Economic and circular economy analysis of including potato waste in beef feedlot diets: a Canadian case study
Bibliographic record
Abstract
Introduction The present study investigates the combined impact of adding a common agricultural waste by-product alternative feed (cull potatoes) to the diet of feedlot cattle on both economic profitability and greenhouse gas (GHG) emissions. Methods A standard enterprise budget model for beef feedlots was used to estimate economic impacts of replacing feed grains with cull potatoes in cattle diets in two beef feedlot regions in Canada. We compared economic outcomes with the estimated GHG emissions associated with these production systems, along with the offset potential from diverting cull potatoes from landfill to feed for finishing beef cattle. Results Inclusion of cull potatoes in beef feedlot diets generated a ‘win-win’ scenario, reducing both the per head feed costs and GHG emissions associated with finishing beef cattle. Diversion of cull potatoes from landfill to feed further offset beef finishing GHG emissions by more than 65% and up to 89% for scenarios with higher inclusion rates of cull potatoes. Discussion Increasing the use of palatable waste by-products like cull potatoes in feedlot diets can potentially reduce both the cost of production and GHG emissions, thereby improving both environmental sustainability and profitability. Facilitating greater diversion of agricultural by-products to livestock can enable the ruminant sector to realize its full potential to upcycle waste by-products in a circular bioeconomy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".