Potato Yield Response and Seasonal Nitrate Leaching as Influenced by Nitrogen Management
Why this work is in the frame
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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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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 it