Effect of Polymer-Coated Urea/Urea Blends on Corn Yields under Short Growing Season Conditions in Eastern Canada
Bibliographic record
Abstract
Polymer-coated urea (PCU) was developed to better synchronize nitrogen (N) supply with crop needs and reduce N losses. The objective of this work was to evaluate the effects of different N rates prepared using combinations of urea and ESN (PCU) on corn (Zea mays L.), grain yield, yield components, in-season nutritional status, and residual soil N. Field experiments were conducted on two sites in eastern Ontario (Canada); Kemptville (sandy loam) and Winchester (clay-loam), and repeated over three years (2011–2013). A total of ten treatments were applied using combinations of three N rates (50, 100, and 150 kg N ha−1) and three fertilizer proportions (100% urea, 75:25 urea:ESN, and 60:40 urea:ESN) for each rate. The tenth treatment consisted of a non-fertilized control (0 N). Grain yield was significantly affected by N source, N rate, site, and year. There was no significant effect of the N source in most sites/years. In the wetter season 2013, treatment 100N60:40 in the sandy site produced a similar yield to treatments receiving 150 kg N ha−1. In the clay-loam site, the 150N75:25 treatment had a yield advantage of 11–12% compared with straight urea. Chlorophyll index generally increased with the higher N application rate. The other grain parameters were little affected by the N rate or source. Soil residual mineral N tended to increase with ESN blends at 100 and 150 kg N ha−1 compared with straight urea. Our findings indicate that replacing a portion of urea with PCU might save N in lighter soils prone to leaching especially in wet years without affecting yields.
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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 | 0.001 | 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".