237 Increasing the threonine-to-lysine ratio improves the growth performance of slow-growing pigs when fed with precision feeding techniques
Bibliographic record
Abstract
Abstract Individual precision feeding (IPF) is a suitable tool to account for the nutritional requirement variability among pigs. It was previously reported that, within IPF, pigs require greater Thr:Lys than conventional group-phase feeding (GPF) pigs. It remains unknown if these differences in Thr:Lys requirements occur due to different growth rates (Gr). This study evaluated whether the Thr:Lys requirements of pigs fed with IPF systems are affected by Gr. Sixty pigs (30 barrows and 30 gilts) with 38.1 ± 3.8 kg of body weight were distributed in a completely randomized 3 × 5 factorial arrangement. Pigs were classified into fast (FGr), medium (MGr) and slow (SGr) Gr based on their average daily gain (ADG) during 6 wk previous to the trial. After classification, pens with 2 barrows and 2 gilts from the same Gr group were randomly assigned to one of five treatments (12 repetitions per treatment). Treatments consisted of a conventional GPF (GPF100) receiving 100% of the optimal 0.65 Thr:Lys and 4 IPF treatments providing 140% (IPF140), 120% (IPF120), 100% (IPF100) and 80% (IPF80) of the optimal ratio. GPF100 pigs were fed a single diet with 0.97% standardized ileal digestible Lys. IPF diets of the pigs were adjusted every 2 d to meet the Lys requirements of the most demanding pig in the pen. Body weight was recorded twice a week, and individual feed intake (ADFI) daily over 21 d. At d 1 and 21, pigs were scanned with dual X-ray to estimate their body composition. Body weight, body protein and lipid masses were analyzed as repeated measures over time. The Dunnett test was used to compare the IPF treatments to GPF100 and the Tukey test to compare Gr groups. The pig was considered the experimental unit. FGr maintained greater (treatment × Gr, P < 0.05) ADFI and ADG and greater (P < 0.05) lipid deposition over the trial when compared with MGr and SGr. However, increasing Thr:Lys enhanced (P < 0.05) ADG and ADFI of SGr pigs within IPF, resulting in similar (P > 0.05) values between Gr groups in IPF120 and IPF140. Although FGr tended (P = 0.05) to have greater final body protein mass, SGr presented leaner gain (P < 0.05), independently of the Thr:Lys. Threonine supplementation supports metabolic processes like glycine and serine synthesis, impacting gut health, protein synthesis, energy production, and oxidative stress, potentially explaining lower lipid deposition in SGr pigs when the same level of energy in the diet. SGr pigs might need a different AA profile where a higher Thr:Lys ratio likely enhances the metabolic capacity to support fast growth in SGr pigs within IPF. Consequently, increasing the Thr:Lys ratio within IPF led to improved growth performance in SGr pigs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".