Schedule - BIC & NAPIA Biennial Meeting

BIC & NAPIA Biennial Meeting Homepage

MONDAY, NOV. 3

1–7 pm Registration

5:30–7:30 pm  Opening Reception, P Street Dining

• Juan M. Osorno, BIC President Professor, Dry Bean Breeding & Genetics, Department of Plant Sciences, North Dakota State University
• Carlos A. Urrea, Dry Bean Breeding Specialist, Department of Agronomy and Horticulture, University of Nebraska–Lincoln
• Michelle Hubbard, NAPIA President, Research Scientist, Pulse Pathology, Swift Current Research and Development Centre, Saskatchewan, Agriculture and Agri-Food Canada, Government of Canada

Wednesday, Nov. 5

7–8 am  NAPIA Board & Germplasm Meeting – NAPIA Alumni

7:30–8:30 am  Breakfast (Regents DEF)

NOV 5: BIC/NAPIA CONCURRENT SESSION
GENOMICS AND BREEDING
(REGENTS BC)

Moderator: Jenna Hershberger, Clemson University

8:30–8:45 am Genomic Insights into Iron and Zinc Biofortification in Common Bean: Leveraging Crop Wild Relatives, Identifying Genomic Hotspots and Introgression Regions    

Jennifer Wilker, Research Scientist, Alliance of Bioversity International and CIAT

Co-authors: Santiago Diaz, Sergio Cruz, Juan Lobaton, Diana Duarte, Stephen Beebe

Common bean (Phaseolus vulgaris) is a critical crop for biofortification, offering an affordable source of iron (Fe) and zinc (Zn) to combat micronutrient deficiency in over 2 billion people. Crop wild relatives (CWRs) are a vital source of genetic diversity for crop improvement. Certain bean CWRs, like P. parvifolius and P. acutifolius, thrive in arid soils where iron is scarce, suggesting they have evolved mechanisms for enhanced iron uptake. 

In this study, we developed two interspecific bean populations incorporating genes from P. parvifolius and P. acutifolius into P. vulgaris. The first, VAP9 × SEN118 (VxS), includes 412 recombinant inbred lines (RILs), while the second, IMAWT, consists of 314 F5.6 families. Field trials were conducted at Alliance Bioversity-CIAT (Palmira, Colombia) in 2023 under irrigated conditions to phenotype seed iron (SdFe) and zinc (SdZn) content using x-ray fluorescence, and to measure phenological and yield traits. Populations were genotyped with genome complexity reduction methodologies (GBS and DArTSeq), and to reduce the reference bias, we assembled de novo VAP9, one of the interspecific parental lines, using Oxford Nanopore long reads technology.

We found that both populations presented a wide range of variation for SdFe (40-120 ppm) and SdZn (16-40 ppm) concentration. QTL mapping in the VxS population identified 53 QTLs, including 10 major QTLs (>10% PVE). VAP9 contributed positively to seed mineral traits, while SEN118 excelled in yield components. Notably, four distinct QTLs for SdFe accumulation (SdFe6.1–SdFe6.4) were repeatedly identified on chromosome Pv06 across different mapping configurations and populations, suggesting a genomic hotspot for iron regulation. For the IMAWT population, 11 QTNs were detected for SdFe and SdZn. Interestingly, the QTN SdFe6.2 was located inside a SdFe hotspot on Pv06. We used the Patterson’s D-statistic to evaluate the introgression content in VAP9. We discovered extensive introgression regions near the identified QTLs for SdFe and yield. These regions represent promising genetic loci for incorporation into future breeding programs. These findings underscore the potential of non-vulgaris gene pools to enhance micronutrient content in common bean and could enhance future breeding for biofortification efforts using marker-assisted and genomic selection strategies.

8:45–9 am A Framework for Genomic Selection in Dry Bean Breeding: From GBS to SNP Chip Development

Jose Figueroa-Cerna, Graduate Student, North Dakota State University    

Co-authors: Nusrat Khan, Phillip E. McClean, Rian Lee, Jayanta Roy

Genomic prediction (GP) has the potential to accelerate genetic gain in dry bean (Phaseolus vulgaris L.) breeding by enabling earlier and more accurate selection decisions, while potentially saving time and resources. This study aimed to develop a GP pipeline tailored to the North Dakota State University (NDSU) dry bean breeding program. Phenotypic data was collected over four years from 427 advanced breeding lines representing eight market classes and both gene pools, encompassing eight traits that spanned agronomic (seed yield, 100-seed weight, plant height, days to flowering, and days to maturity), quality (canning quality), and disease-resistance (White Mold -Sclerotinia sclerotiorum and Common Bacterial Blight -Xanthomonas axonopodis pv. phaseoli). Genotyping-by-sequencing (GBS) produced ~400,000 SNPs, which were quality-filtered to 109,000 high-confidence markers. Three GP models (Bayes A, Bayes B, and rrBLUP) were evaluated using the complete dataset, yielding moderate to high prediction accuracies (0.33 – 0.92) depending on the trait. To reduce the number of SNPs for marker optimization and chip design purposes, genome-wide association studies (GWAS) and GS-based additive effects identified 4,440 SNPs as significantly associated with phenotypic variation among traits. An additional 298 SNPs were included based on reported genomic regions associated to other traits of economic importance. After design and quality control, a custom-made chip containing 3,867 SNPs was developed. For validation purposes, two independent populations were genotyped. The first (192 genotypes) was used in GS for Soybean Cyst Nematodes (SCN, Heterodera glycines Ichinohe) resistance, yielding a prediction accuracy of 0.80 based on cross-validation results. In contrast, the second population (217 genotypes) achieved a prediction accuracy of 0.53 for protein content. These results highlight the value of marker optimization in enhancing GP accuracy and demonstrate a scalable framework for integrating genomic tools into dry bean breeding pipelines. 

9–9:15 am Developing Protein-Rich Dry Beans (Phaseolus vulgaris L.) through Genomic prediction

Carly George, Graduate Student, North Dakota State University

Co-authors: Jose Figueroa-Cerna, Nusrat Khan, Phillip E. McClean, & Juan M. Osorno    

As an important crop in global food systems, dry bean (Phaseolus vulgaris L.) plays a crucial role in food security, making the improvement of its nutritional profile essential for maximizing its benefits to consumers. Known for being a rich source of plant-based protein, dry beans are widely recognized as a nutritious food source. However, combining key agronomic, seed quality, and nutritional traits into a single dry bean cultivar remains challenging. To address this challenge, genomic prediction enables the simultaneous assessment of multiple important traits within the same breeding line before field testing, potentially reducing the time and resources required for selection. This approach uses genome-wide molecular markers and statistical models to estimate the genetic potential of breeding lines. This approach has been successfully applied in other legume crop species- such as lentils (Lens culinaris M.) and peas (Pisum sativum L.) and has shown improved selection efficiency by 1.9- and 2.4- fold for seed yield and 2-fold for protein content. This study evaluates the protein content of approximately 530 breeding lines within the North Dakota State University (NDSU) dry bean breeding program, along with check cultivars, to assess the effectiveness of genomic prediction models for improving protein content. Protein was measured using Near Infrared Reflectance (NIR), while genotyping was conducted utilizing a custom single nucleotide polymorphism (SNP) chip containing ~4K SNP markers. Preliminary results suggest that among the 17 genomic prediction models tested, the Random Forest algorithm achieved the highest predictive accuracy for protein content, with an R² of 0.52 (52%). Model predictions closely matched observed values overall, and the model correctly identified 13% of the top 20% highest-protein lines and 32% of the bottom 20% lowest-protein lines. The model’s stronger ability to identify low-protein lines highlights potential value for negative selection, allowing breeders to discard inferior lines early in the breeding process to focus more on promising material. Beyond protein content, the application of genomic prediction models for negative selection could transform trait improvement strategies in dry bean breeding, enabling faster, more targeted genetic improvement.

