Assessment of Genetic Divergence in Naked Barley (Hordeum vulgare L.) Genotypes Using Cluster and Principal Component Analysis

Assessment of Genetic Divergence in Naked Barley (Hordeum vulgare L.) Genotypes Using Cluster and Principal Component Analysis | 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 Assessment of Genetic Divergence in Naked Barley (Hordeum vulgare L.) Genotypes Using Cluster and Principal Component Analysis Koricho Lemma, Chandra Sekhar Singh, Kumud Kant Awasthi, Garima Awasthi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8993040/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Understanding genetic diversity in crops is essential for developing effective breeding, conservation, and sustainable production strategies. This study evaluated 64 naked barley ( Hordeum vulgare L.) genotypes at Bale Robe, southeastern Ethiopia, during the main cropping season using an alpha lattice design with two replications. Fifteen agronomic traits were recorded to determine the extent of genetic variation. Analysis of variance indicated significant differences among the genotypes for all the traits studied. Grain yield ranged from 2.915 to 6.035 t ha⁻¹, with accession 4138 achieving the highest yield and landrace 206306 the lowest. Principal component analysis identified four major components PC1 (38%), PC2 (19.8%), PC3 (13%), and PC4 (7.9%) with eigenvalues above one (5.7, 2.97, 1.96, 1.19), together explaining 78.8% of the total variation. Cluster analysis grouped the genotypes into four distinct clusters, with the greatest genetic divergence observed between clusters III and IV. The findings highlight the existence of substantial genetic diversity in naked barley, providing valuable opportunities for breeding programs aimed at enhancing yield potential and ensuring sustainable barley production in Ethiopia. Accession Cluster analysis genetic diversity naked barley Principal component analysis Figures Figure 1 Figure 2 Figure 3 Introduction Barley ( Hordeum vulgare L.) is one of the world’s oldest and most important cereal crops, cultivated across diverse agroecological regions for both food and feed purposes. It ranks fourth globally after maize, wheat, and rice in terms of production and area coverage. In Ethiopia, barley holds special cultural and economic significance, serving as a staple crop particularly in highland regions where other cereals perform poorly. The country is widely recognized as a primary center of origin and diversity for barley, possessing extensive morphological and genetic variability among its landraces (Lakew & Assefa, 2011 ) The genus Hordeum consists of 45 taxa and 32 species, including diploid (2n = 2x = 14), tetraploid (2n = 4x = 28), and hexaploidy (2n = 6x = 42) cytotypes based on a basic chromosome number of seven (Bothmer & Jacobsen, 1985 ). Ethiopia, as a key diversity hotspot, harbors a wide array of barley landraces adapted to varied environments and traditional farming systems (Lakew et al. 1997 ). According to the Central Statistical Agency of Ethiopia ( 2022 ), the country is the leading barley producer in Africa, ranking fifth among cereals after maize, wheat, teff, and sorghum. Barley’s utilization in Ethiopia extends far beyond its agronomic importance. It plays a central role in traditional diets and cultural practices. The grain is processed into a range of food products such as Injera , Dabbo (bread), Kitta/Torosho , Shorba (soup), Besso , Genfo , Chiko , Kinche , and Atmit/Muk (Molla & Abebaw, 1998 ; Abebe et al, 2010 ). It is also an indispensable ingredient in local beverages such as Tella and Areki . Particularly, naked barley is preferred for the preparation of Kolo , a roasted grain snack consumed alone or mixed with roasted legumes ( Pisum sativum L., Cicer arietinum L.) and oilseeds ( Guizotia abyssinica L., Carthamus tinctorius L.) (Molla & Abebaw, 1998 ). Despite its importance, barley production in Ethiopia faces multiple constraints, including poor adaptability to changing climatic conditions, low tolerance to abiotic stresses such as drought, lodging, and frost, and susceptibility to major diseases such as scald ( Rhynchosporium secalis Oud.), net blotch ( Helminthosporium teres Sacc.), spot blotch ( Helminthosporium sativum Pum.), and leaf rust ( Puccinia hordei Otth.). Additionally, insect pests like the Russian wheat aphid ( Diuraphis noxia ) and shoot fly continue to limit productivity (Assefa, 2003 ; Tetyannikov & Bome, ( 2020 )). These biotic and abiotic stresses collectively contribute to yield instability and threaten the conservation of valuable genetic resources (Bekele et al. 2005 ). Although large numbers of barley accessions have been collected and conserved in Ethiopia, many remain inadequately characterized for key agronomic and morphological traits. Comprehensive evaluation of this genetic diversity is essential to identify promising genotypes for crop improvement and sustainable utilization (Abtew, 2020 ). Such characterization not only enhances breeding efficiency but also supports conservation strategies to safeguard native germplasm under the pressures of climate change and agricultural modernization. Therefore, the present study was conducted to assess the extent of genetic diversity among naked barley genotypes using multivariate approaches such as cluster and principal component analyses. The findings are expected to provide valuable insights for breeders and conservationists in selecting parental lines for breeding programs and in developing strategies for the sustainable utilization of Ethiopian barley genetic resources. Materials and Methods Description of the Study Area The experiment was conducted at the Madda Walabu University research station, located in the Bale Zone of the Oromia region, Ethiopia, during the main rainy season of 2023. The site lies at 7°7′N latitude and 40°0′E longitude (7.117°N, 40.000°E) at an elevation of 2,480 meters above sea level, receiving an average annual rainfall of 1,100 mm. Experimental Material and Design A total of sixty-four naked barley genotypes were evaluated, including 62 accessions obtained from the Ethiopian Biodiversity Institute (EBI) and two local genotypes sourced from farmers (Table 1 ). The experiment was arranged in a 4 × 16 alpha lattice design with two replications. Seeds were sown manually with an inter-row spacing of 20 cm on plots measuring 0.3 m² (1.5 m × 0.2 m). Plot, block, and replication separations were maintained at 0.2 m, 1 m, and 1 m, respectively. A seed rate of 120 kg ha⁻¹ was used, and recommended fertilizer rates of 150 kg ha⁻¹ urea and 100 kg ha⁻¹ NPS were applied. Source of Plant Material The study evaluated 64 naked barley ( Hordeum vulgare L.) genotypes, including 62 accessions from the Ethiopian Biodiversity Institute and two local genotypes (‘Negele’ and ‘EH-1493’) obtained from farmers in the Ambo area of Oromia, Ethiopia. The accessions originated from several Ethiopian regions including Amhara, Oromia, SNNP, Benishangul-Gumuz, and Tigray. Detailed information on accession numbers and collection locations is provided in Table 1 . Table 1 List of Barley genotypes used as planting material that was collected from different part of Ethiopia No Accession Region Zone Woreda Latitude Longitude 1 1617 Amara S/Shewa D/Berhan 10-47-00-N 37-07-00-E 2 1621 Amara S/Shewa D/Berhan 09-38-00-N 39-27-00-E 3 1696 Oromiya Arssi Tena 07-50-00-N 39-34-00-E 4 1855 Oromiya M/Shewa Waliso& Goro 08-45-00-N 37-50-00-E 5 3552 B/Gumuz Metekel Wenbera 11-15-00-N 35-55-00-E 6 3567 B/Gumuz Metekel Wenbera 10-30-00-N 35-50-00-E 7 3639 Amara S/Gondar Gondar 13-10-00-N 37-54-00-E 8 3701 Amara S/Gondar Debark 13-05-00-N 37-52-00-E 9 3724 Amara S/Gondar Debark 13-06-00-N 37-53-00-E 10 3737 Amara S/Gondar Wegera 12-50-00-N 37-43-00-E 11 3739 Amara S/Gondar Wegera 12-47-00-N 37-41-00-E 12 3746 Amara S/Gondar Gondar 12-47-00-N 37-41-00-E 13 4138 Amara D/Gondar Este 11-29-00-N 37-57-00-E 14 4164 Amara D/Gondar Este 11-37-00-N 38-04-00-E 15 4182 Amara D/Gondar Este 11-37-00-N 38-04-00-E 16 4212 Amara S/Wello Wadla 11-38-00-N 38-53-00-E 17 4395 Amara M/Gojam Sekela 10-59-00-N 37-10-00-E 18 4442 SNNP S/Omo Gofa 06-03-00-N 36-53-00-E 19 4476 Oromiya M/Shewa Cheliya 09-06-00-N 37-24-00-E 20 4490 Oromiya M/Shewa Cheliya 09-08-00-N 37-19-00-E 21 4500 Oromiya M/Wellega J/Horo 09-32-00-N 37-04-00-E 22 4533 Oromiya M/Wellega J/Horo 09-33-00-N 37-02-00-E 23 4605 Amara M/Gojam E/Enawga 10-45-00-N 38-04-00-E 24 4606 Amara M/Gojam E/Enawga 10-46-00-N 38-03-00-E 25 4658 Amara M/Gojam Guzamn 10-32-00-N 37-47-00-E 26 4752 Amara S/Shewa Mojana 09-57-00-N 39-51-00-E 27 4762 Amara S/Shewa D/Berhan 09-48-00-N 39-43-00-E 28 16922 Oromiya Arssi Limu&Bilbilo 07-25-07-N 39-13-41-E 29 16931 Oromiya Arssi Limu&Bilbilo 07-27-02-N 39-12-46-E 30 16946 Oromiya Arssi Limu&Bilbilo 06-34-06-N 39-16-54-E 31 17020 Oromiya Arssi Digeluna Tijo 07-41-38-N 39-15-59-E 32 17240 Amara Agew Awi Kosober 10-56-13-N 36-53-51-E 33 64080 Amara S/Gondar Debark 13-10-00-N 37-55-00-E 34 64164 SNNP S/Omo Chencha 06-19-00-N 37-28-00-E 35 64290 Oromiya M/Wellega Haru 09-36-00-N 37-50-00-E 36 64329 Oromiya M/Shewa Bako Tibe 09-04-00-N 36-36-00-E 37 64332 Oromiya M/Shewa Cheliya 38 206306 Amara D/Gondar Kemekem 39 206359 Amara D/Gondar Lay Gayint 40 206366 Amara D/Gondar Lay Gayint 41 206420 Amara S/Gondar Debark 42 206423 Amara S/Gondar Debark 43 206493 Amara D/Gondar Tach Gayint 44 214219 Amara S/Gondar Debark 45 214228 Amara S/Gondar Debark 46 214229 Amara S/Gondar Debark 47 214235 Amara S/Gondar Debark 48 214797 Amara D/Wello Kalu 49 215204 Oromiya M/Shewa Meta Robi 09-23-00-N 39-24-00-E 50 215223 Amara S/Wello Guba Lafto 11-48-00-N 39-26-00-E 51 215224 Amara S/Wello Guba Lafto 11-48-00-N 39-26-00-E 52 215580 Tigray Debubawi Ofla 12-13-00-N 38-43-00-E 53 219177 Oromiya M/Harerge Deder 09-15-00-N 45-20-00-E 54 219300 SNNP Gedeo Wenago 55 219763 Amara D/Gondar Lay Gayint 11-44-00-N 38-20-00-E 56 228224 Amara S/Wello D/ Delant 57 228228 Oromiya M/Shewa Wonchi 58 243235 Oromiya S/Shewa Kembibit 09-36-19-N 39-29-50-E 59 244772 SNNP Kembata Angacha 60 244904 Oromiya M/Wellega G/Horo 61 244912 Oromiya M/Wellega G/Horo 62 206417 Uknown Uknown Uknown 63 Negele Oromia M/Shewa Ambo 64 EH-1493 Oromia M/hewa Ambo Data Collection Field and laboratory data were collected in accordance with the International Barley Descriptor List (IPGRI, 1994 ), encompassing both plant-based and plot-based traits. Data were recorded from representative samples within each experimental plot to ensure reliability and accuracy. For quantitative traits such as grains per spike, grain weight per spike, spikelets per spike, peduncle length, plant height, productive tillers per plant, spike length, and total tillers per plant, observations were taken from ten randomly selected and individually tagged plants per plot. Each measurement was averaged to obtain a representative value for that plot. Grain yield per plot was determined by harvesting and threshing all plants within the net plot area. The total grain weight was recorded after proper cleaning and drying to approximately 12% moisture content. The plot yield was then converted to grain yield per hectare (kg ha⁻¹) using the formula: $$GrainYield(kg/ha)=\frac{TotalGrainWeight\left(kg\right)}{PlotArea\left(m2\right)}x\text{10,000}$$ Other traits, including biomass yield, days to heading, days to maturity, grain filling period, grain yield, harvest index, and thousand-grain weight, were recorded from the entire plot for each genotype. The harvest index (HI) was calculated as: $$HarvestIndex=\frac{GrainYield}{TotalBiomassYield}$$ , Thousand-grain weight (TGW) was measured as the weight of 1,000 grains in grams. In addition to the field data, laboratory analyses were performed for selected quality parameters, following standard procedures outlined in the barley descriptor manual. Environmental conditions such as