Genetic Diversity for Morphological and Nutritional Traits in Global Lentil Genotypes for Biofortification Breeding in Ethiopia

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This study aims to evaluate the morphological diversity, mineral composition (Zn, Fe), and ash content of 64 lentil genotypes sourced from ICARDA, Aus, Argentina, Jordan and Ethiopian-released varieties. The experiment was carried out using a simple lattice design with two replications. The data were subjected to multivariate analyses, including principal component analysis (PCA) and clustering techniques, to evaluate genetic diversity and trait associations using different packages of R studio. The results revealed significant variation among genotypes in morphological traits, with some showing high Zn and Fe concentrations alongside desirable agronomic characteristics. The essential nutrients showed that iron levels ranged from 67.9 to 313.7mg/kg, zinc from 17.5 to 40mg/kg, ash from 1.5 to 4.45%, and protein from 17.5 to 26. Genotypes from ICARDA and Jordan exhibited larger thousand seed weight and seed yield comparatively. Hierarchical clustering was performed, and the dendrogram divided the genotypes into three groups using the ward method. Based on the principal component analysis, four principal components (PC1 to PC4) eigenvalues range from 1.04 to 3.5, collectively explaining 70.95% of the total variation. The first two principal components PC1 and PC2 contributed 35.02% and 13.09%, respectively.The findings highlight the importance of conservation and utilization of international lentil germplasm to enhance productivity as well as nutritional quality. Molecular and genomic tools must be utilized with conventional selection within future breeding schemes to facilitate the release of climate resistant and biofortified lentil varieties that will contribute to increased food and nutrition security. Cluster Genotype Nutritional quality Lentil variability Figures Figure 1 Figure 2 Figure 3 1. Introduction Lentils (Lens culinaris Medik.) belong to the Leguminosae family. Archaeological evidence indicates lentils were among the earliest cultivated crops in the Fertile Crescent, birthplace of agriculture (Herlan, 1992 ). It is believed to have been domesticated during the Neolithic period, around 8000–7000 BC with the advancement of agriculture, their cultivation spread from initial centers to the Mediterranean basin, the Indian subcontinent, and other regions of the ancient world (Zohary et al., 1988 ). It was one of the first crops to be cultivated by humans and played an essential role in early agriculture. The major producer of lentil is Canada, India, Turkey, USA, Kazakhstan, Nepal, Australia, Russia, Bangladesh, China and Ethiopia (FAO, 2022 ). It become a popular crop that is grown and eaten in many parts of the world (Montejano-Ramírez & Valencia-Cantero, 2024 ). Ethiopia is one of the biggest lentil producing country with altitude range of 1700 to 35000 masl. Lentil is a vital legume crop globally recognized for its nutritional and agronomic significance, rich in essential nutrients, which is crucial role in alleviating malnutrition and ensuring food security. It is rich in protein, fiber, various vitamins and micro nutrients iron (Fe), zinc (Zn), making them a promising crop in the global effort to combat human micronutrient deficiencies (Jha et al., 2022 : Rajpal et al., 2023 ). Lentil has climate-smart characteristics and the cultivation can further strengthen its role in addressing global challenges related to climate change (Gupta et al., 2019 : Khazaei et al., 2016 ). Lentil also provides important economic advantages to the small-scale farm households in providing food in the form of salads, soups, stews, feed, cash income and foreign currency earnings (Chilot et al. 2016; Tolesa and Asrat 2019). As leguminous crop, it contributes to soil fertility through biological nitrogen fixation, reducing the need for synthetic fertilizers. Its root systems also improve soil structure and promote microbial activity, making it a valuable component in sustainable agricultural systems Ethiopia has a vast area dedicated to lentil production, with up to 14 lentil genotypes released for cultivation. These genotypes have been sourced from ICARDA (International Center for Agricultural Research in the Dry Areas) and local germplasm collections to enhance productivity and adaptability. Despite these efforts to improve yield and agronomic traits, the nutritional composition of these high-performing lentil varieties has not been extensively studied (Baggar et al., 2023 ). To address this gap, a study focusing on the multivariate analysis of lentil genotypes for morphological traits, as well as zinc, iron, and ash concentration, is necessary. A primary motivation for this study is the widespread prevalence of micronutrient deficiencies, particularly zinc and iron, among human populations. Lentils serve as a potential source of these essential nutrients, but their concentration can vary significantly across different genotypes (Bhattacharya et al., 2022 ). Identifying lentil varieties with enhanced nutrient content is critical for improving both dietary intake and agricultural sustainability (Karakoy et al. , 2012). There is a growing need to identify and develop improved lentil genotypes with enhanced traits. The utilization of diverse germplasm from ICARDA and other sources is intended to address the limitations in productivity and nutritional quality. Understanding genetic and phenotypic variations is essential for developing high-yielding, nutrient-dense, and climate-resilient lentil varieties. Furthermore, micronutrient deficiencies, particularly zinc and iron, remain significant public health concerns in Ethiopia, where large populations suffer from anemia and other nutrient-related disorders. Enhancing lentil varieties with higher levels of zinc and iron offers a sustainable strategy to combat these deficiencies (Aboutayeb et al., 2023 : Kumar et al., 2018 : Dhaliwal et al., 2021 ). Thus, there is an urgent need to evaluate lentil genotypes from ICARDA, Ethiopian collections and other country using advanced statistical and multivariate techniques. This study aims to fill this knowledge gap by identifying genetic and phenotypic relationships among lentil genotypes and guiding future breeding efforts for enhanced nutritional and agronomic traits. Specifically, the primary goals of this research are to examine genetic diversity in seed iron (Fe) and zinc (Zn) concentrations and to identify key traits across a diverse range of lentil accessions. 2. Materials and Methods 2.1. Description of Experimental Area. The study area for the experiment was located in Moretna Jiru Wereda, situated in the North Shewa Zone of Ethiopia. This region is geographically positioned at latitude 9°52'10.7" North and longitude 39°10'46.5" East, with an elevation of 2,600 meters above sea level. It lies approximately 158 kilometers north of Addis Ababa, the capital city of Ethiopia. The site experiences a subtropical highland climate, with an average annual rainfall of 1,220 mm, making it conducive for agricultural activities. The average maximum temperature in the area is 21.3°C, while the average minimum temperature is 9.6°C, providing a suitable environment for diverse cropping systems. The soil at the site is classified as a Vertisol, which has a clay loam texture. Vertisols are notable for their high fertility and water retention properties, making them particularly advantageous for cultivating crops such as lentils, which were the focus of this study. Agricultural practices in the region are characterized by a mixed farming system that integrates livestock production with crop cultivation. This approach optimizes resource utilization, where animal husbandry and arable farming complement each other to enhance productivity and sustainability. The interaction between livestock and crop farming not only provides essential nutrients for the soil but also supports the livelihoods of the local farming community, ensuring a balanced and efficient use of the land resources in the area. This combination of favourable climatic conditions, fertile soils, and integrated farming practices makes Moretna Jiru Wereda an ideal site for conducting agricultural experiments and developing sustainable farming systems. 2.2. Experimental Materials The study utilized a diverse set of lentil genotypes sourced from various reputable institutions. These included genotypes obtained from the Debre Birhan, Debre Zeit Agricultural Research Center and the International Center for Agricultural Research in the Dry Areas (ICARDA). Sixty-four lentil genotypes were evaluated during the experiment. This collection encompassed both newly introduced and locally adapted genotypes, including five nationally and regionally released improved varieties were used. The description of experimental materials are presented in Table 1 Table 1 The description of experimental materials No Genotype Source Pedigree No Genotype Source Pedigree 1 LC-8603-59-L USA ILL10923 33 FLIP-2010-28L ICARDA ILL8090XILL6783 2 2009S -9651s-L ARGETINA ILL4400XILL7956 34 FLIP-2010-29L ICARDA ILL8090XILL6783 3 2009S -96575-L ARGETINA ILL6434XILL7938 35 FLIP-2010-30L ICARDA ILL8090XILL7685 4 2009S- 96576-L ARGETINA ILL4400XILL7949 36 FLIP-2010-31L ICARDA ILL8090XILL7686 5 2009S-96101-2 ARGETINA ILL323XILL4605 37 FLIP-2010-74L ICARDA WA8649090XILL7559 6 78S-26052 TURKEY ILLU2SELECTION 38 FLIP-2011-20L ICARDA ILL0590XILL5562 7 UIJ-29L JORDAN ILL5244 39 FLIP-2011-21L ICARDA ILL8116XILL5562 8 1b1a-1 JORDAN lbla-1 40 FLIP-2011-22L ICARDA ILL8116XILL5562 9 8IS-15 Jordan UJL197XILL4400 41 FLIP-2011-23L ICARDA ILL7683XILL5562 10 193S-180L AUS ILL10935 42 FLIP-2011-24L ICARDA ILL8116XILL5562 11 94-028L AUS ILL10933 43 FLIP-2010-25L ICARDA ILL8090XILL5769 12 95-005L AUS ILL10932 44 FLIP-2011-25L ICARDA ILL8116XILL5562 13 96-034L AUS ILL10930 45 FLIP-2011-27L ICARDA ILL2126XILL6199 14 97-011L AUS ILL10924 46 FLIP-2011-29L ICARDA ILL8116XILL5562 15 97-011L AUS ILL10933 47 FLIP-2011-30L ICARDA ILL8116XILL5562 16 97-039LX-99R064 AUS ILL10925 48 FLIP-2011-33L ICARDA ILL7949XILL7686 17 97-039LX-99R120 AUS ILL10924 49 FLIP-2011-36L ICARDA ILL7683XILL5562 18 97-039LX99RO60 AUS ILL7683XILL5562 50 FLIP-2011-37L ICARDA ILL8090XILL7685 19 Alem Tena DZARC Released (2004) 51 FLIP-2011-41L ICARDA ILL6467XILL8009 20 Jiru DBARC Released (2015) 52 FLIP-2011-42L ICARDA ILL818XILL5883 21 Teshale Ethiopia Released (2004) 53 FLIP-2011-62L ICARDA ILL7537XILL590 22 Alemaya-98 Ethiopia Released (1997) 54 FLIP-2011-82L ICARDA ILL7620ILL8113 23 Chekol Ethiopia Released (1994) 55 ILL 2261 ICARDA IG 2261 24 FLIP-2007-1L ICARDA ILL7620XILL7686 56 ILL 2303 ICARDA IG2303 25 FLIP-2010-19L ICARDA ILL7949XILL7686 57 ILL-1323 ICARDA NEL 1323 26 FLIP-2010-20L ICARDA ILL0590XILL5769 58 PRECOZ ICARDA ILL4353XILL4400 27 FLIP-2011-56L ICARDA ILL702XILL2125 59 010S- 96105-1 ICARDA ILL8072XILL7162 28 FLIP-2010-21L ICARDA ILL6467XILL8009 60 010S- 96122-3 ICARDA ILL1005XILL5883 29 FLIP-2010-22L ICARDA ILL7012XILL2125 61 010S- 96134-3 ICARDA ILL4400XILL7947 30 FLIP-2010-23L ICARDA ILL712XILL2125 62 010S-96143-4 ICARDA ILL323XILL9977 31 FLIP-2010-24L ICARDA ILL8116XILL5562 63 09S -82109-04 ICARDA ILL1005XILL5883 32 FLIP-2010-26L ICARDA ILL8116024XILL0098 64 09S -83227-04 ICARDA ILL1005XILL5883 2.3. Experimental Design and Procedures The study was conducted using simple lattice design with two replications. Each genotype was sown in uniform plots measuring 4 m × 0.8 m (3.2 m²), with 20 cm row spacing, 0.4 m between plots, and 1.5 m between blocks. Seeds were hand sown in the first week of August using broad bed furrows (BBF). Standard agronomic practices, including weeding and pest management, were followed throughout the growing season. Pea aphids were managed by spraying Dimethoate at a recommended rate of 1.8 L per hectare mixed with 200 L of water. The crop was harvested at maturity when the pods turned yellowish, ensuring proper physiological development. 