Sotirios Archontoulis, an agronomy professor at Iowa State University, is one of a team of scientists who published a paper in the journal Nature Food, calling for a reevaluation of the statistical methods used in determining investments in agricultural research and development. Accurate crop yield prediction is important to the agriculture industry because it helps determine resource allocation, improves food security and increases profitability. Researchers found that in the United States, statistical models depend on best-case scenarios, relying on data from the most fertile soils in the most productive counties during the most favorable weather. Current methods also draw yield potential across large regions that often have wide variations in climates and soils, the study stated. The study compared yield potential and gaps of rain-fed crops such as corn and soybeans and used four statistical models to predict yields. “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,” Archontoulis said in a press release.