Performance and Diversity of Ethiopian Core Tef Germplasm Under Seasonal Conditions

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Information about the natural genetic variation of the crop would be useful for genetically improving it through breeding. Therefore, the current study was designed to determine the extent and pattern of genetic variability among selected tef core germplasm lines and released Varieties from Ethiopia, using morphological traits. A total of 81 tef genotypes were field- evaluated for 17 morphological traits using a 9 × 9 simple lattice designs at Debre Zeit during the 2021 main cropping season and off-season. Among the assessed traits, only a few showed significant differences among the genotypes. Specifically, these traits were thousand seed weight and fertile tiller number in the main season, and peduncle length and number of fertile tillers in the off-season. Cluster analysis grouped the 81 tef genotypes into four clusters, each consisting of 15 to 31 genotypes. Principal component analysis indicated that approximately 71% of the gross variance among the tested genotypes could be explained by six principal components with eigenvalues greater than one. In general, the study revealed highly significant genetic distances between clusters 1 and 2. This suggests that selecting tef materials from these clusters for a cross-breeding program would likely be beneficial. Population Biology Cluster Core germplasm Released Varieties Morphological traits Principal components tef. Figures Figure 1 INTRODUCTION Tef (Eragrostis tef, Zucc. Trotter) is an indigenous and significant food, feed, and cash crop for smallholder Ethiopian farmers. Among all the grain crops (cereals, legumes, and oilseeds) cultivated in Ethiopia, cereals are the most extensively produced, covering 10.56 million hectares, with a production of about 30.21 million tons, involving approximately 15.74 million farmer households[ 1 ]. Consequently, cereals constitute around 81.19% of the total acreage and 88.36% of the overall grain production of all crops grown in the country. Within the category of cereals, tef covers 2.93 million hectares of land, equivalent to 27.8% of the total area allocated to cereal crop cultivation in Ethiopia [ 1 ]. The same source indicates that the annual grain production of 5.51 million tons places tef third in line, following maize and wheat with 10.56 and 5.78 million tons, respectively [ 1 ]. The number of farming households engaged in tef production is approximately 6.87 million, second only to those growing maize (10.19 million) [ 1 ]. Tef is favored by both farmers and consumers due to its versatility in adapting to a wide range of environments. It performs better than other cereals under adverse climatic and soil conditions and its grains can be stored for extended periods as they are not susceptible to storage pests. Tef grains are primarily used to make injera, and sometimes porridge, non-raised bread ("kita"), and local alcoholic drinks like tela and katikala. It provides nutrition similar to more common cereals like wheat [ 2 ], offering balanced amino acids with a high lysine content [ 3 ], and significant iron content [[ 4 ], [ 5 ]]. Additionally, its straw serves as cattle feed, reinforces mud used for plastering walls of huts and local grain storage facilities, and serves as bedding material and mulch. Currently, tef is gaining global popularity as a health and performance food primarily because its grains are gluten-free, which prevents celiac disease. It also serves as a suitable dietary option for individuals with diabetes due to its low glycemic index in comparison to most other cereals [ 6 ]. Consequently, tef grain production has been introduced to industrialized nations, particularly in recent years. Despite its immense significance in Ethiopia, tef production faces numerous constraints and technical as well as socio-economic challenges. These include the lack of cultivars tolerant to lodging and improved varieties suitable for diverse agro-ecologies, reliance on traditional cultural practices, exposure to abiotic stresses (such as drought, salinity, acidity, and cold/heat), susceptibility to pests (including weeds, diseases, and insect pests), weak seed and extension systems, and limitations in the accessibility and affordability of inputs like fertilizers[ 7 ]. To overcome these constraints, assessing the variability in the genotypes and exploiting favorable traits using morphological and molecular markers is vital. Morphological markers are the earlier markers utilized to assess genetic diversity within and between populations. While morphological markers provide straightforward measurements of phenotypes, they exhibit low polymorphism and heritability, and their expressions are influenced by environmental factors [ 8 ]. Nevertheless, they offer a cost-effective means of characterizing germplasm accessions. Several genetic diversity studies [[ 9 ], [ 10 ], [ 11 ], [ 12 ], [ 13 ]] have already revealed substantial phenotypic variations among tef germplasm accessions and/or varieties for pheno-morphic and agronomic traits. However, tef's productivity, with a national average yield of 1.88 t/ha, remains notably low compared to other cereals [ 1 ]. Therefore, enhancing tef's productivity to compete with more productive cereals is imperative. This indicates the presence of critical constraints in tef production. Predominantly, these include lodging, abiotic stresses like drought and acidity, and biotic stresses (diseases and insect pests) [ 14 ]. To address these challenges, a comprehensive evaluation of germplasm variability using morphological markers becomes of paramount importance. Consequently, this study was initiated with the aim of generating useful information on the phenotypic diversity of selected core tef germplasm lines along with some recently released varieties as an aid for designing effective and efficient tef breeding strategies. MATERIALS AND METHODS Experimental Plant Materials The experimental plant materials for both the field and laboratory experiments comprised a total of 81 genotypes including 74 core germplasm lines and seven released varieties obtained from the working collections at the National Tef Research Program of DZARC. The germplasm materials originated from previous collections made from different areas of Ethiopia (Table 1). Description of Experimental Sites and Season The field experiment was carried out at the Debre Zeit Agricultural Research Center (DZARC). It is located about 47 km Southeast of Addis Ababa. The geographical position and the climatic and soil-related data of the sites have been summarized on (Table 2). The experiments were carried out in 2021 both in the off- and main cropping season. The off-season experiment was planted at the end of January 2021 using irrigation and again the experiment was repeated in the main season. The experimental field at this site is characterized by heavy black clay soil with very high moisture retention capacity (Table 2). Experimental Design and Management The field experiments were conducted using a 9x9 simple lattice design. The plot sizes measured 1 m x 1 m with 1 m distances between plots and 1.5 m distances between blocks/replications. Each plot was comprised of 5 rows spaced 20 cm apart. The core collection and varieties were randomly assigned to plots within each replication. Following research recommendations of 10 kg/ha, 1 g/plot of seeds was manually broadcasted along the surfaces of the rows in each plot [15]. Fertilizers were applied at the rate of 40 kg N and 60 kg P2O5 per hectare, following the recommendations for black soil at Debre Zeit. Diammonium phosphate (DAP) was administered at planting, while urea was applied two weeks after sowing and top-dressed during the tillering stage [13]. Hand weeding was made three times during the crop growth stage. All other cultural management practices were performed as per the research recommendations for tef production in the particular test location. Data Collection Phenotypic data