Multivariate analysis for yield and yield-related traits of amaranth Accessions from Malawi

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This study evaluated the genetic diversity and trait-based classification of six amaranth genotypes using multivariate analyses on seven quantitative traits. Results: Principal component analysis revealed that the first two components explained 63.6% of the total variation, effectively distinguishing genotypes based on vegetative vigor and reproductive output. Cluster analysis identified two statistically distinct groups, with grain yield and inflorescence length contributing most to genetic divergence. Strong positive correlations among grain yield, stem girth, leaf length, and inflorescence traits suggest opportunities for indirect selection. Genotypes CK-BH-01 and LL-BH-04 exhibited superior performance in yield-related traits, making them as promising candidates for breeding improvement. Conclusion: These findings provide a robust, data-driven framework for trait-based selection in amaranth breeding, supporting the development of high-yielding, stress-resilient varieties adapted to diverse agroecological zones. Amaranthus spp. Genetic diversity Multivariate analysis Principal component analysis (PCA) Trait-based selection Cluster analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1.0. Introduction Amaranth ( Amaranthus spp.) is gaining global recognition as a promising underutilized crop due to its exceptional nutritional profile, adaptability to diverse environments, and potential to enhance food and nutritional security amid climate change [ 1 ]–[ 3 ]. Rich in high-quality protein, lysine, iron, calcium, and other micronutrients, amaranth offers significant advantages over conventional cereals, particularly in smallholder and low-input farming systems [ 4 ]–[ 6 ]. In Africa, especially sub-Saharan regions, amaranth is increasingly valued for its dual-purpose use as both a leafy vegetable and a grain crop [ 7 ], [ 8 ]. Despite its potential, amaranth remains genetically underexplored, with breeding efforts hindered by limited germplasm characterization, insufficient trait documentation, and the absence of robust trait-based selection frameworks [ 9 ]. While prior research has addressed agronomic performance [ 10 ], stress tolerance [ 11 ], [ 12 ], and phenotypic stability [ 13 ], few studies have leveraged multivariate trait analysis to dissect genetic variation and inform breeding. Multivariate statistical tools such as principal component analysis (PCA) and hierarchical clustering effectively simplify trait complexity and identify key contributors to genetic divergence [ 14 ]. These methods enable trait-based genotype grouping and targeted parental selection, revealing critical relationships between vegetative and reproductive traits that may otherwise remain hidden [ 15 ], [ 16 ]. Understanding correlations and the relative contributions of traits like plant height, leaf size, stem girth, inflorescence structure, and grain yield is essential for effective varietal development [ 17 ], [ 18 ]. Grain yield and inflorescence characteristics particularly influence genetic differentiation in Amaranthus hypochondriacus and A. cruentus , highlighting their value as key selection indices [ 14 ], [ 19 ]. Modern breeding increasingly combines phenotypic data with molecular markers such as SSRs and SNPs to unravel genetic architectures and accelerate marker-assisted selection [ 20 ]–[ 23 ]. Nonetheless, trait-based classification remains a foundational step for identifying elite germplasm before molecular investigations [ 24 ]. For the first time in this germplasm set, we integrate field-based agro-morphological trait data with multivariate statistical methods to quantify the hierarchical contributions of individual traits to genotype divergence, uncovering patterns of trait-based clustering. This addresses a critical gap in amaranth breeding research, where trait interrelationships and their impact on genetic structure remain poorly understood. Specifically, this study aims to: (i) assess agro-morphological diversity among six amaranth accessions, (ii) identify traits most strongly driving genetic divergence, and (iii) establish a trait-based classification framework to support efficient breeding. By combining traditional agronomic evaluation with advanced analytics, our work offers a practical blueprint to accelerate genetic improvement in amaranth and enhance its contribution to sustainable agriculture. 