Search

Objectives: The three objectives of this study were to determine the positive predictive value (PPV) of oestrus diagnosis (heat detection accuracy) by dairy farmers, calculate the diagnostic sensitivity and specificity of the P4 Rapid milk progesterone assay for detecting a corpus luteum and evaluate the economics of using a cow-side milk progesterone assay designed to aid oestrus diagnosis., Methods: Milk samples were collected from 752 cows diagnosed in oestrus by farm personnel on 14 dairy farms. Samples were tested using the P4 Rapid milk progesterone assay to estimate the PPV of oestrus diagnosis at each farm and a crude pooled mean of PPV of oestrus diagnosis across all farms. A further 156 milk samples were collected from cows with luteal tissue status determined by transrectal ultrasound and tested by the P4 Rapid assay to enable calculation of the sensitivity and specificity of the P4 Rapid assay., Results: For pooled farm samples, the PPV was 97.0%, with a range between farms of 88.9-100%. Sensitivity of the P4 Rapid milk progesterone assay for detecting a corpus luteum was 90.1% and specificity was 98.7%. Misclassification of oestrus in cows previously identified as pregnant was the most common cause of false-positive oestrus diagnoses by farm personnel., Conclusion: Routine testing of milk progesterone in all cows diagnosed in oestrus is not economically justified and may even slightly reduce submission rates; conversely, strategic use of cow-side milk progesterone assays can improve herd reproductive performance by facilitating decisions on whether to rebreed cows previously diagnosed as pregnant., (© 2016 Australian Veterinary Association.)

Background: The use of artificial insemination (AI) has great potential to improve smallholder dairy herds in Africa, however poor success and, in some situations, high costs in Kenya, have been discouraging. Effective AI requires accurate oestrus detection and the measurement of progesterone (P4) can be used to indicate oestrus as well as non-pregnancy. A cow-side progesterone lateral flow test, P4 Rapid, was evaluated as an aid to detect oestrus and non-pregnancy in Kenyan dairy cows, and assessed for association with AI efficiency. Methods: A total of 527 cows were enrolled in the study, from two counties in central and southern Kenya. Cattle in the test group (n = 308) were presented when suspected to be in oestrus and tested with the P4 Rapid (low P4 = oestrus, medium P4 = inconclusive, high P4 = not in oestrus/pregnant). Cattle with low P4 were inseminated. Cattle in the control group (n = 219) were inseminated when oestrus behaviour was detected i.e. standard practice. Results: Of the total P4 Rapid tests performed (n = 745), 1.5% were inconclusive, with the true accuracy of the test between 87-97%. Conception rates were not significantly higher in the test group (83.9%) compared to the control group (77.9%). Abortion rates were not significantly different between the control (9.5%) and test groups (8.2%). In the test group, 6.2% (19/308) cows showed a medium or high P4 level on day 0 and nine of these were subsequently found to have been already pregnant. Conclusions: The data indicated that the P4 Rapid test can be a useful tool to assist farmer decision-making in the confirmation of correct timing for AI, and importantly may avoid unnecessary inseminations in pregnant animals, thus reducing the risk of AI-induced abortion.

Objective. Efficiency in heat detection, determination of the duration and type of estrous cycle and progesterone concentration in sheep racial biotypes in the high tropics was evaluated. Materials and methods. In 32 adult ewes, biotype Criolla, Hampshire, Romney Marsh and Corriedale, during the postpartum period, Heat Detection Efficiency - EDC, cycle type: Short - CC, Regular - CR and Long - CL, Cycle Duration was determined. Oestrus – DCE and progesterone concentrations – CP4. CP4 were assessed by ELISA technique. Statistical analysis used a completely randomized model and repeated measures over time. Results. The average EDC was 64.88%, presenting 35.12% CC, 59.92% CR and 4.96% CL. The average of regular DCE was 16.9±0.89 days. The Criollo group presented the highest ranges of variation of CP4, showing changes in its steroidogenic activity of the corpus luteum similar to that observed with the Hampshire group. Between days 4 and 12, the Romney Marsh group presented the lowest CP4., being similar to the behavior of the Corriedale group. Conclusions. CP4 increases from days 2 to 6, remaining high and constant between days 10-12, gradually decreasing until day 18, progressively evidencing a possible functional luteolysis that leads to structural luteolysis. The CP4 may be associated with adaptation phenomena of some racial groups, which affect the reproductive efficiency that is observed to be differentiated between them. [ABSTRACT FROM AUTHOR]

