In Illinois, researchers with NASA, the University of Chicago and the Potsdam Institute for Climate Impact Research found the most effective way to improve crop loss simulations is to add data on planting and harvesting in specific regions. The innovative adaptation could improve the information available for policymakers and markets to brace for the impacts of crop loss. They plan to use this improved model framework to test out crop forecasting throughout the next season in real time.
Current models struggle to predict yields, not only in view of long-term climate change, but simply for the following year’s crops. Researchers tried attacking the problem from a different angle: Instead of assuming farmers grow a single variety of a crop across the globe, they implemented the average times that each region plants and harvests its crops to represent local varieties.
“The model performance just doubles,” said Jägermeyr, a postdoctoral researcher with the UChicago Department of Computer Science, Potsdam and NASA, and the corresponding author on the study. “Getting the growing season right is the single most effective measure to better match observed corn yields.”
With this information, researchers’ models matched up much more closely with actual, observed yields.