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The study, published in Agronomy, uses a newly developed algorithm to predict both end-of-season yield and grain composition -- the proportion of starch, oil, and protein in the kernel -- by analyzing weather patterns during three important stages in corn development. Importantly, the predictions apply to the entire Midwest corn crop in the United States, regardless of corn genotypes or production practices.
"There are several studies assessing factors influencing quality for specific genotypes or specific locations, but before this study, we couldn't make general predictions at this scale," says Carrie Butts-Wilmsmeyer, research assistant professor in the Department of Crop Sciences at U of I and co-author of the study.
Corn - Elevators - Midwest - Season - US
As corn arrives at elevators across the Midwest each season, the U.S. Grains Council takes samples to assess composition and quality for their annual summary reports, which are used for export sales. It was this comprehensive database that Butts-Wilmsmeyer and her colleagues used in developing their new algorithm.
"We used data from 2011 to 2017, which encompassed drought years as well as record-yielding years, and everything in between," says Juliann Seebauer, principal research specialist in U of I's Department of Crop Sciences and co-author of the study.
Researchers - Grain-quality - Data - Weather - Data
The researchers paired the grain-quality data with 2011- 2017 weather data from the regions feeding into each grain elevator. To build their algorithm, they concentrated on the weather during three critical periods -- emergence, silking, and grain fill -- and found that the strongest predictor of both grain yield and compositional quality was water availability during silking and grain fill.
The analysis went deeper, identifying conditions leading to...
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