Agricultural Greenhouse Gas Emissions and Mitigation Strategies for Promoting Sustainable Agroecosystems in Canada – A Review
Bibliographic record
Abstract
Canada's agricultural greenhouse gas emissions are ~73Mt CO 2 eq yr -1 (10% of the total), which importantly includes 29% and 78% of the total CH 4 and N 2 O emissions, respectively.These emissions are caused by enteric fermentation ~24Mt, manure management ~8Mt, agricultural soils/ crop production ~24Mt, and on-farm fuel use ~14Mt.Canada has committed under the Paris Agreement-2015 to reduce its total emissions by 30% below the 2005 levels by 2030, whereas its Emission Reduction Plan (ERP)-2030 of $9.1 billion targets a 40-45% reduction; and become a net-zero country by 2050.The ERP-2030 envisages reducing agricultural emissions by 19Mt CO 2 eq yr -1 by increasing carbon sequestration ($780 million) from wetlands, peatlands, and grasslands; implementing beneficial management practices (BMPs); and reducing fertilizer use.It is a challenging task as agricultural emissions have been stable during the last couple of decades, whereas food and fiber requirements are growing.Nevertheless, as rightly perceived in the ERP, the agriculture sector is unique in reducing net emissions either by decreasing emissions or increasing sequestration through BMPs.Furthermore, the ERP will provide subsidies to the farmers (~$900 million) to adopt sustainable practices and use more energy-efficient equipment while supporting research and knowledge transfer.The subsidies would help address some of the monetary barriers producers face in adopting these practices and technologies, leaving behind the associated social and technical challenges.It is, therefore, important to follow the "observe, evaluate, and improve" strategy for better implementation of the ERP-2030 and better achieve its targets and objectives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".