K-State and John Deere team up for a remote sensing study

Researchers at Kansas State University and John Deere report the findings of a project that they say could “fundamentally change” the way farmers manage and market crops.

K-State Research and Extension agronomist Ignacio Ciampitti said the university is working with John Deere partners to analyze information from remote sensors on and off combine harvesters that will ultimately help farmers improve their protein. cereals from crops.

“Our customers tell us that maximizing grain yield and quality is very important,” said Yancy Wright, head of business agronomy testing at John Deere. “End-users – including millers, animal feeders and other processors – need high-quality cereal crops, and market premiums are beginning to reflect this demand.

“We wanted to validate our current technology development and discover new approaches to consider when developing solutions to help customers maximize their yield and quality, especially grain protein.”

One published paper At the end of 2021, in the journal Remote Sensing, researchers presented an analysis of 84 studies on the accuracy of models that predict grain content in a field crop based on current technology, such as satellite imagery.

Ciampitti said the team was able to compare areas of agricultural fields before harvesting using portable sensors, drones or airplanes; then after harvesting using sensors attached to the combine.

With this information, they compared the areas of the field assessed as low or high quality for the cereal protein concentration and determined where there were variations in crop quality after harvest.

“This is a developing field of research,” Ciampitti said. “Differentiating the quality of field crops becomes important to understand and can increase the competitiveness of American crops that enter both supply chains and local and international markets.”

Ciampitti said the analysis showed that combine sensors are more accurate than remote sensors in predicting grain protein concentrations, although off-combine sensors worked better for seasonal management and separate harvest planning. ; and costs less to implement.

“However,” he adds, “the sensors on the combine can quickly become the gold standard for predicting cereal protein levels during the season.”

According to the recent researcher journal article, a recent survey of 186 soybean farmers in several states indicated that more than 55% of them would invest in technology to assess grain protein concentration if they could earn a $ 0.50 premium per bushel. Because of this, the researchers say, “farmers’ interest is expected to grow as the direct and indirect benefits of (protein concentration in cereals) become more apparent.”

“As we introduce technologies for collecting grain protein concentration data, we will look at this paper to understand how we can achieve some of the proposed uses for this new data layer with internal and partnership solutions. , which will help us bring maximum value to customers who adopt these technologies, ”said Wright.

“This work,” he added, “will lead to the development of technology that will fundamentally change the way farmers manage their grain harvest and trade, as well as the way they manage their crop inputs.”

Ciampitti said the university is moving forward with the development of a remote sensing “decision tool” to differentiate the spatial variation in crop quality before harvest, which will help farmers make decisions before harvesting and marketing their crop.

“In addition, we are working with commodity councils to begin collecting field data to create one of the largest farmer-centric databases on the spatial variation of field crops related to crop quality in the United States,” Ciampitti said. “This is happening in collaboration with many other states and in close partnership with farmers across the country.”

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