Deciphering the Multidimensional Trait Space of Yield and Quality Attributes in Oat (Avena sativa L.) Using Multivariate Analysis

Deciphering the Multidimensional Trait Space of Yield and Quality Attributes in Oat (Avena sativa L.) 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Using Multivariate Analysis HARSHITA NEGI, ROHIT ROHIT, BIRENDRA PRASAD, ANITA SINGH, CHARUPRIYA CHAUHAN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8940229/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Since time immemorial, Oat as an important multipurpose cereal crop have been closely associated with humans. In the Indian subcontinent, it is popularly used as food, feed, and fodder. The present investigation was done at the Experimental Dairy Farm, Nagala, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, for the identification and characterization of the most promising oat lines contributing yield and quality traits through genetic diversity analysis. The findings demonstrated that oat genotypes varied widely in terms of genetic diversity. Grain yield, dry fodder yield, green fodder yield, dry matter percentage, and 100-seed weight all showed strong heritability and high genetic advancement, suggesting additive-type gene action and improvement by simple selection. Eight PCs contributed 87.75 percent of the total variation across the genotypes evaluated for sixteen characters, according to principal component analysis (PCA). PC1 contributed the greatest towards the variability (25.8%), followed by PC2 (19.1%) and PC3 (13.1%). Cluster analysis categorized the accessions under six major clusters, which revealed a reasonable relationship of genetic diversity. The highest inter-cluster distance was observed between clusters V and VI (9.01), followed by clusters VI and I (8.61). In order to create high diversity for efficient selection in the segregating generations for the production of high-yielding oat cultivars, intercrossing between members of these diverse clusters would be necessary. Sufficient diversity was identified in the genotypes based on phenotypic and genotypic variance, principal component analysis, and cluster analysis, which may be employed by researchers in future nutri-agricultural crop improvement programs. Fodder Genetic diversity genetic donor hierarchical cluster analysis Oat Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1.Introduction Oat ( Avena sativa L.) is a valuable crop that belongs to the family Poaceae. It is one of the most utilized winter forage crops and is widely grown by marginal farmers across the world. The crop's ability to be used as a pasture, fodder, and grain has led to its widespread adoption (Chand et al. 2025). This is one of the best dual-purpose cereal crops that is beneficial to both people and cattle. After wheat, rice, corn, barley, sorghum, and millets, it came in at number seven in the world's cereal production rankings. but it was neglected for a long time despite its high nutritious values (Urgesa 2023). Concerning most staple cereals, oat grains are rich in lipids, quality proteins, dietary fiber, phenolic as well as have high antioxidant properties (α-tocotrienol, α- tocopherol, and avenanthramides). These properties have been explored by various scientific communities during the past few decades (Alemayehu et al. 2023; Chen et al. 2017; Vilmane et al. 2015). Breeders have been fascinated by the oat's progress in recent years owing to its nutritionally enhanced livestock fodder and its grains as an animal feed with significant net energy gains, given the rise of the global dairy sector (Dziurka et al. 2019; Ruwali et al.2013; Jaipal and Shekhawat 2016). Before starting a targeted breeding program, it is necessary to examine the genetic diversity using phenotypic characterisation for several variables. The chance for selection is mostly determined by the amount of genetic diversity present in the germplasm (Salgotra et al. 2023). The extensive expansion of dominant cultivars, which consistently yield low crop genetic diversity and high uniformity, may lead to agricultural vulnerability (Khoury et al. 2022; Salgotra and Chauhan 2023). Therefore, incorporating new genetic diversity linked to advantageous commercial traits is crucial to boosting resilience against climate change. In order to achieve genetic gain in breeding programs, populations with a wide range of phenotypic variability are crucial, and the best natural resource for supplying the allelic variation in traits required to create new cultivars is germplasm gathered from different regions (Swarup et al, 2021). Henceforth, with this holistic vision, the present investigation was designed to study the genetic diversity existing in the oat genotypes for identification and characterization ofthe potential genetic donor (lines) which is likely to play an important role during crop improvement programs. 2. Material and methods Fifty-four germplasm lines of oat including three checks were evaluated for various quantitative traits in augmented block design at Experimental Dairy farm, Nagala, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India in year 2021-22 and 2022-23. Test genotypes were not replicated, but three checks were replicated six times. The plot was made of a single row, 1 meter long and distance between rows was regulated to 100 cm. To grow a successful crop during the trial, all the suggested package of practices was used. A representative random sample of 5 individual plants from each row was used for recording the observations. Data were collected on sixteen agronomic and quality traits, including 1.Plant height (PH) 65 days after sowing(cm.) 2.Numbers of tillers per plant(TPP) 3.Plant height (PH) at 50%flowering(cm.) 4.Plant height(PH) at maturity 5.Days to 50% heading(DF) 6.Glume length(GL) 7. Panicle length (PL) 8. Green fodder yield (GFY) per plant(g) Dry matter(DM) (%) 10.Dry matter yield per plant(DMY)11. Leaf stem ratio(LSR) 12. Grain yield (GY)/ meter row 13.100-grain weight 14. Crude Protein (CP) 15. Neutral detergent fiber (NDF) and 16. Acid detergent fiber (ADF). Pooled data over two years were analyzed to estimate genetic variability parameters using the R package variability (Popat et al. 2020). Trait correlations were computed and visualized using Metan (Olivoto and Lúcio 2020). Principal component analysis (PCA) was conducted using FactoMineR (Lê et al. 2008), and biplots were generated with Factoextra (Kassambara and Mundt 2017) and ggplot2 (Wickham 2011). Hierarchical clustering was performed using Euclidean distance and Ward’s linkage method, and dendrograms were constructed to interpret genetic relationships. Graphical outputs were produced using the dendextend (Galili 2015) and circlize (Gu et al. 2014) packages. 3. Result and discussion 3.1. Evaluation of genetic variability parameters for yield and quality traits Significant variations between all 57 oat genotypes for the traits under study were found by the analysis of variance, suggesting that there is a lot of genetic variation that may be used in breeding and selection strategies. (Table 1 ). Oat genotypes showed a wide range of variability for GY and GFY and their component traits. Significant genetic variability was observed for plant height among oat genotypes at different growth stages, ranging from 24.44–59.62 cm at 65 days after sowing, 80.81–161.06 cm at 50% flowering, and 92.56–192.56 cm at maturity. The general mean plant heights at these stages were 45.48 cm, 117.69 cm, and 139.38 cm, respectively, and were in agreement with earlier reports in oats (Negi et al. 2019; Iannucci et al. 2011; Chakraborty et al. 2014). TPP showed considerable variation among oat genotypes, ranging from 6.16 to 14.25 with a general mean of 9.76, and these values were superior to earlier reports (Hisir et al. 2012). DF also varied widely, with a mean of 136 days and a range from 82 days to 184 days, in agreement with previous findings (Ahmed et al. 2011; Bind et al. 2016). According to previous research, GFY showed significant variability, ranging from 76.7 g to 322.83 g with a general mean of 184.77 g (Iannucci et al. 2011). Similar to this, DFY varied significantly amongst oat genotypes, ranging from 10.95 g to 65.30 g, with an average of 32.31 g. The genotypes ranged widely from 61.60 g to 458.77 g, with a mean GY of 186.18 g. Although previous research