Dry Bean Varieties Research Articles

Antioxidant and Anti-Inflammatory Capacities of Three Dry Bean Varieties after Cooking and In Vitro Gastrointestinal Digestion.

The present study delved into the chemical composition, antioxidant, and anti-inflammatory properties of three dry edible beans: Black (BL), Great Northern (GN), and Pinto (PN). The beans were soaked, cooked, and subjected to in vitro gastrointestinal (GI) digestion. BL bean exhibited significantly higher gastric (42%) and intestinal (8%) digestion rates. Comparative assessment of soluble GI-digested fractions (<3 kDa) revealed that the GN bean exhibited the highest abundance of dipeptides (P < 0.05). The BL bean fraction displayed a 4-fold increase in tripeptides (P < 0.05). Both BL and PN bean fractions are high in essential free amino acids, flavonols, and derivatives of hydroxybenzoic acid when compared to the GN bean. All the beans exhibited the ability to mitigate TNF-α-induced pro-inflammatory signaling; however, the BL bean fraction was the most effective at lowering AAPH-induced oxidative stress in HT-29 cells, followed by the GN bean (P < 0.05). In contrast, a low antioxidant effect was observed with PN beans.

Обзор върху антракнозата по фасула (Colletotrichum lindemuthianum) в Бъргария

Bean anthracnose, caused by Colletotrichum lindemuthianum, is a key disease in a number of countries around the world. Depending on the varietal structure and climatic conditions, yield losses vary from 10-58% to the complete destruction of crops. For the conditions of Bulgaria, the disease has a sporadic character in the plain areas, but in the mountainous areas it occurs annually. The pathogen control is related to uninfected seed material, long crop rotation, destruction of plant residues, application of fungicides and use of resistant varieties. C. lindemuthianum is characterized by considerable virulence diversity, which makes it difficult to breeding resistant cultivars and leads to overcoming already achieved resistance. This publication summarizes research on the virulence diversity of C. lindemuthianum in Bulgaria over the last forty years. In parallel, data on the resistance of Bulgarian dry bean varieties, some of which are essential for production in the country, to a set of physiological races of C. lindemuthianum are summarized. The analysis of the identified races shows significant virulence diversity in the pathogenic populations in Bulgaria. During the period 1979-2006, 10 physiological races were identified - 2, 3, 6, 22, 23, 54, 73, 79, 81 and 131. Race 81 was dominantly prevalent (78.4% of isolates) in traditional dry bean growing areas in the country. Races 2, 6, 22, 54 and 131 are established in the Rhodopе Mountains and Pirin Mountains. Races 3, 23, 73 and 79 were incidental to Bulgaria and there is no confirmation of their distribution. Of the 17 varieties presented in the overview, resistance to 11 races of C. lindemuthianum has variety Pukliv 1. Variety GTB Blyan is susceptible to races 6 and 81 and resistant to the remaining 9 physiological races included in the test. The Pirina variety is resistant to 10 of the 11 races tested. The varieties Pukliv 1, Trakya and Pirina are resistant to race 81, which prevails in Bulgaria. All cultivars have resistance to one or more of the investigated physiological races, which makes it possible to include them as resistance donors in the breeding of dry beans, by the pyramidal accumulation of race-specific genes.

Evaluating the Performance of Voting Classifier in Multiclass Classification of Dry Bean Varieties

This study explores the application of a voting classifier, integrating Decision Tree, Logistic Regression, and Gaussian Naive Bayes models, for the multiclass classification of dry bean varieties. Utilizing a dataset comprising 13,611 images of dry bean grains, captured through a high-resolution computer vision system, we extracted 16 features to train and test the classifier. Through a rigorous 5-fold cross-validation process, we assessed the model's performance, focusing on accuracy, precision, recall, and F1-score metrics. The results demonstrated significant variability in the classifier's performance across different data subsets, with accuracy rates fluctuating between 31.23% and 96.73%. This variability highlights the classifier's potential under specific conditions while also indicating areas for improvement. The research contributes to the agricultural informatics field by showcasing the effectiveness and challenges of using ensemble learning methods for crop variety classification, a crucial task for enhancing agricultural productivity and food security. Recommendations for future research include exploring additional features to improve model generalization, extending the dataset for broader applicability, and comparing the voting classifier's performance with other ensemble methods or advanced machine learning models. This study underscores the importance of machine learning in advancing agricultural classification tasks, paving the way for more efficient and accurate crop sorting and grading processes.

