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Exploring the Impact of China’s Retaliatory Tariffs on US Soybean Exports with Machine Learning Techniques

Anastasia W. Thayer et al.

Journal of Agricultural and Applied Economics2025https://doi.org/10.1017/aae.2025.6article
AJG 1ABDC C
Weight
0.41

What the paper says

Abstract The 2018/2019 trade conflict between the United States and China impacted a broad array of agricultural products, including soybeans. Previous trade studies using gravity models fail to account for trends and complex seasonal patterns observed in the data. This study uses a machine learning (ML) approach to estimate losses in soybean export value and volume from the trade war. We find that models using ML techniques outperform traditional models and estimate losses in the value of soybean exports of $10.16 billion/year. The ML models fit the complex export trade data series well, highlighting the importance of utilizing improved modeling approaches.

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https://doi.org/https://doi.org/10.1017/aae.2025.6

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@article{anastasia2025,
  title        = {{Exploring the Impact of China’s Retaliatory Tariffs on US Soybean Exports with Machine Learning Techniques}},
  author       = {Anastasia W. Thayer et al.},
  journal      = {Journal of Agricultural and Applied Economics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1017/aae.2025.6},
}

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Exploring the Impact of China’s Retaliatory Tariffs on US Soybean Exports with Machine Learning Techniques

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.