revealed relatively lower yields, the assessed genotypes in this study recorded greater average GY than the check varieties, demonstrating their superior yield potential (Shehzad et al. 2011; Chakraborty et al. 2014). The 100 SW value had an average mean of 3.81g and ranged from 1.74g to 5.7g. With a mean of 8.82%, the CP content of oat genotypes varied from 6.58% to 11.13%, exhibiting patterns consistent with previous research (Bibi et al. 2012; Siloriya et al. 2014; Surjeet et al. 2015). With mean values of 63.25% for NDF and 54.59% for ADF, the genotypes likewise showed greater detergent fiber contents than the checks, indicating significant variability (Krishna et al. 2014; Bind et al. 2016). The elevated fiber content further highlights the nutritional importance of oats, particularly for infant diets and diabetic patients (Shehzad et al. 2011; Chakraborty et al. 2014; Bind et al. 2016).Thus, based on the above observations it was suggested that the presence of large and exploitable variation in different oat germplasm provides new avenues and ample scope of variation in these traits that could be effectively utilized for crop improvement programs. Estimates of genotypic (GCV), phenotypic (PCV), and environmental (ECV) coefficients of variation revealed that PCV values were higher than GCV for all traits (Table 1 ), indicating the influence of environment on trait expression, as also reported earlier (Köse et al. 2021; Negi et al. 2018; Singh and Singh 2011; Kumar et al. 2016). High GCV and PCV were recorded for DFY, GY, GFY, LSR, DM%, 100SW, suggesting that selection for these traits would be effective for yield improvement in oats. Moderate variability was observed for TPP, PH at different stages, DF, GL, PL, and CP, while low GCV and PCV were noted for NDF and ADF (Pemkumar et al. 2017; Atar et al. 2018; Bibi et al. 2012; Chakraborty et al. 2014; Surje and De 2014). The close association between GCV and PCV for several traits indicated minimal environmental influence on their expression, supporting earlier findings (Singh and Singh 2011; Surje and De 2014). Several traits, including PH at different stages, DF, GL, PL, DFY, GFY, GY, LSR, DM%, 100 SW exhibited high heritability combined with high genetic advance. Similar trends were earlier reported for key yield traits such as GFY, SW, TPP (Chakraborty et al. 2014; Premkumar et al. 2017; Sangwan et al. 2012). The high heritability and genetic advance for these traits indicate the predominance of additive gene action, suggesting that direct selection would be effective for developing superior oat genotypes. Table 1 Basic descriptive statistics for sixteen quantitative characteristics of fifty-four oat germplasm lines Treatment Range Mean CD at 5% ECV(%) GCV(%) PCV(%) Heritability (BS) Genetic Advance as % of mean PH at 65 days (cm) 24.44–59.62 45.48 9.05 9.94 16.21 19.01 0.73 28.46 TPP 6.165–14.255 9.76 3.50 17.93 15.93 23.98 0.44 21.80 PH at 50% flowering 80.815-161.065 117.69 8.84 3.75 15.19 15.64 0.94 30.37 PH at M 92.565-192.565 139.38 38.44 13.77 13.54 19.31 0.49 19.55 DF 82.08-184.165 136.46 11.57 4.23 17.86 18.36 0.95 35.81 GL 2.255–3.755 3.00 0.10 1.68 11.91 12.03 0.98 24.28 PL 46.53–97.44 71.63 1.91 1.33 15.55 15.61 0.99 31.92 GFY 76.7-322.83 184.77 44.96 12.15 32.92 35.09 0.88 63.62 DM% 5.745–27.15 17.76 4.10 11.53 27.42 29.74 0.85 52.06 DFY 10.955–65.305 32.31 3.94 6.08 43.05 43.48 0.98 87.81 LSR 0.09–0.625 0.33 0.08 12.46 39.55 41.46 0.91 77.72 GY 61.605–458.77 186.18 17.25 4.63 45.45 45.68 0.99 93.15 100SW 1.745–5.37 3.81 0.69 9.11 22.25 24.04 0.86 42.42 CP 6.58-11.135 8.82 0.87 4.94 10.25 11.38 0.81 19.02 NDF 60.59–66.13 63.25 2.61 2.06 1.48 2.54 0.34 1.78 ADF 51.505–66.185 54.59 2.67 2.45 3.86 4.57 0.71 6.71 3.2.Estimation of the PCA Principal component analysis efficiently condensed correlated traits into a few major components, with the first eight PCs (Eigenvalue > 0.61) explaining 87.75% of the total variation in oat germplasm (Fig. 1 ). Among these, PC1 contributed 25.78%, followed by PC2 (19.12%) and PC3 (13.12%), together accounting for more than 50% of the total variability (Table 2 ). This substantial agro-morphological diversity was mainly governed by yield and yield-associated traits, confirming their major role in genotype differentiation, in agreement with earlier reports (Clifford and Stephenson 1975; Guei et al. 2005; Kebede et al. 2023). Table 2 Principal component analysis (PcA) using standard data for sixteen quantitative characteristics of Oats Component Eigen Value Proportional Variation (%) cumulative Variation (%) PC1 4.125588 25.78493 25.78493 PC2 3.058693 19.11683 44.90176 PC3 2.098622 13.11639 58.01815 PC4 1.345977 8.412354 66.4305 PC5 1.066595 6.666219 73.09672 PC6 0.997215 6.232591 79.32931 PC7 0.737287 4.608044 83.93736 PC8 0.610156 3.813473 87.75083 The scree plot from PCA of 16 traits in 54 oat accessions showed that PC1 explained 25.78% of the total variance, followed by PC2 (19.12%) and PC3 (13.12%), together accounting for about 58% of the total variability (Fig. 1 ). The contribution declined gradually from PC4 (8.41%), PC5 (6.67%), and PC6 (6.23%), forming a flattened tail, indicating that most meaningful variation is captured by the first few components. This pattern supports retaining the initial PCs for effective interpretation of genetic variability, in agreement with earlier findings in oats (Bichewar et al. 2023; Köse et al. 2021). The PCA biplot depicted the relationships among 54 oat accessions and 16 traits based on the first two principal components, with PC1 and PC2 explaining 25.8% and 19.1% of the total variation, respectively (Fig. 2 ). Traits such as GY, GFY, DFY, LSR, ADF, PH, PL and 100 SW showed strong contributions, as indicated by their long vectors away from the origin. Traits oriented in similar directions, namely GFY, DFY, GY, and LSR, showed positive associations, indicating that higher biomass production was linked with improved yield attributes, while oppositely oriented traits reflected negative relationships. Accessions such as EC-159602, NO1310, EC-605838, UPO-276, OL-1325, and M-27 were closely associated with GY, GFY, DFY and quality traits, highlighting their potential as dual-purpose genotypes. On the other hand, EC-6269 and EC-61704 were linked to fodder quality traits, indicating significant genetic variability and supporting selection for yield, fodder, and quality in oat breeding programs, while OX-435, M-72, and EC-KENT were linked to plant height and morphological traits for biomass improvement. The first two main components were used to divide the PCA biplot into four quadrants. Quadrant I comprised traits contributing positively to both PC1 and PC2, representing accessions with combined yield and quality attributes (Fig. 3). The clustering of yield and quality traits in the same quadrant indicated strong positive associations, showing that oat accessions with vigorous growth and higher biomass also performed better for grain yield, fodder yield, and nutritional quality. PC1 primarily represented a growth and yield axis, while PC2 reflected a quality and biomass axis, making accessions in this quadrant superior for dual-purpose utilization. Quadrant II included quality-oriented traits with positive association to PC2 but negative to PC1, Quadrant IV comprised grain yield and plant stature traits with strong positive loadings on PC1 but negative on PC2, whereas Quadrant III contained traits with minimal contribution to both components, indicating limited influence on overall variability. Figure 3. PCA graph of variables of 16 traits in 54 genotypes of Oat 3.3.Correlation analysis: The correlation coefficient and correlation matrix between the attributes revealed 120 connections, 57 of which were positive (Fig. 4 ). TPP had a strong positive correlation with GFY, DFY, and LSR. A significant positive association of PH at 50% was recorded with PH at maturity, DF and PL. DF showed significant positive correlation with PL whereas GL showed positive correlation with 100 SW. The significant positive correlation as also observed between GFY, LSR and DFY. There have previously been reports of similar phenotypic associations between yield-attributing characteristics and other morphological traits (Yan et al. 2023; Choubey et al. 2001; Bibi et al. 2012; Bukhari et al. 2009. 