Genetic divergence analysis of Ethiopian faba bean (Vicia faba L.) landraces

Genetic divergence analysis of Ethiopian faba bean (Vicia faba L.) landraces | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetic divergence analysis of Ethiopian faba bean (Vicia faba L.) landraces Andualem Hiywotu, Alemu Asfaw, Fisseha Woldekirkos, Temesgen Molla This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8978024/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Genetic divergence is important in plant breeding to select parents for crossing. A study was initiated to assess the extent of genetic divergence and group faba bean accessions based on their similarity and dissimilarity. The experiment was conducted at Mecha district in 2019 main cropping season. A 9x9 simple lattice design was used and 14 important agronomic traits were collected. Cluster analysis distinguished the 81 accessions into seven groups. The discrimination of accessions into so many discrete clusters suggested the presence of genetic diversity in the material evaluated. The maximum inter-cluster distance was detected between cluster I and cluster III (D 2 = 152.28**) followed by cluster III and cluster IV (D 2 = 130.22**). The high values of inter-cluster distances indicate divergence among the accessions and may be used in selection of genetically divergent parents for exploitation in crossing programs for better genetic recombination. The results of principal component (PC) analysis demonstrated that first four PCs explained 73.81% of the total variations among the 81 faba bean accessions. Plant height, number of pods per plant, number of branches per plant, biomass yield, grain yield and disease scores (chocolate spot, ascochyta blight and rust) were important in the first and second PC axes; as a result, they are important in discriminating the faba bean landraces. The study shows the importance of multivariate methods in assessing genetic divergence so as to identify possible parents for crossing considering traits of agronomic importance. Clustering eigenvalue genetic distance genetic divergence principal component Figures Figure 1 Figure 2 INTRODUCTION Faba bean ( Vicia faba L.), also known as fava bean, broad bean and horse bean, is one of the oldest crops cultivated worldwide (Mínguez & Rubiales, 2021 ). Mediterranean countries, Ethiopia, Egypt, China, Afghanistan, India, Northern Europe, and Northern Africa are major producers of faba beans (Rahate et al., 2020 ). Out of more than 50 faba bean-producing countries, about 90% production is concentrated in Asian, European Union (EU) and African region (FAO, 2020). The world production of faba beans was 5.43 million metric tons in 2019, representing a 25% increase compared with 4.35 million metric tons in 1990. Regionally, Asia leads with 33.55% of total faba bean production globally, followed by Europe (EU) and Africa with 29.36% and 27.04% share, respectively (FAO, 2020). China was the leading producer of faba beans, followed by Ethiopia; these two countries represented about 50% of the total global production, whereas among the EU, the United Kingdom and France were among the top five producers. Also, in 2019, Australia was the leading exporter of faba beans with 265,543 metric tons or nearly 30% of total exports, followed by the United Kingdom, Lithuania, Egypt and Latvia (FAO, 2020). Egypt led importers with 309,355 metric tons or 40.48% of total global imports followed by Norway, Germany, Saudi Arabia, and France (FAO, 2020). Faba bean is an important crop for an ecological, nutritional, and economical point of view (Xiao et al., 2021 ). It is a versatile crop providing various ecosystem services, that is, cultivated primarily as a food source for the human population residing in Asia and Africa, as animal feed/silage in the European region, and fixation of atmospheric nitrogen in agricultural soils, thereby significantly reducing the application of synthetic fertilizers (Zhou et al., 2018 ). Nutritionally, mature seeds of faba bean are rich in proteins (26.1%), carbohydrates (58.3%), and dietary fiber (25.0%) (USDA, 2021). Genetic divergence refers to genetic distance among the genotypes under consideration. It is determined by using cluster analysis. D-square statistics (D 2 ) is one of statistical technique developed by Mahalanobis ( 1936 ) used to classify the different genotypes into different groups. The extent of diversity present between genotypes determines the extent of improvement gained through selection and hybridization. The more divergent two genotypes are the more will be the probability of improving genotypes through selection and hybridization. Principal component analysis (PCA) is a multivariate analysis that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables. It is a standard tool in modern data analysis used to extract relevant information from confusing data sets by identifying directions, called principal components (Chatfield and Collin, 1980). Therefore, PCA used to identify and minimize the number of traits for effective selection and improvement of yield and its related trait. The first principal component accounts more of the variability in the data as possible followed by each succeeding component accounts for the remaining variability as possible. Therefore, the present study was initiated to assess the extent of genetic divergence and group faba bean accessions based on their similarity and dissimilarity. MATERIALS AND METHODS Experimental Site, Materials and Design The experiment was conducted at Bahir Dar University research site in Mecha district, Ethiopia in the 2019 main cropping season. Mecha is located at about 525 km Northeast of Addis Ababa and 34 km Southwest of Bahir Dar. It is located at latitude of 10°30' N and longitude of 37°29' E. The mean annual rainfall of the site is 1572 mm. The mean temperature ranges between 24°C and 27 °C; and the altitude is 2009 m.a.s.l. Totally, 81 faba bean genotypes were used in the study. Seventy-eight of the genotypes were accessions obtained from Ethiopian Biodiversity Institute; the two were standard checks obtained from Adet Agricultural Research Center; and one was a local check from local source. The experimental design used was 9 × 9 simple lattice. Each accession was planted on two-row plot. Spacing between rows and within rows were 40 cm and 10 cm, respectively. The single plot size was 0.8m × 1m (0.8 m 2 ). Spacing between blocks and replication was 1 meter. Table 1. List of the 81 Faba bean accessions used in the study Treatment Accession Collection Region District Treatment Accession Collection Region District 1 212565 Amhara Siyadebrina Wayu 42 235709 SNNP Dirashe Special 2 27279 Amhara Machakel 43 219089 Oromia Amigna 3 25299 SNNP Angacha 44 25338 SNNP Meskanena Mareko 4 245140 SNNP Dila Zuria 45 215748 Amhara Dessie Zuria 5 212566 Oromia Wuchalena Jido 46 213211 Oromia Tiro Afeta 6 25006 Amhara Hulet Ej Enese 47 25336 SNNP Meskanena Mareko 7 212568 Amhara Siyadebrina Wayu 48 226125 Amhara Legambo 8 25018 Oromia Wuchalena Jido 49 240497 SNNP Decha 9 27052 Oromia Kofele 50 25346 SNNP Meskanena Mareko 10 25274 Oromia Becho 51 25328 SNNP Selti 11 229303 Amhara Lay Betna Tach Bet 52 213214 Oromia Limu Seka 12 212567 Amhara Siyadebrina Wayu 53 25335 SNNP Meskanena Mareko 13 25298 SNNP Angacha 54 25340 SNNP Meskanena Mareko 14 219355 Oromia Adolana Wadera 55 25339 SNNP Meskanena Mareko 15 208085 Amhara Weremo Wajetuna Mida 56 215129 Amhara Mama Midrina Lalo 16 212811 Amhara Wegera 57 215128 Amhara Mama Midrina Lalo 17 203105 Oromia Dedesa 58 228607 Amhara Goncha Siso Enese 18 235955 Amhara Debark 59 25337 SNNP Meskanena Mareko 19 25279 SNNP Cheha 60 25341 SNNP Meskanena Mareko 20 208114 Amhara Weremo Wajetuna Mida 61 25331 SNNP Selti 21 229310 Amhara Weremo Wajetuna Mida 62 235433 Tigray Kola Temben 22 212572 Amhara Weremo Wajetuna Mida 63 Tumsa - - 23 25003 Oromia Kuyu 64 25325 SNNP Selti 24 25323 SNNP Selti 65 25329 SNNP Selti 25 25022 Oromia Kuyu 66 25309 SNNP Angacha 26 25280 SNNP Cheha 67 25304 SNNP Angacha 27 235956 Amhara Debark 68 25330 SNNP Selti 28 220079 Tigray Adwa 69 25307 SNNP Angacha 29 212576 Amhara Lay Betna Tach Bet 70 25334 SNNP Selti 30 212575 Amhara Lay Betna Tach Bet 71 25306 SNNP Angacha 31 25277 SNNP Cheha 72 25311 SNNP Angacha 32 220076 Tigray Adwa 73 25332 SNNP Sodo 33 27290 Oromia Jimma Arjo 74 25310 SNNP Angacha 34 25264 Oromia Becho 75 25333 SNNP Selti 35 25292 SNNP Limo 76 25302 SNNP Angacha 36 25270 Oromia Becho 77 25327 SNNP Selti 37 212580 Amhara Mama Midrina Lalo 78 Dosha - - 38 25017 Amhara Enarj Enawga 79 25303 SNNP Angacha 39 212578 Amhara Geramidirna Keya 80 25301 SNNP Angacha 40 25010 Oromia Gerar Jarso 81 Local - - 41 25290 SNNP Limo Data Analysis Analysis of variance The data for each trait were subjected to analysis of variance (ANOVA) for simple lattice design using SAS