Factors associated with the differential in actual gestational age and gestational age predicted from transrectal ultrasonography in pregnant dairy cows

Association between days post conception and lactation persistency in dairy cattle

Determining the optimal insemination moment for individual cows is complex, particularly when considering the impact of pregnancy on milk production. The effect of pregnancy on the absolute milk yield has already been reported in several studies. Currently, there is limited quantitative knowledge about the association between days post conception (DPC) and lactation persistency, based on a lactation curve model, and, specifically, how persistency changes during pregnancy and relates to the days in milk at conception (DIMc). Understanding this association might provide valuable insights to determine the optimal insemination moment. This study, therefore, aimed to investigate the association between DPC and lactation persistency, with an additional focus on the influence of DIMc. Available milk production data from 2005 to 2022 were available for 23,908 cows from 87 herds located throughout the Netherlands and Belgium. Persistency was measured by a lactation curve characteristic decay, representing the time taken to halve milk production after peak yield. Decay was calculated for 8 DPC (0, 30, 60, 90, 120, 150, 180 and 210 d after DIMc) and served as the dependent variable. Independent variables included DPC, DIMc (< = 60, 61-90, 91-120, 121-150, 151-180, 181-210, > 210), parity group, DPC × parity group, DPC × DIMc and variables from 30 d before DIMc as covariates. The results showed an increase in decay, i.e., a decrease in persistency, during pregnancy for both parity groups, albeit in different ways. Specifically, from DPC 150 to DPC 210, multiparous cows showed a higher decline in persistency compared with primiparous cows. Furthermore, a later DIMc (cows conceiving later) was associated with higher persistency. Except for the early DIMc groups (DIMc < 90), DIMc does not impact the change in persistency by gestation. The findings from this study contribute to a better understanding of how DPC and DIMc during lactation influence lactation persistency, enabling more informed decision-making by farmers who wish to take persistency into account in their reproduction management.

Estimation of gestational age in buffaloes (Bubalus bubalis) by transabdominal and transrectal ultrasonography

The aims of the presented study were (1) to examine the relationship between foetal measurements and gestational age (GA), (2) to generate GA formulas, and (3) to investigate the estimation of GA by transabdominal ultrasonography in buffaloes. Thirteen pregnant buffaloes were used in the study. Transrectal ultrasonography was performed between Day (D) 28 and 112 of gestational age, whereas transabdominal ultrasonography was between 126 and 294. The diameters of embryonic (EVD) and amniotic (AVD) vesicles, crown-rump length (CRL), occipito-nasal length (ONL), biparietal diameter (BPD), orbit diameter (OD), cervical, thoracic, lumbar and coccygeal vertebrae lengths (CVL, TVL, LVL, CcVL), abdominal diameter (AD), chest diameter (CD), umbilical cord diameter (UCD), scapula, humerus, radius-ulna, metacarpus, femur, tibia, and metatarsus lengths (SL, HL, RUL, McL, FL, TL, MtL), diameters of transversal heart (THD), stomach (SD), kidney (KD), and outer diameter, circumference and area of placentomas (OPD, OPC, OPA) were measured by ultrasonography. All 26 parameters were highly correlated with GA (r = 0.968 - 0.999). The observation of the foetus was evident in all animals via transabdominal ultrasonography, and all parameters except EVD, AVD, and CRL could be measured on D 126. In addition, heartbeats, the sign of foetal vitality, could be observed in 11 of 13 living foetuses. This study is the first to demonstrate that transabdominal ultrasonography can be used to estimate GA in buffaloes. In addition, GA formulas related to ONL, CVL, TVL, LVL, CcVL, extremity bone lengths, THD, UCD, PC, and PA measurements were created for the first time in buffaloes.

