Effect of Sire Fertility and Timing of Artificial Insemination in a Presynch + Ovsynch Protocol on First-Service Pregnancy Rates

Effects of two protocols of ovulation synchronization on corpus luteum size and blood flow, progesterone concentration, and pregnancy rate in beef heifers

The objective of this study was to evaluate the effect of 7-day estradiol-progesterone-based [Treat_(C)] and 5-day Co-Synch plus progesterone [Treat_(Co-Sy)] protocols on ovulation time, pre-ovulatory follicle diameter, corpus luteum (CL) size and blood flow, progesterone (P4) concentration and pregnancy rate (PR) in beef heifers. In Experiment 1, a crossover design was applied (n = 9). For Treat_(C), a progesterone intravaginal (PI) device was inserted, plus 2 mg of estradiol benzoate (day 0). On day 7, 500 µg of cloprostenol plus 0.5 mg of estradiol cypionate were administered, and PI was removed. For Treat_(Co-Sy), on day 0, a PI was inserted plus 100 µg gonadotropin-releasing hormone (GnRH). On day 5, PI was removed, plus 500 µg of cloprostenol and 100 µg of GnRH were administered at 69-70 h later. From day one to ovulation day, dominant follicle was evaluated by ultrasonography. On days 4 and 8 post-ovulation, CL was evaluated by color Doppler, and P4 concentration was determined by chemiluminescence. In Experiment 2, a split-plot experimental design was used. Protocols followed were the same as in Experiment 1 [Treat_(C); n = 310 and Treat_(Co-Sy); n = 314]. Heifers were fixed-time artificially inseminated. Pregnancy was determined on day 41. In Experiment 1, the interval between PI removal and ovulation time was different between protocols (P < 0.01). In addition, P4 concentration was related to the CL size (P < 0.001), CL blood flow (P < 0.01) and protocols (P < 0.03). In Experiment 2, PR did not differ between protocols.

Classifying the fertility of dairy cows using milk mid-infrared spectroscopy

The objective of this study was to investigate the potential of milk mid-infrared (MIR) spectroscopy, MIR-derived traits including milk composition, milk fatty acids, and blood metabolic profiles (fatty acids, β-hydroxybutyrate, and urea), and other on-farm data for discriminating cows of good versus poor likelihood of conception to first insemination (i.e., pregnant vs. open). A total of 6,488 spectral and milk production records of 2,987 cows from 19 commercial dairy herds across 3 Australian states were used. Seven models, comprising different explanatory variables, were examined. Model 1 included milk production; concentrations of fat, protein, and lactose; somatic cell count; age at calving; days in milk at herd test; and days from calving to insemination. Model 2 included, in addition to the variables in model 1, milk fatty acids and blood metabolic profiles. The MIR spectrum collected before first insemination was added to model 2 to form model 3. Fat, protein, and lactose percentages, milk fatty acids, and blood metabolic profiles were removed from model 3 to create model 4. Model 5 and model 6 comprised model 4 and either fertility genomic estimated breeding value or principal components obtained from a genomic relationship matrix derived using animal genotypes, respectively. In model 7, all previously described sources of information, but not MIR-derived traits, were used. The models were developed using partial least squares discriminant analysis. The performance of each model was evaluated in 2 ways: 10-fold random cross-validation and herd-by-herd external validation. The accuracy measures were sensitivity (i.e., the proportion of pregnant cows that were correctly classified), specificity (i.e., the proportion of open cows that were correctly classified), and area under the curve (AUC) for the receiver operating curve. The results showed that in all models, prediction accuracy obtained through 10-fold random cross-validation was higher than that of herd-by-herd external validation, with the difference in AUC ranging between 0.01 and 0.09. In the herd-by-herd external validation, using basic on-farm information (model 1) was not sufficient to classify good- and poor-fertility cows; the sensitivity, specificity, and AUC were around 0.66. Compared with model 1, adding milk fatty acids and blood metabolic profiles (model 2) increased the sensitivity, specificity, and AUC by 0.01, 0.02, and 0.02 unit, respectively (i.e., 0.65, 0.63, and 0.678). Incorporating MIR spectra into model 2 resulted in sensitivity, specificity, and AUC values of 0.73, 0.63, and 0.72, respectively (model 3). The comparable prediction accuracies observed for models 3 and 4 mean that useful information from MIR-derived traits is already included in the spectra. Adding the fertility genomic estimated breeding value and animal genotypes (model 7) produced the highest prediction accuracy, with sensitivity, specificity, and AUC values of 0.75, 0.66, and 0.75, respectively. However, removing either the fertility estimated breeding value or animal genotype from model 7 resulted in a reduction of the prediction accuracy of only 0.01 and 0.02, respectively. In conclusion, this study indicates that MIR and other on-farm data could be used to classify cows of good and poor likelihood of conception with promising accuracy.