9:15–9:30 am Strengths, Weaknesses, and Future Application of k-mer GWAS in Phaseolus vulgaris    

Andrew Wiersma, Archer Daniels Midland Company    

Co-authors: John Hamilton, Brieanne Vaillancourt, Julia Brose, Halima Awale, Evan Wright, Yi-Wen Wang, John Hart, Timothy Porch, James Kelly, C. Robin Buell    

New and improved methods are needed for identifying closely linked marker-trait associations for common beans (Phaseolus vulgaris L.). One major limitation to traditional genome-wide association studies (GWAS) is the reliance on a single reference genome which may be missing essential genetic variation for the expression of target traits (i.e. yield, agronomics, quality, disease resistance, or stress tolerance). Another limitation is that GWAS based on single nucleotide polymorphisms (SNPs) do not adequately represent structural genetic variation such as, copy number variation, presence/absence variation, insertions/deletions, and translocations/inversions. A new method that attempts to overcome these challenges is GWAS based on k-mers (short nucleotide sequences)—which is not reliant on a single reference genome and can mark both SNPs and structural variation. Here, I will review a benchmark application of k-mer GWAS in the common bean Andean Diversity Panel (ADP), where marker-trait associations were identified for plant determinacy, anthracnose (caused by Colletotrichum lindemuthianum) resistance, bean common mosaic virus (BCMV) resistance, and agronomic traits under heat stress. Availability of historical SNP-chip and phenotypic data enabled a direct comparison of GWAS based on three tiers of genotypic data: SNP-chip, whole genome shotgun sequence (WGS)-SNP, and reference-free k-mers. The primary advantages that k-mer GWAS affords are, much higher marker resolution, identification of cis-associated k-mers that are absent from the reference genome, and the identification of structural variation that was unidentified through SNP-based approaches. Notable challenges for implementing k-mer GWAS is that the cost is higher (due to DNA library preparation and sequencing), and the bioinformatics pipeline requires high-performance computing. Additionally, it can be challenging to identify the genetic context of k-mer sequences that are absent from reference genomes—which can be facilitated by whole genome assembly of additional plant genotypes containing those sequences. In spite of these challenges, k-mer GWAS represents an important advancement in crop improvement and is a useful tool for more effectively identifying and incorporating beneficial pangenomic variation into new breeding lines and varieties. 

9:30–10:15 am Frazier Zaumeyer Distinguished Lectureship: Genes, Genomes, and Pan-Genomes: Enabling Discovery Across Scales in Phaseolus    

C. Robin Buell, Georgia Research Alliance Eminent Scholar Chair in Crop Genomics, Crop & Soil Sciences, University of Georgia    

Co-authors: Andrew Wiersma, John P. Hamilton, Yi-Wen Wang, Joshua C. Wood, Leah Tomey, Kathrine Mailloux, Brieanne

Vaillancourt, Nolan Bornowski, Andrew Wiersma, Consuelo Estévez de Jensen, Tim Porch.    
Advancing genome technologies have resulted in access to whole genome assemblies that are complemented by whole genome resequencing datasets of hundreds of accessions. In the last decade, multiple reference genomes, as well as sequenced diversity panels, have been generated for both common as well as tepary bean. Comparative genome analyses between and among P. vulgaris and P. acutifolius genome assemblies have revealed conservation not only within but also between these species as well as structural variation within the species that can be associated with traits. Access to resequencing and additional high throughput genotype data for both species have been coupled with phenotyping datasets to reveal causal loci associated with agronomic traits of interest, retrospective discovery of breeding activities, and an understanding of population structure. While technologies for generating sequence data have advanced rapidly, software for storing, organizing, and accessing large genome datasets for breeders are now beginning to emerge. Pan-genome analyses are focused on genic content differences between accessions and can reveal structural variation that are associated with speciation and agronomic traits. For breeding and quantitative trait analyses, the Practical Haplotype Graph was developed to collate large genome datasets into consensus haplotypes. To facilitate breeding of tepary bean, we constructed a Practical Haplotype Graph, generated whole genome assemblies from additional accessions using long read, third generation technologies to serve as ‘founder’ genomes, and generated consensus haplotypes revealing loci ranging from a single haplotype to eight haplotypes across all founder genomes, reflecting loci of both conserved and diverse haplotypes within the founder genomes. Efforts to impute haplotypes from whole genome sequencing data of 345 tepary diversity panel accessions are underway.

10:15–10:45 am Coffee Break and Networking   

Moderator: Christine Diepenbrock, University of California, Davis

10:45–11:45 am  NAPIA Keynote: Regenerative Ag on the Northern Prairies

 Paul Overby, Lee Farms Verdi-Plus: Value Added Management Solutions, North Dakota

Regenerative agriculture is emerging as an enhancement of previous transitions to no-till farming and use of cover crops by many farmers in the northern prairies . This presentation explores the processes, practices, and lessons that have been learned in a 20 year effort to improve soil health and ecological systems on the Paul and Diane Overby farm in North Dakota.

11:45 am–12 pm Integrating Transcriptomics and Machine Learning to Uncover Genes Underlying High and Moderate levels of Fusarium Root Rot Resistance in Pea   

Stephen Awodele, Graduate Student, Department of Plant Sciences/Crop Development Centre, University of Saskatchewan    

Co-authors: Gali KK, Chatterton S, Tar’an B, Banniza S, and Warkentin TD

Pea (Pisum sativum) is amenable to sustainable agriculture and important for future food security due to its nitrogen-fixation capability, low water usage and short life-cycle. It ranks third in importance in the expanding plant-based protein markets. Fusarium avenaceum (Fav), the most aggressive Fusarium pathogen in the pea root rot complex, causes a significant yield loss in Western Canada and is also prevalent in the Northeastern and Pacific Northwest regions of the United States. It is important to understand the molecular basis of resistance to this pathogen for genomics-assisted breeding of root rot resistant pea cultivars. We utilized reference-based time-course transcriptome analysis, functional genomics, and machine-learning transcriptome mining to discover key molecular mechanisms responsible for higher resistance in pigmented and moderate resistance in white-flowered pea cultivars. Our initial reference gene-based qPCR assay and fungal biomass quantification revealed novel critical time points, 12-, 48-, and 96-hours post inoculation, in the genetic response of pea to Fav. We inoculated and sampled the roots of CDC Dakota (highly resistant, pigmented), RIL PR-20-48 (moderate resistant, white-flowered), and CDC Amarillo (susceptible, white-flowered) for RNA-seq. Number of upregulated differentially expressed genes (DEGs: Adjusted-pvalue<0.05, Fold-change>2) increased and downregulated DEGs reduced overtime in CDC Dakota upon Fav challenge, whereas the number of upregulated and downregulated DEGs mostly reduced overtime in the other genotypes. Based on GO and KEGG annotations, significant enrichment (FDR <0.05) of DEGs in defense response, apoptic response, programed cell death, phenylpropanoid biosynthesis, and isoflavonoid biosynthesis was commonly observed in CDC Dakota vs. CDC Amarillo, and PR-20-48 vs. CDC Amarillo. Whereas, DEGs involved in chitin metabolism, response to water, transmembrane transport, cell wall polysaccharide metabolism, lipid transport, cell wall organization, plant hormone signal transduction, sucrose metabolism, and flavonoid biosynthesis were uniquely enriched in CDC Dakota vs. CDC Amarillo, but not in PR-20-48 vs. CDC Amarillo comparison. These differences in enriched gene functions upon pathogen attack suggest the underlying mechanisms of high and moderate levels of resistance to Fav root rot. Further, we subjected these DEGs to weighted gene co-expression network analysis (WGCNA), LASSO regression algorithm, annotation of the Fav resistance QTL regions, and identified key candidate genes. 

