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“Develop valuable soybean varieties and germplasm for use as genetic resources for companies and for direct on-farm production”
Principal Investigators: Schapaugh, W. - Agronomy
Todd, T. - Plant Pathology
Harold Trick – Plant Pathology
Kansas State University, Manhattan, KS
Outcomes of research on variety development, SCN resistance, genetic gain, drought, and high-throughput phenotyping, FY 22
Variety development
This project enabled the development of over 100 new breeding populations, and advancement of over 300 populations in the F1, F2, F3, F4 and F4:5 generations. Parents used to create these populations were selected for their yield potential, drought tolerance, herbicide resistance (Roundup Ready 1 and STS), seed protein content, oil composition, disease resistance (primarily SCN and Soybean Sudden Death Syndrome), and genetic diversity.
Nearly 11,000 genotypes were evaluated in over 19,000 plots in Kansas in 2021. Over 1400 K-lines were evaluated in our preliminary trials. Over 190 K-lines were evaluated in our KS advanced yield trials. Over 500 (including 20 K-lines) breeding lines from programs across the country were evaluated in our KS Uniform Tests and Uniform Preliminary yield trials. Over 8,900 genotypes, (experimental breeding lines and plant introductions) were evaluated in our drought, remote sensing, and diversity yield trials.
Funding from this project enabled the development and release of KS4822NS (late maturity group (MG) IV, cyst nematode resistant, STS tolerant). This variety can be used for commercial production and as a parent by plant breeders for the development of new varieties.
SCN resistance
Breeding lines: Soybean resistance to HG Types 7, 2.5.7, and 1.2.3.5.6.7 was evaluated in replicated screening trials for ~240 preliminary and advanced breeding lines. Approximately 40% of breeding lines displayed moderate or better levels of resistance (FI = 30) to the HG Type 7 population, while only 3-8% of breeding lines displayed moderate or better levels of resistance (FI = 30) to HG Type 2 populations. Seven lines (~3%) were resistant or moderately resistant to all screening populations, and female indices for two of these averaged < 5. Kansas Soybean Performance Test: Soybean resistance to SCN was evaluated in replicated screening trials for 81 entries in the Kansas Soybean Variety Performance Test (KSVPT). Only 37% of KSVPT entries could be classified as resistant to moderately resistant to the HG Type 7 population, while only 5-10% could be classified as resistant or moderately resistant to the HG Type 2 populations. Four of the ten entries with the lowest female indices for the HG Type 7 population were KAES entries. No entries were resistant or moderately resistant to all SCN screening populations. Female indices for the HG Type 7 population were reasonably predictive of FI for the HG Type 2 populations, confirming that most KSVPT entries shared a common source of resistance (PI 88788).
Genetic gain
In 2021, we used genomic predictions for yield, genetic variation, and seed composition to select, intermate and rapidly cycle F1 plants to achieve three cycles of selection in one calendar year. Progeny from the initial base population and the rapid cycling generations are now being increased in a winter nursery to produce seed for planting in replicated field trials to characterize the effectiveness of the genomic selection and rapid cycling methodology. We also used the same genomic prediction model to create populations from elite public breeding lines that are predicted to produce superior progeny and have a negligible negative correlation between seed yield and seed protein content. The progeny of these crosses will be compared with progeny produced from our standard selection process in the future.
High-throughput phenotyping to increase genetic gain and improve evaluations under drought stress
We continue to develop models utilizing canopy reflectance and canopy thermal properties to estimate relative soybean maturity, seed yield, drought stress, and disease resistance. Entries in our 2017, 2018 and 2019 progeny rows selected based on remote sensing criteria where slightly higher yielding than random selections in our 2018, 2019 and 2020 preliminary yield trials. This information is being summaried and will be submitted for publication.
In 2021, all trials experienced drought stress through August and September. Significant differences in wilting ratings in our breeding material were noted among the test entries. Phenotypic differences in wilting ratings will support the development of drought tolerance varieties and further our understanding of the genetic basis of drought stress. In additional to taking visual wilting ratings, efforts continue to refine techniques to characterize drought stress using high throughput phenotyping with drones.
Opportunities for training and professional development
One graduate student working on objectives related to this project in Agronomy completed his M.S. degree in 2020, and one additional student in Bio and Ag Engineering worked cooperatively using the field plots developed and evaluated through this project also completed an M.S. degree. Currently, one student in Agronomy is pursuing a Ph.D. degree focusing on the application of remote sensing technology in breeding and one M.S. student in Plant Pathology is evaluating the use of transgenic material for Dectes control.
Dissemination of results
Peer-reviewed publications, extension publications, news releases, and experiment station reports, field days, extension meetings and tours are used to share the results of this project. Web pages have been developed to disseminate information on new releases and germplasm and pests. Distribution of results of genotype characterization for resistance are published online. Distribution of SCN survey results to cliental will provide much-needed information for making informed decisions by producers regarding variety selections for SCN management and by soybean breeders for the development of varieties with improved levels of resistance. Publications in 2021 included:
Aguirre-Rojas, L.M.; Buschman, L.L.; McCornack, B.; Schapaugh, W.T.; Scully, E.D.; Zhu, K.Y.; Trick, H.N.; Smith, C.M. 2021. Inheritance of Antibiosis Resistance to the Dectes Stem Borer, Dectes texanus, in Soybean PI165673. Agronomy 2021, 11, 738. https://doi.org/10.3390/agronomy11040738.
Walta, Dylan. 2021. Evaluation of drone imagery as a method for selection criteria in soybean breeding. M.S. Thesis, Kansas State University.
Singh, Asheesh K., Arti Singh, Soumik Sarkar, Baskar Ganapathysubramanian, William Schapaugh, Fernando E. Miguez, Clayton N. Carley et al. "High-Throughput Phenotyping in Soybean." In High-Throughput Crop Phenotyping, pp. 129-163. Springer, Cham, 2021.