Research

Seed banks across the globe store and preserve the genetic diversity of millions of varieties of crops. This massive collection of genetic material ensures crop breeders access to a wealth of genetics with which to breed crops that yield better or resist stress and disease.

But, with a world of corn genetics at their disposal, how do plant breeders know which varieties are worth studying and which ones aren’t? For most of history, that required growing the varieties and studying their performance in the real world. But innovative data analytics and genomics could help plant breeders predict the performance of new varieties without having to go to the effort of growing them.

Daryl Herzmann grew up on a dairy farm in northeast Iowa, and like most farmers, his days revolved around the weather.

"I was always frustrated by the forecast because we based our farming decisions on a two- or three-day forecast," the agronomy systems analyst said. "I decided I had to go to Iowa State and try to figure out how to fix this. That's where it dawned on me that it is not necessarily fixable, but it is a data problem."

The meteorology major began working with the Iowa Environmental Mesonet soon after graduation in 2001, providing better data to improve forecasts.

A new federal grant from the U.S. Department of Agriculture Organic Agriculture Research and Extension Initiative (OREI) will support Iowa State scientists and collaborators as they develop improved seed corn tailored to the needs of the rapidly growing organic industry.

The lead investigator for the four-year, $1,996,500 grant is USDA Agricultural Research Service research geneticist Paul Scott, an affiliate professor of agronomy at Iowa State. Thomas Lübberstedt, the Frey Chair in Agronomy and Director of the Raymond F. Baker Center for Plant Breeding at Iowa State, will partner on the project, along with Martin Bohn from the University of Illinois Urbana-Champaign and Angela Linarez from the University of Puerto Rico.

Our. Dr. Fernando Miguez has been awarded a USDA National Institute of Food and Agriculture grant. These grants focus on big data analytics, machine learning, artificial intelligence, and predictive technologies needed to keep U.S. agriculture on the leading edge of food and agricultural production. These grants are awarded through the USDA-NIFA Agriculture and Food Research Initiative (AFRI) Food and Agriculture Cyberinformatics and Tools (FACT).
 
“Big data and artificial intelligence will increasingly play a vital role in the future of agricultural technologies,” said Parag Chitnis, acting director of USDA-NIFA. “As we work to realize precision nutrition for consumers and enhance farmer profitability and agricultural sustainability, these predictive technologies will keep research and development moving quickly to provide the tools needed for success.”
 

A new study from researchers at the University of Minnesota and our Dr. Matt Liebman finds that diversifying crop rotations can greatly reduce negative environmental and health impacts, while maintaining profitability for farmers.