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The new algorithm analyzes weather patterns for corn yield and grain

Corn is grown annually over 90 million acres throughout the United States. Along with these data, after harvesting, government agencies will analyze the whole production and quality issue. Scientists shorten their timeline to predict the end of the season half-yearly challenges. However, researchers have made fewer alcohol quality predictions, especially in large scale ones. A new study by the University of Illinois begins to fill this gap.

Research published AgronomyIt uses a new algorithm for the prediction of the performance of the last season and the composition of the grain – the proportion of starch, oils and protein kernels – analyzing weather patterns in corn flakes in three major stages. It is important to apply predictions of United States Midwest corn crops, regardless of arth genotypes or production practices.

"There are several studies to evaluate the quality factors that affect specific genotypes or specific locations, but prior to this study, we could not make general predictions on this scale," says Carrie Butts-Wilmsmeyer, assistant professor at the Department of Research Sciences. Research I and co-author.

When craftsmen reach the western Midwest, U.S. The Grains Council includes samples, annual summary reports on the composition and quality of sales, which are used for export sales. Butts-Wilmsmeyer and his colleagues were the integral data used to develop a new algorithm.

"We have used data between 2011 and 2017, droughts and seasons, and throughout the year," says Julian Seebauer, principal specialist at the Laboratory I Department and the author of the research.

The researchers linked ale-quality data to the weather data for the period 2011- 2017 as they feed on the regional grain with each elevator. When their algorithm was built, the weather was divided into three critics: emergence, silk and grain fillings, and found the two strongest predictions of both composition and quality to reduce the usability of dirty dirt and dirt.

The study went deeper, identifying the conditions for obtaining a higher concentration of oil or protein.

The proportion of starch, oil and protein is based on the genotypes, soil availability and weather conditions. But the weather effect is not always easy when it's protein. In drought conditions, stressed plants provide less starch in the grain. For this reason, the grain is more proportional to protein than plants that are not drought-stressed. Good weather can also cause higher protein concentrations. Much water means that more nitrogen is transported to the plant and the proteins enter.

In the study, "the average grain protein and oil levels were less nitrogen-less in early vegetative growth, but at higher temperatures, while oil concentrations were higher than in protein concentrations while lower levels of flowering and lower temperatures," the authors say.

The ability to better prevent the concentration of proteins and oils could be in global markets, taking into account the growing domestic demand and high demand for animal feed applications. With the new algorithm in theory, it is possible to analyze the seasonal performance and quality predictions by analyzing weather patterns before weeks or months before harvest.

"Other researchers have achieved real-time forecast results using more complex data and models, which has a comparative approach, but we added a quality piece of work and achieved accurate accuracy," said Butts-Wilmsmeyer. "The weather variables that can be used in more complex analyzes can be used in more complex analyzes to predict future performance and performance."



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