Potato Yield Response and Seasonal Nitrate Leaching as Influenced by Nitrogen Management
Bibliographic record
Abstract
Nitrate leaching is of great environmental concern, particularly with potatoes grown on sandy soils. This 3-year study evaluated the effect of three N rates (100, 150, and 200 kg ha−1) of single applications of polymer-coated urea (PCU) and a 75% PCU + 25% urea mixture, plus a conventional split application of 200 kg N ha−1 of a 50% ammonium sulfate + 50% calcium ammonium nitrate mixture (CONV) on NO3−-N leaching, potato yield, and N uptake under irrigated and non-irrigated conditions on a sandy soil in Quebec (Canada). Fertilizer N application increased growing season NO3−-N leaching only under irrigation. On average, irrigation increased seasonal NO3−-N leaching by 52%. Under irrigated conditions, PCU reduced NO3−-N leaching compared to PCU + urea. However, both PCU and PCU + urea significantly increased NO3−-N leaching compared to the CONV at the equivalent N rate of 200 kg N ha−1. This was attributed to the timing of soil N availability and deep-water percolation. Total (TY) and marketable (MY) yields in the CONV were similar to those in the PCU applied at the equivalent N rate of 200 kg N ha−1. Despite lower plant N uptake, PCU resulted in greater TY and MY compared to PCU + urea. Residual soil inorganic N was greater for PCU and PCU + urea compared to the CONV, providing evidence that PCU products have the potential to increase NO3−-N leaching after the growing season. In this study, PCU was an agronomically and environmentally better choice than PCU + urea. The results also showed that the efficiency of PCU to reduce seasonal NO3−-N leaching may vary according to the timing of precipitation and irrigation.
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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".