2.0. Materials and Methods 2 .1. Plant Materials and Field Data This study builds upon the experimental work detailed in Sefasi et al.[ 25 ], where six amaranth accessions (MN-BH-01, LL-BH-04, NU-BH-01, PE-UP-BH-01, PE-LO-BH-01, and CK-BH-01) were evaluated for nutritional traits. The plant materials, field conditions, and experimental design are described in detail in that publication and are not repeated here. In the current extension, we focused on agro-morphological traits related to vegetative growth and yield performance to further examine genetic diversity and trait associations among the same accessions. 2.2. Growth Measurement Agro-morphological data collected for the current study included: leaf length (cm), leaf width (cm), stem girth (cm), inflorescence length (cm), plant height (cm), grain yield (kg/ha), and dry weight biomass (kg/ha). All measurements were taken at the blooming stage, following standardized procedures as outlined by Nyasulu et al.[ 13 ], with the exception of dry weight biomass. Dry weight biomass was determined by harvesting the above-ground biomass from a 2.25 m². The harvested biomass was weighed fresh in the field, then oven-dried at 70°C for 72 hours to a constant weight. The final dry weight was converted to kg/ha using the appropriate plot-to-hectare conversion factor. 2.3. Data analysis Data were subjected to multivariate and descriptive statistical analyses to assess the agro-morphological diversity among the six amaranth accessions. All statistical computations, including principal component analysis (PCA), correlation analysis, hierarchical clustering, and silhouette width analysis, were performed using R version 4.2.2 [ 26 ]. PCA was conducted to reduce dimensionality and identify traits contributing most to phenotypic variation, while clustering analyses were used to group genotypes based on trait similarity. The average silhouette width method was used to determine the optimal number of clusters. Trait contributions to clustering were calculated to identify variables most responsible for genotype separation. Bar graphs representing trait means and variability were generated using GraphPad Prism version 10.4.1 for better visual presentation. 3.0. Results 3.1. Descriptive Statistics Descriptive statistics revealed substantial variation among the seven quantitative traits evaluated across the amaranth accessions (Table 1 ). Leaf length ranged from 3.6 to 30.1 cm, with a mean of 16.9 cm and a high coefficient of variation (CV) of 59.5%, indicating wide genetic variability. Leaf width showed a mean of 6.5 cm, ranging from 0.5 to 20.1 cm (CV = 44.5%), while stem girth had a mean of 1.4 cm with values between 0.1 and 4.1 cm (CV = 45.4%). Inflorescence length varied from 2.5 to 70.5 cm with a mean of 20.9 cm (CV = 56.2%). Plant height exhibited the highest mean (140.4 cm), ranging from 13 to 368 cm (CV = 44.1%). Grain yield per plant ranged from 31.5 to 1217.8 g, with a mean of 365.3 g (CV = 40.2%), while dry weight biomass ranged from 513.6 to 15,466.7 g, with a mean of 3212.9 g (CV = 35.3%). Most traits showed positive skewness, suggesting a tendency toward lower values, except for plant height and biomass, which were slightly negatively skewed. Kurtosis values were generally close to zero, indicating near-normal distributions, although inflorescence length and grain yield showed more peaked distributions (kurtosis = 2.0 and 1.2, respectively). Table 1 Descriptive statistics for seven quantitative traits measured across amaranth accessions Trait Mean Standard Deviation Coefficient of variation (%) Minimum Maximum Kurtosis Skewness Leaf Length (cm) 16.9 10.1 59.5 3.6 30.1 0.2 0.1 Leaf Width (cm) 6.5 2.9 44.5 0.5 20.1 1.0 0.7 Stem Girth (cm) 