"Using seasonal forecasts and satellite data, we developed a very advanced yield prediction system for both the national and county levels. Our research demonstrates that we can do better than the USDA's real-time estimation", stated Kaiyu Guan. When put to the test, Kaiyu Guan's tool outperformed the USDA's predictions for 2018 end of season corn yields with greater accuracy. Kaiyu Guan's approach allows more accurate end-of-season predictions to be made earlier in the season. At the end of the growing season, when the corn harvest is complete, it is possible to look back and evaluate the accuracy of each previous month's prediction. Between 2010 and 2016, for example, the WASDE report for June was off, on average, by 17.66 bushels per acre. For the same time frame, Kaiyu Guan's estimate was only off by 12.75 bushels per acre. In August, WASDE was off by an average of 5.63 bushels per acre, whereas Kaiyu Guan and Peng's system got the number down to 4.37.
Kaiyu Guan and his colleagues are not the first to use satellite data to try to predict crop yield, but their combined use of seasonal climate prediction, along with crop growth information from satellite imagery, is unique.
Due to the wide-breadth and innovative nature of Kaiyu Guan's work, he was recently awarded the 2018 Global Environmental Change Early Career award from the American Geophysical Union (AGU), which seeks to award outstanding interdisciplinary contributions in research, education, or society in the area of global environmental change.