EFFECTIVENESS OF SEEDROW-PLACED N WITH POLYMER-COATED AND NBPT-TREATED UREA FOR CANOLA AND WHEAT
Bibliographic record
Abstract
Seedrow-placed urea minimizes soil disturbance in reduced tillage systems, but it generally decreases seedling emergence (or stand density) at nitrogen (N) rates adequate for optimum crop yield. Two three-year field experiments were conducted on canola (Brassica napus L.) and spring wheat (Triticum turgidum L.) at Melfort Research Farm, Saskatchewan, Canada, to determine the influence of N rate (40, 80 and 120 kg N ha−1), N source [untreated urea (urea), polymer-coated urea (ESN), and urea treated with Dicyandiamide (DCD) and N-(n-butyl) thiophosphoric triamide (NBPT or AgrotainTM) (SuperU) in 2007, or NBPT only (AgrotainU) in 2008 and 2009], and placement (side-banded N and seedrow-placed N, using knives to create 2 cm wide band), plus a zero-N control, on seedling emergence, seed and straw yield, protein concentration (PC) in seed, and N uptake in seed and straw. For both crops, side-banded N had no detrimental effect on seedling emergence compared to the zero-N control for all rates and sources. Seedrow-placed ESN had little or no effect on seedling emergence of wheat or canola. Conversely, seedrow-placed urea, SuperU or AgrotainU reduced seedling emergence for wheat at the 80 and 120 kg N ha−1 rates and reduced canola seedling emergence substantially at all rates, but particularly at the 80 and 120 kg N ha−1. Seed yield and N uptake were generally greater with ESN than urea and also SuperU or AgrotainU, when the fertilizers were seedrow-placed at high N rates. The findings suggest the effectiveness of ESN in providing greater seedrow-placed N application options for producers.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".