USING IN-LINE MILK PROGESTERONE DATA TO DETERMINE OVARIAN ACTIVITY PARAMETERS ASSOCIATED WITH REDUCED FERTILITY IN CANADIAN HOLSTEIN COWS
Bibliographic record
Abstract
Improvements in genetic selection and dairy management practices have resulted in dramatic increases in individual milk production over past decades. However, this positive trend has been accompanied by decreased reproductive performance and fertility in dairy cows. Infertility in modern dairy cows is often linked to altered ovarian function; therefore, gaining a better understanding of ovarian activity and factors associated with fertility impairment is of great importance. In this regard, a fully automated technology that measures frequent milk progesterone (P4) concentrations has been recently commercialized. It allows monitoring of real- time ovarian activity and exploration of various components of luteal activity associated with fertility that could not be studied previously. Three studies were conducted that aimed to evaluate components of ovarian activity parameters associated with reduced fertility, by retrospectively assessing postpartum in-line milk P4 data in Holstein cows. Records of milk P4 were obtained through an automated in-line milk analysis system (Herd NavigatorTM, DeLaval, Tumba, Sweden) in two (in first and second study) and four (in third study) commercial dairy herds. On average, milk P4 (ng/mL) was measured every 2 d starting at approximately 20 d postpartum until the determination of artificial insemination (AI) outcomes. Variations in adjusted milk P4 values below and above the designated threshold (5 ng/mL) were used to determine the commencement of luteal activity (CLA), length of luteal phases, and pregnancy. The objectives of the first study (Chapter 3) were to investigate relationships of (1) interval from calving to CLA and (2) luteal phase length and frequency preceding first AI, with parity and AI outcomes in two dairy herds. Primiparous cows had delayed CLA and less abnormal (i.e. short or long) luteal phases than multiparous cows. An early CLA improved fertility in multiparous cows, while having an abnormal luteal phase reduced fertility in primiparous cows. Regardless, the presence of at least two luteal phases preceding first AI greatly improved fertility. In conclusion, a high frequency of luteal phases (including at least one normal luteal phase) preceding first AI is a major factor benefiting AI outcomes. The second study (Chapter 4) aimed to determine the dynamics of pre- and post-AI milk P4 profiles and their associations with parity and AI outcomes in two herds. Differences in milk P4 profiles between parities and among different AI outcome groups were observed. Primiparous ii cows had higher and a more rapid increase in milk P4 levels post-AI than multiparous cows. Also, cows that became pregnant after AI had lower milk P4 levels near the time of AI and higher milk P4 levels beyond d 10 post-AI, compared to cows that were not pregnant after AI. The third study (Chapter 5) aimed to characterize cut-off values for various luteal activity parameters in the early postpartum period, and before and after AI, that were associated with reduced probability of pregnancy, in a larger population. By identifying cut-off values through receiver operating characteristic curve analysis, several parameters associated with reduced fertility were characterized, such as: (1) low number of cycles preceding first AI, (2) prolonged luteal phase preceding AI, (3) delayed AI following decline in milk P4 levels, and (4) sub- optimal P4 concentrations at different time points before and after AI. In conclusion, modern dairy cows have a high prevalence of abnormal luteal activity parameters associated with reduced probability of pregnancy, which might be the underlying reasons for poor fertility in dairy herds. The results from this Master’s thesis research indicate that monitoring in-line milk P4 profiles can be a valuable approach to identify components of infertility and to benchmark the prevalence of these components in modern dairy herds. Thereby, targeted management strategies to overcome abnormal luteal activity conditions can be implemented to improve fertility, such as (1) inducing luteolysis in cows with prolonged luteal phases preceding AI or with elevated P4 near the time of AI, and (2) supplementing P4 in cows with sub-optimal spontaneous P4 levels before or after AI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".