Evaluation of the Root Zone Water Quality Model (RZWQM) for Southern Ontario: Part II. Simulating Long-Term Effects of Nitrogen Management Practices on Crop Yield and Subsurface Drainage Water Quality
Bibliographic record
Abstract
Abstract Loss of nitrogen from the agricultural production system is of concern in Ontario. The challenge for researchers and farmers is to fulfill crop water requirements while limiting chemical movement with surface and subsurface runoff. The main objective of this study was to evaluate the long-term effects of current N management practices for corn production for two different soil types using the Root Zone Water Quality Model (RZWQM) for southern Ontario conditions. The model simulated the amount of subsurface tile drainage, residual soil nitrate-nitrogen (NO3-N), NO3-N in subsurface drainage water, and crop yield. The validated RZWQM for silt loam and sandy loam soils showed that the relative long-term effectiveness of the most economic rate of nitrogen (MERN) for corn production fluctuates significantly from year-to-year in response to weather patterns. In addition, soil type had a small but significant effect on the MERN. Side-dress application of N on sandy loam resulted in significant reduction in corn yield and NO3-N loss to shallow groundwater. Also, crop rotation from corn-soybean to corn-soybean-soybean resulted in a greater reduction of NO3-N loads in the tile outflow on silt loam soil than on sandy loam soil. Overall, the RZWQM simulated tile drain flow, NO3-N loss, and crop yield with reasonable accuracy. However, more field work is needed to assist with identifying suitable values for a number of coefficients used in the RZWQM's nutrient component for Ontario conditions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".