PSXIII-11 A systematic review and meta-analysis of efficacy of exogenous fiber enzymes in modulating growth performances and nutrient digestibility in weanling pigs.
Bibliographic record
Abstract
Abstract Dietary fiber is an anti-nutritive factor that reduces nutrient digestibility, profitability and contributes to greenhouse gas emissions in swine production. Exogenous fiber enzymes have been used in the swine industry for over two decades, but their efficacy in modulating growth performance and nutrient digestibility in weanling pigs remains inconsistent. We hypothesized no significant differences in the in vivo efficacy endpoints in responses to exogenous fiber enzyme supplementation. This study conducted a meta-analysis of 35 studies (2003–2024) to examine the effects of exogenous fiber enzyme supplementation on growth performance and nutrient digestibility in weanling pigs fed commercial diets containing oil meals, cereal grains, by-products and co-products. Results showed no differences (P >0.05) between non-enzyme and enzyme-supplemented groups in initial body weight (7.91±0.35 vs. 7.68±0.28 kg) and dietary contents (as-fed) of net energy (2463±63 vs. 2462±63 kcal/kg), crude fat (7.98±2.72 vs. 8.12±2.73%), neutral-detergent fiber (NDF) (12.16±0.77 vs. 13.14±0.85%) and lignin (0.82±0.23 vs. 0.84±0.28%). Supplementation of exogenous fiber enzymes of cellulases and hemi-cellulases in single and/or in combination had no effects (P >0.05) on growth performance indicators of average daily gain, feed intake, and gain-to-feed ratio. Apparent total tract digestibility of dry matter (82.8±0.8 vs. 83.3±0.9%), NDF (53.5±2.3 vs. 55.6±2.5%) and crude fat (69.2±8.8 vs. 70.3±9.3%) remained unchanged (P >0.05). Our meta-analysis results suggest that currently available commercial exogenous fiber enzymes have limited efficacy for improving growth performances and nutrient digestibility in weanling pigs. More efficacious exogenous fiber enzymes or their modifications need to be further developed to improve profitability and sustainability in swine production.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".