Introduction of imidazolinone herbicide and Clearfield® rice between weedy rice—control efficiency and environmental concerns
Bibliographic record
Abstract
Water scarcity and increasing labor costs of rice cultivation have prompted many agro-ecosystems in the world to adopt the direct-seeded rice (DSR) method instead of the hand-transplanting method. However, there is a downside to this approach, which is the prevalence and spread of weedy rice (WR), a troublesome weed in paddy fields that has the potential to cause a 90% loss of total yield in high-infested areas. The progression, infestation, and dynamics of WR are linked to environmental circumstances, types of rice cultivar, established techniques, and field management. WR is viewed as a critical problem, as it may prove counterproductive in rice cultivation because it causes an overall increase in the production cost of paddy harvesting. For the purpose of our discussion, a method is explored that can be used to eliminate, or at least mitigate, the spread of WR, which is the Clearfield® Production System (CPS). This method consists of imidazolinone (IMI) herbicide, Clearfield® certified seeds, and the Stewardship Guide. However, use of the CPS has been known to negatively affect the environment, as it transfers resistance traits to WR, increasing IMI persistence in the cultivated soils, and contaminating soils and water with herbicide residues. These negative environmental effects could be dealt with by using integrated weed management systems (IWMS) that include the use of all viable tools and should be incorporated with the proper Stewardship Guide to reduce the growth of herbicide-resistant WR. This review aims to elucidate information pertaining to WR infestation, the characteristics thereof, sustainable techniques for WR control, IMI herbicides, and diverse methods for the extraction and determination of IMI residues in the environment. Understanding the conspecific nature of WR serves as a baseline for constructing novel WR control strategies in the future.
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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".