Prediction of persistency for day 305 of lactation at the moment of the insemination decision

When deciding on the voluntary waiting period of an individual cow, it might be useful to have insight into the persistency for the remainder of that lactation at the moment of the insemination decision, especially for farmers who consider persistency in their reproduction management. Currently, breeding values for persistency are calculated for dairy cows but, to our knowledge, prediction models to accurately predict persistency at different moments of insemination are lacking. This study aimed to predict lactation persistency for DIM 305 at different insemination moments (DIM 50, 75, 100, and 125). Available cow and herd level data from 2005 to 2022 were collected for a total of 20,508 cows from 85 herds located in the Netherlands and Belgium. Lactation curve characteristics were estimated for every daily record using the data up to and including that day. Persistency was defined as the number of days it takes for the milk production to decrease by half during the declining stage of lactation, and calculated from the estimated lactation curve characteristic 'decay'. Four linear regression models for each of the selected insemination moment were built separately to predict decay at DIM 305 (decay-305). Independent variables included the lactation curve characteristics at the selected insemination moment, daily milk yield, age, calving season, parity group and other herd variables. The average decay-305 of primiparous cows was lower than that of multiparous cows (1.55 *10-3 vs. 2.41*10-3, equivalent to a persistency of 447 vs. 288 days, respectively). Results showed that our models had limitations in accurately predicting persistency, although predictions improved slightly at later insemination moments, with R2 values ranging between 0.27 and 0.41. It can thus be concluded that, based only on cow and herd milk production information, accurate prediction of persistency for DIM 305 is not feasible.

Predicting male dairy calf live weight for use in calf management decision support

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A robust yet simple statistical model for predicting young male dairy calf live weight was developed.

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The predictive model can be adapted to estimate the age when a calf reaches a target live weight.

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The model can be integrated into a decision support tool to inform calf labor and infrastructure management.

The growing awareness and scrutiny of the management of young dairy calves, especially male calves, necessitates a support tool to aid in the planning of resource allocation on dairy farms. There is a desire among some vendors for a minimum calf weight when purchasing young dairy bull calves. Hence, the objective of the present study was to investigate whether live weight of young calves (approximately 10–50 d old) can be predicted using readily accessible animal-level features, especially features that may be available in advance of birth. A multiple linear regression mixed model was developed with the live weight of 602 dairy bull calves aged between 10 and 42 d as the dependent variable; the age at which an animal is predicted to reach a predefined live weight was then estimated based on the model regression coefficients. Fixed effects included in the multiple regression model were dam parity, gestation length, and parental average genetic merit for relevant traits available in Ireland; namely, birth weight, birth size, and carcass weight. Herd of origin was included as a random effect, with all calves having been sold directly from the farm of birth. Live weight data were recorded at the point of sale when calves were, on average, 26 d old with a mean live weight of 56.6 kg. Animals were randomly assigned to 10 separate (i.e., folds) cross-validation data sets without replacement (i.e., each fold consisted of a different 10% of the data to test the model, with the remaining 90% of data being used to train the model) to quantify the accuracy of prediction. Across all data, the correlation between actual and predicted live weight was 0.76; the regression coefficient of actual live weight on predicted live weight across all data was 0.99. The root mean squared error of prediction varied from 4.40 to 6.66 kg per fold. Across all data, the root mean squared error was 5.61 kg, implying that 68% of live weight predictions were within 5.61 kg of the actual live weight. Given the potential availability of all model features in advance of birth (gestation length can be predicted from ultrasound examination of the pregnant uterus, although substituting parental average genetic merit for gestation length had minimal effect on model performance), predictions can be integrated into a dairy farm decision support tool to aid in the management of labor and infrastructure resources to achieve minimum live weight specifications before sale.

Invited review: Beef-on-dairy-The generation of crossbred beef × dairy cattle

Because a growing proportion of the beef output in many countries originates from dairy herds, the most critical decisions about the genetic merit of most carcasses harvested are being made by dairy producers. Interest in the generation of more valuable calves from dairy females is intensifying, and the most likely vehicle is the use of appropriately selected beef bulls for mating to the dairy females. This is especially true given the growing potential to undertake more beef × dairy matings as herd metrics improve (e.g., reproductive performance) and technological advances are more widely adopted (e.g., sexed semen). Clear breed differences (among beef breeds but also compared with dairy breeds) exist for a whole plethora of performance traits, but considerable within-breed variability has also been demonstrated. Although such variability has implications for the choice of bull to mate to dairy females, the fact that dairy females themselves exhibit such genetic variability implies that "one size fits all" may not be appropriate for bull selection. Although differences in a whole series of key performance indicators have been documented between beef and beef-on-dairy animals, of particular note is the reported lower environmental hoofprint associated with beef-on-dairy production systems if the environmental overhead of the mature cow is attributed to the milk she eventually produces. Despite the known contribution of beef (i.e., both surplus calves and cull cows) to the overall gross output of most dairy herds globally, and the fact that each dairy female contributes half her genetic merit to her progeny, proxies for meat yield (i.e., veal or beef) are not directly considered in the vast majority of dairy cow breeding objectives. Breeding objectives to identify beef bulls suitable for dairy production systems are now being developed and validated, demonstrating the financial benefit of using such breeding objectives over and above a focus on dairy bulls or easy-calving, short-gestation beef bulls. When this approach is complemented by management-based decision-support tools, considerable potential exists to improve the profitability and sustainability of modern dairy production systems by exploiting beef-on-dairy breeding strategies using the most appropriate beef bulls.

