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Forecasting global crop productivity using novel satellite data and process-based models

Kaiyu Guan, University of Illinois at Urbana-Champaign

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The ultimate goal of this project is to improve predictability for global crop yield by integrating site measurements, advanced remote sensing observation, and process-based modeling. We took our first step forward by focusing on the high-temperature impacts on corn/soybean yield in the U.S. Corn Belt. Different pathways of high-temperature impacts on crop yield are considered in our newly developed CLM-APSIM modeling framework, which combines the strengths of earth system model and agronomy crop models. We are conducting parameter sensitivity analysis and optimization as well as a set of historical simulation experiments aimed at disentangling the contribution of different mechanisms to high-temperature impacts on crop yield. Projection runs will also be conducted shortly to explore the impact of high temperatures on crop yield under various climate change scenarios.