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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".