Postpartum Uterine Involution and Embryonic Development Pattern in Chinese Holstein Dairy Cows

Understanding the postpartum uterine involution pattern and embryonic development could facilitate bovine reproduction management, improve reproductive efficiency, and diagnosis of the reproductive disorder, which would contribute to the success of the dairy business. This study aimed to investigate postpartum uterine involution and embryonic developmental patterns or postconceptional marks of embryonic fetal development in Chinese Holstein dairy cows using B-mode ultrasonography. The results revealed a significant decline in the involution period with an increase of parity and age. The uterine involution period was shorter in multiparous cows when compared with cows with lower parities. Consistently, cows over 4 years old recovered faster than younger cows (2 or 3 years). Besides, the elder cows (over 4 years) had a relatively larger size of resumed cervix uteri and horns. Postpartum uterine involution pattern analysis revealed that the reproductive tract recovered very fast during the first 16 days postpartum for all the parity. Results of postconceptional marks of embryo development revealed a slow increase in diameter of the gravid uterine horn and crown-rump length (CRL) before day 60. In contrast, this increase was dramatic and rapid after the 60th day. We also established two models to estimate gestational age based on gravid uterine horn diameter or CRL. A formula was established to determine the gravid uterine horn size during postconceptional on day 30th–day 90th (r = 0.8714, P < 0.01). In addition, a significant positive correlation between CRL and gestational age (r = 0.98151, P < 0.01) was built. In conclusion, these results illustrated that parity and calving age had significant effects on uterine involution in Chinese Holstein cows. Crown-rump length and gravid uterine horn diameter are both efficient for evaluating the embryo growth. These current findings broaden the understanding of basic reproductive pattern in Chinese Holstein cows and could benefit bovine reproductive management primarily in postpartum and early pregnant cows to reduce the calving interval and avoid periparturient metabolic diseases.

Prediction of the onset of parturition in horses and cattle

Economic losses due to dystocia or neonatal death as well as animal welfare and ethical concerns demand a reliable prediction of parturition with the aim to improve survival of the dam and her offspring. In this review, estimation of gestational age by ultrasound and prediction of parturition based on clinical signs, behaviour changes and changes in body temperature, composition of mammary gland secretions as well as hormonal changes are discussed in comparison between horses and cattle. Based on the physiological changes associated with the end of gestation and onset of labor, several systems and methods to predict parturition in horses and cattle have been developed. Physiological differences related to breed, maternal age and parity, pathological changes due to intrauterine growth retardation, placental problems or general illness of the dam but also housing and management systems bias a reliable prediction of parturition. This may be acceptable in cattle as long as birth alarm systems give satisfying results at herd level. The situation is different in the horse where the economic and emotional value of an individual mare and her foal usually reaches different dimensions than in cows. The possibilities for prediction of parturition can thus be discussed together, the consequences, however, may differ considerably between the two species.

