A team of agronomists, including Iowa State University professor Sotirios Archontoulis, is urging a shift in how crop yield potential and yield gaps are estimated. In a new study published in Nature Food, the researchers argue that current statistical models used in the U.S. rely too heavily on best-case scenarios—such as highly productive counties with ideal weather and fertile soil—skewing yield potential estimates. Archontoulis says, “The approach recommended by our team should better capture yield gaps, which can help identify regions with the largest room to increase crop production. This should provide a more accurate picture to orient agricultural research and development programs.” The study compared estimates of corn, soybean, and wheat yields using traditional statistical methods against a bottom-up model based on local weather, soil data, and crop science. Through this new process, researchers found estimates better accounted for regional variation and long-term conditions. Accurate yield gap estimates are crucial for directing agricultural investment and identifying areas with the greatest potential for growth. The study included co-authors from Kansas State University, the University of Nebraska-Lincoln, and France’s CIRAD Institute.
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