Corn is planted on approximately 90 million acres across the United States every year. With all that data, it takes months after harvest for government agencies to analyze total yield and grain quality. Scientists are working to shorten that timeline, making predictions for end-of-season yield by mid-season. However, fewer researchers have tackled predictions of grain quality, especially on large scales. A new University of Illinois study starts to fill that gap.
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.
Read more at College of Agricultural, Consumer and Environmental Sciences
Image: Average corn grain quality across the Midwest, with red areas showing the highest-protein levels graduating to purple, showing the lowest protein but highest yields. CREDIT: College of Agricultural, Consumer and Environmental Sciences