software version 9.2 (SAS, 2008). Clustering of accessions and genetic divergence analysis Cluster analysis was made using SAS Software version 9.2 (SAS Institute, 2008). Based on the squared distances (D 2 ) values, clustering of accessions was done using Tocher’s method as described by Singh and Chaudhary (1985). Unweighted Pair Group Methods with Arithmetic-average (UPGMA) based on the generalized D 2 distances by average linkage method of agglomerative hierarchical clustering (successive merger) was built to group the faba bean genotypes into genetically distinct classes based on phenotypic traits. The number of clusters was determined by using points where local peaks of Pseudo F-statistics join with small values of the Pseudo t 2 statistics followed by a larger Pseudo t 2 for the next cluster fusion. The dendrogram was constructed using JMP software. Principal component analysis Principal component analysis (PCA) was used to find out the traits, which accounted more to the total variation. The data were standardized to mean zero and variance of one before computing principal component analysis. Principal components were calculated based on correlation matrix using SAS computer software. As suggested by Johnson and Wichern (1988), eigenvector greater than half divided by the standard deviation (square root) of the eigen value of the respective PC was employed as general guideline for weighing the relative significance of traits constituting to the PCs. RESULTS AND DISCUSSION Analysis of variance The result of analysis of variance showed that the accessions vary significantly (p < 0.01) for all the traits investigated except days to maturity and number of seeds per pod (Hiywotu et al., 2022 ). Clustering among accessions Cluster analysis distinguished the 81 accessions into seven groups of different sizes (Table 2 and Fig. 1 ), members within a cluster being assumed to be more closely related in terms of the traits under consideration with each other than those members in different clusters. Similarly, members in clusters with non-significant distance were assumed to have more close relationships with each other than they are with those in significantly distant clusters. The number of accessions in each of the seven clusters ranged from 3 to 24 in the smallest and largest cluster, respectively. Table 2 List of faba bean accessions grouped into seven clusters Cluster Number of Name of accessions accessions C I 8 212565, 212568, 25018, 212567, 208085, 212575, 25010, 215748 C II 9 245140, 212566, 229303, 219355, 212811, 25003, 25323, 213214, 228607 C III 3 Tumsa, Dosha, Local C IV 24 27279, 25006, 27052, 203105, 25279, 208114, 25022, 25280,235956, 220079, 25277, 220076, 25264,212580, 212578, 25338, 226125, 25346, 25340, 215128, 25341, 25329, 25332, 25333 C V 12 25274, 229310, 25270, 25290, 235709, 219089, 240497, 25337, 235433, 25325, 25334, 25310 C VI 8 212572, 212576, 27290, 25336, 25328, 25339, 215129, 25331 C VII 17 25299, 25298, 235955, 25292, 25017, 213211, 25335, 25309, 25304, 25330, 25307,25306, 25311, 25302, 25327, 25303, 25301 Clusters CI and CVI had 8 accessions for each. Clusters CII and CIII had 9 and 3 accessions, respectively. Cluster CIV was the largest cluster group consisting of 24 accessions followed by CVII which had 17 accessions. CV had 12 accessions. These indicate the presence of adequate variability among the accessions. The discrimination of accessions into so many discrete clusters suggested the presence of genetic diversity in the material evaluated. The presence of substantial genetic diversity among the accessions implied that these accessions may serve as good source for selecting the diverse parents for a hybridization program aimed at isolating desirable segregants for grain yield and other important traits. Similarly, substantial genetic divergence in the faba bean germplasm was reported previously (Keneni et al., 2005 ; Qi and Xing, 2009; Berma et al., 2016 and Chaudhary et al., 2018 ). Genetic distance among clusters Pair wise generalized distances (D 2 ) among the seven clusters were presented in Table 3 . Out of 21 possible pairs of clusters, differences between 14 pairs were highly significant (P < 0.01) and 4 pairs were significant (P < 0.05), while those between the rest of the clusters were non-significant. The maximum inter-cluster distance (D 2 = 152.28**) was detected between CI and CIII. Cluster CI constituted accessions from two regions (Amhara and Oromia) except one while cluster CIII constituted standard and local checks as shown in Table 3 of the 81 faba bean accessions used in the study. The high values of inter-cluster distances indicated divergence among the accessions and might be used in breeding programs for better genetic recombination and selection of genetically divergent parents for exploitation in crossing programs. This finding is consistent with Fikreselassie and Seboka (2012) and Keneni et al. ( 2005 ) who used 25 and 160 faba bean genotypes respectively and found high D 2 value. The second most divergent clusters were cluster CIII and CIV (D 2 = 130.22**). Cluster CIV constituted accessions from all regions (Amhara, Oromia, Tigray and Southern nation nationalities people). This indicates accessions from different regions might have similar genetic background. The third most divergent clusters were CIII and CVI (D 2 = 121.99**). Cluster CVI constituted accessions from three regions (Amhara, Oromia and Southern nation nationalities people). The results implied that crossing of accessions from CIII with accessions from CI, CIV and CVI will result in useful segregants in the F 1 and subsequent generations. Similar findings were reported by Lal et al. (2019) who clustered 83 faba bean genotypes in to ten clusters. The minimum distance (D 2 = 6.93) was observed between CIV and CVII suggesting that the materials may be very closely related. Cluster CVII constituted accessions from Southern nation nationalities people region except three accessions. In the present investigation, the highest intra-cluster distance was found for CI followed by CIV, CII, CV, CVII, CVI and CIII. This implies that the accessions present in a cluster have little genetic divergence from each other with respect to aggregate effect of twelve traits under study, while more genetic diversity was observed between the accessions belonging to different clusters. Table 3 Estimates of average intra- (diagonal) and inter-cluster (above diagonal) distances for 7 clusters of 81 faba bean accessions Cluster CI CII CIII CIV CV CVI CVII CI 206.99 27.25** 152.28** 19.54 58.57** 49.81** 29.65** CII 170.43 104.81** 13.92 22.94* 37.03** 19.69* CIII 10.90 130.22** 109.29** 121.99** 93.40** CIV 195.76 23.29* 24.57* 6.93 CV 91.32 29.22** 25.57** CVI 27.21 26.11** CVII 61.58 ** = highly significant at probability level p < 0.01 (χ 2 = 24.72), * = Significant at probability level of p < 0.05 (χ 2 = 19.67) Traits mean of clusters Cluster means showed significant differences among the clusters for most of the traits (Table 4 ). Cluster CI constituted inferior accessions (susceptible to chocolate spot, rust, the shortest in plant height, low 100 seed weight and harvest index) for most of the traits. Similar results were obtained by Keneni et al. ( 2005 ) for chocolate spot, the shortest in plant height, and low 100 seed weight. Clusters CII, CIV and CV comprised intermediate accessions for most of the traits, but CV superior in grain yield. Cluster CVI showed the highest mean for harvest index and lowest mean of biological yield, ascochyta blight score and rust disease score. Cluster CVII was characterized by the least days to flowering, number of pods per plant and number of branches/plant but superior in ascochyta blight disease. Cluster CIII constituted superior accessions for most of the traits including chocolate spot disease, rust disease, plant height, pod length, number of pods per plant, number of branches per plant, biological yield and 100 seed weight. Overall, the results showed wide variation from one cluster to another in respect of cluster means for twelve traits, which indicated that accessions having distinctly different mean performance for various traits were separated into different clusters. The crossing between the entries belonging to cluster pairs having large inter-cluster distance and possessing high cluster means for one or other traits to be improved may be recommended for isolating desirable recombinants in the segregating generations in faba bean. However, the results suggested that selection of parents should consider not only the distance between clusters but also the special merits of each cluster and each accession within a cluster depending on the specific objectives of hybridization as suggested by Singh ( 1990 ). Table 4 Cluster means for 12 traits of 81 faba bean accessions Traits CI CII CIII