12–12:15 pm Genetic Mapping, Proximal Sensing, and Crop Modeling of Nutritional and Agronomic Traits in a Cowpea MAGIC Population Across Three Californian Environments   

Jonathan Berlingeri, Graduate Student, University of California, Davis            

Co-authors: Sassoum Lo, Ismael Mayanja, Isaac Kazuo Uyehara, Hamid Kamangir, Margaret Riggs, Astrid Lao, Antonia Palkovic, Bao-Lam Huynh, Brian Bailey, J. Mason Earles, Christine Diepenbrock

Cowpea (Vigna unguiculata) is an important crop in the U.S., including in Cowpea (Vigna unguiculata) is an important crop in the U.S., including in California where ~8,000 acres are grown annually. Cowpea produces mineral- and protein-rich grains supporting global food and nutritional security. Due to cowpea’s architecture, with pods and flowers visible from top-down views, cowpea breeding is well-suited to benefit from proximal remote sensing. Sensing can provide predictions of yield, as well as reproductive and canopy dynamics, which in turn can serve as inputs to genetic mapping and crop modeling efforts aimed at parsing the genetic and physiological bases of these traits.

In this study, a cowpea MAGIC population was grown for two years in three Californian environments: two in the Central Valley (differing primarily in temperature) and one in Coachella Valley. Agronomic traits were scored via traditional (groundtruth) assessments, while grain nutritional composition was assessed using near-infrared spectroscopy. The Central Valley environments were sensed weekly including thermal and RGB deployed via drone and rover. Reproductive traits, including visible flower and pod counts, were estimated from rover-based RGB. Additionally, a reproductive dynamics study combined manual weekly scoring of flower and pod traits with smartphone sensing to predict yield and peak flowering time (date when the greatest number of flowers were open). A machine learning pipeline leveraging sensor images and synthetic images from a 3D crop model was also developed to predict stomatal traits. Genetic mapping was performed on groundtruth nutritional and agronomic traits, rover- and drone-based predictions of reproductive and canopy traits, and modeled stomatal conductance.
Significant genomic regions were identified for several traits. Our results showcase three key advantages that the integration of sensing and modeling present to molecular breeding of cowpea: 1) overlapping significant regions between sensed and manually scored trait analogs demonstrate potential of sensing as a scalable proxy for manual scoring; 2) sensing-enabled crop models provide access to physiological traits not previously measurable at population scale; and 3) spatiotemporal resolution of trait assessment is enhanced through repeated sensing campaigns. Our findings illustrate the potential of combining sensing, crop modeling, and genomics to accelerate molecular breeding of cowpea among other leguminous and/or partially occluded crops.

12:15–12:30 pm  Breeding for Increased Seed Protein Concentration in Chickpea    

George Vandemark, Research Scientist, USDA    

Co-author: Dil Thavarajah

Chickpea has been an important source of plant-based protein for human diets across the world for several millennia. However, global demand for plant-based protein has increased over the past decade for numerous reasons and increasing protein content in chickpea has been proposed to be the most important research area in chickpea breeding and genetics. A recent study in Washington State conducted across eight location-years demonstrated that the cultivar ‘Sierra’ typically had higher seed protein concentration than other cultivars. The objective of this study is to develop new chickpea breeding lines with increased seed protein concentrations. Briefly, crosses were made between two kabuli chickpea cultivars; ‘Sierra’ and ‘Sawyer’, and two high protein USDA National Plant Germplasm Accessions; PI 360122, a desi type, and PI 360291, a kabuli type. In 2022, preliminary breeding lines were evaluated in single plots at Pullman, WA for agronomic traits and seed protein concentration. In 2023, Sierra and 19 breeding lines were evaluated in a replicated yield trial at Pullman, WA. Sierra had a mean seed protein concentration of 22.2% and the means of the breeding lines ranged from 21.6 – 25.9%. Based on 2023 results, eight breeding lines and Sierra were evaluated in replicated yield trials at three locations in 2024. The mean seed protein concentration among entries ranged from a low of 21.8% for Sierra to a high of 24.1% for the breeding line CA21900073. Three breeding lines had significantly higher seed protein concentration than Sierra but significantly lower yields. Results will be presented that include data from 2025 yield trials and examine relationships between seed protein concentration and other nutritional and agronomic traits. 

12:30–1:30 pm   Lunch (Atrium-ALL)
 

NOV 5: AFTERNOON SPLIT SESSIONS

BIC AFTERNOON SESSION – BIOTIC STRESS
(Regents BC)

Moderator: Mohammad Erfatpour, University of Guelph

1:30–1:45 pm High-Throughput Phenotyping and Deep Learning for Pod-Based Yield Prediction and Rust Screening in Dry Beans    

Maria De Oliveira, Graduate Student, North Dakota State University 

Co-authors: Juan M. Osorno, Jithin Mathew, Anup Kumar Das, Cristhian Perdigon, Paulo Flores    

Developing dry bean (Phaseolus vulgaris L.) cultivars with high yield potential and disease resistance is essential to meet grower and market demands. Assessing seed yield early in the breeding program, along with screening for major diseases such as rust (Uromyces appendiculatus Pers. Link), can accelerate the release of competitive varieties. In this study, we tested whether data collected by two high-throughput phenotyping (HTP) platforms at later breeding stages could be effectively used to support predictive modeling, with the goal of applying these models at early generations. The first platform focused on pod detection and counting in the field, which can be used as an indirect estimate of potential seed yield. Data collection took place in Hatton, North Dakota during the 2024 growing season. Videos were collected from advanced yield trials across six market classes using a custom-made rover equipped with depth cameras mounted 20 cm above the soil surface. Initially, only black beans were analyzed, since their upright growth habit reduced lodging allowed clearer imaging. About 18,000 images were extracted, annotated with Make Sense AI, and split into training and testing sets (90:10). Results based on data from the field platform showed that YOLOv11 deep learning model achieved an overall accuracy of 86% for pod detection. The model correctly identified most pods (73% precision), found the majority of pods present (77% recall), and showed 61% accuracy when evaluated across stricter thresholds, demonstrating promising initial accuracy for pod detection. The second platform was a greenhouse-based platform designed to generate image-based data on foliar traits, including the detection and quantification of rust symptoms caused by U. appendiculatus (race 20-3). A panel of 360 advanced breeding lines from the NDSU dry bean program, along with checks, were grown in two replications and evaluated with both RGB imaging and conventional visual scoring. Preliminary GWAS conducted with visual rust scores identified significant associations on chromosomes Pv4 and Pv11. The next step is to extract image-based foliar traits, to run correlation and heritability analyses, followed by GWAS, providing a direct comparison between conventional and image-based rust evaluations. 

1:45–2 pm Management of Emerging Pod Spotting and Russeting Diseases of Fresh Market Snap Bean in Virginia    

Harleen Kaur, Postdoc, Virginia Tech

Co-authors: Alyssa K. Betts, Emmalea G. Ernest 

In Virginia, fresh market snap beans generate more than $10 million annually. However, recent outbreaks of pod blemishes, specifically, pod spotting and russeting, have caused serious threats to snap bean crop quality and grower profitability. Some fields had nearly 50% of beans rejected as suitable for fresh markets resulting in losses of over $800,000. To better understand the cause of these outbreaks, symptomatic snap bean samples were collected in 2025 from two fields in northern Virginia, four fields in southwestern Virginia, and three locations on the Eastern Shore. Fungal isolations consistently recovered Alternaria alternata and Plectosporium tabacinum based on morphological characteristics. Molecular identification through amplification and sequencing of the internal transcribed spacer (ITS), large subunit ribosomal RNA (LSU), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) gene regions is in progress. A. alternata is known to cause Alternaria leaf and pod spot, producing small, irregular, water-soaked spots that turn reddish-dark brown or black and sometimes form long streaks. Whereas P. tabacinum, previously reported as a more aggressive pathogen, is associated with russeting, which appears as superficial light brown areas or flecks with undefined borders. Disease management is challenging due to the lack of resistant cultivars and regulations limiting fungicide applications near harvest. Snap bean growers rely on Quinone outside Inhibitor (QoIs; FRAC 11) fungicides, but recent control failures indicate potential fungicide resistance. Resistance to FRAC 11 fungicides has been documented in A. alternata on other crops, but limited information is available for P. tabacinum. This study focuses on understanding the knowledge gaps by characterizing pathogen incidence and possible population shifts from A. alternata to P. tabacinum, screening isolates for fungicide sensitivity to group FRAC 11, and evaluating fungicide efficacy with alternative modes of action through field trials. Findings from this research will improve understanding in identification of pathogens causing pod spotting and russeting and establish effective management strategies, providing valuable information for Virginia growers and for other snap bean production regions facing similar challenges. 

