The contribution of red clover to nitrate loading in an agricultural watershed
Bibliographic record
Abstract
Excessive nitrogen (N) input into the environment is a major contributor to the global contamination of drinking water sources and the eutrophication of aquatic ecosystems. This study investigated the impact of land use on nitrate loading in the Dunk River Watershed in Prince Edward Island, Canada, using the Soil and Water Assessment Tool (SWAT) by focusing on the role of red clover in potato rotation systems. Nitrogen uptake and accumulation of the main crops were refined using local measurements. The SWAT model estimated an annual average nitrate loading of 316 Mg N (or 22 kg N/ha) in the watershed. Although the potato–cereal–red clover (PBC) rotation land only accounted for 56% of the watershed, it contributed 90% of total nitrate loading, emphasizing the need to improve N-use efficiency. Annual nitrate loading varied with a coefficient of 24.8% but showed no significant long-term trend. Fluctuations were correlated with stream discharge and potato production area, as higher levels of both increased nitrate loading. Groundwater delivered approximately 98% of total nitrate loading. Red clover was estimated to accumulate 201 kg N/ha annually, comparable to the total N demand of potatoes in the region, contributing significant N to the crop rotation system. Substituting soybean for red clover in the PBC rotation resulted in a significant 16.7% reduction in nitrate loading. This reduction was driven by decreases in nitrate loading in potato (169.7–141.4 Mg N/yr.), cereal (77.3–70.5 Mg N/yr.), and red clover lands (36.8–0 Mg N/yr.), despite an increase in soybean land (5.9–26.5 Mg N/yr.). These findings highlight the importance of adequately accounting for N credits from red clover or substituting it with a crop that recycles less N, such as soybean, to enhance the sustainability of potato production. These findings also underscore the importance of properly modeling leguminous forages on nitrate loading estimations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".