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 NO 3 –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 NO 3 –N leaching. A single application of PCU increased total seasonal NO 3 –N leaching in 2011 compared with AN and AS, which was attributed to a greater soil NO 3 –N concentration under the PCU treatment in combination with increased rainfall during the tuber bulking phase (60−90 d after planting). Total seasonal NO 3 –N leaching in 2012 was reduced with PCU and AS compared with AN, which was attributed to reduced soil NO 3 –N concentrations between planting and hilling when rainfall was high. Regardless of the fertilizer N source, NO 3 –N leaching was primarily driven by precipitation, as leaching occurred when elevated soil NO 3 –N concentrations coincided with excess water in the soil. The results suggest that a single application of PCU is an effective strategy for reducing NO 3 –N leaching in years when there is significant rainfall during the period between planting and hilling.
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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) | 0.000 | 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 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".