Jumeler des arbres avec les cultures : une combinaison rentable pour le climat
Bibliographic record
Abstract
Pairing trees with crops-a profitable combination for the climateTo help Canada achieve its objectives under the Paris Climate Agreement (2015), scientists from more than 20 departments, universities and organizations in Canada and USA conducted the first study to evaluate and measure the medium-and long-term effects of adopting 24 Natural Climate Solutions (NCS) in forests, grasslands, agricultural areas, and wetlands.NCS are readily deployable options that can contribute to reduce greenhouse gas (GHG) emission.By capitalizing on the natural carbon storage processes in ecosystems, NCS can already reduce emissions without waiting for new carbon capture technologies.Agriculture and Agri-Food Canada (AAFC) agroforestry scientists focused on NCS which integrate trees and shrubs into farming practices to reduce GHG emissions.Aside from shelterbelts, common in Western Canada, intercropping, silvopasture, and riparian buffer strips have the most potential.Intercropping entails planting, right in the middle of cultivated fields, rows of high economic value native (or sometimes non-native) trees and shrubs (e.g.hybrid poplar, red oak, black walnut, Norway spruce, seabuckthorn, and buffaloberry) that are effective at capturing carbon dioxide (CO 2 ).Of all the agroforestry practices, intercropping would reduce the most GHG emissions; it ranks fourth among the 11 agricultural NCS evaluated in the study.If adopted on nearly 800,000 hectares (ha) of agricultural land in just Quebec and Ontario, this practice would capture around 4 million tons of CO 2 equivalent [1] (CO 2 e) per year.Silvopasture involves rearing livestock in treed (tame and seeded) pasture and forested areas where the animals feed on spontaneous undergrowth vegetation.If adopted on an average of 20 ha per livestock farm (totalling 985,000 ha in the 10 provinces), silvopasture would capture 2.8 million tons of CO 2 e/year.Riparian buffer strips are areas of perennial vegetation planted along the watercourses of farm fields.They help reduce soil loss, improve water quality, and stabilize the banks.Planting 30 metres of riparian buffer strips along watercourses on farms located in naturally forested areas (200,000 ha in total for nine provinces) would capture 1.62 million tons of CO 2 e/year.Prediction models show that transitioning to these three agroforestry practices by 2030 could capture almost 8.5 million tons of CO 2 e/year.Scientists also calculated the monetary value of these practices when traded on the carbon exchange market.With values ranging from $10 to $50/tonne of CO 2 equivalent, the results indicated that agroforestry practices would generate significant additional income for farmers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".