Evaluating sire effects on cow fertility: Timed AI and repeat-breeder dairy cows

In this single herd observational study, there is investigation of the effects of 81 sires (with 11,424 artificial inseminations) on conception rates in 1790 Holstein cows for 5 years. Sires were catagorized based on the published sire conception rate (SCR) into different sire fertility groups (low, average and high fertility sires). The performance of different-sire fertility groups was assessed in timed artificial insemination (TAI) and repeat-breeder (RB) cows. With this aim, two logistic regression models with sire, inseminator, cow, and lactation random effects were applied to data on pregnancies assessed at days 30 and 70 post-insemination. Fixed effects of sire fertility group, sire breed, cow-fertility status, insemination type, postpartum problems, milk yield, temperature humidity index, and year were evaluated. Results from the analysis indicated there was a significant individual sire effect on conception rates, and large heterogeneity in values for this variable among sires. Results indicate that SCR could be assessed to predict low fertility sires reasonably well, and the predicted probabilities for pregnancy per AI (P/AI) at 30 and 70 days post-insemination for high fertility sires were consistent for the most part with values for the SCR. The sire breed did not affect conception rates at days 30 and 70 post-insemination nor its interactions with insemination type (estrous detection AI (EDAI) compared with TAI) and cow-fertility status (RB compared with non-RB). Predicting response probabilities for sires with at least 100 inseminations in each insemination group resulted in greater conception probabilities in cows in which there was EDAI than those in the TAI group.

Vitrification of bovine oocytes: implications of follicular size and sire on the rates of embryonic development

PURPOSE

The objectives were to test how the source of oocytes and semen impacted vitrification of large numbers of bovine oocytes and subsequent IVF and early embryo development to test procedures that may assist with assisted reproductive technologies in humans.

METHODS

Bovine oocytes were vitrified from follicles of different diameters, small (< or =4 mm) and medium (4 to 10 mm), using nylon mesh. Oocytes were exposed to the cryoprotectant composed of 40% (v/v) ethylene glycol, 18% (w/v) Ficoll-70, and 0.3 M sucrose in three stepwise dilutions. Thawing was conducted with a series of 0.5, 0.25 and 0.125 M sucrose dilutions in 20% fetal bovine serum.

RESULTS

The cleavage (39.1% vs. 58.5%) and blastocyst rates (5.1% vs. 22.9%) were significantly lower for the vitrified oocytes. Follicle size had a significant impact on the development of embryos. Sires had significant effects on embryonic developmental rates.

CONCLUSIONS

We conclude that differences in development exist due to follicle source and sire used for IVF after vitrification.

Altering the time of the second gonadotropin-releasing hormone injection and artificial insemination (AI) during Ovsynch affects pregnancies per AI in lactating dairy cows

Based on previous research, we hypothesized that Cosynch at 72 h [GnRH-7 d-PGF(2alpha)-72 h-GnRH + artificial insemination (AI)] would result in a greater number of pregnancies per AI (P/AI) than Cosynch at 48 h. Further, we hypothesized that P/AI would be improved to a greater extent when GnRH was administered at 56 h after PGF(2alpha) before AI at 72 h due to a more optimal interval between the LH surge and AI. Nine hundred twenty-seven lactating dairy cows (n = 1,507 AI) were blocked by pen, and pens rotated through treatments. All cows received GnRH followed 7 d later by PGF(2alpha) and then received one of the following: 1) GnRH + timed AI 48 h after PGF(2alpha) (Cosynch-48); 2) GnRH 56 h after PGF(2alpha) + timed AI 72 h after PGF(2alpha) (Ovsynch-56); or 3) GnRH + timed AI 72 h after PGF(2alpha) (Cosynch-72). Pregnancy diagnoses were performed by ultrasound at 31 to 33 d post-AI and again at 52 to 54 d post-AI. Overall P/AI were similar for the Cosynch-48 (29.2%) and Cosynch-72 (25.4%) groups. The Ovsynch-56 group had a greater P/AI (38.6%) than Cosynch-48 or Cosynch-72. Presynchronized first-service animals had greater P/AI than cows at later services in Cosynch-48 (36.2 vs. 23.0%) and Ovsynch-56 (44.8 vs. 32.7%) but not in Cosynch-72 (24.6 vs. 26.2%). Similarly, primiparous cows had greater P/AI than multiparous cows in Cosynch-48 (34.1 vs. 22.9%) and Ovsynch-56 (41.3 vs. 32.6%), but not Cosynch-72 (29.8 vs. 25.3%). In conclusion, we found no advantage to Cosynch at 72 h vs. 48 h. In contrast, we found a clear advantage to treating with GnRH at 56 h, 16 h before a 72-h AI, probably because of more-optimal timing of AI before ovulation.