A novel system for on-farm fertility monitoring based on milk progesterone

Geerinckx

et al. 2019

Preprint

Self Cite

INTERPRETATIVE SUMMARYValidation of luteolysis monitoring tool Adriaens.Recently, a novel progesterone-based monitoring algorithm using synergistic control, PMASC, was developed. This algorithm employs the known luteal dynamics of the progesterone profile to estimate the moment of luteolysis with minimal dependence on the absolute measurement values. As its value and implementability strongly dependends on the number of samples it requires to obtain the desired accuracy, its performance on cost-effective, ‘smart’ sampled and on-farm measured data is demonstrated.SHORT COMMUNICATIONValidation of a novel milk progesterone based tool to monitor luteolysis in dairy cows. Performance on cost-effective, on-farm measured data

supporting

confidence: 90%

mentioning

confidence: 99%

SHORT COMMUNICATION: Validation of a novel milk progesterone based tool to monitor luteolysis in dairy cows. Performance on cost-effective, on-farm measured data

Geerinckx

et al. 2019

Preprint

Self Cite

“…Each alert not associated with a REP was considered a false alarm (false positive, FP). Progesterone-based alerts were generated with a recently developed on-line monitoring system, P4 Monitoring Algorithm using Synergistic Control (PMASC), which consists of a mathematical model describing the different parts of the P4 profile (Adriaens et al, 2017) and a statistical process control chart to indicate luteolysis (Adriaens et al, 2018). An alert was identified as TP if followed by a REP within 24 h; all other alerts were considered FP.…”

mentioning

confidence: 99%

Short communication: Sensitivity of estrus alerts and relationship with timing of the luteinizing hormone surge

Journal of Dairy Science

Lamberigts

et al. 2019

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Both the sensitivity of an estrus detection system and the consistency of alarms relative to ovulation determine its value for a farmer. The objective of this study was to compare an activity-based system and a milk progesterone-based system for their ability to detect estrus reliably, and to investigate how their alerts are linked to the time of the LH surge preceding ovulation. The study was conducted on an experimental research farm in Flanders, Belgium. The activity alerts were generated by a commercial activity meter (ActoFIT, DeLaval, Tumba, Sweden), and milk progesterone was measured using a commercial ELISA kit. Sensitivity and positive predictive value of both systems were calculated based on 35 estrus periods over 43 d. Blood samples were taken for determination of the LH surge, and the intervals between timing of the alerts and the LH surge were investigated based on their range and standard deviation (SD). Activity alerts had a sensitivity of 80% and a positive predictive value of 65.9%. Alerts were detected from 39 h before until 8 h after the LH surge (range: 47 h, SD: 16 h). Alerts based on milk progesterone were obtained from a recently developed monitoring algorithm using a mathematical model and synergistic control. All estruses were correctly identified by this algorithm, and the LH surge followed, on average, 62 h later. Using the mathematical model, model-based indicators for the estimation of ovulation time can be calculated. Depending on which modelbased indicator was used, ranges of 33 to 35 h and SD of about 11 h were obtained. Because detection of the LH surge was very labor intensive, only a limited number of potential estrus periods could be studied.

“…The first algorithm tested in this paper, PMASC, consists of a mathematical model describing the luteal dynamics (Adriaens et al, 2017), and a statistical process control chart to detect luteolysis (Adriaens et al, 2018a). The mathematical model consists of two sigmoidal functions, a symmetrical Hill function to characterize the increase in P4 during luteal development, and a Gompertz function to describe the decrease during luteolysis.…”

Section: Methodsmentioning

confidence: 99%

“…The set threshold’s value might depend on the P4 measurement technique or the calibration method, but is generally taken between 4 and 6 ng/mL (Friggens et al, 2008; Bruinjé et al, 2017). In contrast, the P4 monitoring algorithm using synergistic control ( PMASC ) enables the identification of fertility events on farm using the underlying physiological basis of the related P4 dynamics (Adriaens et al, 2017, 2018a). It employs a combination of mathematical functions to describe the development and regression of the CL and a statistical control chart for detection of luteolysis.…”

Section: Introductionmentioning

confidence: 99%

Validation of a novel milk progesterone-based tool to monitor luteolysis in dairy cows. Timing of the alerts and robustness against missing values

Martin

et al. 2019

Preprint

Self Cite

Automated monitoring of fertility in dairy cows using milk progesterone is based on the accurate and timely identification of luteolysis. In this way, well-adapted insemination advice can be provided to the farmer to further optimize the fertility management. To properly evaluate and compare the performance of new and existing data-processing algorithms, a test dataset of progesterone time-series that fully covers the desired variability in progesterone profiles is needed. Further, the data should be measured with a high frequency to allow rapid onset events, such as luteolysis, to be precisely determined. Collecting this type of data would require a lot of time, effort and budget. In the absence of such data, an alternative was developed using simulated progesterone profiles for multiple cows and lactations, in which the different fertility statuses were represented. To these, relevant variability in terms of cycle characteristics and measurement error was added, resulting in a large cost-efficient dataset of well-controlled but highly variable and farm-representative profiles. Besides the progesterone profiles, information on (the timing of) luteolysis was extracted from the modelling approach and used as a reference for the evaluation and comparison of the algorithms. In this study, two progesterone monitoring tools were compared: a multiprocess Kalman filter combined with a fixed threshold on the smoothed progesterone values to detect luteolysis, and a progesterone monitoring algorithm using synergistic control ‘PMASC’, which uses a mathematical model based on the luteal dynamics and a statistical control chart to detect luteolysis. The timing of the alerts and the robustness against missing values of both algorithms were investigated using two different sampling schemes: one sample per cow every eight hours versus one sample per day. The alerts for luteolysis of the PMASC algorithm were on average 20 hours earlier compared to the ones of the multiprocess Kalman filter, and their timing was less sensitive to missing values. This was shown by the fact that, when one sample per day was used, the Kalman filter gave its alerts on average 24 hours later, and the variability in timing of the alerts compared to simulated luteolysis increased with 22%. Accordingly, we postulate that implementation of the PMASC system could improve the consistency of luteolysis detection on farm and lower the analysis costs compared to the current state of the art.Interpretative SummaryValidation of luteolysis monitoring tool for dairy cows.AdriaensIn this study, the performance of two monitoring algorithms to detect luteolysis using milk progesterone measurements was validated on a simulated dataset of realistic milk progesterone profiles. The synergistic control-based algorithm, PMASC, was able to identify luteolysis almost simultaneously with its occurrence. It was found to be more robust against missing samples and less dependent on the absolute milk progesterone values compared to a multiprocess Kalman filter combined with a fixed threshold. This research showed that implementation of PMASC could improve progesterone-based fertility monitoring on farm.