Evaluating the effects of BMPs on agricultural contaminants using a novel method accounting for uncertainty in water flow and contaminant loads
Bibliographic record
Abstract
Field-scale studies have shown that beneficial management practices (BMPs), such as nutrient management plans and grass buffers, can reduce the downstream transport of non-point source contaminants. This study presents a novel method for evaluating the effectiveness of BMPs using in situ data. From 2005 to 2012, hydrometric monitoring and water quality monitoring were carried out at the outlet and along two main branches of a micro-watershed (236 ha) with a high proportion of cultivated land. The method was based on evaluating the uncertainty associated with the determination of water flow and agricultural contaminant loads, with the latter being based on statistical distributions of nutrient or sediment concentrations. Distribution of loads (i.e. April–November) was estimated in order to assess the cumulative effectiveness of all implemented BMPs with an emphasis in riparian buffers established on the micro-watershed under study at different spatio-temporal scales. Results showed the concentrations and loads of total nitrogen (TN), total phosphorus (TP), nitrate-nitrogen (NO3−−N) and particulate phosphorus (PP) were significantly lower following riparian buffer implementation. A significant decrease in NO2−−N and ammonium nitrogen (NH4+−N) in the loads also occurred after riparian buffers were established. Spatially, a ratio approach based on comparing an export fraction (loads [kg] to nutrient balances [kg]) downstream from riparian buffers with that at the outlet of the same stream showed a significant reduction in the ratio downstream from the riparian buffer for TN and TP in 2009, with no significant reduction in 2010, 2011 and 2012. Ratios calculated on a seasonal basis showed the riparian buffers were less effective in the spring, as well as during seasons marked by one or more intense rainfall events.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".