Variety Development in Soybean and Millets

A.K. Singh’s group is recognized for extensive advancements in breeding high-yielding, pest-resistant, and climate-resilient varieties of soybean. The group has recently initiated an effort on small millets breeding. Over 29 new soybean varieties have been developed and filed for intellectual property protection at Iowa State University, with six varieties launched commercially and adopted by regional producers. These cultivars demonstrate enhancements in seed yield, protein content, disease resistance, including soybean cyst nematode (SCN), sudden death syndrome and other stem diseases. They have also developed varieties with improved seed composition traits such as high protein content, improved carbohydrate profile (increased sucrose, low raffinose and stachyose), increased seed size and yellow hilum. Parallel progress in millets complements efforts toward broadening genetic diversity and addressing nutritional security. Collaborations and technology-driven breeding strategies underpin scientific innovations in variety development complemented with interdisciplinary research.

Papers of interest:

  • Mariana V Chiozza, Johnathon M Shook, Liza Van der Laan, Asheesh K Singh, Fernando E Miguez. 2025. Comprehensive assessment of soybean seed composition from field trials spanning 22 US states and 24 years: Predictive insights. Crop Science. 65:4. E70142. https://doi.org/10.1002/csc2.70142
  • Krause MD, KOG Dias, AK Singh, WD Beavis. 2025. Using soybean historical field trial data to study genotype by environment variation and identify mega‐environments with the integration of genetic and non‐genetic factors. Volume117, Issue1. e70023. https://doi.org/10.1002/agj2.70023
  • Carroll ME, LG Riera, BA Miller, PM Dixon, B Ganapathysubramanian, S Sarkar, AK Singh. 2024. Leveraging soil mapping and machine learning to improve spatial adjustments in plant breeding trials. Early view. https://doi.org/10.1002/csc2.21336
  • Krause MD, HP Piepho, KOG Dias, AK Singh, WD Beavis. 2023. Models to Estimate Genetic Gain of Soybean Seed Yield from Annual Multi-Environment Field Trials. Theoretical Applied Genetics 136 (12), 252. https://doi.org/10.1007/s00122-023-04470-3
  • Shook J, T Gangopadhyay, L Wu, B Ganapathysubramanian, S Sarkar, AK Singh. 2021. Crop yield prediction integrating genotype and weather variables using deep learning. Plos one 16 (6), e0252402. https://doi.org/10.1371/journal.pone.0252402
  • Parmley KA, RH Higgins, B Ganapathysubramanian, S Sarkar, AK Singh. 2019. Machine Learning Approaches for Prescriptive Plant Breeding. Scientific Reports. Scientific Reports volume 9, Article number: 17132. https://doi.org/10.1038/s41598-019-53451-4