2.4. Data Collections The morpho-agronomic traits and yield components were collected from a field trial. Measurements were taken from 10 randomly selected plants in the central two rows of each plot, and the average values were calculated for traits such as plant height (cm), seed yield, above-ground biomass, and hundred-seed weight (g). Additionally, phenological traits, including days to 50% flowering and days to maturity, were recorded on a plot basis. Sample preparation and Methods for quality traits For the analysis of quality traits such as seed ash, protein content, and mineral composition (iron and zinc), a 10 g seed sample was collected from each genotype and ground into a fine powder. To avoid contamination during grinding, gloves were worn throughout the process. All analyses were performed using the ground seed samples. The concentrations of iron (Fe) and zinc (Zn) in the lentil seeds were determined using the dry ashing method, followed by Atomic Absorption Spectroscopy (AAS), as described by Chapman and Pratt ( 1961 ). The procedure was placing 0.5 g portions of ground plant material in to 50ml crucible. Place crucible into cool the furnace and increase temperature gradually to 550ºc ash for 5 hours. After ashing is complete cool the furnace and takeout the crucible and keep in the hood carefully. Dissolve the ash in conc. HCl (first add 1mi H2O) then 1ml conc. HCl. Use glass rod to stir the contents. Filter the contents (use 100ml volumetric flask) and wash the filter paper with distilled H2O and make up to the mark. Iron and zinc concentrations were measured using an Atomic Absorption Spectrophotometer. Protein Content (%) Crude protein content (%) was determined from a 1 g lentil flour sample using the micro-Kjeldahl method for nitrogen (N) analysis. The nitrogen percentage obtained was then converted to crude protein by multiplying it by a factor of 6.25, following the AOAC (2000) official method 979.09. Sample preparation: Accurately weigh 1 g of finely ground lentil flour and transfer it into a Kjeldahl digestion flask. Add approximately 15 mL of concentrated sulfuric acid (H₂SO₄) along with a digestion catalyst mixture and copper sulfate (CuSO₄). Gently heat the flask until the sample becomes clear, then allow the digest to cool. Transfer the cooled digest to the automatic steam distillation unit. Add an excess of 40% sodium hydroxide (NaOH) to make the solution alkaline, which converts ammonium ions to ammonia (NH₃). Distill the liberated NH₃ into a receiving flask containing boric acid solution with a mixed indicator. Continue the distillation for 5–10 minutes until all the ammonia is transferred. Titrate the collected distillate with standard 0.1 N hydrochloric acid (HCl) until the endpoint is reached. Then run a blank (without sample) using the same procedure to correct for background nitrogen. The analyses were performed with Kjeldahl method (wet digestion method), and using the formula to calculate; $$\:\text{C}\text{P}\left(\text{%}\right)=\frac{\left(T-B\right)*N*14*100*6.25}{Ws*100}$$ Where, CP = Crude protein = Titration reading = Blank titration reading = HCl normality Ws = Sample weight, 1000 = to convert in to mg. Ash concentration (mgkg-1) : It was determined by using dry ashing method. The sample was determined according to the method of AOAC (1990) as follows: One dry gram ground sample was placed in a clean dry pre-weighed crucible, and then the crucible with its content ignited in a muffle furnace at about 105 o c in aluminum disc overnight in oven. The crucible was removed from the furnace to desiccators to cool and then weighed. The crucible was reignited in the furnace and allow to cool until constant weight was obtained. Ash content was calculated using following equation (calculated on dry matter bases): AC%= \(\:\frac{(W2-W1)*100}{Ws}\) Where, AC = Ash content, Ws = Weight of sample W1 = Weight of empty crucible, W2 = Weight of crucible with ash. 2.5. Statistical analysis Associations among the traits were determined using principal component analysis (PCA) (Hatcher, 1994) in R version 4.2 (R Project for Statistical Computing, https://www.r-project.org ). The principal components were extracted from the correlation matrix. Data were standardized to estimate the genetic distance matrix using the Euclidean distance approach, and hierarchical clustering was then performed using Ward’s method (Murtagh and Legendre, 2014). Bartlett’s test of sphericity was significant (p 1 were retained based on the Kaiser criterion. Circular dendrograms and phylogenetic trees were visualized using the fviz_dend function from the ggplot2 package. Principal component variable graphs were displayed using fviz_pca_var, and correlation plots for the measured genotype traits were generated using the ggcorrplot function. 3. Results and Discussions 3.1. Analysis of variance The analysis of variance revealed significant variability among lentil genotypes for key morphological traits. These are days to 50% flowering, plant height, days to maturity, thousand seed weight, biomass and seed yield varied significantly between the genotypes from different collection of the world and Ethiopian released genotype. Significant variation on lentil yield and yield attributing traits were also reported by (Abdipur et al., 2011 : Adhikari et al .2018 Kumar et al . 2016: Sehgal et al., 2021 : Preiti et al ., 2024: AL-Boush., 2025). 3.1.1. Genotypic mean performance The performance of different genotypes was assessed based on several agronomic traits. The genotypes exhibited significant variation in days to 50% flowering and days to maturity. The earliest flowering genotypes included Jiru and Alem Tena (both at 58.5 days), while 193S-180L had the longest days to 50% flowering at 86 days. Regarding days to maturity, Alemaya-98 and FLIP-2011-82L matured the earliest (115.5 days), whereas FLIP-2010-28L had the longest maturity period at 143 days. Early-maturing genotypes are desirable for environments with a short growing season, while late-maturing ones may benefit from extended vegetative growth in favorable conditions. Plant height ranged from 23.5 cm (010S 96134-3) to 38.4cm (PRECOZ). Taller plants are generally advantageous for biomass production, while shorter plants may be more resistant to lodging. Jiru, ILL 2303, and PRECOZ exhibited the tallest plants, suggesting their potential for higher biomass production. Genotypes from ICARDA and Jordan exhibited larger thousand seed weight ranging from 26.1 g (FLIP-2010-30L) to 54.0 g (FLIP-2010-26L). Genotypes with high thousand seed weight, such as 1b1a-1, UIJ-29L, 010S 96122-3, 010S 96105-1 and FLIP-2010-26L are advantageous for grain quality and market preference. Genotype 010S-96143-4, 97-039LX-99R064, PRECOZ, ILL 2303, 96-034L, Jiru and Alemaya-98 had scored higher seed yield (Table 2). The above result suggesting potential of variability of genotype for the best trait of interest. The observed variations in agronomic traits suggest that different genotypes can be selected for specific breeding objectives. Genotypes 010S-96143-4, Jiru, Alemaya-98, and ILL 2303 exhibited superior seed yield, greater biomass, and better plant height, making them promising candidates variety development. Conversely, early-maturing genotypes such as Alem Tena and FLIP-2011-82L may be preferred for regions with shorter growing seasons. Table.2. Mean performance comparison of agronomic traits of lentil genotype grown in main season at Moretna Jiru Ethiopia Name of genotype DF DM PH TSW (gm) BM (kg/ha) SY (kg/ha) Alemaya-98 61p-s 115.5i 31.7c-n 37.5h-s 5301.2a-c 2295.9ab Chekol 60.5p-s 123f-i 32.1c-m 31.6s-v 4149.3d-i 1789.5f-h Jiru 58.5s 119i-h 37.4a-b 46.0b-e 5539.2a 2370.5a Teshale 59.5q-s 119i-h 28.5i-p 36.2k-u 3530.7h-p 1317.4m-o Alem Tena 58.5s 123.5f-i 30.5e-p 35.9l-u 3090.7k-x 1297.9m-p 8IS-15 79a-g 140a-c 32.3b-m 34.7m-u 2610q-z 1019.09s-y FLIP-2011-33L 76a-j 131.5a-g 30.2f-p 32.5q-u 3383.5h-u 1061.04r-y FLIP-2010-21L 78.5a-h 136a-e 32.1c-m 37.5h-s 3189.5j-w 1173.8o-s FLIP-2011-22L 70.5e-p 139a-d 27.2n-q 33.q-u 2581.1r-z 983.75u-y FLIP-2010-26L 68.5g-s 137.5a-e 28.8h-p 54.0a 2686.7o-z 1085.5r-x 94-028L 76a-j 134.5a-f 33.2b-i 37.6h-s 3109.3k-x 1139.08r-s 95-005L 76a-j 134.5a-f 28.8h-p 35.4m-u 2138.3y-z 998.29t-y FLIP-2011-30L 76.5a-i 138a-e 34.7a-f 33.7o-u 2743o-z 1021.54s-y FLIP-2010-22L 77a-h 139.5a-d 27.7k-q 40.7d-k 2929.3n-z 1007.33s-y LC-8603-59-L 59r-s 128c-h 35a-f 36.2k-u 4011.1d-k 1512.0k-l FLIP-2010-23L 72c-o 133.5a-g 31.7c-n 34.7m-u 2735.8o-z 1059.05r-y FLIP-2010-20L 74.5b-l 132a-g 28.8h-p 44.5d-g 2593.4q-z 1415.6l-m FLIP-2011-20L 82.5a-c 139a-d 31.7c-n 35.7l-u 2346.1v-z 992.38t-y FLIP-2010-31L 71d-p 130b-h 32.4b-m 31.6s-v 3620.6g-o 1359.3l-m FLIP-2010-19L 81.5a-d 136a-e 26p-q 34.6m-u 2082.3y-z 908.88y-z 96-034L 68h-s 127.5d-h 35.1a-f 36.6j-t 4676.0b-e 2201.5bc FLIP-2011-25L 73.5b-l 137.5a-e 33.0b-j 36.0l-u 2990.5m-z 1144.7p-u FLIP-2011-37L 65k-s 128c-h 32.5b-l 31.8s-v 3041.3m-y 1009.09s-y 97-039LX-99R064 78a-h 138a-e 36.2a-c 42.0d-k 4626.6c-e 1896.2ef FLIP-2011-23L 78a-h 137.5a-e 33b-j 32.5q-u 2669.8p-z 1030r-y 97-039LX-99R120 69.5f-r 137a-e 32.5b-l 38.4g-r 2456.8u-z 869.38y-z FLIP-2011-21L 77a-h 136.5a-e 31.9c-m 30.0q-u 2429.4v-z 881.59y-z 97-011L 63qs 129.5b-h 35.1a-f 40.8d-k 3974.3e-l 1786.6f-h FLIP-2010-21L 78a-h 141a-b 28.9h-p 38.5g-r 2019.9y-z 924.25x-z FLIP-2010-24L 82.5a-c 139a-d 30.1f-p 35.0m-u 2916.9n-z 1029.71r-y FLIP-2011-29L 77.5a-h 134a-f 30.8dp 34.0n-u 2348.1v-z 961.79u-y FLIP-2011-27L 77a-h 138.5a-d 33.0b-j 36.8i-t 3114.3k-x 1130.83r-s FLIP-2010-30L 69.5f-r 136a-e 33.4a-i 26.1v 2827.2o-z 990.67t-y FLIP-2010-28L 83.5a-b 143a 30.5e-p 30.7uv 2624.8q-z 925.5x-z FLIP-2011-36L 70f-p 134.5a-f 29.1g-p 34.5m-u 2609.9q-z 879.33y-z FLIP-2007-1L 66i-s 131.5a-g 26p-q 33.5p-u 2560.5s-z 898.84y-z 193S-180L 86a 140a-c 32.5b-l 32.1r-u 2499.3t-z 925.04x-z 97-011L 80a-f 139a-d 26.3o-q 29.8uv 2757.1o-z 909.34y-z FLIP-2011-24L 81.5a-d 137a-e 31.9c-m 38.6g-p 3245.4i-v 1069.58r-y 97-039LX99RO60 76.5a-i 141a-b 33.1b-i 34.6m-u 3182.8j-w 1014.42s-y FLIP-90-25L 70.5e-p 128.5c-h 32.8b-k 32.5q-u 3145.4k-x 1104.96r-v 78S-26052 73.5b-l 137a-e 35.3a-f 40.2e-n 3424.4h-t 1191.5o-r PRECOZ 60.5p-s 121.5g-i 38.4a 44.2d-h 4479.7c-g 1996.1de UIJ-29L 77a-h 135a-f 32.5b-l 51.0a-c 2815.9o-z 1152.3p-t FLIP-2011-62L 62.5q-s 118h-i 35.8a-d 39.7f-p 3894.3e-m 1621.4i-k FLIP-2011-42L 64l-s 128.5c-h 27.5l-q 40.5e-n 2268.3w-z 956.88x-z FLIP-2011-82L 60.5p-s 115.5i 34.8a-f 40.9d-k 3598.3g-p 1728.8g-j FLIP-2011-41L 73b-n 134.5a-f 37.3a-b 37.4i-s 3844.4e-m 1639.3h-j FLIP-2010-74L 62o-s 123.5g-i 32.5b-l 38.7g-p 4231.3d-h 1752.0f-i 09S 82109-04 64.5l-s 135a-f 33.9a-h 40.0e-p 3505.5h-r 1092.71r-w 010S 96134-3 69g-s 123f-i 23.5q 42.6d-j 2210.9x-z 949.5x-z ILL-1323 64l-s 128.5c-h 27.9j-q 38.4g-r 2615.4q-z 1082.46r-x 2009S 96575-1 62o-s 128c-h 34.2a-g 38.8g-p 3468h-s 1139.42r-u ILL 2261 65.5j-s 133a-g 32.1c-m 38.8g-p 2771.6o-z 902.96y-z 2009S 9657s-1 62o-s 133.5a-g 31.2c-o 42.9d-j 4182.6d-h 1495.2k-l 2009s -96511-10 75.5a-j 134a-f 32.6b-l 46.0b-e 3761.2f-n 1486.1k-l 1b1a-1 78a-h 133a-g 27.7k-q 47.0b-c 2771.4o-z 1005.88s-y 010S 96122-3 70.5e-p 133a-g 28.8h-p 51.5ab 3069.2l-y 1212.3n-q 2009S-96101-2 68h-s 129b-h 26.6n-q 35.7l-u 4009.4d-k 1388.2l-m 09S 83227-04 81a-e 134.5a-f 32.4b-m 37.9h-s 3117.6k-x 1047.84r-y ILL 2303 59r-s 126e-i 35.7a-e 37.0i-t 5334.9a-b 2092.5cd 010S-96143-4 69g-s 130b-h 34.7a-f 43.2d-i 4860.5a-c 1833.2fg 010S 96105-1 59.5q-s 123.5g-i 28.5i-p 51.8a-b 4076.3d-j 1595.6j-k FLIP-2010-29L 78.5a-h 134.5a-f 32.3b-m 35.3m-u 4141.2d-i 1496.6k-l Means 71.03 132.12 31.62 37.95 3274.06 1269.53 DF = Days to 50% flowering, DM = Days to maturity, PH = Plant height, BY = Above ground biomass kg/ ha, SY = Seed yield kg/ha, TSW = Thousand seed weight (g) The classification of genotypes based on their iron and zinc concentrations provides critical insights into their potential applications in agricultural and nutritional strategies (Kumar et al., 2015 : Banerjee et al., 2023 : Senguttuve et al ., 2023). The strategic use of genotypes from different categories ensures a comprehensive approach to addressing global micronutrient malnutrition challenges while maintaining agricultural productivity. Different studies show genotypic variation among genotype in iron and zinc concentration. In this study the genotypes were classified in iron and zinc concentration level. Iron concentration in lentil seeds ranged from 68 to 313 mg/kg. These high Concentration of irons (> 150 mg/kg) were recorded by genotype UIJ-29L, 8IS-15 ,010s- 96130-2 and FLIP-2011-37L, and FLIP-2011-30L (Fig. 1 .a). These genotypes originated from Jordan and ICARDA respectively. Different studies showed slight variation in iron concentration Mahra et al. (2018) reported variation in iron concentration among 2000 lentil genotypes ranges 42–168 mg/kg and Kumar et al. ( 2024 ) also tested 128 lentil accession the iron concentration ranged from 16-183mg/kg. The wide variation in iron concentration suggests significant genetic diversity among lentil genotypes, potentially influenced by environmental factors. And also, this finding suggests that the geographical origin of the tested genetic material plays a key role in terms of richness in iron. These genotypes are ideal candidates for bio fortification programs aimed at addressing iron deficiencies in human diets. Their superior iron levels make them valuable for breeding programs targeting improved nutritional quality (Joshi-Saha et al., 2022 ). Low Concentration (< 100 mg/kg) of iron also scored from genotype LC-8603-59-L and FLIP-2010-28L making them less suitable for direct biofortification. However, they can still be utilized as parental lines in breeding programs aiming to introduce other desirable traits. Zinc concentration varies between 17.5 and 40 mg/kg. High Concentration of zinc (> 35 mg/kg) (Fig. 1 .b) were recorded from FLIP-2011-24L, 2009S-96575-1, 97-011L, and 96-034L making them strong candidates for combating zinc deficiencies. The first two were from ICARDA and the last two were from AUS. These genotypes are especially important for regions where zinc deficiency is prevalent and can significantly improve dietary zinc intake. Genotype FLIP-2010-31L, 78S-26052 and Chekol originated from ICARDA, Turkey and Commercial cultivar of Ethiopia respectively, were also recorded low level of zinc concentration. Various studies have supported this finding (Mahra et al ., 2018: Aboutayeb et al .,2023: kumar et al .,2024), indicating a genotypic variation in zinc concentration. ( Bhattacharya et al., 2022 ) also studied 125 lentil genotype score variation in zinc concentration ranged from (17.45 to 77.25 mg/kg). This difference may be due to environmental factor. Generally, the genotype, which have high-concentration of both iron and zinc, such as 2009S-96575-1, and FLIP-2011-37L making it a strong dual-nutrient candidate are particularly promising for biofortification programs aimed at mitigating micronutrient deficiencies. Low-concentration genotypes, although less suitable for direct nutritional interventions, hold importance in broader breeding programs. Overall, the strategic use of genotypes from different categories ensures a comprehensive approach to addressing global micronutrient malnutrition challenges while maintaining agricultural productivity. 