2–2:15 pm Evaluation of Multiple Botrytis Species Causing Chocolate Spot on Faba Bean (Vicia faba) in the Mid-Atlantic and Fungicide Management Options    

Marian Grskovich, Graduate Student, Virginia Polytechnic Institute and State University    

Co-author: Douglas Higgins    

In the Mid-Atlantic region, faba bean (Vicia faba) is an emerging rotational crop valued for nitrogen fixation, health benefit, and added revenue. Its production is limited by chocolate spot, an economically important disease reducing plant photosynthetic activity and yield. The disease is caused by three Botrytis species with only B. cinerea and B. fabae appearing to be reported in the U.S. The objective of this study is to identify the Botrytis spp. causing chocolate spot on faba beans and to evaluate the efficacy of foliar fungicides for disease management. In spring 2025, symptomatic faba bean leaves were collected from three field sites across eastern Virginia. Symptomatic tissue was isolated onto various media, and Botrytis-like conidiophores and conidia observed in vivo were transferred directly to artificial media. However, most colonies failed to sporulate on a variety of media and growth conditions. Both B. cinerea (n=1) and B. fabiopsis (n=14) were preliminarily identified using species specific random amplified polymorphic DNA (RAPD) primer sets. B. caroliniana, a newly described species, has an overlapping geographical range with B. fabiopsis and is its closest relative which may confound the identification based on a single locus. A multi-locus sequence analysis is in progress to confirm the identity of the isolates collected. A fungicide efficacy field trial was conducted in 2025 using fifteen fungicides applied weekly under natural disease pressure. Plants were evaluated five times for foliar disease incidence and severity of chocolate spot symptoms. Disease pressure was low, with maximum severity of 20% leaf coverage and incidence of 83% in untreated controls. Most fungicides reduced the area under the disease progress curve for incidence and severity, with pydiflumetofen+fludioxonil, fluopyram+trifloxystrobin, thiophanate-methyl, chlorothalonil, fluxapyroxad+pyraclostrobin, and boscalid being among the most effective. Fenhexamid, polyoxin D zinc salt, and captan+fenhexamid were less effective than most of the other fungicides. Accurate species identification and evaluation of fungicide efficacy will guide future management recommendations for this emerging crop. 

2:15–2:30 pm Genome-wide Association Study to Identify Genomic Regions Associated with Resistance to Soybean Cyst Nematode HG type 2.5.7 in Dry Beans    

Dalvir Singh, Graduate Student, North Dakota State University

Co-authors: Prabhat Poudyal, Gurminder Singh, Rekha Neupane, Jose Figueroa Cerna, Juan M. Osorno, and Guiping Yan    

Soybean cyst nematode (SCN; Heterodera glycines Ichinohe) is the most destructive pathogen of soybean [Glycine max (L.) Merr.], causing substantial yield losses in the US. Recent reports have shown that SCN can also reproduce on dry bean (Phaseolus vulgaris L.), raising concerns that dry bean could become an additional host crop with similar yield risks. In North Dakota and the upper Midwest regions, where both soybean and dry bean are important crops, the spread of virulent SCN populations represents a growing threat. Among these, HG type 2.5.7 is of particular concern because it can reproduce on PI 88788-derived resistance (the predominant source used in soybean) and become prevalent in surveyed fields in North Dakota. Host resistance is well established as an effective and eco-friendly management strategy in soybean; however, in dry beans, resistant sources are poorly characterized and used. In this study, we evaluated 263 dry bean genotypes spanning the Middle American and Andean gene pools and multiple market classes (great northern, black, navy, slow-darkening pinto, light red kidney, dark red kidney). Two independent greenhouse experiments were conducted using HG type 2.5.7, and SCN infection was quantified as the female index (FI) relative to the susceptible soybean check ‘Barnes’. Phenotypic responses across experiments were estimated using best linear unbiased predictions (BLUPs). Genotyping was performed with a newly developed custom 3.8K Bean Panel Chip. After quality control, 2,019 single-nucleotide polymorphism (SNP) markers were retained and evenly distributed across the 11 dry bean chromosomes. A genome-wide association study was conducted in GAPIT using the BLINK model, with quantile-quantile (QQ) plots confirming appropriate model fit. Two significant SNP-trait associations were identified: S02_50534194 on chromosome Pv2, which explained 57.88% of the phenotypic variance, and S07_37564475 on Pv7, which explained 1.84%. These marker-trait associations point to genomic regions that may underlie resistance to HG type 2.5.7 in dry bean and provide a foundation for candidate gene discovery. This study provides a basis for developing high-throughput markers to support marker-assisted and genomic selection to expedite the breeding of dry bean cultivars with improved resistance to SCN. 

2:30–2:45 pm Validation of Molecular Markers for Aphanomyces Root Rot Resistance in Common Bean (Phaseolus vulgaris L.)    

Rebecca O. Arias, Graduate Student, University of Wisconsin-Madison        

Co-authors: Ken Kmiecik, Douglas Rouse, Mikah Fowler, Shelby Ellison    

Common bean (Phaseolus vulgaris L.) is a globally significant legume that serves as a source of cultural, dietary, and economic importance. Not only is common bean a major source of plant-based protein, but it also contains a large repertoire of nutrients such as iron, magnesium, zinc, folic acid, non-lactic calcium, and soluble dietary fiber. For many, common bean provides an accessible protein substitute for meat, which has led to its cultural importance across Europe, Africa, Central and South America, and the Caribbean. Despite its importance, common bean remains vulnerable to soilborne pathogens, particularly those within the root rot complex. Among these, Aphanomyces euteiches is a destructive oomycete that causes severe root rot damage, limiting yield and crop viability. To investigate the underlying genetics of resistance to this pathogen, we developed F2 and Recombinant Inbred Line (RIL) populations between an Aphanomyces resistant Pinto parent and susceptible Great Northern parent. Genotyping-by-sequencing (GBS) revealed a major QTL for resistance located on Chromosome 10 within a 1 Mb interval. Candidate resistance genes sequenced using Oxford Nanopore were used to develop co-dominant, PCR-based allele specific markers. Three PACE markers were developed and tested across the RIL population and a broader diversity panel. Of these markers, one shows strong predictive potential for resistance across diverse genetic backgrounds and market classes. Ultimately, this work supports the development of efficient molecular tools for breeding Aphanomyces-resistance common bean cultivars, enhancing disease resilience and supporting sustainable production for growers worldwide.

2:45–3 pm Marker-Assisted Backcrossing to Introduce the Non-Darkening Trait into Cranberry Bean (Phaseolus vulgaris) Varieties     

Sajida Noor, Graduate Student, Department of Plant Agriculture, University of Guelph, Guelph, Canada

Co-author: K. P. Pauls 

Seed coat darkening is an undesirable post-harvest quality trait of several market classes of dry beans (Phaseolus vulgaris L.). It is determined by crop genetics and exacerbated by environmental conditions, such as high temperatures, humidity, and excessive light exposure. This trait reduces market acceptance, as darkened seeds are often perceived as old and slow to cook, leading to economic losses for producers. The current research was designed to introduce the non-darkening trait, controlled by a homozygous recessive condition for the j gene on chromosome 10 in P. vulgaris, into high-yielding, regular-darkening cranberry bean varieties, OAC Navabi and OAC Firestripe, without compromising their qualities. The non-darkening variety Wit-rood was crossed with OAC Navabi and OAC Firestripe to introduce the recessive j allele. The resulting F1s were backcrossed with the elite parents to establish non-darkening OAC Navabi and OAC Firestripe backcross (BC) breeding populations. The BC seedlings were screened with a Kompetitive Allele Specific PCR (KASP) marker to identify Jj individuals, which were used for additional backcrossing with the elite parents. Progenies with high recovery of the recurrent parent genome (RPG), along with the donor gene for non-darkening, were selected throughout the breeding program by screening genomic DNA from KASP-selected individuals with a BARCBean12K bead chip. Almost 1,000 polymorphic markers between the parents were used to evaluate the plants in subsequent generations. Background analysis revealed that the extent of RPG recovery ranged from 41% to 90% in OAC Firestripe and 59% to 92% in OAC Navabi from BC1 to BC4. These results demonstrate that marker-assisted backcrossing, combining foreground selection for the j allele with background selection for RPG recovery, is effective for developing non-darkening cranberry bean lines while retaining the genetic background of elite cultivars. 