CIV CV CVI CVII Mean DF 50.88 a 49.72 45.17 44.71 44.58 43.94 43.56 b 46.08 PH 84.04 b 103.52 121.71 a 93.01 109.02 91.42 98.63 100.19 PL 3.46 3.84 5.78 a 3.44 b 3.75 3.54 4.04 3.98 NPP 9.47 10.30 19.37 a 9.75 16.53 13.03 8.86 b 12.47 NBP 1.78 2.18 2.40 a 1.55 2.06 1.45 1.35 b 1.82 BY 7283.69 9926.04 13349.67 a 7520.44 10402.26 5211.28 b 8145.63 8834.14 GY 1438.02 b 1979.36 2962.10 1703.18 3231.91 a 1958.92 1910.47 2169.14 HSW 33.29 b 38.13 82.32 a 37.92 42.38 40.06 46.76 45.83 HI 20.22 b 20.65 22.52 23.58 32.10 38.98 a 23.80 25.98 AB 4.25 3.78 2.33 3.38 2.33 4.50 a 2.29 b 3.27 CS 5.25 a 2.11 1.00 b 3.75 1.67 3.00 2.82 2.80 RT 4.63 a 1.00 b 1.00 b 2.17 1.17 4.63 a 2.35 2.42 a=Highest value, b=Lowest value. DF= days to flowering, PH= plant height, PL = pod length, NPP= number of pods per plant, NBP= number of branches per plant, BY= biomass yield, GY= grain yield, HSW = 100 seed weight, HI= harvest index, AB= ascochyta blight, CS= chocolate spot, RT= rust disease score. Principal component analysis In the present study, the first four principal components exhibited eigen values more than one (4.856, 2.006, 1.672 and 1.063) and explained about 73.81% of the total variations among the 81 faba bean accessions (Table 5 ). The first four PCs were given due importance for further explanation. Similar findings were reported by Tiwari ( 2019 ) who found the first three principal components exhibiting eigen values more than one and explained about 75.53% of the total variations. The first principal component accounted for 37.35% of the total variations. Traits such as plant height, pod length, number of pods per plant, biological yield, grain yield and 100 seed weight had high contribution to the first PC. These indicated that these traits had higher relative contribution to the total diversity and they were the ones that most differentiated the accessions. Keneni et al. ( 2005 ) reported traits with relatively greater weight in PC1 like grain yield and plant height had higher relative contribution to the total diversity and they were the ones that most differentiated the populations. The second component accounted for 15.43% of the total variation and predominantly illustrates variation in days to flowering and number of branches per plant. The third principal component accounted for 12.86% of the total variation and it was chiefly accounted by variation in number of pods per plant, grain yield and harvest index. The fourth principal component accounted for 8.17% of the total variation and indicated with high variation in pod length, number of branches per plant, 100 seed weight and rust disease score. Table 5 Eigenvectors, eigenvalues and variance explained by the first four principal components Traits PC-1 PC-2 PC-3 PC-4 Days to flowering -0.090 0.537 -0.152 0.285 Plant height 0.363 0.113 0.102 0.061 Pod length 0.325 -0.099 -0.156 0.542 Number of pods per plant 0.322 0.221 0.307 -0.264 Number of branches per plant 0.184 0.481 0.162 0.302 Biomass yield 0.333 0.171 -0.324 -0.247 Grain yield 0.373 0.055 0.325 -0.123 Hundred-seed weight 0.303 -0.285 -0.213 0.463 Harvest index 0.046 -0.139 0.706 0.180 Ascochyta blight -0.208 0.087 0.138 0.071 Chocolate spot -0.313 0.050 0.081 0.098 Rust disease -0.304 -0.094 0.202 0.344 Eigen value 4.856 2.006 1.672 1.063 Proportion (%) 37.35 15.43 12.86 8.17 Cumulative (%) 37.35 52.78 65.64 73.82 As it is visualized in Fig. 2 , grain yield was highly correlated with number of pods per plant, biomass yield and plant height, whereas weaker but positive correlated with number of branches per plant while negatively correlated with ascochyta blight, chocolate spot and rust disease score. Treatment 8 and 16 were plotted far apart from others. Treatment 8, 16, 7, 45, 15 and 29 highly contributed to PC1 in negative direction and treatment 62, 63, 78, and 81 contributed highly in positive direction for PC1. The biplot gave more opportunity to assess which accessions were good for which traits that would help as excellent baseline information for faba bean improvement. Accordingly, treatment 4, 10, 14, 21, 23, 24, 25, 36, 41, 42, 43, 52, 58, 62, 63 and 64 can be selected for grain yield improvement, whereas treatment 21 and 58 were better for biomass yield. This implies a substantial amount of diversity in the faba bean accessions and possibility of its improvement by selection. Conclusion Cluster analysis grouped the accessions into seven clusters based on their similarity and dissimilarity. The maximum inter-cluster distance (D 2 = 152.28**) was detected between CI and CIII followed by CIII-CIV and CIII-CVI. The high values of inter-cluster distances indicate divergence among the accessions and might be used in breeding programs for better genetic recombination and selection of genetically divergent parents for exploitation in crossing programs. The principal component analysis shows that the first four principal components exhibited eigen values more than one and explained about 73.81% of the total variations. Declarations Acknowledgements We would like to thank the staff at Bahir Dar University Department of Plant Sciences and Adet Agricultural Research Center for their invaluable technical support and for supplying all facilities during the experiments. Author contributions Andualem Muche Hiywotu : Conceived and designed the experiments; performed the experiments and wrote the paper. Fisseha Worede : Conceived and designed the experiments; edited the paper. Alemu Abate : Analyzed and interpreted the data; materials, analysis tools, or data. All authors have read and agreed to the definitive version of the manuscript. Temesgen Asmare Molla: Performed the experiments, wrote the paper, and prepared the manuscript. Funding The authors declare that they didn’t receive any funds, grants, or other forms of support while preparing this manuscript. Data availability All data generated or analyzed during this study are included in this published article. Ethics approval and consent to participate Ethical approval: This article contains no studies with human participants or animals performed by any of the authors, “not applicable”. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Clinical trial number: not applicable References Andualem Muche Hiywotu, Alemu Abate, Fisseha Worede and Abunu Marefia (2022). Genetic variability in Ethiopian faba bean ( Vicia faba L.) accessions. Cogent Food & Agriculture, 8:1, 2132847, DOI: 10.1080/23311932.2022.2132847. Berma, I. K., Yadav, C. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8978024","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607272188,"identity":"10848c86-8a3a-4c59-9ba1-df4aed5f16e8","order_by":0,"name":"Andualem Hiywotu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYHACAxCRwMBw+OCDD0AWGzvRWhiPJRvOAGlhJloL8xkzaR4Qk5AW/tnN26Ruttnl8bMBtdj82ibPx8zA+OFjDm4tEneOlUnntiUXS/YcK7bO7btt2MbMwCw5cxsea27kmAG1HEjccOPwxtu5PbcZgVrYmHnxaJGHadl//4GBtGXPbXuCWgzgtjAcMZJm+HE7kaAWwxtpxdY555ITZxwABnJvw+3kNmbGZrx+kbuRvPF2TpldYn8DMCp//LltO7+9+eCHj/i8jwIY28BkA7HqQeAPKYpHwSgYBaNgpAAAFElXErmCj4gAAAAASUVORK5CYII=","orcid":"","institution":"Debark University, College of Agriculture and Environmental Conservation, Department of Plant Science, Debark, Ethiopia","correspondingAuthor":true,"prefix":"","firstName":"Andualem","middleName":"","lastName":"Hiywotu","suffix":""},{"id":607272189,"identity":"3e2bb2bc-811d-4f9c-8dca-9cb3c0bc9775","order_by":1,"name":"Alemu Asfaw","email":"","orcid":"","institution":"Debark University, College of Agriculture and Environmental Conservation, Department of Plant Science, Debark, Ethiopia","correspondingAuthor":false,"prefix":"","firstName":"Alemu","middleName":"","lastName":"Asfaw","suffix":""},{"id":607272190,"identity":"651684bf-e1d5-4558-be94-cad32abe3f8d","order_by":2,"name":"Fisseha Woldekirkos","email":"","orcid":"","institution":"Ethiopian Institute of Agricultural Research, Fogera National Rice Research and Training Center, Bahir Dar, Ethiopia","correspondingAuthor":false,"prefix":"","firstName":"Fisseha","middleName":"","lastName":"Woldekirkos","suffix":""},{"id":607272191,"identity":"11486801-0d74-4a9b-ab11-3fa1a7d3223a","order_by":3,"name":"Temesgen Molla","email":"","orcid":"","institution":"Department of Plant sciences, college of Agriculture and Environmental Sciences, Bahir Dar University, P.O.Box 79, Bahir Dar, Ethiopia","correspondingAuthor":false,"prefix":"","firstName":"Temesgen","middleName":"","lastName":"Molla","suffix":""}],"badges":[],"createdAt":"2026-02-26 12:53:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8978024/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8978024/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104890131,"identity":"19d77779-714b-4c3e-9ab1-4e4e3540abab","added_by":"auto","created_at":"2026-03-18 10:31:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42599,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram showing relationships among 81 Ethiopian faba bean accessions constructed by using 12 traits\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8978024/v1/7b6501c70b02c0a427f6b3ab.png"},{"id":105034310,"identity":"fa294e8f-388b-4d59-bba0-479dca013ea2","added_by":"auto","created_at":"2026-03-20 07:23:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69536,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis biplot describing the relative position of 81 faba bean accessions and 12 traits. Identification of treatments is as listed in Table 1.