were recorded both on a whole-plot basis and an individual plant basis. Data collected on a whole-plot basis : For the entire plot, data were recorded on days to panicle emergence (heading), days to maturity, grain filling period, grain yield, shoot biomass yield, harvest index, and lodging index. The lodging index was assessed using Caldicott and Nuttall’s [16] method, which considers both the extent and severity of lodging. Severity or angle lodging was graded on a scale of 0 to 5, based on the angle of lodging (0 = no lodging = 100% upright, 5 = completely lodged = 100% flat). Intermediate degrees of lodging were represented by scores 1 to 4 corresponding to the angle of lodging. Subsequently, the prevalence of lodging in a plot, expressed as the percentage of the affected plot area for each degree score, was recorded. The lodging index was ultimately calculated as the average product sum of each degree of lodging and its corresponding prevalence percentage. Data collected on a single-plant basis: Information at an individual plant level was collected from five random plant samples per plot, and the average data from these five sample plants were used for analysis. Traits assessed on an individual plant basis encompassed plant height, panicle length, culm length, peduncle (uppermost culm internode) length, total and fertile tiller counts, fertile floret counts per spikelet, main shoot panicle weight, and grain yield per panicle (main shoot). Statistical Analyses Prior to executing analysis of variance, data obtained from the field experiments across two Seasons were checked for normal distribution and homogeneity of error variances, assessed through Bartlett’s tests[17] and the SAS software package [18]. Test genotypes were grouped variance results for the two seasons were analyzed separately, since the analysis of variance given that, data for all traits didn't exhibit consistent error variance homogeneity [19]. For all the multivariate analyses techniques employed, the means of each of the traits were pre- Standardized to mean zero and variance unity to prevent bias due to differences in Measurement. The ANOVA was conducted using the model outlined in (Table 3): Pij = µ + gi + rj + bki + eij Where, Pij = phenotypic value of the ith genotype under the jth replication within replication j; µ = Grand mean; gi = the effect of the ith genotype; rj = the effect of replication j; bki = the effect of the ith Incomplete blocks in replication, and eij = the residual or effect of random error. The analysis of variance (ANOVA) was executed separately for all characteristics using the SAS Statistical package (SAS 9.0). ANOVA was employed to determine the presence of significant variation among the test materials. F-tests within the ANOVA were regarded as significant at p≤0.05. For all subsequent multivariate statistical analyses, including cluster analysis (CA), distance analysis, and principal components analysis (PCA), the standardized means of the 81 tef test genotypes were applied. The collective hierarchical cluster analysis approach was used to examine the grouping pattern of the 81 tested genotypes based on their similarity, constructed using the corresponding means of all 17 studied traits. The cluster analysis was performed using the average linkage method, with the determination of the number of clusters based on local peaks of the pseudo-F-statistic joining with small values of the pseudo-t2 statistic, followed by a larger pseudo-t2 for the subsequent cluster combination. This process was executed using the SAS statistical package [18] . The dendrogram was constructed using the average linkage and the Euclidean distance as a measure of dissimilarity, employing the SAS statistical package. Similarly, a cluster based on origin was formed by computing the means of the genotypes per origin and then standardizing the data, as was done for the genotype clustering, using the SAS statistical package. Genetic distances between clusters, as standardized, were calculated using Mahalanobis's D2 statistics [20]: D2ij = (xi - xj)' cov-1(xi - xj) Where, D2ij = the distance between cases i and j; xi and xj = vectors of the values of the variables for cases i and j. To assess the significance level of genetic diversity between and within clusters, a chi-square table (X2 table) was utilized, based on the degree of freedom of traits at a significance level of 0.05 or 0.010. The principal components (PC) examinations were utilized to identify the characteristics contributing a large portion of the whole variation among the 81 test genotypes. The characters with bigger absolute values closer to one inside each principal component impact the clustering more than those with lower total values closer to zero [21]. Only PCs with eigenvalues greater than one were considered as important. As recommended by Johnson and Wichern [22] , a characteristic coefficient or eigenvector greater than half divided by the standard deviation (square root) of the eigenvalue of the respective PC was utilized as a general rule to evaluate the relative importance of traits constituting the PCs for 'P' degrees of freedom, where P represents the number of traits considered [23]. RESULTS AND DISCUSSION Genetic variability in breeding materials is essential for a successful plant breeding program. Understanding the magnitude of variability in crop species is crucial because it forms the foundation for selection. Trait Ranges and Analysis of Variance In the present study, all traits except biomass yield did not exhibit homogeneity of error variances, as indicated by P-values below the significance levels (0.05 and 0.01). Consequently, separate analyses were conducted. Based on data from two seasons, substantial ranges between maximal and minimal mean values were observed for all evaluated traits (Table 4). For instance, in the main season, the mean value ranges for days to heading, days to maturity, and grain filling period were 32-44, 86- 101.8, and 44-62.8 days, respectively. In the off-season, these ranges were 43.0-54.1, 87.0- 97.4, and 43.3-44 days, respectively (Table 4). Similarly, broad ranges were also noted for all studied traits (Table 4). Across the two seasons of field experiments, the ranges of mean values were 50.6-114.2 cm for plant height, 28-71.6 cm for culm length, 22.2-50.6 cm for panicle length, and 11.0-28.4 cm for peduncle length (Table 4). The minimum and maximum mean values across the two seasons were 0.023 and 0.5 g for thousand seed weight, 350 and 3500 kg/ha for grain yield, 2000 and 24000 kg/ha for biomass yield, 0.01 and 1.53 for grain yield per panicle, 21-88 for lodging index, and 0.042 and 22.1% for harvest index. The separate analyses of variance for the two seasons revealed that, except for thousand seed weight and fertile tiller number per plant in the main season, and peduncle length and number of fertile tillers in the off-season, most of the genotypes did not show statistically significant differences in terms of variance (P ≤ 0.01 and 0.05) for all evaluated traits (Table 5).Likewise, notable difference concerning origin (regions) were observed in the mean squares of a few traits: biomass yield, grain yield per panicle, and main shoot panicle weight during the off- season, as well as plant height in the main season (Table 5). The current results from the analyses of variance contrast with previous findings in genetic diversity studies of tef germplasm, where substantial variations were reported for many of the evaluated traits [[9], [10], [11], [24]]. These discrepancies could be due to differences in genotypes and testing environments [25]. However, the range values of traits are in alignment with the report by Assefa et al. [9], except for total and fertile tillers per plant, grain yield, biomass yield, and harvest index. As indicated the fertile tillers per plant ranged from 4.6 to 25 in the main season and 1.8 to 10.6 in the off-season, the mean value for the main season was higher than that reported by Assefa et al.