Effect of Nitrogen Source and Weed Management Systems on No-Till Corn Yields
Bibliographic record
Abstract
Field research was conducted at upstate Missouri to evaluate the impact of weed management systems and pre-plant nitrogen source selection [polymer-coated urea, (PCU); anhydrous ammonia (AA), urea, and ammonium nitrate (AN)] and side dressed urea ammonium nitrate (UAN) at 168 kg N ha-1 on no-till corn grain yield and weed growth. Small-seeded broadleaf weed heights responded differently to PCU and anhydrous ammonia in the two years of study. Corn heights were greater with AN and urea compared to PCU, AA, and side dressed UAN 7 to 9 weeks after planting. Nitrogen fertilizer source selection and weed management system affected total weed biomass (giant foxtail, common waterhemp, and common lambsquarters) at physiological maturity of corn. However, these factors showed no interactive effect on corn grain yields. An early postemergence application of atrazine + dimethenamid-P + glyphosate reduced total weed biomass 86% and 92% compared to atrazine + dimethenamid-P applied preemergence following AA and the non-fertilized control, respectively. A two-pass postemergence system (glyphosate followed by glyphosate) had 74 to 79% greater weed biomass compared to residual systems when following PCU. All weed management systems increased yield 1.5 to 5.09 Mg ha-1 compared to the non-treated control, and no yield difference was observed among weed management systems. PCU, AA, and side dressed UAN are preferred over broadcast urea for integrated weed management of no-till corn production in this region.
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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 | 0.001 |
| 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".