3.4. Estimates of genetic diversity through hierarchical cluster analysis : Cluster analysis grouped the accessions into six distinct clusters, facilitating the selection of diverse genotypes. Cluster II had the maximum number of accessions (33), followed by cluster V (14), cluster IV (3), cluster III (2), cluster I (1), and cluster VI (1) (Table 3 ; Fig. 6 ). Cluster V and cluster VI had the largest inter-cluster distance (9.01), which was more than intra-cluster distances (Fig. 5). These distances are followed by cluster VI and cluster I (8.61), while the highest intra-cluster distance was observed in cluster IV, indicating wide genetic diversity among clusters (Subramanian and Subbaraman 2010; Singh and Singh 2011; Ahmed et al. 2011; Bhattarai et al. 2017). Trait-wise cluster means showed that cluster VI and cluster III were superior for green fodder yield and dry fodder yield, grain yield was highest in cluster VI and cluster IV, detergent fiber content was high in cluster III and cluster I, and days to 50% flowering was lowest in cluster I and cluster IV. The appreciable variation among cluster means further confirmed genetic diversity in oat germplasm, as also reported earlier by Iannucci et al. (2011), Ahmed et al. (2011), and Krishna et al. (2014). Selection of parents from cluster I and cluster IV for earliness, combined with high fodder yield from cluster VI and cluster III, would be useful for hybridization programmes aimed at developing short-duration, high-yielding oat varieties (Ahmed et al. 2011; Krishna et al. 2014; Jaipal and Shekhawat 2016). Crossing lines from different clusters may result in high heterosis, which can be utilized for oat improvement (Singh and Singh 2011; Bhattarai et al, 2017). Cluster mean comparisons suggest selecting genotypes from clusters with high mean values for specific traits (Subramanian and Subbaraman 2010; Singh and Singh 2011). Cluster VI showed the highest means for most traits, followed by cluster III, indicating the presence of promising genotypes for hybridization to improve oat yield. These findings are in agreement with previous studies (Singh and Singh 2011; Krishna et al. 2014; Bind et al. 2016). Table 3 Quantitative variable means across several oat germplasm clusters Cluster variables Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 PH65 56.95 48.28 49.88 56.29 36.99 51.96 TPP 13.05 9.77 12.49 9.05 8.86 13.17 PH50% 111.57 124.65 98.73 105.07 106.46 146.52 PHM 123.65 147.27 109.61 123.48 128.14 174.07 DF 82.08 145.87 123.58 109.75 123.08 172.75 GL 2.97 3.04 3.61 3.04 2.79 3.26 PL 73.53 74.29 56.76 66.53 65.91 84.13 GFY 218.54 199.25 228.12 218.87 117.88 322.83 DM% 17.39 17.98 22.16 6.76 18.27 19.46 DFY 38.00 35.39 49.75 14.95 21.02 62.97 LSR 0.44 0.35 0.39 0.50 0.20 0.63 GY 274.94 183.87 199.19 279.94 137.52 458.77 100SW 4.50 3.93 4.86 4.17 3.28 4.79 CP 9.39 8.78 10.33 9.09 8.68 8.33 NDF 62.67 63.11 64.66 63.60 63.70 64.19 ADF 66.19 54.68 56.85 54.65 53.75 52.32 3.5. Assessing the most promising germplasm lines with important traits Significant variability for agronomic, yield, and quality traits was found when oat germplasm was compared to the best check, suggesting a large genetic potential for targeted improvement. In terms of plant height, EC-131306 showed dwarfism at both stages, which is a desirable characteristic for lodging resistance, whereas M-72 continuously displayed tall stature at both 50% flowering and maturity, making it appropriate for fodder biomass enhancement. There was clear phenological variation, and EC-6269 was found to be an early flowering genotype that was useful for multiple cropping systems and stress escape. In contrast, M-72 and OX-435 flowered later, indicating longer vegetative growth and more accumulation of fodder. EC-61704 showed superior basal tillering ability among yield-related traits. With exceptional results in green fodder yield, leaf–stem ratio, and grain yield per meter row, EC-159602 emerged as a very promising genotype with strong dual-purpose potential. EC-43555 had a higher dry fodder yield, while N0-982 had the highest dry matter content—a crucial factor for the quality and storage of fodder. High-quality characteristics further distinguished the germplasm. While OX-435 and EC-6269 were found to be important sources of neutral detergent fiber and acid detergent fiber, respectively, EC-24900 demonstrated a higher crude protein content. effectively used in oat breeding programs to create nutritionally enhanced and high-yielding varieties. Overall, the findings demonstrate the existence of trait-specific elite germplasm, including EC-159602, M-72, EC-6269, and EC-131306, which may be successfully used in oat breeding programs to create high-yielding and nutritionally enhanced varieties (Table 4 ). Conclusion The wide, structured, and exploitable genetic diversity among oat genotypes for agro-morphological, yield, and quality traits is clearly demonstrated by this study. The evaluated germplasm exhibits strong yield and nutritional improvement potential, as evidenced by the significant variation across all traits and the superior performance of several genotypes over the checks. The effectiveness of direct selection for quick genetic gain is confirmed by high GCV, heritability, and genetic advance for important traits like green fodder yield, grain yield, dry fodder yield, leaf–stem ratio, dry matter content, and seed weight, which show the predominance of additive gene action. Further multivariate analyses showed that the main causes of genetic divergence are yield and yield-associated traits. PCA effectively separated genotypes according to productivity, biomass, and quality attributes, capturing the majority of the variability within the first few components and proving that high yield and superior fodder quality can be enhanced at the same time. Significant inter-cluster divergence was revealed by cluster analysis, offering opportunities for heterosis breeding through deliberate hybridization between genetically distant clusters. The identification of elite, trait-specific donor lines- particularly EC-159602, M-72, EC-6269, EC-131306, and EC-61704- offers immediately usable genetic resources for developing dual-purpose, high-yielding, and nutritionally superior oat cultivars. All things considered, the results support the crucial role that genetic diversity has played in the evolution of oats and offer a solid, empirically supported framework for upcoming breeding initiatives targeted at sustainable productivity and quality improvement. Declarations Acknowledgments The help and support provided during an investigation by the Department of Genetics and Plant Breeding, College of Agriculture, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India is duly acknowledged. Authors Contributions HN, R, and BP conceptualized the manuscript. HN and R wrote the manuscript. BP and AS assisted in writing, updated information and edited the manuscript. BP and CC contributed to critically revising the draft and updating the manuscript for publication. Funding and grants received This research did not receive any specific grant from funding agencies in the public, commercial, or, not-for-profit sectors. Ethics approval and consent to participate The plant material used in this study ( Avena sativa L. ) consisted of cultivated oat germplasm maintained for research purposes. The collection and use of plant materials complied with institutional guidelines of Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, India, and relevant national regulations. No wild or endangered plant species were used in this study; therefore, no specific permissions or licenses were required. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Data Availability Statement The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. References Ahmed S, Roy AK, Majumdar AB (2011) Genetic diversity and variability analysis in oat ( Avena sativa L.). Range Manag Agrofor 32:96–99. Araus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19:52–61. Arora RN (2013) Characterization and evaluation of one and two-harvest of oats. Forage Res 39:59–63. 