

AIM: To assess the use and performance of activity-based oestrus detection systems (ODS) on two commercial dairy farms using a gold standard based on profiles of concentrations of progesterone in milk, artificial insemination (AI) records and pregnancy diagnosis results.

METHODS: Two activity-based ODS were evaluated in mature cows on two large pasture-grazed dairy farms (>500 cows) over the first 3 weeks of AI. Farm 1 (n=286 cows) used a leg-mounted device and cows were drafted automatically based on activity alerts. Decisions regarding AI were then made based on tail-paint and cow history for these cows. Farm 2 (n=345 cows) used a collar-mounted device and activity alerts were used in conjunction with other information, before the farmer manually selected cows for AI. The gold standard to define the timing of oestrus was based on profiles of concentrations of progesterone in milk measured twice-weekly, used in conjunction with AI records and pregnancy diagnosis results. Sensitivity and positive predictive value (PPV) were calculated for the activity-based ODS data only, and then for AI decisions, against the gold standard.

RESULTS: Farm 1 had 195 confirmed oestrus events and 209 activity alerts were generated. The sensitivity of the activity-based ODS was 89.2% with a PPV of 83.3%. Using tail-paint and cow history to confirm activity-based alerts 175 cows were inseminated, resulting in a sensitivity of 89.2% and an improved PPV of 99.4%. Farm 2 had 343 confirmed oestrus events, and 726 alerts were generated by the activity-based ODS, giving a sensitivity of 69.7% with a PPV of 32.9%. A total of 386 cows had AI records, giving a sensitivity of 81.3% and PPV of 72.3%.

CONCLUSIONS: The two activity-based ODS were used differently on-farm; one automatically selecting cows and the other supporting the manual selection of cows in oestrus. Only one achieved a performance level suggested to be acceptable as a stand-alone ODS. Use of additional tools, such as observation of tail paint to confirm activity-based oestrus alerts before AI, substantially improved the PPV.

CLINICAL RELEVANCE: A well performing activity-based ODS can be a valuable tool in identifying cows in oestrus prior to visual confirmation of oestrus status. However the performance of these ODS technologies varies considerably.

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