Review: Reducing residual soil nitrogen losses from agroecosystems for surface water protection in Quebec and Ontario, Canada: Best management practices, policies and perspectives
Bibliographic record
Abstract
Rasouli, S., Whalen, J. K. and Madramootoo, C. A. 2014. Review: Reducing residual soil nitrogen losses from agroecosystems for surface water protection in Quebec and Ontario, Canada: Best management practices, policies and perspectives. Can. J. Soil Sci. 94: 109-127. Eutrophication and cyanobacteria blooms, a growing problem in many of Quebec and Ontario's lakes and rivers, are largely attributed to the phosphorus (P) and nitrogen (N) emanating from intensively cropped agricultural fields. In fact, 49% of N loading in surface waters comes from runoff and leaching from fertilized soils and livestock operations. The residual soil nitrogen (RSN), which remains in soil at the end of the growing season, contains soluble and particulate forms of N that are prone to being transported from agricultural fields to waterways. Policies and best management practices (BMPs) to regulate manure storage and restrict fertilizer and manure spreading can help in reducing N losses from agroecosystems. However, reduction of RSN also requires an understanding of the complex interactions between climate, soil type, topography, hydrology and cropping systems. Reducing N losses from agroecosystems can be achieved through careful accounting for all N inputs (e.g., N credits for legumes and manure inputs) in nutrient management plans, including those applied in previous years, as well as the strategic implementation of multiple BMPs and calibrated soil N testing for crops with high N requirements. We conclude that increasing farmer awareness and motivation to implement BMPs will be important in reducing RSN. Programs to promote communication between farmers and researchers, crop advisors and provincial ministries of agriculture and the environment are recommended.
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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 | 0.000 |
| 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".