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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Loss of nitrogen from the agricultural production system is of concern in Ontario. The challenge for researchers and farmers is to fulfi ll 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 fl uctuates signifi cantly from year-to-year in response to weather patterns. In addition, soil type had a small but signifi cant effect on the MERN. Side-dress application of N on sandy loam resulted in signifi cant 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 outfl ow on silt loam soil than on sandy loam soil. Overall, the RZWQM simulated tile drain fl ow, NO3-N loss, and crop yield with reasonable accuracy. However, more fi eld work is needed to assist with identifying suitable values for a number of coeffi cients used in the RZWQM’s nutrient component for Ontario conditions. Key words: water pollution, computer modeling, nutrient management, crop yield
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it