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8978024/v1/60569e6a742b19fa10e2446c.png"},{"id":105036541,"identity":"048693c7-6cd9-4344-80b0-cc84697a76b8","added_by":"auto","created_at":"2026-03-20 07:34:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1631752,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8978024/v1/ec95bcf1-f46d-49d9-bb38-d9051073d014.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic divergence analysis of Ethiopian faba bean (Vicia faba L.) landraces","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eFaba bean (\u003cem\u003eVicia faba\u003c/em\u003e L.), also known as fava bean, broad bean and horse bean, is one of the oldest crops cultivated worldwide (M\u0026iacute;nguez \u0026amp; Rubiales, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Mediterranean countries, Ethiopia, Egypt, China, Afghanistan, India, Northern Europe, and Northern Africa are major producers of faba beans (Rahate et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Out of more than 50 faba bean-producing countries, about 90% production is concentrated in Asian, European Union (EU) and African region (FAO, 2020). The world production of faba beans was 5.43\u0026nbsp;million metric tons in 2019, representing a 25% increase compared with 4.35\u0026nbsp;million metric tons in 1990. Regionally, Asia leads with 33.55% of total faba bean production globally, followed by Europe (EU) and Africa with 29.36% and 27.04% share, respectively (FAO, 2020). China was the leading producer of faba beans, followed by Ethiopia; these two countries represented about 50% of the total global production, whereas among the EU, the United Kingdom and France were among the top five producers. Also, in 2019, Australia was the leading exporter of faba beans with 265,543 metric tons or nearly 30% of total exports, followed by the United Kingdom, Lithuania, Egypt and Latvia (FAO, 2020). Egypt led importers with 309,355 metric tons or 40.48% of total global imports followed by Norway, Germany, Saudi Arabia, and France (FAO, 2020).\u003c/p\u003e \u003cp\u003eFaba bean is an important crop for an ecological, nutritional, and economical point of view (Xiao et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is a versatile crop providing various ecosystem services, that is, cultivated primarily as a food source for the human population residing in Asia and Africa, as animal feed/silage in the European region, and fixation of atmospheric nitrogen in agricultural soils, thereby significantly reducing the application of synthetic fertilizers (Zhou et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Nutritionally, mature seeds of faba bean are rich in proteins (26.1%), carbohydrates (58.3%), and dietary fiber (25.0%) (USDA, 2021).\u003c/p\u003e \u003cp\u003eGenetic divergence refers to genetic distance among the genotypes under consideration. It is determined by using cluster analysis. D-square statistics (D\u003csup\u003e2\u003c/sup\u003e) is one of statistical technique developed by Mahalanobis (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1936\u003c/span\u003e) used to classify the different genotypes into different groups. The extent of diversity present between genotypes determines the extent of improvement gained through selection and hybridization. The more divergent two genotypes are the more will be the probability of improving genotypes through selection and hybridization.\u003c/p\u003e \u003cp\u003ePrincipal component analysis (PCA) is a multivariate analysis that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables. It is a standard tool in modern data analysis used to extract relevant information from confusing data sets by identifying directions, called principal components (Chatfield and Collin, 1980). Therefore, PCA used to identify and minimize the number of traits for effective selection and improvement of yield and its related trait. The first principal component accounts more of the variability in the data as possible followed by each succeeding component accounts for the remaining variability as possible. Therefore, the present study was initiated to assess the extent of genetic divergence and group faba bean accessions based on their similarity and dissimilarity.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eExperimental Site, Materials and Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experiment was conducted at Bahir Dar University research site in Mecha district, Ethiopia in the 2019 main cropping season. Mecha is located at about 525 km Northeast of Addis Ababa and 34 km Southwest of Bahir Dar. It is located at latitude of 10\u0026deg;30\u0026apos; N and longitude of 37\u0026deg;29\u0026apos; E. The mean annual rainfall of the site is 1572 mm. The mean temperature ranges between 24\u0026deg;C and 27 \u0026deg;C; and the altitude is 2009 m.a.s.l. Totally, 81 faba bean genotypes were used in the study. Seventy-eight of the genotypes were accessions obtained from Ethiopian Biodiversity Institute; the two were standard checks obtained from Adet Agricultural Research Center; and one was a local check from local source.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe experimental design used was 9 \u0026times; 9 simple lattice. Each accession was planted on two-row plot. Spacing between rows and within rows were 40 cm and 10 cm, respectively. The single plot size was 0.8m \u0026times; 1m (0.8 m\u003csup\u003e2\u003c/sup\u003e). Spacing between blocks and replication was 1 meter.\u003c/p\u003e\n\u003cp\u003eTable 1. List of the 81 Faba bean accessions used in the study\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"861\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eAccession\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eCollection Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eDistrict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eAccession\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eCollection Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eDistrict\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e212565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eSiyadebrina Wayu\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e42\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e235709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eDirashe Special\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e27279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eMachakel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e43\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e219089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eAmigna\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eAngacha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e44\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eMeskanena Mareko\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e245140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eDila Zuria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e215748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eDessie Zuria\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e212566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eWuchalena Jido\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e46\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e213211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eTiro Afeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eHulet Ej Enese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e47\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eMeskanena Mareko\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e212568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eSiyadebrina Wayu\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e48\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e226125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eLegambo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eWuchalena Jido\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e240497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eDecha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e27052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eKofele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eMeskanena