[9] , Jifar et al. [11], and Fikre et al. [13]. Nonetheless, the mean values of tiller number in the off-season were nearly similar with the reports of Assefa et al. [9], Jifar et al. [11], and Fikre et al. [13]. Similarly, mean value ranges for shoot biomass and grain yield in the main season were 5.2 to 24.0 t/ha and 0.35 to 3.50 t/ha, respectively. In the off-season, these ranges were 0.7-2.3 t/ha and 5.0-19.0 t/ha. This indicates that the main season biomass and grain yield per hectare were roughly comparable to those reported by Jifar et al. [26], while off-season yields were relatively lower. Additionally, the range of harvest index in this study was 1.8-22.1 in the main season and 0.042-14.18 in the off-season. These values significantly deviate from the ranges of 5.0- 38.8 and 14.7-24.3 reported by Assefa et al. [9] and Jifar et al. [27], respectively. Such differences can be attributed to seasonal variations in moisture and temperature, as well as differences in test genotypes [25] Cluster and Distance Analysis Cluster analysis grouped the genotypes into four clusters based on their similarity. The first cluster (C1, n=31=38.27%) comprised the largest number of core germplasm lines, originating from Jimma (6), Gojam (4), Tigray (4), Wello (11), Wellega (4), and East Shoa (2) (Figure 1 and Table 6). Subsequently, the fourth cluster (n=19=23.46%) included lines from West Shoa (17) and East Shoa (2), followed by the second cluster (n=16=19.75%) encompassing all released varieties (7), as well as germplasm lines from Arsi (5) and West Shoa (4). The smallest cluster was cluster three (C3, n=15=18.51%), consisting of 15 genotypes, all originating from East Shoa (Figure 1 and Table 6). The clusters displaying the least genetic divergence were clusters 3 and 4, with a D2 value of 11.80, while relatively high divergence was observed between cluster 1 and cluster 2, followed by cluster 1 and cluster 4 (Table 7). Conversely, within-cluster divergence was relatively high for cluster three, followed by cluster two, with the least observed within cluster 1 (Table 7). When comparing the four clusters formed based on the similarity or differentiation of 17 pheno-morphic and agronomic traits, significant differences among clusters were only observed for three traits: grain filling period, plant height, and grain yield per panicle (Table8). The findings of this study diverge in terms of the number of clusters from those reported by different authors using different sets of tef genotypes. For example, the reported number of clusters was 3 for 18 genotypes[12] , 6 for 28 semi-dwarf genotypes [28], 6 for 188 genotypes [27], and 7 for 49 genotypes [13]. The tested tef genotypes, including germplasm lines from various zones and released varieties from the same origin, clustered into different classes, while those from different origins were grouped together. This confirms the conclusion drawn by Assefa et al.[10] That the genetic diversity level in tef germplasm is comparatively higher within populations (origin) than among populations (origins). Consequently, accessions originating from the same region and altitude were not distinctly separated into distant clusters. Therefore, although this study indicates relatively lower diversity, tef genotypes did not cluster into a small number of groups, as noted in earlier studies [[29],[26]]. In this study, the least genetic divergence was observed between cluster 3 and cluster 4, with a D2 value of 11.8, while high significant divergence was noted between cluster 2 and cluster 1 (D2 = 85.49), followed by the divergence between cluster 1 and cluster 4 (D2 = 45) (Table 7). The notably high inter-cluster distance between cluster 1 and cluster 2 may be attributed to the inclusion of released varieties. Consequently, it is advisable to consider crosses from this cluster for enhanced heterotic expression [27]. Furthermore, within clusters, relatively high distances were observed for cluster 3 and 2 (Table 7), indicating the presence of diverse genotypes within the same cluster, which could hold potential for further breeding. Principal Component Analysis The principal component analysis revealed that the first six principal components, each with eigenvalues greater than one, collectively accounted for approximately 70.6% of the total variation among the 74 tef core germplasm lines and 7 released varieties assessed for 17 traits (Table 9). Among these, the first principal component (PC) explained 20% of the overall phenotypic variation among the tef genotypes, primarily attributed to variations in panicle length, number of fertile florets per spikelet, culm length, plant height, days to maturity, number of total and fertile tillers, and grain filling period. The second principal component, which accounted for 15.20% of the total variation, was primarily influenced by variations in grain yield per plot, biomass yield, harvest index, and peduncle length. The third principal component, contributing to 10.80% of the total variation, was chiefly due to variations in grain yield per panicle, main panicle shoot weight, and peduncle length. The fourth principal component, responsible for 8.90% of the total variation, primarily resulted from high variations in the number of fertile and total tillers per plant. Similarly, the fifth principal component, explaining 7.80% of the total variation, was mainly influenced by the number of fertile florets per spikelet and thousand seed weight. The sixth principal component, also accounting for 7.8% of the total variation, was chiefly affected by variations in the number of days to heading. The observed variation in principal components in this study is lower than that reported in studies by Assefa et al. [29], Jifar et al. [26], Jifar et al. [28], and Fikre et al. [13], where the first PC accounted for 40%, 44.7%, 41.3%, and 30.65% of the gross variability, respectively. Furthermore, the proportion of variation explained by the first three principal components in this study (46%) was lower than values previously reported: 64.7% by Assefa et al. [10][10], 68.67% by Assefa et al. [29], 74.66% by Adnew et al. [30], 71.03% by Plaza-Wüthrich et al.[12] , 78.3% by Jifar et al. [26], 69.1% by Jifar et al. [28], and 55.9% by Fikre et al. [13]. This suggests that the phenotypic diversity among the tested tef genotypes cannot be adequately explained solely by a few principal components. This observation persists despite the fact that the analyses of variance did not reveal substantial variations among the genotypes in most of the evaluated traits. CONCLUSIONS AND RECOMMENDATIONS An understanding of genetic diversity and population structure of tef using morphological and powerful molecular marker are important steps for breeding. In this study, the morphological diversity analyses depicted highly significant genetic distances between clusters 1 and 2, and this implies that it would be worth selecting tef materials from these clusters for cross-breeding program especially for the traits of grain falling period, yield per panicle and plant height. And the phenotypic diversity among the test tef genotypes cannot be explained in terms of few PCs, in spite of the fact that the analyses of variance did not show substantial variations among the genotypes in most of the traits evaluated The germpalsm materials used in the present study did not cover all the different agro-ecologies of the country because most of the lines in the core germplasm set lack passport data. Hence, for a fruitful tef breeding and enhancement of the precision genetic diversity investigations for breeders to exploit the hereditary potential of the crop in improving its generation and efficiency, the core germplasm collection should represent all the various tef growing agro- ecologies of the country. Consequently, it is recommendable to first revisit the core germplasm set, and assemble a truly representative core germplasm based on ensuring inclusion of accessions from all the diverse agro-ecologies followed by systematic evaluation and charcaterization based on both phenotyping using important