AI-Supported Agricultural Information Systems for National Yield Forecasting and Food Price Stabilization in the United States: A Mixed-Methods Investigation

Abstract

AI-supported agricultural information systems have emerged as critical tools for improving crop yield forecasting and strengthening food price monitoring in modern agricultural economies. This study investigated the effectiveness of artificial intelligence–driven predictive analytics in enhancing national yield forecasting accuracy and examining its relationship with food price stability in the United States. A quantitative longitudinal design was applied using integrated datasets consisting of historical crop yield records, climatic indicators, satellite-derived vegetation indices, and national food price series. Machine learning models including Random Forest, Gradient Boosting, and Support Vector Regression were compared with conventional regression-based forecasting models. The analysis incorporated cross-validation and out-of-sample testing to evaluate predictive reliability and model generalization across multiple production seasons. The findings demonstrated that AI-supported forecasting models significantly outperformed traditional statistical approaches. Regression models produced prediction errors ranging between 4.0% and 5.0% across the analyzed years, whereas machine learning models reduced prediction errors to approximately 1.1%–1.2%, indicating a substantial improvement in forecasting precision. Model comparison results showed that Gradient Boosting achieved the highest predictive performance with an R² value of 0.88 and the lowest root mean square error (RMSE) of 4.02 compared with 6.84 in linear regression models. Environmental predictors played a critical role in forecasting performance, with satellite-derived vegetation indices demonstrating the strongest standardized effect size (β = 0.47, p < 0.001), followed by rainfall variability (β = 0.32, p < 0.001) and temperature variability (β = 0.21, p < 0.05). Regional analysis further indicated that AI-based forecasting systems improved predictive accuracy by approximately 25%–34% across major agricultural regions, with the largest gains observed in the Midwest and Great Plains. In addition to improving yield prediction, the results revealed measurable statistical relationships between predicted crop supply levels and food price dynamics. Years with reduced crop supply indices, such as 2019 and 2022, corresponded with higher food price volatility scores of 0.29 and 0.31 respectively, whereas years with higher predicted supply exhibited lower volatility levels near 0.19–0.22. These findings indicate that AI-supported agricultural intelligence systems can provide early signals of supply fluctuations that influence commodity market behavior. Overall, the study demonstrates that integrating machine learning algorithms, remote sensing technologies, and large-scale agricultural datasets significantly improves the predictive capacity of national agricultural monitoring systems while providing valuable insights into the interaction between agricultural production variability and food price stability.