Observed progeny performance validates the benefit of mating genetically elite beef sires to dairy females

While several studies in cattle have confirmed the improved performance achievable from selection on total merit indexes, these studies have solely been confined to specific-purpose beef or dairy total merit indexes. Validation studies of total merit indexes used to select beef sires for use on dairy females are lacking. The objective here was to fill this void by quantifying the performance of beef × dairy progeny where the sire excels in either a total merit index encompassing calving performance and beef performance traits (dairy-beef index; DBI) or excels in a subindex based solely on calving performance (CLV); for comparative purposes, these beef × dairy progeny were also compared with dairy × dairy progeny. A total of 123,785 calving records from 101,773 dairy cows calving in 3,065 dairy herds were used; of these, 48,875 progeny also had carcass information. The beef sires were stratified into 5 equally sized groups based separately on their DBI or CLV. Linear and threshold mixed models were used to compare calving and carcass performance of all 3 sire genotypes. Of the 415 sires that ranked in the highest of the 5 strata on the CLV subindex, only 52% of them ranked in the highest stratum for the DBI. The percentage of primiparae requiring any assistance at calving was 2 to 3 percentage units greater for the higher DBI sires relative to both the higher CLV beef sires and the dairy sires (not ranked on anything); no difference existed in multiparae. The extent of calving difficulty in primiparae was, however, less in higher DBI beef sires relative to both the higher CLV beef sires and the dairy sires, although the differences were biologically small. Perinatal mortality was greatest in the beef sires relative to the dairy sires, but no difference existed between the high CLV or high DBI beef sires. No difference in progeny gestation length was evident between the high DBI or high CLV beef sires, although both were >2 d longer than progeny from dairy sires. The higher DBI sires produced progeny with heavier, more conformed carcasses relative to the progeny from both high CLV beef sires and dairy sires. No differences existed between the progeny of the beef sires ranked highly on the CLV versus those ranked highly on the DBI for the probability of achieving the specification for carcass weight (between 270 and 380 kg) or fat score; the higher DBI animals, however, had a 4 to 10% greater probability of achieving the minimum carcass conformation required. In all instances, the beef sires had a greater probability of achieving all specifications relative to the progeny from the dairy sires with the difference for conformation being particularly large. Results indicate that more balanced progeny can be generated using a DBI, helping meet the requirements of both dairy and beef producers. Ignoring market failure across sectors, using higher DBI sires could increase dairy herd profit by 3 to 5% over and above the status quo approach to selection in dairy (i.e., CLV subindex).

Choice of artificial insemination beef bulls used to mate with female dairy cattle

Understanding the preferences of dairy cattle producers when selecting beef bulls for mating can help inform beef breeding programs as well as provide default parameters in mating advice systems. The objective of the present study was to characterize the genetic merit of beef artificial insemination (AI) bulls used in dairy herds, with particular reference to traits associated with both calving performance and carcass merit. The characteristics of the beef AI bulls used were compared with those of the dairy AI bulls used on the same farms. A total of 2,733,524 AI records from 928,437 females in 5,967 Irish dairy herds were used. Sire predicted transmitting ability (PTA) values and associated reliability values for calving performance and carcass traits based on national genetic evaluations from prior to the insemination were used. Fixed effects models were used to relate both genetic merit and the associated reliability of the dairy and beef bulls used on the farm with herd size, the extent of Holstein-Friesian × Jersey crossbreeding adopted by the herd, whether the herd used a technician insemination service or do-it-yourself, and the parity of the female mated. The mean direct calving difficulty PTA of the beef bulls used was 1.85 units higher than that of the dairy bulls but with over 3 times greater variability in the beef bulls. This 1.85 units equates biologically to an expectation of 1.85 more dystocia events per 100 dairy cows mated in the beef × dairy matings. The mean calving difficulty PTA of the dairy AI bulls used reduced with increasing herd size, whereas the mean calving difficulty PTA of the beef AI bulls used increased as herd size increased from 75 cows or fewer to 155 cows; the largest herds (>155 cows) used notably easier-calving beef bulls, albeit the calving difficulty PTA of the beef bulls was 3.33 units versus 1.67 units for the dairy bulls used in these herds. Although we found a general tendency for larger herds to use dairy AI bulls with lower reliability, this trend was not obvious in the beef AI bulls used. Irrespective of whether dairy or beef AI bulls were considered, herds that operated more extensive Holstein-Friesian × Jersey crossbreeding (i.e., more than 50% crossbred cows) used, on average, easier calving, shorter gestation-length bulls with lighter expected progeny carcasses of poorer conformation. Mean calving difficulty PTA of dairy bulls used increased from 1.39 in heifers to 1.79 in first-parity cows and to 1.82 in second-parity cows, remaining relatively constant thereafter. In contrast, the mean calving difficulty PTA of the beef bulls used increased consistently with cow parity. Results from the present study demonstrate a clear difference in the mean acceptable genetic merit of beef AI bulls relative to dairy AI bulls but also indicates that these acceptable limits vary by herd characteristics.

