The objectives of this study were to evaluate (1) the performance of an automated health-monitoring system (AHMS) to identify cows with metritis based on an alert system (health index score, HIS) that combines rumination time and physical activity; (2) the number of days between the first HIS alert and clinical diagnosis (CD) of metritis by farm personnel; and (3) the daily rumination time, physical activity, and HIS patterns around CD. In this manuscript, the overall performance of HIS to detect cows with all disorders of interest in this study [ketosis, displaced abomasum, indigestion (companion paper, part I), mastitis (companion paper, part II), and metritis] is also reported. Holstein cattle (n=1,121; 451 nulliparous and 670 multiparous) were fitted with a neck-mounted electronic rumination and activity monitoring tag (HR Tags, SCR Dairy, Netanya, Israel) from at least -21 to 80 d in milk (DIM). Raw data collected in 2-h periods were summarized per 24 h as daily rumination and activity. An HIS (0 to 100 arbitrary units) was calculated daily for individual cows with an algorithm that used rumination and activity. A positive HIS outcome was defined as an HIS of <86 units during at least 1 d from -5 to 2 d after CD. Blood concentrations of nonesterified fatty acids, β-hydroxybutyrate, total calcium, and haptoglobin were determined in a subgroup of cows (n=459) at -11±3, -4±3, 0, 3±1, 7±1, 14±1, and 28±1 DIM. The overall sensitivity of HIS was 55% for all cases of metritis (n=349), but it was greater for cows with metritis and another disorder (78%) than for cows with metritis only (53%). Cows diagnosed with metritis and flagged based on HIS had substantial alterations in their rumination, activity, and HIS patterns around CD, alterations of blood markers of metabolic and health status around calving, reduced milk production, and were more likely to exit the herd than cows not flagged based on the HIS and cows without disease, suggesting that cows flagged based on the HIS had a more severe episode of metritis. Including all disorders of interest for this study, the overall sensitivity was 59%, specificity was 98%, positive predictive value was 58%, negative predictive value was 98%, and accuracy was 96%. The AHMS was effective for identifying cows with severe cases of metritis, but less effective for identifying cows with mild cases of metritis. Also, the overall accuracy and timing of the AHMS alerts for cows with health disorders indicated that AHMS that combine rumination and activity could be a useful tool for identifying cows with metabolic and digestive disorders, and more severe cases of mastitis and metritis.

The objectives of this study were to evaluate (1) the performance of an automated health-monitoring system (AHMS) to identify cows with metabolic and digestive disorders-including displaced abomasum, ketosis, and indigestion-based on an alert system (health index score, HIS) that combines rumination time and physical activity; (2) the number of days between the first HIS alert and clinical diagnosis (CD) of the disorders by farm personnel; and (3) the daily rumination time, physical activity, and HIS patterns around CD. Holstein cattle (n=1,121; 451 nulliparous and 670 multiparous) were fitted with a neck-mounted electronic rumination and activity monitoring tag (HR Tags, SCR Dairy, Netanya, Israel) from at least -21 to 80 d in milk (DIM). Raw data collected in 2-h periods were summarized per 24 h as daily rumination and activity. A HIS (0 to 100 arbitrary units) was calculated daily for individual cows with an algorithm that used rumination and activity. A positive HIS outcome was defined as a HIS of <86 during at least 1 d from -5 to 2 d after CD. Blood concentrations of nonesterified fatty acids, β-hydroxybutyrate, total calcium, and haptoglobin were determined in a subgroup of cows (n=459) at -11±3, -4±3, 0, 3±1, 7±1, 14±1, and 28±1 DIM. The sensitivity of the HIS was 98% [95% confidence interval (CI): 93, 100] for displaced abomasum (n=41); 91% (95% CI: 83, 99) for ketosis (n=54); 89% (95% CI: 68, 100) for indigestion (n=9); and 93% (95% CI: 89, 98) for all metabolic and digestive disorders combined (n=104). Days (mean and 95% CI) from the first positive HIS <86 and CD were -3 (-3.7, -2.3), -1.6 (-2.3, -1.0), -0.5 (-1.5, 0.5), and -2.1 (-2.5, -1.6) for displaced abomasum, ketosis, indigestion, and all metabolic and digestive disorders, respectively. The patterns of rumination, activity, and HIS for cows flagged by the AHMS were characterized by lower levels than for cows without a health disorder and cows not flagged by the AHMS from -5 to 5 d after CD, depending on the disorder and parameter. Differences between cows without health disorders and those flagged by the AHMS for blood markers of metabolic and health status confirmed the observations of the CD and AHMS alerts. The overall sensitivity and timing of the AHMS alerts for cows with metabolic and digestive disorders indicated that AHMS that combine rumination and activity could be a useful tool for identifying cows with metabolic and digestive disorders.