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Kueger S, Steinhauser D, Willmitzer L, Giavalisco P (2012) High-resolution plant metabolomics: from mass spectral features to metabolites and from whole-cell analysis to subcellular metabolite distributions. Plant J 70:39–50. Kumar P, Phogat DS, Bhukkar A (2016) Genetic diversity analysis in oat. Forage Res 42:96–100. Kumar Y, Jhorar BS, Sangwan O, Singh P (2004) Character association between grain yield and its components in oat ( Avena sativa L.). Natl J Plant Improv 6:4–6. Lê S, Josse J, Husson F (2008) FactoMineR: An R package for multivariate analysis. J Stat Softw 25:1–18. Moradi M, Rezai A, Arzani A (2005) Path analysis for yield and related traits in oats. J Sci Technol Agric Nat Resour 9:173–180. Negi H, Prasad B, Kumar A, Kumar S (2019) Simple correlation and phenotypic path coefficient analysis in oat germplasm. Int J Chem Stud 7:1174–1178. Negi H, Prasad B, Lohani P (2018) Estimates of genetic variability parameters in oat germplasm for improving fodder yield. J Hill Agric 9:371–376. Olivoto T, Lúcio ADC (2020) metan: An R package for multi-environment trial analysis. Methods Ecol Evol 11:783–789. Poonia A, Phogat DS, Versha, Kumar S (2021) Principal component analysis in oat ( Avena sativa L.) genotypes for green fodder yield and its attributing traits. Popat R, Patel R, Parmar D (2020) Variability: Genetic variability analysis for plant breeding research. R package documentation. https://cran.r-project.org/web/packages/variability/variability.pdf Premkumar R, Nirmalakumari A, Anandakumar CR (2017) Studies on genetic variability and character association among yield and yield attributing traits in oats ( Avena sativa L.). Int J Curr Microbiol Appl Sci 6:4075–4083. Pundir SR, Dutt Y, Grewal RPS (2008) Genetic variability for forage characters in oat ( Avena sativa L.). Forage Res 28:228–229. Ruwali Y, Singh K, Kumar S, Kumar L (2013) Molecular diversity analysis in selected fodder and dual-purpose oat ( Avena sativa L.) genotypes using RAPD markers. Afr J Biotechnol 12:3425–3429. Salgotra RK, Chauhan BS (2023) Genetic diversity, conservation, and utilization of plant genetic resources. Genes 14:174. https://doi.org/10.3390/genes14010174 Sangwan O, Avtar R, Arora RN, Singh A (2012) Variability and character association studies in fodder oat ( Avena sativa L.). Forage Res 38:56–58. Shehzad M, Ayub M, Nadeem MA, Pervez M, Nadeem M, Sarwar N (2011) Comparative study on forage yield and quality of different oat ( Avena sativa L.) varieties under agroecological conditions. Afr J Agric Res 6:3388–3391. Sheoran OP, Tonk DS, Kaushik LS, Hasija RC, Pannu RS (1998) Statistical software package for agricultural research workers. In: Hooda DS, Hasija RC (eds) Recent advances in information theory, statistics and computer applications . CCS Haryana Agricultural University, Hisar, pp 139–143. Siloriya RN, Rathi GS, Meena VD (2014) Relative performance of oat ( Avena sativa L.) varieties for growth and seed yield. Afr J Agric Res 9:425–431. Singh A, Vyas RP, Kumar S, Singh HC, Deep A, Malik P, Singh A (2018) Genetic variability and correlation of seed yield and related characters in oat ( Avena sativa L.). Int J Chem Stud 6:1532–1537. Singh SB, Singh AK (2009) Genetic variability, character association and path analysis for green fodder yield and its component characters in oat ( Avena sativa ). Prog Res 4:159–162. Singh SB, Singh AK (2011) Genetic variability and divergence analysis in oat ( Avena sativa ) under rainfed environment of intermediate Himalayan hills. Indian J Plant Genet Resour 24:57–62. Subramanian A, Subbaraman N (2010) Hierarchical cluster analysis of genetic diversity in maize germplasm. Electron J Plant Breed 1:431–436. Surje DT, Barma SD, Satpute SB, Kale VA, Das A, De DK (2015) Variability and cause–effect analysis for fodder and grain yield characters in oat ( Avena sativa L.) genotypes. Forage Res 41:85–91. Surje DT, De DK (2014) Correlation coefficient study in oat ( Avena sativa L.) genotypes for fodder and grain yield characters. J Agric Sci Technol 1:89–93. Swarup S, Cargill EJ, Crosby K, Flagel L, Kniskern J, Glenn KC (2021) Genetic diversity is indispensable for plant breeding to improve crops. Crop Sci 61:839–852. https://doi.org/10.1002/csc2.20377 Vilmane L, Zute S, Straumīte E, Galoburda R (2015) Protein, amino acid and gluten content in oat ( Avena sativa L.) grown in Latvia. Proc Latv Acad Sci B 4:170–177. Wickham H (2011) ggplot2. Wiley Interdiscip Rev Comput Stat 3:180–185. Yan W, Hadinezhad M, Dehaan B, Hayes M, Orozovic S, Nilsen KT et al (2023) Exploring trait–yield association patterns in different oat mega-environments of Canada. Crop Sci 63:3356–3366. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8940229","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618249815,"identity":"20fa8fc1-cc27-4d84-a69e-84ac7f20e936","order_by":0,"name":"HARSHITA NEGI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYBACAyBmBpPs/R8ffADSbOzEaQFSPAeMDWeAtDATpQVESTiYSfMwQLh4gTn72cefCwr+yJtLMCQb2/zaJs/HzMD44WMObi2WPelm0jMMDAx3zm44+Di377ZhGzMDs+TMbXgcdiCNjZnHwIBxw52Dzca5PbcZgVrYmHnxaTn/jPkzUIv9hhvJbNKWPbftCWu5kcYgDdSSuOFGGps0w4/biURoecYG1GKcvOHMGWbD3obbyW3MjM34/XI+DeiwP3K2G473MD748ee27fz25oMfPuLRggoY28BkA7HqQeAPKYpHwSgYBaNgpAAAK+tPCL91bskAAAAASUVORK5CYII=","orcid":"","institution":"Graphic Era Hill University","correspondingAuthor":true,"prefix":"","firstName":"HARSHITA","middleName":"","lastName":"NEGI","suffix":""},{"id":618249816,"identity":"8f0ffc8e-30dd-4a9d-82ae-279d0766eeec","order_by":1,"name":"ROHIT ROHIT","email":"","orcid":"","institution":"G. B. Pant University of Agriculture and Technology","correspondingAuthor":false,"prefix":"","firstName":"ROHIT","middleName":"","lastName":"ROHIT","suffix":""},{"id":618249817,"identity":"9c583b99-1d38-4cff-b68f-14f81e1973da","order_by":2,"name":"BIRENDRA PRASAD","email":"","orcid":"","institution":"G. B. Pant University of Agriculture and Technology","correspondingAuthor":false,"prefix":"","firstName":"BIRENDRA","middleName":"","lastName":"PRASAD","suffix":""},{"id":618249818,"identity":"0f91c2ba-2b51-4ba9-8aa1-ab40a5da8dbd","order_by":3,"name":"ANITA SINGH","email":"","orcid":"","institution":"Graphic Era Hill University","correspondingAuthor":false,"prefix":"","firstName":"ANITA","middleName":"","lastName":"SINGH","suffix":""},{"id":618249821,"identity":"fb853083-c629-47ca-8245-815b5aebf3f5","order_by":4,"name":"CHARUPRIYA CHAUHAN","email":"","orcid":"","institution":"Acharya Narendra Deva University of Agriculture and Technology","correspondingAuthor":false,"prefix":"","firstName":"CHARUPRIYA","middleName":"","lastName":"CHAUHAN","suffix":""}],"badges":[],"createdAt":"2026-02-22 15:54:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8940229/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8940229/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106403416,"identity":"ca4a6f1a-f48e-4376-89ba-abf31269ad42","added_by":"auto","created_at":"2026-04-08 09:14:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":125195,"visible":true,"origin":"","legend":"\u003cp\u003eScree plot showing percentage of variance explained by principal components\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8940229/v1/ee039ab459c0139c1e290329.png"},{"id":106366016,"identity":"9cec5eb2-db00-4daf-9014-0e174ff69fdb","added_by":"auto","created_at":"2026-04-07 23:44:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":146660,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBiplot of 54 oat genotypes on principal component axis 1 and 2.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8940229/v1/575688f8555f7e617e0414e1.png"},{"id":106404683,"identity":"23f4749a-e084-40d0-8ade-78d3a2df0d7f","added_by":"auto","created_at":"2026-04-08 09:16:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":100788,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCA graph of variables of 16 traits in 54 genotypes of Oat\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8940229/v1/1652625b53b6ac4746759822.png"},{"id":106366020,"identity":"ff752662-e3db-4f3e-823c-eace17913c91","added_by":"auto","created_at":"2026-04-07 23:44:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":257371,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePairwise relationships between several quantitative characteristics of oat germplasm lines. The correlation's numerical values are displayed in the upper panel. Correlation significance at the 0.05, 0.01, and 0.001 probability levels is indicated by the *, **, and *** stars, respectively. The scatter plots are displayed in the lower panel.