pheno-morphic and agronomic traits as well as genotyping using suitable and modern molecular markers. Declarations ACKNOWLEDGEMENTS The principal author acknowledges the assistance and support of the Ethiopian Institute of Agricultural Research (EIAR), Debre Zeit Agricultural Research Center and its staff, and all the tef research team and especially Mr. Nigussu Hussein. Contribution of the Authers Derejaw Tesfa and Kebebew Assefa: Conceived and designed the experiments; collected data; analyzed and interpreted the data; wrote the original draft. Kebebew Assefa, Dejene Girma and Tileye Feyissa: supervision, conceptualization, methodology, review and editing Consent form The authors have approved the manuscript for publication in this journal Conflict of Interest The author declares that no conflict of interest. Data availability statement All the data that supports the findings of this study is referenced in the article in form of tables and graph. References CSA (2021). Central Statistical Agency. Agricultural Sample Survey 2020/21 (2013 E.C.), Volume I, Report on Area and Production of Major Crops (Private Peasant Holdings, Meher Season), Statistical Bulletin 590, Addis Ababa, Ethiopia. Baye K (2014). Synopsis: Tef Nutrient Composition and Health Benefits. Ethiopia Strategy Support Program. (2013–2014). Jansen G, Dimaio L, Hause N (1962). 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Agri.and Food Security 4: 7-16. Jifar H, Tesfaye K, and Assefa K (2018). Agro-morphological traits diversity in tef [Eragrostis tef ( Zucc.) Trotter] genotypes from various sources. Ethiopian. J. Agric. Sci. 28(3): 131-148. Jifar H, Tesfyae K. Assefa K., Chanyalew S, Tadele Z (2017). Semi-dwarf tef (Eragrostis tef) lines for high seed yield and lodging tolerance in central Ethiopia. Afric. Crop Sci. J. 25 (4): 419 - 439. Assefa K, Merker A, Tefera H (2003b). Multivariate analysis of diversity of tef (Eragrostis tef (Zucc.) Trotter) germplasm from western and southern Ethiopia. Hereditas 138: 228–236. Adnew T, Ketema S, Tefera. H. and Sridhara H (2005). Genetic diversity in tef [Eragrostis tef (Zucc.) Trotter) germplasm. Genetic Resour. and Crop Evol. 53: 891-902. Tables Tables 1 to 9 are available in the Supplementary Files section Additional Declarations The authors declare no competing interests. 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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-4826900","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":333623480,"identity":"4355d217-4730-4183-9cf3-7ecbecf61b18","order_by":0,"name":"Derejaw Tesfa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie2PvaoCMRBGI4HZJrrtBERfIbAgwn2ZW6WKbyBiZeXPq1htrYSbbfYBFiyusmBhJSyIlZqo9W5KwRwY5iu+AzOEBAKfyMYO2oF++YxdbwVjIn9dZH6Kq/KpEi40K51scdgPx3oiNnl1KsZDRiL9t65TeJ4lAo1GsV2kP8rYw5iURZ0iCgmIsEOh22miwCrIBvXK/zG64s0qhh0TdfNRCgDCZzvkc0bL0cxD4bmkyJd3vkIY0NESGTT90slMq8KLjAFpWanLpBdH2tQqDoqvDc8NTXVH6/xWzz7tQCAQ+D4e3OxGyYXeW+oAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0005-9060-9112","institution":"ARARI","correspondingAuthor":true,"prefix":"","firstName":"Derejaw","middleName":"","lastName":"Tesfa","suffix":""}],"badges":[],"createdAt":"2024-07-30 08:16:40","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-4826900/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4826900/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61471055,"identity":"2b6b2219-d598-41ab-9cf2-c265a5f32575","added_by":"auto","created_at":"2024-07-31 06:46:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92998,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram showing relationships among the 81 tef genotypes based on average linkage and Euclidean distance using the mean of 17 Quantitative traits based on UPGMA 305\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4826900/v1/701d14e6c176576aea975672.png"},{"id":61471740,"identity":"a6e12e9a-1502-4120-9a7d-f36ab4ff0d35","added_by":"auto","created_at":"2024-07-31 06:54:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":429051,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4826900/v1/5bfac478-fec9-4239-b5ff-c34662b5f5c2.pdf"},{"id":61471056,"identity":"97186c96-d91b-4f65-b584-874b524e67a2","added_by":"auto","created_at":"2024-07-31 06:46:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":68793,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4826900/v1/4baa447d923856aac6ef8159.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePerformance and Diversity of Ethiopian Core Tef Germplasm Under Seasonal Conditions\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eTef (Eragrostis tef, Zucc. Trotter) is an indigenous and significant food, feed, and cash crop for smallholder Ethiopian farmers. Among all the grain crops (cereals, legumes, and oilseeds) cultivated in Ethiopia, cereals are the most extensively produced, covering 10.56\u0026nbsp;million hectares, with a production of about 30.21\u0026nbsp;million tons, involving approximately 15.74\u0026nbsp;million farmer households[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Consequently, cereals constitute around 81.19% of the total acreage and 88.36% of the overall grain production of all crops grown in the country. Within the category of cereals, tef covers 2.93\u0026nbsp;million hectares of land, equivalent to 27.8% of the total area allocated to cereal crop cultivation in Ethiopia [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The same source indicates that the annual grain production of 5.51\u0026nbsp;million tons places tef third in line, following maize and wheat with 10.56 and 5.78\u0026nbsp;million tons, respectively [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The number of farming households engaged in tef production is approximately 6.87\u0026nbsp;million, second only to those growing maize (10.19\u0026nbsp;million) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTef is favored by both farmers and consumers due to its versatility in adapting to a wide range of environments. It performs better than other cereals under adverse climatic and soil conditions and its grains can be stored for extended periods as they are not susceptible to storage pests.\u003c/p\u003e \u003cp\u003eTef grains are primarily used to make injera, and sometimes porridge, non-raised bread (\"kita\"), and local alcoholic drinks like tela and katikala. It provides nutrition similar to more common cereals like wheat [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], offering balanced amino acids with a high lysine content [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and significant iron content [[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]]. Additionally, its straw serves as cattle feed, reinforces mud used for plastering walls of huts and local grain storage facilities, and serves as bedding material and mulch.\u003c/p\u003e \u003cp\u003eCurrently, tef is gaining global popularity as a health and performance food primarily because its grains are gluten-free, which prevents celiac disease. It also serves as a suitable dietary option for individuals with diabetes due to its low glycemic index in comparison to most other cereals [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Consequently, tef grain production has been introduced to industrialized nations, particularly in recent years.\u003c/p\u003e \u003cp\u003eDespite its immense significance in Ethiopia, tef production faces numerous constraints and technical as well as socio-economic challenges. These include the lack of cultivars tolerant to lodging and improved varieties suitable for diverse agro-ecologies, reliance on traditional cultural practices, exposure to abiotic stresses (such as drought, salinity, acidity, and cold/heat), susceptibility to pests (including weeds, diseases, and insect pests), weak seed and extension systems, and limitations in the accessibility and affordability of inputs like fertilizers[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo overcome these constraints, assessing the variability in the genotypes and exploiting favorable traits using morphological and molecular markers is vital. Morphological markers are the earlier markers utilized to assess genetic diversity within and between populations. While morphological markers provide straightforward measurements of phenotypes, they exhibit low polymorphism and heritability, and their expressions are influenced by environmental factors [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Nevertheless, they offer a cost-effective means of characterizing germplasm accessions.