192 Environmental impacts associated with feeding bread by-products to growing and finishing feedlot cattle
Bibliographic record
Abstract
Abstract Global environmental challenges including the need to reduce food waste and greenhouse gas (GHG) emissions have led to an increasing need to improve the sustainability of our food systems. To address this need, we examined the environmental impacts of feedlot cattle when corn grain was substituted with bread by-products (BBy) in feedlot diets. Land and water use, off-farm feed production and ammonia (NH3) emissions were estimated with the use of spreadsheet models, while Holos was used to model whole-farm GHG emissions. Data were used from two experiments with growing (Dvorak et al., 2001) and finishing (Guiroy et al., 2000) steers fed BBy at 40% and 55% of diet DM, respectively, as compared to corn-based diets. Simulations using data from the growing steer experiment indicated that inclusion of BBy resulted in 45% less land and 37% less water being used, with GHG and NH3 emissions reduced by 14 and 4%, respectively. Simulations for the finishing experiment indicated that BBy reduced land and water use by 63% and 61%, respectively, while GHG emissions declined by 19% but NH3 emissions were unaffected. Furthermore, GHG emissions associated with BBy were 24% and 53% lower when it was diverted from landfill to growing and finishing feedlot diets, respectively. These differences in environmental impact between BBy and corn-based diets may be attributed to lower DMI and improved feed:gain of steers fed BBy compared to those offered a corn-based diet. Emission reductions from feedlots could potentially be even greater if a carbon credit was awarded to feedlot cattle producers for diverting food waste organic matter away from landfill. This could be in the form of a direct credit that recognizes the avoidance of landfill emissions or by not assigning emissions to the food waste that is included in feedlot diets. Consequently, inclusion of bread waste in feedlot diets is an effective strategy to reduce the environmental footprint of feedlot cattle.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".