Growing season nitrate leaching as affected by nitrogen management in irrigated potato production
Bibliographic record
Abstract
Abstract Nitrate leaching from potato (Solanum tuberosum L.) production is of great concern because of its potential effects on water resources. Suction lysimeters were used in combination with a one‐dimensional flow model to quantify NO3–N leaching under irrigated potato produced on sandy soils near Québec City, QC, Canada. The 3‐yr (2010−2012) study compared a single application of polymer‐coated urea (PCU) and split‐applied soluble N fertilizers (ammonium nitrate, AN; ammonium sulfate, AS) at three N rates (120, 200, and 280 kg N ha−1) in addition to an unfertilized control. Fertilizer N application increased total seasonal NO3–N leaching. A single application of PCU increased total seasonal NO3–N leaching in 2011 compared with AN and AS, which was attributed to a greater soil NO3–N concentration under the PCU treatment in combination with increased rainfall during the tuber bulking phase (60−90 d after planting). Total seasonal NO3–N leaching in 2012 was reduced with PCU and AS compared with AN, which was attributed to reduced soil NO3–N concentrations between planting and hilling when rainfall was high. Regardless of the fertilizer N source, NO3–N leaching was primarily driven by precipitation, as leaching occurred when elevated soil NO3–N concentrations coincided with excess water in the soil. The results suggest that a single application of PCU is an effective strategy for reducing NO3–N leaching in years when there is significant rainfall during the period between planting and hilling.
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 | 0.000 | 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".