Selecting for Weed Resistance: Herbicide Rotation and Mixture
Bibliographic record
Abstract
Herbicide rotations and mixtures are widely recommended to manage herbicide resistance. However, little research has quantified how these practices actually affect the selection of herbicide resistance in weeds. A 4-yr experiment was conducted in western Canada from 2004 to 2007 to examine the impact of herbicide rotation and mixture in selecting for acetolactate synthase (ALS) inhibitor resistance in the annual broadleaf weed, field pennycress, co-occurring in wheat. Treatments consisted of the ALS-inhibitor herbicide, ethametsulfuron, applied in a mixture with bromoxynil/MCPA formulated herbicide (photosystem-II inhibitor/synthetic auxin), or in rotation with the non-ALS inhibitor at an ALS-inhibitor application frequency of 0, 25, 50, 75, and 100% (i.e., zero to four applications, respectively) over the 4-yr period. The field pennycress seed bank at the start of the experiment contained 5% ethametsulfuron-resistant seed. Although weed control was only marginally reduced, resistance frequency of progeny of survivors increased markedly after one ALS-inhibitor application. At the end of the experiment, the level of resistance in the seed bank was buffered by susceptible seed, increasing from 29% of recruited seedlings after one application to 85% after four applications of the ALS inhibitor. The level of resistance in the seed bank for the mixture treatment after 4 yr remained similar to that of the nontreated (weedy) control or 0% ALS-inhibitor rotation frequency treatment. The results of this study demonstrate how rapidly ALS-inhibitor resistance can evolve as a consequence of repeated application of herbicides with this site of action, and supports epidemiological information from farmer questionnaire surveys and modeling simulations that mixtures are more effective than rotations in mitigating resistance evolution through herbicide selection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".