rainfall, temperature, and soil characteristics of the experimental site were also recorded to support interpretation of genotype performance. All data were collected from three replications to minimize experimental error and to allow for robust statistical analysis in subsequent multivariate evaluations. Data Analysis Data analysis was carried out to evaluate the extent and pattern of genetic diversity among the naked barley genotypes based on the measured quantitative traits. Prior to multivariate analyses, all trait data were standardized to a mean of zero and variance of one to eliminate the effects of scale differences among variables. Results and Discussion Analysis of Variance Analysis of variance revealed highly significant differences (p < 0.05) among the 64 naked barley genotypes for all 15 agronomic traits, indicating the presence of substantial genetic variability within the evaluated population. Grain yield ranged from 2.915 to 6.035 t ha⁻¹, with accession 4138 recording the highest yield and landrace 206306 the lowest. Similarly, wide variation was observed for other quantitative traits such as plant height, spike length, productive tillers per plant, and grain weight per spike, reflecting the diverse genetic backgrounds of the genotypes. This phenotypic variability suggests a broad genetic base, offering valuable opportunities for selection and improvement. Principal Component Analysis (PCA) Principal Component Analysis (PCA) was conducted to identify the major components explaining the overall phenotypic variation and to determine the relative contribution of each trait to total diversity among the naked barley genotypes. Components with eigenvalues greater than one (λ > 1) were considered significant according to the Kaiser criterion, and their corresponding loadings were used to determine the traits contributing most to genotype differentiation. PCA grouped the evaluated traits into four principal components with eigenvalues greater than one (λ > 1). The first four componentsPC1 (38.0%), PC2 (19.8%), PC3 (13.0%), and PC4 (7.9%) together accounted for 78.8% of the total phenotypic variation among the genotypes (Table 2 ). The corresponding eigenvalues were 5.70, 2.97, 1.96, and 1.19, respectively. The first two principal components contributed most to the overall diversity, highlighting the key traits responsible for genotype differentiation. PC1 (38.0%) exhibited strong positive loadings for spike length, spikelets per spike, grain weight per spike, thousand-grain weight, biomass yield, and grain yield, indicating that these traits were the major contributors to total variation and are essential determinants of yield potential. PC2 (19.8%) captured variation in phenological traits, particularly days to heading and days to maturity, while PC3 (13.0%) was influenced mainly by tiller number. PC4 (7.9%) reflected variation in grains per spike, plant height, harvest index, and peduncle length. Traits with higher absolute loadings on PC1 exerted a stronger influence on clustering, suggesting that genotype differentiation was driven by multiple traits rather than isolated characteristics (Chahal and Gosal, 2002 ). These findings are consistent with previous reports (Enyew et al. 2020; Shtaya and Abdallah, 2019; Angassa and Mohammed, 2021), which indicated that the first four principal components explained 75–81% of the total variation in barley genotypes and were largely influenced by similar yield-related and morphological traits. Table 2 Vector loadings and percentage of explained variation by the first four Principal Components Variables PC1 PC2 PC3 PC4 DH 0.07 0.49 0.18 0.18 GFP -0.09 0.47 -0.04 -0.06 SL 0.30 0.11 0.10 0.00 NSPS 0.35 0.00 0.11 0.14 ProTil 0.09 0.17 -0.64 -0.08 ToTil 0.09 0.18 -0.64 -0.09 PH 0.24 0.22 0.18 -0.34 DM 0.00 0.53 0.13 0.14 PCL 0.13 -0.16 -0.03 -0.67 NGPS -0.25 0.29 0.04 -0.30 GWPS 0.37 -0.03 0.02 0.16 TGW 0.39 -0.09 -0.07 0.14 BY 0.39 0.02 0.03 -0.06 GY 0.35 0.01 -0.11 0.14 HI -0.23 -0.08 -0.23 0.44 Eigenvalue 5.70 2.97 1.96 1.19 Variability (%) 37.97 19.81 13.05 7.94 Cumulative (%) 37.97 57.78 70.83 78.77 Foot Note BY= Biomass yield, DHA=Days to heading, DM=Days to maturity, GFP= Grain filling period, GWPS= Grain weight spike − 1 , HI= Harvest index, NGPS=Number of grain spike − 1 , NSPS=Number of spikelet spike − 1 , PCL= Peduncle length, PH=Plant height, ProTil= Productive tiller plant − 1 , SL =Spike length, TGW = Thousand grain weight, ToTil= Total tiller plant. A PCA biplot (Fig. 1 ) was generated to visualize correlations between genotypes and quantitative traits. Genotypes are represented as blue points, while red arrows denote traits. Longer arrows indicate stronger contributions to the principal components. Traits such as peduncle length, biomass yield, spikelets per spike, and grain weight per spike aligned closely with Dim1, indicating their primary influence on horizontal variability. Conversely, days to maturity, grain-filling period, and grains per spike were more associated with Dim2, affecting vertical variability. Trait correlations are further reflected by the angles between arrows: acute angles (e.g., biomass yield vs. grain weight per spike) indicate strong positive correlations, whereas obtuse angles (e.g., grains per spike vs. biomass yield) suggest negative correlations. Genotypes located near the origin represent average trait performance, whereas those at extremes are outliers with distinct profiles. These findings confirm the relevance of key agronomic and phenological traits in distinguishing naked barley genotypes, providing critical insights for parental selection and breeding programs, including under stress conditions (Yadav et al.; Pour-Aboughadareh). Cluster Analysis Cluster Analysis (CA) was performed using the agglomerative hierarchical method based on Euclidean distance as a measure of dissimilarity among genotypes. The average linkage method (UPGMA) was employed to construct the dendrogram, grouping genotypes with similar trait performance. The number of clusters was determined by examining the fusion levels and corroborating the grouping pattern with PCA-based scatter plots to ensure consistency. All statistical and graphical analyses were conducted in R statistical software using the packages cluster , factoextra , prcomp , and ggplot2 (R Core Team, 2023 ). Cluster analysis grouped the 64 naked barley genotypes (62 accessions and 2 local checks) into four distinct clusters. Cluster I comprised the largest group, containing 50 genotypes (78.13%), followed by Cluster II with 9 genotypes (14.06%), Cluster III with 4 genotypes (6.25%), and Cluster IV represented solely by the local check genotype ‘Negele’ (1.56%). The highest inter-cluster distance was observed between Clusters III and IV, followed by I and IV, and II and IV, indicating considerable genetic divergence among these groups (Table 3 ). The clustering pattern highlights substantial genetic variability among the evaluated genotypes, providing a valuable basis for selecting genetically diverse parents for future hybridization and breeding programs. Table 3 Distribution of 64 naked barley genotypes into their respective cluster Cluster No. of genotypes Proportion % Genotypes 4752 3639 64290 4500 64080 1617 214229 206306 3552 1621 206359 206420 219177 215224 215580 I 50 78.13% 64164 64329 4605 215223 244904 64332 16946 214228 206366 4490 206417 4762 4533 219763 4164 244772 206493 215204 4476 214797 17240 3746 3701 3724 214219 4658 214235 16922 244912 4395 243235 228228 244772 16931 1855 II 9 14.06% 219300 4138 17020 4212 4182 4442 1696 206423 4606 III 4 6.25% 3737 3739 3567 EH-1493 IV 1 1.56% Negele Cluster Visualization Based on Principal Components The cluster visualization derived from principal component analysis (PCA) revealed four distinct groups of naked barley genotypes, corresponding well with the hierarchical clustering results (Fig. 2 ). The first two principal components-Dim1 and Dim2-explained 38.0% and 19.8% of the total variation, respectively, together accounting for 57.8% of the overall genetic variability among the genotypes. Cluster 1 (red) exhibited the widest genetic dispersion, indicating high intra-cluster diversity and representing genotypes with broad genetic backgrounds and multiple favorable traits. In contrast, Clusters 2 (green) and 3 (blue) were relatively compact and well-separated, reflecting distinct and stable trait combinations-potentially corresponding to specific adaptive or morphological characteristics. Cluster 4 (purple) was represented by a small group of rare or unique genotypes, suggesting the presence of specialized or regionally adapted accessions that may harbor valuable alleles for stress tolerance or specific yield attributes. A moderate overlap between Clusters 1 and 2 suggests partial genetic similarity or shared phenotypic traits among genotypes in these groups, which may be due to common ancestry or adaptive convergence. These clustering trends are consistent with the findings of Abebe et al. (2024), who reported similar groupings among Ethiopian barley genotypes associated with yield and drought-resistance traits. Likewise, Zhang et al. (2024). observed comparable clustering patterns in global barley germplasm, linking genetic diversity to environmental adaptation and stress tolerance. Collectively, these results reaffirm the utility of PCA-based clustering for visualizing genetic relationships and for identifying diverse parental lines for targeted breeding programs. 3.5. Genetic Divergence Analysis Genetic divergence among the 64 naked barley genotypes was assessed based on Euclidean distance matrices computed in R using the cluster package (Table 2 ). The analysis revealed varying degrees of genetic dissimilarity among the four clusters. The smallest genetic distance was recorded between Clusters I and III (11.42), followed by Clusters I and II (18.19), indicating relatively close genetic relationships among these groups. Moderate divergence was observed between Cluster I and Cluster IV (33.07). The highest inter-cluster distance was observed between Clusters III and IV (38.54), suggesting that Cluster IV is genetically distinct and contains genotypes with considerable variability relative to the others. It is well established that greater inter-cluster distances correspond to higher genetic divergence, which can be exploited in breeding to produce transgressive segregants and enhance heterosis (Kumar et al. 2020 ) In this study, the pronounced divergence of Cluster IV from Clusters I, II, and III suggests that crosses involving genotypes from Cluster IV are likely to generate substantial genetic recombination, resulting in enhanced heterotic expression in F₁ hybrids and broader variability in segregating F₂ populations. Conversely, crosses between genetically closer clusters, such as I and III or I and II, may yield more uniform progeny with narrower genetic variability (Fantahun et al. 2023 ) Overall, the genetic distance analysis highlights substantial genetic variability among the naked barley genotypes studied, providing a valuable foundation for parental selection, hybridization strategies, and genetic improvement. These findings emphasize the importance of genetically distant parents particularly those from Cluster IV for developing superior and diverse barley cultivars suited to different agroecological conditions (Table 4 ). Table 4 Inter-cluster distances between centroids for 64 naked barley genotypes Cluster I II III IV I 0 II 18.19 III 11.42 20.13 IV 36.58 33.07 38.54 0 χ 2 = 23.68 and 29.14 at 5% and 1% probability level respectively 3.6. Cluster Mean Analysis Analysis of mean trait values across the four clusters revealed significant genetic variation among the evaluated naked barley genotypes, consistent with prior studies. Cluster III exhibited the highest performance for grain yield (5.38 t ha⁻¹), biomass yield (17.48 