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

Abstract

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.

Short communication: Validation of a novel milk progesterone-based tool to monitor luteolysis in dairy cows using cost-effective, on-farm measured data

“…When the sampling is based on decision rules implemented in PMASC, outside the scope of this research, these luteolyses can also be detected by PMASC. The intermediate values causing additional PMASC alerts in the second case (2) are mainly due to the calibration technique of the online measurement device, as also discussed in Adriaens et al (2019a).…”

Sample frequency and reactivity of farmers influences correct insemination window on farms using on-farm milk progesterone for fertility management.

“…To generate these smooth values (ng/mL), a multiprocess "extended" Kalman filter is used. This algorithm takes the probability and direction and change of the P4 measurements into account (Friggens et al, 2008;Løvendahl and Chagunda, 2010;Adriaens et al, 2018aAdriaens et al, , 2019, thereby reacting conservatively to outliers and quickly to measurements that rapidly change level or slope (e.g., during luteolysis). This results in the fact that the ability of the HN bio-model to distinguish between cycle phases and detect luteolysis depends on the accuracy of the smooth P4 (SmP4) values on and on the sampling frequency of the raw P4 (RaP4) values.…”

“…Progesterone sampling frequency and interpretation. The HN system determines the sampling frequency such that the maximum amount of information is extracted from the P4 data with as little as possible measurements (Adriaens et al, 2019). To this end, a low sample frequency of about one measurement per 3 á 4 days is maintained in the first 15 days of a cycle, after which it increases to one measurement per 0.5 á 1 day when luteolysis is expected.…”

“…To limit analysis costs, this device combines a smart sampling scheme based on the calving date, the time in cycle and the P4 concentration with an algorithm to interpret the P4 dynamics and predict the ideal insemination window. The performance of the latter algorithm depends on how often the cows are milked, on the raw P4 concentration measured, the reactivity of the farmer upon the alerts and how meticulously they inseminate the cows, as also pointed out by (Adriaens et al, 2019).…”

The Effect of Supplementation of Rumen-Protected Choline on Reproductive and Productive Performances of Dairy Cows

“…Choline reduces fatty liver conditions, and due to its presence in biological structures, it helps in reproductive tract recovery after the post-calving period. Changes in progesterone (P4) concentration are useful for predicting reproductive function recovery [ 25 ]. Researchers proved that rumen-protected choline positively affects the days leading to first observed heat, service period (days open), number of services, conception rate, and pregnancy rate [ 26 ].…”