The objectives of this study were to evaluate (1) the performance of an automated health-monitoring system (AHMS) to identify cows with mastitis based on an alert system (health index score, HIS) that combines rumination time and physical activity; (2) the number of days between the first HIS alert and clinical diagnosis (CD) of mastitis by farm personnel; and (3) the daily rumination time, physical activity, and HIS patterns around CD. Holstein cows (n=1,121; 451 nulliparous and 670 multiparous) were fitted with a neck-mounted electronic rumination and activity monitoring tag (HR Tags, SCR Dairy, Netanya, Israel.) from at least -21 to 80 d in milk (DIM). Raw data collected in 2-h periods were summarized per 24 h as daily rumination and activity. An HIS (0 to 100 arbitrary units) was calculated daily for individual cows with an algorithm that used rumination and activity. A positive HIS outcome was defined as an HIS of <86 units during at least 1 d from -5 to 2 d after CD. Blood concentrations of nonesterified fatty acids, β-hydroxybutyrate, total calcium, and haptoglobin were also determined in a subgroup of cows (n=459) at -11±3, -4±3, 0, 3±1, 7±1, 14±1, and 28±1 DIM. The sensitivity of the HIS was 58% [95% confidence interval (CI): 49, 67] for all cases of clinical mastitis (n=123), and 55% (95% CI: 46, 64; n=114) and 89% (95% CI: 68, 100; n=9) for cases of mastitis alone or concurrent with other health disorders, respectively. Among clinical cases, sensitivity was 80.7% (95% CI: 67, 97) for cases caused by Escherichia coli (n=31) and ranged from 45 to 48% for cases caused by gram-positive bacteria (n=39; Streptococcus agalactiae, Streptococcus dysgalactiae, Streptococcus uberis, Streptococcus spp., Staphylococcus spp., and Trueperella pyogenes), Staphylococcus aureus (n=11), or cases with no bacterial growth (n=25). Days between the first HIS <86 and CD were -0.6 (95% CI: -1.1, -0.2) for all cases of mastitis. Cows diagnosed with mastitis had alterations of their rumination, activity, HIS patterns, and reduced milk production around CD depending on the type of mastitis case. Cows with mastitis also had some alterations of their calcium and haptoglobin concentrations around calving. The AHMS used in this study was effective for identifying cows with clinical cases of mastitis caused by E. coli and cows with another disease occurring during an event of mastitis, but it was less effective in identifying cows with mastitis not caused by E. coli.

The objective of this study was to compare the reproductive performance of lactating dairy cows using a treatment (TRT) program for second and subsequent artificial insemination (AI) services aimed at (1) increasing AI upon estrus detection based on increased physical activity (AIAct) and (2) increasing fertility of timed AI (TAI) services for cows not AIAct through presynchronization of the estrous cycle and improved physiological milieu before TAI. Cows in the control (CON) group were managed with a program that combined AIAct and TAI after the Ovsynch protocol. After nonpregnancy diagnosis (NPD) by transrectal ultrasonography at 31 ± 3 d after AI, cows received the following treatments: (1) CON (n=634), AIAct any time after a previous AI and resynchronization with the Ovsynch-56 protocol (GnRH-7d-PGF2α-56 h-GnRH-16 h-TAI) 1d after NPD, or (2) TRT (n = 616): cows with a corpus luteum (CL) ≥ 20 mm (TRT-CL) received a PGF2α injection 1d after NPD, whereas cows with no CL or a CL < 20 mm (TRT-NoCL) received a GnRH injection 3d after NPD. Cows in TRT-CL and TRT-NoCL not AIAct were enrolled in a 5-d Ovsynch + progesterone protocol (GnRH + controlled internal drug release-5d-PGF2α + controlled internal drug release removal-24 h-PGF2α -32 h-GnRH-16 h-TAI) 9 and 7d after the PGF2α or GnRH injection, respectively, to receive TAI. The hazard of pregnancy up to 270 DIM was similar for cows in the CON and TRT group (hazard ratio = 1.07, 95% CI = 0.95 to 1.21), but it was affected by parity (primiparous greater than multiparous cows). Median days to pregnancy for the CON and TRT group were 111 and 110 d, respectively. When evaluated after 104 DIM (first time point at which cows were affected by the treatments), the hazard of pregnancy was similar for the CON and TRT group (hazard ratio = 1.15, 95% CI = 0.95 to 1.39). Based on this analysis, median days to pregnancy for the CON and TRT group were 161 and 178 d, respectively. Thus, in spite of increasing the proportion of cows AIAct (29 and 10% for TRT and CON), median days to insemination after NPD were greater for cows in the TRT (17 d) than the CON (10 d) group, which coupled with similar fertility to AIAct, and TAI failed to improve overall reproductive performance. A low proportion of cows with a CL at NPD (65.2%) and a poor response to PGF2α may explain the poor estrus detection efficiency in the TRT group. We concluded that, when compared with a typical estrus detection and TAI program for cows failing to conceive to previous AI services, a program aimed at increasing the proportion of cows AIAct after NPD and fertility of TAI services increased the proportion of cows AIAct but failed to reduce days to pregnancy during lactation because of greater days to AI after NPD.

