USING IN-LINE MILK PROGESTERONE DATA TO DETERMINE OVARIAN ACTIVITY PARAMETERS ASSOCIATED WITH REDUCED FERTILITY IN CANADIAN HOLSTEIN COWS
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».