Rotation diversifiée des cultures et usage prudent de l’azote : clés de l’agriculture durable
Bibliographic record
Abstract
Diverse crop rotations and prudent nitrogen use -key to sustainable agricultureNitrogen, an essential nutrient required for healthy plant growth, is often added to the soil through nitrogen-based fertilizers.However, excess nitrogen added to the soil is not used by plants.The excess nitrogen escapes into the air as ammonia and nitrous oxide, or into surface and ground waters as nitrate.These losses not only negatively impact farmers' profitability (buying more nitrogen-fertilizers than is necessary for their crops), but also can cause a variety of environmental and health issues and contribute to greenhouse gas (GHG) emissions.Agriculture and Agri-Food Canada (AAFC) scientists have found two promising methods for reducing nitrogen application, and accordingly GHG emissions without negatively affecting farmers' profits or crop yields.The scientists studied six different crop rotations at seven sites in Manitoba, Alberta, and Saskatchewan, and used 12-year average prices to analyze the cost and revenue for each crop.They found that growing crops (such as wheat and canola) chosen for their high potential market returns was more profitable but led to higher GHG emissions.However, choosing a more diverse rotation of crops, including peas and faba beans planted along with winter wheat or malt barley, produced a similar crop yield and market value while using less nitrogen.In fact, the diverse rotation required 52% less nitrogen fertilizer, which is a significant reduction in fertilizer cost for farmers.Although long-term economic effects of these rotations still have to be observed, the results of these studies show that producers can maintain their profitability while reducing the use of nitrogen-based fertilizers, and nitrous oxide emissions.The team also found that using slightly less amounts of nitrogen-based fertilizer, than recommended by the "most economic rate of nitrogen" (MERN) for a crop, could reduce GHG emissions without negatively affecting yield or profitability.A crop's MERN, which is the application rate of nitrogen fertilizer that balances crop yield with the price of nitrogen to produce the optimal economic return, varies by year and even from field to field; farmers could, however, use test strips on their land to get a better idea of where fields lie on the nitrogen response curve.With growing interest in reducing nitrogen emissions for increased sustainability without cutting yields, this research offers practical on-farm advice that could be easily implemented on Canadian farms.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".