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The aim of this study was to establish a model to induce cystic ovarian follicles (COFs) in cattle using the cyclooxygenase inhibitor, indomethacin. Eighteen Holstein-Frisian cattle were synchronized with PGF2α and GnRH. Ultrasound-guided transvaginal intrafollicular injections were performed in 23 preovulatory follicles with different concentrations of indomethacin 16 hours after GnRH administration. An injection of 0.2 ml 35 µM (resulting in a final concentration of 8 µg/ml in the follicular fluid) indomethacin solution was the minimal dosage leading to COF formation. The induced COFs reached a maximum mean diameter of 36.9 ± 4.5mm eleven days after injection. The estrous cycle was extended to 25-39 days. Luteinization was first observed 4 days after injection, accompanied by a slight increase in plasma progesterone concentration. The bioactivity of indomethacin was demonstrated by the decrease of prostaglandin E 2 in the follicular fluid of three animals. The method presented here is minimally invasive and allows for the generation of defined COFs for further investigations.

The reliable detection of estrus is an important scientific and practical challenge in dairy cattle farming. Female vocalization may indicate reproductive status, and preliminary evidence suggests that this information can be used to detect estrus in dairy cattle. The aim of this study was to associate the changes in the vocalization rate of dairy heifers with behavioral estrus indicators as well as test the influence of the type of estrus (natural estrus vs. superovulation-induced estrus). We analyzed 6 predefined estrus-related behavior patterns (standing to be mounted, head-side mounting, active mounting, chin resting, being mounted while not standing, and active sniffing in the anogenital region) and vocalization rates in the peri-estrus period (day of estrus ± 1 d) of 12 German Holstein heifers using audio-visual recordings. Each heifer was observed under natural estrus and a consecutive superovulation induced by FSH and cloprostenol. Estrus was determined by behavioral patterns and confirmed by clinical examination (vaginoscopy and ultrasound imaging of the ovaries) as well as by the concentration of peripheral progesterone. Estrus behavior and vocalization rates were analyzed in 3-h intervals (an average of 19 intervals for each heifer), and an estrus score was calculated based on the 6 behaviors. The interval with the highest estrus score (I0) was considered the estrus climax. We demonstrated similar time courses for the estrus score and vocalization rate independent of estrus type. However, in natural estrus, the maximum vocalization rate (±SE) occurred in the interval before estrus climax (I-1; 42.58 ± 21.89) and was significantly higher than that in any other interval except estrus climax (I0; 27.58 ± 9.76). During natural estrus, the vocalization rate was significantly higher within the interval before estrus climax (I-1; 42.58 ± 21.89 vs. 11.58 ± 5.51) than under superovulation. The results underscore the potential use of vocalization rate as a suitable indicator of estrus climax in automated estrus detection devices. Further studies and technical development are required to record and process individual vocalization rates.

Oestrus detection remains a problem in the dairy cattle industry. Therefore, automatic detection systems have been developed to detect specific behavioural changes at oestrus. Vocal behaviour has not been considered in such automatic oestrus detection systems in cattle, though the vocalisation rate is known to increase during oestrus. The main challenge in using vocalisation to detect oestrus is correctly identifying the calling individual when animals are moving freely in large groups, as oestrus needs to be detected at an individual level. Therefore, we aimed to automate vocalisation recording and caller identification in group-housed dairy cows. This paper first presents the details of such a system and then presents the results of a pilot study validating its functionality, in which the automatic detection of calls from individual heifers was compared to video-based assessment of these calls by a trained human observer, a technique that has, until now, been considered the ‘gold standard’. We developed a collar-based cattle call monitor (CCM) with structure-borne and airborne sound microphones and a recording unit and developed a postprocessing algorithm to identify the caller by matching the information from both microphones. Five group-housed heifers, each in the perioestrus or oestrus period, were equipped with a CCM prototype for 5 days. The recorded audio data were subsequently analysed and compared with audiovisual recordings. Overall, 1404 vocalisations from the focus heifers and 721 vocalisations from group mates were obtained. Vocalisations during collar changes or malfunctions of the CCM were omitted from the evaluation. The results showed that the CCM had a sensitivity of 87% and a specificity of 94%. The negative and positive predictive values were 80% and 96%, respectively. These results show that the detection of individual vocalisations and the correct identification of callers are possible, even in freely moving group-housed cattle. The results are promising for the future use of vocalisation in automatic oestrus detection systems.

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