2026 Soybean Breeders' Workshop
James Specht Poster Competition Winners
2026 James Specht Poster Competition Winner's Abstracts
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Sawyer Goodwin, North Dakota State University
Investigation soybean yield improvement in North Dakota State University experimental lines
Soybean in North Dakota lags behind other midwestern states in its average yield despite being the state's most planted crop by acreage, as well as being the state’s most valuable crop at over 2.3 billion dollars in 2024. With a plateau in yields of experimental soybean lines from the NDSU Soybean Breeding Program, a clear need for introduction of outside germplasm was established. Early maturing parents from North Dakota germplasm were selected to be crossed with high yielding lines identified from Uniform Trial data in maturity groups II, III, and IV. Progeny from these crosses were evaluated for their relative maturity during the 2024 field season to identify lines capable of growing in North Dakota. Selected progeny were advanced to preliminary yield trials during the 2025 field season to test yield versus checks and further verify relative maturity of these experimental lines. Data collected during the 2025 field season is to be analyzed alongside skim-sequencing data of greater than 0.3x coverage for each line to attempt to draw correlation between expressed yield phenotypes and possible genomic regions of interest. After one year of yield data was collected, preliminary results have revealed that the integration of new genetic material was successful, with a clear separation in grouping of crossed progeny and ND x ND experimental lines. Preliminary results have also revealed several possible regions of interest across multiple chromosomes; though, a second year of data is needed before any conclusions can be drawn.
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Emanuel Ferrari do Nascimento, University of Arkansas
Quantifying Southern Root-Knot Nematode Impact in Soybean Using High-Throughput UAV-Based Phenotyping
The southern root-knot nematode [SRKN, Meloidogyne incognita (Kofoid & White) Chitwood] represents one of the most yield-limiting soybean pathogens worldwide. With limited control options, genetic resistance remains the primary management strategy. This study aims to assess phenotypic differences between resistant and susceptible soybean genotypes across naturally SRKN-infested and non-infested environments using high-throughput UAV-based phenotyping. In 2025, field experiments were conducted across three Arkansas locations, including a highly SRKN-infested site in Kerr, and two non-infested sites in Stuttgart and Pine Tree. A total of fifteen genotypes were evaluated, including six lines carrying the resistance allele at the major quantitative trait locus (QTL) on chromosome 10, six susceptible lines lacking this allele, and three commercial cultivars. Composite soil samples were collected from each two-row plot at planting and again at 70 days after planting to quantify temporal changes in nematode population density in the soil between resistant and susceptible genotypes in the SRKN-infested field and to confirm the absence of the nematode at the other locations. Unmanned Aerial Vehicle (UAV) flights were conducted five times throughout the season—twice during the vegetative stage and three times during the reproductive stage—to monitor canopy responses across growth stages and enable early discrimination between the resistant and susceptible lines. Yield data from each plot were collected to assess genotype performance across contrasting nematode pressure environments. Under SRKN-infested conditions, resistant genotypes outyielded susceptible lines by 28.7% (p < 0.001), whereas the yield advantage under non-infested conditions was only 14.9%. These findings underscore the importance of genetic resistance for mitigating yield losses under SRKN pressure and support the integration of UAV-based high-throughput phenotyping into soybean breeding programs. Together, these results establish a foundation for future analyses evaluating the potential of UAV-based approaches for early, field-based discrimination of resistant genotypes under variable nematode pressure.
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Joscif Raigne, Iowa State University
Phenomic-assisted Selection in Soybean Variety Development
Predicting seed yield for phenomic-assisted selection (PAS) in plant breeding programs requires efficient, scalable phenotyping tools that operate across thousands of genotypes and environments. Traditional yield trials are resource-intensive and often limit the pace of genetic gain. This study assesses Uncrewed Aerial Vehicle (UAV)-based multispectral sensing for high-throughput SY prediction in a soybean cultivar development program.UAV data were collected at multiple growth stages across >54,000 soybean breeding plots over three years in Iowa, using multispectral reflectance bands and vegetation indices (VIs) as model inputs. Minimal performance loss was observed across ground sampling distances of 2.89 cm to 5.78 cm, suggesting that higher-altitude flights are viable. Random forest models were trained using raw bands, RGB VIs, and multispectral VIs for progeny rows and yield trials. Integrating at least two time points, especially later ones, significantly improved predictive power compared to a single time point.Model performance was evaluated on independent field trials to assess PAS. Raw bands, multispectral VIs, and RGB VIs showed similar predictive power in progeny rows (R^2 = 0.61) and yield trials (R^2 = 0.74). At 10–30% selection thresholds, models achieved up to 0.58 sensitivity, 0.93 specificity, and 0.87 accuracy.