Liquid feeding corn-based diets to growing pigs: practical considerations and use of co-products
Bibliographic record
Abstract
Liquid feeding has many potential benefits over conventional dry feeding of pigs, such as improved gut health, use of inexpensive liquid co-products from the food and biofuel industry, flexibility and ease of feed delivery, and manipulation of feeding value of ingredients with enzymes and microbial inoculants. These benefits can result in improved growth performance and feed efficiency, reduce the reliance on feeding antibiotics and improve public views on pork production and pork products. In the province of Ontario, Canada, about 20% of growingfinishing pigs are currently raised on liquid feeding systems and experience has been gained with liquid feeding corn-based diets. Based on growth performance of high health status pigs, there is limited benefit of liquid feeding corn-based diets to growing-finishing pigs. This is in contrast to European findings, where swine liquid feeding research is more focused on wheatand barley-based diets. Recent research shows that liquid feeding allows for an effective use of liquid corn distillers solubles and corn steep water. In general, and when used at 15% or less of feed dry matter content, the use of corn distillers solubles and corn steep water does not result in major changes in pig growth performance, or carcass and meat quality. The feeding value of wheat shorts appears improved in liquid fed pigs. There is potential to further enhance the value of feed ingredients for the pig by steeping with enzymes and controlled fermentation with microbial inoculants. Uncontrolled (proteolytic) fermentation which contributes to reduced feed palatability and nutritional value of liquid feeds can be minimized via control of feed pH and lactic acid content. When using liquid feeding systems, pigs should have access to an additional source of water. Management of liquid feeding systems requires computer and engineering skills and attention to detail, especially when using co-products with variable nutrient content.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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 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".