Mareko\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eBecho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e51\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eSelti\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e229303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eLay Betna Tach Bet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e52\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e213214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eLimu Seka\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e212567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eSiyadebrina Wayu\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e53\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eMeskanena Mareko\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eAngacha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e54\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eMeskanena Mareko\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e219355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eAdolana Wadera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e55\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eMeskanena Mareko\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e208085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eWeremo Wajetuna Mida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e215129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eMama Midrina Lalo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e212811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eWegera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e57\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e215128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eMama Midrina Lalo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e203105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eDedesa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e58\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e228607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eGoncha Siso Enese\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e235955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eDebark\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e59\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eMeskanena Mareko\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eCheha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eMeskanena Mareko\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e208114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eWeremo Wajetuna Mida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e61\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eSelti\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e229310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eWeremo Wajetuna Mida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e62\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e235433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eTigray\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eKola Temben\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e212572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eWeremo Wajetuna Mida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e63\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eTumsa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eKuyu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e64\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eSelti\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eSelti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eSelti\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eKuyu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e66\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eAngacha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eCheha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eAngacha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e235956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eDebark\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e68\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eSelti\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e220079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eTigray\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eAdwa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e69\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eAngacha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e212576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eLay Betna Tach Bet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e70\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eSelti\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e212575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eLay Betna Tach Bet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e71\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eAngacha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e31\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eCheha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e72\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eAngacha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e220076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eTigray\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eAdwa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e73\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eSodo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e27290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eJimma Arjo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e74\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eAngacha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eBecho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eSelti\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eLimo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e76\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eAngacha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eBecho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e77\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eSelti\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e212580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eMama Midrina Lalo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e78\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eDosha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e38\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eEnarj Enawga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e79\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eAngacha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e212578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAmhara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eGeramidirna Keya\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003eAngacha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eGerar Jarso\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e81\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eLocal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e41\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e25290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eSNNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eLimo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of variance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for each trait were subjected to analysis of variance (ANOVA) for simple lattice design using SAS software version 9.2 (SAS, 2008).\u003c/p\u003e\n\u003ch3\u003eClustering of accessions and genetic divergence analysis\u003c/h3\u003e\n\u003cp\u003eCluster analysis was made using SAS Software version 9.2 (SAS Institute, 2008). Based on the squared distances (D\u003csup\u003e2\u003c/sup\u003e) values, clustering of accessions was done using Tocher\u0026rsquo;s method as described by Singh and Chaudhary (1985). Unweighted Pair Group Methods with Arithmetic-average (UPGMA) based on the generalized D\u003csup\u003e2\u003c/sup\u003e distances by average linkage method of agglomerative hierarchical clustering (successive merger) was built to group the faba bean genotypes into genetically distinct classes based on phenotypic traits. The number of clusters was determined by using points where local peaks of Pseudo F-statistics join with small values of the Pseudo t\u003csup\u003e2\u003c/sup\u003e statistics followed by a larger Pseudo t\u003csup\u003e2\u003c/sup\u003e for the next cluster fusion. The dendrogram was constructed using JMP software.