A retrospective analysis of historical data of multi‐environment trials for dry bean (Phaseolus vulgaris L.) in Michigan

AbstractPlant breeding programs use multi‐environment trials (METs) to test multiple genotypes with the aim of selecting superior lines and increasing genetic gain. However, METs can be affected by the genotype × environment interactions (GEI) which hinders the ability to identify stable and high‐yielding dry bean varieties. This study investigates the impact of GEI on grain yield (GY) in three dry bean market classes across multiple environments in Michigan. The goal is to identify genotypes with superior performance under GEI effects and describe the genetic correlation between locations and other agronomic traits. Furthermore, we evaluate genetic yield trends on three major dry bean market classes in Michigan. Using linear mixed models, stability analysis, and a multi‐trait index, this study accounted for the effects of the environment on genotype performance to improve the prediction of GY. Our results highlight the importance of considering GEI in dry bean breeding programs to enhance the selection accuracy of GY. The genotype by environment interaction biplot analysis and the GEI model aided in identifying and classifying mega‐environments, leading to specific strategies for testing commercial bean classes across target production environments. Multiple‐trait performance selection showed positive results, although limited gains were observed for early maturing genotypes. Finally, the analysis of genetic trends reveals a positive trend in yield for black beans, while navy beans and small red beans show a slight decrease which can be attributed to unfavorable growing conditions and lack of genetic diversity in these market classes.

Sensory properties of selected biofortified common bean (Phaseolus vulgaris) varieties grown in Burundi.

The dry common bean is an important grain legume used for human consumption worldwide. In Eastern Africa, Burundi has a significantly high per capita consumption of the crop. There has been significant research on the underlying agronomic traits of dry biofortified common beans, such as disease resistance. However, there is limited systematic information describing the sensory properties of these bean varieties, particularly in Burundi. This study evaluated the sensory properties of eight cooked dry biofortified common bean varieties using a panel of fifty-four (fourteen plus forty) persons for descriptive sensory evaluation and consumer acceptability tests. Kinure, a traditional non-biofortified common bean variety, was the control. Based on differences in the attributes of the bean varieties, two-dimensional principal component analysis (PCA) explained 58.94% of the variation. The attributes of astringency, consistency, color, juiciness, beany aroma, stickiness, and bean size contributed mostly to the differentiation of the bean varieties. A 95% PCA prediction ellipse displayed stronger congruity in the descriptive attributes of NUV130, NUV91, RWV1129, RWV1272, and RWR2245. In contrast, a deviation in the descriptive attributes of MAC44, MAC70, and RWR2154 was discerned. Regarding consumer acceptability tests, the varieties RWR2245 and MAC44 garnered significantly higher (p < .05) sensory scores on color, aroma, taste, texture, and overall acceptability. Therefore, the physical traits of cooked biofortified common bean varieties are a major contributor to varietal disparities in consumer acceptance studies. These parameters can greatly impact the adoption of dry biofortified common beans and could be of concern to common bean breeders.

Identification of Dry Bean Varieties Based on Multiple Attributes Using CatBoost Machine Learning Algorithm

Dry beans are the most widely grown edible legume crop worldwide, with high genetic diversity. Crop production is strongly influenced by seed quality. So, seed classification is important for both marketing and production because it helps build sustainable farming systems. The major contribution of this research is to develop a multiclass classification model using machine learning (ML) algorithms to classify the seven varieties of dry beans. The balanced dataset was created using the random undersampling method to avoid classification bias of ML algorithms towards the majority group caused by the unbalanced multiclass dataset. The dataset from the UCI ML repository is utilised for developing the multiclass classification model, and the dataset includes the features of seven distinct varieties of dried beans. To address the skewness of the dataset, a Box-Cox transformation (BCT) was performed on the dataset’s attributes. The 22 ML classification algorithms have been applied to the balanced and preprocessed dataset to identify the best ML algorithm. The ML algorithm results have been validated with a 10-fold cross-validation approach, and during validation, the CatBoost ML algorithm achieved the highest overall mean accuracy of 93.8 percent, with a range of 92.05 percent to 95.35 percent.