3–3:30 pm Coffee Break and Networking 

3:30–5:30 pm  Poster Presentation (Regents DEF) All presenters of even-numbered posters need to be next to their posters.   

7–9 pm Awards Banquet (Regents BC)

• Music by Evan LeBouef Quintet
• Join us to celebrate the BIC/NAPIA community science. Leadership, technical achievement, and graduate/undergraduate student oral and poster competition. Thanks to our sponsors for their generosity.
• Presentation Awards: Carlos Urrea, BIC Award Committee Chair, and Michelle Hubbard, NAPIA President
• Closing Remarks, Juan Osorno (BIC President)
   

                          
NOV 5: NAPIA AFTERNOON SESSION
(Regents A)

Moderator: Marie Celestin, Montana State University

1:30-1:45 pm From Field to Insight: Managing Breeding Data with BIMS    

Sook Jung, Associate Professor, Washington State University        

Co-authors: Taein Lee, Chun-Huai Chen, Jodi Humann, Rebecca McGee, Ksenija Gasic, Sushan Ru, Fred Gmitter, and Dorrie Main    

With advancements in scientific technology, breeding programs now generate big data across diverse types, including image, phenotype, genotype, and pedigree data. The Breeding Information Management System (BIMS), a fully web-based system freely available through the Pulse Crop Database with just an account, enables efficient management of this large and complex breeding data. BIMS is compatible with the Android application Field Book, which allows breeders to collect data using a phone or tablet. Both BIMS and Field Book app are BrAPI-compliant, meaning the trait and image data collected via Field Book can be automatically loaded into BIMS. Recent updates to BIMS support efficient handling of image and multi-measurement datasets, essential for current breeding applications, and include improved search and download functionality. This talk will demonstrate how transitioning to electronic data collection and managing breeding programs through the BIMS’s user friendly web-based system can significantly enhance program efficiency. Future directions for BIMS will also be discussed, including the development of a data analysis portal and the integration of AI-assisted image analysis tools to further support data-driven breeding decisions. 

1:45–2 pm First Steps Toward Adapting Lentil for Organic Production in the Southeastern U.S.    

Mark Dempsey, Graduate Student, Clemson University                

Co-authors: Bridges, W., Thavarajah, D.    

Lentil is a nutrient-rich cool-season pulse typically grown as a summer crop in the Northern Great Plains of North America, but has yet to be adapted to the Southeastern (SE) U.S., where it can be grown as a winter crop. Organic lentil production offers growers a significant price advantage; yet, weed and disease pressure are high in organic production. The objective of this research was to identify candidate lentil genotypes for adaptation to organic production in the SE U.S., with emphasis on low disease incidence and the ability to compete with weeds. To achieve this, 446 genotypes were evaluated at two locations in South Carolina, U.S., over three years (2022–2024; five environments) in an alpha-lattice design, followed by quantification of seven agronomic traits: disease incidence, plant height, canopy cover, days to flower (DTF), days to maturity (DTM), lodging, and seed yield. Genetic data were obtained by genotyping-by-sequencing and whole-genome sequencing. Lentil diseases were diagnosed by Schutter Diagnostic Lab and Clemson Plant and Pest Diagnostic Clinic. Root rots were the most prevalent disease throughout the study, and Fusarium spp. was the most frequently identified root rot-associated pathogen. Hierarchical cluster analysis of all traits × environments identified several groups of genotypes with low disease incidence and lodging, and high plant height, canopy, and seed yield. Genome-wide association study identified one significant single nucleotide polymorphism (SNP) where genotypes with the rare allele had lower disease incidence. This finding suggests this SNP is part of a genomic region associated with tolerance or resistance to the dominant pathogen(s) present in the field study, and warrants further investigation. Follow-up research should include screening lentil genotypes using pathogens isolated from fields in the SE U.S. to confirm disease tolerance or resistance, and to further elucidate the genetic underpinnings of disease tolerance or resistance. The results of this research support both regional and global breeding programs, especially those geared toward organic production, by identifying potential breeding parents and the genomic region(s) associated with disease tolerance or resistance, and can improve the global supply of lentils. 

2–2:15 pm Genome-Wide Association Mapping of Resistant Starch for Improved Nutritional Traits in Lentils     

Hatice Sari, Post-Doc, Washington State University, Post-Doc             

Co-authors: Dorrie Main, Yu Ma, Alecia Kiszonas, Sean Finnie, Zhiwu Zhang, Sintayehu Daba, Stephen Ficklin, Rebecca J. McGee   

Lentil (Lens culinaris Medik.) is a nutrient-dense legume that plays a critical role in global food security and human health. Beyond its well-known benefits as a rich source of protein, fiber, and micronutrients, lentil is also a natural source of resistant starch, a key component of dietary fiber associated with improved glycemic response, enhanced gut microbiota, and reduced inflammation. 

In this study, resistant starch concentration was quantified in a diverse panel of 192 lentil accessions and five check lines grown across three environments. Genome-wide association studies (GWAS) were performed using 5,583 high-quality SNP markers to identify loci associated with resistant starch concentration. A total of nine significant marker–trait associations were detected, each explaining between 9.1% and 38.5% of the phenotypic variance. Several candidate genes were identified near the associated markers, based on functional annotations related to starch metabolism and carbohydrate processing.
These findings advance our understanding of the genetic basis of resistant starch accumulation in lentil seeds and provide genomic resources to support the breeding of lentil cultivars with enhanced dietary fiber and improved nutritional profiles.

2:15–2:30 pm Genome-Wide Association Analysis of Root Architectural Traits in Hydroponically Grown Interspecific Chickpea Population 

Tamanna Jahan, Post-Doc, University of Saskatchewan                

Co-author: Bunyamin Tar’an

Phosphorus (P) availability is a major limiting factor for chickpea (Cicer arietinum L.) production, as it is critical for plant metabolism, nodule development, and nitrogen fixation. Excessive application of P fertilizers often forms insoluble complexes with other minerals, reducing P availability to plant roots. Improving root architecture represents a promising approach to enhancing nutrient uptake efficiency and tolerance to P deficiency. To investigate the genetic basis of root traits, we evaluated an interspecific population of 200 lines derived from a cross between cultivated Cicer arietinum and wild Cicer reticulatum. Plants were grown in a hydroponic system under standard nutrient conditions to minimize environmental variations. Root traits, including total root length, cumulative root length, root surface area, taproot length, root projected area, and number of tips, were quantified using WinRhizo imaging software alongside manual measurements. Shoot biomass was also recorded. Descriptive statistical analysis revealed substantial variation in root traits within the population, with selected shoot traits also exhibiting moderate variation. Pearson correlation analysis indicated strong associations among most root traits (p ≤ 0.01), suggesting common genetic control of root system architecture. Genome-wide association studies (GWAS) identified numerous significant single-nucleotide polymorphisms (SNPs) across multiple chromosomes associated with root architectural traits and shoot biomass. Additional SNPs were associated specifically with cumulative root length and root surface area. These results indicate polygenic control of root and shoot traits and highlight genomic regions that can be targeted for marker-assisted selection. The identified SNPs will be further analyzed for their association with phosphorus use efficiency (PUE), facilitating the identification of candidate genes that may be leveraged in chickpea breeding programs to improve phosphorus acquisition and overall root system performance. Ongoing research focuses on validating these loci and integrating phenotypic, genotypic, and functional genomic data from this interspecific population. This study demonstrates that combining interspecific populations with GWAS allowed for dissecting the genetic control of root architecture and shoot development, providing a foundation for breeding chickpea cultivars with enhanced PUE and resilience to nutrient-limited conditions.