3.2. Genetic Divergent 3.2.1. Clustering of Lentil Genotypes The Euclidean distance matrix of lentil genotypes estimated from eleven quantitative and nutritional traits was used to construct dendrogram based on the Unweighted Pair-group methods with Arithmetic Means (UPGMA). Accordingly, these 64 lentil genotypes were grouped into 3 distinct clusters (Fig. 2 ). Cluster I, II, and III consisted of 19 (29.6%), 25 (39.1%) and 20(31.3%) genotypes respectively. Cluster I comprised 19(29.6%) lentil genotypes including 8 germplasm from ICARDA, two from AUS, four from Argentina and five Ethiopian commercially release lentil genotypes. Cluster II contained 25 genotypes. Of these, 19 were from the International Centre for Agricultural Research in the Dry Areas (ICARDA). Additionally, there were 5 genotypes from AUS, and 1 genotype, specifically LC-8603-59-L, from the USA. Cluster III contained 16 genotypes from ICARDA included 4 genotypes each from Argentina, Turkey, Jordan, and AUS. The first cluster, showed high performance in iron concentration (Table 3 and Figure. 2). In contrast, the second cluster comprises best performance some agronomic traits. In cluster, three showed good for almost all agronomic trait zinc and protein content. To establish high-yielding lentil genotypes with superior nutritional quality attributes, crossing genotypes from different clusters may produce suitable recombinants, according to the results, which demonstrated genetic divergence among the genotypes. This is because the cluster analysis isolates genotypes into clusters that show a high degree of heterogeneity. The five nationally and regionally released varieties grouped into one cluster. The result suggested that the genotypes grouped under the same cluster had similarity for many characters but dissimilarity to other genotypes in other clusters with one or more traits. Several authors reported the presence of divergence among the lentil genotypes indicating grouping in different numbers of distinct clusters. Hussan et al . (2018) studied 45 lentil genotypes of genetic diversity; genotypes grouped into seven clusters. Paliya et al . (2015) studied genetic diversity on 100 lentil genotypes; identified 10 distinctive clusters. Fikiru et al. ( 2010 ) classified seventy Ethiopian lentil landrace accessions into two clusters based on Euclidian distance considering 8 morphological traits. Asghar et al. (2010) reported that metro glyph analysis distributed 30 lentil genotypes into 10 distinct groups, the first and the second showed close genetic relationship in respect of number of pods and seed yield. Roy et al . (2013) studied genetic diversity on 110 segregated lentil accessions into six clusters. Maurya et al . (2018) revealed that divergence analysis of 74 Lentil genotypes, along with four checks collected from different origin grouped in 9 clusters. Nigussie et al ., (2024) studied the agro-morphological characterization and comparative performance of 615 Ethiopian and exotic lentil were grouped in to two cluster. 3.2.2. Clusters Mean Analysis The mean value for ten quantitative and nutritional trait across the three clusters are shown in Table 3 . Cluster I was distinguished by having high mean iron value compared to the overall mean of the genotype in other clusters. This indicates a great potential for developing varieties through selection and further evaluation of genotype from the two clusters. This cluster also had longer flowering and maturity. however, it recorded the lowest mean value of other trait compared to the genotype in the remaining clusters. Cluster II had higher plant height, thousand seed weight, biomass and seed yield relative to cluster one. Cluster III is distinguished by its short maturing period and days to 50% flowering, taller plant height, higher thousand-seed weight, greater above-ground biomass, higher grain yield, protein content, and zinc concentration compared to other clusters. This cluster is particularly important for escaping environmental fluctuations, such as rainfall shortages during the growth period. It also exhibits good nutritional quality traits and has the highest mean values greater than the overall mean performance of genotypes for all traits, except for iron concentration. The larger seed size (thousand seed weight) in Cluster III may improve market value and consumer preference. However, the reduction in iron content could be a concern for nutritional quality. The higher grain yield in Cluster III is a crucial factor, especially for farmers aiming to maximize production. This cluster's superior performance in biomass and grain yield suggests a strong adaptation to favourable conditions. Traits such as ash content and zinc content show minimal variation among the clusters, indicating that these parameters may be less influenced by cluster-specific factors or genetic variability. A cross-made between selected genotypes in those clusters could possibly create high yielding and nutritional trait as well as other yield components through hybridization of genotypes from different clusters and subsequent selection. Table 3 Mean value of 10 quantitative and qualitative traits of three clusters for 64 lentil genotypes Clusters DF DM PH TSW BM GY PRO AS FE ZN I 74.8 135.7 29.8 37.0 2557.8 991.7 21.3 2.9 139.6 32.1 II 71.1 131.7 32.4 37.7 3294.1 1195.8 21.8 2.9 129.5 32.1 III 64.8 126.7 33.8 39.8 4466.6 1821.3 22.2 2.8 120.6 32.5 D F=Days to 50% flowering, DM = Days to maturity, PH=Plant height, BY=Above ground biomass k g/ ha, SY =Seed yield kg/ha, TSW= Thousand seed weight (g) PC= protein content (%) and Ash (%), Fe=iron (mg/kg), ZN=zinc (mg/kg) 3.3. Principal Component Analysis Principal component analysis (PCA) was employed to assess the individual contributions of various variables to the overall data variation. As stated by (Kaiser, 1960 : Zalewski et al. 2015 ), only the principal components (PCs) with eigen values greater than 1 were considered. It reflects the proportion of variance each principal component (PC) accounts for, with this criterion retaining PCs that explain more variance than the average variable. The principal component analysis is used to visualize the variation among 64 genotypes for 11 quantitative and nutritional traits in (Table 4 and figure. 3). As a result, the principal component analysis identified four principal components (PC1 to PC4) with eigenvalues ranging from 1.04 to 3.5, collectively explaining 70.95% of the total variation (Table 4 ). The first two principal components PC1 and PC2 contributed 35.02% and 13.09%, respectively from the total variation. The eigenvectors represent the contribution of each trait to the principal components. Higher absolute values indicate a stronger contribution of the trait to that principal component. The most important traits for PC1 were days to 50% flowering, days to maturity, plant height, aboveground biomass and seed yield. This suggests that PC1 captures traits related to productivity (yield and biomass) versus developmental timing (flowering and maturity days). These variables were also the most interrelated with each other, suggesting significant potential for improving the crop by selecting for these traits. Hundred seed weight, Ash and Zinc concentration contributed more to (PC2). The finding is agreed with (Hussain et al., 2022 and Preiti et al ., 2024) who observed that above ground biomass, plant height and Seed yield contributed much to PC1 in genotype. (Tripathi et al., 2022 : AL-Boush., 2025) reported that days to maturity and days to 50%flowering, above ground biomass and seed yield contributed much to PC1 in genotype who reported that days to maturity, primary branches and plant height contributed to PC2. In principal component (PC3) likely reflects a mix of plant growth characteristics and micronutrient content such as, Days to 50% flowering, days to maturity, plant height above ground biomass, seed yield, Zinc and iron concentration mg- 1 kg had relatively more contribution to the total variance. For PC4, hundred seed weight and Iron concentration contributed the most. Karakoy et al . (2012) reported 1000 seed weight, iron concentration in PC4.The output of principal component analysis (PCA) showed that the different traits contributed varying amounts to the overall variation. It is common practice to select one variable from each of the identified groups of related traits. Hence, for the first group seed yield (0.490) is best choice, which had the largest loading from component ones, Zinc Concentration (0.598) for the second, Plant height (0.641) for the third, Iron (0.774) for the fourth group. Table 4 Eigenvalue of the first four principal components for traits of 64 lentil genotypes Eigenvectors Traits PC1 PC2 PC3 PC4 Days to 50% flowering -0.4169 0.0870 0.3048 -0.0955 Days to maturity -0.4347 0.0810 0.3082 0.0029 Plant height (cm) 0.2810 0.0111 0.6419 -0.0515 1000 seed weight(g) 0.1823 0.3212 -0.2499 0.5963 Above ground biomass kgha- 1 0.4774 0.0751 0.2448 -0.0986 Seed yield kgha- 1 0.4900 0.0341 0.2007 -0.0072 Protein (%) 0.2119 -0.0955 -0.2316 -0.0435 Ash (%) -0.0114 0.7071 -0.1769 -0.0303 Iron mg- 1 kg -0.1022 -0.0920 0.3252 0.7740 Zinc mg- 1 kg -0.0399 0.5984 0.2162 -0.1449 Eigen value 3.502 1.309 1.241 1.043 Percent of total variance explained 35.02 13.09 12.41 10.43 The blue vector represents agronomic and nutritional quality traits. Black dots denote lentil genotypes. Dim1 (x-axis) explains 35% of the total variance and Dim2 (y-axis) explains 13.1% of the total variance. Hence, these two dimensions account for 48.1% of the variability in the data. The closer a sample is to an arrow, the more strongly it is associated with the variable represented by that arrow. The samples near the center are likely similar across many variables. Outliers like PRECOZ and Jiru suggest unique characteristics. Traits like DF: Days to 50% flowering, DM: Days to maturity, Ash content, and TSW: Thousand seed weight point in specific directions, indicating they contribute significantly to the variation in that direction. Variables with long arrows (DF, DM and AS) are highly influential in differentiating the samples along their respective dimensions. Variables pointing in the same direction are positively correlated. Jiru and Alemaya-98 are located on the left side of the plot, suggesting they might have lower values for traits pointing to the right (DF, DM, FE) and potentially higher values for traits pointing to the left (BM, GY). Genotypes like FLIP-2010-24L and FLIP-2010-19L are on the right, suggesting higher values for DF and DM. 4. Conclusion Multivariate analysis of lentil genotypes from ICARDA, Ethiopia and other countries has successfully identified significant variations in morphological traits like days to 50% flowering, days to maturity, plant height, above ground biomass, 1000 seed weight and seed yield as well as zinc, iron, and ash concentrations. The study confirms significant genetic diversity among lentil genotypes in terms of agronomic and nutritional traits. Certain genotypes show promise for breeding programs aimed at improving both yield and micronutrient content. FLIP-2011-82L, FLIP-2010-74L, ILL 2303, Jiru, 010S-96143-4, 97-011L and 96-034L had high yielder and have medium concentration of iron and zinc sourced from ICARD, AUS and Ethiopia. However, genotype 010s- 96130-2, FLIP-2011-37L, UIJ-29L have high concentrations of iron more than double from the commercial release varieties but exhibit low yield potential. These genotypes are important for the biofortification of lentil breeding. Multivariate analysis is an effective tool for identifying lentil varieties with superior traits. Mean values for ten quantitative and nutritional traits are classified into three clusters. Cluster III appears to be the most productive group, while Cluster I has the lowest yield and biomass and high iron concentration. Cluster II represents an intermediate category with moderate productivity. Ultimately, these findings contribute to developing more nutritious and resilient lentil varieties, benefiting both producers and consumers. Future research should investigate physiological and environmental factors influencing Zn and Fe accumulation to further accelerate breeding progress. Declarations Data availability statement We are willing to provide the data used in this manuscript to the editorial office upon request. Declaration of interest statement The authors declare no conflict of interest. Generative AI statement The author(s) declare that no Generative AI was used in the creation of this manuscript. Funding statement This work was supported by the Debre Berhan Agricultural Research Center (DBARC), Ethiopia. Clinical trial number: not applicable Ethics Declaration The plant materials used in this study were collected from different national and Regional agricultural research institute from in Ethiopia and abroad. The collection and use of these materials complied with all relevant national and institutional guidelines. No wild plant materials were collected, and no special permits or licenses were required for obtaining the cultivated genotypes used in this research. Author contributions ET: Participated in conception, design, analysis and writing of the manuscript and serve as the lead author of the paper. FM is participated in designing, revising and writing the manuscript. All authors approve the final version of the manuscript. 