Yield improvement
Soybean production and profitability are impacted by crop achieving its yield potential. There are two main components of realizing the true genetic potential: (1) the assembly of favorable genetic combination of grain yield genes, and (2) the protection of yield from various deterrents including pests and diseases, and performance maximizing in a diverse set of growing and soil conditions. Our goals are to improve agricultural production output and positively impact IA farmers and the agricultural industry through the development of new products (cultivars and germplasm), gene discovery, research insights on pertinent topics of importance to farmers, processors, and consumers, and developing selection strategies that lead to higher yield and new products that will improve market penetration and expand export markets.

Our research projects combine hardware and software solutions to solve phenotyping bottleneck, which streamlines breeding and trait study pipeline for yield gain and improve protection traits. Our team uses digital phenotyping above- and below-ground traits and gleans insights using machine learning analytics. Each graduate student develops hardware tools and software solutions in a collaborative initiative; and are supported by scientific and professional staff to accomplish these goals. We mentor the next generation of plant breeders, develop and participate in teams that can work towards improved productivity and profitability of producers and processors, and provide enhanced nutritional quality for animal and human health. We integrate breeding and research activities with student mentoring and learning experiences. It is important to point out that most of incoming students came from a farming background with minimal or no computer programming knowledge, but have now developed expertise in this area making them valued in the job market, and also take this knowledge to their farms to help improve their crop production. They are the first generation of high-tech expert plant breeder employees building cultivars for our farmers geared toward technology driven higher profitability and sustainability. The fact that they come from farming background ensures that the future generation of US plant breeders will continue to be passionate about farmer issues and work for advancing their interests.
We utilize team based collaborations with faculty members from different disciplines, departments and colleges including engineering, social sciences, and statistics. Our group has become a research nucleus that brings together multiple researchers from several departments and industry. This creates wealth for the state and opportunities for IA farmers. We routinely receive requests from various stakeholders to access intellectual property we have created.

Our project goals are to develop superior soybean cultivars for Iowa farmers, to provide high yield and protection against crop stressors to keep Iowa farmers competitive and profitable. Our long-term team goal is to build highly desirable trait packages for our Iowa farmers. These will include an arsenal of performance genes and resistance against insects and diseases in soybean. This project will lead to new and improved varieties, new digital technology insights, and research in the area of aerial and ground robot based phenotyping. Our efforts to integrate performance using high throughput phenotyping (ground and aerial) continues to provide national and international prominence to our breeding and research activities and lead to benefits to Iowa farmers, farm economy and industries.

The main objectives of this project are to (1) increase soybean seed yield using genetic and phenomics tools, (2) improve seed quality traits (for example, develop clear hilum, high oleic) varieties for increased market capture, and (3) develop breeding population to improve protection traits (biotic and abiotic stress tolerance). We will focus on infusion of engineering and data analytic tools, as well as work with a larger population pool.

• 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].

Update:
In the reporting period, a safe and successful harvest was completed. Post-harvest, seed processing and trait data acquisition was also completed in late fall and winter. Selection decision on advancements were made. Additionally, plant material was sent to winter nursery to get the second crop cycle. Preparations for 2022 planting were initiated.
Student updates on their research projects:
1. Sam Blair and Matt Blair are working on implementation of drone based maturity estimation, and on use of ground robot for image based yield estimation.
2. Clayton Carley is working on role in development and positioning of soybean nodule for overall plant growth and productivity.
3. Sarah Jones is working on a project that is combining multiple sensors to study plant growth and development for creating a tool that helps predict plant response in multiple scenarios.
4. Liza van der Laan is working on a project that is helping with the identification of heat tolerance soybean accessions.
5. Joscif Raigne is working on a project that is helping incorporate multiple sources of SCN resistance in elite varieties.
Based on multi-state testing for yield, agronomics, quality and yield protection traits, we will disclose new varieties in 2022 for foundation seed increase. These new varieties will include high sucrose, low raffinose and low stachyose, and also new varieties with high protein, as well as combination of improved carbohydrate profile in high protein. 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.

