New Method for Yield and Grain Composition Estimation

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A new study from University of Illinois may help improve U.S. total yield and grain composition prediction models by estimating total end-season yield by mid-season. Researchers developed an algorithm comparing grain quality data collected from elevators with weather patterns experienced during emergence, silking, and grain fill. They assessed weather factors that influenced levels of starch, oil, and protein during those critical periods. The study evaluated data from 2011-2017, corn production years that included broad variability from drought to record-breaking yield years.

The study, published in Agronomy reports, “above-average grain protein and oil levels were favored by less nitrogen leaching during early vegetative growth, but also higher temperatures at flowering, while greater oil than protein concentrations resulted from lower temperatures during flowering and grain fill,” the authors say in the study. Improvements in predicting grain protein and oil content more effectively could influence global markets. Click here to learn more.