Researchers from University of Illinois (UI) are attempting to bridge two types of computational crop models to become more reliable predictors of crop production in the U.S. Corn Belt.

“One class of crop models is agronomy-based and the other is embedded in climate models or earth system models. They are developed for different purposes and applied at different scales. Because each has its own strengths and weaknesses, our simple idea is to combine the strengths of both types of models to make a new crop model with improved prediction performance,” said Kaiyu Guan, the principal investigator of this research.

Guan and his team implemented and evaluated a new maize growth model by combining superior features in both Community Land Model and Agricultural Production Systems Simulator.

NIFA supports this project through the Agriculture and Food Research Initiative.

Read the full story at College News at UI. USDA Photo.

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