Interactive Effects of Landscape Position and Time of Application on the Response of Spring Wheat to Fall‐Banded Urea
Bibliographic record
Abstract
The objective of this experiment was to quantify the effect of landscape position and time of application on the agronomic efficiency of fall‐banded urea [CO(NH2)2] for spring wheat (Triticum aestivum L.) grown in the eastern prairie region of Canada. Landscape positions in this experiment were defined as high and low based on their relative elevations to one another within the field. Fertilizer treatments included urea banded at three different times in the fall (early, mid‐, and late fall), in the spring at planting, plus a control with no fertilizer N added. In the low landscape positions, grain yield, total crop N uptake, grain yield increases (GYI), and crop nitrogen use efficiency (NUE) for fall‐banded urea (all relative to spring‐banded urea) increased linearly with delayed application dates and declining soil temperatures on date of application. However, only crop N uptake and NUE were related to cumulative soil heat units (SHU) from date of fertilizer application until freeze‐up, and no measure of crop response to N was related to cumulative nitrification heat units (NHU). In the high landscape positions, the performance of fall‐banded urea was not related to any measures of time and/or soil temperature. These results can be used to predict the increase in crop response to fall‐banded N as a result of delaying application in low areas of the landscape. Our study also shows that date of application and soil temperature are robust and practical indicators for determining the appropriate time to fall‐band urea fertilizer in these areas.
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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) | 0.001 | 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".