Precision feeding in gestating sows: a retrospective analysis of lysine intake and pregnancy outcomes
Bibliographic record
Abstract
• Precision feeding can reduce overfeeding of amino acids in gestating sows. • Lysine balance is associated with foetal survival during gestation and at birth. • Imbalance in amino acids in mid gestation may compromise foetal development. • A margin of 1.5 g/d should be maintained when calculating lysine requirements. • Birth weight was least in piglets from parity one sows. From day 30 of gestation, 288 mixed parity sows were fed a diet varying in the inclusion of lysine and other amino acids, to meet changing requirements throughout gestation more closely (precision feeding). Lysine inclusion for precision fed sows was varied by parity group and was altered every few days by blending two diets differing in lysine at varying ratios. As a reference, 280 mixed parity sows were fed a single gestation diet consistent in lysine and well above requirements (NRC 2012). To investigate the role of lysine intake in prenatal development, standardised ileal digestible (SID) lysine requirements were estimated retrospectively based on realised maternal gain and litter weights at farrowing. SID Lysine excess was calculated over day 30 to 80 of gestation, the period of accelerated placenta growth, and defined as lysine intake minus requirements. SID Lysine excess was lower for precision fed sows, and sows with lowest lysine excess (1 g/d or less) had lower foetal survival (90.8 % vs 93 to 95 %), more mummies per litter (0.6 vs 0.2 to 0.3), and more litters with at least one mummy (38 % vs 12 to 21 %), compared to sows with SID lysine excess of 1 g/d and more. In conclusion, when feeding amino acids closer to requirements, in some sows this may result in feeding under requirements, potentially compromising foetal development. This study provides a previously undefined safety margin for SID lysine of 1.5 g/d, which can be used to formulate gestation diets and feeding strategies.
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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 | 0.002 | 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 | 0.000 |
| 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".