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003ch3\u003ePrincipal component analysis\u003c/h3\u003e\n\u003cp\u003ePrincipal component analysis (PCA) was used to find out the traits, which accounted more to the total variation. The data were standardized to mean zero and variance of one before computing principal component analysis. Principal components were calculated based on correlation matrix using SAS computer software. As suggested by Johnson and Wichern (1988), eigenvector greater than half divided by the standard deviation (square root) of the eigen value of the respective PC was employed as general guideline for weighing the relative significance of traits constituting to the PCs.\u003c/p\u003e"},{"header":"RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of variance\u003c/h2\u003e \u003cp\u003eThe result of analysis of variance showed that the accessions vary significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) for all the traits investigated except days to maturity and number of seeds per pod (Hiywotu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClustering among accessions\u003c/h3\u003e\n\u003cp\u003eCluster analysis distinguished the 81 accessions into seven groups of different sizes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), members within a cluster being assumed to be more closely related in terms of the traits under consideration with each other than those members in different clusters. Similarly, members in clusters with non-significant distance were assumed to have more close relationships with each other than they are with those in significantly distant clusters. The number of accessions in each of the seven clusters ranged from 3 to 24 in the smallest and largest cluster, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of faba bean accessions grouped into seven clusters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eName of accessions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eaccessions\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e212565, 212568, 25018, 212567, 208085, 212575, 25010, 215748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245140, 212566, 229303, 219355, 212811, 25003, 25323, 213214, 228607\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTumsa, Dosha, Local\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27279, 25006, 27052, 203105, 25279, 208114, 25022, 25280,235956,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220079, 25277, 220076, 25264,212580, 212578, 25338, 226125,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25346, 25340, 215128, 25341, 25329, 25332, 25333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25274, 229310, 25270, 25290, 235709, 219089, 240497, 25337,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235433, 25325, 25334, 25310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC VI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e212572, 212576, 27290, 25336, 25328, 25339, 215129, 25331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC VII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25299, 25298, 235955, 25292, 25017, 213211, 25335, 25309, 25304,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25330, 25307,25306, 25311, 25302, 25327, 25303, 25301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eClusters CI and CVI had 8 accessions for each. Clusters CII and CIII had 9 and 3 accessions, respectively. Cluster CIV was the largest cluster group consisting of 24 accessions followed by CVII which had 17 accessions. CV had 12 accessions. These indicate the presence of adequate variability among the accessions. The discrimination of accessions into so many discrete clusters suggested the presence of genetic diversity in the material evaluated. The presence of substantial genetic diversity among the accessions implied that these accessions may serve as good source for selecting the diverse parents for a hybridization program aimed at isolating desirable segregants for grain yield and other important traits. Similarly, substantial genetic divergence in the faba bean germplasm was reported previously (Keneni et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Qi and Xing, 2009; Berma et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e and Chaudhary et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenetic distance among clusters\u003c/h2\u003e \u003cp\u003ePair wise generalized distances (D\u003csup\u003e2\u003c/sup\u003e) among the seven clusters were presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Out of 21 possible pairs of clusters, differences between 14 pairs were highly significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and 4 pairs were significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while those between the rest of the clusters were non-significant. The maximum inter-cluster distance (D\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;152.28**) was detected between CI and CIII. Cluster CI constituted accessions from two regions (Amhara and Oromia) except one while cluster CIII constituted standard and local checks as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e of the 81 faba bean accessions used in the study. The high values of inter-cluster distances indicated divergence among the accessions and might be used in breeding programs for better genetic recombination and selection of genetically divergent parents for exploitation in crossing programs. This finding is consistent with Fikreselassie and Seboka (2012) and Keneni et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) who used 25 and 160 faba bean genotypes respectively and found high D\u003csup\u003e2\u003c/sup\u003e value. The second most divergent clusters were cluster CIII and CIV (D\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;130.22**). Cluster CIV constituted accessions from all regions (Amhara, Oromia, Tigray and Southern nation nationalities people). This indicates accessions from different regions might have similar genetic background.\u003c/p\u003e \u003cp\u003eThe third most divergent clusters were CIII and CVI (D\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;121.99**). Cluster CVI constituted accessions from three regions (Amhara, Oromia and Southern nation nationalities people). The results implied that crossing of accessions from CIII with accessions from CI, CIV and CVI will result in useful segregants in the F\u003csub\u003e1\u003c/sub\u003e and subsequent generations. Similar findings were reported by Lal \u003cem\u003eet al.\u003c/em\u003e (2019) who clustered 83 faba bean genotypes in to ten clusters. The minimum distance (D\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;6.93) was observed between CIV and CVII suggesting that the materials may be very closely related. Cluster CVII constituted accessions from Southern nation nationalities people region except three accessions. In the present investigation, the highest intra-cluster distance was found for CI followed by CIV, CII, CV, CVII, CVI and CIII. This implies that the accessions present in a cluster have little genetic divergence from each other with respect to aggregate effect of twelve traits under study, while more genetic diversity was observed between the accessions belonging to different clusters.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimates of average intra- (diagonal) and inter-cluster (above diagonal) distances for 7 clusters of 81 faba bean accessions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCIII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCIV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCVII\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e206.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.25**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e152.28**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.57**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.81**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29.65**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e170.43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e104.81**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.94*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37.03**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.69*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCIII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e10.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e130.22**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e109.29**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e121.99**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e93.40**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCIV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e195.76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.29*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.57*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e91.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.22**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25.57**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCVI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e27.