Mathematical characterization of the milk progesterone profile as a leg up to individualized monitoring of reproduction status in dairy cows

Adriaens, Ines; Huybrechts, Tjebbe; Geerinckx, Katleen; Daems, Devin; Lammertyn, Jeroen; Ketelaere, Bart De; Saeys, Wouter; Aernouts, Ben

doi:10.1016/j.theriogenology.2017.07.040

Cited by 22 publications

(41 citation statements)

References 29 publications

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“…The Progesterone ( P4 ) monitoring algorithm using synergistic control ( PMASC ) enables to identify fertility events stooled on the underlying physiological basis of the related progesterone dynamics (Adriaens et al, 2017, 2018b). Therefore, PMASC employs a combination of mathematical functions describing the increasing and decreasing P4 concentrations during the development and regression of the corpus luteum ( CL ) and a statistical control chart which allows to identify luteolysis.…”

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confidence: 99%

“…In practice, this would allow to ensure that luteolysis has taken place, while there is still sufficient time left to arrange the inseminator. More concretely, the following rule was implemented: if a measurement was out of control, all measurements following within a period of 48 hours were evaluated using the current, non-updated model parameters of the increasing Hill function (Adriaens et al, 2017). If in that period another residual was also below the current LCL, we assumed luteolysis had taken place and an alert was raised.…”

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confidence: 99%

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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

Adriaens

Saeys

Geerinckx

et al. 2019

Preprint

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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

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confidence: 99%

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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

Adriaens

Saeys

Geerinckx

et al. 2019

Preprint

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“…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%

“…Estrus detection based on milk P4 dynamics therefore relies on the accurate and timely identification of luteolysis preceding ovulation. Since recently, it is possible to automatically measure milk P4 on farm, in which regular milk analyses clearly show the P4 dynamics during an estrous cycle (Adriaens et al, 2017; Bruinjé et al, 2017). The current state-of-the-art in P4-based fertility monitoring is to smooth the raw measured values with a multi-process Kalman filter ( MPKF ), after which a fixed threshold ( T ) to these smoothed values is applied to detect luteal activity and luteolysis (Friggens and Chagunda, 2005; Friggens et al, 2008).…”

Section: Introductionmentioning

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%

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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

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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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Effect of carbons’ structure and type on AC electrical properties of polymer composites: predicting the percolation threshold of permittivity through different models

Rahaman

Gupta²,

Hossain

et al. 2023

Colloid Polym Sci

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The AC electrical properties of EVA- and NBR-based composites filled with different conductive fillers were investigated. Result shows several magnitudes of increment in AC electrical conductivity and dielectric permittivity after the addition of these conductive fillers, indicating that these materials can be used as supercapacitors. The magnitude of increment was varied according to polymer and filler types. Herein, we also have tested the applicability of different sigmoidal models to find out the percolation threshold value of permittivity for these binary polymer composite systems. It is observed that except sigmoidal–Boltzmann and sigmoidal–dose–response models, other sigmoidal models exhibit different values of percolation threshold when considered for any particular polymer composite system. The paper discusses the variation in results of percolation threshold with an emphasis on the advantages, disadvantages and limitations of these models. We also have applied the classical percolation theory to predict the percolation threshold of permittivity and compared with all the reported sigmoidal models. To judge the unanimous acceptability of these models, they tested vis-à-vis the permittivity results of various polymer composites reported in published literature. To comprehend, all the models except the sigmoidal–logistic-1 model were successfully applicable for predicting the percolation threshold of permittivity for polymer composites. Supplementary Information The online version contains supplementary material available at 10.1007/s00396-023-05120-2.

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Mathematical characterization of the milk progesterone profile as a leg up to individualized monitoring of reproduction status in dairy cows

Cited by 22 publications

References 29 publications

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

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

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

Effect of carbons’ structure and type on AC electrical properties of polymer composites: predicting the percolation threshold of permittivity through different models

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