IEEPA Tariff Escalation: What It Means for U.S. Food and Ag-Input Imports
Bibliographic record
Abstract
The August 2025 NDSU Agricultural Trade Monitor analyzes the impact of the IEEPA tariffs on U.S. agri-food imports and farm inputs, alongside the latest export data. The trade-weighted effective tariff on agri-food imports rises from 4% MFN to 15%, though USMCA carve-outs and an EU deal blunt the aggregate shock; non-exempt suppliers such as Brazil, India, Switzerland, and China face steep rates (30–50%) that especially hit coffee, bottled water, and packaged foods. The effective tariff on agricultural inputs jumps from 1% to 12%, with pesticides near 25% and tractors/parts 13–16%, while fertilizer impacts remain muted due to Canadian exemptions. China extends its Section 301 exclusion window (applications through Oct 30; approvals through Dec 13) amid a 90-day tariff truce, yet U.S. exports to China are still down 53% year-to-date. Overall, June export value rose 3% year-over-year but is 2% year-to-date, with corn and ethanol remaining firm while soybeans, beef, and poultry sit at multi-year lows; underscoring higher input costs, uneven import exposure, and a fragile outlook for U.S. agricultural producers and exporters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".