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8940229/v1/284e4c447d850bb7a00180bf.png"},{"id":106366018,"identity":"385b2056-5481-4a8d-a052-7c4eee6e36f3","added_by":"auto","created_at":"2026-04-07 23:44:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":216806,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAverage intra and inter cluster distances for various clusters\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8940229/v1/57437658a4dc2af36aef0d58.png"},{"id":106415185,"identity":"d79f1f52-66a7-4fd9-8279-b83ca855d12e","added_by":"auto","created_at":"2026-04-08 10:33:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":407593,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDendrogram depicting relationships among the oat genotype for 16 traits\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8940229/v1/d3db488585eca121f4cf0a01.png"},{"id":106416409,"identity":"d930f27b-b0c3-4aa2-9ae1-68161c26597a","added_by":"auto","created_at":"2026-04-08 10:45:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2475872,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8940229/v1/23c7feb4-dfdd-454b-b5ef-4876a215d75f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deciphering the Multidimensional Trait Space of Yield and Quality Attributes in Oat (Avena sativa L.) Using Multivariate Analysis","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eOat (\u003cem\u003eAvena sativa\u003c/em\u003e L.) is a valuable crop that belongs to the family Poaceae. It is one of the most utilized winter forage crops and is widely grown by marginal farmers across the world. The crop's ability to be used as a pasture, fodder, and grain has led to its widespread adoption (Chand et al. 2025). This is one of the best dual-purpose cereal crops that is beneficial to both people and cattle. After wheat, rice, corn, barley, sorghum, and millets, it came in at number seven in the world's cereal production rankings. but it was neglected for a long time despite its high nutritious values (Urgesa 2023). Concerning most staple cereals, oat grains are rich in lipids, quality proteins, dietary fiber, phenolic as well as have high antioxidant properties (α-tocotrienol, α- tocopherol, and avenanthramides). These properties have been explored by various scientific communities during the past few decades (Alemayehu et al. 2023; Chen et al. 2017; Vilmane et al. 2015). Breeders have been fascinated by the oat's progress in recent years owing to its nutritionally enhanced livestock fodder and its grains as an animal feed with significant net energy gains, given the rise of the global dairy sector (Dziurka et al. 2019; Ruwali et al.2013; Jaipal and Shekhawat 2016). Before starting a targeted breeding program, it is necessary to examine the genetic diversity using phenotypic characterisation for several variables. The chance for selection is mostly determined by the amount of genetic diversity present in the germplasm (Salgotra et al. 2023). The extensive expansion of dominant cultivars, which consistently yield low crop genetic diversity and high uniformity, may lead to agricultural vulnerability (Khoury et al. 2022; Salgotra and Chauhan 2023). Therefore, incorporating new genetic diversity linked to advantageous commercial traits is crucial to boosting resilience against climate change. In order to achieve genetic gain in breeding programs, populations with a wide range of phenotypic variability are crucial, and the best natural resource for supplying the allelic variation in traits required to create new cultivars is germplasm gathered from different regions (Swarup et al, 2021). Henceforth, with this holistic vision, the present investigation was designed to study the genetic diversity existing in the oat genotypes for identification and characterization ofthe potential genetic donor (lines) which is likely to play an important role during crop improvement programs.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cp\u003eFifty-four germplasm lines of oat including three checks were evaluated for various quantitative traits in augmented block design at Experimental Dairy farm, Nagala, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India in year 2021-22 and 2022-23. Test genotypes were not replicated, but three checks were replicated six times. The plot was made of a single row, 1 meter long and distance between rows was regulated to 100 cm. To grow a successful crop during the trial, all the suggested package of practices was used. A representative random sample of 5 individual plants from each row was used for recording the observations. Data were collected on sixteen agronomic and quality traits, including 1.Plant height (PH) 65 days after sowing(cm.) 2.Numbers of tillers per plant(TPP) 3.Plant height (PH) at 50%flowering(cm.) 4.Plant height(PH) at maturity 5.Days to 50% heading(DF) 6.Glume length(GL) 7. Panicle length (PL) 8. Green fodder yield (GFY) per plant(g) Dry matter(DM) (%) 10.Dry matter yield per plant(DMY)11. Leaf stem ratio(LSR) 12. Grain yield (GY)/ meter row 13.100-grain weight 14. Crude Protein (CP) 15. Neutral detergent fiber (NDF) and 16. Acid detergent fiber (ADF). Pooled data over two years were analyzed to estimate genetic variability parameters using the R package variability (Popat et al. 2020). Trait correlations were computed and visualized using Metan (Olivoto and L\u0026uacute;cio 2020). Principal component analysis (PCA) was conducted using FactoMineR (L\u0026ecirc; et al. 2008), and biplots were generated with Factoextra (Kassambara and Mundt 2017) and ggplot2 (Wickham 2011). Hierarchical clustering was performed using Euclidean distance and Ward\u0026rsquo;s linkage method, and dendrograms were constructed to interpret genetic relationships. Graphical outputs were produced using the dendextend (Galili 2015) and circlize (Gu et al. 2014) packages.\u003c/p\u003e"},{"header":"3. Result and discussion","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Evaluation of genetic variability parameters for yield and quality traits\u003c/h2\u003e \u003cp\u003eSignificant variations between all 57 oat genotypes for the traits under study were found by the analysis of variance, suggesting that there is a lot of genetic variation that may be used in breeding and selection strategies. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Oat genotypes showed a wide range of variability for GY and GFY and their component traits. Significant genetic variability was observed for plant height among oat genotypes at different growth stages, ranging from 24.44\u0026ndash;59.62 cm at 65 days after sowing, 80.81\u0026ndash;161.06 cm at 50% flowering, and 92.56\u0026ndash;192.56 cm at maturity. The general mean plant heights at these stages were 45.48 cm, 117.69 cm, and 139.38 cm, respectively, and were in agreement with earlier reports in oats (Negi et al. 2019; Iannucci et al. 2011; Chakraborty et al. 2014). TPP showed considerable variation among oat genotypes, ranging from 6.16 to 14.25 with a general mean of 9.76, and these values were superior to earlier reports (Hisir et al. 2012). DF also varied widely, with a mean of 136 days and a range from 82 days to 184 days, in agreement with previous findings (Ahmed et al. 2011; Bind et al. 2016). According to previous research, GFY showed significant variability, ranging from 76.7 g to 322.83 g with a general mean of 184.77 g (Iannucci et al. 2011). Similar to this, DFY varied significantly amongst oat genotypes, ranging from 10.95 g to 65.30 g, with an average of 32.31 g. The genotypes ranged widely from 61.60 g to 458.77 g, with a mean GY of 186.18 g. Although previous research revealed relatively lower yields, the assessed genotypes in this study recorded greater average GY than the check varieties, demonstrating their superior yield potential (Shehzad et al. 2011; Chakraborty et al. 2014). The 100 SW value had an average mean of 3.81g and ranged from 1.74g to 5.7g. With a mean of 8.82%, the CP content of oat genotypes varied from 6.58% to 11.13%, exhibiting patterns consistent with previous research (Bibi et al. 2012; Siloriya et al. 2014; Surjeet et al. 2015). With mean values of 63.25% for NDF and 54.59% for ADF, the genotypes likewise showed greater detergent fiber contents than the checks, indicating significant variability (Krishna et al. 2014; Bind et al. 2016). The elevated fiber content further highlights the nutritional importance of oats, particularly for infant diets and diabetic patients (Shehzad et al. 2011; Chakraborty et al. 2014; Bind et al. 2016).Thus, based on the above observations it was suggested that the presence of large and exploitable variation in different oat germplasm provides new avenues and ample scope of variation in these traits that could be effectively utilized for crop improvement programs.