Association between body condition score fluctuations and pregnancy loss in Holstein cows

Factors influencing pregnancy loss between days 30 and 70 in a single cattle herd: A comprehensive analysis of sire, cow, and other contributing factors

This retrospective study utilized a mixed-effects logistic model analysis to investigate variables associated with the probability of pregnancy loss (PL) between days 30 and 70 in a dataset comprising 9507 pregnancies from a single cattle herd over 10 years. The model incorporated fixed-effect variables including cow breed (Holstein, Crossbred, and Brown Swiss), parity (1st, 2nd, 3rd, and 4th or more), insemination seasons, insemination number (≤3 vs >3), estrus nature (spontaneous vs synchronized), postpartum problems, sire breed (Holstein, Fleckvieh, Brown Swiss, and Montbéliarde), zygote genotypic (pure vs crossbred), days in milk (DIM) at insemination, actual 305-day milk yield, and sire conception rate. Additionally, random effects included sire (n = 129), cow (n = 3463), and production years (n = 10). The results revealed that cows inseminated with Brown Swiss sires or sires with lower initial conception rates had higher PL rates. Biparous cows, cows with lower 305-day milk yield, cows inseminated later in DIM, cows receiving the 4th or subsequent insemination, and cows inseminated during winter or autumn had lower PL rates. The estrus type and zygote genotype did not significantly impact PL. The random effects of cow, sire, and production years were estimated at 0.230, 0.054, and 0.112, respectively. In conclusion, the study findings suggested that improving management practices for high-yielding cows, cows in early lactation stages, and cows exposed to thermal stress conditions, along with utilizing Brown Swiss cows and sires with high initial conception rates per insemination, could potentially decrease overall PL rates on the farm. Nonetheless, the results did not support the use of Crossbred cows, sires from different breeds, or specific sires to mitigate PL rates on the farm.

Recent advances in early pregnancy loss diagnosis in dairy cows: New approaches

Early pregnancy loss is a primary cause of low reproductive rates in dairy cows, posing severe economic losses to dairy farming. The accurate diagnosis of dairy cows with early pregnancy loss allows for oestrus synchronization, shortening day open, and increasing the overall conception rate of the herd. Several techniques are available for detecting early pregnancy loss in dairy cows, including rectal ultrasound, circulating blood progesterone, and pregnancy-associated glycoproteins (PAGs). Yet, there is a need to improve on existing techniques and develop novel strategies to identify cows with early pregnancy loss accurately. This manuscript reviews the applications of rectal ultrasound, circulating blood progesterone concentration, and PAGs in the diagnosis of pregnancy loss in dairy cows. The manuscript also discusses the recent progress of new technologies, including colour Doppler ultrasound (CDUS), interferon tau-induced genes (ISGs), and exosomal miRNA in diagnosing pregnancy loss in dairy cows. This study will provide an option for producers to re-breed cows with pregnancy loss, thereby reducing the calving interval and economic costs. Meanwhile, this manuscript might also act as a reference for exploring more economical and precise diagnostic technologies for early pregnancy loss in dairy cows.

Impact of postpartum time period associated body condition score on reproductive performance in dairy cows

This study was designed to identify the time period at which the body condition score (BCS) most critically affects the reproductive performance after calving in dairy cows. Data were collected from 4865 lactation records (1821 from primiparous and 3044 from multiparous cows) on 28 dairy farms, including the BCS (at calving, and month 1 and the first artificial insemination [AI] after calving), the presence of peri- or postpartum disorders, reproductive data, and the weather. The data on the extent of BCS loss from calving to the first AI was divided into two periods, namely, between calving and month 1 after calving (period 1), and between month 1 and the first AI after calving (period 2). Cows with BCS of 3.0, 3.25 and ≥ 3.5 at the first AI after calving were more likely (P < 0.05-0.01) to be pregnant at 30 (odds ratio [OR]: 1.36, 1.64, and 1.90) and 45 (OR: 1.39, 1.75, and 1.99) days post-AI and more likely (P < 0.05-0.01) to be pregnant within 180 days after calving (hazard ratio [HR]: 1.18, 1.43, and 1.58) than cows with BCS of ≤2.75. In addition, cows who suffered a BCS loss of ≥0.5 units during period 1 tended to be less likely (HR: 0.79, P < 0.1) to be pregnant within 180 days after calving than cows without a BCS loss. Cows with BCS of 3.0, 3.25, and ≥3.5 at calving were less likely (P < 0.1-0.05) to lose their pregnancy (OR: 0.37, 0.33, and 0.16) than cows with BCS of ≤2.75. These results indicate that the linearly higher BCS (3.0, 3.25, and ≥3.5) at the first AI is positively associated with the probability of pregnancy per AI after the first AI and the likelihood of pregnancy within 180 days after calving, whereas a higher BCS loss of ≥0.5 units during period 1 was adversely associated with the likelihood of pregnancy within 180 days after calving.