Relative effect of milk constituents on fertility performance of milk-recorded, spring-calving dairy cows in Ireland

Several studies have reported associations between milk composition data and fertility performance. However, no work to date has estimated the effect of milk constituents on fertility performance in cows with low milk constituent concentrations. The objective of this study was to assess the association between milk constituents, animal characteristics, and time from mating start date (MSD) to conception using survival analysis. Furthermore, we aimed to investigate the relative effect of each variable by predicting median times to conception for animals with different combinations of characteristics and milk compositions. The final data set consisted of 87,227 cow lactation records from 64,519 cows in 2,049 herds with calving dates from 2010 to 2013. Milk recording data from each lactation were used, including test day recordings at 0 to 30, 31 to 60, and 61 to 90 d in milk (DIM). The analysis was limited to spring-calving cows (i.e., animals calving from January to May inclusive). Mating start date was determined for each unique herd in each year. A cow-specific MSD (MSD_cow) was defined taking into consideration the MSD for each herd and the calving date and a minimum calving to insemination interval of each herd year. The conception date for each cow was estimated using the subsequent calving date. Cows with no subsequent calving date were assumed not to have conceived. Time from MSD_cow to approximate conception date was analyzed using survival analysis. Cox proportional hazard models were constructed for each of the 3 recording windows: 0 to 30, 31 to 60, and 61 to 90 DIM. A fourth model was used to assess the dynamics of milk composition over the 3 windows. To investigate the effect of these variables, model outputs were used to create parametric accelerated failure time models to predict median survival times for animals at the 10th and 90th percentiles of the variable of interest but otherwise identical across the rest of the variables. Results demonstrated that fertility breeding subindex had the largest effect on time from MSD_cow to conception, with an additional 62 d open for those in the 10th percentile versus those in the 90th percentile. Of the milk constituents, milk lactose concentration had the greatest effect on MSD to conception, particularly when measured from 0 to 30 DIM. An additional 10 d open resulted from comparing those in the 10th and 90th percentiles. Milk protein concentration, although statistically significant, had a lower effect on fertility outcome when comparing cows in the 10th and 90th percentiles for this exposure variable. The greatest effect was found in the 61 to 90 DIM recording window, where cows in the 10th percentile had an additional 9 d open at the subsequent breeding season compared with those in the 90th percentile. Overall, our study shows that although the associations between milk constituents and fertility are statistically significant, their overall influence in determining MSD to conception in this study population is relatively modest, particularly compared with fertility breeding subindex, when comparing cows at the 10th and 90th percentiles. Of the milk constituents measured, milk lactose concentration measured at 0 to 30 DIM had the greatest effect in determining fertility outcome when comparing cows at the 10th and 90th percentiles. The predictive value of early-lactation test day milk composition data on hazard of pregnancy during the following breeding period, within a spring-calving context, appears to be relatively modest at the individual-cow level. Further work is required to test the usefulness of these associations at the herd level.

Genomic Regions Associated With Gestation Length Detected Using Whole-Genome Sequence Data Differ Between Dairy and Beef Cattle