\u003c/p\u003e \u003cp\u003eEstimates of genotypic (GCV), phenotypic (PCV), and environmental (ECV) coefficients of variation revealed that PCV values were higher than GCV for all traits (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), indicating the influence of environment on trait expression, as also reported earlier (K\u0026ouml;se et al. 2021; Negi et al. 2018; Singh and Singh 2011; Kumar et al. 2016). High GCV and PCV were recorded for DFY, GY, GFY, LSR, DM%, 100SW, suggesting that selection for these traits would be effective for yield improvement in oats. Moderate variability was observed for TPP, PH at different stages, DF, GL, PL, and CP, while low GCV and PCV were noted for NDF and ADF (Pemkumar et al. 2017; Atar et al. 2018; Bibi et al. 2012; Chakraborty et al. 2014; Surje and De 2014). The close association between GCV and PCV for several traits indicated minimal environmental influence on their expression, supporting earlier findings (Singh and Singh 2011; Surje and De 2014).\u003c/p\u003e \u003cp\u003eSeveral traits, including PH at different stages, DF, GL, PL, DFY, GFY, GY, LSR, DM%, 100 SW exhibited high heritability combined with high genetic advance. Similar trends were earlier reported for key yield traits such as GFY, SW, TPP (Chakraborty et al. 2014; Premkumar et al. 2017; Sangwan et al. 2012). The high heritability and genetic advance for these traits indicate the predominance of additive gene action, suggesting that direct selection would be effective for developing superior oat genotypes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic descriptive statistics for sixteen quantitative characteristics of fifty-four oat germplasm lines\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCD at 5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eECV(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGCV(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePCV(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHeritability (BS)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGenetic Advance as % of mean\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePH at 65 days (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.44\u0026ndash;59.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e28.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTPP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.165\u0026ndash;14.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e21.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePH at 50%\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eflowering\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.815-161.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e30.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePH at M\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92.565-192.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e19.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.08-184.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e35.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.255\u0026ndash;3.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e24.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.53\u0026ndash;97.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e31.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGFY\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.7-322.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e184.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e63.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDM%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.745\u0026ndash;27.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e52.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDFY\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.955\u0026ndash;65.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e87.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLSR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09\u0026ndash;0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e77.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGY\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.605\u0026ndash;458.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e186.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e93.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e100SW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.745\u0026ndash;5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e42.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.58-11.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e19.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNDF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.59\u0026ndash;66.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eADF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.505\u0026ndash;66.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.71\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 \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2.Estimation of the PCA\u003c/h2\u003e \u003cp\u003ePrincipal component analysis efficiently condensed correlated traits into a few major components, with the first eight PCs (Eigenvalue\u0026thinsp;\u0026gt;\u0026thinsp;0.61) explaining 87.75% of the total variation in oat germplasm (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among these, PC1 contributed 25.78%, followed by PC2 (19.12%) and PC3 (13.12%), together accounting for more than 50% of the total variability (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This substantial agro-morphological diversity was mainly governed by yield and yield-associated traits, confirming their major role in genotype differentiation, in agreement with earlier reports (Clifford and Stephenson 1975; Guei et al. 2005; Kebede et al. 2023).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ePrincipal component analysis (PcA) using standard data for sixteen quantitative characteristics of Oats\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEigen Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportional Variation (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecumulative Variation (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.125588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.78493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.78493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.058693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.11683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.90176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.098622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.11639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.01815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.345977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.412354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.4305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.066595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.666219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.09672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.997215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.232591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.32931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.737287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.608044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.93736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.610156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.813473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.75083\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\u003eThe scree plot from PCA of 16 traits in 54 oat accessions showed that PC1 explained 25.78% of the total variance, followed by PC2 (19.12%) and PC3 (13.12%), together accounting for about 58% of the total variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The contribution declined gradually from PC4 (8.41%), PC5 (6.67%), and PC6 (6.23%), forming a flattened tail, indicating that most meaningful variation is captured by the first few components. This pattern supports retaining the initial PCs for effective interpretation of genetic variability, in agreement with earlier findings in oats (Bichewar et al. 2023; K\u0026ouml;se et al. 2021).