Fertilizer nitrogen rate and the response of weeds to herbicides
Bibliographic record
Abstract
Differences in plant community composition have been attributed to abiotic field characteristics, crop type, localized predation, farm implement traffic, and natural dispersal mechanisms. Nitrogen (N) fertilizer rates and herbicides also are known to influence weed community structure, although their interaction has not been reported in the literature. A growth room experiment was conducted using three weed species (green foxtail, redroot pigweed, and velvetleaf) and five herbicides (nicosulfuron, atrazine, glufosinate, glyphosate, and mesotrione) differing in their mode of action and efficacy to the selected species. The experiment was conducted in growth chambers with two levels of N fertilization (low: 0.7 mM N and high: 7.7 mM N). Weeds were grown to the two- to five-leaf stage (depending on species), treated with the appropriate herbicide, and harvested approximately 2 wk after treatment. The herbicide dose at which a 50% reduction in biomass occurred (GR50) was determined using log-logistic analysis. Herbicide susceptibility of the different weed species was influenced by N level. Green foxtail grown under low N required approximately six times the dose of nicosulfuron compared with plants grown under high N. Similarly, higher doses of nicosulfuron, glufosinate, mesotrione, and glyphosate were required to achieve a 50% reduction in redroot pigweed biomass grown under low N. In contrast, N did not influence the efficacy of mesotrione, glufosinate, or atrazine when applied to velvetleaf. This indicated specificity among herbicide–species combinations. Differences in herbicide efficacy resulting from soil N levels may alter weed community structure and may potentially explain possible weed control failures on farm fields.
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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".