Nitrogen and green foxtail (<i>Setaria viridis</i>) competition effects on corn growth and development
Bibliographic record
Abstract
Agronomic research on the effects of nitrogen fertilizer and weed control in corn has focused primarily on maintaining or increasing yield. Few studies have examined the effect of nitrogen (N) fertilizer rate or weed competition (or both) on whole plant growth and development. The objectives of this research were to determine how N influences the growth and development of corn and to explore how green foxtail density affects this relationship. Field experiments were conducted on a sandy low organic matter soil from 1999 to 2001. The experiment was designed as a factorial with N rate ranging from 0 to 200 kg N ha−1and targeted green foxtail density ranging from 0 to 300 plants m−2. Under weed-free conditions, a higher rate of N fertilizer increased corn leaf and grain N content, leaf area index (LAI), plant height, and aboveground dry matter (DM) production, including kernel weight. However, in the presence of green foxtail, corn leaf N content, LAI, growth rate, plant height, and aboveground DM were reduced at each N level. Despite having significant main effects, there was no interaction between N rate and green foxtail density. Results indicate that in corn grown on a coarse-textured soil with low organic matter, the additional stress brought about by the presence of green foxtail exacerbated the effect of low N rates on corn growth and development. More intensive weed management may be required in corn if N fertilizer rates are reduced.
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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".