Price, Inventory, and Trade Dynamics in the U.S. Soybean Market with Structural Breaks and VAR Analysis
DOI:
https://doi.org/10.54536/ajase.v5i1.6992Keywords:
Granger Causality, Soyabean Export, Soybean Price, Stationary, Vector AutoregressionAbstract
Soybeans play a vital role in global food, feed, and energy systems. Therefore, this study combines global and U.S. secondary data from FAOSTAT (2000-2023) with USDA balance sheets and trade statistics (2000/01-2023/24). Descriptive analysis employs indicators of self-sufficiency, import dependency, export intensity, and stock-to-use ratios. Market dynamics are examined by identifying multiple structural breaks using the Bai-Perron method, followed by Vector Autoregressive (VAR) estimation and Granger causality analysis linking U.S. soybean production, harvest price, export value, and ending stocks. Results show that global soybean harvested area expanded by 84%, while production increased by 130%. In contrast, U.S. cultivated area increased by only 13.8%, whereas production rose by 51%, confirming productivity-led growth. Brazil and the U.S. together account for more than two-thirds of global soybean production, and within the U.S., approximately 34-39% of total output is concentrated in the top three producing states. Econometric results identify four major structural shifts occurring around 2006, 2010, 2014, and 2020. Vector Autoregression (VAR) estimates indicate strong production persistence (lag 1 = 1.229, p < .001), a significant negative effect of ending stocks on production (-0.289, p < .001), and a short-run negative impact of production on prices (-0.901, p < .10), while export values exert a positive influence on prices (0.647, p < .01). Granger causality results reveal bidirectional relationships among prices, stocks, and production, suggesting that prices and inventories serve as key adjustment mechanisms. Hence, future research should focus on biofuel policy, inventory management, and energy–agriculture linkages to enhance market sustainability.
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