Topping the list of Australia’s major crops, wheat is grown on more than half the country’s cropland and is a key export commodity.
Topping the list of Australia’s major crops, wheat is grown on more than half the country’s cropland and is a key export commodity. With so much riding on wheat, accurate yield forecasting is necessary to predict regional and global food security and commodity markets. A new study published in Agricultural and Forest Meteorology shows machine-learning methods can accurately predict wheat yield for the country two months before the crop matures.
“We tested various machine-learning approaches and integrated large-scale climate and satellite data to come up with a reliable and accurate prediction of wheat production for the whole of Australia,” says Kaiyu Guan, assistant professor in the Department of Natural Resources and Environmental Sciences at the University of Illinois, Blue Waters professor at the National Center for Supercomputing Applications, and principal investigator on the study. “The incredible team of international collaborators contributing to this study has significantly advanced our ability to predict wheat yield for Australia.”
People have tried to predict crop yield almost as long as there have been crops. With increasing computational power and access to various sources of data, predictions continue to improve. In recent years, scientists have developed fairly accurate crop yield estimates using climate data, satellite data, or both, but Guan says it wasn’t clear whether one dataset was more useful than the other.
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