Diallel Analysis of Dry Bean Varieties for Seed Yield and Important Traits for Calcareous Soils

Diallel Analysis of Dry Bean Varieties for Seed Yield and Important Traits for Calcareous Soils

Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine

Since dry bean varieties have different qualities and economic values, their separation is of great importance in the field of agriculture. In recent years, the use of artificial intelligence-supported and image-based systems has become widespread for this process. This study aims to create a data set consisting of 14 classes in the detection of dry beans and to investigate the effectiveness of the hybrid structure of the extreme learning machine (ELM) model with GoogLeNet transfer learning on this dataset. At the same time, the salp swarm algorithm (SSA), which is one of the swarm intelligence algorithms, was used to test its applicability in ELM classifier by optimizing ELM parameters. The performance of these models was compared with ELM-based particle swarm optimization, harris hawks optimization, artificial bee colony, and traditional machine learning algorithms such as support vector machine and k-nearest neighbor. The suggested SSA-ELM model successfully classifies 14 different types of dry beans with a success rate of 91.43%. The comparable results demonstrate that the proposed hybrid model had better classification accuracy and performance metrics than traditional machine learning algorithms. In addition, it is seen that the use of image data, extraction of deep features, and classification with optimized ELM in the classification of dry beans have achieved comparable success in the literature.

Determination of the Effect of Different Sowing Dates on Growth and Yield Parameters of Some Dry Bean (Phaseolus vulgaris L.) Varieties

Determination of the Effect of Different Sowing Dates on Growth and Yield Parameters of Some Dry Bean (Phaseolus vulgaris L.) Varieties

Determination of the Effect of Different Sowing Dates on Growth and Yield Parameters of Some Dry Bean (Phaseolus vulgaris L.) Varieties

The sowing date is one of the environmental factors that significantly affected the growth and development of dry beans. Recently, the rapid change of climate required arrangement in its sowing dates. This study aimed to evaluate the effect of different sowing dates (10/03/2018, 04/03/2018, and 04/05/2018) on the phonological, morphological, and yield performances of six dry bean varieties. The experiment was carried out at Dicle University, Faculty of Agriculture Department of Field Crops in the 2018 spring and summer seasons. The experiment established randomized complete blocks design in split plots with three replications. The sowing dates significantly affected the days to seed emergence, days to first flowering, days to 50% flowering, days to maturity, first pod height, number of branches per plant, pod width, plant weight, and pod weight per plant, number of pods per plant, number of seeds per plant, seed yield per plant, biological yield and seed yield. As the sowing date was delayed, there was a decrease in all traits. The highest biological and seed yield was in early to mid-March sowing (10/03/2018) date in Cihan and Aras-98 varieties (1610.90 kg ha-1, 1850.30 kg ha-1, respectively). The early sowing date provided better growth and development in cold tolerance varieties. Adabeyazi, Cihan and Ahlat varieties were the most tolerant to low temperatures, thus the crop growth and yield increased for these varieties in the early to mid-March sowing date.

​​Evaluation of Dry Bean Cultivars in Turkey for Resistance against Common Bacterial Blight Disease

Background: Common bacterial blight (CBB) caused by Xanthomonas axonopodis pv. phaseoli (Xap) has been recorded as one of the most important seed-borne destructive diseases of beans, affecting its production worldwide. Since chemical compounds cause serious damage to natural ecosystems and in some instances fail in controlling CBB, the use of genetically resistant cultivar is the most economically efficient and environmentally friendly measure in the control of CBB disease. The objective of this study was to assess the resistance reactions of widely grown and popular common bean cultivars against Xap under controlled conditions. Methods: In this study, 16 dry bean cultivars comprising eight widely grown dry bean varieties, six improved and promising genotypes and two known CBB susceptible varieties, were evaluated for their reaction to the CBB in a completely randomized design with three replications. The reaction of the bean cultivars to Xap was assessed using disease incidence, disease severity, per cent severity index, the area under disease progress curve (AUDPC) and incubation period. Result: Data analysis indicated that there were highly significant differences (p≤0.01) among common bean cultivars for CBB disease incidence, disease severity, per cent severity index and AUDPC among cultivars. The cultivated varieties Noyanbey and Özmen were identified as resistant cultivars. In addition, SAP6 and BAC6 resistance genes were found in these two cultivars. As a consequence, it was thought that these cultivars could be a potential source of disease resistance for CBB in breeding programs and sustainable, eco-friendly and good agricultural practices.

QTL Mapping of Seed Quality Traits Including Cooking Time, Flavor, and Texture in a Yellow Dry Bean (Phaseolus vulgaris L.) Population.

