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.

Blue Waters Annual Reports