t ha⁻¹), and number of grains per spike (54.45), indicating its superior productivity and potential for direct use in high-yielding cultivar development. Similar patterns were reported by Belay et al. ( 2024 ) who identified clusters with enhanced yield and biomass traits in Ethiopian barley genotypes. Cluster II showed the best performance for spike length (8.93 cm), thousand-grain weight (46.97 g), and productive tillers per plant (7.06), reflecting favorable grain development and vegetative growth. These findings are in agreement with Güngör and Türkoğlu ( 2024 ), who observed similar trait patterns among barley clusters. Cluster IV, while exhibiting the highest harvest index (36.66%), had lower overall grain yield, highlighting a trade-off between resource allocation and productivity, also noted by Güngör and Türkoğlu ( 2024 ). Cluster I showed relatively lower grain yield (4.23 t ha⁻¹) but a longer grain-filling period (38.05 days), suggesting potential for breeding programs targeting extended grain-filling duration or stress resilience. These results underscore the utility of cluster mean analysis for identifying barley genotypes with desirable combinations of traits, including yield, biomass, and physiological efficiency. Such insights provide valuable guidance for parental selection in hybridization and for developing cultivars with improved productivity and adaptability (Table 5 ). Table 5 Cluster mean of 15 quantitative traits for 64 Naked Barley genotypes Variable Cluster I Cluster II Cluster III Cluster IV Grand Mean DH 69.12 66.83 77.75 59.00 68.18 GFP 38.05 32.67 38.13 34.00 35.71 SL 7.35 8.93 8.50 5.72 7.63 NSPS 19.30 25.89 22.80 14.70 20.67 ProTil 6.88 7.06 6.55 6.90 6.85 ToTil 7.40 7.57 7.08 7.50 7.38 PH 94.17 97.62 100.55 83.02 93.84 DM 123.59 117.17 134.50 104.50 119.94 PCL 6.58 8.16 6.03 9.52 7.57 NGPS 57.37 26.53 54.45 44.00 45.59 GWPS 1.19 1.83 1.90 1.43 1.59 TGW 34.97 46.97 45.78 44.00 42.93 BY 12.15 16.85 17.48 13.93 15.10 GY 4.23 5.36 5.38 4.88 4.96 HI 35.11 32.11 30.83 36.66 33.68 Foot Note BY= Biomass yield, DHA=Days to heading, DM=Days to maturity, GFP= Grain filling period, GWPS= Grain weight spike − 1 , HI= Harvest index, NGPS=Number of grain spike − 1 , NSPS=Number of spikelet spike − 1 , PCL= Peduncle length, PH=Plant height, ProTil= Productive tiller plant − 1 , SL =Spike length, TGW = Thousand grain weight, ToTil= Total tiller plant − 1 . The dendrogram depicting the four clusters based on 15 agronomic variables using Euclidean distance is presented in Fig. 3 . In the figure, genotypes within the same cluster are marked with similar colors, while different clusters are distinguished by distinct colors. Closely related genotypes are connected within the same cluster, and genotype codes corresponding to accessions are indicated along the bottom axis. Discussion The considerable genotypic variation observed across all measured traits highlights the broad genetic base of Ethiopian naked barley accessions. This diversity represents an invaluable resource for global barley improvement efforts, particularly in breeding for resilience to biotic and abiotic stresses. The magnitude of variation recorded in grain yield and yield-related traits indicates that Ethiopian naked barley constitutes an untapped reservoir of adaptive alleles, consistent with its status as one of the world’s oldest centers of barley domestication. Comparable trends in high intra-population variability have also been reported in Asian and European landrace collections (Horsley et al., 2022 ; Wang et al., 2023 ), emphasizing that landrace populations continue to play a crucial role in broadening the genetic base of cultivated barley. Principal Component Analysis (PCA) revealed that a limited number of principal components explained most of the total phenotypic variability, with yield- and tillering-related traits exerting the strongest influence on genetic divergence. This pattern agrees with findings from large-scale germplasm evaluations in China and the Himalayas (Zhang et al., 2023 ; Chodron et al., 2024 ), where similar traits were identified as key discriminators among genotypes. Importantly, our analysis provides new evidence that Ethiopian naked barley exhibits trait interrelationships like those observed in global barley panels, thereby supporting its potential inclusion in international pre-breeding programs. The strong positive association between grain yield, productive tillers, and biomass yield suggests that these parameters can serve as efficient selection indices for yield improvement under marginal environments. Cluster analysis further confirmed the existence of pronounced genetic divergence, partitioning genotypes into four distinct groups with substantial inter-cluster distances. The wide separation between clusters III and IV suggests the potential for exploiting heterosis through strategic hybridization between genetically distant genotypes. Similar diversity patterns have been documented among Tibetan and Central Asian naked barley accessions (Zeng et al., 2023; Chodron et al., 2024 ), underscoring the parallel adaptive diversification of barley under contrasting highland environments. The present study therefore contributes novel comparative insight by demonstrating that Ethiopian naked barley, though geographically isolated, exhibits genetic structures and trait associations comparable to other ancient barley centers of diversity. Overall, these findings highlight the international relevance of Ethiopian naked barley as a unique genetic resource for global barley improvement. Despite being based on a single growing season, the study provides a valuable baseline for future multi-environment and genomic validation studies. The identification of distinct genetic clusters and trait-based principal components offers a clear framework for selecting parental lines that can contribute to the development of high-yielding, stress resilient barley cultivars suited to diverse agroecological conditions worldwide. Conclusion The present study revealed considerable genetic variability among 64 naked barley ( Hordeum vulgare L.) genotypes for all evaluated agronomic traits. The significant differences observed across traits such as grain yield, productive tillers, and spike morphology confirm the existence of a broad genetic base within the Ethiopian naked barley germplasm. Principal component analysis identified a small number of key components explaining the majority of total variation, with grain yield, biomass yield, and productive tillers emerging as major contributors. Cluster analysis further demonstrated clear genetic differentiation, grouping the genotypes into four distinct clusters with varying inter-cluster distances. These results collectively underscore the potential of Ethiopian naked barley accessions as valuable genetic resources for breeding and genetic improvement. Genotypes from highly divergent clusters particularly those showing superior performance for yield and yield-related traits should be prioritized as parents in hybridization programs to maximize heterosis and broaden the genetic base of future breeding populations. Moreover, the identification of diverse and high-performing genotypes supports targeted selection for specific environments, aiding the development of cultivars better adapted to the country’s varied agroecological zones. Continued characterization of these landraces using molecular markers and multi-environment field trials will further enhance their utilization in sustainable barley improvement and conservation strategies. Declarations Author Contributions Koricho Lemma designed and conducted the study, collected and analyzed the data, and drafted the manuscript. Chandra Sekhar Singh, Kumud Kant Awasthi, & Garima Awasthi critically reviewed the manuscript, provided suggestions, and assisted with data analysis. All authors read and approved the final version of the manuscript. Funding This research received no external funding . Data availability All data generated or analysed during this study are included in this published article [and its supplementary information files]. Permissions to Collect Plant Material The barley germplasm used in this study included 62 accessions obtained from the Ethiopian Biodiversity Institute (EBI), Ethiopia, and two local genotypes collected from farmers in the Ambo area of the Oromia region. The plant materials were provided for research purposes and their use complied with relevant national regulations and institutional guidelines of Ethiopia. No endangered or protected species were involved. Plant Ethics / Collection Guidelines (Ethics and Consent to Participate) All plant materials used in this study were cultivated barley genotypes obtained from recognized germplasm collections and local farmers. Their use complied with national and institutional guidelines for plant genetic resource research in Ethiopia. No protected or endangered species were used. Competing Interests The authors declare no competing interests. References Abebe K. Cluster and principal component analysis for yield and yield-related traits of food barley ( Hordeum vulgare L.) genotypes at Woreilu district, South Wollo, Ethiopia. Int J Crop Sci. 2024;59(4):202–15. Abebe TD, Bauer AM, Léon J. Morphological diversity of Ethiopian barleys ( Hordeum vulgare L.) in relation to geographic regions and altitudes. Hereditas. 2010;147(4):154–64. ttps://doi.org/10.1111/j.1601-5223.2010.02173.x. Abtew WG. 2020. Characterization of genetic variation among Ethiopian barley (Hordeum vulgare L.) genotypes [Master’s thesis, Jimma University]. Angassa D, Mohammed J. Agro-morphological variability study of Ethiopian barley ( Hordeum vulgare L.) accessions for their important agronomical traits at Hadiya Zone, Southern Ethiopia. J Plant Sci. 2022;10(1):19–26. ttps://doi.org/10.11648/j.jps.20221001.13. Assefa A. 2003. Genetic variability and breeding potential of barley (Hordeum vulgare L.) landraces from North Shewa in Ethiopia [Unpublished master’s thesis]. Addis Ababa University. Bekele B, Alemayehu F, Lakew B. 2005. Food barley in Ethiopia. In S. Grando & H. Gomez Macpherson, editors, Food barley: Importance, uses and local knowledge,53–82. CABI Publishing. https://cabidigitallibrary.org. Belay F, Alemu A, Abate Z. Cluster and principal component analysis for yield and yield-related traits of barley genotypes. J Agricultural Sci Technol. 2024;26(1):45–59. Bothmer VR, Jacobsen N. 1985. Origin, taxonomy, and related species. In D. C. Rasmusson, editor, Barley (pp. 19–56). American Society of Agronomy. ttps://doi.org/10.2134/agronmonogr26.c2 Central Statistical Agency of Ethiopia. 2022. Production, area and yield by crops in Ethiopia. Public Knoema Data Hub. https://public.knoema.com/xnghehb/production-area-and-yield-by-crops-in-ethiopia Chahal GS, Gosal SS. Principles and procedures of plant breeding: Biotechnology and conventional approaches. Alpha Science International; 2002. Chodron N, Tashi T, Dorji T, Yangzom P, Wangmo K, Dorji S. 2024. Abundant genetic diversity harbored by traditional naked barley varieties on Tibetan Plateau: Implications in their effective conservation and utilization. Biology , 13 (12), 1018. https://www.mdpi.com/2079-7737/13/12/1018 Enyew M, Dejene T, Lakew B, Worede F. Clustering and principal component analysis of barley ( Hordeum vulgare L.) landraces for major morphological traits from northwestern Ethiopia. Int J Agricultural Sci Food Technol. 2019;5(1):58–63. ttps://doi.org/10.17352/2455-815X.000043. Fantahun B, Woldesemayate T, Fadda C, Gebrehawaryat Y, Pe E, Dell’Acqua M. Multivariate analysis in the dissection of phenotypic variation of Ethiopian cultivated barley ( Hordeum vulgare ssp. vulgare L.) genotypes. Cogent Food Agric. 2023;9(1):2157104. ttps://doi.org/10.1080/23311932.2022.2157104. Güngör NB, Türkoğlu N. GT biplot and cluster analysis of barley ( Hordeum vulgare L.) genotypes based on agro-morphological traits. Agronomy. 