Issues on Adoption, Import Regulations, and Policies for Biotech Commodities in China with a Focus on Soybeans
Bibliographic record
Abstract
Since the introduction of biotech commodities in 1996, farmers in the United States have rapidly adopted this new technology for production, primarily for soybeans, cotton, and corn (Nelson, 2001). The United States is the largest grower of biotech crops in the world, with 101.5 million acres under cultivation in 2003 (United States Department of Agriculture [USDA] National Agricultural Statistics Service [NASS], 2003). In the United States, adoption of biotech soybeans reached 81% in 2003, 73% for biotech cotton, and 40% for biotech corn. Globally, in 2002 about 45% of soybean acreage was planted with biotech soybeans, 11% with biotech corn, 20% with biotech cotton, and 11% with biotech rapeseed (USDA Foreign Agricultural Service [FAS], 2003a). China has become the fourth largest grower of transgenic commodities, following the United States, Argentina, and Canada. Chinas dominant biotech commodity is Bt cotton, with 5.2 million acres planted in 2002 (USDA FAS, 2003a). As of this writing, China has not approved the adoption of other major transgenic agricultural commodities, such as soybeans, corn, rice, or wheat. Given food safety concerns, there is global controversy about biotech foods. Many countries (particularly developed countries that import food) have implemented regulations to restrict adoption and import of biotech food products. In the late 1990s, six European Union (EU) member nations (Austria, France, Germany, Greece, Italy, and Luxembourg) banned imports of transgenic corn and rapeseed that were approved by the European Union (USDA FAS, 2003b). In late 1998, the EU imposed a five-year de facto moratorium on approving new transgenic varieties, which effectively prohibits most US corn exports to Europe. In May 2003, the United States, Argentina, and Canada filed a World Trade Organization (WTO) dispute against the EU over its moratorium (USDA, 2003; USDA FAS, 2003b). The first step in a WTO dispute is to request and conduct consultations during the next 60 days. WTO procedures were designed to encourage parties to resolve their differences (USDA FAS, 2003b). However, after consultations, in August 2003 the US took the next step by requesting a dispute settlement panel to hear arguments in its WTO challenge to the EUs biotech moratorium. Dispute settlement procedures, including appeal, typically take a total of 18 months (USDA FAS, 2003b, 2003f). Japan also has strict regulations for biotech food imports. In 2000, Japanese legislation was introduced to prevent imports of food products that contain transgenic varieties not yet approved in Japan (USDA FAS, 2003d). Japans biotech testing focuses on transgenic products approved for commercialization abroad but not yet approved in Japan (e.g., StarLink corn is not approved for any use in Japan). In Japan, foods found Mary A. Marchant University of Kentucky
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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 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".