Integrated weed management strategies for the depletion of multiple herbicide-resistant waterhemp (<i>Amaranthus tuberculatus</i>) seed in the soil seedbank
Bibliographic record
Abstract
Abstract The development of an integrated weed management (IWM) strategy for control of multiple herbicide-resistant (MHR) waterhemp can provide field crop producers with a strategy to deplete the number of waterhemp seeds in the soil seedbank. Field experiments were established on two commercial farms in Ontario, Canada, with MHR waterhemp in 2017. The number of waterhemp seeds in the seedbank at the Cottam and Walpole Island sites prior to establishing the experiments was 413 and 40 million seeds ha −1 , respectively. The goal of this 9-yr study is to document the depletion in the number of waterhemp seeds in the seedbank after Years 3, 6, and 9 (spring 2020, 2023, and 2026) and to identify management practices that can reduce the number of waterhemp seeds by 95% or more. Relative to the number of seeds in the soil seedbank when the experiment was initiated, at the Cottam site after 3 yr of this experiment, in the “control” treatment (continuous soybean seeded in rows spaced 75 apart, and sprayed with glyphosate) there was a numeric 31% increase in the number of waterhemp seeds in the seedbank; in contrast, in the three-crop rotation of corn/soybean/winter wheat (with or without a cover crop after winter wheat harvest), soybean seeded in rows spaced 37.5 cm apart, with herbicide applications using a total of eight different herbicide modes of action resulted in a 65% to 66% decrease in the number of waterhemp seeds in the soil seedbank. At the Walpole Island site after 3 yr of this experiment, the number of waterhemp seeds in the seedbank was not affected by the IWM programs evaluated. Results indicate that a diversified integrated waterhemp management program dramatically decreased the number of waterhemp seeds in the seedbank at one of two sites.
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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 | 0.002 |
| Science and technology studies | 0.000 | 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".