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Senguttuvel, P., CN, N., V, J., P, B., SV, S.P., LV, S.R., AS, H., K, S., RM, S. and Govindaraj, M., 2023. Rice biofortification: breeding and genomic approaches for genetic enhancement of grain zinc and iron contents. Frontiers in Plant Science , 14 :1138408. Bhattacharya, S., Das, A., Banerjee, J., Mandal, S.N., Kumar, S. and Gupta, S., 2022. Elucidating genetic variability and genotype× environment interactions for grain iron and zinc content among diverse genotypes of lentils ( Lens culinaris ). Plant Breeding , 141(6):786-800. Zalewski, D., Galek, R., Kozak, B.and Sawicka-Sienkiewicz, E. 2015. Pheno-morphological and agronomic diversity in a collection of wild and domesticated species of the genus Lupinus. Turkish Journal of Field Crops 20(1):43-48 Zohary, B. D., Hopf, M., Zohary, D., Hopf, M., Zohary, D., & Hopf, M. (1988). Domestication of plants in the old world: the origin and spread of cultivated plants in West Asia, Europe, and the Nile Valley. In Choice Reviews Online . 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14:02:29","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":166467,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8036492/v1/c1100559e56bb08d7f026cd7.html"},{"id":97003182,"identity":"16e27a84-fbde-4011-99db-a43fbd77d5e0","added_by":"auto","created_at":"2025-11-28 14:02:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":219529,"visible":true,"origin":"","legend":"\u003cp\u003e(a). \u0026nbsp;Mean value of Iron (mg/kg) concentration (b). Mean value of Zinc(mg/kg) concentration\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8036492/v1/9b28e5be82d2641c401d676a.png"},{"id":97139366,"identity":"0620a0cf-4583-4751-affc-af460a52c777","added_by":"auto","created_at":"2025-12-01 10:00:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153586,"visible":true,"origin":"","legend":"\u003cp\u003ea). Heat map showing similarity distances among lentil germplasm where relative importance of different quantitative traits and nutritional is highlighted by the different color distributions\u003c/p\u003e\n\u003cp\u003eb). Clusters, based on Ward’s method, for lentil germplasms using Euclidian distance.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8036492/v1/ab730cf94348f4b614df26fe.png"},{"id":97139608,"identity":"68fbbc78-deeb-4359-9a81-8b54e3556952","added_by":"auto","created_at":"2025-12-01 10:00:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":112560,"visible":true,"origin":"","legend":"\u003cp\u003eGenotype by traits Biplot showing the relationship between traits and mean performance of lentil.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8036492/v1/4f9e04b84c9803d7c723847c.png"},{"id":109204863,"identity":"8058742a-cd0d-43d7-bd6c-24a8f7ced840","added_by":"auto","created_at":"2026-05-13 15:02:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":998823,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8036492/v1/9e827f28-53f7-437e-b1fe-7d198f47a679.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic Diversity for Morphological and Nutritional Traits in Global Lentil Genotypes for Biofortification Breeding in Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLentils (Lens culinaris Medik.) belong to the Leguminosae family. Archaeological evidence indicates lentils were among the earliest cultivated crops in the Fertile Crescent, birthplace of agriculture (Herlan, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). It is believed to have been domesticated during the Neolithic period, around 8000\u0026ndash;7000 BC with the advancement of agriculture, their cultivation spread from initial centers to the Mediterranean basin, the Indian subcontinent, and other regions of the ancient world (Zohary et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). It was one of the first crops to be cultivated by humans and played an essential role in early agriculture. The major producer of lentil is Canada, India, Turkey, USA, Kazakhstan, Nepal, Australia, Russia, Bangladesh, China and Ethiopia (FAO, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It become a popular crop that is grown and eaten in many parts of the world (Montejano-Ram\u0026iacute;rez \u0026amp; Valencia-Cantero, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEthiopia is one of the biggest lentil producing country with altitude range of 1700 to 35000 masl. Lentil is a vital legume crop globally recognized for its nutritional and agronomic significance, rich in essential nutrients, which is crucial role in alleviating malnutrition and ensuring food security. It is rich in protein, fiber, various vitamins and micro nutrients iron (Fe), zinc (Zn), making them a promising crop in the global effort to combat human micronutrient deficiencies (Jha et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e: Rajpal et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Lentil has climate-smart characteristics and the cultivation can further strengthen its role in addressing global challenges related to climate change (Gupta et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e: Khazaei et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Lentil also provides important economic advantages to the small-scale farm households in providing food in the form of salads, soups, stews, feed, cash income and foreign currency earnings (Chilot \u003cem\u003eet al.\u003c/em\u003e 2016; Tolesa and Asrat 2019). As leguminous crop, it contributes to soil fertility through biological nitrogen fixation, reducing the need for synthetic fertilizers. Its root systems also improve soil structure and promote microbial activity, making it a valuable component in sustainable agricultural systems\u003c/p\u003e\u003cp\u003eEthiopia has a vast area dedicated to lentil production, with up to 14 lentil genotypes released for cultivation. These genotypes have been sourced from ICARDA (International Center for Agricultural Research in the Dry Areas) and local germplasm collections to enhance productivity and adaptability. Despite these efforts to improve yield and agronomic traits, the nutritional composition of these high-performing lentil varieties has not been extensively studied (Baggar et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To address this gap, a study focusing on the multivariate analysis of lentil genotypes for morphological traits, as well as zinc, iron, and ash concentration, is necessary. A primary motivation for this study is the widespread prevalence of micronutrient deficiencies, particularly zinc and iron, among human populations. Lentils serve as a potential source of these essential nutrients, but their concentration can vary significantly across different genotypes (Bhattacharya et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Identifying lentil varieties with enhanced nutrient content is critical for improving both dietary intake and agricultural sustainability (Karakoy \u003cem\u003eet al.\u003c/em\u003e, 2012). There is a growing need to identify and develop improved lentil genotypes with enhanced traits. The utilization of diverse germplasm from ICARDA and other sources is intended to address the limitations in productivity and nutritional quality. Understanding genetic and phenotypic variations is essential for developing high-yielding, nutrient-dense, and climate-resilient lentil varieties. Furthermore, micronutrient deficiencies, particularly zinc and iron, remain significant public health concerns in Ethiopia, where large populations suffer from anemia and other nutrient-related disorders. Enhancing lentil varieties with higher levels of zinc and iron offers a sustainable strategy to combat these deficiencies (Aboutayeb et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e: Kumar et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e: Dhaliwal et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThus, there is an urgent need to evaluate lentil genotypes from ICARDA, Ethiopian collections and other country using advanced statistical and multivariate techniques. This study aims to fill this knowledge gap by identifying genetic and phenotypic relationships among lentil genotypes and guiding future breeding efforts for enhanced nutritional and agronomic traits. Specifically, the primary goals of this research are to examine genetic diversity in seed iron (Fe) and zinc (Zn) concentrations and to identify key traits across a diverse range of lentil accessions.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Description of Experimental Area.\u003c/h2\u003e\n \u003cp\u003eThe study area for the experiment was located in Moretna Jiru Wereda, situated in the North Shewa Zone of Ethiopia. This region is geographically positioned at latitude 9\u0026deg;52\u0026apos;10.7\u0026quot; North and longitude 39\u0026deg;10\u0026apos;46.5\u0026quot; East, with an elevation of 2,600 meters above sea level. It lies approximately 158 kilometers north of Addis Ababa, the capital city of Ethiopia. The site experiences a subtropical highland climate, with an average annual rainfall of 1,220 mm, making it conducive for agricultural activities. The average maximum temperature in the area is 21.3\u0026deg;C, while the average minimum temperature is 9.6\u0026deg;C, providing a suitable environment for diverse cropping systems. The soil at the site is classified as a Vertisol, which has a clay loam texture. Vertisols are notable for their high fertility and water retention properties, making them particularly advantageous for cultivating crops such as lentils, which were the focus of this study.\u003c/p\u003e\n \u003cp\u003eAgricultural practices in the region are characterized by a mixed farming system that integrates livestock production with crop cultivation. This approach optimizes resource utilization, where animal husbandry and arable farming complement each other to enhance productivity and sustainability. The interaction between livestock and crop farming not only provides essential nutrients for the soil but also supports the livelihoods of the local farming community, ensuring a balanced and efficient use of the land resources in the area. This combination of favourable climatic conditions, fertile soils, and integrated farming practices makes Moretna Jiru Wereda an ideal site for conducting agricultural experiments and developing sustainable farming systems.