Seasonal variation of nitrate-nitrogen in the soil profile in a subsurface drained field
Bibliographic record
Abstract
Astatkie, T., Madani, A., Gordon, R., Caldwell, K. and Boyd, N. 2001. Seasonal variation of nitrate-nitrogen in the soil profile in a subsurface drained field. Canadian Biosystems Engineering/Le genie des biosystemes au Canada 43:1.1-1.6. There is evidence in both the public media and technical literature that leaching of nitrate to groundwater has become the most important environmental aspect of agricultural fertilizer use. In a study initiated to examine the leaching of nitrate in the crop root zone, nitrate-nitrogen (NO3-N) concentrations were determined in soil samples collected in 1995 and 1996 at four depths and two sampling locations, over shallow and deep drainage tiles on five sampling dates throughout the growing season at Onslow, Nova Scotia, Canada. Repeated measures analysis (a statistical method) was used to account for non constant variance and dependence among NO3-N concentrations, and hence error terms, from nearby sampling dates. The results from Proc Mixed of SAS suggested that NO3-N concentrations in the soil depend on the sampling depth, the time in the growing season, and subsurface drainage depth. At the shallowest soil sample depth (0to 150-mm), soil NO3-N concentrations were low at the beginning of the growing season, increased substantially after fertilizer/manure application in late May, then gradually declined through to harvest of corn (Zea mais). It then remained low through winter to the beginning of the next growing season. At 600to 900-mm depth, NO3-N concentrations remained low throughout the year. In both years, soil where the drainage tile was installed deep (800 mm) contained more NO3-N than did the shallow tiles (500 mm deep). This research provides insight into the seasonal distribution of NO3-N in the soil, which is useful for responsible fertilizer use and watertable management.
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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) | 0.000 | 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 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".