2:30–2:45 pm Evaluation of Chickpea Germplasm for Potential Tolerance to Different Modes of Actions of Herbicides     

Bella Amyotte, Graduate Student, University of Saskatchewan                

Co-authors: Carmen Breitkreutz, Shaun Sharpe , Bunyamin Tar’an

Chickpeas are an important and affordable source of plant protein, nutrients and minerals for human consumption. However, chickpea production faces challenges from weed pressures throughout the growing season on Saskatchewan farms. Chickpea is a poor competitor with weeds, with yield loss in chickpea ranging from 24% to 88% due to weed pressures if the infestation is not controlled (Khan et al, 2023). Therefore, it is important to find ways to alleviate weed pressures in chickpea through the incorporation of greater and broader herbicide tolerance. This allows farmers to have more options to control weeds at any growth stage of the crop as part of the integrated weed management system.

The aim of this research is to identify tolerance to different modes of action of herbicides in chickpeas to offer better weed control, especially for broadleaf weeds. By breeding for improved tolerance to group 2, group 5 and group 14 modes of action it will allow farmers to use these herbicides for in-crop weed control. Three populations of chickpeas, a mutated, an interspecific and a cultivated population available at the Crop Development Centre (CDC), University of Saskatchewan were used in indoor screenings in controlled growth chambers to evaluate the individual plants for potential herbicide tolerance. At the six to ten node stage, the plants were sprayed in separate screenings with Metribuzin (Sencor ®) Imidazolinone (Odyssey®), Sulfonylurea (Pinnacle®) and Saflufenacil/Trifludimoxazin (Voraxor®). The individual plants were evaluated based on a 0 to 10 scale developed at the University of Saskatchewan at 7, 14 and 21 days post herbicide application. Screenings for group 2 chemistries of Imidazolinone and Sulfonylurea have been completed on the EMS (Ethyl methyl sulfonate) treated population, along with a screening of metribuzin on the cultivated population and varying degrees of tolerance have been determined in each trial. With some plants displaying a rating of one, and with others being up to nine within the group 2 screenings and a low of three in the group 5 screening. Selected plants for tolerance were collected and grown in greenhouse environments for future use as a source for tolerance in breeding and for genetic analyses. Field trials of the selected germplasm will be performed at different locations to validate the tolerance under field conditions. 

2:45–3 pm  Evaluation of Spring and Winter Pea Varieties in Western Nebraska    

Dipak Santra, Professor, Department of Agronomy and Horticulture, University of Nebraska-Lincoln    

Co-authors: Vernon Florke, Rituraj Khound

Pea (Pisum sativum L.) emerges as an important crop for plant-based protein ingredients to the US food industry since it is non-GMO, non-soy, climate-friendly to produce, human-healthy, and easy to blend with any food formulation. People, especially the millennial population, prefer plant-based protein. In the US, traditional pea growing regions are MT, ND, and the Pacific Northwest. Pea is relatively a new crop in Nebraska, which started in 2013. Can Nebraska play an important role in producing this important plant-protein ingredient crop? The objective of this study was to evaluate commercial pea varieties of both spring and winter types in western Nebraska production conditions for yield, seed size, protein content, flowering, and plant height. The last 12 years’ results (2014-2025) of spring pea varieties and the last 6 years’ results (2020-2025) of winter pea varieties are presented here. The yield, test weight, seed protein %, and other traits of both spring and winter varieties varied significantly over the years and locations. We identified location non-specific (i.e., similar yields over the locations and years) and location-specific (significantly different yields at different locations) of high performing varieties. Similar results (location non-specific and location-specific) were also observed in the varieties for other traits, such as test weight, seed size, seed protein %, flowering, and plant height, across the locations in western Nebraska and years. The result was used to recommend high yielding spring and winter pea varieties for commercial production of both spring and winter pea in western Nebraska. 

3–3:15 pm  Improving Drought and Cold Tolerance in Winter Pea

Lyndon Porter, Research Scientist, USDA-ARS

Co-authors: 

Food-grade winter peas were approved for production in the US in 2009 and efforts to develop cold and drought tolerant varieties that can survive cold winters (-23°C) and low rainfall (≤ 25.4 cm) areas of Eastern Washington are desired to provide growers additional options besides a wheat-fallow rotation. A field trial evaluating 3,400 pea accessions of Pisum sativum from the Pisum Collection located at the Western Regional Plant Introduction Station in Pullman, WA were planted in Prosser, WA and assessed for cold and drought tolerance. Fall emergence, frost damage, winter survivability, spring plant vigor and yield were determined. Several accessions (735) demonstrated cold and drought tolerance and were reevaluated an additional growing season. This research determined the best white-flowered and pigmented-flowered lines with cold and drought tolerance for this pea growing region were white-flowered accessions: PI 663943, PI 210642, PI 641005, PI 210641, PI 654745, and PI 392018 and pigmented-flowered accessions: PI 505066, PI 269815, PI 306592, PI 304603, PI 505087, and PI 598077. These lines can be used by winter pea breeders to improve future genetic resistance to cold and drought tolerance. 

3:15–3:30 pm  Coffee Break and Networking

3:30–5:30 pm  Poster Presentation (Regents DEF)  All presenters of even-numbered posters need to be next to their posters. 

7–9 pm  Awards Banquet (Regents BC)

• Music by Evan LeBouef Quintet
• Join us to celebrate the BIC/NAPIA community science. Leadership, technical achievement, and graduate/undergraduate student oral and poster competition. Thanks to our sponsors for their generosity.
• Presentation Awards: Carlos Urrea, BIC Award Committee Chair, and Michelle Hubbard, NAPIA President
• Closing Remarks: Juan Osorno (BIC President)
   

Thursday, Nov. 6

7–8 am Breakfast  (Alumni)    

NOV 6: BIC SESSION – NUTRITION, QUALITY, AND BREEDING
(REGENTS BC)

Moderator: Donna Harris, University of Wyoming

8–8:30 am Keynote Speaker: Genetic Analysis of Traits in Dry Beans that affect the Human Gut Microbiome

Andrew Benson, Professor, University of Nebraska–Lincoln, Nebraska

Coauthors: Mallory J. Suhr Van Haute, Qinnan Yang, Nate Korth, Mary M. Happ, Car Reen Kok, Chenyong Miao, Jennifer L. Clarke, Kelsey Karnik, Kent M. Eskridge, Carlos A. Urrea, David L. Hyten, James C. Schnable, Devin J. Rose

Common bean varieties can produce diverse bioactive molecules, but none have been systematically studied for their variation, nor is it well understood how these bioactive molecules may affect the human gut microbiome. We used Automated in vitro Microbiome Screening (AiMS) to measure taxonomic shifts and metabolite production of three human gut microbiomes cultured with 299 common bean cultivars under conditions that mimic the nutrient availability of the human colon. Common bean population structure (landrace and market class) had significant effects on microbiota diversity, composition, and metabolite production across diverse microbiomes from the three human subjects. Genome-wide association analysis further identified seven multiple-effect loci (MEL) where genetic variation in the common bean genome affected multiple species of microbes. One MEL (MEL-C) on chromosome Pv05 had impacts on the abundances of several genera belonging to the bacterial families Lachnospiraceae and Ruminococcaceae, and the allelic effects of variation at MEL-C on members of these families were validated across multiple human microbiomes. Three closely spaced SNPs at MEL-C accounted for variation in 31/34 microbiome traits mapping to this locus, and all three SNPs fell within a tandem array of seven -amyrin synthase genes.  Molecular complementation experiments, which introduced purified saponins into pools of pre-digested bean with the major allele at the most prominent SNP at MEL-C, mimicked the effects of the minor allele on the microbiome, suggesting that variation in the biosynthesis of saponins at this MEL was the mechanism driving the variability in microbiota composition and function. This study provides an innovative understanding of how historical breeding and standing genetic variation in common beans affect the human gut microbiome and potentially human health.       

