Accuracy of predicted calving dates in Holstein-Friesian dairy cows based on fetal ages estimated using manual rectal palpation

The Role of Transrectal Sonography in Pregnancy Diagnosis in Cattle

Objective:

A reproduction management strategy is an essential component of any cattle production operation. Options include palpation per rectum, transrectal sonography, or biochemical blood tests. To determine the best method, veterinary costs must be weighed against operational costs of maintaining open cattle.

Materials and Methods:

An online literature search using PubMed, Medline, CINAHL, EBSCO, and Google Scholar was completed to find relevant articles regarding pregnancy detection in cattle, costs of reproductive management in cattle, and pregnancy loss in cattle. Priority was given to original research articles pertaining to palpation per rectum, transrectal ultrasound, fetal gender, and pregnancy loss.

Results:

Transrectal sonography can detect a pregnancy up to 15 days earlier than palpation per rectum and has sensitivities and specificities as high as 97% when performed between days 21 and 35 post artificial insemination.

Conclusion:

Cattle producers can be confident that transrectal sonography is a useful tool in the beef and dairy cattle industry. Its use facilitates reproductive management decisions, such as re-breeding or culling, thus reducing expenses, and increasing profitability.

Components of the covariances between reproductive performance traits and milk protein concentration and milk yield in dairy cows

Reproductive performance in dairy cows can be improved through genetic selection and herd management. Milk protein concentration is strongly associated with various measures of reproductive performance, but the relative importance of genetic and environmental components of these associations have not been defined. The primary objective of this study was to estimate the magnitudes of correlations and covariances between 9 reproductive performance traits in dairy cows and each of milk protein concentration and milk yield at 4 levels: genetic, permanent environmental effects of cow, herd-year-season, and residual levels. A retrospective single cohort study was conducted using data collected from seasonally and split calving dairy herds. We used animal models to partition covariances for the relationships between 9 fertility traits and each of milk protein concentration and milk yield at lactation level, with up to 80,203 lactations from 27,244 cows that were 780 herd-year-seasons in 65 herds. For the fertility traits, of the explained covariance with milk protein concentration, between 33 and 79% (median 53%) was genetic and 21 to 67% (median 47%) was nongenetic. We concluded that research should be conducted to identify management strategies that capture the nongenetic components of relationships between milk protein concentration and reproductive performance. Genetic correlations with milk protein concentration were generally similar to genetic correlations with milk yield, but the correlation with milk protein concentration was closer (i.e., the absolute value of the correlation coefficient was nearer to 1) for pregnant by wk 6, a key trait for seasonally and split calving dairy herds (correlation coefficient ± standard error = 0.28 ± 0.05 and -0.17 ± 0.07 for milk protein concentration and milk yield, respectively). As the associations also have substantial genetic components, it is possible that reliabilities of estimated breeding values for fertility may be improved by including milk protein concentration in multitrait genetic evaluation models for fertility traits. From our preliminary analyses, reliabilities were only slightly higher when pregnancy by wk 6 of the breeding period was analyzed with milk protein concentration rather than alone or with milk yield, but further research should be considered to assess this question. Importantly, the benefits of these strong relationships can only be fully harnessed through joint use of both management strategies and genetic strategies.

Training Method and Other Factors Affecting Student Accuracy in Bovine Pregnancy Diagnosis

To optimize bovine pregnancy diagnosis (PD) training, factors influencing student performance were investigated. The objective was to determine whether training method, gender, background (farm, urban, or mixed), previous experience in bovine PD, and current career interest influenced the accuracy of bovine PD by trans-rectal palpation (TRP). Fourth-year (of a 6-year program) veterinary students (n=138) received one PD training session in groups using either simulator training on Breed'n Betsy (BB) or training on live cows (C). Students completed a questionnaire on gender, background, and career interest. Students' PD accuracy (pregnancy status and stage) was determined after training when each student palpated six cows with known pregnancy status. Students' accuracy in determining pregnancy status was measured as sensitivity and specificity (the ability to correctly identify the presence and absence of pregnancy respectively). Factors that influenced overall accuracy with a higher student sensitivity of bovine PD by TRP were training method, farming background, an interest in a mixed animal career, and stage of gestation. Gender of students and previous experience in bovine PD did not have an influence. Training on BB simulators was associated with lower student sensitivity for pregnancy detection in cows <6 months pregnant. Student sensitivity for pregnancy detection in cows >6 months pregnant was similar for training on BB simulators and live cows. No evaluated factors were significantly associated with specificity of PD. Teaching efforts focusing on specificity of PD and repeated simulator-based training in conjunction with live cow exposure are recommended.