1.4 0.6 45.4 0.1 4.1 0.2 0.6 Inflorescence Length (cm) 20.9 11.8 56.2 2.5 70.5 2.0 1.2 Plant Height (cm) 140.4 61.9 44.1 13 368 -0.5 0.9 Grain yield/kg 365.3 5.3 40.2 31.5 1217.8 1.2 1.1 Dry weight Biomass 3212.9 25.1 35.3 513.6 15466.7 -1.0 -0.1 3.2. Analysis of Variance (ANOVA) Analysis of variance showed significant variation among amaranth accessions, locations, and their interactions for most measured traits (Table 2 ), with the values presented representing mean squares from the ANOVA. Accession had a highly significant effect (p < 0.001) on leaf length, leaf width, stem girth, inflorescence length, and plant height, and a significant effect on grain yield (p 0.05). Location significantly influenced all traits, showing highly significant effects (p < 0.001) except for dry weight biomass, which was significantly affected at a lower level (p 0.05). Residual mean squares were comparatively low, indicating reliable experimental precision. Overall, these results highlight the genetic variability and environmental effects on amaranth growth and yield traits, while dry biomass remained relatively stable across genotypes and locations. Table 2 Mean squares from two-way analysis of variance for growth and yield traits of amaranth accessions evaluated across multiple locations Source of Variation df Leaf Length Leaf Width Stem Girth Inflorescence Length Plant Height Grain yield Dry weight biomass Accession 5 567.1 *** 412.0 *** 6.3 *** 2416 *** 81592 *** 113746 ** 7114767 ns Location 4 1106.6 *** 203.7 *** 22.3 *** 4157 *** 374422 *** 582501 *** 14858203 ** Accession*Location 20 114.5 *** 38.8 *** 1.9 *** 870 *** 20933 *** 93378 *** 14858203 ns Residual 811 9.4 3.7 0.2 52 1009 25334 4003708 Total 840 Note : * = P < 0.05; ** = P < 0.01; *** = P < 0.001; ns = not significant. 3.3. Principal Component Analysis (PCA) Principal component analysis (PCA) extracted six principal components (PC1 to PC6) with eigenvalues ≥ 0.24, cumulatively accounting for 97.42% of the total variance (Table 3 ). PC1 explained 46.24% of the variance, followed by PC2 (17.41%), PC3 (12.90%), PC4 (10.78%), PC5 (6.59%), and PC6 (3.50%). High loadings on PC1 were observed for stem girth (0.50), leaf length (0.48), plant height (0.47), and leaf width (0.43). Grain yield (0.65) and dry weight biomass (0.45) loaded strongly on PC2, while inflorescence length (− 0.83) contributed most to PC3. Dry weight biomass (− 0.79) dominated PC4, leaf width (− 0.85) PC5, and leaf length (− 0.63) and plant height (0.71) PC6. Table 3 Eigenvectors and eigenvalues of the first six principal components (PCs) for 7 quantitative characters of 6 genotypes. Traits Principal Components PC1 PC2 PC3 PC4 PC5 PC6 Eigenvalue 3.24 1.22 0.91 0.76 0.46 0.24 Variance. Percent 46.24 17.41 12.90 10.78 6.59 3.50 Cumulative. Variance. Percent 46.24 63.65 76.55 87.33 93.92 97.42 eigenvalues Leaf Length 0.48 -0.02 0.31 -0.00 0.33 -0.63 Leaf Width 0.43 0.18 0.04 0.23 -0.85 -0.12 Stem Girth 0.50 -0.17 0.08 0.05 0.25 0.02 Inflorescence Length 0.17 -0.46 -0.83 0.14 0.00 -0.19 Plant Height 0.47 -0.30 0.13 -0.14 0.03 0.71 Grain Yield 0.20 0.65 -0.27 0.53 0.33 0.24 Dry Weight Biomass 0.23 0.45 -0.33 -0.79 -0.01 -0.04 3.4. Scree plot In order to determine the optimal number of principal components to retain for further analysis, a scree plot was used to visualize the eigenvalues and identify the point at which additional components contribute minimally to the total variance. The first two components were considered significant, as their eigenvalues were greater than or equal to 1. Beyond these, eigenvalues decreased sharply, indicating reduced importance, and thus these components were excluded from subsequent analyses. As shown in Fig. 1 , PC1 explained 46.2% of the variance, followed by PC2 (17.4%), PC3 (12.9%), PC4 (10.8%), PC5 (6.6%), and PC6 (3.5%), with corresponding eigenvalues of 3.24, 1.22, 0.91, 0.76, 0.46, and 0.24, respectively. 