Effects of Polymer‐coated Urea on Nitrate Leaching and Nitrogen Uptake by Potato
Bibliographic record
Abstract
Increasing groundwater nitrate concentrations in potato (Solanum tuberosum L.) production regions have prompted the need to identify alternative nitrogen management practices. A new type of polymer-coated urea (PCU) called Environmentally Smart Nitrogen (Agrium, Inc., Calgary, AB) is significantly lower in cost than comparable PCUs, but its potential to reduce nitrate leaching and improve fertilizer recovery has not been extensively studied in potato. In 2006 and 2007, four rates of PCU applied at emergence were compared with equivalent rates of soluble N split-applied at emergence and post-hilling. Additional treatments included a 0 N control, two PCU timing treatments (applied at preplant or planting), and a soluble N fertigation simulation. Nitrate leaching, fertilizer N recovery, N use efficiency (NUE), and residual soil inorganic N were measured. Both 2006 and 2007 were low leaching years. Nitrate leaching with PCU (21.3 kg NO(3)-N ha(-1) averaged over N rates) was significantly lower than with split-applied soluble N (26.9 kg NO(3)-N ha(-1)). The soluble N fertigation treatment resulted in similar leaching as PCU at equivalent N rates. Apparent fertilizer N recovery with PCU (65% averaged over four rates) tended to be higher than split-applied soluble N (55%) at equivalent rates (p = 0.059). Residual soil N and NUE were not significantly affected by N source. Under the conditions of this study, PCU significantly reduced leaching and tended to improved N recovery over soluble N applied in two applications and resulted in similar N recovery and nitrate leaching as soluble N applied in six applications.
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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".