From Surplus to Deficit: Decoding the Fundamental Shift in US Agricultural Trade
Why this work is in the frame
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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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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 | 0.000 |
| Science and technology studies | 0.000 | 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 it