“In a time of economic uncertainty and in a nation where agriculture is a major part of the economy, it is an honor to have the chance to remove one of the challenges that is hindering our farmers.”
EDWARDSVILLE, Ill. (PRWEB) May 19, 2020
Southern Illinois University Edwardsville has been awarded $499,113 from the National Institute of Food and Agriculture of the United States Department of Agriculture (USDA) for a collaborative project involving the University of Illinois (U of I) and industry partners. The researchers aim to create a new modeling technique that will improve agricultural production systems and reduce postharvest loss by considering metabolic, environmental and genomic factors (MEG).
SIUE Center for Predictive Analysis (C-PAN) Director Carolyn Butts-Wilmsmeyer, PhD, associate professor in the Department of Biological Sciences, is principal investigator (PI) of the four-year project, entitled “MEG Models: A Holistic, Systems-Based Modeling Technique for Improved Agricultural Production System Performance and Reduced Postharvest Loss.” Co-PI’s include Tiffany Jamann, PhD, and Martin Bohn, PhD, with the U of I at Urbana-Champaign.
“During this multi-institutional project, we will be studying a pathogen known as Fusarium graminearum, which causes Gibberella ear rot in corn and head blight in wheat,” Butts-Wilmsmeyer explained. “Each year in the U.S. alone, ear rots cause almost three-quarters of a billion dollars in postharvest losses. Fusarium is particularly problematic, because not only does it cause the seed to rot, but also it can cause the seed to harbor hidden mycotoxins, chemicals that have been tied to a number of negative health impacts in both animals and humans.”
“As a scientist in the public sector, you hope you can give back to society,” she continued. “In a time of economic uncertainty and in a nation where agriculture is a major part of the economy, it is an honor to have the chance to remove one of the challenges that is hindering our farmers.”
According to the research team, in agriculture, and in all aspects of biology and human health, almost all of the traits and diseases of economic or societal importance are highly influenced by both genetics and the environment. However, almost all genetic models only make use of genetic data, don’t often consider the environment in detail, and rarely consider metabolic factors that might be responsible for natural resistance to diseases.
“We are developing a highly-sophisticated model that looks at known metabolic factors that help deter the disease and prevent it from spreading, the environmental conditions that we know make this disease a bigger problem, and the genetic variation that is present in our corn varieties,” Butts-Wilmsmeyer said. “We are taking an approach that considers pathology, plant breeding and statistical modeling, so that we can build something that is even more predictive than the genomic selection models that are currently used across all disciplines of science.”
SIUE, U of I and industry partners will evaluate field trials across multiple states in the U.S. Corn Belt, looking at genetically representative corn lines both under natural conditions and intense disease pressure. Ultimately, they will develop and evaluate multiple models to determine which is best and why.
Butts-Wilmsmeyer notes the research team’s extraordinary, collaborative ability to successfully carry out this project.
“We are all bringing the best of what each of our institutions has to offer,” she emphasized. “We are utilizing the computational resources of SIUE’s C-PAN, the excellent laboratory resources and expertise at the SIUE Biotechnology Laboratory Incubator and Shimadzu Laboratory. And, Drs. Jamann and Bohn with the U of I are highly-qualified scientists in the areas of plant pathology and quantitative plant breeding, respectively. Finally, the ability of our team to look at the entirety of Illinois and multiple states across the Midwest is possible thanks to the generous contributions of our industry partners.”
Importantly, Butts-Wilmsmeyer underscores the pertinent experiences involvement in this project will offer students. “Many of our students graduate and wish to remain in the St. Louis area, which boasts the highest concentration of agriculture PhDs in the world but which is also a growing technology hub in need of a workforce with experience in machine learning and advanced statistical approaches to complex biological problems.”
Despite challenges presented by the COVID-19 pandemic, the research team is charging ahead. They began planting the first part of the experiment in May and will evaluate the first field studies in summer 2021.
“This work is funded through 2024, and we expect to have published a comparison of multiple models by the end of the project’s funding or shortly thereafter,” Butts-Wilmsmeyer explained. “After publishing the models and the study’s results, we plan to make all data, models and code freely available to the public so that the results of this study can be immediately useful to the agricultural industry and other scientists.”
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