Background: The use of artificial insemination (AI) has great potential to improve smallholder dairy herds in Africa, however high costs and often poor success rates have been discouraging. Effective AI requires accurate oestrus detection and the measurement of progesterone (P4) can be used to indicate oestrus as well as non-pregnancy. A cow-side progesterone lateral flow test, P4 Rapid, was evaluated as an aid to detect oestrus and non-pregnancy in Kenyan dairy cows, and assessed for association with AI efficiency. Methods: A total of 527 cows were enrolled in the study, from two counties in central and southern Kenya. Cattle in the test group (n = 308) were presented when suspected to be in oestrus and tested with the P4 Rapid (low P4 = oestrus, medium P4 = inconclusive, high P4 = not in oestrus/pregnant). Cattle with low P4 were inseminated. Cattle in the control group (n = 219) were inseminated when oestrus behaviour was detected i.e. standard practice. Results: Of the total P4 Rapid tests performed (n = 745), 1.5% were inconclusive, with the true accuracy of the test between 87-97%. Conception rates were not significantly higher in the test group (83.9%) compared to the control group (77.9%). Abortion rates were not significantly different between the control (9.5%) and test groups (8.2%). In the test group, 6.2% (19/308) cows showed a medium or high P4 level on day 0 and 9 of these were subsequently found to have been already pregnant. Conclusions: The data indicated that the P4 Rapid test can be a useful tool to assist farmer decision-making in the confirmation of correct timing for AI, and importantly may avoid unnecessary inseminations in pregnant animals, thus reducing the risk of AI-induced abortion.

Hourly pedometer counts and irregularly measured concentration of the hormone progesterone were available for a large number of dairy cattle. A hidden semi-Markov was applied to this bivariate time-series data for the purposes of monitoring the reproductive status of cattle. In particular, the ability to identify oestrus is investigated as this is of great importance to farm management. Progesterone concentration is a more accurate but more expensive method than pedometer counts, and we evaluate the added benefits of a model that includes this variable. The resulting model is biologically sensible, but validation is difficult. We utilize some auxiliary data to demonstrate the model's performance. [ABSTRACT FROM AUTHOR]

Abstract: Automated monitoring and detection of oestrus in dairy cows is attractive for reasons of economy in dairy farming. While high performance detection has been shown possible using high-priced progesterone measurements, detection results were less reliable when only low-cost sensor data were available. Aiming at improving detection scheme reliability with the use of low-cost sensor data, this study combines information from step count and leg tilt sensors. Introducing a lying balance for the individual animal, a novel change detection scheme is derived from observed distributions of the step count data and the lying balance. Detection and hypothesis testing are based on generalised likelihood ratio optimisation combined with time-wise joint probability windowing based on the duration of oestrus and oestrus intervals. It is shown to be essential that cow-specific parameters and test statistics are derived on-line from data to cope with behaviours of individuals. Performance is validated on 18 sequences of data where definite proof of prior oestrus was available in form of subsequent pregnancy. These data were extracted from data sequences from 44 dairy cows over an 8 months period. The results show sensitivity 88.9% and error rate 5.9.%, which is very satisfactory when only cheap sensor data are used. [Copyright &y& Elsevier]

Abstract: Reproductive management, in particular timely oestrus detection, is important for profitable dairy production. The aim of this study was to develop a biological model to predict reproductive state on the basis of milk progesterone measures. A number of additional inputs were incorporated to make use of other known effectors of reproductive performance that are not reflected in progesterone levels. These are: days from calving, breed, parity, signs of behavioural oestrus, insemination dates, pregnancy determinations, energy status, body fat status, milk urea content and reproductive disorders associated with calving. A dynamic, deterministic model was developed. It is designed to run each time a new trigger input (progesterone, behavioural oestrus, inseminations, pregnancy determinations) occurs using the current and previous values and can run in the absence of the additional inputs. The milk progesterone values are smoothed using an extended Kalman filter before being processed in the biological component of the model. The model predicts the reproductive status of the cow, which can be one of three mutually exclusive states: postpartum anoestrus, oestrus cycling, and potentially pregnant. The other model outputs are all reproductive status specific with the exception of days to next sample (DNS), which is calculated in each model run regardless of reproductive status. DNS is designed to feedback to the sampling system so that the frequency of milk sampling (i.e. progesterone measurement) can be varied according to the predicted likelihood of a future reproductive event, such as onset of oestrus cycling. The other model outputs are: risk of prolonged postpartum anoestrus, risk and type of ovarian cyst, onset of oestrus, likelihood of a potential insemination succeeding, and likelihood of being pregnant (following oestrus). The model was evaluated using three simulated datasets consisting of a timeseries of progesterone values centred on each of the three reproductive statuses and including relevant additional information. Test runs were carried out on the full datasets and then on reduced data. The data reductions were made by using only those values that would have been available if the model days to next sample function was used to control sampling frequency. The sensitivity of the model to noise in the raw progesterone data was examined by adding 1, 2, or 3 residual standard deviations (1.85ng/ml) random variation to the original data and evaluating model performance. The model was found to be able to readily identify and distinguish reproductive states. A reduction in sampling frequency to 36% of original sample resulted in an average increase in days to detection of oestrus of 0.36. The addition of 1 S.D. noise did not cause additional oestruses to be detected and all oestruses were correctly identified. However, when 2 or 3 S.D. noise were added, the model found on average 1.4 and 3 extra oestruses. It was concluded that reproductive status can be predicted from milk progesterone values using a biological model and that such a model is robust to reductions in sampling frequency number and to a doubling in the random variation in the raw progesterone values. It therefore has the potential to provide the basis for a useful reproductive management tool. [Copyright &y& Elsevier]