2024;14(10):2188. ttps://doi.org/10.3390/agronomy14102188. Horsley RD, Li C, Tinker NA. Genetic diversity and association mapping in global barley germplasm collections. Theor Appl Genet. 2022;135(2):455–69. IPGRI. Descriptors for barley (Hordeum vulgare L) . International Plant Genetic Resources Institute; 1994. Kumar Y, Niwas R, Nimbal S, Dalal MS. Hierarchical cluster analysis in barley genotypes to delineate genetic diversity. Electron J Plant Breed. 2020;11(3):742–8. ttps://doi.org/10.37992/2020.1103.122. Lakew B, Assefa A. 2011. Advances and experiences in barley landrace improvement in Ethiopia. In Proceedings of the 2nd National Barley Research and Development Review Workshop (pp. 31–46). Holetta Agricultural Research Center. Lakew B, Semeane Y, Alemayehu F, Gebre H, Grando S, van Leur JA, Ceccarelli S. Exploiting the diversity of barley landraces in Ethiopia. Genet Resour Crop Evol. 1997;44(2):109–16. ttps://doi.org/10.1023/A:1008609201169. Melaku W, Tesfaye T, Regassa F. In: Brush SB, editor. Keeping diversity alive: An Ethiopian perspective. Lewis; 2000. Molla A, Abebaw M. Barley utilization in Ankober Wereda, North Shewa. In: Yirga C, Alemayehu F, Sinebo W, editors. Barley-based farming systems in the highlands of Ethiopia. Ethiopian Agricultural Research Organization; 1998. pp. 85–94. Tetyannikov NV, Bome NA. 2020. Sources of characters useful for breeding in hulless barley. Proceedings on Applied Botany, Genetics and Breeding, 3 (2), 49–55. ttps://doi.org/10.30901/2227-8834-2020-2-49-55 Pour-Aboughadareh A, Mohammadi R, Ahmadi J, Mehrabi AA, Etminan A, Sididi M. Biplot analysis of agro-morphological traits in barley ( Hordeum vulgare L.) genotypes under drought stress conditions. Agron J. 2023;115(2):524–38. ttps://doi.org/10.1002/agj2.21594. R Core Team. 2023. R: A language and environment for statistical computing (Version 4.4.2) [Computer software]. R Foundation for Statistical Computing. https://www.r-project.org Shtaya MJY, Abdallah JM. Assessment of phenotypic diversity of barley genotypes through cluster and principal component analyses. J Anim Plant Sci. 2021;31(5):1345–51. ttps://doi.org/10.36899/JAPS.2021.5.0336. Wang X, Liu H, Zhang J. Genome-wide association analysis of yield-related traits in a diverse barley panel. Plant Breeding. 2023;142(3):345–58. Yadav R, Kumari P, Sharma S. Multivariate biplot analysis of exotic barley genotypes based on agro-morphological variables. Plant Archives. 2022;22(1):302–9. Zewodu A, Mohammed W, Shiferaw E. Analysis of genetic diversity and population structure of some Ethiopian barley ( Hordeum vulgare L.) accessions using SSR markers. PLoS ONE. 2024;19(6):e0305945. ttps://doi.org/10.1371/journal.pone.0305945. Zhang L, He Y, Xu R. Multivariate analysis of agronomic and morphological traits in Chinese barley germplasm. Crop Sci. 2023;63(1):75–86. Zhang Y. Genetic diversity and environmental adaptation in barley: A clustering approach for stress resistance and yield traits. Crop Sci Technol. 2024;62(1):98–112. Additional Declarations No competing interests reported. Supplementary Files image1.png Graphical Abstract Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 11 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviews received at journal 04 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviews received at journal 28 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers invited by journal 17 Apr, 2026 Editor invited by journal 26 Mar, 2026 Editor assigned by journal 17 Mar, 2026 Submission checks completed at journal 17 Mar, 2026 First submitted to journal 13 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-8993040","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627548445,"identity":"820c9c3c-1084-4f23-8ce5-18311bffa61b","order_by":0,"name":"Koricho Lemma","email":"","orcid":"","institution":"Madda Walabu University, Bale Robe, Ethiopia","correspondingAuthor":false,"prefix":"","firstName":"Koricho","middleName":"","lastName":"Lemma","suffix":""},{"id":627548447,"identity":"c91bb9d9-8314-45e8-95da-c6e532480f6c","order_by":1,"name":"Chandra Sekhar Singh","email":"","orcid":"","institution":"Mangalayatan University, Jabalpur, Madhya Pradesh, India","correspondingAuthor":false,"prefix":"","firstName":"Chandra","middleName":"Sekhar","lastName":"Singh","suffix":""},{"id":627548448,"identity":"72bbd930-3a69-4294-9262-39f9d838239d","order_by":2,"name":"Kumud Kant Awasthi","email":"","orcid":"","institution":"K R Mangalam University, Gurugram, Haryana, India","correspondingAuthor":false,"prefix":"","firstName":"Kumud","middleName":"Kant","lastName":"Awasthi","suffix":""},{"id":627548449,"identity":"5e2574e4-303e-490e-ac35-bd0cff9736ca","order_by":3,"name":"Garima Awasthi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYJCCAwlAgp+Z+QCQkpAhXotkOxuIkuAh3iqD8zwGIJqwFnn3sw8PPMyxk2c4zPP51Y0aCx4G9sNHN+DTYngm3eBA4rZkw8Zm3m3WOceADuNJS7uBV0tDGgNQCzNjMzPvNuMcNqAWCR4z/Fr6n4G01Nu3MfM8M875R4QWeQmwLYcTe5h5mB/nthGhxUACbMvx5BnMbGbMuX0SPGyE/CLfn8b88ee2atv95w8//pzzrU6On/3wMfy2HECw2STAJD7lYFsaEGzmD4RUj4JRMApGwcgEADSlR5NJsRDbAAAAAElFTkSuQmCC","orcid":"","institution":"K R Mangalam University, Gurugram, Haryana, India","correspondingAuthor":true,"prefix":"","firstName":"Garima","middleName":"","lastName":"Awasthi","suffix":""}],"badges":[],"createdAt":"2026-02-28 08:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8993040/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8993040/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107717905,"identity":"e5ef216c-57c8-4782-a76b-e44c41445f03","added_by":"auto","created_at":"2026-04-24 10:20:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":200266,"visible":true,"origin":"","legend":"\u003cp\u003eBi plot of PC1 and PC2 showing the relationships of genotypes by traits. The blue color represents genotypes (n=64) and the red color represents the traits under study.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8993040/v1/c436993f162ee220e25b9471.png"},{"id":107868814,"identity":"4b8fb5d9-4fa9-41d7-ad98-0dfa8b5a52a5","added_by":"auto","created_at":"2026-04-27 07:34:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17656,"visible":true,"origin":"","legend":"\u003cp\u003eCluster Visualization of 64 naked barley genotypes for 15 studied variables using Euclidean distance (average linkage method)\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8993040/v1/4603ac0affcf98fcc8fa33d8.png"},{"id":107717908,"identity":"1568dd16-923c-40d8-9b55-c51bac74b9f8","added_by":"auto","created_at":"2026-04-24 10:20:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":67507,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram of 64 naked barley genotypes for 15 studied variables using Euclidean distance cluster analysis (average linkage method)\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8993040/v1/3fa92cd31bb3a7bbb7b1d2e9.png"},{"id":107871771,"identity":"08fb1082-8b79-413f-add9-8277727206fe","added_by":"auto","created_at":"2026-04-27 07:54:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":939980,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8993040/v1/bd887ad1-4f4a-4176-96d0-a2a1498adf93.pdf"},{"id":107717906,"identity":"78663f8a-9c2f-4d37-a26b-e48e0a5b990b","added_by":"auto","created_at":"2026-04-24 10:20:42","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2655242,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8993040/v1/97b0c90f3fe772b97a25e098.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of Genetic Divergence in Naked Barley (Hordeum vulgare L.) Genotypes Using Cluster and Principal Component Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBarley (\u003cem\u003eHordeum vulgare\u003c/em\u003e L.) is one of the world\u0026rsquo;s oldest and most important cereal crops, cultivated across diverse agroecological regions for both food and feed purposes. It ranks fourth globally after maize, wheat, and rice in terms of production and area coverage. In Ethiopia, barley holds special cultural and economic significance, serving as a staple crop particularly in highland regions where other cereals perform poorly. The country is widely recognized as a primary center of origin and diversity for barley, possessing extensive morphological and genetic variability among its landraces (Lakew \u0026amp; Assefa, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe genus \u003cem\u003eHordeum\u003c/em\u003e consists of 45 taxa and 32 species, including diploid (2n\u0026thinsp;=\u0026thinsp;2x\u0026thinsp;=\u0026thinsp;14), tetraploid (2n\u0026thinsp;=\u0026thinsp;4x\u0026thinsp;=\u0026thinsp;28), and hexaploidy (2n\u0026thinsp;=\u0026thinsp;6x\u0026thinsp;=\u0026thinsp;42) cytotypes based on a basic chromosome number of seven (Bothmer \u0026amp; Jacobsen, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Ethiopia, as a key diversity hotspot, harbors a wide array of barley landraces adapted to varied environments and traditional farming systems (Lakew et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). According to the Central Statistical Agency of Ethiopia (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the country is the leading barley producer in Africa, ranking fifth among cereals after maize, wheat, teff, and sorghum.\u003c/p\u003e \u003cp\u003eBarley\u0026rsquo;s utilization in Ethiopia extends far beyond its agronomic importance. It plays a central role in traditional diets and cultural practices. The grain is processed into a range of food products such as \u003cem\u003eInjera\u003c/em\u003e, \u003cem\u003eDabbo\u003c/em\u003e (bread), \u003cem\u003eKitta/Torosho\u003c/em\u003e, \u003cem\u003eShorba\u003c/em\u003e (soup), \u003cem\u003eBesso\u003c/em\u003e, \u003cem\u003eGenfo\u003c/em\u003e, \u003cem\u003eChiko\u003c/em\u003e, \u003cem\u003eKinche\u003c/em\u003e, and \u003cem\u003eAtmit/Muk\u003c/em\u003e (Molla \u0026amp; Abebaw, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Abebe et al, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). It is also an indispensable ingredient in local beverages such as \u003cem\u003eTella\u003c/em\u003e and \u003cem\u003eAreki\u003c/em\u003e. Particularly, naked barley is preferred for the preparation of \u003cem\u003eKolo\u003c/em\u003e, a roasted grain snack consumed alone or mixed with roasted legumes (\u003cem\u003ePisum sativum\u003c/em\u003e L., \u003cem\u003eCicer arietinum\u003c/em\u003e L.) and oilseeds (\u003cem\u003eGuizotia abyssinica\u003c/em\u003e L., \u003cem\u003eCarthamus tinctorius\u003c/em\u003e L.) (Molla \u0026amp; Abebaw, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite its importance, barley production in Ethiopia faces multiple constraints, including poor adaptability to changing climatic conditions, low tolerance to abiotic stresses such as drought, lodging, and frost, and susceptibility to major diseases such as scald (\u003cem\u003eRhynchosporium secalis\u003c/em\u003e Oud.), net blotch (\u003cem\u003eHelminthosporium teres\u003c/em\u003e Sacc.), spot blotch (\u003cem\u003eHelminthosporium sativum\u003c/em\u003e Pum.), and leaf rust (\u003cem\u003ePuccinia hordei\u003c/em\u003e Otth.). Additionally, insect pests like the Russian wheat aphid (\u003cem\u003eDiuraphis noxia\u003c/em\u003e) and shoot fly continue to limit productivity (Assefa, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Tetyannikov \u0026amp; Bome, (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)). These biotic and abiotic stresses collectively contribute to yield instability and threaten the conservation of valuable genetic resources (Bekele et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough large numbers of barley accessions have been collected and conserved in Ethiopia, many remain inadequately characterized for key agronomic and morphological traits. Comprehensive evaluation of this genetic diversity is essential to identify promising genotypes for crop improvement and sustainable utilization (Abtew, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Such characterization not only enhances breeding efficiency but also supports conservation strategies to safeguard native germplasm under the pressures of climate change and agricultural modernization.