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Experimental Materials\u003c/h2\u003e\n \u003cp\u003eThe study utilized a diverse set of lentil genotypes sourced from various reputable institutions. These included genotypes obtained from the Debre Birhan, Debre Zeit Agricultural Research Center and the International Center for Agricultural Research in the Dry Areas (ICARDA). Sixty-four lentil genotypes were evaluated during the experiment. This collection encompassed both newly introduced and locally adapted genotypes, including five nationally and regionally released improved varieties were used. The description of experimental materials are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe description of experimental materials\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePedigree\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePedigree\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLC-8603-59-L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL10923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2010-28L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL8090XILL6783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009S -9651s-L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARGETINA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL4400XILL7956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2010-29L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL8090XILL6783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009S -96575-L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARGETINA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL6434XILL7938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2010-30L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL8090XILL7685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009S- 96576-L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARGETINA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL4400XILL7949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2010-31L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL8090XILL7686\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2009S-96101-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARGETINA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL323XILL4605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2010-74L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWA8649090XILL7559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78S-26052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTURKEY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILLU2SELECTION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2011-20L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL0590XILL5562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUIJ-29L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJORDAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL5244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2011-21L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL8116XILL5562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1b1a-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJORDAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elbla-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2011-22L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL8116XILL5562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8IS-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJordan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUJL197XILL4400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2011-23L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL7683XILL5562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193S-180L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL10935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2011-24L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL8116XILL5562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94-028L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL10933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2010-25L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL8090XILL5769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95-005L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL10932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2011-25L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL8116XILL5562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96-034L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL10930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2011-27L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL2126XILL6199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97-011L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL10924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2011-29L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL8116XILL5562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97-011L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL10933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2011-30L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL8116XILL5562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97-039LX-99R064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL10925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2011-33L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL7949XILL7686\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97-039LX-99R120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL10924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2011-36L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL7683XILL5562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97-039LX99RO60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL7683XILL5562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2011-37L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL8090XILL7685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlem Tena\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDZARC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReleased (2004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2011-41L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL6467XILL8009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJiru\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDBARC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReleased (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2011-42L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL818XILL5883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTeshale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReleased (2004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2011-62L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL7537XILL590\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlemaya-98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReleased (1997)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2011-82L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL7620ILL8113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChekol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReleased (1994)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL 2261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIG 2261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2007-1L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL7620XILL7686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL 2303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIG2303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2010-19L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL7949XILL7686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL-1323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNEL 1323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2010-20L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL0590XILL5769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRECOZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL4353XILL4400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2011-56L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL702XILL2125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e010S- 96105-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL8072XILL7162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2010-21L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL6467XILL8009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e010S- 96122-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL1005XILL5883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2010-22L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL7012XILL2125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e010S- 96134-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL4400XILL7947\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2010-23L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL712XILL2125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e010S-96143-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL323XILL9977\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2010-24L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL8116XILL5562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09S -82109-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL1005XILL5883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFLIP-2010-26L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL8116024XILL0098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e09S -83227-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICARDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILL1005XILL5883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Experimental Design and Procedures\u003c/h2\u003e\n \u003cp\u003eThe study was conducted using simple lattice design with two replications. Each genotype was sown in uniform plots measuring 4 m \u0026times; 0.8 m (3.2 m\u0026sup2;), with 20 cm row spacing, 0.4 m between plots, and 1.5 m between blocks. Seeds were hand sown in the first week of August using broad bed furrows (BBF). Standard agronomic practices, including weeding and pest management, were followed throughout the growing season. Pea aphids were managed by spraying Dimethoate at a recommended rate of 1.8 L per hectare mixed with 200 L of water. The crop was harvested at maturity when the pods turned yellowish, ensuring proper physiological development.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Data Collections\u003c/h2\u003e\n \u003cp\u003eThe morpho-agronomic traits and yield components were collected from a field trial. Measurements were taken from 10 randomly selected plants in the central two rows of each plot, and the average values were calculated for traits such as plant height (cm), seed yield, above-ground biomass, and hundred-seed weight (g). Additionally, phenological traits, including days to 50% flowering and days to maturity, were recorded on a plot basis.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSample preparation and Methods for quality traits\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFor the analysis of quality traits such as seed ash, protein content, and mineral composition (iron and zinc), a 10 g seed sample was collected from each genotype and ground into a fine powder. To avoid contamination during grinding, gloves were worn throughout the process. All analyses were performed using the ground seed samples.\u003c/p\u003e\n \u003cp\u003eThe concentrations of iron (Fe) and zinc (Zn) in the lentil seeds were determined using the dry ashing method, followed by Atomic Absorption Spectroscopy (AAS), as described by Chapman and Pratt (\u003cspan class=\"CitationRef\"\u003e1961\u003c/span\u003e). The procedure was placing 0.5 g portions of ground plant material in to 50ml crucible. Place crucible into cool the furnace and increase temperature gradually to 550\u0026ordm;c ash for 5 hours. After ashing is complete cool the furnace and takeout the crucible and keep in the hood carefully. Dissolve the ash in conc. HCl (first add 1mi H2O) then 1ml conc. HCl. Use glass rod to stir the contents. Filter the contents (use 100ml volumetric flask) and wash the filter paper with distilled H2O and make up to the mark. Iron and zinc concentrations were measured using an Atomic Absorption Spectrophotometer.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eProtein Content (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eCrude protein content (%) was determined from a 1 g lentil flour sample using the micro-Kjeldahl method for nitrogen (N) analysis. The nitrogen percentage obtained was then converted to crude protein by multiplying it by a factor of 6.25, following the AOAC (2000) official method 979.09.\u003c/p\u003e\n \u003cp\u003eSample preparation: Accurately weigh 1 g of finely ground lentil flour and transfer it into a Kjeldahl digestion flask. Add approximately 15 mL of concentrated sulfuric acid (H₂SO₄) along with a digestion catalyst mixture and copper sulfate (CuSO₄). Gently heat the flask until the sample becomes clear, then allow the digest to cool. Transfer the cooled digest to the automatic steam distillation unit. Add an excess of 40% sodium hydroxide (NaOH) to make the solution alkaline, which converts ammonium ions to ammonia (NH₃). Distill the liberated NH₃ into a receiving flask containing boric acid solution with a mixed indicator. Continue the distillation for 5\u0026ndash;10 minutes until all the ammonia is transferred. Titrate the collected distillate with standard 0.1 N hydrochloric acid (HCl) until the endpoint is reached. Then run a blank (without sample) using the same procedure to correct for background nitrogen. The analyses were performed with Kjeldahl method (wet digestion method), and using the formula to calculate;\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:\\text{C}\\text{P}\\left(\\text{%}\\right)=\\frac{\\left(T-B\\right)*N*14*100*6.25}{Ws*100}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere, CP\u0026thinsp;=\u0026thinsp;Crude protein\u0026thinsp;=\u0026thinsp;Titration reading\u0026thinsp;=\u0026thinsp;Blank titration reading\u0026thinsp;=\u0026thinsp;HCl normality Ws\u0026thinsp;=\u0026thinsp;Sample weight, 1000\u0026thinsp;=\u0026thinsp;to convert in to mg.