Recycling nutrients in the beef supply chain through circular manuresheds: Data to assess tradeoffs
Bibliographic record
Abstract
Nutrient circularity can help supply chain participants meet sustainability targets. Across the segmented beef supply chain, opportunity exists to reinforce and introduce nutrient circularity by recycling surplus manure nutrients from cattle feedlots to lands where cattle feed is produced. We describe four datasets developed to evaluate options in U.S. and Canadian beef systems. The datasets delineate three "circular manuresheds," each encompassing a hay-grazing landscape where beef cattle are raised on grazingland and supplemented with hay grown nearby, and the distant feedlots where those cattle produce manure nutrients for potential import back to the hayfields. We selected the hay-grazing landscapes of New Mexico, USA; Florida, USA; and western Canada (Manitoba, Saskatchewan, Alberta, British Columbia) because of their significant grazingland production and potential to substitute feedlot manure for commercial fertilizer on hayfields. In each circular manureshed, the manure nutrients from major feedlot destinations could supply a considerable proportion of the P used by hay for grazing cattle: 34% of the P requirements in New Mexico, 36% in Florida, and 6% in western Canada. The average distance to return the resource was 647 km for New Mexico, 1,884 km for Florida, and 1,587 km for western Canada. These magnitudes and distances suggest that the New Mexico circular manureshed may be the most economically viable in the current agri-food system, but this reflects only part of a greater, multi-factor assessment of tradeoffs. The circular manureshed concept provides a platform for simultaneous consideration of competing factors for sustainability via circularity.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".