\u003c/p\u003e \u003cp\u003eSeveral genetic diversity studies [[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]] have already revealed substantial phenotypic variations among tef germplasm accessions and/or varieties for pheno-morphic and agronomic traits. However, tef's productivity, with a national average yield of 1.88 t/ha, remains notably low compared to other cereals [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Therefore, enhancing tef's productivity to compete with more productive cereals is imperative. This indicates the presence of critical constraints in tef production. Predominantly, these include lodging, abiotic stresses like drought and acidity, and biotic stresses (diseases and insect pests) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. To address these challenges, a comprehensive evaluation of germplasm variability using morphological markers becomes of paramount importance.\u003c/p\u003e \u003cp\u003eConsequently, this study was initiated with the aim of generating useful information on the phenotypic diversity of selected core tef germplasm lines along with some recently released varieties as an aid for designing effective and efficient tef breeding strategies.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eExperimental Plant Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental plant materials for both the field and laboratory experiments comprised a total of 81 genotypes including 74 core germplasm lines and seven released varieties obtained from the working collections at the National Tef Research Program of DZARC. The germplasm materials originated from previous collections made from different areas of Ethiopia (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescription of Experimental Sites and Season\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe field experiment was carried out at the Debre Zeit Agricultural Research Center (DZARC). It is located about 47 km Southeast of Addis Ababa. The geographical position and the climatic and soil-related data of the sites have been summarized on (Table 2). The experiments were carried out in 2021 both in the off- and main cropping season. The off-season experiment was planted at the end of January 2021 using irrigation and again the experiment was repeated in the main season. The experimental field at this site is characterized by heavy black clay soil with very high moisture retention capacity (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperimental Design and Management\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe field experiments were conducted using a 9x9 simple lattice design. The plot sizes measured 1 m x 1 m with 1 m distances between plots and 1.5 m distances between blocks/replications. Each plot was comprised of 5 rows spaced 20 cm apart. The core collection and varieties were randomly assigned to plots within each replication. Following research recommendations of 10 kg/ha, 1 g/plot of seeds was manually broadcasted along the surfaces of the rows in each plot\u0026nbsp;[15].\u003c/p\u003e\n\u003cp\u003eFertilizers were applied at the rate of 40 kg N and 60 kg P2O5 per hectare, following the recommendations for black soil at Debre Zeit. Diammonium phosphate (DAP) was administered at planting, while urea was applied two weeks after sowing and top-dressed during the tillering stage\u0026nbsp;[13]. Hand weeding was made three times during the crop growth stage. All other cultural management practices were performed as per the research recommendations for tef production in the particular test location.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhenotypic data were recorded both on a whole-plot basis and an individual plant basis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData collected on a whole-plot basis\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e:\u0026nbsp;\u003c/em\u003eFor the entire plot, data were recorded on days to panicle emergence (heading), days to maturity, grain filling period, grain yield, shoot biomass yield, harvest index, and lodging index. The lodging index was assessed using Caldicott and Nuttall\u0026rsquo;s [16] method, which considers both the extent and severity of lodging. Severity or angle lodging was graded on a scale of 0 to 5, based on the angle of lodging (0 = no lodging = 100% upright,\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;5 = completely lodged = 100% flat). Intermediate degrees of lodging were represented by scores 1 to 4 corresponding to the angle of lodging. Subsequently, the prevalence of lodging in a plot, expressed as the percentage of the affected plot area for each degree score, was recorded. The lodging index was ultimately calculated as the average product sum of each degree of lodging and its corresponding prevalence percentage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData collected on a single-plant basis:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eInformation at an individual plant level was collected from five random plant samples per plot, and the average data from these five sample plants were used for analysis. Traits assessed on an individual plant basis encompassed plant height, panicle length, culm length, peduncle (uppermost culm internode) length, total and fertile tiller counts, fertile floret counts per spikelet, main shoot panicle weight, and grain yield per panicle (main shoot).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to executing analysis of variance, data obtained from the field experiments across two Seasons were checked for normal distribution and homogeneity of error variances, assessed through Bartlett\u0026rsquo;s tests[17]\u0026nbsp;and the SAS software package\u0026nbsp;[18]. Test genotypes were grouped variance results for the two seasons were analyzed\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;separately, since the analysis of variance given that, data for all traits didn\u0026apos;t exhibit consistent error variance homogeneity\u0026nbsp;[19].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;For all the multivariate analyses techniques employed, the means of each of the traits were pre- Standardized to mean zero and variance unity to prevent bias due to differences in Measurement.\u003c/p\u003e\n\u003cp\u003eThe ANOVA was conducted using the model outlined in (Table 3):\u003c/p\u003e\n\u003cp\u003ePij = \u0026micro; + gi + rj + bki + eij\u003c/p\u003e\n\u003cp\u003eWhere, Pij = phenotypic value of the ith genotype under the jth replication within replication j; \u0026micro; = Grand mean; gi = the effect of the ith genotype; rj = the effect of replication j; bki = the effect of the ith Incomplete blocks in replication, and eij = the residual or effect of random error.\u003c/p\u003e\n\u003cp\u003eThe analysis of variance (ANOVA) was executed separately for all characteristics using the SAS Statistical package (SAS 9.0). ANOVA was employed to determine the presence of significant variation among the test materials. F-tests within the ANOVA were regarded as significant at p\u0026le;0.05.\u003c/p\u003e\n\u003cp\u003eFor all subsequent multivariate statistical analyses, including cluster analysis (CA), distance analysis, and principal components analysis (PCA), the standardized means of the 81 tef test genotypes were applied. The collective hierarchical cluster analysis approach was used to examine the grouping pattern of the 81 tested genotypes based on their similarity, constructed using the corresponding means of all 17 studied traits. The cluster analysis was performed using the average linkage method, with the determination of the number of clusters based on local peaks of the pseudo-F-statistic joining with small values of the pseudo-t2 statistic, followed by a larger pseudo-t2 for the\u0026nbsp;subsequent cluster combination. This process was executed using the SAS statistical package\u0026nbsp;[18]\u0026nbsp;.