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAsh concentration (mgkg-1)\u003c/strong\u003e: It was determined by using dry ashing method. The sample was determined according to the method of AOAC (1990) as follows: One dry gram ground sample was placed in a clean dry pre-weighed crucible, and then the crucible with its content ignited in a muffle furnace at about 105\u003csup\u003eo\u003c/sup\u003ec in aluminum disc overnight in oven. The crucible was removed from the furnace to desiccators to cool and then weighed. The crucible was reignited in the furnace and allow to cool until constant weight was obtained. Ash content was calculated using following equation (calculated on dry matter bases):\u003c/p\u003e\u003cp\u003eAC%= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{(W2-W1)*100}{Ws}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eWhere, AC\u0026thinsp;=\u0026thinsp;Ash content, Ws\u0026thinsp;=\u0026thinsp;Weight of sample W1\u0026thinsp;=\u0026thinsp;Weight of empty crucible, W2\u0026thinsp;=\u0026thinsp;Weight of crucible with ash.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e\u003cp\u003eAssociations among the traits were determined using principal component analysis (PCA) (Hatcher, 1994) in R version 4.2 (R Project for Statistical Computing, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org\u003c/span\u003e\u003c/span\u003e). The principal components were extracted from the correlation matrix. Data were standardized to estimate the genetic distance matrix using the Euclidean distance approach, and hierarchical clustering was then performed using Ward\u0026rsquo;s method (Murtagh and Legendre, 2014). Bartlett\u0026rsquo;s test of sphericity was significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the Kaiser measure was 0.78, indicating sampling adequacy for PCA. Only principal components with eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1 were retained based on the Kaiser criterion. Circular dendrograms and phylogenetic trees were visualized using the fviz_dend function from the ggplot2 package. Principal component variable graphs were displayed using fviz_pca_var, and correlation plots for the measured genotype traits were generated using the ggcorrplot function.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and Discussions","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Analysis of variance\u003c/h2\u003e\u003cp\u003eThe analysis of variance revealed significant variability among lentil genotypes for key morphological traits. These are days to 50% flowering, plant height, days to maturity, thousand seed weight, biomass and seed yield varied significantly between the genotypes from different collection of the world and Ethiopian released genotype. Significant variation on lentil yield and yield attributing traits were also reported by (Abdipur et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e: Adhikari \u003cem\u003eet al\u003c/em\u003e.2018 Kumar \u003cem\u003eet al\u003c/em\u003e. 2016: Sehgal et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e: Preiti \u003cem\u003eet al\u003c/em\u003e., 2024: AL-Boush., 2025).\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1. Genotypic mean performance\u003c/h2\u003e\u003cp\u003eThe performance of different genotypes was assessed based on several agronomic traits. The genotypes exhibited significant variation in days to 50% flowering and days to maturity. The earliest flowering genotypes included Jiru and Alem Tena (both at 58.5 days), while 193S-180L had the longest days to 50% flowering at 86 days. Regarding days to maturity, Alemaya-98 and FLIP-2011-82L matured the earliest (115.5 days), whereas FLIP-2010-28L had the longest maturity period at 143 days. Early-maturing genotypes are desirable for environments with a short growing season, while late-maturing ones may benefit from extended vegetative growth in favorable conditions. Plant height ranged from 23.5 cm (010S 96134-3) to 38.4cm (PRECOZ). Taller plants are generally advantageous for biomass production, while shorter plants may be more resistant to lodging. Jiru, ILL 2303, and PRECOZ exhibited the tallest plants, suggesting their potential for higher biomass production. Genotypes from ICARDA and Jordan exhibited larger thousand seed weight ranging from 26.1 g (FLIP-2010-30L) to 54.0 g (FLIP-2010-26L). Genotypes with high thousand seed weight, such as 1b1a-1, UIJ-29L, 010S 96122-3, 010S 96105-1 and FLIP-2010-26L are advantageous for grain quality and market preference. Genotype 010S-96143-4, 97-039LX-99R064, PRECOZ, ILL 2303, 96-034L, Jiru and Alemaya-98 had scored higher seed yield (Table\u0026nbsp;2). The above result suggesting potential of variability of genotype for the best trait of interest.\u003c/p\u003e\u003cp\u003eThe observed variations in agronomic traits suggest that different genotypes can be selected for specific breeding objectives. Genotypes 010S-96143-4, Jiru, Alemaya-98, and ILL 2303 exhibited superior seed yield, greater biomass, and better plant height, making them promising candidates variety development. Conversely, early-maturing genotypes such as Alem Tena and FLIP-2011-82L may be preferred for regions with shorter growing seasons.\u003c/p\u003e\u003cp\u003eTable.2. Mean performance comparison of agronomic traits of lentil genotype grown in main season at Moretna Jiru Ethiopia\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\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\u003eName of genotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTSW (gm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBM (kg/ha)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSY (kg/ha)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlemaya-98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61p-s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115.5i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.7c-n\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37.5h-s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5301.2a-c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2295.9ab\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChekol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.5p-s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123f-i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.1c-m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.6s-v\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4149.3d-i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1789.5f-h\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJiru\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.5s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119i-h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.4a-b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46.0b-e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5539.2a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2370.5a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeshale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59.5q-s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119i-h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.5i-p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.2k-u\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3530.7h-p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1317.4m-o\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlem Tena\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.5s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123.5f-i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.5e-p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35.9l-u\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3090.7k-x\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1297.9m-p\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8IS-15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79a-g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140a-c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.3b-m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.7m-u\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2610q-z\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1019.09s-y\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFLIP-2011-33L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76a-j\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131.5a-g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.2f-p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.5q-u\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3383.5h-u\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1061.04r-y\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFLIP-2010-21L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78.5a-h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136a-e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.1c-m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78a-h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e133a-g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.7k-q\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47.0b-c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2771.4o-z\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1005.88s-y\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e010S 96122-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.5e-p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e133a-g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.8h-p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51.5ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3069.2l-y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1212.3n-q\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2009S-96101-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68h-s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129b-h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.6n-q\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35.7l-u\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4009.4d-k\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1388.2l-m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e09S 83227-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81a-e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134.5a-f\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.4b-m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37.9h-s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3117.6k-x\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1047.84r-y\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eILL 2303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59r-s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126e-i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.7a-e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37.0i-t\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5334.9a-b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2092.5cd\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e010S-96143-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69g-s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130b-h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.7a-f\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43.2d-i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4860.5a-c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1833.2fg\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e010S 96105-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59.5q-s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123.5g-i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.5i-p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51.8a-b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4076.3d-j\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1595.6j-k\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFLIP-2010-29L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78.5a-h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134.5a-f\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.3b-m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35.3m-u\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4141.2d-i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1496.6k-l\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeans\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e132.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3274.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1269.