What are the factors associated with pregnancy loss after timed-artificial insemination in Bos indicus cattle?
Bibliographic record
Abstract
Pregnancy loss (PL) has important impacts on the profitability of livestock production systems, although it is not widely reported, particularly in Bos indicus cattle. The present study retrospectively evaluated PL after timed-artificial insemination (TAI) in Bos indicus (Nelore) beef cows corresponding to several factors, such as parity, body condition score (BCS), presence of corpus luteum (CL) at the beginning of TAI protocols, expression of estrus, and hormonal manipulations during the TAI protocol. Data from two experiments performed during three breeding seasons (BS) were reanalyzed. Both experiments evaluated adding GnRH treatment at TAI in a 7-d estradiol (E2) plus progesterone (P4)-based protocol, with intravaginal P4 implant removal on Day 7 combined with treatment with 0.5 or 1.0 mg E2 cypionate and 300 IU eCG, and TAI on Day 9. In addition, during BS 2 and 3 (Exp 2), cows were randomized to receive or not a PGF treatment on Day 0 (beginning of the TAI protocol). In all BS, presence of CL and BCS were evaluated at the beginning of TAI protocols, follicle size and expression of estrus were evaluated at TAI. The PL was assessed between the first pregnancy diagnosis (∼35d) and parturition. There were no effects of hormonal manipulations within TAI protocols of different BS on PL. There was no interaction between GnRH treatment at TAI and the other variables within BS, and there was no main effect of GnRH treatment on PL (without = 10.1% [102/1007] vs. with = 10.4% [114/1100]). The addition of PGF on Day 0 had no effect on PL (11.5% [102/886] vs. 10.5% [89/850]), as well as EC dose to induce final ovulation (10.8% [89/827] vs. 11.2% [102/909] for 0.5 and 1.0 mg, respectively). Primiparous had greater PL than multiparous cows (14.0% [77/550] vs. 8.9% [139/1557]), and cows not expressing estrus near TAI had greater PL than those expressing estrus (13.5% [57/422] vs. 9.7% [156/1617]). There was no interaction between follicle size at TAI and GnRH treatment on PL. However, probability of PL decreased linearly as follicle size at TAI increased. There were no effects of service number (first TAI or resynchronization), BCS, or presence of CL on D0 on PL. In addition, PL was not affected by sire within any of the BS. In conclusion, some factors that are known to impact pregnancy per AI also influenced PL, such as parity and expression of estrus, although, other aspects such as BCS, number of services, and presence of CL on D0 did not affect PL. Moreover, commonly implemented treatments to increase fertility (e.g., PGF on Day 0 and increasing EC dose to 1.0 mg) did not affect PL. Finally, the GnRH treatment at TAI had no effect on PL and did not interact with any of the variables, an important result, since GnRH at TAI also increases fertility in Bos indicus beef cows.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".