Impact of bread waste inclusion in feedlot diets on the environmental footprint of growing and finishing beef cattle
Bibliographic record
Abstract
The objective of this research was to examine the impact of including bread waste in feedlot diets on the environmental footprint of beef cattle. Existing data including diet composition and animal performance metrics (body weight, average daily gain, dry matter intake, feed:gain ratio) were obtained from two previous feeding trials in which steers were fed bread by-product (BBy) at rates of 40% (growing steers) and 55% DM (finishing steers). Environmental footprint metrics were estimated through modeling approaches and included land use requirements, greenhouse gas (GHG) emissions, ammonia (NH3) emissions, and water use requirements. Including bread waste in the diets of growing steers resulted in a 45% decrease in land use (ha hd-1) for feed crops, a 14% decrease in GHG emission intensity (kg CO2e hd-1), a 4% decrease in NH3 emission intensity (kg NH3 hd-1), and a 37% decrease in water use intensity (L kg-1 live weight) compared to steers fed a conventional corn-based diet. Finishing steers fed a BBy-based diet had a 63% reduction in land use (ha hd-1) for feed production, a 19% reduction in GHG emission intensity (kg CO2e hd-1), 1% reduction in NH3 emissions (kg NH3 hd-1), and a 61% reduction in water use intensity (L kg-1 live weight). Furthermore, GHG emissions associated with BBy from production to waste management were 24% and 53% lower when diverting BBy from landfill to growing and finishing diets, respectively. Utilizing bread waste in feedlot diets not only reduces the environmental footprint of growing and finishing cattle but makes use of land, water and fertilizer resources that have already been expended. Furthermore, as bread waste is priced lower than conventional feedstuffs, its inclusion in feedlot diets is expected to reduce the cost of production for growing and finishing cattle. Despite the benefits, current challenges that must be considered include availability and proximity of bread waste to feedlots, short shelf life, and regulatory restrictions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".