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e26.11**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCVII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e61.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e** = highly significant at probability level p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;24.72), * = Significant at probability level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;19.67)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTraits mean of clusters\u003c/h2\u003e \u003cp\u003eCluster means showed significant differences among the clusters for most of the traits (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Cluster CI constituted inferior accessions (susceptible to chocolate spot, rust, the shortest in plant height, low 100 seed weight and harvest index) for most of the traits. Similar results were obtained by Keneni et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) for chocolate spot, the shortest in plant height, and low 100 seed weight. Clusters CII, CIV and CV comprised intermediate accessions for most of the traits, but CV superior in grain yield. Cluster CVI showed the highest mean for harvest index and lowest mean of biological yield, ascochyta blight score and rust disease score. Cluster CVII was characterized by the least days to flowering, number of pods per plant and number of branches/plant but superior in ascochyta blight disease. Cluster CIII constituted superior accessions for most of the traits including chocolate spot disease, rust disease, plant height, pod length, number of pods per plant, number of branches per plant, biological yield and 100 seed weight.\u003c/p\u003e \u003cp\u003eOverall, the results showed wide variation from one cluster to another in respect of cluster means for twelve traits, which indicated that accessions having distinctly different mean performance for various traits were separated into different clusters. The crossing between the entries belonging to cluster pairs having large inter-cluster distance and possessing high cluster means for one or other traits to be improved may be recommended for isolating desirable recombinants in the segregating generations in faba bean. However, the results suggested that selection of parents should consider not only the distance between clusters but also the special merits of each cluster and each accession within a cluster depending on the specific objectives of hybridization as suggested by Singh (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCluster means for 12 traits of 81 faba bean accessions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCIII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCIV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCVII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.88\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43.56\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e46.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.04\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121.71\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e98.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e100.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.78\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.44\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.37\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.86\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.40\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.35\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7283.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9926.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13349.67\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7520.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10402.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5211.28\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8145.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8834.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1438.02\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1979.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2962.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1703.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3231.91\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1958.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1910.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2169.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.29\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.32\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.22\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.98\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e25.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.29\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.25\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.63\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.63\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ea=Highest value, b=Lowest value. DF= days to flowering, PH= plant height, PL\u0026thinsp;=\u0026thinsp;pod length, NPP= number of pods per plant, NBP= number of branches per plant, BY= biomass yield, GY= grain yield, HSW\u0026thinsp;=\u0026thinsp;100 seed weight, HI= harvest index, AB= ascochyta blight, CS= chocolate spot, RT= rust disease score.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal component analysis\u003c/h2\u003e \u003cp\u003eIn the present study, the first four principal components exhibited eigen values more than one (4.856, 2.006, 1.672 and 1.063) and explained about 73.81% of the total variations among the 81 faba bean accessions (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The first four PCs were given due importance for further explanation. Similar findings were reported by Tiwari (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) who found the first three principal components exhibiting eigen values more than one and explained about 75.53% of the total variations.\u003c/p\u003e \u003cp\u003eThe first principal component accounted for 37.35% of the total variations. Traits such as plant height, pod length, number of pods per plant, biological yield, grain yield and 100 seed weight had high contribution to the first PC. These indicated that these traits had higher relative contribution to the total diversity and they were the ones that most differentiated the accessions. Keneni et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) reported traits with relatively greater weight in PC1 like grain yield and plant height had higher relative contribution to the total diversity and they were the ones that most differentiated the populations. The second component accounted for 15.43% of the total variation and predominantly illustrates variation in days to flowering and number of branches per plant. The third principal component accounted for 12.86% of the total variation and it was chiefly accounted by variation in number of pods per plant, grain yield and harvest index. The fourth principal component accounted for 8.17% of the total variation and indicated with high variation in pod length, number of branches per plant, 100 seed weight and rust disease score.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEigenvectors, eigenvalues and variance explained by the first four principal components\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC-2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC-3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePC-4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays to flowering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePod length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of pods per plant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of branches per plant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomass yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrain yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHundred-seed weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarvest index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscochyta blight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChocolate spot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRust disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEigen value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs it is visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, grain yield was highly correlated with number of pods per plant, biomass yield and plant height, whereas weaker but positive correlated with number of branches per plant while negatively correlated with ascochyta blight, chocolate spot and rust disease score. Treatment 8 and 16 were plotted far apart from others. Treatment 8, 16, 7, 45, 15 and 29 highly contributed to PC1 in negative direction and treatment 62, 63, 78, and 