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

Adriaens, Ines; Saeys, Wouter; Geerinckx, Katleen; Ketelaere, Bart De; Aernouts, Ben

doi:10.1101/526061

Cited by 2 publications

(10 citation statements)

References 9 publications

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“…This shows the sensitivity of MPKF+T to the actual entered data and their absolute values. For PMASC, the FNR was very similar to that for the ‘ALL’ sampling scheme (1.1% and 0.8% respectively for ‘1D’ and ‘ALL’), which shows that the algorithm can work with less samples, as also presented in Adriaens et al, (2019). The MPKF+T seems to be more sensitive to this as the FNR increased from 1.4 to 3.6% when using less samples.…”

Section: Resultssupporting

confidence: 59%

“…Although taking only one sample per day might not seem a ‘random’ way to mimic missing samples during luteolysis, the variability in luteolysis length and the independency of the P4 profiles to the simulated time of REF LUT ensures that the timing of missed samples compared to luteolysis was variable. In a real on-farm setting, it is more probable that not in all cows samples are skipped, which would make the variability in alerts compared to real luteolysis for OOC and MPKF+T even larger (see also Adriaens et al, 2019). Furthermore, based on the results of this study, we can assume that the TB85 indicator remains consistent in its estimation of the timing luteolysis, independent of the sampling scheme or interval, which supports its use for monitoring purposes.…”

Section: Resultsmentioning

confidence: 99%

“…The second step entailed the addition of measurement noise corresponding to the on-line LFIA technique (resulting profile shown in lower panel of Figure 1). The characteristics of this measurement noise were defined from an available dataset containing 10,958 on-line measured P4 measurements collected at the Hooibeekhoeve in Geel, Belgium, which was described in (Adriaens et al, 2019). The P4 data in this LFIA dataset was smoothed using a second order Savitzky-Golay filter with a span of 7 measurements.…”

Section: Methodsmentioning

confidence: 99%

“…The framework for the MPKF was set up based on the information provided in Smith and West, (1983), West and Harrison, (1997), Korsgaard and Løvendahl, (2002) and Friggens et al, (2007). The parameters were estimated based on the raw and smoothed P4 values of the same P4 dataset described before and in (Adriaens et al, 2019). The mean squared difference between our implementation of the MPKF and the smoothed values obtained from the on-line device was 0.094 ng/mL which was considered to be sufficiently low to compare results and derive relevant conclusions.…”

Section: Methodsmentioning

confidence: 99%

“…In a previous validation study, PMASC was shown to be successful in correctly identifying luteolysis using milk P4 data measured on-farm with a lateral flow immunoassay ( LFIA ) (Adriaens et al, 2019). Nevertheless, as those P4 data originated from a commercial sensor system, the sampling frequency was set by the system software and the effect of the measurement frequency and timing, as well as the effect of missing values on the performance of PMASC could not be evaluated.…”

Section: Introductionmentioning

confidence: 99%

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

Adriaens

Martin

Saeys

et al. 2019

Preprint

Self Cite

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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.

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Section: Resultssupporting

confidence: 59%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Adriaens

Martin

Saeys

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

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

Adriaens

Martin

Saeys

et al. 2019

Journal of Dairy Science

Self Cite

View full text Add to dashboard 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 fertility management. To properly evaluate and compare the performance of new and existing data-processing algorithms, a test data set 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 data set of well-controlled but highly variable and farm-representative profiles. Besides the progesterone profiles, information on (the timing of) luteolysis was extracted from the modeling approach and used as a reference for the evaluation and comparison of the algorithms. In this study, 2 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 2 different sampling schemes: one sample per cow every 8 h versus 1 sample per day. The alerts for luteolysis of the PMASC algorithm were on average 20 h earlier compared with the ones of the multiprocess Kalman filter, and their timing was less sensitive to missing values. This was shown by the fact that, when 1 sample per day was used, the Kalman filter gave its alerts on average 24 h later, and the variability in timing of the alerts compared with 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 with the current state of the art.

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

Cited by 2 publications

References 9 publications

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

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

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

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