Herbicide Options for Control of Palmer Amaranth (<i>Amaranthus palmeri</i>) and Common Waterhemp (<i>Amaranthus rudis</i>) in Double-Crop Soybean
Bibliographic record
Abstract
Abstract Double-crop soybean after winter wheat is a component of many cropping systems across eastern and central Kansas. Until recently, control of Palmer amaranth and common waterhemp has been both easy and economical with the use of sequential applications of glyphosate in glyphosate-resistant soybean. Many populations of Palmer amaranth and common waterhemp have become resistant to glyphosate. During 2015 and 2016, a total of five field experiments were conducted near Manhattan, Hutchinson, and Ottawa, KS, to assess various non-glyphosate herbicide programs at three different application timings for the control of Palmer amaranth and waterhemp in double-crop soybean after winter wheat. Spring-POST treatments of pyroxasulfone (119 g ai ha –1 ) and pendimethalin (1065 g ai ha –1 ) were applied to winter wheat to evaluate residual control of Palmer amaranth and waterhemp. Less than 40% control of Palmer amaranth and waterhemp was observed in both treatments 2 wk after planting (WAP) double-crop soybean. Preharvest treatments of 2,4-D (561 g ae ha –1 ) and flumioxazin (107 g ai ha –1 ) were also applied to the winter wheat to assess control of emerged Palmer amaranth and waterhemp. 2,4-D resulted in highly variable Palmer amaranth and waterhemp control, whereas flumioxazin resulted in control similar to PRE treatments that contained paraquat (841 g ai ha –1 ) plus residual herbicide(s). Excellent control of both species was observed 2 WAP with a PRE paraquat application; however, reduced control of Palmer amaranth and waterhemp was noted 8 WAP due to subsequent emergence. Results indicate that Palmer amaranth and waterhemp control was 85% or greater 8 WAP for PRE treatments that included a combination of paraquat plus residual herbicide(s). PRE treatments that did not include both paraquat and residual herbicide(s) did not provide acceptable control.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".