81 contributed highly in positive direction for PC1. The biplot gave more opportunity to assess which accessions were good for which traits that would help as excellent baseline information for faba bean improvement. Accordingly, treatment 4, 10, 14, 21, 23, 24, 25, 36, 41, 42, 43, 52, 58, 62, 63 and 64 can be selected for grain yield improvement, whereas treatment 21 and 58 were better for biomass yield. This implies a substantial amount of diversity in the faba bean accessions and possibility of its improvement by selection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eCluster analysis grouped the accessions into seven clusters based on their similarity and dissimilarity. The maximum inter-cluster distance (D\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;152.28**) was detected between CI and CIII followed by CIII-CIV and CIII-CVI. The high values of inter-cluster distances indicate divergence among the accessions and might be used in breeding programs for better genetic recombination and selection of genetically divergent parents for exploitation in crossing programs. The principal component analysis shows that the first four principal components exhibited eigen values more than one and explained about 73.81% of the total variations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the staff at Bahir Dar University Department of Plant Sciences and Adet Agricultural Research Center for their invaluable technical support and for supplying all facilities during the experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAndualem Muche Hiywotu\u003c/strong\u003e: Conceived and designed the experiments; performed the experiments and wrote the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFisseha Worede\u003c/strong\u003e: Conceived and designed the experiments; edited the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlemu Abate\u003c/strong\u003e: Analyzed and interpreted the data; materials, analysis tools, or data. All authors have read and agreed to the definitive version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTemesgen Asmare Molla:\u0026nbsp;\u003c/strong\u003ePerformed the experiments, wrote the paper, and prepared the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they didn\u0026rsquo;t receive any funds, grants, or other forms of support while preparing this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval: This article contains no studies with human participants or animals performed by any of the authors, \u0026ldquo;not applicable\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eAndualem Muche Hiywotu, Alemu Abate, Fisseha Worede and Abunu Marefia\u003c/strong\u003e (2022). Genetic variability in Ethiopian faba bean (\u003cem\u003eVicia faba\u003c/em\u003e L.) accessions. Cogent Food \u0026amp; Agriculture, 8:1, 2132847, DOI: 10.1080/23311932.2022.2132847.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBerma, I. K., Yadav, C. B., Ram, N., \u0026amp; Gautam, S. C.\u003c/strong\u003e (2016). Genetic divergence analysis among the germplasm collections of faba bean (Vicia faba L.) in normal soil under irrigated condition. Progressive Research, 11, 4013\u0026ndash;4017.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eChatfield, C., \u0026amp; Collins, A. J.\u003c/strong\u003e (1980). Principal component analysis (pp. 57\u0026ndash;81). In \u003cem\u003eIntroduction to Multivariate Analysis\u003c/em\u003e. Springer. https://doi.org/10.1201/9780203749999-6\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eChaudhary, A. K., Yadav, C. B., Prakash, H. P., Shrivastav, S. P., \u0026amp; Hitaishi, S. K.\u003c/strong\u003e (2018). 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Kalyani Publishers.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTiwari, J. K., \u0026amp; Singh, A. K.\u003c/strong\u003e (2019). Principal component analysis for yield and yield traits in faba bean (\u003cem\u003eVicia faba\u003c/em\u003e L.). \u003cem\u003eJournal of Food Legumes, 32\u003c/em\u003e(1), 13\u0026ndash;15. https://doi.org/10.5281/zenodo.8066586\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eU.S. Department of Agriculture.\u003c/strong\u003e (2021). \u003cem\u003eFoodData Central (Nutrient Database)\u003c/em\u003e. Agricultural Research Service. https://fdc.nal.usda.gov/\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eXiao, J. X., Zhu, Y. A., Bai, W. L., Liu, Z. Y., Tang, L., \u0026amp; Zheng, Y.\u003c/strong\u003e (2021). Yield performance and optimal nitrogen and phosphorus application rates in wheat and faba bean intercropping. \u003cem\u003eJournal of Integrative Agriculture, 20\u003c/em\u003e(11), 3012\u0026ndash;3025. https://doi.org/10.1016/S2095-3119(20)63489-X\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eZhou, R., Hyldgaard, B., Yu, X., Rosenqvist, E., Ugarte, R. M., Yu, S., Wu, Z., Ottosen, C. O., \u0026amp; Zhao, T.\u003c/strong\u003e (2018). Phenotyping of faba beans (\u003cem\u003eVicia faba\u003c/em\u003e L.) under cold and heat stresses using chlorophyll fluorescence. \u003cem\u003eEuphytica, 214\u003c/em\u003e, 68. https://doi.org/10.1007/s10681-018-2154-y\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Clustering, eigenvalue, genetic distance, genetic divergence, principal component","lastPublishedDoi":"10.21203/rs.3.rs-8978024/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8978024/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eGenetic divergence is important in plant breeding to select parents for crossing. A study was initiated to assess the extent of genetic divergence and group faba bean accessions based on their similarity and dissimilarity. The experiment was conducted at Mecha district in 2019 main cropping season. A 9x9 simple lattice design was used and 14 important agronomic traits were collected. Cluster analysis distinguished the 81 accessions into seven groups. The discrimination of accessions into so many discrete clusters suggested the presence of genetic diversity in the material evaluated. The maximum inter-cluster distance was detected between cluster I and cluster III (D\u003c/em\u003e\u003csup\u003e\u003cem\u003e2 \u003c/em\u003e\u003c/sup\u003e\u003cem\u003e= 152.28**) followed by cluster III and cluster IV (D\u003c/em\u003e\u003csup\u003e\u003cem\u003e2 \u003c/em\u003e\u003c/sup\u003e\u003cem\u003e= 130.22**). The high values of inter-cluster distances indicate divergence among the accessions and may be used in selection of genetically divergent parents for exploitation in crossing programs for better genetic recombination.\u003c/em\u003e \u003cem\u003eThe results of principal component (PC) analysis demonstrated that first four PCs explained 73.81% of the total variations among the 81 faba bean accessions. Plant height, number of pods per plant, number of branches per plant, biomass yield, grain yield and disease scores (chocolate spot, ascochyta blight and rust) were important in the first and second PC axes; as a result, they are important in discriminating the faba bean landraces. The study shows the importance of multivariate methods in assessing genetic divergence so as to identify possible parents for crossing considering traits of agronomic importance.\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Genetic divergence analysis of Ethiopian faba bean (Vicia faba L.) landraces","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 10:31:02","doi":"10.21203/rs.3.rs-8978024/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-30T21:39:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T09:38:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-18T16:54:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3634945309675660626873759194261139037","date":"2026-04-10T09:14:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T12:13:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108659019555197504665746894873664718394","date":"2026-03-22T03:32:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253877833786204071698280037882900354917","date":"2026-03-17T03:06:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-16T23:16:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-05T09:24:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-03T09:58:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-03T09:54:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Applied Sciences","date":"2026-02-26T12:40:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4526ffe4-6c75-4a9e-9eab-26f98889074f","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-04-30T21:39:48+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T18:23:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 10:31:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8978024","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8978024","identity":"rs-8978024","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}