DOI: 10.31220/agriRxiv.2022.00133

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  Abstract: Abstract Improving fertility of dairy cows on farm remains difficult, as it is a complex trait which depends on physiological, management and external factors. Even small improvements in fertility (e.g. in terms of conception rate) potentially make a big difference for cow longevity, lifetime production and as such, sustainability. The availability of 1) new sensor data to get a better grip on physiological status of cows and 2) new systems to accurately predict the best insemination window based on milk progesterone (P4), offers new opportunities to analyse and understand fertility performance, and subsequently improve this trait. Before external and physiological factors that impact conception success can be investigated, it is crucial that we have certainty on whether the correct insemination time was applied. This study uses an extensive dataset from 5 modern dairy farms with a Herd Navigator system (HN), which farmers use to determine the optimal moment of insemination upon a drop in milk P4 preceding luteolysis. Despite that in many cases the estimation of the insemination moment goes well, inconsistencies in detection of luteolysis arise when sampling rate or milking frequency deviate from optimal conditions, which subsequently impacts fertility performance. More specifically, the use of an algorithm for which a time lag is introduced that depends on both absolute P4 concentration, rate of change of P4 during luteolysis and a fixed threshold, leads to inconsistencies in detection of luteolysis when sampling rate or milking frequency deviate. In this study, we analysed the factors influencing correct identification of the insemination window, to better separate cases in which conception failed due to human or system errors and cases in which the cow physiology caused poor fertility performance. To this end, we analysed different traits related to insemination and sampling frequency of the Herd Navigator and looked at their effect on success of conception across the 5 farms. We found that, in farms with worse fertility performance, the time between the HN alarm and the true luteolysis (i.e. the first raw P4 measurement below 5 ng/mL) was greater and less consistent. When this time was used to distinct between high- and low P4 sampling rate cycles, successful insemination that followed a low P4 sampling rate has significantly less time between an insemination and the preceding HN alarm compared to unsuccessful inseminations following a low P4 sampling rate. Moreover, successful insemination that followed a low P4 sampling rate, are associated with less time between an insemination and the preceding HN alarm compared to successful inseminations that followed a high P4 sampling rate. These finding confirm that when the time between the HN alarm and the first raw P4 measurement below 5 ng/mL is greater, farmers should inseminate sooner. From this analysis, we can make a better selection of the cycles in which insemination time was correct, and thus depended on other factors. This is a first step towards improved understanding of fertility performance on farm, and as such, of a more sustainable dairy sector.

  Key words: p4; insemination; sampling rate; hn; fertility performance; luteolysi

  Submit time: 1 January 2022

  Copyright: The copyright holder for this preprint is the author/funder, who has granted agriXiv a license to display the preprint in perpetuity.
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