8:30–9 am  Keynote Speaker: Beans: The Ultimate Superfood    

Chelsea Didinger, A Legume a Day            

Beans may be a perfect food, but that doesn't mean consumers are eating enough of them to reap all the incredible benefits. This presentation details several of the key reasons beans are such a beneficial food, ranging from nutrition and health benefits, to sustainability, to their incredible versatility and deliciousness. The bean industry can work together to increase production and consumption, capitalizing on consumer trends and innovating new products to ensure beans are both accessible and in vogue. After all, the phrase is "cool beans" for a reason.

9–9:15 am Iron Nutrition from Great Northern Beans    

Raymond Glahn, Research Scientist, USDA        

Co-authors: Jason Wiesinger, Karen Cichy, Juan Osorno, Carlos Urrea, Phil Miklas. 

The compounds present in the bean seed coat that define the color class, known as polyphenols or flavonoids, are the primary factors influencing iron (Fe) nutrition from beans. These compounds complex with Fe and can inhibit or promote Fe absorption depending on the amount and profile; however, in white beans such as the Great Northerns, flavonoids that inhibit Fe bioavailability are absent. As a result, Great Northern beans and other white beans have strong potential to deliver bioavailable iron. Therefore, the objective of this study was to characterize Fe bioavailability from Great Northern beans from multiple locations within the US. Thirty dry bean varieties were grown in research plots of Michigan, Nebraska, North Dakota, and Washington in 2023 and 2024. Samples were sent to the Robert Holley Center for Agriculture and Health, Ithaca NY, for analysis of Fe content (via ICP-ES) and Fe bioavailability via the Caco-2 Cell Bioassay. Samples from Michigan were canned and evaluated. A common red bean (Merlot), a black bean (Eclipse), and 2 slow darkening pintos (ND-Palomino, USDA Diamondback) varieties were included from each location as reference controls. For Fe content, beans from Washington were significantly higher than the other three locations, with Nebraska having the lowest Fe content. Iron bioavailability was not significantly different across locations; however, five varieties (Eiger, Andromeda, Panhandle Pride, Starlight, and Marquis) consistently showed very high Fe bioavailability across all locations. Additionally, the canning process significantly increased Fe bioavailability for all Great Northern beans, including the reference checks of red (Merlot) and slow-darkening pintos (ND-Palomino, USDA-Diamondback ). This effect occurred despite a decrease in Fe content of approximately 10%. No beneficial effect from canning was observed in the black bean (Eclipse). The mechanism for the promotional effect of canning is unknown, and further research is needed. Overall, the results indicate that most Great Northern beans are excellent sources of bioavailable Fe. This study is the first to document the beneficial effect of canning on the bioavailability of Fe in beans. Such information is highly valuable for dietitians and nutritionists seeking to design meal plans with enhanced Fe delivery. 

9:15–9:30 am A Two-Gene Strategy to Improve the Iron Bioavailability Across Multiple Market Classes of Dry Beans (Phaseolus vulgaris L.)   

Jason Wiesinger, Research Associate, Robert W. Holley Center for Agriculture and Health, , Ithaca, New York        

Co-authors: Rie Sadohara, Karen Cichy, Raymond Glahn, Kevin Howe, K. Peter Pauls, Lyndsay Schram, Juan M. Osorno, Jayanta Roy, Rian Lee, and Phillip McClean

The genetic architecture of iron bioavailability (FeBio) in beans is not well understood. Recent findings point to genes that control flavonoid synthesis and post-harvest seed coat darkening. Slow darkening (Psd) pintos and non-darkening (recessive j) yellow beans have reduced concentrations of darkening compounds in their seed coats (flavan-3ols and proanthocyanidins) and have improved FeBio compared to regular darkening (RD) beans. However, the degree to which the non-darkening (ND) trait impacts FeBio varies widely among the different market classes - having a recessive j gene does not guarantee a large boost in bean FeBio. This study aimed to use a panel of breeding lines and QTL mapping populations of ND yellow, pinto, and cranberry beans to identify the seed coat characteristics and genomic regions associated with the high FeBio phenotype. Flavonoids were analyzed with UPLC-MS (Waters™ H-Class®-QDa) and FeBio was measured using a Caco2 Cell Bioassay. A RIL population of 242 F5:F7-F8 lines was developed from a cross between a RD yellow (PI527538; dark hilum; poor FeBio) and ND yellow (Ervilha; clear hilum, high FeBIO). The RILs were genotyped using the BARCBean12K BeadChip with the physical SNP positions based on Phaseolus vulgaris v2.1 genome. One key physical trait that distinguished ND yellow, pinto and cranberry lines with high and low FeBio was the hilum ring color. ND cranberry lines with clear hilum had 3x more bioavailable iron than ND cranberry lines with dark hilum, and 6x more FeBio than RD cranberry beans. Major QTLs were detected on chromosomes Pv10 (recessive j) and Pv3 near the Z gene, which controls hilum color. Twenty ND RILs (clear hilum; high FeBio) and 20 ND RILs (dark hilum; low FeBio) were bulked for Khufu QTL-seq analysis, which confirmed a major signal at the Z gene. A SNP mutation-based (PACE) Z marker exhibited 100% co-segregation with FeBio between the two groups of ND RILs, and flavonoid analysis revealed no flavan-3ols or proanthocyanidins in ND RILs with high FeBio. These results indicate recessive alleles at the J and Z genes are associated with FeBio, and together, are candidates for marker-assisted selection of beans with improved iron nutrition. 

9:30–9:45 am The POPBEANS Project: Developing Protein-Rich Wholesome Popping Beans to Enhance Agricultural Production, Nutrition and Sustainability    

David Gang, Associate Professor and Fellow, Institute of Biological Chemistry, Director of the Tissue Imaging and Proteomics Laboratory, Washington State University    

Co-authors: Girish Ganjyal, Hanu Pappu, Amjad Ahmad, Karen Cichy, Sarah Dohle, Gina Greenway, James Myers, Juan Osorno, Diane Smith, Carlos Urrea, Gayatri Mishra, Ted Kisha    

Popping beans, derived from crosses of Peruvian Nuña beans with other common bean varieties (Phaseolus vulgaris L.), pop (much like popcorn) when heated. This new market class of dry bean is being developed due to its unique processing qualities and food application potential, making it ideally suited for snacks and other foods that are highly nutritious and high in protein and fiber. Popping beans have great potential for novel culinary use. Beans possessing the popping trait hold significant promise for development of new foods that can help combat significant national health concerns, particularly in school-aged children, such as obesity and diabetes. The POPBEANS project was established and funded by NIFA through an SCRI grant this past year to further develop popping beans as a market class of dry beans. The project uses systems-based, transdisciplinary approaches to address four major objectives: 1) improving bean advanced breeding lines with the popping and other enhanced qualities/traits; 2) determining field performance of accessions and advanced and newly developed breeding lines under varying field conditions, focusing on agronomic and bean quality traits, as well as disease resistance; 3) developing nutritious snack foods based on the project’s advanced breeding lines, focusing on popping trait-derived properties while maintaining high nutrition; and 4) generating consumer acceptance data for the new advanced bean breeding lines/varieties and food products developed, evaluating economics of commercial popping bean production and providing information regarding market development potential. Progress to date in the major project areas will be outlined, particularly focusing on a description of newly released popping bean varieties, phenolic analysis of popping bean varieties vs other common beans, food development efforts, disease evaluation efforts, initial consumer acceptance results, and field evaluation coupled to breeding efforts.