Factors associated with the time and magnitude of the nadir body condition score in early lactation and its subsequent effects on fertility of Holstein cows

The objective of this study was to identify variables associated with the time and magnitude of nadir BCS (nBCS) during early lactation and to characterize the effects of these two measurements on the subsequent pregnancy at first artificial insemination (P/AI1) and pregnancy loss of Holstein cows. A retrospective observational study was completed using data collected from 12,042 lactations in 7,626 Holstein cows calving between April 2019 and January 2022 in a commercial dairy operation located in Colorado, USA. Scores generated by an automated BCS camera system at dry-off (BCSdry), calving (BCS1), nadir (nBCS; defined as the lowest daily BCS from calving to 100 DIM), and 21 d post nadir were selected for the analyses and subsequently categorized into quartiles (Q1 = lower nBCS). Changes in BCS were calculated from dry-off to calving (multiparous cows); from calving to nadir; and from nadir to 21 d post nadir and assigned into quartile categories considering Q1 as the 25% of cows with greater loss (pre nadir) or smaller gain (post nadir) of BCS. Data were examined using logistic regression, ANOVA, and time to event analyses. Initial univariable models were followed by multivariable models that considered parity category, calving season, BCS1, and average milk yield up to 30 DIM (M30) as covariables. Sick cows and high producing cows had increased DIM at nadir, while lower BCSdry, greater M30, and occurrence of disease were associated with lower nBCS. Cows that conceived at AI1 had their nBCS earlier (45.1 ± 0.32 d) than cows that remained open after first AI (46.4 ± 0.35 d). On the contrary, DIM at nBCS did not differ by pregnancy loss status. The logistic regression analyses reported smaller odds of P/AI1 in cows in the lower nBCS categories. Likewise, cows with larger loss in BCS between calving and nadir had smaller odds of P/AI1. Although cows that lost their pregnancy had lower nBCS, this association was not consistent across the analyses. The time to and magnitude of nBCS, together with the ∆BCS from calving to nadir could be useful measures to evaluate the transition and early lactation status of dairy cows.

Associations of automated body condition scores at dry-off and through early lactation with milk yield of Holstein cows

The objective of this study was to analyze the associations of body condition score (BCS) and BCS change (∆BCS) during the dry period and the first 100 d of lactation with daily milk yield. Examining the involvement of health status in the associations between BCS and milk yield was a secondary objective of this research. Data included 12,042 lactations in 7,626 Holstein cows calving between April 2019 and January 2022 in a commercial dairy operation located in Colorado, USA. BCSs were generated daily by an automated BCS camera system located at the exit of the milking parlor. The assessment points selected for this study were dry-off (BCSdry), calving (BCS1), 7 DIM (BCS7), 14 DIM (BCS14), 21 DIM (BCS21), and nadir (nBCS; defined as the lowest daily BCS from calving to 100 DIM). Subsequently, these BCS were categorized considering quartiles (Q1 = 25% lowest BCS; Q4 = 25% greatest BCS), separately for primiparous and multiparous cows. Changes in BCS were calculated from dry-off to calving (multiparous); and from calving to 7 DIM, 14 DIM, 21 DIM, and nadir and assigned into quartile categories considering Q1 as the 25% of cows with the greatest decrease of BCS. Lactations were classified based on the number of health events before nadir as healthy, affected by one event, or having multiple events. Data were examined in primiparous and multiparous cows separately using ANOVA. The least square means for daily milk at 60 DIM and 305 DIM were calculated by category of BCS and ∆BCS at multiple time points and time periods. Subsequently, lactation curves were created by BCS and ∆BCS categories and by health status. Multivariable models included calving season and BCS1 as covariables. The largest differences in milk yield among categories of BCS and ∆BCS were identified for BCS originated at nadir and for the ∆BCS between calving and nadir. The differences in average daily milk yield between cows in the lowest and the greatest nBCS category (Q1 vs. Q4) were 3.3 kg/d (60 DIM) and 3.4 kg/d (305 DIM) for primiparous cows and 2.4 kg/d (60 DIM) and 2.1 kg/d (305 DIM) for multiparous cows. During the period from calving to nadir, primiparous cows in Q1 (greatest decrease of BCS) produced 4.3 kg/d (60 DIM) and 3.8 kg/d (305 DIM) more than cows in Q4. For multiparous cows, the differences were 3.0 kg/d (60 DIM) and 1.9 kg/d (305 DIM) in favor of Q1 cows. Overall, the associations between BCS and ∆BCS categories and milk yield were not consistent across time and they depended on the parity category. Nonetheless, as the assessment of BCS and ∆BCS approached the nadir, the association between greater milk yield and lower BCS or greater reduction in BCS became more evident.