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe PCA biplot depicted the relationships among 54 oat accessions and 16 traits based on the first two principal components, with PC1 and PC2 explaining 25.8% and 19.1% of the total variation, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Traits such as GY, GFY, DFY, LSR, ADF, PH, PL and 100 SW showed strong contributions, as indicated by their long vectors away from the origin. Traits oriented in similar directions, namely GFY, DFY, GY, and LSR, showed positive associations, indicating that higher biomass production was linked with improved yield attributes, while oppositely oriented traits reflected negative relationships. Accessions such as EC-159602, NO1310, EC-605838, UPO-276, OL-1325, and M-27 were closely associated with GY, GFY, DFY and quality traits, highlighting their potential as dual-purpose genotypes. On the other hand, EC-6269 and EC-61704 were linked to fodder quality traits, indicating significant genetic variability and supporting selection for yield, fodder, and quality in oat breeding programs, while OX-435, M-72, and EC-KENT were linked to plant height and morphological traits for biomass improvement.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe first two main components were used to divide the PCA biplot into four quadrants. Quadrant I comprised traits contributing positively to both PC1 and PC2, representing accessions with combined yield and quality attributes (Fig.\u0026nbsp;3). The clustering of yield and quality traits in the same quadrant indicated strong positive associations, showing that oat accessions with vigorous growth and higher biomass also performed better for grain yield, fodder yield, and nutritional quality. PC1 primarily represented a growth and yield axis, while PC2 reflected a quality and biomass axis, making accessions in this quadrant superior for dual-purpose utilization. Quadrant II included quality-oriented traits with positive association to PC2 but negative to PC1, Quadrant IV comprised grain yield and plant stature traits with strong positive loadings on PC1 but negative on PC2, whereas Quadrant III contained traits with minimal contribution to both components, indicating limited influence on overall variability.\u003cdiv description=\"C:\\Users\\hp\\Desktop\\CURRENT PAPER WORK\\oat D2 new\\PCA GRAPH.jpg\" class=\"Drawing\" id=\"3\" name=\"Picture 1\"\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;3. PCA graph of variables of 16 traits in 54 genotypes of Oat\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3.Correlation analysis:\u003c/h2\u003e \u003cp\u003eThe correlation coefficient and correlation matrix between the attributes revealed 120 connections, 57 of which were positive (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). TPP had a strong positive correlation with GFY, DFY, and LSR. A significant positive association of PH at 50% was recorded with PH at maturity, DF and PL. DF showed significant positive correlation with PL whereas GL showed positive correlation with 100 SW. The significant positive correlation as also observed between GFY, LSR and DFY. There have previously been reports of similar phenotypic associations between yield-attributing characteristics and other morphological traits (Yan et al. 2023; Choubey et al. 2001; Bibi et al. 2012; Bukhari et al. 2009.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.4. Estimates of genetic diversity through hierarchical cluster analysis\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eCluster analysis grouped the accessions into six distinct clusters, facilitating the selection of diverse genotypes. Cluster II had the maximum number of accessions (33), followed by cluster V (14), cluster IV (3), cluster III (2), cluster I (1), and cluster VI (1) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Cluster V and cluster VI had the largest inter-cluster distance (9.01), which was more than intra-cluster distances (Fig.\u0026nbsp;5). These distances are followed by cluster VI and cluster I (8.61), while the highest intra-cluster distance was observed in cluster IV, indicating wide genetic diversity among clusters (Subramanian and Subbaraman 2010; Singh and Singh 2011; Ahmed et al. 2011; Bhattarai et al. 2017).\u003c/p\u003e \u003cp\u003eTrait-wise cluster means showed that cluster VI and cluster III were superior for green fodder yield and dry fodder yield, grain yield was highest in cluster VI and cluster IV, detergent fiber content was high in cluster III and cluster I, and days to 50% flowering was lowest in cluster I and cluster IV. The appreciable variation among cluster means further confirmed genetic diversity in oat germplasm, as also reported earlier by Iannucci et al. (2011), Ahmed et al. (2011), and Krishna et al. (2014). Selection of parents from cluster I and cluster IV for earliness, combined with high fodder yield from cluster VI and cluster III, would be useful for hybridization programmes aimed at developing short-duration, high-yielding oat varieties (Ahmed et al. 2011; Krishna et al. 2014; Jaipal and Shekhawat 2016).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCrossing lines from different clusters may result in high heterosis, which can be utilized for oat improvement (Singh and Singh 2011; Bhattarai et al, 2017). Cluster mean comparisons suggest selecting genotypes from clusters with high mean values for specific traits (Subramanian and Subbaraman 2010; Singh and Singh 2011). Cluster VI showed the highest means for most traits, followed by cluster III, indicating the presence of promising genotypes for hybridization to improve oat yield. These findings are in agreement with previous studies (Singh and Singh 2011; Krishna et al. 2014; Bind et al. 2016).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQuantitative variable means across several oat germplasm clusters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCluster 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCluster 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCluster 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCluster 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCluster 5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCluster 6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e105.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e106.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e146.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e123.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e109.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e123.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e128.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e174.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e145.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e123.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e109.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e123.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e172.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e84.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e218.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e199.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e228.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e218.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e117.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e322.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e62.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e274.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e183.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e199.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e279.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e137.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e458.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100SW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e53.