Manteca yellow dry beans (Phaseolus vulgaris L.) have many quality traits that appeal to consumers, including fast cooking times, creamy texture, and sweet, buttery flavor. They are native to Chile and consumed in regions of South America and Africa but are largely unfamiliar to United States consumers. While cooking time, flavor, and texture have not been prioritized in United States dry bean breeding programs, genetic variability exists such that these traits could be addressed through breeding. In this study, a recombinant inbred line (RIL) population was developed from a cross between Ervilha (Manteca) and PI527538 (Njano), yellow dry beans with contrasting cooking time and sensory attributes. The population and parents were grown for 2 years in Michigan and evaluated for cooking time and sensory attribute intensities, including total flavor, beany, vegetative, earthy, starchy, sweet, bitter, seed-coat perception, and cotyledon texture. Cooking time ranged 19–34 min and exhibited high broad-sense heritability (0.68). Sensory attribute intensities also exhibited variation among RILs, although broad-sense heritability was low, with beany and total flavor exhibiting the highest (0.33 and 0.27). A linkage map of 870 single nucleotide polymorphisms markers across 11 chromosomes was developed for quantitative trait loci (QTL) mapping, which revealed QTL for water uptake (3), cooking time (6), sensory attribute intensities (28), color (13), seed-coat postharvest non-darkening (1), seed weight (5), and seed yield (2) identified from data across 2 years. Co-localization was identified for starchy, sweet, and seed-coat perception on Pv01; for total flavor, beany, earthy, starchy, sweet, bitter, seed-coat perception, cotyledon texture, and color on Pv03; water uptake and color on Pv04; total flavor, vegetative, sweet, and cotyledon texture on Pv07; cooking time, starchy, sweet, and color on Pv08; and water uptake, cooking time, total flavor, beany, starchy, bitter, seed-coat perception, cotyledon texture, color, and seed-coat postharvest non-darkening on Pv10. The QTL identified in this work, in particular CT8.2 and CT10.2, can be used to develop molecular markers to improve seed quality traits in future dry bean varieties. Considering yellow dry beans already excel in quality and convenience, they might be an ideal market class to signal a new focus on consumer-valued traits in the United States.

Host suitability of different common bean varieties in a growth room to the plant-parasitic nematodes Pratylenchus thornei and P. neglectus

Summary Pratylenchus thornei and P. neglectus attack common bean and cause economic yield losses throughout cultivated areas in Turkey. The most effective management strategy for the P. thornei and P. neglectus infections is crop rotation with non-host crops and breeding resistant/tolerant varieties. However, parent bean genotypes immune to P. thornei and P. neglectus are not available for breeding programmes; thus, resistant varieties are commonly incorporated as parents. In the present research, a total of 36 common bean varieties were tested in a growth room for their host response to these two nematode pests. The reproduction factor and the population density of both nematode species were calculated. All tested bean varieties showed varying levels of resistance and susceptibility to P. thornei and P. neglectus. Among the dry bean varieties, nine were found to be resistant to P. thornei and three to P. neglectus, with three varieties (‘Kantar-05’, ‘Önceler-98’ and ‘Karacasehir-90’) resistant to both species. Among fresh bean varieties, ten were found to be resistant to P. thornei and four to P. neglectus, with four varieties (‘Helda’, ‘Gina’, ‘Gelincik’ and ‘Bourgondia’) resistant to both species. The resistant common bean varieties identified in this study are a valuable untapped genetic pool that will offer improved resistance levels to P. thornei and P. neglectus, especially ‘Gina’ and ‘Önceler-98’, which seem to possess a great source of resistance to P. thornei and P. neglectus, respectively, and can be used in breeding programmes in the near future.

The effect of plant population on chlorophyll content and grain yield of dry bean (&lt;em&gt;Phaseolus vulgaris&lt;/em&gt; L.) at Dzindi irrigation scheme in South Africa

The trial was conducted at Dzindi irrigation scheme in Vhembe District in the Limpopo Province during the 2012 growing season. It was a 3x2 factorial experiment involving three plant populations (210 000, 150 000 and 70 000 plants ha-1) and two varieties of dry bean which are OPS-RS2 and Jenny with three replications. The results revealed that the interaction relationship between dry bean variety and plant population significantly influenced grain yield, 100 seed mass, number of seeds, plant height, chlorophyll content and dry matter production. The highest grain yield per plant was produced by Jenny with 70 000 plants ha-1. The highest grain yield was produced by OPS-RS2 with 150 000 plants ha-1 (3.8 tha-1) which was not significantly different to Jenny at 210 000 plants ha-1 and Jenny at 150 000 plants ha-1. A plant population of 150 000 plants ha-1 was suitable for both determinate and indeterminate growth type dry beans.

