2022
Breeding high yielding soybean cultivars for Iowa Farmers
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Contributor/Checkoff:
Iowa Soybean Association
Category:
Sustainable Production
Keywords:
Crop protectionDiseaseField management
Parent Project:
Breeding for high yield, yield protection and compositional traits in soybean for Iowa farmers
Lead Principal Investigator:
Asheesh Singh, Iowa State University
Co-Principal Investigators:
Project Code:
Contributing Organization (Checkoff):
Iowa Soybean Association
$250,000
Leveraged Funding (Non-Checkoff):
We received federal funding from the National Science Foundation and USDA-NIFA. These include funding through NSF Smart and Connected Communities on smart and connect farms ($1.5 M), NSF Cyber-Physical Systems on production agriculture advances using modern technologies ($7 M through NSF and USDA), and Artificial Intelligence Institute for Resilient Agriculture ($20 M from USDA-NIFA) programs. Each of these three grants involves multiple partners across the nation and brings together a diverse team or varying disciplines. These projects do not fund breeding activities but will help develop technological solutions that can be useful to develop better soybean varieties. In each of these grants, ISA is a funded partner and ensures that farmer's are engaged in the defining the future of agriculture research and receive information in a timely manner from project outcomes.
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Institution Funded:
Iowa State University
$250,000
Brief Project Summary:

To realize true genetic potential, soybeans need favorable genetic combinations of grain yield genes, protection from deterrents like pests and diseases, and maximized performance in diverse growing and soil conditions. This breeding program’s goals are to improve soybean production through the development of new cultivars and germplasm, gene discovery, research insights for farmers, processors, and consumers, and developing seed selection strategies. This project combines hardware and software solutions to solve phenotyping bottleneck, which streamlines breeding and trait study pipeline for yield gain and improve protection traits. Primary objectives are to increase soybean seed yield using genetic and phenomics tools, improve seed quality for increased market capture, and develop breeding population to improve protection traits.

Key Benefactors:
farmers, agronomists, Extension agents, soybean breeders, seed companies

Information And Results
Project Deliverables

• Short term: development of breeding populations for objectives listed in this project.
• Long term: cultivar and germplasm lines that meet the requirements of Iowa farmers and industry of high yield trait with yield preservation.
• Proposed specific deliverables (long term; 2022): Checks in MG 1 (IAS19C3), MG2 (IAS25C1), and MG3 (IAS31C1) will be used as benchmarks for yellow hilum soybean. These three varieties will be used as checks in internal tests. Additionally, multi-location and -state testing will include conventional checks for yellow and non-yellow hilum seed. These checks include varieties used in multiple institution cooperative trials. Usage of checks from private and public programs will ensure farmers will have the ability to compare ISU lines with competitive checks.
We will test ISU lines in the Iowa Crop Performance Test (ICPT) that includes commercial company most advanced lines enabling us to demonstrate performance of our lines relative to current commercial company checks. In 2020 and in 2021, the ISU lines performed among the top entries [data available through ICPT website].

Final Project Results

Update:
Variety development update:
Foundation seed production of three varieties was done through CAD: IAS19C3, IAS25C1, IAS31C1. These varieties are non-GM, and yellow hilum with an excellent package of traits, and suitability for the state of IA. Contracts were established with multiple companies for seed sale of these lines demonstrating success of these lines.
Based on multi-state testing for yield, agronomics, quality and yield protection traits, we disclosed IAS25HPHS1. This line combines good seed yield with high protein (meets or exceeds 48% meal protein), higher sucrose, low raffinose, low stachyose, and larger seed size. It has a maturity 2.3-2.7 and will meet the need of companies and farmers interested to grow a food grade soybean due to clear hilum color along with combination of yield, protein, carbohydrate and seed size traits. With previous efforts in soybean focused on high yield alone, we are lagging behind on high protein and therefore there is a demand for high protein soybean. Increased sucrose is a desirable trait for food grade applications. Foundation seed of this line was completed in 2022 field season.

Breeding population updates:
We developed new populations, and F1, F2, F3 generations from segregating populations for breeding objectives were advanced using US and/or winter nurseries. We grew early generations in space planted nurseries and single pod picks were collected, while F4:5 single plants were pulled based on selection criteria. We identified single plant pulls for late MG1, MG2 and early MG3. Yield testing of progeny rows, preliminary yield tests, and advanced yield tests were completed. We continued our research work on robot-based yield estimation, and aerial imagery for trait selection.

