Improving Weed Management Based on the Timing of Emergence Peaks: A Case Study of Problematic Weeds in Northeast USA
Bibliographic record
Abstract
We reviewed the timing of the peak rate of emergence for 15 problematic weed species as well as ways to use this knowledge to improve control. Much of the previous literature modeled emergence based on growing-degree-days. For these models, we input average temperature data from several zones of Northeast USA. Within species, model-predicted peak emergence in the warmest and coolest zones differed by an average of 39 days. Also within species, there was some variation between models, likely reflecting different conditions in study locations and population-level differences that will need to be addressed in future modelling efforts. Summarizing both observed and modelled results, emergence typically peaked early-season for barnyardgrass, Canada thistle, common lambsquarters, common ragweed, giant foxtail, large crabgrass, perennial sowthistle, and smooth crabgrass. Emergence typically peaked mid-season for hairy galinsoga, mouseear chickweed, and red sorrel. Emergence typically peaked late-season for annual bluegrass. Several species emerged in a protracted manner, including common chickweed, quackgrass, and redroot pigweed. With this improved knowledge, farmers may target key problematic species of a particular field in several ways. Weed seedling control efforts can be timed at the highest densities or most vulnerable phenological stage. Residual herbicides and suppressive mulches can be timed to maximize effectiveness prior to their breakdown. And if management flexibility allows, crop selection and associated planting dates may be adjusted to improve crop competition or facilitate seedbank depletion through timely bare fallow periods. Such improvements to weed management based on timing of emergence will likely become even more impactful as predictive model reliability continues to improve.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 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".