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.

Animal board invited review: precision livestock farming for dairy cows with a focus on oestrus detection

Dairy cows are high value farm animals requiring careful management to achieve the best results. Since the advent of robotic and high throughput milking, the traditional few minutes available for individual human attention daily has disappeared and new automated technologies have been applied to improve monitoring of dairy cow production, nutrition, fertility, health and welfare. Cows milked by robots must meet legal requirements to detect healthy milk. This review focuses on emerging technical approaches in those areas of high cost to the farmer (fertility, metabolic disorders, mastitis, lameness and calving). The availability of low cost tri-axial accelerometers and wireless telemetry has allowed accurate models of behaviour to be developed and sometimes combined with rumination activity detected by acoustic sensors to detect oestrus; other measures (milk and skin temperature, electronic noses, milk yield) have been abandoned. In-line biosensors have been developed to detect markers for ovulation, pregnancy, lactose, mastitis and metabolic changes. Wireless telemetry has been applied to develop boluses for monitoring the rumen pH and temperature to detect metabolic disorders. Udder health requires a multisensing approach due to the varying inflammatory responses collectively described as mastitis. Lameness can be detected by walk over weigh cells, but also by various types of video image analysis and speed measurement. Prediction and detection of calving time is an area of active research mostly focused on behavioural change.

Reproductive performance of seasonal-calving, pasture-based dairy herds in four regions of New Zealand

AIMS

The primary objective of this study was to describe the reproductive performance of a selection of New Zealand dairy herds in 2009/10 and 2010/11. A secondary objective was to explore associations between a number of biophysical and socio-demographic herd-level variables and herd-level reproductive outcomes.

METHODS

Seasonal-calving, pasture-based herds from the Waikato (n=16), Taranaki (n=16), North Canterbury (n=17) and South Canterbury (n=14) regions of New Zealand were enrolled as part of a larger study. Submission, conception and pregnancy risks ("in-calf risks") were calculated for 2009/10 and 2010/11 based on cow-level records and from the results of strategically timed pregnancy diagnoses. Additional herd-level information was collected during interviews with the herd key decision maker (KDM). Associations between independent variables and reproductive performance were examined using univariable and multivariable generalised linear models.

RESULTS

Mean 6-week in-calf risk of the herds over both study years was 67 (min 46, max 86)%, mean 3-week submission risk was 81 (min 44, max 95)% and mean probability of pregnancy at first insemination was 48 (min 25, max 71)%. On univariable analyses, the herd's 6-week in-calf risk in the year before enrollment (2008/09) was the strongest identified predictor of each of submission risk, probability of pregnancy at first insemination and 6-week in-calf risk (all p<0.01), while other predictor variables considered were non-significant. From multivariable modelling, herds using extensive production systems had better reproductive performance than more intensively managed systems. Higher 3-week submission risks were achieved in herds where the KDM were farm managers rather than owner operators, and where the predominant breed was Jersey rather than Friesian.

CONCLUSIONS

The New Zealand dairy industry has set a target for a mean 6-week in-calf risk of 78%, with minimal hormonal intervention, by 2016. Achieving this target appears unlikely unless there are substantial increases in both submission risks and pregnancy at first inseminations. The mean 6-week in-calf risk of New Zealand dairy herd does not appear to have changed in the last decade. However the large differences between herds indicate that better reproductive performance is currently possible in many herds. The herd's 6-week in-calf risk in the year before enrollment (2008/09) was the strongest identified predictor of current 6-week in-calf risk, suggesting that rate of improvement in reproductive performance would be slow within current management systems unless substantial changes are introduced.