Assessing Saskatchewan forage production with regard to carbon and nitrogen emissions
Bibliographic record
Abstract
CONTEXT Policy issues in most nations include adapting primary agricultural production to reduce greenhouse gas (GHG) emissions. Commitments have been established through multi-lateral agreements targeting GHG emission reductions to abate climate change impacts. 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. GHG emissions arising from livestock production contribute to a damaging narrative surrounding agriculture, particularly beef production. OBJECTIVE The purpose of this study is three-fold, quantifying (a) net emissions, 2 (b) changes in practice, and (c) economic outcomes attributed to the forage production facet of cow-calf production. METHODS The Saskatchewan Forage Production Survey was developed to gather forage management practices data, 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 AND CONCLUSIONS Results indicate carbon sequestration increased between the periods of 1991–94 and 2016–19. Gross emissions decreased to a larger degree and net emission results for the forage production facet of the Saskatchewan cow calf sector are −0.123 Mg CO2e/ha/yr in 2016–19. SIGNIFICANCE Recommendations include the renewal of forage rejuvenation funding programs that may improve forage yields and carbon sequestration potential. Further, the expansion of term conservation easement programs to include non-native forage lands is recommended to incentivize the retention of forage land.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".