Ensemble Agroecosystem Modeling Enhances Predictions of Crop Yields and Soil Carbon Across the United States

Abstract. Accurately estimating crop yields and soil organic carbon (SOC) dynamics is essential for agricultural planning, carbon accounting, and sustainable land management. However, process-based agroecosystem models often produce divergent estimates due to variations in model structure, parameterization, and underlying assumptions. In this study, we developed a multi-model ensemble framework that integrates three widely used process-based models-Daily Century (DAYCENT), DeNitrification DeComposition (DNDC), and Ecosystem model (ECOSYS)-to simulate crop yields and SOC stock changes (0–30 cm) across cultivated lands of the continental United States (CONUS) at 4 km² spatial resolution. Each model was parameterized using harmonized environmental, soil, and management datasets and evaluated using observed crop yields from the National Agricultural Statistics Service and measured SOC data from the Rapid Carbon Assessment. For the baseline period (2014-2023) under conventional corn-soybean rotation, the ensemble mean showed strong agreement with observations (corn: 7.7 vs. 8.5 Mg ha^-1, RMSE = 3.0; soybean: 2.5 vs. 3.0 Mg ha^-1, RMSE = 1.0), while simulated SOC stocks (5.5 vs. 4.8 kg C m^-2, RMSE = 2.5) closely matched measured data. Spatially, the ensemble model projected SOC gains in the Midwest and Southeastern regions and losses in the Great Plains and Western United States, underscoring the importance of region-specific management practices. Overall, the ensemble approach improved predictive accuracy and reduced uncertainty relative to individual models, providing a scalable pathway for robust, data driven assessments of soil carbon and crop productivity across U.S. agroecosystems.