Dynamics of pre- and post-insemination progesterone profiles and insemination outcomes determined by an in-line milk analysis system in primiparous and multiparous Canadian Holstein cows
Introduction
Milk progesterone (mP4) data have been widely used to characterize ovarian activity [1], [2], [3], [4], [5], [6], pregnancy status [1], [6], [7] and to evaluate associations between progesterone (P4) levels around time of artificial insemination (AI) and pregnancy [7], [8]. However, characterizing complete P4 profiles from early postpartum period until pregnancy establishment in lactating dairy cows through manual milk sampling is labor-intensive; hence rarely done. The in-line milk analysis system (IMAS; Herd Navigator™, DeLaval International, Tumba, Sweden) is a relatively new herd management tool that allows evaluation of postpartum mP4 profiles both in individual cows and in whole herds from about 3 wk postpartum until pregnancy establishment [6]. The IMAS uses a bio-model [9] that drives automatic milk sampling in every cow and measures mP4 at frequent intervals to estimate luteal function, estrus, time to AI and AI outcomes (non-pregnancy, pregnancy, pregnancy loss) [10]. In addition to assisting with reproductive management decisions, the assessment of frequent mP4 data generated by the IMAS gives a new opportunity to evaluate parameters of luteal activity [6], [11], [12], such as mP4 levels at specific time points, and their associations with fertility. Given the considerable variability in luteal phase length in the modern dairy cow [5], [6], evaluating characteristics of luteal activity through mP4 profiles in cows that conceived and maintained a pregnancy would enhance the understanding of the dairy cow fertility.
The IMAS (Herd Navigator™) has been commercially available since 2008 in Europe and since 2011 in Canada. In addition to monitoring reproductive events, it is designed for early detection of ketosis, mastitis, and to monitor milk urea profiles. Although data from European herds using the IMAS have been used to establish benchmarks of luteal activity [11] and endocrine fertility traits [12], [13], no report exists on evaluating mP4 profiles in relation to fertility. The IMAS bio-model offers a novel approach to monitor cyclic status (i.e. commencement of postpartum luteal activity) [6], [11], abnormal luteal cycles [6], and detection of estrus [14].
After ovulation, an optimal P4 environment is required for establishment of pregnancy [15], and increasing concentrations of P4 following AI support embryo development through uterine secretions of proteins and growth factors [16]. To understand the influence of P4 on fertility, recent studies have investigated the effects of P4 supplementation on pregnancy outcomes during or following synchronization protocols [17], [18], [19] with inconsistent results in post-AI evaluations. These inconsistencies could be at least partially explained by confounding effects on luteal activity parameters which are still poorly characterized, such as parity, as the levels of feed intake and milk production (expected to be lower in primiparous than in multiparous cows) affect P4 concentrations [20]. While it is frequently reported that pregnancy per AI is greater in primiparous than in multiparous cows [21], [22], [23], [24], the underlying factors contributing to the increased fertility in primiparous cows are not fully understood. Recent reports using IMAS in commercial dairy herds [6], [11] or continuous manual milk sampling for mP4 determination in a research herd [5] indicate that differences in ovarian function exist between primiparous and multiparous cows, such as in characteristics of luteal cycles early postpartum [5], [6], [11]. Frequent automated mP4 sampling can help identify characteristics of P4 profiles associated with successful pregnancies, pregnancy losses, and potential differences in P4 dynamics among parity groups, which have not been studied previously.
Therefore, a retrospective study was conducted using data generated through an automated IMAS to determine the dynamics of pre- and post-AI mP4 profiles and their associations with parity (primiparous, multiparous) and AI outcomes (non-pregnant, pregnant, pregnancy loss) in Canadian Holstein cows. As milk yield affects P4 metabolism [20] and elevated P4 post-AI is essential for pregnancy [15], we tested the hypothesis that mP4 levels before and after AI and the rate-of-daily-change in mP4 post-AI are greater (more rapid increase) in primiparous than in multiparous cows and in cows that become pregnant than in cows with other AI outcomes.
Section snippets
Herds and management
Data relating to 605 AI of 115 primiparous (1st lactation) and 249 multiparous (2nd+ lactation) Holstein cows that calved between June 2014 and December 2015, had not been subjected to reproductive hormone interventions during the luteal phases evaluated (see Section 2.3), and had been inseminated beyond 40 d postpartum, were obtained from two dairies located in Alberta, Canada using the IMAS (Herd Navigator™). Records were accessed through a dairy-management software program (AlPro™, DeLaval
Results
Summary statistics for mP4 levels and rate-of-daily-change between time points, after adjusting for outlier exclusion (stated within the missing values category), are presented in Table 1. First and second AI occurred at 70 ± 17 (range 42–170) and 99 ± 20 (range 62–180) d postpartum, respectively. Mean PrePeak mP4 was 19.4 ng/mL with a minimum value of 5.5 ng/mL. The mean (±SD) interval between PrePeak and AI was 6.6 ± 2.7 d, and between AI and PostPeak was 17.4 ± 2.3 d. At mP4-decline (d −2),
Discussion
Primiparous cows had greater post-AI mP4 levels than multiparous cows, and greater rate-of-daily-change in mP4 from d −2 to 5 and d 5 to 10, supporting our hypothesis that mP4 levels and their rate-of-daily-change are greater post-AI, at least for most time points, in primiparous cows. The greater mP4 levels in primiparous than in multiparous cows was expected as a higher milk yield in multiparous cows would be associated with greater dry matter intake and, consequently, accelerated metabolic
Acknowledgements
Research supported by Alberta Milk, Alberta Livestock and Meat Agency, and the Livestock Research & Extension Branch, Alberta Agriculture and Forestry (Agriculture Funding Consortium Grant #2016F056R), Edmonton, AB, Canada. The authors acknowledge the producers involved and the DeLaval Canada for their in-kind support in obtaining the data. Tony C. Bruinjé was a recipient of the Master of Science Scholarship Award from the Faculty of Graduate Studies and Research, University of Alberta.
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