The relationship between postpartum vaginal discharge symptoms and progesterone profile characteristics in lactating dairy cows in Denmark

Progesterone profiles in postpartum dairy cows with inflammatory disorders

The objective of this prospective cohort study was to determine if progesterone (P4) profiles differed between dairy cows with or without inflammatory disorders early postpartum. A total of 708 cows from 2 commercial herds were enrolled 3 wk before parturition and examined for clinical health disorders (difficult calving, retained placenta, metritis, displaced abomasum, mastitis, or lameness) until 5 wk postpartum. Serum haptoglobin (Hp) was measured in blood at 2 and 6 DIM (range ±2 d); metritis was assessed at 4, 8, 11, and 15 DIM; and purulent vaginal discharge and endometritis (≥6% PMN in endometrial cytology sampled by cytobrush) were assessed at 35 ± 3 DIM. As Hp ≥0.8 g/L or endometritis were associated with ovarian dysfunction in previous studies, cows with serum Hp ≥0.8 g/L at either time point and endometritis, regardless of clinical disease, were classified as the cohort with inflammatory disorders (INFLAM; n = 139). Clinically healthy cows without difficult calving, with singleton birth, with Hp <0.8 g/L at both sampling times, without endometritis or purulent vaginal discharge, and BCS ≥3.00 (1 to 5 scale) were classified as healthy (n = 133). Cows with only one of the 2 conditions (high Hp or endometritis) were excluded. Cohorts had serum P4 measured twice weekly from 35 to 70 DIM (±3 d), and the first detected luteal phase (LP) during the sampling period was defined as the period from onset of luteal activity (P4 increase to ≥1 ng/mL) until decline of P4 to <1 ng/mL. The odds of prolonged LP (≥21 d long), average LP length, peak P4, and time to P4 decline (hazard rate) were analyzed using multivariable mixed logistic, linear, or Cox proportional hazard regression models including INFLAM status, parity, sampling day (when applicable), and herd as a random effect considering the covariates of season, milk yield at first DHIA test, and DIM at onset of cyclicity or LP length (when applicable). Cows with INFLAM had greater odds of prolonged LP (LSM ± SEM; 67% vs. 37% ± 7%), greater average LP length (17 vs. 15 ± 2 d), lesser P4 at d 4 (4.6 vs. 5.5 ± 0.3 ng/mL) and d 7 (6.0 vs. 7.7 ± 0.3 ng/mL) of the LP, and lesser peak P4 (6.9 vs. 8.2 ± 0.3 ng/mL) during the LP than healthy cows. Status of INFLAM was associated with time to P4 decline in multiparous but not primiparous cows; the LP of INFLAM multiparous cows was less likely to have luteolysis (P4 decline) by d 14 (adjusted hazard ratio [AHR] and 95% CI: 0.54; 0.31 to 0.94) or by d 21 (AHR: 0.32; 0.12 to 0.84) than in healthy multiparous cows. In conclusion, postpartum cows with markers of systemic inflammation at wk 1 and uterine inflammation at wk 5 had altered luteal function (prolonged LP and lower P4 concentrations) before first breeding, which is a possible pathway linking postpartum health disorders and reduced fertility.

Effect of BCS and parity on uterine involution, ovarian rebound and various fertility parameters in postpartum dairy cows

The objective of present study was to investigate the effect of body condition score and parity on uterine involution, resumption of ovarian cyclicity and subsequent fertility parameters after calving in Jersey crossbred cows. Primiparous (10) and pluriparous cows (13) having normal parturition were examined. Body condition score was recorded at the time of calving and a significant difference was observed between primiparous and pluriparous cows. The monitoring of uterine involution of previous gravid and non-gravid uterine horn and first postpartum ovulatory follicle (OF) and subsequent corpus luteum (CL) was performed by transrectal ultrasound scanning at weekly interval upto 43 days postpartum. Uterine involution was significantly faster in primiparous cows as compared to pluriparous cows. However, ovarian rebound was significantly earlier in pluriparous cows based on the presence of first postpartum OF and subsequent CL. Monitoring of fertility parameters (days to first artificial insemination, number of inseminations per conception and days open) was done until next conception. Days to first artificial insemination and days open were significantly less in pluriparous cows as compared to primiparous cows. Also, the number of inseminations required for conception were less in pluriparous cows. In conclusion, as the parity increases, postpartum fertility parameters improve despite the rapid uterine involution in primiparous cows.

Milk Production and Fertility in Cattle

Evolutionary biology provides reasons for why the intensive selection for milk production reduces reproductive success rates. There is considerable exploitable genetic variation in reproductive performance in both dairy and beef cattle, and examination of national genetic trends demonstrates that genetic gain for both reproductive performance and milk production is possible in a well-structured breeding program. Reproductive failure is often postulated to be a consequence of the greater negative energy balance associated with the genetic selection for increased milk production. However, experimental results indicate that the majority of the decline in reproductive performance cannot be attributed to early lactation energy balance, per se; reproductive success will, therefore, not be greatly improved by nutritional interventions aimed at reducing the extent of negative energy balance. Modeling can aid in better pinpointing the key physiological components governing reproductive success and, also, the impact of individual improvements on overall fertility, helping to prioritize variables for inclusion in breeding programs.

Lameness detection via leg-mounted accelerometers on dairy cows on four commercial farms

Lameness in dairy herds is traditionally detected by visual inspection, which is time-consuming and subjective. Compared with healthy cows, lame cows often spend longer time lying down, walk less and change behaviour around feeding time. Accelerometers measuring cow leg activity may assist farmers in detecting lame cows. On four commercial farms, accelerometer data were derived from hind leg-mounted accelerometers on 348 Holstein cows, 53 of them during two lactations. The cows were milked twice daily and had no access to pasture. During a lactation, locomotion score (LS) was assessed on average 2.4 times (s.d. 1.3). Based on daily lying duration, standing duration, walking duration, total number of steps, step frequency, motion index (MI, i.e. total acceleration) for lying, standing and walking, eight accelerometer means and their corresponding coefficient of variation (CV) were calculated for each week immediately before an LS. A principal component analysis was performed to evaluate the relationship between the variables. The effects of LS and farm on the principal components (PC) and on the variables were analysed in a mixed model. The first four PC accounted for 27%, 18%, 12% and 10% of the total variation, respectively. PC1 corresponded to Activity variability due to heavy loading by five CV variables related to standing and walking. PC2 corresponded to Activity level due to heavy loading by MI walking, MI standing and walking duration. PC3 corresponded to Recumbency due to heavy loading by four variables related to lying. PC4 corresponded mainly to Stepping due to heavy loading by step frequency. Activity variability at LS4 was significantly higher than at the lower LS levels. Activity level was significantly higher at LS1 than at LS2, which was significantly higher than at LS4. Recumbency was unaffected by LS. Stepping at LS1 and LS2 was significantly higher than at LS3 and LS4. Activity level was significantly lower on farm 3 compared with farms 1 and 2. Stepping was significantly lower on farms 1 and 3 compared with farms 2 and 4. MI standing indicated increased restlessness while standing when cows increased from LS3 to LS4. Lying duration was only increased in lame cows. In conclusion, Activity level differed already between LS1 and LS2, thus detecting early signs of lameness, particularly through contributions from walking duration and MI walking. Lameness detection models including walking duration, MI walking and MI standing seem worthy of further investigation.