Process-based crop simulation models are useful for simulating the impacts of climate change on crop yields. Currently, estimation of spatially calibrated soil parameters for crop models can be challenging, as it requires the availability of long-term and detailed input data from several sentinel sites. The use of aggregated regional data for model calibrations has been proposed but not been employed in regional climate change studies. The study: 1) employed the use of county-level data to estimate spatial soil parameters for the calibration of CROPGRO-Soybean model and 2) used the calibrated model, assimilated with future climate data, in assessing the impacts of climate change on soybean yields. The CROPGRO-Soybean model was calibrated using major agricultural soil types, crop yield and current climate data at county level, for selected counties in Alabama for the period 1981-2010. The calibrated model simulations were acceptable with performance indicators showing Root Mean Square Error percent of between 27 - 43 and Index of Agreement ranging from 0.51 to 0.76. Projected soybean yield decreased by an average of 29% and 23% in 2045, and 19% and 43% in 2075, under Representative Concentration Pathways 4.5 and 8.5, respectively. Results showed that late-maturing soybean cultivars were most resilient to heat, while late-maturing cultivators needed optimized irrigation to maintain appropriate soil moisture to sustain soybean yields. The CROPGRO-Soybean phenological and yield simulations suggested that the negative effects of increasing temperatures could be counterbalanced by increasing rainfall, optimized irrigation, and cultivating late-maturing soybean cultivars.
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