Impact of Precision Feeding During Gestation on the Performance of Sows over Three Cycles
Bibliographic record
Abstract
This study evaluated the impact of precision feeding and bump feeding strategies during gestation on the reproductive performance of sows monitored over three cycles. Four treatments were compared: two constant-concentration feeding strategies (0.53% standardized ileal digestible lysine content; SID Lys) with the feed supply remaining constant (flat feeding; FF) or variable (bump feeding; BF) and two precision feeding strategies based on the InraPorc model considering performance by parity (precision feeding per parity; PFP) or the weight of each sow at breeding (precision feeding by individual; PFI). Sows were followed over three gestation and lactation cycles. In the first cycle (n = 502), the birth-to-weaning piglet mortality for PFP (8.7%) and PFI (10.3%) was lower than for BF (13.8%), with FF (11.3%) being intermediate (p = 0.001). No differences were observed in litter performance during the second cycle (n = 340). During the third cycle (n = 274), the stillborn rate was lower for PFP (6.2%) than for BF (9.1%) and FF (10.4%), with PFI (7.0%) being intermediate (p = 0.01). The BF strategy did not significantly improve sow or litter performance during lactation. Meanwhile, precision feeding could reduce nitrogen (10–13%) and total phosphorus intake (6–9%) with PFP and PFI strategies. Also, the results showed that it could even reduce piglet mortality during lactation.
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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) | 0.000 | 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".