A team of Iowa State researchers from the plant sciences and engineering disciplines have received a $733,795 U.S. Department of Agriculture’s (USDA) National Institute of Food and Agriculture (NIFA) grant to help advance research to develop more resilient and nutritious crops. More specifically, the Iowa State research project will develop methodologies to optimize plant selection and mating parameters to improve genomic selection (GS) strategies, as well as develop a user-friendly interface for the new GS platform that will increase the efficiency of crop breeding programs.
Patrick Schnable, director of the Plant Sciences Institute located at Iowa State is the Principal Investigator (PI) on the project along with co-PI’s Lizhi Wang and Guiping Hu, associate professors of industrial and manufacturing systems engineering at Iowa State.
According to Schnable, a consequence of growing populations, changing diets, and the challenges of variable weather patterns, mean our agricultural systems must produce more with less. More meaning meeting greater demand for agricultural products such as food, feed, energy and fiber, and less meaning doing so with reduced agricultural inputs such as water, fertilizer, pesticides and a reduced environmental footprint, all on less land.
Schnable said a key tool for making agriculture systems more productive, sustainable and resilient is genetic improvement via breeding. However, he said, traditional methods used in plant breeding programs, namely, crossbreeding plants repeatedly to get the best traits, do not optimize selection and mating parameters to their fullest potential.
By harnessing Big Data, the scientists are able to make the plant breeding process more focused and less time consuming.
“What’s been done previously is you identify the parent plants of the next generation — with each generation you have to pick who the next generations’ parents will be,” Schnable said. “What has not been explored extensively before and where optimization is being applied here in a novel way is identifying which specific parents should be crossed to other specific parents to produce the next generation.”
Using advanced mathematical programming and optimization techniques, Schnable’s team is also working to develop user-friendly tools to allow breeders to optimize the selection and breeding parameters for their own breeding scenarios.