Simulating nitrogen pollution potential in surface and subsurface runoff in Ontario using EPIC model
Bibliographic record
Abstract
This study focuses on the possible potential of nitrogen contamination of water resources due to land applicationof manure and fertilizer in agricultural regionsof Ontario. The EPIC (Erosion/Productivity Impact Calculator) model wasused to partition nitrogen loads in percolating and runoff waters under corn production system by dividing the province into fourregions. Water balance and nitrogen loads were estimated for different types of soils, land slope gradient, soil organic matter,rate of nitrogen application, typesof tillage, and the presence and absence of subsurface drainage systems. Results indicatedthat reasonable annual nitrogen leaching predictions could be obtained through accurate soil hydraulic characterizationand monthly runoff curve number adjustment. The regression equationsreflected the general trendsof rate of application andorganic matter and were found to be the most important factor in quantifying the nitrate loadsin the infiltrating water for all soiltypes. Increasing slope gradient translated into an increasing. portion of nitrate moving with subsurface lateral flow based on the model results. The statistical analyses also showed that theequations can satisfactorily predict nitrate loads. The strength of the equationsover the MCLONE4 module iss hown by thereduced annual deviation from the observed nitrogen loads. In addition, the current recommended rate seemed to represent acut-off limit, below which nitrogen loads in percolating water did not substantially decrease. Although, crop yield did not significantlyincrease with increased nitrogen application rates, the nitrogen loadsdid. It showsthat the current recommended ratesfor the different regions are probably appropriate to minimize nitrogen losses while maximizing yield. The high nitrogen lossesfrom the Western region may result not only from higher. nitrogen application rates, but also from the higher probability. of leaching events caused by the longer cropping period. Overall,the approach and the results of the study could be helpful ineffective source water protection planning.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".