The objective was to compare the reproductive performance of lactating Holstein cows managed with a strategy that included the Ovsynch protocol with exogenous progesterone (P4) supplementation or presynchronization with GnRH 7d before Ovsynch to treat cows without a corpus luteum (CL), a CL <15 mm, or cystic at the time of the PGF2α injection of Resynch (GnRH-7 d-PGF2α-56 h-GnRH-16 to 20 h-TAI). In a preliminary study, blood collection and transrectal ovarian ultrasonography were conducted (n=555) at the PGF2α of Resynch [coincident with nonpregnancy diagnosis (NPD)] to define a cutoff value for CL size that better predicted fertility after timed artificial insemination (TAI). A CL size of 15 mm was selected based on statistical differences in pregnancies per AI (P/AI) [33.2 vs. 10.3 P/AI for CL ≥15 mm (n=497) vs. no CL ≥15 mm (n=58; no CL, CL <15 mm, or cystic)]. Subsequently, in a completely randomized experiment, cows were enrolled in a management strategy that used Ovsynch with P4 supplementation [Ovsynch+P4; GnRH and controlled internal drug release device (CIDR)-7 d-PGF2α and CIDR removal-56 h-GnRH-16 to 20 h-TAI] or a PreG-Ovsynch protocol [PreG-Ovsynch; GnRH-7 d-GnRH-7 d-PGF2α-56 h-GnRH-16 to 20 h-TAI] to treat cows without a CL, a CL <15 mm, or cystic at NPD and the PGF2α of Resynch. Cows with a CL ≥15 mm at the PGF2α of Resynch completed the protocol and received TAI. Data were available from 212, 192, and 1,797 AI services after Ovsynch+P4, PreG-Ovsynch, and Resynch, respectively. At 39d after AI, P/AI tended to be greater for Ovsynch+P4 and PreG-Ovsynch combined (35.1%) than for Resynch cows (31.1%), whereas P/AI were similar for Ovsynch+P4 (34.4%) and PreG-Ovsynch (35.9%). The hazard of pregnancy for cows that received the experimental treatments at least once was similar for cows in the Ovsynch+P4 (n=124) and the PreG-Ovsynch (n=132) group (hazard ratio 1.15; 95% confidence interval: 0.87 to 1.53). Median days to pregnancy were 52 and 59 for cows in the Ovsynch+P4 and the PreG-Ovsynch groups, respectively. The presynchronizing GnRH injection of PreG-Ovsynch induced ovulation in 86.0% of the cows. At the first GnRH of Ovsynch, the proportion of cows with a CL based on ultrasound (86.6 vs. 15.0%), P4 >1 ng/mL (82.8 vs. 31.8%), a follicle ≥ 10 mm (98.0 vs. 84.4%), and P4 concentrations (3.7 vs. 1.1 ng/mL) was greater in PreG-Ovsynch than in Ovsynch+P4. Conversely, more cows ovulated in response to the first GnRH of Ovsynch in Ovsynch+P4 (71.9%) than PreG-Ovsynch (58.3%). At the PGF2α before TAI, more cows had a CL based on ultrasound (92.1 vs. 77.0%) and P4 concentrations were greater in PreG-Ovsynch than in Ovsynch+P4 (4.1 vs. 2.6 ng/mL); however, a similar proportion of cows had P4 >1 ng/mL (79.1 vs. 82.7%). We conclude that the Ovsynch+P4 and PreG-Ovsynch treatments for cows without a CL, a CL <15 mm, or cystic at the PGF2α injection of Resynch led to P/AI similar to that of cows with a CL ≥15 mm, and that both management strategies resulted in similar time to pregnancy.