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eDF\u0026thinsp;=\u0026thinsp;Days to 50% flowering, DM\u0026thinsp;=\u0026thinsp;Days to maturity, PH\u0026thinsp;=\u0026thinsp;Plant height, BY\u0026thinsp;=\u0026thinsp;Above ground biomass kg/ ha, SY\u0026thinsp;=\u0026thinsp;Seed yield kg/ha, TSW\u0026thinsp;=\u0026thinsp;Thousand seed weight (g)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe classification of genotypes based on their iron and zinc concentrations provides critical insights into their potential applications in agricultural and nutritional strategies (Kumar et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e: Banerjee et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e: Senguttuve \u003cem\u003eet al\u003c/em\u003e., 2023). The strategic use of genotypes from different categories ensures a comprehensive approach to addressing global micronutrient malnutrition challenges while maintaining agricultural productivity. Different studies show genotypic variation among genotype in iron and zinc concentration. In this study the genotypes were classified in iron and zinc concentration level. Iron concentration in lentil seeds ranged from 68 to 313 mg/kg. These high Concentration of irons (\u0026gt;\u0026thinsp;150 mg/kg) were recorded by genotype UIJ-29L, 8IS-15 ,010s- 96130-2 and FLIP-2011-37L, and FLIP-2011-30L (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.a). These genotypes originated from Jordan and ICARDA respectively. Different studies showed slight variation in iron concentration Mahra \u003cem\u003eet al.\u003c/em\u003e (2018) reported variation in iron concentration among 2000 lentil genotypes ranges 42\u0026ndash;168 mg/kg and Kumar et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) also tested 128 lentil accession the iron concentration ranged from 16-183mg/kg. The wide variation in iron concentration suggests significant genetic diversity among lentil genotypes, potentially influenced by environmental factors. And also, this finding suggests that the geographical origin of the tested genetic material plays a key role in terms of richness in iron. These genotypes are ideal candidates for bio fortification programs aimed at addressing iron deficiencies in human diets. Their superior iron levels make them valuable for breeding programs targeting improved nutritional quality (Joshi-Saha et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Low Concentration (\u0026lt;\u0026thinsp;100 mg/kg) of iron also scored from genotype LC-8603-59-L and FLIP-2010-28L making them less suitable for direct biofortification. However, they can still be utilized as parental lines in breeding programs aiming to introduce other desirable traits.\u003c/p\u003e\u003cp\u003eZinc concentration varies between 17.5 and 40 mg/kg. High Concentration of zinc (\u0026gt;\u0026thinsp;35 mg/kg) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.b) were recorded from FLIP-2011-24L, 2009S-96575-1, 97-011L, and 96-034L making them strong candidates for combating zinc deficiencies. The first two were from ICARDA and the last two were from AUS. These genotypes are especially important for regions where zinc deficiency is prevalent and can significantly improve dietary zinc intake. Genotype FLIP-2010-31L, 78S-26052 and Chekol originated from ICARDA, Turkey and Commercial cultivar of Ethiopia respectively, were also recorded low level of zinc concentration. Various studies have supported this finding (Mahra \u003cem\u003eet al\u003c/em\u003e., 2018: Aboutayeb \u003cem\u003eet al\u003c/em\u003e.,2023: kumar \u003cem\u003eet al\u003c/em\u003e.,2024), indicating a genotypic variation in zinc concentration. \u003cb\u003e(\u003c/b\u003eBhattacharya et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also studied 125 lentil genotype score variation in zinc concentration ranged from (17.45 to 77.25 mg/kg). This difference may be due to environmental factor. Generally, the genotype, which have high-concentration of both iron and zinc, such as 2009S-96575-1, and FLIP-2011-37L making it a strong dual-nutrient candidate are particularly promising for biofortification programs aimed at mitigating micronutrient deficiencies.\u003c/p\u003e\u003cp\u003eLow-concentration genotypes, although less suitable for direct nutritional interventions, hold importance in broader breeding programs. Overall, the strategic use of genotypes from different categories ensures a comprehensive approach to addressing global micronutrient malnutrition challenges while maintaining agricultural productivity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Genetic Divergent\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1. Clustering of Lentil Genotypes\u003c/h2\u003e\u003cp\u003eThe Euclidean distance matrix of lentil genotypes estimated from eleven quantitative and nutritional traits was used to construct dendrogram based on the Unweighted Pair-group methods with Arithmetic Means (UPGMA). Accordingly, these 64 lentil genotypes were grouped into 3 distinct clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Cluster I, II, and III consisted of 19 (29.6%), 25 (39.1%) and 20(31.3%) genotypes respectively. Cluster I comprised 19(29.6%) lentil genotypes including 8 germplasm from ICARDA, two from AUS, four from Argentina and five Ethiopian commercially release lentil genotypes. Cluster II contained 25 genotypes. Of these, 19 were from the International Centre for Agricultural Research in the Dry Areas (ICARDA). Additionally, there were 5 genotypes from AUS, and 1 genotype, specifically LC-8603-59-L, from the USA. Cluster III contained 16 genotypes from ICARDA included 4 genotypes each from Argentina, Turkey, Jordan, and AUS. The first cluster, showed high performance in iron concentration (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Figure. 2). In contrast, the second cluster comprises best performance some agronomic traits. In cluster, three showed good for almost all agronomic trait zinc and protein content. To establish high-yielding lentil genotypes with superior nutritional quality attributes, crossing genotypes from different clusters may produce suitable recombinants, according to the results, which demonstrated genetic divergence among the genotypes. This is because the cluster analysis isolates genotypes into clusters that show a high degree of heterogeneity.\u003c/p\u003e\u003cp\u003eThe five nationally and regionally released varieties grouped into one cluster. The result suggested that the genotypes grouped under the same cluster had similarity for many characters but dissimilarity to other genotypes in other clusters with one or more traits. Several authors reported the presence of divergence among the lentil genotypes indicating grouping in different numbers of distinct clusters. Hussan \u003cem\u003eet al\u003c/em\u003e. (2018) studied 45 lentil genotypes of genetic diversity; genotypes grouped into seven clusters. Paliya \u003cem\u003eet al\u003c/em\u003e. (2015) studied genetic diversity on 100 lentil genotypes; identified 10 distinctive clusters. Fikiru et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) classified seventy Ethiopian lentil landrace accessions into two clusters based on Euclidian distance considering 8 morphological traits. Asghar \u003cem\u003eet al.\u003c/em\u003e (2010) reported that metro glyph analysis distributed 30 lentil genotypes into 10 distinct groups, the first and the second showed close genetic relationship in respect of number of pods and seed yield. Roy \u003cem\u003eet al\u003c/em\u003e. (2013) studied genetic diversity on 110 segregated lentil accessions into six clusters. Maurya \u003cem\u003eet al\u003c/em\u003e. (2018) revealed that divergence analysis of 74 Lentil genotypes, along with four checks collected from different origin grouped in 9 clusters. Nigussie \u003cem\u003eet al\u003c/em\u003e., (2024) studied the agro-morphological characterization and comparative performance of 615 Ethiopian and exotic lentil were grouped in to two cluster.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2. Clusters Mean Analysis\u003c/h2\u003e\u003cp\u003eThe mean value for ten quantitative and nutritional trait across the three clusters are shown in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Cluster I was distinguished by having high mean iron value compared to the overall mean of the genotype in other clusters. This indicates a great potential for developing varieties through selection and further evaluation of genotype from the two clusters. This cluster also had longer flowering and maturity. however, it recorded the lowest mean value of other trait compared to the genotype in the remaining clusters. Cluster II had higher plant height, thousand seed weight, biomass and seed yield relative to cluster one.\u003c/p\u003e\u003cp\u003eCluster III is distinguished by its short maturing period and days to 50% flowering, taller plant height, higher thousand-seed weight, greater above-ground biomass, higher grain yield, protein content, and zinc concentration compared to other clusters. This cluster is particularly important for escaping environmental fluctuations, such as rainfall shortages during the growth period. It also exhibits good nutritional quality traits and has the highest mean values greater than the overall mean performance of genotypes for all traits, except for iron concentration. The larger seed size (thousand seed weight) in Cluster III may improve market value and consumer preference. However, the reduction in iron content could be a concern for nutritional quality. The higher grain yield in Cluster III is a crucial factor, especially for farmers aiming to maximize production. This cluster's superior performance in biomass and grain yield suggests a strong adaptation to favourable conditions. Traits such as ash content and zinc content show minimal variation among the clusters, indicating that these parameters may be less influenced by cluster-specific factors or genetic variability. A cross-made between selected genotypes in those clusters could possibly create high yielding and nutritional trait as well as other yield components through hybridization of genotypes from different clusters and subsequent selection.\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 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean value of 10 quantitative and qualitative traits of three clusters for 64 lentil genotypes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClusters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTSW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGY\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePRO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eFE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eZN\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e135.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2557.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e991.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e21.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e139.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e32.1\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\u003e71.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e131.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3294.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1195.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e21.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e129.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e32.1\u003c/p\u003e\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\u003e64.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e126.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e39.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4466.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1821.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e22.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e120.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e32.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003cem\u003eD\u003c/em\u003e\u003cem\u003eF=Days to 50% flowering, DM = Days to maturity, PH=Plant height, BY=Above ground biomass\u003c/em\u003e\u003cem\u003e\u0026nbsp;k\u003c/em\u003e\u003cem\u003eg/ ha, SY =Seed yield kg/ha, TSW= Thousand seed weight (g)\u0026nbsp;\u003c/em\u003e\u003cem\u003ePC= protein content (%) and Ash (%), Fe=iron (mg/kg), ZN=zinc (mg/kg)\u003c/em\u003e\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Principal Component Analysis\u003c/h2\u003e\u003cp\u003ePrincipal component analysis (PCA) was employed to assess the individual contributions of various variables to the overall data variation. As stated by (Kaiser, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1960\u003c/span\u003e: Zalewski et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), only the principal components (PCs) with eigen values greater than 1 were considered. It reflects the proportion of variance each principal component (PC) accounts for, with this criterion retaining PCs that explain more variance than the average variable. The principal component analysis is used to visualize the variation among 64 genotypes for 11 quantitative and nutritional traits in (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e and figure. 