Impact of xylanase and protease supplementation on digestibility in growing pigs fed diets with varying levels of zinc and copper
Bibliographic record
Abstract
This study aimed to evaluate the effect of dietary copper (Cu) and zinc (Zn) levels and xylanase and protease supplementation on apparent digestibility of nutrients at the end of the ileum (AID) and large intestine (ALID). Using a 2 × 2 factorial design, 24 weaned pigs were assigned to one of the four barley-wheat-soybean meal diets, supplemented with two levels of Cu/Zn (20/125 and 40/250 mg/kg) and either a mix of xylanase and protease (ENZ) or no enzyme supplementation. Neither the Cu/Zn level nor ENZ affected AID of nutrients. However, ALID of dry matter and fibres (ADF and NDF) increased with ENZ supplementation (p < 0.010). Enzyme supplementation also influenced the ALID of crude protein and phosphorus, depending on the Cu/Zn levels (Mineral × ENZ, p < 0.050). Specifically, ENZ increased the ALID of crude protein in the high Cu/Zn diet, while ALID of phosphorus increased by 63% with ENZ in low Cu/Zn diet but only by 23% in the high Cu/Zn diet. The high Cu/Zn level increased the ALID of dry matter but had no effect on the ALID of other nutrients. In conclusion, ENZ supplement increased the apparent digestibility of nutritional compounds, but this effect was dependent on the level of Cu/Zn in the diet. Further research is needed to explore the interaction between xylanase/protease and nutritional components of feed to maximise the benefits of these supplements.
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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".