27 Greenhouse Gas Emissions and a Partial Life Cycle Assessment When Growing Pigs Are Fed High Wheat Millrun Diets
Bibliographic record
Abstract
Abstract The impact of feeding growing pigs with high wheat millrun diets on the global warming potential (GWP) of pork production was investigated. In study 1, a 2 × 2 factorial arrangement of wheat millrun (0 or 30%) and multi-carbohydrase enzyme (0 or 1 mg kg-1) as main effects was utilized. For each of 16 reps, 6 pigs (60.2±2.2 kg BW) were housed in environmental chambers for 14d. Air samples were collected and analyzed for carbon dioxide (CO2); nitrous oxide (N2O); and methane (CH4). In study 2, data from study 1 and performance data obtained from a previous feeding trial were utilized in a life cycle assessment (LCA) framework that included feed production. The Holos farm model (Agriculture and Agri-Food Canada, Lethbridge. AB) was used to estimate emissions from feed production. In study 1, total manure output from pigs fed 30% wheat millrun diets was 30% greater than pigs on the 0% wheat millrun diets (P < 0.05), however, Feeding diets with 30% millrun did not affect greenhouse gas (GHG) output (CH4, 4.7, 4.9; N2O, 0.45, 0.42; CO2, 1610, 1711; mg s-1 without or with millrun inclusion, respectively; P > 0.78). Enzyme supplementation had no effect on GHG production (CH4, 4.5, 5.1; N2O, 0.46, 0.42; CO2, 1808, 1513; mg s-1 without or with enzymes, respectively; P > 0.51). In study 2, the LCA indicated that the inclusion of 30% wheat millrun in diets for growing pigs resulted in approximately a 25% reduction in GWP when compared to the no wheat millrun diets. Our results demonstrate that 30% wheat millrun did not increase GHG output from the pigs, and thus the inclusion of wheat millrun in diets of growing pigs can reduce the GWP of pork production.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".