Placing Bounds on Extreme Temperature Response of Maize to Improve Crop Model Intercomparison

Monday, 14 December 2015: 09:15
3005 (Moscone West)
Christopher Anderson1, Bruce Babcock1, Yixing Peng2, Philip W Gassman1 and Todd Campbell1, (1)Iowa State University, Ames, IA, United States, (2)None, None, United States
We propose the development of community-based estimates for bounds on maize sensitivity to extreme temperature. We use model-based, observation-driven soil moisture climatology in a high maize production region in the United States to develop bounds on high temperature sensitivity through its dependence on available water. For the portion of the region with relatively long growing season, yield reduction per degree-C is 10% for high water availability and 32.5% for low water availability. Where the growing season is shorter, yield reduction per degree-C is 6% for high water availability and 27% for low water availability. High temperature sensitivity is indeterminate where extreme temperature yield effect does not yet exceed excessive water yield effect.

We suggest new soil moisture climatology from reanalysis datasets could be used to develop community-based estimates of high temperature sensitivity that would significantly improve the accuracy of maize temperature sensitivity bounds, their regional variability, and their importance relative to other weather yield shocks. A community-based estimate would substantially improve evaluation of crop system simulation models and provide baseline information for evaluation of adaptation options. For instance, since process models are needed for evaluation of crop system adaptation response under climate projections, a community-developed estimate would provide a clear target for process model evaluation. Furthermore, the range of extreme temperature sensitivity from empirical models would provide a lower bound on variability that could be achieved from process models. If the process models achieved this bound, it would mean the uncertainty among their simulations would be primarily from observational limitations than differences in model response. While we demonstrate the potential in the context of maize, the concept could be implemented within any crop production system.