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52.32\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 \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Assessing the most promising germplasm lines with important traits\u003c/h2\u003e \u003cp\u003eSignificant variability for agronomic, yield, and quality traits was found when oat germplasm was compared to the best check, suggesting a large genetic potential for targeted improvement. In terms of plant height, EC-131306 showed dwarfism at both stages, which is a desirable characteristic for lodging resistance, whereas M-72 continuously displayed tall stature at both 50% flowering and maturity, making it appropriate for fodder biomass enhancement. There was clear phenological variation, and EC-6269 was found to be an early flowering genotype that was useful for multiple cropping systems and stress escape. In contrast, M-72 and OX-435 flowered later, indicating longer vegetative growth and more accumulation of fodder. EC-61704 showed superior basal tillering ability among yield-related traits. With exceptional results in green fodder yield, leaf\u0026ndash;stem ratio, and grain yield per meter row, EC-159602 emerged as a very promising genotype with strong dual-purpose potential. EC-43555 had a higher dry fodder yield, while N0-982 had the highest dry matter content\u0026mdash;a crucial factor for the quality and storage of fodder. High-quality characteristics further distinguished the germplasm. While OX-435 and EC-6269 were found to be important sources of neutral detergent fiber and acid detergent fiber, respectively, EC-24900 demonstrated a higher crude protein content. effectively used in oat breeding programs to create nutritionally enhanced and high-yielding varieties. Overall, the findings demonstrate the existence of trait-specific elite germplasm, including EC-159602, M-72, EC-6269, and EC-131306, which may be successfully used in oat breeding programs to create high-yielding and nutritionally enhanced varieties (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe wide, structured, and exploitable genetic diversity among oat genotypes for agro-morphological, yield, and quality traits is clearly demonstrated by this study. The evaluated germplasm exhibits strong yield and nutritional improvement potential, as evidenced by the significant variation across all traits and the superior performance of several genotypes over the checks. The effectiveness of direct selection for quick genetic gain is confirmed by high GCV, heritability, and genetic advance for important traits like green fodder yield, grain yield, dry fodder yield, leaf\u0026ndash;stem ratio, dry matter content, and seed weight, which show the predominance of additive gene action. Further multivariate analyses showed that the main causes of genetic divergence are yield and yield-associated traits. PCA effectively separated genotypes according to productivity, biomass, and quality attributes, capturing the majority of the variability within the first few components and proving that high yield and superior fodder quality can be enhanced at the same time. Significant inter-cluster divergence was revealed by cluster analysis, offering opportunities for heterosis breeding through deliberate hybridization between genetically distant clusters. The identification of elite, trait-specific donor lines- particularly EC-159602, M-72, EC-6269, EC-131306, and EC-61704- offers immediately usable genetic resources for developing dual-purpose, high-yielding, and nutritionally superior oat cultivars. All things considered, the results support the crucial role that genetic diversity has played in the evolution of oats and offer a solid, empirically supported framework for upcoming breeding initiatives targeted at sustainable productivity and quality improvement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe help and support provided during an investigation by the Department of Genetics and Plant Breeding, College of Agriculture, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India is duly acknowledged.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHN, R, and BP conceptualized the manuscript. HN and R wrote the manuscript. BP and AS assisted in writing, updated information and edited the manuscript. BP and CC contributed to critically revising the draft and updating the manuscript for publication. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding and grants received\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or, not-for-profit sectors.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cbr\u003eThe plant material used in this study (\u003cem\u003eAvena sativa L.\u003c/em\u003e) consisted of cultivated oat germplasm maintained for research purposes. The collection and use of plant materials complied with institutional guidelines of Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, India, and relevant national regulations. No wild or endangered plant species were used in this study; therefore, no specific permissions or licenses were required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cbr\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003cbr\u003e The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed S, Roy AK, Majumdar AB (2011) Genetic diversity and variability analysis in oat (\u003cem\u003eAvena sativa\u003c/em\u003e L.). \u003cem\u003eRange Manag Agrofor\u003c/em\u003e 32:96\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAraus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. \u003cem\u003eTrends Plant Sci\u003c/em\u003e 19:52\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArora RN (2013) Characterization and evaluation of one and two-harvest of oats. \u003cem\u003eForage Res\u003c/em\u003e 39:59\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhattarai RP, Thapa DB, Ojha BR, Kharel R, Sapkota M (2017) Cluster analysis of elite spring wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) genotypes based on yield and yield attributing traits under irrigated condition. \u003cem\u003eInt J Exp Rev\u003c/em\u003e 10:9\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBibi A, Shahzad AN, Sadaqat HA, Tahir MH, Fatima B (2012) Genetic characterization and inheritance studies of oats (\u003cem\u003eAvena sativa\u003c/em\u003e L.) for green fodder yield. \u003cem\u003eInt J Biol Pharm Sci\u003c/em\u003e 1:450\u0026ndash;460.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBichewar N, Mehta AK, Bhargava K, Ramakrishana S (2023) Principal component analysis in advanced breeding lines of oat (\u003cem\u003eAvena sativa\u003c/em\u003e \u0026times; 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In the Indian subcontinent, it is popularly used as food, feed, and fodder. The present investigation was done at the Experimental Dairy Farm, Nagala, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, for the identification and characterization of the most promising oat lines contributing yield and quality traits through genetic diversity analysis. The findings demonstrated that oat genotypes varied widely in terms of genetic diversity. Grain yield, dry fodder yield, green fodder yield, dry matter percentage, and 100-seed weight all showed strong heritability and high genetic advancement, suggesting additive-type gene action and improvement by simple selection. Eight PCs contributed 87.75 percent of the total variation across the genotypes evaluated for sixteen characters, according to principal component analysis (PCA). PC1 contributed the greatest towards the variability (25.8%), followed by PC2 (19.1%) and PC3 (13.1%). Cluster analysis categorized the accessions under six major clusters, which revealed a reasonable relationship of genetic diversity. The highest inter-cluster distance was observed between clusters V and VI (9.01), followed by clusters VI and I (8.61). In order to create high diversity for efficient selection in the segregating generations for the production of high-yielding oat cultivars, intercrossing between members of these diverse clusters would be necessary. Sufficient diversity was identified in the genotypes based on phenotypic and genotypic variance, principal component analysis, and cluster analysis, which may be employed by researchers in future nutri-agricultural crop improvement programs.\u003c/p\u003e","manuscriptTitle":"Deciphering the Multidimensional Trait Space of Yield and Quality Attributes in Oat (Avena sativa L.) 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