Effects of postpartum energy intake on pregnancy rates in beef cattle subjected to GnRH- or CIDR-based timed artificial insemination protocols
Bibliographic record
Abstract
The objectives were to determine the effects of three levels of postpartum metabolisable energy (ME) intake on pregnancy rates in beef cattle subjected to either GnRH-based (OVS) or progestin-based (CIDR) protocols for fixed time artificial insemination (TAI). Hereford cross cows were assigned to ME and TAI treatments (within ME) on the basis of parity and predicted calving date. The postpartum grass-silage-based diet was formulated to provide either Low (93 MJ d-1), Medium (103 MJ d-1) or High (120 MJ d-1)] ME from calving (January to February) to turnout (May 25). Lactating cows [n = 175, 5.7 ± 1.1 mean (± SD) body condition score at calving] were subjected to their assigned TAI protocol; OVS [i.m. treatments of GnRH (100 μg) on day 0, PGF2α (25 mg) on day 7, a second GnRH on day 9 and TAI 16 to 18 h later], or CIDR [i.m. treatment with 1 mg estradiol benzoate and 100 mg progesterone concurrent with CIDR (1.9 g progesterone) insertion on day 0, PGF2α treatment at CIDR removal on day 7, a second estradiol treatment on day 8 and TAI 28 to 30 h later). Cows were 60 ± 13 d post-partum at the time of insemination. Lower ME intakes reduced (P < 0.05) maternal body weight and calf weight gain, but ME intake did not affect (P > 0.05) the proportion cycling (113/175 = 65%, based on serum progesterone concentrations on days -5 and -14), ovulation following TAI, or the TAI pregnancy rates (based on ultrasonography). Timed insemination pregnancy rates were greater for CIDR- than OVS-treatment (63 vs. 45%, respectively, P < 0.05), regardless of ME intake. Key words: Beef cow, estrus synchronization, Ovsynch, CIDR, energy intake, reproduction, calf gains
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".