Manipulation of Dietary Protein and Nonstarch Polysaccharide to Control Swine Manure Emissions
Bibliographic record
Abstract
Odor and greenhouse gas (GHG) emissions from stored pig (Sus scrofa) manure were monitored for response to changes in the crude protein level (168 or 139 g kg(-1), as-fed basis) and nonstarch polysaccharide (NSP) content [i.e., control, or modified with beet pulp (Beta vulgaris L.), cornstarch, or xylanase] of diets fed to pigs in a production setting. Each diet was fed to one of eight pens of pigs according to a 2 x 4, full-factorial design, replicated over three time blocks with different groups of animals and random assignment of diets. Manure from each treatment was characterized and stored in a separate, ventilated, 200-L vessel. Repeated measurements of odor, carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) emissions from the vessels were taken every two weeks for eight weeks. Manure from high-protein diets had higher sulfur concentration and pH (P < or = 0.05). High-NSP (beet pulp) diets resulted in lower manure nitrogen and ammonia concentrations and pH (P < or = 0.05). Odor level and hedonic tone of exhaust air from the storage vessel headspaces were unaffected by the dietary treatments. Mean CO2 and CH4 emissions (1400 and 42 g d(-1) m(-3) manure, respectively) increased with lower dietary protein (P < or = 0.05). The addition of xylanase to high-protein diets caused a decrease in manure CO2 emissions, but an increase when added to low-protein diets (P < or = 0.05). Nitrous oxide emissions were negligible. Contrary to other studies, these results do not support the use of dietary protein reduction to reduce emissions from stored swine manure.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".