Carbon and Cattle: Deriving and analyzing the net emissions of livestock feed in Saskatchewan's cow-calf sector
Bibliographic record
Abstract
Policy issues in most nations include environmental sustainability, the mitigation of climate change, and agri-food systems. Commitments have been established through multi-lateral agreements targeting greenhouse gas (GHG) emission reductions to abate climate change impacts. These agreements generate domestic policy initiatives to incentivize behavioural changes of economic actors for environmental betterment. In response to policy initiatives targeted at industries such as agriculture, producers are adopting innovative production methods and technologies to provide environmental services and mitigate emissions.\nGHG emissions arising from livestock production contribute to a damaging narrative surrounding agriculture, particularly beef production; however, Canadian cow-calf producers remain guarded regarding the adoption of new technologies. Consequently, if consumers' and policy makers' attitudes towards the cow-calf industry become negative concerning environmental impact, industry development may be hindered. \nThe purpose of this study is three-fold, quantifying (a) net emissions, (b) changes in practice, and (c) economic outcomes attributed to the forage production facet of cow-calf production. The Saskatchewan Forage Production Survey was developed to gather data on forage management practices, placing emphasis on land use and land management changes. Canada’s whole-farm assessment model, Holos, was applied as a carbon accounting framework to derive the net emissions of the forage production cycle. Results indicate that net emissions were -0.123 Mg CO2e/ha per annum in 2016-19, a marked decrease from 1991-94. Economic assessments place the value of stored carbon between $0.57/ha and $6.61/ha. Recommendations include the renewal of forage rejuvenation funding programs and the expansion of term conservation easement programs to include non-native forage lands.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| 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".