Control of common waterhemp (<i>Amaranthus tuberculatus</i> var. <i>rudis</i>) in corn and soybean with sequential herbicide applications
Bibliographic record
Abstract
Common waterhemp (Amaranthus tuberculatus) is an aggressive annual broadleaf whose distribution is expected to increase rapidly in agricultural land in eastern Canada. Eight field experiments (four in corn and four in soybean) over a 2-yr period (2005 and 2006) were established on two Ontario farms (near Comber and Petrolia, Ontario) with waterhemp infestations to evaluate the efficacy of various PRE- and POST-emergence herbicides applied alone or in sequence for the control of waterhemp in corn and soybean. There was minimal injury (up to 3.8%) to corn and soybean from the herbicide treatments evaluated. In corn, sequential herbicide programs of isoxaflutole + atrazine PRE fb either dicamba POST, dicamba/diflufenzopyr POST, dicamba/atrazine POST or mesotrione + atrazine POST provided consistent full-season control of waterhemp. Corn yield was reduced 48% when waterhemp was not controlled. Corn yield was equivalent to the weed-free check with the herbicide treatments evaluated. In soybean, PRE or POST herbicides alone provided 41 to 94% control of waterhemp, however, waterhemp control was increased to 90 to 99% with the sequential herbicide programs. Dimethenamid (PRE; 1250 g ha -1 ) followed by glyphosate (POST1; 900 g ha -1 ) followed by glyphosate (POST2; 900 g ha -1 ) controlled waterhemp 99%. Results with waterhemp density and biomass were similar to visible control. Soybean yield was reduced 41% when waterhemp was not controlled. Soybean yield was equivalent to the weed-free check with all the herbicide treatment except dimethenamid PRE, acifluorfen POST1 and fomesafen POST1 where the yield was 30, 19, and 19% lower, respectively. Key words: Waterhemp, weed control, glyphosate, corn, soybean, pre-emergence herbicide, post-emergence herbicide
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".