The international trade of U.S. organic agri-food products: export opportunities, import competition and policy impacts
Bibliographic record
Abstract
Abstract International markets are an important destination and source of U.S. organic agri-food products. This paper offers new insights concerning the current status and trends of U.S. organic imports and exports U.S. policies relevant to the international trade of U.S. organic agri-food products are described, characterizing specific products and partners. In addition, the impact of organic equivalency agreements (OEAs), which the U.S. has signed with Canada, the EU, Japan, South Korea and Switzerland, are examined to determine the extent to which they facilitate trade. Using highly disaggregated international trade data (HS-10) from the U.S. International Trade Commission and Statistics Canada, this analysis finds that fresh agricultural products dominate both U.S. exports and imports. Between 2017 and 2019, apples grapes, strawberries and spinach were the predominant fresh exports, while tomato sauces, vinegar and roasted coffee are the most exported processed food products. A significant majority of these exports are destined for Canada and Mexico. The most imported organic agri-food products include unroasted coffee, bananas, olive oil and soybeans. There is much more diversity in the country of origin of these imports with Mexico, Peru, Brazil, Spain and Argentina among the major organic food suppliers to the U.S. OEAs allow for mutual recognition of national organic standards between countries. This analysis finds that, while, in aggregate, OEAs were not found to impact U.S. organic imports or exports, results evaluating individual agreements do suggest that they can be effective trade policy instruments. In particular, the U.S.–Canada and the U.S.–Switzerland OEAs were found to be effective in facilitating U.S. exports. Taken together these findings offer important insights into current trade patterns, and U.S. international market and organic policy opportunities.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".