Environmental and Agronomic Implications of Water Table and Nitrogen Fertilization Management
Bibliographic record
Abstract
Nitrate (NO3-) pollution of surface and subsurface waters has become a major problem in agricultural ecosystems. Field trials were conducted from 1996 to 1998 at St-Emmanuel, Quebec, Canada, to investigate the combined effects of water table management (WTM) and nitrogen (N) fertilization on soil NO3- level, denitrification rate, and corn (Zea mays L.) grain yield. Treatments consisted of a combination of two water table treatments: free drainage (FD) with open drains at a 1.0-m depth from the soil surface and subirrigation (SI) with a design water table of 0.6 m below the soil surface, and two N fertilizer (ammonium nitrate) rates: 120 kg N ha(-1) (N120) and 200 kg N ha(-1) (N200). Compared with FD, SI reduced NO3(-)-N concentrations in the soil profile by 37% in spring 1997 and 2% in spring 1998; and by 45% in fall 1997 and 19% in fall 1998 (1 mg NO3(-)-N L(-1) equals approximately 4.43 mg NO3- L(-1)). The higher rate of N fertilization resulted in greater levels of NO3(-)-N in the soil solution. Denitrification rates were higher in SI than in FD plots, but were unaffected by N rate. The N200 rate produced higher yields than N120 in 1996 and 1997, but not 1998. Corn yields in SI plots were 7% higher than FD plots in 1996 and 3% higher in 1997, but 25% lower in 1998 because the SI system was unable to drain the unusually heavy June rains, resulting in waterlogging. These findings suggest that SI can be used as an economical means of reducing NO3- pollution without compromising crop yields during normal growing seasons.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".