Nutritional and technological parameters of common bean varieties bred at Omsk State Agrarian University

In the southern forest-steppe of Western Siberia the shortage of proteins can be overcome cultivating legumes such as beans, peas, broad beans and chickpeas. The varieties of dry and green beans bred at Omsk State Agrarian University are adapted to the conditions of the southern forest-steppe; they are characterized by high and stable productivity and possess high compensatory abilities as regards unfavourable environmental factors (drought, waterlogging, ground frost and others). The aim of the research was to determine the technological and nutritional parameters of the grains of common bean varieties bred at the University (protein and sucrose content, seed cooking rate). 6 varieties of common beans bred at the University served as research materials. As a results, the following varieties were identified as possessing valuable technological and nutritional properties: high protein content – Fizkulturnitsa (23.75 %), Lukerya (23.6 %), Omskaya yubileinaya (24.6 %) and Olivkovaya (25.5 %); high zinc content – Fizkulturnitsa (36.86 mg/kg), Lukerya (40.93 mg/kg) and Olivkovaya (40.35 mg/kg); high iron content – Olivkovaya (108.00 mg/kg); high iodine content – Lukerya (0.23 mg/kg) and Olivkovaya (0.21 mg/kg); high calcium content – Fizkulturnitsa (1.86 %); fast seed cooking rate – Fizkulturnitsa, Omichka and Lukerya (57-59 min); high sucrose content in the leaves – Omichka, Olivkovaya, Lukerya and Fizkulturnitsa (0.10-0.15 %).

Evaluation of the adaptability of dry bean varieties grown under conditions of organic farming

The acceleration of the process of import substitution is becoming a crucial issue in contemporary Russia and priority areas of such process are identified in plant breeding and seed production. A hot topic in the breeding of beans consists in creation of highly productive varieties with stable productivity. Field experiments were conducted at the Omsk State Agrarian University in 2014-2018 in order to evaluate the adaptation properties of prominent varieties of dry beans in terms of yielding capacity. All sorts were grown using the methods of organic farming, without application of pesticides and fertilizers, which allowed for an accurate evaluation of their biological productivity. The parameters of stability and plasticity of the genotypes under study were calculated following the methods of regression analysis (Eberhart, Russell, 1966). The associated trait of the studied feature in dry beans is mainly influenced by “environmental factors” (75%), to a lesser degree by the correlation “genotype x environment” (17%) and by “genotype” (7%). Based on the evaluation of the adaptation capacity and stability of the genotypes of common beans, samples with a high responsiveness to the improvement of cultivation conditions (Omichka, Luker’ya, Sizaya, Shokoladnitsa, Olivkovaya and Omskaya yubileinaya) and with a high genotype stability (Biichanka, Sibakovskaya-100, Rubin, Nerussa, Petukh) have been identified.

Multiclass classification of dry beans using computer vision and machine learning techniques

There is a wide range of genetic diversity of dry bean which is the most produced one among the edible legume crops in the world. Seed quality is definitely influential in crop production. Therefore, seed classification is essential for both marketing and production to provide the principles of sustainable agricultural systems. The primary objective of this study is to provide a method for obtaining uniform seed varieties from crop production, which is in the form of population, so the seeds are not certified as a sole variety. Thus, a computer vision system was developed to distinguish seven different registered varieties of dry beans with similar features in order to obtain uniform seed classification. For the classification model, images of 13,611 grains of 7 different registered dry beans were taken with a high-resolution camera. A user-friendly interface was designed using the MATLAB graphical user interface (GUI). Bean images obtained by computer vision system (CVS) were subjected to segmentation and feature extraction stages, and a total of 16 features; 12 dimension and 4 shape forms, were obtained from the grains. Multilayer perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Decision Tree (DT) classification models were created with 10-fold cross validation and performance metrics were compared. Overall correct classification rates have been determined as 91.73%, 93.13%, 87.92% and 92.52% for MLP, SVM, kNN and DT, respectively. The SVM classification model, which has the highest accuracy results, has classified the Barbunya, Bombay, Cali, Dermason, Horoz, Seker and Sira bean varieties with 92.36%, 100.00%, 95.03%, 94.36%, 94.92%, 94.67% and 86.84%, respectively. With these results, the demands of the producers and the customers are largely met about obtaining uniform bean varieties.

The role of genotype and production environment in determining the cooking time of dry beans (Phaseolus vulgaris L.)