The Soynomics team won the college of agriculture and list sciences team award in 2021. The Soynomics Team consist of leads Asheesh (Danny) Singh and Arti Singh, agronomy; Baskar Ganapathysubramanian and Soumik Sarkar, mechanical engineering; Daren Mueller and Greg Tylka, plant pathology and microbiology; Matt O’Neal, entomology, and their team members consisting of staff, post-doctoral fellows, and students. This team formed in 2014 to work on digital phenotyping and phenomics using sensors in a high-throughput manner that make use of aerial systems, ground robots and smartphones complemented with advanced data analytics. Their work has led to national projects in AI Institute, Cyber-Physical Systems, Smart and Connected Communities, and USDA’s Food and Agriculture Cyberinformatics and Tools program. The Soynomics Team is working on machine learning applications to agriculture, and outcomes from this work have advanced both plant sciences and engineering domains through interdisciplinary and interdepartmental contributions to research, education and outreach. The team takes a transdisciplinary focus, with the participation of farmers in helping shape the research directions, and have led to milestone outputs. The team is provides international leadership in cross-disciplinary mentoring of graduate students, preparing the future workforce in digital agriculture. The team has published more than 50 refereed journal/conference articles, and presented more than 50 invited talks in multiple countries. This award is a reflection of the public-producer partnership in identification of topics that matter to farmers and working together to solve them. More information on Soynomics award can be seen here: https://www.cals.iastate.edu/soynomics-team-receives-team-award

In 2021, ISU experimental lines were entered in the Uniform Soybean tests, which are grown in locations across several mid-western states. This information was also provided in 2021 final report.
In UTIIA (Uniform test, maturity group 2 early): A14011-77 (i.e., IAS19C3) yielded 1% less than the mean of the checks with data coming from 13 locations across the mid-west US. IAS19C3 compared to the only yellow hilum seed color, IA2102, yielded about 2% more than this check with similar days to maturity. Seed protein of IAS19C3 was 41.2% (on dry weight basis) while mean of checks was 38.6%. This variety has seem a healthy market demand with seed requests from multiple private companies.

In UTIIA (Uniform test, maturity group 2 early): A14004-126 (i.e., IAS25C1) yielded 1% more than the mean of the checks with data coming from 13 locations across the mid-west US. IAS25C1 compared to the only yellow hilum seed color, IA2102, yielded about 3% more than this check and two more days to maturity. Seed protein of IAS25C1 was 38.7% (on dry weight basis) while mean of checks was 38.6%. IAS25C1 has intermediate resistance against soybean aphid. Seed requests from private companies and farmers were made, and foundation seed was supplied by CAD.
In UTIIB (Uniform test, maturity group 2 late): A14004-58 (i.e., IAS31C1) yielded 1.5% more than the mean of the checks with data coming from 13 locations across the mid-west US. IAS31C1 compared to the only yellow hilum seed color, IA2102, yielded about 4% more than this check and three more days to maturity. Seed protein of IAS31C1was 38.7% (on dry weight basis) while mean of checks was also 38.7%. IAS31C1has intermediate resistance against soybean aphid. Seed requests from private companies and farmers were made, and foundation seed was supplied by CAD.
These three varieties were also tested in state-wide test that includes private (including herbicide tolerant traited varieties) and public program including ISU.
• In northern Iowa testing (early season) based on 2 year mean results, IAS19C3 ranked 6th in the state. IAS19C3 is the highest yielding conventional line, suitable for food applications. Only one variety significantly outyielded IAS19C3 based on 2020 and 2021 data. IAS19C3 variety also has high seed protein, therefore has an excellent combination of hard to achieve high yield and high protein.
• In Northern Iowa testing (full season) based on 2 year mean results, IAS25C1 was the highest yielding conventional line, suitable for food applications. In Central Iowa districts (2-year means), IAS25C1 was the second highest yielding conventional variety. IAS25C1 has improved aphid tolerance, which has been a high priority for organic food grade soybean growers.
• In Central Iowa testing (full season) based on 2 year mean results, IAS31C1 ranked 4th in the state competing against private company entries. IAS31C1 is the highest yielding conventional line, suitable for food applications. No variety significantly outyielded IAS31C1 based on 2020 and 2021 data from Central full-season districts. In 2021, this variety was the third highest yielding variety in Central Iowa full season district tests competing against private company varieties. IAS31C1 has improved aphid tolerance. This line did not perform as well in southern districts (2-year means), and we are continuing our efforts to develop a highly competitive variety for southern parts of IA.

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 technology developed from this project, including varieties, germplasm, phenomic tools, phenotyping and screening methods will help the IA farmers, private and public breeders, and research community looking for outputs on increased yield, protection traits, and seed quality. Varieties developed for this project are dual purpose: generic and food grade therefore create and cater to a premium paying market. ISU varieties provide a seed cost advantage due to their lower price, and yield competitive with commercial varieties. The ISU lines do not have a traited herbicide gene, therefore can be used by small or large seed companies to integrate any herbicide trait for a more competitive market. The training of high caliber workforce to work as breeders and scientist in seed, chemical and agricultural companies is immeasurable but highly valued. ISA funding is also bringing in federal investments through research funding agencies, including new partnership projects between our team and ISA.