Performance, digestive function, and mucosal responses to selected feed additives for pigs
Bibliographic record
Abstract
Since the advent of antimicrobial feed additives for growth promotion, a number of feed additives have been made available to the swine industry. Select feed additives (feed enzymes, probiotics, organic acids, pharmacological level of zinc oxide, and copper sulfate) used in swine nutrition are reviewed to identify their principle biological effects and characterize their effects on growth performance, digestive function, and mucosal responses. Feed enzymes are the most researched and used additives to degrade antinutritional factors such as phytic acid or fractions that are not degraded sufficiently or indeed at all by the conditions and the array of digestive enzymes in the gastrointestinal tract. Supplemental phytase is almost ubiquitous in swine diets, and the economic and environmental consequences are well documented. Carbohydrases and proteases often show improvement on digestibility but exhibit inconsistent responses in growth performance. However, feed ingredients are exceedingly structurally complex, and the use of a defined blend of enzymes displaying a range of activities holds tremendous opportunity for extending benefits to gut health and function. In this context, there is increasing evidence feed enzymes can impact gut microbial ecology by reducing undigested substrates and producing oligosaccharides in situ from complex dietary carbohydrates with potential prebiotic effects. Probiotics and pharmacological levels of zinc oxide and copper sulfate are fed to modulate the gut microbial balance which may positively influence animal health. However, responses are dependent on age of the pig, disease level, genetics, environmental factors, and type of diet or feedstuffs. Recent developments in feed probiotic applications have extended responses to nutritional and environmental effects; however, only a few studies have been reported thus far. Organic acids generally reduce the pH of the diet, but effects on the pH of the digestive tract contents as well as effects on gut microbiota are often variable. The ability to find and evolve the next generation of feed additives will be driven by, among other factors, understanding the mechanisms of action and the implications on animal health and performance. Furthermore, most feed additive evaluation and interpretation takes a simplistic view of a single additive, whereas pigs often receive diets containing a mix of distinct additives. It is imperative to consider evaluating combinations of additives to document animal responses and potential synergies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".