173 Greenhouse gas emissions and land use associated with the removal of growth-enhancing technologies from backgrounding and finishing cattle in Canada: A case study
Bibliographic record
Abstract
Abstract Greenhouse gas emissions from backgrounding and finishing cattle with and without the use of growth-enhancing technologies (GET) were estimated using a whole-farm model, Holos (www.agr.gc.ca/holos-ghg). Model inputs were obtained from a four-year performance study using heifers (H) and steers (S) with six treatments (n = 40 hd treatment-1 yr-1): 1) H control (HCon); 2) H implanted (HTBA); 3) H melengestrol acetate (HMGA); 4) S control (SCon); 5) S implanted (STBA); and 6) S implanted + ractopamine hydrochloride (SRAC; conducted in the last two years). All cattle were finished to achieve a consistent number of days on feed (DOF; n = 233 ± 8). Lighter finish weights were observed for HCon and SCon. As a result, DOF were adjusted (-1 to 65 d) to achieve the same final weight as GET-treated cattle. Total emissions (kg CO2e head-1) were greater for Hcon_AdjHTBA (2967 ± 183) and HCon_AdjHMGA (2766 ± 84), than HTBA (2897 ± 184) and HMGA (2730 ± 81). Similarly, total emissions (kg CO2e head-1) for SCon_AdjSTBA (3169 ± 192) and SCon_AdjSRAC (3252 ± 202) were greater than STBA (2998 ± 153) and SRAC (3097 ± 185), respectively. On an intensity basis, (kg CO2e kg slaughter weight-1), emissions from GET-treated cattle [HTBA (4.49 ± 0.19), HMGA (4.39 ± 0.13), STBA (4.28 ± 0.17), and SRAC (4.31 ± 0.25)] were lower than HCon_AdjHTBA (4.60 ± 0.21), HCon_AdjHMGA (4.45 ± 0.18), SCon_AdjSTBA (4.52 ± 0.23), and SCon_AdjSRAC (4.52 ± 0.28), respectively. Furthermore, land-use (ha 100 kg slaughter weight-1) was reduced for GET-treated cattle (HTBA (0.068 ± 0.001), HMGA (0.067 ± 0.001), STBA (0.066 ± 0.001), and SRAC (0.067 ± 0.000)) compared to HCon_AdjTBA (0.071 ± 0.001), HCon_AdjMGA (0.069 ± 0.002), SCon_AdjTBA (0.072 ± 0.002), and SCon_AdjRAC (0.0073 ± 0.001), respectively. This study demonstrates that GETs can reduce the environmental footprint of beef 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".