Incorporating Climate-Crop Feedback to Enhance Seasonal Agricultural Predictions

Recent studies have unveiled that interactions between climate and crops can trigger a positive feedback loop, leading to improved regional climates and increased crop yields. This intricate feedback mechanism involves complex interplays among climate, hydrological, and agricultural processes across local to continental scales, coupled with teleconnections and delayed responses. To incorporate this feedback, we have developed an advanced regional prediction platform that couples the Climate–Weather Research and Forecasting model (CWRF) with the Decision Support System for Agrotechnology Transfer (DSSAT). This coupled system has been optimized for seasonal climate and agricultural predictions over the U.S. Corn Belt. We have conducted historical simulations using the standalone versus coupled CWRF-DSSAT as driven by ECMWF 5^th generation reanalysis (ERA5) for the period 1980-2023. In this presentation, by comparing these simulations, we will characterize the climate-crop feedback and its impact on predicting spatial patterns and interannual anomalies of regional climate, water balance, and crop yield over the U.S. agricultural heartland. Furthermore, we will identify the key physical processes and mechanisms that underly the feedback as well as its regional impacts and teleconnections. Our findings emphasize the critical significance of integrating climate-crop interactions into seasonal agricultural predictions. In this context, the coupled CWRF-DSSAT system presents a uniquely promising predictive platform.