\u003c/p\u003e \u003cp\u003eTherefore, the present study was conducted to assess the extent of genetic diversity among naked barley genotypes using multivariate approaches such as cluster and principal component analyses. The findings are expected to provide valuable insights for breeders and conservationists in selecting parental lines for breeding programs and in developing strategies for the sustainable utilization of Ethiopian barley genetic resources.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDescription of the Study Area\u003c/h2\u003e \u003cp\u003eThe experiment was conducted at the Madda Walabu University research station, located in the Bale Zone of the Oromia region, Ethiopia, during the main rainy season of 2023. The site lies at 7\u0026deg;7\u0026prime;N latitude and 40\u0026deg;0\u0026prime;E longitude (7.117\u0026deg;N, 40.000\u0026deg;E) at an elevation of 2,480 meters above sea level, receiving an average annual rainfall of 1,100 mm.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExperimental Material and Design\u003c/h3\u003e\n\u003cp\u003eA total of sixty-four naked barley genotypes were evaluated, including 62 accessions obtained from the Ethiopian Biodiversity Institute (EBI) and two local genotypes sourced from farmers (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The experiment was arranged in a 4 \u0026times; 16 alpha lattice design with two replications. Seeds were sown manually with an inter-row spacing of 20 cm on plots measuring 0.3 m\u0026sup2; (1.5 m \u0026times; 0.2 m). Plot, block, and replication separations were maintained at 0.2 m, 1 m, and 1 m, respectively. A seed rate of 120 kg ha⁻\u0026sup1; was used, and recommended fertilizer rates of 150 kg ha⁻\u0026sup1; urea and 100 kg ha⁻\u0026sup1; NPS were applied.\u003c/p\u003e\n\u003ch3\u003eSource of Plant Material\u003c/h3\u003e\n\u003cp\u003eThe study evaluated 64 naked barley (\u003cem\u003eHordeum vulgare\u003c/em\u003e L.) genotypes, including 62 accessions from the Ethiopian Biodiversity Institute and two local genotypes (\u0026lsquo;Negele\u0026rsquo; and \u0026lsquo;EH-1493\u0026rsquo;) obtained from farmers in the Ambo area of Oromia, Ethiopia. The accessions originated from several Ethiopian regions including Amhara, Oromia, SNNP, Benishangul-Gumuz, and Tigray. Detailed information on accession numbers and collection locations is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of Barley genotypes used as planting material that was collected from different part of Ethiopia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccession\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWoreda\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Shewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD/Berhan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10-47-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37-07-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Shewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD/Berhan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e09-38-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39-27-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArssi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTena\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e07-50-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39-34-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/Shewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWaliso\u0026amp; Goro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e08-45-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37-50-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB/Gumuz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMetekel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWenbera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11-15-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35-55-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB/Gumuz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMetekel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWenbera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10-30-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35-50-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13-10-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37-54-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDebark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13-05-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37-52-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDebark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13-06-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37-53-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWegera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12-50-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37-43-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWegera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12-47-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37-41-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12-47-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37-41-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e 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colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11-37-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38-04-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e 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colname=\"c3\"\u003e \u003cp\u003eOromiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/Shewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCheliya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e09-06-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37-24-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/Shewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCheliya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e09-08-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37-19-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/Wellega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJ/Horo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e09-32-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37-04-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/Wellega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJ/Horo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e09-33-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37-02-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/Gojam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eE/Enawga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10-45-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38-04-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/Gojam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eE/Enawga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10-46-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38-03-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/Gojam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGuzamn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10-32-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37-47-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Shewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMojana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e09-57-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39-51-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Shewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD/Berhan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e09-48-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39-43-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArssi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLimu\u0026amp;Bilbilo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e07-25-07-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39-13-41-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArssi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLimu\u0026amp;Bilbilo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e07-27-02-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39-12-46-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArssi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLimu\u0026amp;Bilbilo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e06-34-06-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39-16-54-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArssi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDigeluna Tijo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e07-41-38-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39-15-59-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAgew Awi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKosober\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10-56-13-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36-53-51-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDebark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13-10-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37-55-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Omo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChencha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e06-19-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37-28-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/Wellega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHaru\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e09-36-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37-50-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/Shewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBako Tibe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e09-04-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36-36-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/Shewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCheliya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKemekem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLay Gayint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLay Gayint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDebark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDebark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTach Gayint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDebark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDebark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDebark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDebark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD/Wello\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKalu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/Shewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMeta Robi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e09-23-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39-24-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Wello\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGuba Lafto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11-48-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39-26-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Wello\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGuba Lafto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11-48-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39-26-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTigray\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDebubawi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOfla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12-13-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38-43-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/Harerge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDeder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e09-15-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45-20-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGedeo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWenago\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD/Gondar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLay Gayint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11-44-00-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38-20-00-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Wello\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD/ Delant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/Shewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWonchi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e243235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS/Shewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKembibit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e09-36-19-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39-29-50-E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e244772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKembata\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAngacha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e244904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/Wellega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG/Horo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e244912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/Wellega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG/Horo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/Shewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAmbo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEH-1493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOromia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/hewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAmbo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eField and laboratory data were collected in accordance with the International Barley Descriptor List (IPGRI, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), encompassing both plant-based and plot-based traits. Data were recorded from representative samples within each experimental plot to ensure reliability and accuracy. For quantitative traits such as grains per spike, grain weight per spike, spikelets per spike, peduncle length, plant height, productive tillers per plant, spike length, and total tillers per plant, observations were taken from ten randomly selected and individually tagged plants per plot. Each measurement was averaged to obtain a representative value for that plot. Grain yield per plot was determined by harvesting and threshing all plants within the net plot area. The total grain weight was recorded after proper cleaning and drying to approximately 12% moisture content. The plot yield was then converted to grain yield per hectare (kg ha⁻\u0026sup1;) using the formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$GrainYield(kg/ha)=\\frac{TotalGrainWeight\\left(kg\\right)}{PlotArea\\left(m2\\right)}x\\text{10,000}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eOther traits, including biomass yield, days to heading, days to maturity, grain filling period, grain yield, harvest index, and thousand-grain weight, were recorded from the entire plot for each genotype. The harvest index (HI) was calculated as:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$HarvestIndex=\\frac{GrainYield}{TotalBiomassYield}$$\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003eThousand-grain weight (TGW) was measured as the weight of 1,000 grains in grams. In addition to the field data, laboratory analyses were performed for selected quality parameters, following standard procedures outlined in the barley descriptor manual. Environmental conditions such as rainfall, temperature, and soil characteristics of the experimental site were also recorded to support interpretation of genotype performance. All data were collected from three replications to minimize experimental error and to allow for robust statistical analysis in subsequent multivariate evaluations.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eData analysis was carried out to evaluate the extent and pattern of genetic diversity among the naked barley genotypes based on the measured quantitative traits. Prior to multivariate analyses, all trait data were standardized to a mean of zero and variance of one to eliminate the effects of scale differences among variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Variance\u003c/h2\u003e \u003cp\u003eAnalysis of variance revealed highly significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) among the 64 naked barley genotypes for all 15 agronomic traits, indicating the presence of substantial genetic variability within the evaluated population. Grain yield ranged from 2.915 to 6.035 t ha⁻\u0026sup1;, with accession 4138 recording the highest yield and landrace 206306 the lowest. Similarly, wide variation was observed for other quantitative traits such as plant height, spike length, productive tillers per plant, and grain weight per spike, reflecting the diverse genetic backgrounds of the genotypes. This phenotypic variability suggests a broad genetic base, offering valuable opportunities for selection and improvement.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrincipal Component Analysis (PCA)\u003c/h3\u003e\n\u003cp\u003ePrincipal Component Analysis (PCA) was conducted to identify the major components explaining the overall phenotypic variation and to determine the relative contribution of each trait to total diversity among the naked barley genotypes. Components with eigenvalues greater than one (λ\u0026thinsp;\u0026gt;\u0026thinsp;1) were considered significant according to the Kaiser criterion, and their corresponding loadings were used to determine the traits contributing most to genotype differentiation.\u003c/p\u003e \u003cp\u003ePCA grouped the evaluated traits into four principal components with eigenvalues greater than one (λ\u0026thinsp;\u0026gt;\u0026thinsp;1). The first four componentsPC1 (38.0%), PC2 (19.8%), PC3 (13.0%), and PC4 (7.9%) together accounted for 78.8% of the total phenotypic variation among the genotypes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The corresponding eigenvalues were 5.70, 2.97, 1.96, and 1.19, respectively. The first two principal components contributed most to the overall diversity, highlighting the key traits responsible for genotype differentiation.\u003c/p\u003e \u003cp\u003ePC1 (38.0%) exhibited strong positive loadings for spike length, spikelets per spike, grain weight per spike, thousand-grain weight, biomass yield, and grain yield, indicating that these traits were the major contributors to total variation and are essential determinants of yield potential. PC2 (19.8%) captured variation in phenological traits, particularly days to heading and days to maturity, while PC3 (13.0%) was influenced mainly by tiller number. PC4 (7.9%) reflected variation in grains per spike, plant height, harvest index, and peduncle length. Traits with higher absolute loadings on PC1 exerted a stronger influence on clustering, suggesting that genotype differentiation was driven by multiple traits rather than isolated characteristics (Chahal and Gosal, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings are consistent with previous reports (Enyew et al. 2020; Shtaya and Abdallah, 2019; Angassa and Mohammed, 2021), which indicated that the first four principal components explained 75\u0026ndash;81% of the total variation in barley genotypes and were largely influenced by similar yield-related and morphological traits.\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\u003eVector loadings and percentage of explained variation 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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePC4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProTil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToTil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.09\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNGPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGWPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.06\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariability (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.94\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\u003e37.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.77\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\u003e \u003cstrong\u003eFoot Note\u003c/strong\u003e \u003cp\u003eBY= Biomass yield, DHA=Days to heading, DM=Days to maturity, GFP= Grain filling period, GWPS= Grain weight spike\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, HI= Harvest index, NGPS=Number of grain spike\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, NSPS=Number of spikelet spike\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, PCL= Peduncle length, PH=Plant height, ProTil= Productive tiller plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, SL =Spike length, TGW\u0026thinsp;=\u0026thinsp;Thousand grain weight, ToTil= Total tiller plant.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eA PCA biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was generated to visualize correlations between genotypes and quantitative traits. Genotypes are represented as blue points, while red arrows denote traits. Longer arrows indicate stronger contributions to the principal components. Traits such as peduncle length, biomass yield, spikelets per spike, and grain weight per spike aligned closely with Dim1, indicating their primary influence on horizontal variability. Conversely, days to maturity, grain-filling period, and grains per spike were more associated with Dim2, affecting vertical variability. Trait correlations are further reflected by the angles between arrows: acute angles (e.g., biomass yield vs. grain weight per spike) indicate strong positive correlations, whereas obtuse angles (e.g., grains per spike vs. biomass yield) suggest negative correlations. Genotypes located near the origin represent average trait performance, whereas those at extremes are outliers with distinct profiles. These findings confirm the relevance of key agronomic and phenological traits in distinguishing naked barley genotypes, providing critical insights for parental selection and breeding programs, including under stress conditions (Yadav et al.; Pour-Aboughadareh).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCluster Analysis\u003c/h2\u003e \u003cp\u003eCluster Analysis (CA) was performed using the agglomerative hierarchical method based on Euclidean distance as a measure of dissimilarity among genotypes. The average linkage method (UPGMA) was employed to construct the dendrogram, grouping genotypes with similar trait performance. The number of clusters was determined by examining the fusion levels and corroborating the grouping pattern with PCA-based scatter plots to ensure consistency. All statistical and graphical analyses were conducted in \u003cb\u003eR\u003c/b\u003e statistical software using the packages \u003cem\u003ecluster\u003c/em\u003e, \u003cem\u003efactoextra\u003c/em\u003e, \u003cem\u003eprcomp\u003c/em\u003e, and \u003cem\u003eggplot2\u003c/em\u003e (R Core Team, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCluster analysis grouped the 64 naked barley genotypes (62 accessions and 2 local checks) into four distinct clusters. Cluster I comprised the largest group, containing 50 genotypes (78.13%), followed by Cluster II with 9 genotypes (14.06%), Cluster III with 4 genotypes (6.25%), and Cluster IV represented solely by the local check genotype \u0026lsquo;Negele\u0026rsquo; (1.56%). The highest inter-cluster distance was observed between Clusters III and IV, followed by I and IV, and II and IV, indicating considerable genetic divergence among these groups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The clustering pattern highlights substantial genetic variability among the evaluated genotypes, providing a valuable basis for selecting genetically diverse parents for future hybridization and breeding programs.