Two types of crop models are better than one to predict climate change effects


In Illinois, scientists now have a new tool to predict the future effects of climate change on crop yields. Researchers from University of Illinois are attempting to bridge the strengths of two types of computational crop models to become more reliable predictors of crop production in the U.S. Corn Belt.

Kaiyu Guan, an environmental scientist at the University of Illinois and the principal investigator on the research, and his research team implemented and evaluated a new maize growth model by combining the superior features in the Community Land Model and the Agricultural Production Systems sIMulator.

Bin Peng, a postdoctoral researcher in Guan’s lab and also the lead author said, “Our solution is incorporating the life cycle development scheme of APSIM, which has 12 stages, into the CLM model. Through this integration, stresses induced by high temperature, soil water and nitrogen deficits, can be taken into account in the new model.”