3). As a result, the principal component analysis identified four principal components (PC1 to PC4) with eigenvalues ranging from 1.04 to 3.5, collectively explaining 70.95% of the total variation (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The first two principal components PC1 and PC2 contributed 35.02% and 13.09%, respectively from the total variation. The eigenvectors represent the contribution of each trait to the principal components. Higher absolute values indicate a stronger contribution of the trait to that principal component. The most important traits for PC1 were days to 50% flowering, days to maturity, plant height, aboveground biomass and seed yield. This suggests that PC1 captures traits related to productivity (yield and biomass) versus developmental timing (flowering and maturity days). These variables were also the most interrelated with each other, suggesting significant potential for improving the crop by selecting for these traits. Hundred seed weight, Ash and Zinc concentration contributed more to (PC2). The finding is agreed with (Hussain et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e and Preiti \u003cem\u003eet al\u003c/em\u003e., 2024) who observed that above ground biomass, plant height and Seed yield contributed much to PC1 in genotype. (Tripathi et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e: AL-Boush., 2025) reported that days to maturity and days to 50%flowering, above ground biomass and seed yield contributed much to PC1 in genotype who reported that days to maturity, primary branches and plant height contributed to PC2. In principal component (PC3) likely reflects a mix of plant growth characteristics and micronutrient content such as, Days to 50% flowering, days to maturity, plant height above ground biomass, seed yield, Zinc and iron concentration mg-\u003csup\u003e1\u003c/sup\u003ekg had relatively more contribution to the total variance. For PC4, hundred seed weight and Iron concentration contributed the most. Karakoy \u003cem\u003eet al\u003c/em\u003e. (2012) reported 1000 seed weight, iron concentration in PC4.The output of principal component analysis (PCA) showed that the different traits contributed varying amounts to the overall variation. It is common practice to select one variable from each of the identified groups of related traits. Hence, for the first group seed yield (0.490) is best choice, which had the largest loading from component ones, Zinc Concentration (0.598) for the second, Plant height (0.641) for the third, Iron (0.774) for the fourth group.\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 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEigenvalue of the first four principal components for traits of 64 lentil 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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEigenvectors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePC3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePC4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDays to 50% flowering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.4169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0955\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDays to maturity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.4347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlant height (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0515\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1000 seed weight(g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.2499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5963\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbove ground biomass kgha-\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0986\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeed yield kgha-\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProtein (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.2316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0435\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsh (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.1769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0303\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIron mg-\u003csup\u003e1\u003c/sup\u003ekg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7740\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZinc mg-\u003csup\u003e1\u003c/sup\u003ekg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.1449\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEigen value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePercent of total variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.43\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\u003c/p\u003e\u003cp\u003eThe blue vector represents agronomic and nutritional quality traits. Black dots denote lentil genotypes. Dim1 (x-axis) explains 35% of the total variance and Dim2 (y-axis) explains 13.1% of the total variance. Hence, these two dimensions account for 48.1% of the variability in the data. The closer a sample is to an arrow, the more strongly it is associated with the variable represented by that arrow. The samples near the center are likely similar across many variables. Outliers like PRECOZ and Jiru suggest unique characteristics. Traits like DF: Days to 50% flowering, DM: Days to maturity, Ash content, and TSW: Thousand seed weight point in specific directions, indicating they contribute significantly to the variation in that direction. Variables with long arrows (DF, DM and AS) are highly influential in differentiating the samples along their respective dimensions. Variables pointing in the same direction are positively correlated. Jiru and Alemaya-98 are located on the left side of the plot, suggesting they might have lower values for traits pointing to the right (DF, DM, FE) and potentially higher values for traits pointing to the left (BM, GY). Genotypes like FLIP-2010-24L and FLIP-2010-19L are on the right, suggesting higher values for DF and DM.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eMultivariate analysis of lentil genotypes from ICARDA, Ethiopia and other countries has successfully identified significant variations in morphological traits like days to 50% flowering, days to maturity, plant height, above ground biomass, 1000 seed weight and seed yield as well as zinc, iron, and ash concentrations. The study confirms significant genetic diversity among lentil genotypes in terms of agronomic and nutritional traits. Certain genotypes show promise for breeding programs aimed at improving both yield and micronutrient content. FLIP-2011-82L, FLIP-2010-74L, ILL 2303, Jiru, 010S-96143-4, 97-011L and 96-034L had high yielder and have medium concentration of iron and zinc sourced from ICARD, AUS and Ethiopia. However, genotype 010s- 96130-2, FLIP-2011-37L, UIJ-29L have high concentrations of iron more than double from the commercial release varieties but exhibit low yield potential. These genotypes are important for the biofortification of lentil breeding. Multivariate analysis is an effective tool for identifying lentil varieties with superior traits. Mean values for ten quantitative and nutritional traits are classified into three clusters. Cluster III appears to be the most productive group, while Cluster I has the lowest yield and biomass and high iron concentration. Cluster II represents an intermediate category with moderate productivity.\u003c/p\u003e\u003cp\u003eUltimately, these findings contribute to developing more nutritious and resilient lentil varieties, benefiting both producers and consumers. Future research should investigate physiological and environmental factors influencing Zn and Fe accumulation to further accelerate breeding progress.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are willing to provide the data used in this manuscript to the editorial office upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Declaration of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenerative AI statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare that no Generative AI was used in the creation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Debre Berhan Agricultural Research Center (DBARC), Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003enot\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eapplicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe plant materials used in this study were collected from different national and Regional agricultural research institute from in Ethiopia and abroad. The collection and use of these materials complied with all relevant national and institutional guidelines. No wild plant materials were collected, and no special permits or licenses were required for obtaining the cultivated genotypes used in this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eET: Participated in conception, design, analysis and writing of the manuscript and serve as the lead author of the paper. FM is participated in designing, revising and writing the manuscript. All authors approve the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank to Worku Zikarge, for their support during the experimentation. We would like to extend our thanks and appreciations to Holeta Agricultural Research Center for lab work and Debre Berhan Agricultural Research Center and Amhara Agricultural Research Center for providing research budget and facilitate the process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdipur, M., Vaezi, B., Bavei, V. and Heidarpur, N.A., 2011. Evaluation of morpho-physiological selection indices to improve of drought tolerant lentil genotypes (Lens culinaris Medik) under rainfed condition. \u003cem\u003eAmerican-Eurasian Journal of Agricultural \u0026amp; Environmental Sciences\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(2) :275-281.\u003c/li\u003e\n\u003cli\u003eAboutayeb, R., Baidani, A., Zeroual, A., Benbrahim, N., Aissaoui, A.E., Ouhemi, H., Houasli, C., Mazzucotelli, E., Gadaleta, A. and Idrissi, O., 2023. 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In \u003cem\u003eChoice Reviews Online\u003c/em\u003e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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, Genotype, Nutritional quality, Lentil, variability","lastPublishedDoi":"10.21203/rs.3.rs-8036492/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8036492/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLentil is a nutritionally valuable legume, rich in essential minerals such as zinc (Zn) and iron (Fe). This study aims to evaluate the morphological diversity, mineral composition (Zn, Fe), and ash content of 64 lentil genotypes sourced from ICARDA, Aus, Argentina, Jordan and Ethiopian-released varieties. The experiment was carried out using a simple lattice design with two replications. The data were subjected to multivariate analyses, including principal component analysis (PCA) and clustering techniques, to evaluate genetic diversity and trait associations using different packages of R studio. The results revealed significant variation among genotypes in morphological traits, with some showing high Zn and Fe concentrations alongside desirable agronomic characteristics. The essential nutrients showed that iron levels ranged from 67.9 to 313.7mg/kg, zinc from 17.5 to 40mg/kg, ash from 1.5 to 4.45%, and protein from 17.5 to 26. Genotypes from ICARDA and Jordan exhibited larger thousand seed weight and seed yield comparatively. Hierarchical clustering was performed, and the dendrogram divided the genotypes into three groups using the ward method. Based on the principal component analysis, four principal components (PC1 to PC4) eigenvalues range from 1.04 to 3.5, collectively explaining 70.95% of the total variation. The first two principal components PC1 and PC2 contributed 35.02% and 13.09%, respectively.The findings highlight the importance of conservation and utilization of international lentil germplasm to enhance productivity as well as nutritional quality. Molecular and genomic tools must be utilized with conventional selection within future breeding schemes to facilitate the release of climate resistant and biofortified lentil varieties that will contribute to increased food and nutrition security.\u003c/p\u003e","manuscriptTitle":"Genetic Diversity for Morphological and Nutritional Traits in Global Lentil Genotypes for Biofortification Breeding in Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 14:02:23","doi":"10.21203/rs.3.rs-8036492/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":"18e849ad-655a-4201-b3f2-3860b7729742","owner":[],"postedDate":"November 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T13:21:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-28 14:02:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8036492","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8036492","identity":"rs-8036492","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}