\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\u003eDistribution of 64 naked barley genotypes into their respective cluster\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=\"left\" 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=\"left\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of genotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4752\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3639\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64290\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4500\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e64080\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e214229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e206306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1621\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e206359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e206420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e219177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e215224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e215580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e215223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e244904\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e214228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e206366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4490\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e206417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e219763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4164\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e244772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e206493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e215204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e214797\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e214219\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e214235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e244912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4395\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e243235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e228228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e244772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.06%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e219300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4182\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e206423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEH-1493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegele\u003c/p\u003e \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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCluster Visualization Based on Principal Components\u003c/h2\u003e \u003cp\u003eThe cluster visualization derived from principal component analysis (PCA) revealed four distinct groups of naked barley genotypes, corresponding well with the hierarchical clustering results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The first two principal components-Dim1 and Dim2-explained 38.0% and 19.8% of the total variation, respectively, together accounting for 57.8% of the overall genetic variability among the genotypes.\u003c/p\u003e \u003cp\u003eCluster 1 (red) exhibited the widest genetic dispersion, indicating high intra-cluster diversity and representing genotypes with broad genetic backgrounds and multiple favorable traits. In contrast, Clusters 2 (green) and 3 (blue) were relatively compact and well-separated, reflecting distinct and stable trait combinations-potentially corresponding to specific adaptive or morphological characteristics. Cluster 4 (purple) was represented by a small group of rare or unique genotypes, suggesting the presence of specialized or regionally adapted accessions that may harbor valuable alleles for stress tolerance or specific yield attributes.\u003c/p\u003e \u003cp\u003eA moderate overlap between Clusters 1 and 2 suggests partial genetic similarity or shared phenotypic traits among genotypes in these groups, which may be due to common ancestry or adaptive convergence. These clustering trends are consistent with the findings of Abebe et al. (2024), who reported similar groupings among Ethiopian barley genotypes associated with yield and drought-resistance traits. Likewise, Zhang et al. (2024). observed comparable clustering patterns in global barley germplasm, linking genetic diversity to environmental adaptation and stress tolerance. Collectively, these results reaffirm the utility of PCA-based clustering for visualizing genetic relationships and for identifying diverse parental lines for targeted breeding programs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.5. Genetic Divergence Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGenetic divergence among the 64 naked barley genotypes was assessed based on Euclidean distance matrices computed in R using the \u003cem\u003ecluster\u003c/em\u003e package (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The analysis revealed varying degrees of genetic dissimilarity among the four clusters. The smallest genetic distance was recorded between Clusters I and III (11.42), followed by Clusters I and II (18.19), indicating relatively close genetic relationships among these groups. Moderate divergence was observed between Cluster I and Cluster IV (33.07). The highest inter-cluster distance was observed between Clusters III and IV (38.54), suggesting that Cluster IV is genetically distinct and contains genotypes with considerable variability relative to the others. It is well established that greater inter-cluster distances correspond to higher genetic divergence, which can be exploited in breeding to produce transgressive segregants and enhance heterosis (Kumar et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) In this study, the pronounced divergence of Cluster IV from Clusters I, II, and III suggests that crosses involving genotypes from Cluster IV are likely to generate substantial genetic recombination, resulting in enhanced heterotic expression in F₁ hybrids and broader variability in segregating F₂ populations. Conversely, crosses between genetically closer clusters, such as I and III or I and II, may yield more uniform progeny with narrower genetic variability (Fantahun et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eOverall, the genetic distance analysis highlights substantial genetic variability among the naked barley genotypes studied, providing a valuable foundation for parental selection, hybridization strategies, and genetic improvement. These findings emphasize the importance of genetically distant parents particularly those from Cluster IV for developing superior and diverse barley cultivars suited to different agroecological conditions (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\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\u003eInter-cluster distances between centroids for 64 naked barley genotypes\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=\"left\" 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\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.19\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\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\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;23.68 and 29.14 at 5% and 1% probability level respectively\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.6. Cluster Mean Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAnalysis of mean trait values across the four clusters revealed significant genetic variation among the evaluated naked barley genotypes, consistent with prior studies. Cluster III exhibited the highest performance for grain yield (5.38 t ha⁻\u0026sup1;), biomass yield (17.48 t ha⁻\u0026sup1;), and number of grains per spike (54.45), indicating its superior productivity and potential for direct use in high-yielding cultivar development. Similar patterns were reported by Belay et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) who identified clusters with enhanced yield and biomass traits in Ethiopian barley genotypes. Cluster II showed the best performance for spike length (8.93 cm), thousand-grain weight (46.97 g), and productive tillers per plant (7.06), reflecting favorable grain development and vegetative growth. These findings are in agreement with G\u0026uuml;ng\u0026ouml;r and T\u0026uuml;rkoğlu (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who observed similar trait patterns among barley clusters. Cluster IV, while exhibiting the highest harvest index (36.66%), had lower overall grain yield, highlighting a trade-off between resource allocation and productivity, also noted by G\u0026uuml;ng\u0026ouml;r and T\u0026uuml;rkoğlu (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Cluster I showed relatively lower grain yield (4.23 t ha⁻\u0026sup1;) but a longer grain-filling period (38.05 days), suggesting potential for breeding programs targeting extended grain-filling duration or stress resilience.\u003c/p\u003e \u003cp\u003eThese results underscore the utility of cluster mean analysis for identifying barley genotypes with desirable combinations of traits, including yield, biomass, and physiological efficiency. Such insights provide valuable guidance for parental selection in hybridization and for developing cultivars with improved productivity and adaptability (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\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\u003eCluster mean of 15 quantitative traits for 64 Naked Barley genotypes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCluster I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCluster II\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCluster III\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCluster IV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGrand Mean\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProTil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToTil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.38\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e93.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e123.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e134.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e104.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e119.