Efficiency of fall-banded urea for spring wheat production in Manitoba: Influence of application date, landscape position and fertilizer additives
Bibliographic record
Abstract
A 2-yr study was conducted to investigate the effects of application date, landscape position and a urease and nitrification inhibited formulation of urea on the efficiency of fall-banded N fertilizer under Manitoba conditions. To date, no studies have investigated how these factors interact to influence the efficiency of fall-banded N in western Canada. The effects of landscape position were apparent at three of the four sites, with significantly greater grain yields, straw yields and total recovery of N in the high landscape positions than in the low landscape positions. In the high landscape positions, there were no significant differences in crop response or recovered N among application dates in the fall and spring. However, in the low landscape positions, grain yields, grain yield increases and apparent recovered fertilizer N in the aboveground portion of the crop and in the soil (0–120 cm) were significantly greater for spring and late fall applications, when compared with early and mid-fall applications. At one site in the first year of the study, early fall-banded N with the urease and nitrification inhibitors produced greater increases in grain yield than early fall-banded N without the inhibitors in the low landscape positions. However, overall there was little agronomic benefit to the use of the additives, as there were few significant differences in crop yields or N uptake by the crop with the inhibitors than without, in either year or landscape position. The results demonstrate that selection of suitable timing for application of fertilizer N to optimize crop yields is much more critical for poorly drained areas within a field, or for poorly drained fields, than for better drained land. Key words: Fall-banded N, spring-banded N, landscape position, N-(n-butyl) thiophosphoric triamide (NBPT), Dicyandiamide (DCD), wheat (Triticum aestivum)
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".