While many association studies exist that have attempted to relate genomic markers to phenotypic performance in cattle, very few have considered gestation length as a phenotype, and of those that did, none used whole genome sequence data from multiple breeds. The objective of the present study was therefore to relate imputed whole genome sequence data to estimated breeding values for gestation length using 22,566 sires (representing 2,262,706 progeny) of multiple breeds [Angus (AA), Charolais (CH), Holstein-Friesian (HF), and Limousin (LM)]. The associations were undertaken within breed using linear mixed models that accounted for genomic relatedness among sires; a separate association analysis was undertaken with all breeds analysed together but with breed included as a fixed effect in the model. Furthermore, the genome was divided into 500 kb segments and whether or not segments harboured a single nucleotide polymorphism (SNP) with a P ≤ 1 × 10^-4 common to different combinations of breeds was determined. Putative quantitative trait loci (QTL) regions associated with gestation length were detected in all breeds; significant associations with gestation length were only detected in the HF population and in the across-breed analysis of all 22,566 sires. Twenty-five SNPs were significantly associated (P ≤ 5 × 10^-8) with gestation length in the HF population. Of the 25 significant SNPs, 18 were located within three QTLs on Bos taurus autosome number (BTA) 18, six were in two QTL on BTA19, and one was located within a QTL on BTA7. The strongest association was rs381577268, a downstream variant of ZNF613 located within a QTL spanning from 58.06 to 58.19 Mb on BTA18; it accounted for 1.37% of the genetic variance in gestation length. Overall there were 11 HF animals within the edited dataset that were homozygous for the T allele at rs381577268 and these had a 3.3 day longer (P < 0.0001) estimated breeding value (EBV) for gestation length than the heterozygous animals and a 4.7 day longer (P < 0.0001) EBV for gestation length than the homozygous CC animals. The majority of the 500 kb windows harboring a SNP with a P ≤ 1 × 10^-4 were unique to a single breed and no window was shared among all four breeds for gestation length, suggesting any QTLs identified are breed-specific associations.

Sonogram of coccygeus muscle in dairy cows with different gestational ages

BACKGROUND

The change in size and weight of the female reproductive organs during gestation and birth might be affect the perineal muscles and this condition in dairy cow not been reported. This study aimed to assess the ultrasonographic image of coccygeus muscle in 11 inseminated dairy cows with different gestational ages and postpartum.

METHODS

Gestational age was calculated based on the record of artificial insemination and confirmed by using transrectal brightness mode ultrasonography. Perineal hair between the sacrum and ischium bones was shaved along 3-5 cm before being ultrasound. The images of perineal area were obtained by transcutaneous ultrasound using a 5.0 MHz transducer. The thickness and intensity of the coccygeus muscle were measured and analyzed by gestational status and postpartum to show the differences.

RESULTS

The results showed that the thickness of coccygeus muscle increased with the increase in gestational age. Muscle intensity only increased at young gestational age. However, it decreased with the increase in gestational age (P < 0.05).

CONCLUSIONS

The ultrasound image of coccygeus muscle was affected by gestational status, thus this method may be used as one of the new methods of indirect gestational detection on dairy cows.

Accuracy of fetal age estimates using transrectal ultrasonography for predicting calving dates in dairy cows in seasonally calving herds in New Zealand

AIM

To describe the accuracy of transrectal ultrasonography for predicting calving dates in dairy cows under typical New Zealand conditions and to assess potential risk factors for differences between predicted and actual calving dates.

METHODS

Data were collected from 116 seasonally calving herds over 2 years in a retrospective single cohort study. Transrectal ultrasonography was undertaken by experienced veterinarians (n=12) to determine if cows were pregnant, and if so to estimate fetal age. Predicted calving date was calculated by adding 282 days to the estimated conception date. Accuracy was assessed using differences between predicted and actual calving dates for each animal. Potential risk factors for animals calving >10 days before or after their predicted calving date were assessed using multinomial logistic regression models.

RESULTS

The study population comprised 83,104 cows over the 2 years of the study; 75,037 (90.3%) cows calved within 10 days of their predicted calving date, 3,683 (4.4%) calved >10 days earlier, and 4,384 (5.3%) >10 days later, than predicted. Risk factors for calving >10 days before or after the predicted calving date included having >1 artificial insemination (AI) before pregnancy diagnosis (p=0.03), where the cow's most recent AI was <21 days before the end of the herd's AI period (p<0.01), and where the diagnosis was made at the second or third herd-visit (p<0.01). The probability of calving being >10 days later than predicted also increased when the fetus was ≥13 weeks old at pregnancy diagnosis (p<0.01).

CONCLUSIONS AND CLINICAL RELEVANCE

In this study, >90% of cows diagnosed pregnant by veterinarians using transrectal ultrasonography calved within 10 days of the predicted calving date. In herds where herd reproductive performance is high, it would be expected that more cows would conceive to their first AI, and potentially fewer cows would have AI close to the end of the herd's AI period, which would increase diagnostic accuracy. Where herd managers rely on accurate predicted calving dates they should be informed about realistic expected accuracy. For greatest accuracy, a complete AI history should be made available to the person performing the pregnancy diagnoses and cows at most risk of having inaccurate predicted calving dates should be identified.