Evaluation of nutrient beneficial management practices on nitrate loading to groundwater in a Southern Ontario agricultural landscape
Bibliographic record
Abstract
Evaluation of the performance of agricultural Beneficial Management Practices (BMPs) intended to protect groundwater resources that may be impacted by the leaching of excess agricultural nutrients is both essential and problematic. Many field-monitoring techniques are hampered by the substantial lag time that often exists between when the BMP is implemented and when a related impact on the groundwater quality might be observed. As a result, agricultural nitrogen models that are adapted to site-specific field conditions are often utilized in concert with field observations to provide estimates of BMP performance. In the current work, the Root Zone Water Quality Model was used to evaluate the long-term reduction of nitrate loading as a result of regional nutrient reduction BMP implementation across agricultural fields located within a municipal well field capture zone. Soil nitrate concentration and soil moisture content profiles were collected from a series of monitoring locations. These data, in conjunction with a heuristic optimization algorithm, were used to calibrate and validate the model. Validation results showed that the simulated moisture content profiles matched very well with the observed profiles, and that the simulated soil nitrate concentration was in general agreement with field observations except for the highly reactive and transient rooting zone. The calibrated model was used to investigate a series of potential nutrient reduction BMP scenarios. Results indicated that the annual nitrate loading varied both spatially and temporally relative to the subsurface conditions and the agricultural land management. The overall results indicate that BMP effectiveness needs to be investigated over relatively long time periods and that short-term, point-scale field measurements may not provide sufficient information to evaluate BMP performance. The results obtained through the integration of field data and an agriculture nitrogen model indicates that this approach can be highly beneficial to assess or evaluate the potential long-term performance of agricultural BMPs.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 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".