Herbicide Rate, Glyphosate/Glufosinate Sequence and Corn/Soybean Rotation Effects on Weed Seed Banks
Bibliographic record
Abstract
The effect of herbicide rates on weed control and crop yield is the subject of countless and ongoing research projects. Weed seed banks receive very little attention in comparison. The seed bank resulting from 3 yr (2006 to 2008) of single herbicide rates in a cropping system where glyphosate/glufosinate and corn/soybean were rotated or not was evaluated in a field located in St-Augustin-de-Desmaures, Québec, Canada. Field plots under conventional tillage were seeded in corn every year, or corn and soybean (1 yr). These plots received the same herbicide every year or various glyphosate/glufosinate 3-yr sequences. Subplots were sprayed with a single POST application of the recommended rate of glyphosate (900 g ae ha −1 ) or glufosinate (500 g ai ha −1 ) or lower rates. Subplots received the same full (1.0×, recommended) or reduced (0.5×, 0.75×) rate every year. After crop harvest in 2008, soil cores were extracted and the weed seed bank was evaluated. Including soybean in the cropping system resulted in lower seed banks compared to those under continuous corn cropping. Including glufosinate in a glyphosate herbicide sequence increased weed seed banks due to the lower efficacy of the glufosinate rates tested at reducing the seed bank of annual grasses. Higher herbicide rates translated into lower seed banks, up to a certain rate. After 3 yr, the lowest seed bank (full glyphosate rates every year) still had 4,339 ± 836 seeds m −2 and was higher than the initial seed bank (2,826 ± 724 seeds m −2 ).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".