Weeds and the Red to Far-Red Ratio of Reflected Light: Characterizing the Influence of Herbicide Selection, Dose, and Weed Species
Bibliographic record
Abstract
Crop seedlings detect the presence of neighboring competitors by means of the red to far-red ratio (R/FR) of light reflected from the leaf surfaces of adjacent seedlings. Although previous studies have suggested that shifts in the R/FR initiate crop–weed competition, no studies have documented the R/FR of light reflected from weeds or explored how weed management practices may affect the R/FR. Experiments were conducted to test the following hypotheses: (1) the duration of R/FR signals reflected from the leaf surface of weed seedlings will vary among herbicides following treatment and will decline faster as the dose of a given herbicide increases, (2) the R/FR of reflected light will differ among weed species, and (3) the R/FR of reflected light will decrease as weed seedling leaf area and stage of development increases. Velvetleaf was used as a model weed species to examine herbicide chemistry and dose, and six weed species including Powell amaranth, velvetleaf, Eastern black nightshade, barnyardgrass, proso millet, and green foxtail were evaluated in order to characterize the R/FR of light reflected from their leaf surfaces. Results of this study confirm that the R/FR reflected from the leaf surface of weeds is affected by: herbicide chemistry, herbicide dose, weed species, stage of weed development, and distance of the weed from the crop. The relative decline in the R/FR (as a percent of the untreated control) was most rapid following treatment with paraquat, followed by glufosinate and then glyphosate. As glyphosate dose decreased, so did the reduction in the relative R/FR. Based on reflected R/FR, weed species tended to be grouped into monocots and dicots, with the latter reflecting a lower R/FR per unit leaf area than the former. This disparity was attributed to the compact leaf arrangement and orientation of dicot weed canopies, which may contribute to the greater competitiveness of dicot weeds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".