Weed management in herbicide resistant crops – A review
Bibliographic record
Abstract
Weeds infesting crops must be controlled or they reduce crop yields, hinder harvest operations and contaminate produce. Herbicides offer excellent weed control in various crops and cropping systems. Over dependence on one or few herbicides has resulted in the development of herbicide resistance in many weeds. Selective herbicides like atrazine, alachlor, metolachlor and simazine are contaminating surface and ground water due to high residual soil activity. Under such situations non selective herbicides offer excellent control of wide spectrum of weeds besides nil or very low soil residual activity. Exciting developments in plant biotechnology mark a new era in agriculture because herbicide-resistant crops (HRC's) are the first products of biotechnology to be grown on an economic scale. Worldwide spread of transgenic crops cultivation is 67.9 m. ha. In this 73% area (49.70 m. ha) is occupied by HRC'S. More than 40 HRC's are available for commercial cultivation in US, Canada, Australia, Europe, Brazil etc. Resistance in crops is available for different groups of herbicides like Sulfonylureas, Imidazolinones, Triazines, Glufosinates, Glyphosate etc. Roundup ready soybean, cotton and maize are popular in US. IWM approach is required to prolong the life of HRC's. Better weed control is obtained in HRC's when one or more of the practices are combined. Weed management in herbicide resistant crops should involve integrated weed management practices for retaining long-term potential of herbicides like glyphosate. Rotate HRC's with other crops, rotate herbicides, rotate HRC's that are tolerant to herbicide with different mode of action and other agronomic practices for effective weed control, better yields and prevention of herbicide resistance development.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".