Multi-regional research study focuses on soybean flower, pod retention
By: Norman Martin
Texas Tech University will lead a new multi-institutional research effort of five organizations putting their heads together to achieve full genetic yield potential of the soybean. The new partnership, the first of its kind in more than 40 decades, aims to increase soybean flower and pod retention. This unrealized value could bring $50 per acre or $400 million in economic return for U.S. soybean farmers.
The collaborative focus will test how heat and drought impact flower bud retention. Flower production dictates the final pod number and, ultimately, yield in soybeans. Although flower retention is a leading cause of soybean yield loss in the U.S., no organized effort exists to address it. Farmers experience about 30% of flower loss under favorable conditions and up to 80% under drought and heat stress.
Texas Tech, in collaboration with Kansas State University, the University of Missouri and the University of Tennessee, will lead the research on the national effort. At the helm, Principal Investigator Krishna Jagadish at Texas Tech will compile data from dryland, irrigated, severe drought and heat stress growing conditions. In total, 250 diverse genotypes with publicly available whole genome resequencing data will be assessed over the span of this three-year farmer investment.
Jagadish serves as a Professor and Thornton Distinguished Chair within Tech's Department of Plant & Soil Science. He also serves as Director of the Texas Coalition for Sustainable Integrated Systems, and Coordinator of the Texas Alliance for Water Conservation.
In addition, the Texas Tech research team includes Department of Plant & Soil Science faculty members Glen Ritchie, the J.A. Love Endowed Chair and Department Chair; Gunvant Patil, an Assistant Professor in Molecular Crop Improvement from IGCAST; Impa Somayanda, a Research Assistant Professor of Crop & Forage Physiology; as well as Sari-Sarraf Hamed, a Professor within Tech's Department of Electrical & Computer Engineering.
“Integrating physiological, genetic and machine learning approaches to develop novels ways to reduce flower abortion in soybean is exciting as it gives us an opportunity to enhance yield potential and minimize yield loss under harsh environments,” Jagadish said.
The multi-regional and trans-disciplinary team will develop an image-based field phenotyping system, integrated with deep-learning tools to capture large genetic variation in flower loss and pod retention under different soil and climatic conditions. A genetically diverse set of 250 genotypes including late group II, group III and early group IV will be tested under natural dryland conditions in Missouri and Kansas, and under irrigated and severe drought and heat stress conditions in Texas and Tennessee.
A genetically diverse set of 50 genotypes including late group II, group III and early group IV will be tested under natural dryland conditions in Missouri and Kansas, and under irrigated and severe drought and heat stress conditions in Texas and Tennessee.
Officials with the United Soybean Board point out that understanding the genetic diversity of flower loss opens the door to untapped yield potential in soybeans. The novel phenotyping system, which detects expressions of the various genotypes, will capture genetic variation across cultivars; identify molecular switches to enhance flower and pod retention; and help develop advanced breeding lines. Ultimately, the goal is to increase flower and pod retention by 20% to 30%. That in turn could enhance yields by 10% to 15%.
“Partnering on research of this magnitude is especially important as we continue to experience challenging weather events,” said Suzanne Shirbroun, president of the North Central Soybean Research Program and Iowa farmer. “This is one example of how we can invest checkoff dollars collectively that benefits soybean farmers across 30-plus states,” she said.
CONTACT: Glen Ritchie, Department Chair, Department of Plant & Soil Science, Texas Tech University at (806) 742-4325 or firstname.lastname@example.org
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Editor: Norman Martin
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