Publications in the reporting period:
• Riera LG, ME Carroll, Z Zhang, JM Shook, S Ghosal, T Gao, A Singh, S Bhattacharya, B Ganapathysubramanian, AK Singh, S Sarkar. 2021. Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications. SPG Plant Phenomics 2021 (9846470)
• Elango, D., K. Rajendran, L. Van der Laan, S. Sebastiar, J. Raigne, N.A. Thaiparambil, N. El Haddad, B. Raja, W. Wang, A. Ferela, K.O. Chiteri, M. Thudi, R.K. Varshney, S. Chopra, A. Singh, A.K. Singh (2022). Raffinose Family Oligosaccharides: Friend or Foe for Human and Plant Health?. Frontiers in plant science, 13, 829118. https://doi.org/10.3389/fpls.2022.829118
• Bonds, Darcy, J.A. Koziel, M. De, B. Chen, A.K. Singh, M.A. Licht. 2022. "Dataset Documenting the Interactions of Biochar with Manure, Soil, and Plants: Towards Improved Sustainability of Animal and Crop Agriculture" Data 7, no. 3: 32. https://doi.org/10.3390/data7030032
• Banik C, JA Koziel, D Bonds, AK Singh, MA Licht. 2021. Comparing Biochar-Swine Manure Mixture to Conventional Manure Impact on Soil Nutrient Availability and Plant Uptake—A Greenhouse Study. Land 10 (4), 372 (1-20).
• Banik C, JA Koziel, M De, D Bonds, B Chen, AK Singh, M Licht. 2021. Soil nutrients and carbon dynamics in the presence of biochar-swine manure mixture under controlled leaching experiment using a Midwestern Mollisols. Frontiers in Environmental Science 9, 66
• Kar S., K. Nagasubramanian, D. Elango, A. Nair, D. S. Mueller, M. E. O’Neal, A. K. Singh, S. Sarkar, B. Ganapathysubramanian, A. Singh. Self-Supervised Learning Improves Agricultural Pest Classification. AI for Agriculture and Food Systems Workshop in Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2022), Virtual (Conference paper)
• Cho M., K. Nagasubramanian, A. K. Singh, A. Singh, B. Ganapathysubramanian, S. Sarkar, C. Hegde. 2022. Privacy-Preserving Deep Models for Plant Stress Phenotyping. AI for Agriculture and Food Systems Workshop in Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2022), Virtual (conference paper)
• Chiranjeevi S., T. Young, T.Z. Jubery, K. Nagasubramanian, S. Sarkar, A.K. Singh, A. Singh, B. Ganapathysubramanian. 2021. Exploring the use of 3D point cloud data for improved plant stress rating. AI for Agriculture and Food Systems. AIAFS 2022. Vancouver, BC, Canada Feb 28 2021. (conference paper)

Invited presentations in the reporting period:
• Singh AK (2022). “Soybean yield improvement strategies with digital technologies.” DIGICROP 2022. Virtual
• Singh AK (2022). “Digital Phenotyping and Soybean Breeding.” June 21, 2022. NCERA 137 and NC 1197 Meeting, Nebraska.
• Singh AK (2021). “Enhancing Crop Improvement Capabilities Using Machine Learning.” Symposium - Application of Machine Learning and Artificial Intelligence in Plant Breeding, Tri-Societies meetings. Sat Lake City, November 9, 2021.
• Singh AK, D. Elango, A. Ferela, L van der Laan (2021). Soybean Protein Improvement. Iowa Soybean Research Center. Oct 27, 2021.

Student updates on their research projects:
1. Sam Blair and Matt Blair worked on drone based maturity estimation, and on use of ground robot for image based yield estimation. Both projects aim to implement automated phenotyping in 2023 field season.
2. Clayton Carley completed a project that investigated the development and positioning of soybean nodule for overall plant growth and productivity.
3. Sarah Jones continued her work that aims to combine multiple sensors to study plant growth and development to create a tool that helps predict plant response in multiple scenarios.
4. Liza van der Laan completed her screening of several hundred soybean accessions to identify heat tolerant soybean accessions.
5. Joscif Raigne is working on a project that is helping incorporate multiple sources of SCN resistance in elite varieties.

Staff, post-doctoral fellow, and graduate students continue to support the soybean breeding and research program.

This project successfully developed high yielding, high protein, improved carbohydrate soybean varieties that meet the needs of industry and farmers. Several of our soybean varieties were adopted by companies demonstrating their success. We continued to work on the development of sophisticated engineering tools and solutions to improve soybean breeding and production. Research partnerships continued through federal funded projects, providing enhanced return on investment to Iowa farmers. Graduate students continue to advance breeding and production science.

The United Soybean Research Retention policy will display final reports with the project once completed but working files will be purged after three years. And financial information after seven years. All pertinent information is in the final report or if you want more information, please contact the project lead at your state soybean organization or principal investigator listed on the project.