Strategic Feeding Alternating a 2-months’ Feeding Restriction and a Short Boost of the Feeding Level Increases Conception Rate in Sheep
Bibliographic record
Abstract
This study aimed to investigate if feeding patterns prior to and after artificial insemination (AI) affect the reproductive performances of ewes. Two breeds were used; the Barbarine (n = 133) and Queue Fine de l’Ouest (QFO; n = 129). For each breed, 2 experimental groups balanced for age and live weight were formed. For 75 days before AI, ewes in treatment High daily grazed for 6 hours and were supplemented with 0.6 kg of concentrate. For those in treatment Low-High, grazing was restricted to 3 hours only. From 21 days before insemination and the following 20 days, feeding pattern for Low-High ewes was switched to the High feeding regime. Changes in live weight and ovarian activity were monitored; conception rate and litter size were recorded. At the end of the restriction period and for both breeds, Low-High ewes reached lower live weights than High ewes (p < 0.05). Prior to AI and for both breeds, Low-High ewes weighed less than those in the High treatment group but no statistical differences were observed. At the end of the restriction period, more QFO ewes were cycling than for the Barbarine breed (75/129 vs. 55/133; p < 0.01). Further, less QFO ewes in the Low-High treatment were cycling than High ewes (30/65 vs. 45/64; p < 0.01). More QFO ewes conceived to AI than Barbarine counterparts (77/112 vs. 73/130; p < 0.05). For both breeds, higher proportions of ewes in the Low-High treatment groups conceived to AI but differences reached statistical significance only for Barbarine breed. Following a food-restriction period between weaning and mating, improved conception rates are achieved if the plane of nutrition is increased few weeks prior to and after AI in comparison to a continuous increase in live weight during the same period.
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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 | 0.000 |
| 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".