Palmer Amaranth (<i>Amaranthus palmeri</i>) and Common Waterhemp (<i>Amaranthus rudis</i>) Control with Very‐Long‐Chain Fatty Acid Inhibiting Herbicides
Bibliographic record
Abstract
Core Ideas Palmer amaranth and common waterhemp are difficult to control weeds and reduce yield in many crops. VLCFA inhibiting herbicides can provide excellent residual control of Palmer amaranth and common waterhemp. VLCFA inhibiting herbicides are labeled in many common crops. The highest rate of VLCFA inhibiting herbicide should be used for the most residual control of Palmer amaranth and common waterhemp. Herbicides must be used as part of an integrated weed management strategy. Field experiments were established in 2015 and 2016 near Manhattan, Hutchinson, and Ottawa, Kansas to assess residual control of Palmer amaranth (Amaranthus palmeri S. Watson) and common waterhemp (Amaranthus rudis Sauer) with very‐long‐chain fatty acid (VLCFA) inhibiting herbicides. Six VLCFA inhibiting herbicides and pendimethalin were applied at three different rates (high, middle, and low) based on labeled rate ranges for soybean [Glycine max (L.) Merr.]. All treatments were applied preemergence (PRE) in a non‐crop scenario after the plot area was clean tilled with a field cultivator. The experiment was conducted one time in 2015 and four times in 2016 at two locations for a total of five site years. Percent Palmer amaranth and common waterhemp control was visually estimated at 4 and 8 weeks after treatment (WAT). At Manhattan, pyroxasulfone, S‐metolachlor, and dimethenamid‐P resulted in the greatest Palmer amaranth control at both 4 and 8 WAT. At Hutchinson and Ottawa, pyroxasulfone, S‐metolachlor, and non‐encapsulated acetochlor resulted in the highest Palmer amaranth and common waterhemp control at both 4 and 8 WAT. Pyroxasulfone and S‐metolachlor were often the most effective herbicides; whereas, pendimethalin resulted in the least effective Palmer amaranth and common waterhemp control at all sites and observation times. The high use rate across all herbicides resulted in better control when compared to the low use rate across all herbicides at all sites and observation times. This research demonstrates the value of utilizing VLCFA inhibiting herbicides as an effective site of action for residual control of Palmer amaranth and common waterhemp as part of integrated weed management plan.
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 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".