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Syngenta touts new soybean genetic process

By Staff | Nov 21, 2015

“This tool is about better analysis of the data, to make better decisions.” —Joe Byrum Head of Syngenta’s soybean product development



AMES – Syngenta’s new operational research and advanced analytics programs is touted to speed up, with more accuracy and less research cost, soybean genetic developments.

Left unexplained during a Nov. 13 event in Ames, is how the investment savings may be realized for farmers who plant the seed.

Swiss-owned Syngenta, which has a major presence in North America, celebrated a major award at Iowa State University Nov. 13 calling for a math revolution in agriculture.

Attended by plant breeders, ag graduate students and college faculty at the Scheman Building on ISU’s campus, Syngenta officials explained how it has incorporated advanced analytics into its soybean breeding procedures with assistance from ISU faculty and others.

The team’s success won Syngenta the 2015 Franz Edelman Award for achievement in operations research and the management sciences in mid-April.

At the Nov. 13 event, Syngenta’s “Good Growth Through Advanced Analytics” presented in April for its award, was offered at the Scheman Building.

The process is directed by computer computations of the vast amount of data involved in genetics, working through a host of possible breeding combinations, leading to more exact breeder for specific results. It eliminates guess work, the audience was told.

According to Joe Byrum, head of Syngenta’s soybean product development, a five-year overhauling how the company’s plant breeders approach new soybean genetics, led in 2014, a saving of $287 million in research development, and in a shorter time frame, than new genetics would have been put forth without the advanced analytics method.

When asked how Iowa farmers would benefit from this process, Byrum did not address seed costs, but said it gave farmers soybean products they can trust.

He compared the process to stepping onto an airliner

“No one doubts the plane’s ability to lift into the air,” Byrum said.

Travelers have confidence that the designers at Boeing knew their aeronautical analytics was exact, he said, and didn’t guess.

Birum said advanced analytics gives Syngenta a new way to compete and farmers can have confidence “that the transformation is robust, it will perform.

“We’ve integrated the technologies.

“This tool is about better analysis of the data, to make better decisions.”

According to Matt Dar, an ag biosystems engineer at ISU, and a member of the team, told Farm News that operational research and advanced analytics cannot work well for individual farmers and their operational management.

The data sets for a farm, Dar said, are too small.

OR and AA, he said, are for managing big data, such as genetic developments.

A speaker’s panel described big data in this context was described as data so big, it defies traditional analysis.

For example, during Syngenta’s presentation, the cross-breeding possibilities within each soybean’s 46,000 genes, is 1.16×10 to the 12th power. That a number with 130 zeros behind it.

Byrum said more powerful computers can sift through the data faster and present outcomes more efficiently, than simply planting more seed plots around the world.

Prior to OR and AA, creating a new soybean products through cross-pollination took six years, Birum said. Now, Syngenta’s process is a fraction of the time, with substantial research savings.

Syngenta couched its new processes as the effort to feed a fast-growing world population- 750 million every decade – without hurting the earth’s soils.

OR and AA have helped other companies become more efficient, Byrum said, but this is one of the first major efforts to bring AA into agriculture, specifically to soybean development.

U.S. soybean “exports are valued at $42 billion,” Byrum said, “and is the largest protein source for livestock and poultry in the world.”

Tracy Dubler, head of Syngenta’s soybean breeding, said environmental conditions – such as soil types and climate – are the biggest challenges in developing new soybean genetics.

The new AA computations can show which genetics will work when developing soybean with characteristics specific to a region or environmental condition.

The analysis adds anticipated cost of the development and the chances for success.

Byrum said AA can be applied to breeding other food crops, specifically naming apples, corn, sunflowers and watermelons.

Speaking to attending graduate students, Byrum said agriculture historically is not efficient in managing big data and a math revolution is required. “There’s no reason not to know the math, especially with the computing power we have now.”

“Most companies will not be able to function without operational research,” Byrum said. “It’ll be priceless for agriculture.

“Those who have the OR skills will be sought in ag.”

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