\u003c/p\u003e\n\u003cp\u003eThe dendrogram was constructed using the average linkage and the Euclidean distance as a measure of dissimilarity, employing the SAS statistical package. Similarly, a cluster based on origin was formed by computing the means of the genotypes per origin and then standardizing the data, as was done for the genotype clustering, using the SAS statistical package.\u003c/p\u003e\n\u003cp\u003eGenetic distances between clusters, as standardized, were calculated using Mahalanobis\u0026apos;s D2 statistics\u0026nbsp;[20]:\u003c/p\u003e\n\u003cp\u003eD2ij = (xi - xj)\u0026apos; cov-1(xi - xj)\u003c/p\u003e\n\u003cp\u003eWhere, D2ij = the distance between cases i and j; xi and xj = vectors of the values of the variables for cases i and j.\u003c/p\u003e\n\u003cp\u003eTo assess the significance level of genetic diversity between and within clusters, a chi-square table (X2 table) was utilized, based on the degree of freedom of traits at a significance level of 0.05 or 0.010.\u003c/p\u003e\n\u003cp\u003eThe principal components (PC) examinations were utilized to identify the characteristics contributing a large portion of the whole variation among the 81 test genotypes. The characters with bigger absolute values closer to one inside each principal component impact the clustering more than those with lower total values closer to zero\u0026nbsp;[21].\u003c/p\u003e\n\u003cp\u003eOnly PCs with eigenvalues greater than one were considered as important. As recommended by Johnson and Wichern [22] , a characteristic coefficient or eigenvector greater than half divided by the standard deviation (square root) of the eigenvalue of the respective PC was utilized as a general rule to evaluate the relative importance of traits constituting the PCs for \u0026apos;P\u0026apos; degrees of freedom, where P represents the number of traits considered [23].\u003c/p\u003e"},{"header":"RESULTS AND DISCUSSION","content":"\u003cp\u003eGenetic variability in breeding materials is essential for a successful plant breeding program. Understanding the magnitude of variability in crop species is crucial because it forms the foundation for selection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrait Ranges and Analysis of Variance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the present study, all traits except biomass yield did not exhibit homogeneity of error variances, \u0026nbsp;as \u0026nbsp;indicated \u0026nbsp; by P-values \u0026nbsp;below \u0026nbsp;the \u0026nbsp; significance \u0026nbsp;levels \u0026nbsp;(0.05 \u0026nbsp; and \u0026nbsp;0.01). Consequently, separate analyses were conducted. Based on data from two seasons, substantial ranges between maximal and minimal mean values were observed for all evaluated traits (Table 4). For instance, in the main season, the mean value ranges for days to heading, days to maturity, and grain filling period were 32-44, 86- 101.8, and 44-62.8 days, respectively. In the off-season, these ranges were 43.0-54.1, 87.0- 97.4, and 43.3-44 days, respectively (Table 4). Similarly, broad ranges were also noted for all studied traits (Table 4).\u003c/p\u003e\n\u003cp\u003eAcross the two seasons of field experiments, the ranges of mean values were 50.6-114.2 cm for plant height, 28-71.6 cm for culm length, 22.2-50.6 cm for panicle length, and 11.0-28.4 cm for peduncle length (Table 4). The minimum and maximum mean values across the two seasons were 0.023 and 0.5 g for thousand seed weight, 350 and 3500 kg/ha for grain yield, 2000 and 24000 kg/ha for biomass yield, 0.01 and 1.53 for grain yield per panicle, 21-88 for lodging index, and 0.042 and 22.1% for harvest index.\u003c/p\u003e\n\u003cp\u003eThe separate analyses of variance for the two seasons revealed that, except for thousand seed weight and fertile tiller number per plant in the main season, and peduncle length and number of fertile tillers in the off-season, most of the genotypes did not show statistically significant differences in terms of variance (P \u0026le; 0.01 and 0.05) for all evaluated traits (Table 5).Likewise, notable difference concerning origin (regions) were observed in the mean squares of a few traits: biomass yield, grain yield per panicle, and main shoot panicle weight during the off- season, as well as plant height in the main season (Table 5).\u003c/p\u003e\n\u003cp\u003eThe current results from the analyses of variance contrast with previous findings in genetic diversity studies of tef germplasm, where substantial variations were reported for many of the evaluated traits [[9],\u0026nbsp;[10],\u0026nbsp;[11],\u0026nbsp;[24]]. These discrepancies could be due to differences in genotypes and testing environments\u0026nbsp;[25].\u0026nbsp;However, the range values of traits are in alignment with the report by Assefa et al.\u0026nbsp;[9], except for total and fertile tillers per plant, grain yield, biomass yield, and harvest index. As indicated the fertile tillers per plant ranged from 4.6 to 25 in the main season and 1.8 to 10.6 in the off-season, the mean value for the main season was higher than that reported by Assefa et al.[9]\u0026nbsp;, Jifar et al.\u0026nbsp;[11], and Fikre et al.\u0026nbsp;[13]. Nonetheless, the mean values of tiller number in the off-season were nearly similar with the reports of Assefa et al.\u0026nbsp;[9], Jifar et al.\u0026nbsp;[11], and Fikre et al.\u0026nbsp;[13].\u003c/p\u003e\n\u003cp\u003eSimilarly, mean value ranges for shoot biomass and grain yield in the main season were 5.2 to 24.0 t/ha and 0.35 to 3.50 t/ha, respectively. In the off-season, these ranges were 0.7-2.3 t/ha and 5.0-19.0 t/ha. This indicates that the main season biomass and grain yield per hectare were roughly comparable to those reported by Jifar et al. [26], while off-season yields were relatively lower. Additionally, the range of harvest index in this study was 1.8-22.1 in the main season and 0.042-14.18 in the off-season. These values significantly deviate from the ranges of 5.0- 38.8 and 14.7-24.3 reported by Assefa et al. [9] and Jifar et al. [27], respectively. Such differences can be attributed to seasonal variations in moisture and temperature, as well as differences in test genotypes [25]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster and Distance Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCluster analysis grouped the genotypes into four clusters based on their similarity. The first cluster (C1, n=31=38.27%) comprised the largest number of core germplasm lines, originating from Jimma (6), Gojam (4), Tigray (4), Wello (11), Wellega (4), and East Shoa (2) (Figure 1 and Table 6). Subsequently, the fourth cluster (n=19=23.46%) included lines from West Shoa (17) and East Shoa (2), followed by the second cluster (n=16=19.75%) encompassing all released varieties (7), as well as germplasm lines from Arsi (5) and West Shoa (4). The smallest cluster was cluster three (C3, n=15=18.51%), consisting of 15 genotypes, all originating from East Shoa (Figure 1 and Table 6).\u003c/p\u003e\n\u003cp\u003eThe clusters displaying the least genetic divergence were clusters 3 and 4, with a D2 value of 11.80, while relatively high divergence was observed between cluster 1 and cluster 2, followed by cluster 1 and cluster 4 (Table 7). Conversely, within-cluster divergence was relatively high for cluster three, followed by cluster two, with the least observed within cluster 1 (Table 7).\u003c/p\u003e\n\u003cp\u003eWhen comparing the four clusters formed based on the similarity or differentiation of 17 pheno-morphic and agronomic traits, significant differences among clusters were only observed for three traits: grain filling period, plant height, and grain yield per panicle (Table8). The findings of this study diverge in terms of the number of clusters from those reported by different authors using different sets of tef genotypes. For example, the reported number of clusters was 3 for 18 genotypes[12]\u0026nbsp;, 6 for 28 semi-dwarf genotypes\u0026nbsp;[28], 6 for 188 genotypes\u0026nbsp;[27], and 7 for 49 genotypes\u0026nbsp;[13].