Forage Legume Establishment under Exposure to Progressive Declines in Aminocyclopyrachlor and Aminopyralid in Temperate Pastures
Bibliographic record
Abstract
Legumes such as alfalfa (Medicago sativa L.) and white clover (Trifolium repens L.) increase forage productivity and quality in northern temperate pastures, but require re-establishment following broadleaf weed control using herbicides. To quantify the residual effects of two herbicides (aminocyclopyrachlor and aminopyralid) on potential legume re-establishment we examined alfalfa and clover recruitment at two field sites over two years. Sites were over-seeded with alfalfa and clover to populate the seed bank, and then sprayed with herbicide, after which seedling densities were monitored in late summer and fall of the current growing season. Defoliation (via mowing) effects were also assessed to evaluate the role of vegetation competition on legume establishment. Herbicides were applied at recommended rates (1.0), and 0.5, 0.25, 0.125, 0.0625, and 0 times recommended field rates, emulating exponential herbicide degradation (one through four half-lives). Alfalfa and white clover seedling densities were negatively impacted by all rates of herbicide, with modestly greater negative impacts from aminopyralid than aminocyclopyrachlor, although responses to herbicides remained site and legume specific. Reductions in alfalfa and clover were particularly evident through the 0.25 (i.e., two half-life) herbicide rate, with reductions in alfalfa ranging from 78% to 95%, and in clover from 73% to 88%. Legume densities at the 0.125 (three half-life) rate were 39%–68% lower than those in nonsprayed control plots. Our results suggest that at least three half-lives of degradation must occur, and likely four or more, before these legumes can re-establish at densities acceptable for pasture production. These findings have implications for producers seeking to promptly re-establish forage legumes within pastures sprayed for broadleaf weed control in northern temperate regions.
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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 | 0.000 |
| 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".