Nitrogen management will influence threshold values of green foxtail (<i>Setaria viridis</i>) in corn
Bibliographic record
Abstract
Environmental legislation may impose limitations on the quantity of nitrogen (N) used in corn production on the basis of soil type and ground water flow. If N rates are reduced, this might influence the relative competitiveness of weed species. Therefore, the objectives of this research were to develop a surface response model to provide estimations of the effect of differing N rates on threshold values of green foxtail in corn and to use this model as a theoretical framework for hypothesis testing. Field experiments were conducted from 1999 to 2001 to examine the interaction of N rate and green foxtail density on corn grain yield. The experiment was designed as a two-factor factorial with N levels ranging from 0 to 200 kg N ha−1and targeted green foxtail densities ranging from 0 to 300 green foxtail plants m−2. The addition of up to 200 kg N ha−1increased corn grain yield in both weed-free and weedy treatments. Corn yield loss attributed to green foxtail ranged from 35 to 40% at 0 kg N ha−1to 12 to 17% at 200 kg N ha−1. Ridge analysis of the response surfaces indicated that optimal corn grain yield could be achieved at derived values of 131 to 138 kg N ha−1while maintaining a green foxtail density of 8 to 9 green foxtail plants m−2on a sandy soil with less than 2% organic matter. The analyses of simulation results led to the generation of hypotheses of practical relevance to N management. On the basis of the generated hypotheses, a legislated reduction in N or an increase in the cost of N fertilizer would result in a lower threshold value for green foxtail in corn. If legislation were to ban the use of all herbicides in corn production, higher N rates or an increase in mechanical weed control measures would be required to offset yield losses caused by green foxtail. The human health and environmental consequences of such legislation would be significant.
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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".