Nitrogen source and rate effects on residual soil nitrate and overwinter NO<sub>3</sub>-N losses for irrigated potatoes on sandy soils
Bibliographic record
Abstract
Residual soil NO3-N (RSN) is susceptible to loss during the non-growing season. This 5 yr study investigated the effects of three N fertilizer sources [ammonium nitrate (AN), ammonium sulfate (AS), and polymer-coated urea (PCU)] applied at four rates (60, 120, 200, and 280 kg N ha−1) plus an unfertilized control on RSN following potato production and on overwinter NO3-N changes in an irrigated sandy soil in Quebec, Canada. Composite soil samples were collected at the 0–15, 15–30, 30–60, and 60–90 cm depths immediately after potato harvest in fall and again in the following spring from 2008 to 2012. Residual soil NO3-N content within the 0–30 cm depth (RSN0–30) was highly correlated with the RSN content in the 0–90 cm depth (RSN0–90), indicating that RSN0–30 can be used as an indicator of soil profile NO3-N accumulation. Overall, RSN0–90 increased with fertilizer N application rate, particularly for above the minimum fertilizer N rate required to maximize yield (Nmax), and was generally higher for years with greater pre-plant soil NO3-N. The split application of AN and AS resulted in lower RSN0–90 than the single application of PCU at above Nmax. Overwinter losses of soil NO3-N were generally increased with increasing RSN0–90 in fall. The results suggest that reducing the fertilizer N rate is more important than the choice of N source in managing RSN.
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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".