EVOLUTION AND GLOBAL DISTRIBUTION OF GENETICALLY MODIFIED SOYBEAN AREA IN THE PERIOD 2014-2018
Bibliographic record
Abstract
Currently, genetically modified (GM) crops are an important part of world agriculture, offering numerous benefits to farmers. This study investigates the dynamics of the cultivated area with GM crops, especially with genetically modified (GM) soybean, for the period 2014–2018, by country and by transgenic trait, using ISAAA and FAOSTAT databases. In the studied period, the top 5 GM crops countries were USA, Brazil, Argentina, Canada and India which totalled 166.1 million hectares or 89.5% of the global area. Regarding GM soybean, the top 5 countries were USA, Brazil, Argentina, Paraguay and Canada which cultivated 89.9 million hectares or 96.8% of the global area with this crop. The linear regression and the Pearson correlation coefficient have pointed out a general increasing trend for both cultivated areas. The USA and Brazil occupied the leading position in world with 32.93 and 32.55 million hectares of GM soybean, respectively. Herbicide tolerance (HT) has been consistently the dominant transgenic trait for GM soybean in USA, Argentina, Paraguay and Canada. In Brazil, the cultivated areas with stacked HT/IR traits have been larger than the areas cultivated with a single HT trait, in the last 3 years. As a conclusion, the global area of GM soybean will continue to increase due to its important economic role in the agriculture development and environmental benefits.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".