72 Environmental impacts associated with the removal of productivity enhancing technologies from three different post-weaning feed management strategies in Saskatchewan: A case study
Bibliographic record
Abstract
Abstract The purpose of this research was to examine the use of productivity enhancing technologies (PETs) and post-weaning management on the environmental impacts of beef steers in western Canada. The PETs considered in the current study included ionophores (i.e., monensin, 33 ppm) and hormone implants (i.e., Ralgro, 36 mg zeranol). The production management systems considered were direct finishing (Heavy), confined backgrounding prior to finishing (Medium), and confined and pasture-based backgrounding prior to finishing (Light). The objectives were to model and compare environmental outcomes [greenhouse gas emissions (GHG), ammonia (NH3) emissions, land requirements and water use] using a whole-farm perspective and better understand the environmental footprint of the Canadian beef industry. Compared with natural steers, PETs led to a 10 to 13% reduction in GHG emissions (CO2e kgּ boneless beef-1), a 10 to 32% decrease in NH3 emissions (kg NH3ּּ kg boneless beef-1), a 9 to 22% reduction in land use (haּ kg boneless beef-1) and 12 to 25% decrease in water (m3ּ kg boneless beef-1) use. Direct finishing compared with backgrounded, and 2-stage backgrounded steers reduced GHG by 32% and 39% kg CO2eּ kg boneless beef-1, NH3 emissions by 36% and 52% kg NH3ּ kg boneless beef-1, land requirements by 25% and 73% haּ kg boneless beef-1 and water use by 18% and 51% m3ּ kg boneless beef-1, respectively. Varying post-weaning strategies allows producers to value-add (additional weight through backgrounding) and maximize the use of available feed (grazing). The full environmental impacts of removal of PETs from Canadian beef production must consider a whole-systems approach including economic viability, ecosystem services, as well as consumer demand and social acceptance.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".