Effect of enzymes, flavor and organic acids on nutrient digestibility, performance and carcass traits of growing–finishing pigs fed diets containing dehydrated lucerne meal
Bibliographic record
Abstract
Abstract BACKGROUND: We recently determined that 150 g kg−1 of lucerne meal can be fed during the finishing period without detrimental effects on pig performance but lucerne should be limited to less than 75 g kg−1 during the growing period. Lucerne has a high fiber content, is poorly digested and contains antinutritional factors such as saponins which reduce palatability. Therefore, pig performance could be enhanced by supplementing lucerne‐containing diets with feed additives such as flavors, enzymes or organic acids. The objective of this study was to determine the effects of these additives on nutrient digestibility, performance and carcass traits when fed to growing‐finishing swine fed diets containing lucerne. RESULTS: Flavor and enzyme had no effect on nutrient digestibility, pig performance or carcass traits. During the growing period, inclusion of propionic and acetic acids increased (P < 0.05) the digestibility of dry matter, protein and energy. Weight gain and intake also increased (P < 0.05). None of the additives had any effect on pig performance during the finishing period or on carcass traits. CONCLUSION: During the growing period, including 200 g kg−1 lucerne was detrimental to pig performance. Addition of organic acid improved pig performance and nutrient digestibility. During the finishing period, 200 g kg−1 lucerne can be successfully incorporated into pig diets without negative effects on pig performance. Copyright © 2008 Society of Chemical Industry
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".