Optimal time and placement of nitrogen fertilizer with direct and conventionally seeded winter wheat
Bibliographic record
Abstract
Direct seeding of winter wheat ( Triticum aestivum L.) has rapidly become an accepted practice in the Chinook region of the southwestern Canadian prairies. Continuously cropped Chernozemic soils are frequently N deficient. To determine best N fertilizer management practices, we examined conventional versus direct seeding to establish winter wheat and to determine the effects of banded and seed-placed N fertilizer treatments in the fall versus broadcast N in the s pring. The research was conducted using two experiments. The first experiment compared band placement of N fertilizer in soil that was conventionally cultivated and seeded, to direct seeding with seed placement of fertilizer using 10% and 50% seedbed utilizations. The second experiment determined optimal time of N application (i.e., fall/spring split vs. spring only) for direct seeded winter wheat. Direct seeding proved to be successful for germination and emergence of winter wheat and was either as good as or superior to conventionally tilled and seeded treatments. Nitrogen fertilizer was successfully applied in the fall without increased risk of winterkill and application at the time of seeding was generally equal or superior to spring broadcast N. Based on these results, producers could either apply all N fertilizer at the time of seeding or use a split application strategy by applying a portion of N in the fall, and in the spring apply the remaining N required, based on soil test N and spring soil moist re conditions. Key Words: Winter wheat, ammonium nitrate, urea, nitrogen fertilizer placement, direct seeding, conventional seeding
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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".