\u003c/p\u003e\n\u003cp\u003eThe tested tef genotypes, including germplasm lines from various zones and released varieties from the same origin, clustered into different classes, while those from different origins were grouped together. This confirms the conclusion drawn by Assefa et al.[10] \u0026nbsp; That the genetic diversity level in tef germplasm is comparatively higher within populations (origin) than among populations (origins). Consequently, accessions originating from the same region and altitude were not distinctly separated into distant clusters. Therefore, although this study indicates relatively lower diversity, tef genotypes did not cluster into a small number of groups, as noted in earlier studies [[29],[26]].\u003c/p\u003e\n\u003cp\u003eIn this study, the least genetic divergence was observed between cluster 3 and cluster 4, with a D2 value of 11.8, while high significant divergence was noted between cluster 2 and cluster 1 (D2 = 85.49), followed by the divergence between cluster 1 and cluster 4 (D2 = 45) (Table 7). The notably high inter-cluster distance between cluster 1 and cluster 2 may be attributed to the inclusion of released varieties. Consequently, it is advisable to consider crosses from this cluster for enhanced heterotic expression [27]. Furthermore, within clusters, relatively high distances were observed for cluster 3 and 2 (Table 7), indicating the presence of diverse genotypes within the same cluster, which could hold potential for further breeding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrincipal Component Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe principal component analysis revealed that the first six principal components, each with eigenvalues greater than one, collectively accounted for approximately 70.6% of the total variation among the 74 tef core germplasm lines and 7 released varieties assessed for 17 traits (Table 9). Among these, the first principal component (PC) explained 20% of the overall phenotypic variation among the tef genotypes, primarily attributed to variations in panicle length, number of fertile florets per spikelet, culm length, plant height, days to maturity, number of total and fertile tillers, and grain filling period.\u003c/p\u003e\n\u003cp\u003eThe second principal component, which accounted for 15.20% of the total variation, was primarily influenced by variations in grain yield per plot, biomass yield, harvest index, and peduncle length. The third principal component, contributing to 10.80% of the total variation, was chiefly due to variations in grain yield per panicle, main panicle shoot weight, and peduncle length. The fourth principal component, responsible for 8.90% of the total variation, primarily resulted from high variations in the number of fertile and total tillers per plant.\u003c/p\u003e\n\u003cp\u003eSimilarly, the fifth principal component, explaining 7.80% of the total variation, was mainly influenced by the number of fertile florets per spikelet and thousand seed weight. The sixth principal component, also accounting for 7.8% of the total variation, was chiefly affected by variations in the number of days to heading.\u003c/p\u003e\n\u003cp\u003eThe observed variation in principal components in this study is lower than that reported in studies by Assefa et al.\u0026nbsp;[29], Jifar et al.\u0026nbsp;[26], Jifar et al.\u0026nbsp;[28], and Fikre et al.\u0026nbsp;[13], where the first PC accounted for 40%, 44.7%, 41.3%, and 30.65% of the gross variability, respectively. Furthermore, the proportion of variation explained by the first three principal components in this study (46%) was lower than values previously reported: 64.7% by Assefa et al.\u0026nbsp;[10][10], 68.67% by Assefa et al.\u0026nbsp;[29], 74.66% by Adnew et al.\u0026nbsp;[30],\u0026nbsp;71.03% by Plaza-W\u0026uuml;thrich et al.[12]\u0026nbsp;, 78.3% by Jifar et al.\u0026nbsp;[26], 69.1% by Jifar et al.\u0026nbsp;[28], and 55.9% by Fikre et al.\u0026nbsp;[13].\u003c/p\u003e\n\u003cp\u003eThis suggests that the phenotypic diversity among the tested tef genotypes cannot be adequately explained solely by a few principal components. This observation persists despite the fact that the analyses of variance did not reveal substantial variations among the genotypes in most of the evaluated traits.\u003c/p\u003e"},{"header":"CONCLUSIONS AND RECOMMENDATIONS","content":"\u003cp\u003eAn understanding of genetic diversity and population structure of tef using morphological and powerful molecular marker are important steps for breeding. In this study, the morphological diversity analyses depicted highly significant genetic distances between clusters 1 and 2, and this implies that it would be worth selecting tef materials from these clusters for cross-breeding program especially for the traits of grain falling period, yield per panicle and plant height. And the phenotypic diversity among the test tef genotypes cannot be explained in terms of few PCs, in spite of the fact that the analyses of variance did not show substantial variations among the genotypes in most of the traits evaluated\u003c/p\u003e\n\u003cp\u003eThe germpalsm materials used in the present study did not cover all the different agro-ecologies of the country because most of the lines in the core germplasm set lack passport data. Hence, for a fruitful tef breeding and enhancement of the precision genetic diversity investigations for breeders to exploit the hereditary potential of the crop in improving its generation and efficiency, the core germplasm collection should represent all the various tef growing agro- ecologies of the country. Consequently, it is recommendable to first revisit the core germplasm set, and assemble a truly representative core germplasm based on ensuring inclusion of accessions from all the diverse agro-ecologies followed by systematic evaluation and charcaterization based on both phenotyping using important pheno-morphic and agronomic traits as well as genotyping using suitable and modern molecular markers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe principal author acknowledges the assistance and support of the Ethiopian Institute of Agricultural Research (EIAR), Debre Zeit Agricultural Research Center and its staff, and all the tef research team and especially Mr. Nigussu Hussein.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContribution of the Authers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDerejaw Tesfa and Kebebew Assefa: Conceived and designed the experiments; collected data; analyzed and interpreted the data; wrote the original draft. Kebebew Assefa, Dejene Girma and Tileye Feyissa: supervision, conceptualization, methodology, review and editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent form\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have approved the manuscript for publication in this journal\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data that supports the findings of this study is referenced in the article in form of tables and graph.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eCSA (2021). Central Statistical Agency. Agricultural Sample Survey 2020/21 (2013 E.C.), Volume I, Report on Area and Production of Major Crops (Private Peasant Holdings, Meher Season), Statistical Bulletin 590, Addis Ababa, Ethiopia.\u003c/li\u003e\n \u003cli\u003eBaye K (2014). Synopsis: Tef Nutrient Composition and Health Benefits. Ethiopia Strategy Support Program. (2013\u0026ndash;2014).\u003c/li\u003e\n \u003cli\u003eJansen G, Dimaio L, Hause\u0026nbsp; \u0026nbsp;\u0026nbsp;N (1962). Amino acid composition and lysine Supplementation of teff. J. Agri. Food Chem. 10: 62-64.