From Surplus to Deficit: Decoding the Fundamental Shift in US Agricultural Trade
Bibliographic record
Abstract
ABSTRACT The United States has been the world's largest agricultural exporter, consistently recording substantial surpluses in agricultural trade for decades. However, this landscape has shifted dramatically in recent years, with the US incurring a trade deficit ($1 billion) for the first time in 2019 since the USDA trade statistics became available in 1967. This deficit climbed to a staggering $21 billion in 2023 and continues to grow. This study provides an in‐depth analysis of the shifting US trade patterns from 1985 to 2023, focusing on bilateral agricultural trade with major trade partners and key commodity flows. Structural break analysis is employed to identify significant turning points. Breaks are found in trade with China, Canada, Association of Southeast Asian Nations (ASEAN), and Australia. Trade with China stands out as the most disrupted, with structural breaks closely aligned with the imposition of retaliatory tariffs during the US–China trade war. No structural breakpoints are detected in US–Mexico agricultural trade. The rapid and consistent growth in imports from Mexico in recent years has been a significant force behind the spiking US agricultural trade deficits. The potential driving factors behind the observed trends and identified structural breaks are discussed.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".