9:45–10 am Breeding Popping Beans for Temperate US Production    

James Myers, Department of Horticulture, Oregon State University    

Co-authors: Emma Landgraver, Joel Davis and Shinji Kawaii

Nuña or popping beans are a form of dry bean that is cooked without hydration. When heated for only a few minutes, the beans will ‘puff’ and soften, rendering them ready to eat. Once popped, they store well and are lightweight and easy to transport. Nuña beans arose in the high Andes of Peru and Bolivia in a genetic background that is adapted to short days, cool temperatures and intense sunlight. Attempts to grow these beans at higher latitudes outdoors fail because the lines will not flower until late September when day length is less than 12 hours, with no season remaining to mature the crop. We have worked with nuña beans for the past 30 years with breeding efforts to transfer the popping trait into a North American adapted genetic background. We recently released two popping bean cultivars that perform well in most bean growing areas of the US. These cultivars were derived from the 1996 cross ‘Pava’ nuña x ‘5-593’ using the pedigree breeding method. Selection for determinate growth habit and early flowering and maturity was imposed in the F2 generation. The population was advanced in subsequent generations to the F5 as single plants, with selection for desirable field traits and for popping ability (tested by popping a small portion from each single plant). Beginning in the F6, plants were bulked by family for testing in replicated plots in Oregon and other locations. In 2024, the USDA-NIFA-SCRI grant “POPBEANS SREP: Protein-Rich Wholesome Popping Beans to Enhance Agricultural Production, Nutrition, and Sustainability” was funded, which provided additional support to characterize these materials and support their release as cultivars. The long time-frame to releasing these cultivars was due to limited funding along with two patents on popping beans that were in effect for 12 years. Further details of the breeding process, the popping bean patents and recent progress under the SCRI grant will be discussed.

10-10:15 am  Developing New Bruchid-Resistant Varieties Along With the Development and Validation of Molecular Markers

Kelvin Kamfwa, Professor, University of Zambia

Coauthors: Maria Mazala, Philip E. McClean, Rian Lee, Nusrat Khan, Mohammad Erfatpour, Jayanta Roy, Modreen Chinji, Carlos A. Urrea, and Juan M. Osorno

Common bean weevils (Acanthoscelides obtectus, Say), also known as bruchids, is the most damaging storage pest of common bean (Phaseolus vulgaris L.), causing up to 100% losses in seed quality and quantity. Previous research identified a wild tepary bean (P. acutifolius) accession with resistance to A. obtectus and was successfully transferred into common bean. Resistance is suggested to be linked to the presence of the complex APA gene protein family on chromosome Pv04. A set of 1900 F5 bruchid-resistant breeding lines were developed using bruchid-resistant germplasm AO-1012-29-3-3A and multiple commercial parents from Africa. Conventional screening methods for bruchid resistance in large dry bean populations are time-consuming and labor-intensive. Therefore, molecular markers linked to the APA locus would expedite the screening process. In addition, the introgression of APA locus could cause negative effects on the agronomic and/or cooking quality. Three available molecular markers for tracking APA introgression were validated in 30 genotypes, including 16 bruchid-resistant, 6 susceptible, 6 parents, and 2 resistant checks. Agronomic traits were measured in field trials in USA, Mozambique, Malawi, and Zambia. Cooking quality was measured using a Mattson cooker. The α-AI-1 marker demonstrated 100% accuracy. In addition, the APA introgression did not affect cooking time. Two resistant lines are currently considered for commercial release in Zambia.

10:15–10:45 am  Coffee Break and Networking     

Moderator: Michael Wunsch, North Dakota State University           

10:45–11:00 am  Identification of Loci and Germplasm for Breeding for Adaptation to High Temperatures in Cowpea   

Luis Getino, Post-Doc, Universidad de Leon        

Co-authors: R. Akakpo, H. Akinmade, J.F. Harling, K. Volz, T. Marimagne, O. Oyatomi, M. Abberton, K. Boote, O. Boukar, E. Rios, P.L. Morrell and M. Muñoz-Amatriaín    

Heat is among the environmental factors that most negatively impact crop yields. Cowpea (Vigna unguiculata (L.) Walp.) is a crop that is relatively well adapted to high temperatures, making it a good model to understand the genetics of adaptation to heat stress. However, elevated night temperatures can damage reproductive processes, reducing yield. For this reason, it is of great importance to identify loci associated with high temperature tolerance as well as accessions carrying beneficial alleles that can be used in breeding programs. 

There are numerous cowpea germplasm collections around the world, which include landraces adapted to different agroclimatic regions. The largest cowpea collection is located at the International Institute of Tropical Agriculture (IITA) in Nigeria. Its Core collection contains more than 2,000 accessions that have been genotyped with 51,128 SNPs.

In a current project funded by the Foundation for Food and Agriculture Research (ICRC20-0000000032), we are using the IITA Cowpea Core collection to identify genetic variants that contribute to local adaptation to high temperatures. Geographic coordinates within passport data have been curated and used to extract bioclimatic variables related to temperature. Genome-environment association studies (envGWAS) using four different models (MLM, MLMM, FarmCPU, and BLINK) in GAPIT v3 identified multiple loci associated with temperature variables. From those, 7 SNPs were identified in more than one model and had their minor allele associated with greater temperatures. Furthermore, the beneficial allele in these SNPs was not fixed in West African breeding germplasm. These results, together with additional analyses including allele frequency outliers, allowed the selection of accessions carrying multiple beneficial alleles at targeted loci. These selected accessions are being validated experimentally in field and greenhouse trials at the University of Florida and at IITA in Nigeria, and will also be re-sequenced at the University of Minnesota.

11–11:15 am  A Web-Based Application for Automated Stand Counting in Dry Bean Using UAV RGB Imagery and YOLOv11 Segmentation    

Aliasghar Bazrafkan, Post-Doc, Michigan State University        

Corresponding author: Valerio Hoyos-Villegas    

Accurate early-season stand counting is essential for optimizing crop management decisions, estimating yield potential, and identifying replanting needs in dry bean production. Manual counting methods are labor-intensive, time-consuming, and often infeasible at scale. This work presents the development and preliminary evaluation of a fully automated, user-friendly web application designed to perform stand counting in dry bean using unmanned aerial system (UAS) imagery and deep learning. RGB images were collected using a DJI Mavic 3 drone during early vegetative stages of dry bean crops grown in Michigan between 2022 and 2025. A YOLOv11 object detection model was trained to segment vegetation from background soil within these images. The training process involved annotation of diverse image samples to ensure model generalization across varied field conditions, including lighting variability and soil textures. Once trained, the model was integrated into a Flask-based web platform that allows users with no coding experience to perform stand counting tasks with ease. The web application enables users to upload raw RGB imagery, draw a region of interest (ROI) directly on the interface, and initiate the analysis with a single click. The YOLOv11 model runs in the background, segmenting vegetation and distinguishing it from soil, after which the system estimates and displays the number of individual plants within the selected ROI. On the validation dataset, the model achieved an F1 score of 0.61 at the optimal confidence threshold (0.076), with a precision of ~0.616 and recall of ~0.616. The mean average precision (mAP) at IoU 0.5 was 0.616, and mAP across IoU thresholds 0.5–0.95 was ~0.29. Preliminary field observations indicate that early-season stand counts derived from this method are positively correlated with final harvested yield, consistent with the role of uniform plant establishment in determining yield potential in dry beans. Quantifying this relationship will be a focus of future work, enabling integration of automated stand counts into predictive yield models. The application outputs both a visual map overlay of the segmented vegetation and a numerical plant count for the user. Future work will also focus on improving detection accuracy through additional training data, incorporating multi-temporal analysis, and expanding support for other crop types and imaging modalities. This tool provides a practical and accessible solution for researchers, agronomists, and growers seeking efficient, automated stand counting using widely available UAS technology and RGB imagery.

11:15–11:45 am  BIC Business Meeting and Conference Closing

11:45 am–12:45 pm  Phaseolus Crop Germplasm Committee Meeting, PCGC Members

1–2 pm  Lunch    

2–5 pm  Field Tour, University of Nebraska Food Innovation and Nebraska Food for Health Centers, Cody Kaarstad, University of Nebraska–Lincoln           

OTHER BUSINESS AND OPEN MEETINGS 

2–4 pm   USA Multistate Reports, W4150 and W5150 Members 
  
4–5 pm   Bean Genetics Committee, Bean Genetics Committee Members    

5 pm   Dinner on your own  

BACK TO TOP