\u003c/li\u003e\n \u003cli\u003eMengesha H (1966). Chemical composition of tef (Eragrostis tef) compared with that of wheat, barley and grain sorghum. Econ. Bot. 20:268-27\u003c/li\u003e\n \u003cli\u003eCostanza S, Dewet J, Harlan J (1979). Literature‐ review and numerical taxonomy of Eragrostis tef (t\u0026rsquo;ef). Economic Botany 33: 413\u0026ndash;424.\u003c/li\u003e\n \u003cli\u003eSaturni L, Ferretti G, Bacchetti T (2010). The gluten-free diet: safety and nutritional quality. Nutri. 2: 16-34.\u003c/li\u003e\n \u003cli\u003eAssefa K, Yu J, Zeid M, Belay G,Tefera H, Sorells M (2011). Breeding tef [Eragrostis tef (Zucc.) Trotter]: Conventional and molecular approaches. Plant Breeding 130: 1-9.\u003c/li\u003e\n \u003cli\u003eMondini, L, Noorani A, Pagnotta, MA (2009). Assessing plant genetic diversity by molecular tools. Diversity, 1(1), 19-35\u003c/li\u003e\n \u003cli\u003eAssefa K, Ketema S, Tefera H, Kefyalew T, Hundera F (2000). Trait diversity,heritability and genetic advance in selected germplasm lines of tef [Eragrostis tef (Zucc.) Trotter]. Hereditas 133: 29\u0026ndash;37.\u003c/li\u003e\n \u003cli\u003eAssefa K, Tefera H, Merker A, Kefyalew T, Hundera F (2001b). Variability, heritability and genetic advance in pheno-morphic and agronomic traits of tef [Eragrostis tef (Zucc.) Trotter] germplasm from eight regions of Ethiopia. Hereditas 134: 103-113\u003c/li\u003e\n \u003cli\u003eJifar H, Assefa K, Bekele E (2011). Genetic variability in released tef [Eragrostis tef (Zucc.) Trotter] varieties of Ethiopia. Proceedings of the Thirteenth Biennial Conference of Crop Science Society of Ethiopia. Sebil 160-169.\u003c/li\u003e\n \u003cli\u003ePlaza-W\u0026uuml;thrich S, Cannarozzi G, Tadele,Z (2013).Genetic and phenotypic diversity in Selected varieties of tef [Eragrostis tef (Zucc.)Trotter]. Afri. J. of Agri. Res.8(12): 1042-1049.\u003c/li\u003e\n \u003cli\u003eFikre T,Assefa K, Tesfaye K (2020). Extent and pattern of genetic diversity for pheno- Agro-morphological traits in Ethiopian improved and selected farmers\u0026rsquo; varieties of tef ( Eragrostis tef (Zucc.) Trotter ). Afr. J. Agric. Res. 16(6): 892\u0026ndash;901.\u003c/li\u003e\n \u003cli\u003eAssefa K, Cannarozzi G,Girma D, Kamies R, ChanyalewS,Sonia Plaza-W\u0026uuml;thrich R, Rindisbacher A, Rafudeen s and Tadele Z (2015). Genetic diversity in tef [Eragrostistef (Zucc.) Trotter]. Front. Plant Sci .6:177\u003c/li\u003e\n \u003cli\u003eArefaine A, Adhanom D, Tekeste N (2020). Response of teff (Eragrostis tef (Zucc.) Trotter) to seeding rate and methods of owing on yield and yield attributes in a sub- humid environment, Northern Ethiopia. Inter. J. of Agronomy Volume 2020,\u003c/li\u003e\n \u003cli\u003eCaldicott J.B, Nutall A.M (1979). A method for the assessment of lodging in cereal crops. Journal of National Institute of Botany 15:88-91.\u003c/li\u003e\n \u003cli\u003eAslam M (2020). Design of the Bartlett and Hartley tests for homogeneity of variances under indeterminacy environment. J. of Taibah University for Sci. 14(1):6\u0026ndash;10.\u003c/li\u003e\n \u003cli\u003eSAS Institute (2002). SAS/STAT Guide for Personal Computers, Version 9.00 editions.\u003c/li\u003e\n \u003cli\u003eManly B (1986). Multivariate Statistical Methods: A Primer. Chapman and Hall. London.\u003c/li\u003e\n \u003cli\u003eMahalanobis PC (1936). On generalized distance in statistics. Proc. Natl. Sci India B. 2: 49-55.\u003c/li\u003e\n \u003cli\u003eChahal G, Gosal \u0026nbsp;S \u0026nbsp;(2002). \u0026nbsp; Principles and\u0026nbsp; \u0026nbsp;Procedures of \u0026nbsp;Plant\u0026nbsp; \u0026nbsp;\u0026nbsp;Breeding: Biotechnological and Conventional Approaches. Narosa Publishing House, New Delhi, India\u003c/li\u003e\n \u003cli\u003eJohnson R, Wichern D (1988). Applied Multivariate Statistical Analysis. 2nd Edition, John Wiley \u0026amp; Sons Inc., New York.\u003c/li\u003e\n \u003cli\u003eSingh R, Chaudhary B (1985). Biometrical Methods in Quantitative Genetic Analysis. Kalyani Publishers, New Delhi-Ludhiana, India.\u003c/li\u003e\n \u003cli\u003eAssefa K, Tefera H, Merker A (2002). Variation and inter-relationships of quantitative traits in tef [Eragrostis tef (Zucc.) Trotter] germplasm from western and southern Ethiopia. Hereditas 136:116-125.\u003c/li\u003e\n \u003cli\u003eHammer G, McLean G, Chapman S, Zheng B, Doherty A, Harrison M, Oosterom E,Jordan D (2014). Crop design for specific adaptation in variable \u0026nbsp;dryland production Environments. Crop and Pasture Sci. 65:614-626\u003c/li\u003e\n \u003cli\u003eJifar H, Assefa K, Tadele Z (2015). Grain yield variation and association of major traits in brown seeded varieties of tef [Eragrostis tef (Zucc.) Trotter]. Agri.and Food Security 4: 7-16.\u003c/li\u003e\n \u003cli\u003eJifar H, Tesfaye K, and Assefa K (2018). Agro-morphological traits diversity in tef [Eragrostis tef ( Zucc.) Trotter] genotypes from various sources. Ethiopian. J. Agric. Sci. 28(3): 131-148.\u003c/li\u003e\n \u003cli\u003eJifar H, Tesfyae K. Assefa K., Chanyalew S, Tadele Z (2017). Semi-dwarf tef (Eragrostis tef) lines for high seed yield and lodging tolerance in central Ethiopia. Afric. Crop Sci. J. 25 (4): 419 - 439.\u003c/li\u003e\n \u003cli\u003eAssefa K, Merker A, Tefera H (2003b). Multivariate analysis of diversity of tef (Eragrostis tef (Zucc.) Trotter) germplasm from western and southern Ethiopia. Hereditas 138: 228\u0026ndash;236.\u003c/li\u003e\n \u003cli\u003eAdnew T, Ketema S, Tefera. H. and Sridhara H (2005). Genetic diversity in tef [Eragrostis tef (Zucc.) Trotter) germplasm. Genetic Resour. and Crop Evol. 53: 891-902.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 9 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Ethiopian Institution of Agricultural Research","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cluster, Core germplasm, Released Varieties, Morphological traits, Principal components, tef.","lastPublishedDoi":"10.21203/rs.3.rs-4826900/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4826900/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTef is an indigenous and important food, feed, and cash crop for smallholder Ethiopian farmers. Information about the natural genetic variation of the crop would be useful for genetically improving it through breeding. Therefore, the current study was designed to determine the extent and pattern of genetic variability among selected tef core germplasm lines and released Varieties from Ethiopia, using morphological traits. A total of 81 tef genotypes were field- evaluated for 17 morphological traits using a 9 × 9 simple lattice designs at Debre Zeit during the 2021 main cropping season and off-season. Among the assessed traits, only a few showed significant differences among the genotypes. Specifically, these traits were thousand seed weight and fertile tiller number in the main season, and peduncle length and number of fertile tillers in the off-season. Cluster analysis grouped the 81 tef genotypes into four clusters, each consisting of 15 to 31 genotypes. Principal component analysis indicated that approximately 71% of the gross variance among the tested genotypes could be explained by six principal components with eigenvalues greater than one. In general, the study revealed highly significant genetic distances between clusters 1 and 2. This suggests that selecting tef materials from these clusters for a cross-breeding program would likely be beneficial.\u003c/p\u003e","manuscriptTitle":"Performance and Diversity of Ethiopian Core Tef Germplasm Under Seasonal Conditions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-31 06:46:42","doi":"10.21203/rs.3.rs-4826900/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2affc3eb-db28-4c49-9a19-21e240499d37","owner":[],"postedDate":"July 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":35327857,"name":"Population Biology"}],"tags":[],"updatedAt":"2024-07-31T06:46:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-31 06:46:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4826900","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4826900","identity":"rs-4826900","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}