Two studies were conducted to assess the performance of a commercially available neck-mounted activity meter to detect cows about to ovulate in two paddock-based Holstein-Friesian dairy herds. The activity monitoring system recorded cow activity count in 2-hourly periods. Study I investigated the ability of the system to detect cow ovulatory periods in dairy herds managed in two different Australian environments and breeding systems using five activity alert algorithms. Herd 1 consisted of approximately 130 milking cows calving year-round in a sub-tropical environment and kept in a single dry lot paddock. Herd 2 consisted of approximately 400 milking cows calving seasonally in a temperate climate and fed pasture by rotation through multiple grazing paddocks. Ovulatory periods and non-ovulatory days were identified using milk progesterone monitoring alone or in combination with ovarian ultrasonography; using these 'gold standards' 141 and 135 ovulatory periods were identified in 64 and 135 cows in Herds 1 and 2 respectively. Sensitivity of the activity monitoring system for detecting cow ovulatory periods ranged from 79.4% to 94.1%, specificity from 90.0% to 98.2% and positive predictive value from 35.8% to 75.8%. Study II investigated the ability of the activity meter system to predict the timing of ovulations in paddock-based pasture-fed dairy cattle (Herd 2). The time of ovulation was estimated by repeat trans-rectal ovarian ultrasonography at approximately 0, 12, 24 and 36 h after artificial insemination (AI). The mean times (± SD) from onset and end of increased activity to ovulation were 33.4 ± 12.4 and 17.3 ± 12.8 h respectively (n = 94). Fifty per cent of cows (n = 47) ovulated within the 8-h period between 30 to 38 hs after the onset of increased activity, 76.6% (n = 72) within the 16 h between 24 to 40 h, 85.1% (n = 80) within the 24 h between 18 and 42 h and 90.4% (n = 85) within the 32 h from 19 to 51 h after the onset of increased activity. Results from these studies show that in paddock-based dairy cows in two diverse management systems, this neck-mounted activity meter system detects high proportions of cows that are about to ovulate and provides a useful indication of when ovulation is likely to occur. However, the specificities and positive predictive values using the algorithms assessed may be lower than desirable.

A prospective observational study was conducted in two Australian dairy herds to assess the potential for improving pregnancy rates (proportions of inseminations that result in pregnancy) to artificial insemination (AI) if the time of ovulation could be predicted with more certainty. Herd 1 calved year-round and inseminations were performed during two periods each day. Herd 2 calved during autumn-winter and inseminations were performed only after the morning milking each day. In both herds, the AI to ovulation interval of enrolled cows was determined by trans-rectal ovarian ultrasonography approximately 0, 12, 24 and 36 h after AI, and pregnancy was assessed by palpation per rectum 35-56 days after AI. Also, in Herd 1 vaginal electrical resistance (VER) measurements were taken at approximately 0, 12, 24 and 36 h after AI, and in Herd 2 cows were fitted with neck mounted activity meters that monitored cow activity count in 2-h periods. There was substantial variation in the intervals from AI to ovulation within and between herds (mean ± SD 21.2 ± 10.7, n = 102; 14.7 ± 10.4, n = 100 in herds 1 and 2, respectively). Pregnancy rates were higher for inseminations close to, but preceding, ovulation. Using combined herd data (n = 202), the highest pregnancy rate (50.8%) was observed for inseminations between 0 and 16 h before ovulation, a period in which only a modest proportion of inseminations (31.2%) occurred. In contrast, pregnancy rate was significantly lower (28.7%; risk ratio 0.6; 95% CI 0.4-1.0; p = 0.039) for inseminations between 16 and 32 h before ovulation, a period where the highest proportion of inseminations (53.2%) occurred. Thus pregnancy rates could potentially be improved if a greater proportion of inseminations were conducted shortly before ovulation. In Herd 1, mean VER during the peri-ovulatory period varied with time from ovulation. Lowest values (mean ± SEM, VER = 64.8 ± 1.2, n = 55) occurred approximately 18 h before ovulation and were significantly lower than measurements approximately 6 h before ovulation (67.4 ± 1.0; n = 73; p = 0.003). Further work is required to determine if VER can be used to identify ovulation time and hence the optimal time to inseminate in individual animals. In Herd 2 a modest proportion of inseminations (26.9%) occurred between 24 and 40 h after the onset of increased cow activity where the highest pregnancy rate (67.9%) was observed, whereas a significantly lower pregnancy rate (42.4%; risk ratio 0.6; 95% CI 0.4-0.9; p = 0.036) was observed for inseminations between 8 and 24 h after the onset of increased cow activity where the highest proportion of inseminations (56.7%) occurred. Thus cow activity monitoring may be useful to identify the optimal time to inseminate cows. Results from this study indicate that improved methods of ovulation prediction may allow better insemination timing relative to ovulation and consequently increased pregnancy rates.

The primary objective of this study was to determine whether a single measurement of intravaginal electrical resistance (VER), using the commercially available Ovatec probe, can discriminate between dioestrus and oestrus in Bos indicus females, which had been treated to synchronize oestrus. Santa Gertrudis heifers (n = 226) received one of three oestrous synchronization treatments: double PGF(2alpha) 10 days apart, 8-day controlled internal drug release (CIDR) treatment or CIDR pre-synchronization + PGF(2alpha) 10 days after CIDR removal. The heifers were inseminated within 12 h following observed oestrus, or, if not observed, at a fixed time approximately 80 h, following the last synchronization treatment. They were palpated per rectum for signs of pregnancy 9 weeks after artificial insemination (AI). Vaginal electrical resistance measurements were taken at the completion of synchronization treatments (presumed dioestrus), immediately prior to AI (oestrus), and then at 3 and 9 weeks post-AI. Mean VER differed between presumed dioestrus and oestrus (113.7 vs 87.4, p < 0.001). The area under the receiver operating characteristics (ROC) curve was 0.925, indicating that VER was highly discriminatory between dioestrus and oestrus. Vaginal electrical resistance at time of AI was negatively associated with odds of conception when all inseminations were included in the analyses [odds ratio (OR) = 0.97; 95% CI 0.95-1.00; p = 0.018], but not when fixed time AIs were excluded (OR = 1.00; 95% CI 0.97-1.03; p = 0.982). Mean VER readings differed between pregnant and non-pregnant animals at both 3 weeks (120.5 vs 96.7, p < 0.001) and 9 weeks (124.0 vs 100.3, p < 0.001) post-AI. However, 3- and 9-week VER measurements were not highly discriminatory between pregnancy and non-pregnancy (area under ROC curve = 0.791 and 0.736, respectively). Mean VER at time of AI for animals diagnosed in oestrus differed between each of the oestrous synchronization treatments (84.7, 73.6 and 78.9, groups 1-3 respectively, p < 0.001). These findings suggest that measurement of VER may improve accuracy of oestrus diagnoses when selecting cattle for AI following oestrous synchronization programmes involving tropically adapted cattle.

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