Integrated weed management and weed species diversity
Bibliographic record
Abstract
Alternative practices for weed management, such as integrated weed management (IWM) may allow the persistence of weed populations below a given economic threshold. Increased species diversity of weeds also may result. If diversity increases, and the number of ecological interactions also increases, weed species should be viewed as an interactive community, rather than an unrelated set of targets for control. In this review we summarize how diversity is evaluated in unmanaged Systems, examine how IWM techniques may alter the diversity of weed species and suggest how strategies can be developed for managing weed diversity under IWM. Methods used to evaluate diversity in natural Systems may be used to evaluate weed diversity in alternative Systems of weed management. We made preliminary calculations of diversity for reduced tillage, modified herbicide use, crop rotation, critical period of weed control, techniques to improve crop competitiveness, and alternative control methods. Many of these IWM techniques potentially may result in changes in weed species diversity. We examined potential effects of these changes in weed diversity within six primary elements of community ecology: colonization, disturbance, the physical environment, interactions with other communities, community interactions and community dynamics. Opportunities to develop strategies of community management exist within each of these elements. If diversity could be managed while maintaining acceptable crop yields, some previously unrealized benefits of the presence of weeds could be seen, as predicted by relationships among plants of unmanaged communities. Moreover, the goal of producing a more sustainable System that incorporates the diversity of the weed community would be complemented by trends in policy towards encouraging biodiversity in agroecosystems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".