Glyphosate‐resistant crops: adoption, use and future considerations
Bibliographic record
Abstract
BACKGROUND: Glyphosate-resistant crops (GRCs) were first introduced in the United States in soybeans in 1996. Adoption has been very rapid in soybeans and cotton since introduction and has grown significantly in maize in recent years. GRCs have grown to over 74 million hectares in five crop species in 13 countries. The intent of this paper is to update the hectares planted and the use patterns of GRC globally, and to discuss briefly future applications and uses of the technology. RESULTS: The largest land areas of GRCs are occupied by soybean (54.2 million ha), maize (13.2 million ha), cotton (5.1 million ha), canola (2.3 million ha) and alfalfa (0.1 million ha). Currently, the USA, Argentina, Brazil and Canada have the largest plantings of GRCs. Herbicide use patterns would indicate that over 50% of glyphosate-resistant (GR) maize hectares and 70% of GR cotton hectares receive alternative mode-of-action treatments, while approximately 25% of GR soybeans receive such a treatment in the USA. Alternative herbicide use is likely driven by both agronomic need and herbicide resistance limitations in certain GR crops such as current GR cotton. Tillage practices in the USA indicate that > 65% of GR maize hectares, 70% of GR cotton hectares and 50% of GR soybean hectares received some tillage in the production system. Tillage was likely used for multiple purposes ranging from seed-bed preparation to weed management. CONCLUSION: GRCs represent one of the more rapidly adopted weed management technologies in recent history. Current use patterns would indicate that GRCs will likely continue to be a popular weed management choice that may also include the use of other herbicides to complement glyphosate. Stacking with other biotechnology traits will also give farmers the benefits and convenience of multiple pest control and quality trait technologies within a single seed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".