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNGPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGWPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42.93\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.10\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.96\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.68\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\u003e \u003cstrong\u003eFoot Note\u003c/strong\u003e \u003cp\u003eBY= Biomass yield, DHA=Days to heading, DM=Days to maturity, GFP= Grain filling period, GWPS= Grain weight spike\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, HI= Harvest index, NGPS=Number of grain spike\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, NSPS=Number of spikelet spike\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, PCL= Peduncle length, PH=Plant height, ProTil= Productive tiller plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, SL =Spike length, TGW\u0026thinsp;=\u0026thinsp;Thousand grain weight, ToTil= Total tiller plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe dendrogram depicting the four clusters based on 15 agronomic variables using Euclidean distance is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In the figure, genotypes within the same cluster are marked with similar colors, while different clusters are distinguished by distinct colors. Closely related genotypes are connected within the same cluster, and genotype codes corresponding to accessions are indicated along the bottom axis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDiscussion\u003c/h3\u003e\n\u003cp\u003eThe considerable genotypic variation observed across all measured traits highlights the broad genetic base of Ethiopian naked barley accessions. This diversity represents an invaluable resource for global barley improvement efforts, particularly in breeding for resilience to biotic and abiotic stresses. The magnitude of variation recorded in grain yield and yield-related traits indicates that Ethiopian naked barley constitutes an untapped reservoir of adaptive alleles, consistent with its status as one of the world\u0026rsquo;s oldest centers of barley domestication. Comparable trends in high intra-population variability have also been reported in Asian and European landrace collections (Horsley et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), emphasizing that landrace populations continue to play a crucial role in broadening the genetic base of cultivated barley.\u003c/p\u003e \u003cp\u003ePrincipal Component Analysis (PCA) revealed that a limited number of principal components explained most of the total phenotypic variability, with yield- and tillering-related traits exerting the strongest influence on genetic divergence. This pattern agrees with findings from large-scale germplasm evaluations in China and the Himalayas (Zhang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chodron et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), where similar traits were identified as key discriminators among genotypes. Importantly, our analysis provides new evidence that Ethiopian naked barley exhibits trait interrelationships like those observed in global barley panels, thereby supporting its potential inclusion in international pre-breeding programs. The strong positive association between grain yield, productive tillers, and biomass yield suggests that these parameters can serve as efficient selection indices for yield improvement under marginal environments.\u003c/p\u003e \u003cp\u003eCluster analysis further confirmed the existence of pronounced genetic divergence, partitioning genotypes into four distinct groups with substantial inter-cluster distances. The wide separation between clusters III and IV suggests the potential for exploiting heterosis through strategic hybridization between genetically distant genotypes. Similar diversity patterns have been documented among Tibetan and Central Asian naked barley accessions (Zeng et al., 2023; Chodron et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), underscoring the parallel adaptive diversification of barley under contrasting highland environments. The present study therefore contributes novel comparative insight by demonstrating that Ethiopian naked barley, though geographically isolated, exhibits genetic structures and trait associations comparable to other ancient barley centers of diversity.\u003c/p\u003e \u003cp\u003eOverall, these findings highlight the international relevance of Ethiopian naked barley as a unique genetic resource for global barley improvement. Despite being based on a single growing season, the study provides a valuable baseline for future multi-environment and genomic validation studies. The identification of distinct genetic clusters and trait-based principal components offers a clear framework for selecting parental lines that can contribute to the development of high-yielding, stress resilient barley cultivars suited to diverse agroecological conditions worldwide.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study revealed considerable genetic variability among 64 naked barley (\u003cem\u003eHordeum vulgare\u003c/em\u003e L.) genotypes for all evaluated agronomic traits. The significant differences observed across traits such as grain yield, productive tillers, and spike morphology confirm the existence of a broad genetic base within the Ethiopian naked barley germplasm. Principal component analysis identified a small number of key components explaining the majority of total variation, with grain yield, biomass yield, and productive tillers emerging as major contributors. Cluster analysis further demonstrated clear genetic differentiation, grouping the genotypes into four distinct clusters with varying inter-cluster distances. These results collectively underscore the potential of Ethiopian naked barley accessions as valuable genetic resources for breeding and genetic improvement. Genotypes from highly divergent clusters particularly those showing superior performance for yield and yield-related traits should be prioritized as parents in hybridization programs to maximize heterosis and broaden the genetic base of future breeding populations. Moreover, the identification of diverse and high-performing genotypes supports targeted selection for specific environments, aiding the development of cultivars better adapted to the country\u0026rsquo;s varied agroecological zones. Continued characterization of these landraces using molecular markers and multi-environment field trials will further enhance their utilization in sustainable barley improvement and conservation strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKoricho Lemma designed and conducted the study, collected and analyzed the data, and drafted the manuscript. Chandra Sekhar Singh, Kumud Kant Awasthi, \u0026amp; Garima Awasthi critically reviewed the manuscript, provided suggestions, and assisted with data analysis. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files].\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePermissions to Collect Plant Material\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe barley germplasm used in this study included 62 accessions obtained from the Ethiopian Biodiversity Institute (EBI), Ethiopia, and two local genotypes collected from farmers in the Ambo area of the Oromia region. The plant materials were provided for research purposes and their use complied with relevant national regulations and institutional guidelines of Ethiopia. No endangered or protected species were involved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlant Ethics / Collection Guidelines (Ethics and Consent to Participate)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll plant materials used in this study were cultivated barley genotypes obtained from recognized germplasm collections and local farmers. Their use complied with national and institutional guidelines for plant genetic resource research in Ethiopia. No protected or endangered species were used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbebe K. Cluster and principal component analysis for yield and yield-related traits of food barley (\u003cem\u003eHordeum vulgare\u003c/em\u003e L.) genotypes at Woreilu district, South Wollo, Ethiopia. Int J Crop Sci. 2024;59(4):202\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbebe TD, Bauer AM, L\u0026eacute;on J. Morphological diversity of Ethiopian barleys (\u003cem\u003eHordeum vulgare\u003c/em\u003e L.) in relation to geographic regions and altitudes. 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Crop Sci Technol. 2024;62(1):98\u0026ndash;112.\u003c/span\u003e\u003c/li\u003e\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-plants","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Plants](https://link.springer.com/journal/44372)","snPcode":"44372","submissionUrl":"https://submission.springernature.com/new-submission/44372/3","title":"Discover Plants","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Accession, Cluster analysis, genetic diversity, naked barley, Principal component analysis","lastPublishedDoi":"10.21203/rs.3.rs-8993040/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8993040/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding genetic diversity in crops is essential for developing effective breeding, conservation, and sustainable production strategies. This study evaluated 64 naked barley (\u003cem\u003eHordeum vulgare\u003c/em\u003eL.) genotypes at Bale Robe, southeastern Ethiopia, during the main cropping season using an alpha lattice design with two replications. Fifteen agronomic traits were recorded to determine the extent of genetic variation. Analysis of variance indicated significant differences among the genotypes for all the traits studied. Grain yield ranged from 2.915 to 6.035 t ha⁻¹, with accession 4138 achieving the highest yield and landrace 206306 the lowest. Principal component analysis identified four major components PC1 (38%), PC2 (19.8%), PC3 (13%), and PC4 (7.9%) with eigenvalues above one (5.7, 2.97, 1.96, 1.19), together explaining 78.8% of the total variation. Cluster analysis grouped the genotypes into four distinct clusters, with the greatest genetic divergence observed between clusters III and IV. The findings highlight the existence of substantial genetic diversity in naked barley, providing valuable opportunities for breeding programs aimed at enhancing yield potential and ensuring sustainable barley production in Ethiopia.\u003c/p\u003e","manuscriptTitle":"Assessment of Genetic Divergence in Naked Barley (Hordeum vulgare L.) Genotypes Using Cluster and Principal Component Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-24 10:20:34","doi":"10.21203/rs.3.rs-8993040/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-11T14:52:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119679096835222095935251211369081806592","date":"2026-05-05T07:28:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T11:06:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268021559521933414800462970366774609292","date":"2026-05-04T08:24:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T07:46:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95321932393149026890975584214679006073","date":"2026-04-20T08:30:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-17T07:07:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-26T15:58:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-17T18:42:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-17T07:16:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Plants","date":"2026-03-13T06:31:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-plants","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Plants](https://link.springer.com/journal/44372)","snPcode":"44372","submissionUrl":"https://submission.springernature.com/new-submission/44372/3","title":"Discover Plants","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed6b02c0-162e-4089-99e2-17d48042a64f","owner":[],"postedDate":"April 24th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-11T14:52:03+00:00","index":69,"fulltext":""},{"type":"reviewerAgreed","content":"119679096835222095935251211369081806592","date":"2026-05-05T07:28:04+00:00","index":67,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T11:06:54+00:00","index":66,"fulltext":""},{"type":"reviewerAgreed","content":"268021559521933414800462970366774609292","date":"2026-05-04T08:24:09+00:00","index":65,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-24T10:20:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-24 10:20:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8993040","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8993040","identity":"rs-8993040","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}