AbstractDry bean (Phaseolus vulgaris L.) is a nutrient‐dense food rich in proteins and minerals. Although a dietary staple in numerous regions, including Eastern and Southern Africa, greater utilization is limited by its long cooking time as compared with other staple foods. A fivefold genetic variability for cooking time has been identified for P. vulgaris, and to effectively incorporate the cooking time trait into bean breeding programs, knowledge of how genotypes behave across diverse environments is essential. Fourteen bean genotypes selected from market classes important to global consumers (yellow, cranberry, light red kidney, red mottled, and brown) were grown in 10 to 15 environments (combinations of locations, years, and treatments), and their cooking times were measured when either presoaked or unsoaked prior to boiling. The 15 environments included locations in North America, the Caribbean, and Eastern and Southern Africa that are used extensively for dry bean breeding. The cooking times of the 14 presoaked dry bean genotypes ranged from 16 to 156 min, with a mean of 86 min across the 15 production environments. The cooking times of the 14 dry bean genotypes left unsoaked ranged from 77 to 381 min, with a mean cooking time of 113 min. The heritability of the presoaked cooking time was very high (98%) and moderately high for the unsoaked cooking time (~60%). The genotypic cooking time patterns were stable across environments. There was a positive correlation between the presoaked and unsoaked cooking times (r = .64, p &lt; 0.0001), and two of the fastest cooking genotypes when presoaked were also the fastest cooking genotypes when unsoaked (G1, Cebo, yellow bean; and G4, G23086, cranberry bean). Given the sufficient genetic diversity found, limited crossover Genotype × Environment interactions, and high heritability for cooking time, it is feasible to develop fast cooking dry bean varieties without the need for extensive testing across environments.

Baking performance of 25 edible dry bean powders: Correlation between cookie quality and rapid test indices

This study was designed to evaluate the baking performances of 25 edible dry bean (Phaseolus vulgaris L.) varieties and to investigate correlations among cookie features and rapid test indices (i.e., water and lactic acid retention capacities, oil binding capacity and Rapid Visco Analyzer indices). Two bean powder particle sizes (≤0.5 mm, ≤1.0 mm) were investigated. Cookies were evaluated in terms of nutritional, geometrical and textural properties. Bean powders doubled the amount of cookie protein and increased cookie resistant starch content. Baking potential varied according to bean genotype and powder particle size: coarse powders resulted in larger (+26%) and thinner (−19%) cookies characterized by easier breaking texture (fracture strengths of 41–157 vs. 48–226 kPa for fine powders). Water retention and oil binding capacities and pasting properties significantly (p < 0.05) correlated with cookie features. In conclusion, these accumulated findings can be used in designing value-added traditional and gluten-free cookies.

Agronomic Performance and Nitrogen Fixation of Heirloom and Conventional Dry Bean Varieties Under Low-Nitrogen Field Conditions.

Common beans (Phaseolus vulgaris) form a relationship with nitrogen-fixing rhizobia and through a process termed symbiotic nitrogen fixation (SNF) which provides them with a source of nitrogen. However, beans are considered poor nitrogen fixers, and modern production practices involve routine use of N fertilizer, which leads to the down-regulation of SNF. High-yielding, conventionally bred bean varieties are developed using conventional production practices and selection criteria, typically not including SNF efficiency, and may have lost this trait over decades of modern breeding. In contrast, heirloom bean genotypes were developed before the advent of modern production practices and may represent an underutilized pool of genetics which could be used to improve SNF. This study compared the SNF capacity under low-N field conditions, of collections of heirloom varieties with and conventionally bred dry bean varieties. The heirloom-conventional panel (HCP) consisted of 42 genotypes from various online seed retailers or from the University of Guelph Bean Breeding program seedbank. The HCP was genotyped using a single nucleotide polymorphism (SNP) array to investigate genetic relatedness within the panel. Field trials were conducted at three locations in ON, Canada from 2014 to 2015 and various agronomic and seed composition traits were measured, including capacity for nitrogen fixation (using the natural abundance method to measure seed N isotope ratios). Significant variation for SNF was found in the panel. However, on average, heirloom genotypes did not fix significantly more nitrogen than conventionally bred varieties. However, five heirloom genotypes fixed >60% of their nitrogen from the atmosphere. Yield (kg ha-1) was not significantly different between heirloom and conventional genotypes, suggesting that incorporating heirloom genotypes into a modern breeding program would not negatively impact yield. Nitrogen fixation was significantly higher among Middle American genotypes than among Andean genotypes, confirming previous findings. The best nitrogen fixing line was Coco Sophie, a European heirloom white bean whose genetic makeup is admixed between the Andean and Middle American genepools. Heirloom genotypes represent a useful source of genetics to improve SNF in modern bean breeding.