We plan to have the same experiment setup for the second year, to captureclimate variability and also to provide more robust results for response curves from experiments. We will deploy temperature treatments at six temperature increases from +1 degrees C to + 6 degrees C withevery 1 degree C interval, and each treatment has one replicate, with a total of 12 plots, and 2 plots for the controlexperiments (in total 14 plots). Project Methods The current study combines field experiment (T-FACE heating experiment), empirical data analysis, and process-based crop modeling.First, we will conduct the field work in the first two years of growing season to addressObjective 1. The advanced SoyFACE facility and the Blue Waters Supercomputing facility at theUniversity of Illinois provide unique advantages to achieve the project deliverables. The team includes plant physiologists and a crop modeler, with rich experience in the proposed experiments and modeling. Finally, we will use CLM-APSIM to project crop yield for the whole Corn Belt under the various climate scenarios, and quantify the contribution of each mechanism.

Using the calibrated model, we will attribute the historical yield loss due to increase temperature to different physiological mechanisms. We will then use the experiment results to improve and calibrate the CLM-APSIM model at the site level. Infrared heating arrays will be used to heat three soybean varieties (maturity groups II, III, IV) representing the major groups planted across the Midwest for two growing seasons, and multiple physiological and biochemical measurements will be taken simultaneously. We consider the following major processes: (1) direct temperature effects on photosynthesis and respiration (2) acceleration of the crop growth rate and shortening of the growing season (3) direct heat stress effects on reproductive stages, including number of flowers and pods produced and aborted and (4) high-temperature induced increase of atmospheric water demands. In this project, we aim to quantify mechanisms of high temperature impacts on soybean yield at the whole plant level, combining the temperature free-air controlled enhancement (T-FACE) experiment and a newly developed crop modeling framework (CLM-APSIM).

Parsing different mechanisms through experiments and then incorporating them into process-based models will allow better predictions of the future impact of rising temperatures on agricultural production and will identify promising targets for adaptation. Multiple physiological or metabolic mechanisms are involved in crop responses to high temperature. Non Technical Summary Higher growing season temperatures associated with global climate change will challenge future agricultural production.

Soybean apsim model