3.5. Trait-Based Grouping of Genotypes Using Principal Component Analysis The principal component analysis (PCA) revealed distinct trait-based grouping among the six amaranth genotypes based on their performance across seven agronomic traits (Fig. 2 ). The first two principal components (Dim1 and Dim2) accounted for a cumulative 63.6% of the total variation, with Dim1 explaining 46.2% and Dim2 contributing 17.4%. The genotypes grouped according to their associated traits, highlighting key differentiating characteristics. CK-BH-01 and LL-BH-04 were positioned on the positive side of Dim1 and strongly associated with inflorescence length (IF) and grain yield (GY), indicating their superior yield potential. NU-BH-01 was distinct on the positive axis of Dim2, reflecting a unique trait profile compared to the other accessions. In contrast, MN-BH-01 was grouped based on its strong alignment with vegetative traits such as plant height (PH), stem girth (SG), and leaf length (LL). Meanwhile, PE-LO-BH-01 and PE-UP-BH-01 clustered nearer to the center, showing moderate associations with leaf width (LW) and dry weight biomass (DWB). To complement the PCA-based grouping, a Pearson correlation analysis was conducted to assess the strength and direction of relationships among the seven agronomic traits (Fig. 3 ). The resulting correlation matrix revealed consistently strong positive associations, indicating that improvements in one trait are likely to enhance others. Notably, grain yield (GY) showed a high correlation with leaf length (LL, r = 0.94), stem girth (SG, r = 0.89), and inflorescence fresh weight (IF, r = 0.76), suggesting these traits are major contributors to yield performance. Similarly, LL was strongly correlated with SG ( r = 0.98), IF ( r = 0.90), and dry biomass weight (DWB, r = 0.90), reinforcing its central role in biomass accumulation. Plant height (PH) was most closely associated with leaf width (LW, r = 0.96), and DWB also showed significant positive correlations with IF ( r = 0.90) and PH ( r = 0.87). These findings support the PCA-based trait groupings and highlight key trait linkages that can be targeted in selection strategies aimed at improving yield and biomass productivity in amaranth. 3.6. Cluster Analysis To determine the optimal number of clusters for grouping the amaranth genotypes, a silhouette width analysis was conducted (Fig. 4 ). The silhouette plot indicated that the highest average silhouette width occurred at K = 2, suggesting that partitioning the genotypes into two distinct clusters provides the most appropriate structure for the dataset. Beyond K = 2, the average silhouette width declined, with slight fluctuations observed at higher values of K , indicating less distinct separation and reduced clustering quality. Therefore, two clusters were considered optimal for subsequent genotype classification, reflecting underlying patterns in trait performance and supporting the trait-based differentiation observed in the PCA and correlation analyses. Cluster I comprised two genotypes (PE-UP-BH-01 and PE-LO-BH-01), while Cluster II included four genotypes (LL-BH-04, NU-BH-01, CK-BH-01, and MN-BH-01) (Fig. 5 ). The analysis of morphological and yield-related traits across the two clusters revealed minimal differences (Table 4 ). Cluster I exhibited slightly higher mean values in leaf length (16.18 cm), leaf width (6.48 cm), stem girth (1.40 cm), inflorescence length (21.14 cm), plant height (138.51 cm), grain yield (371.12 kg/ha), and dry biomass weight (3240 kg/ha) compared to Cluster II. The grand means for these traits were generally consistent across clusters, reflecting overall homogeneity within the population. Independent t-tests showed no statistically significant differences between clusters for any of the traits (p > 0.05). Table 4 Mean performance of different quantitative traits in each cluster. . Traits Clusters Grand Mean T-test (P value) I II Leaf Length (cm) 16.18 16.07 16.125 0.75 Leaf Width (cm) 6.48 6.41 6.445 0.68 Stem Girth (cm) 1.40 1.37 1.385 0.40 Inflorescence Length (cm) 21.14 20.83 20.985 0.55 Plant Height (cm) 138.51 137.47 137.99 0.82 Grain Yield (kg/ha) 371.12 362.35 366.735 0.62 Dry Weight Biomass (kg/ha) 3240 3199.32 3219.66 0.70 3.5. Intra and inter-cluster distances Cluster analysis based on the seven quantitative traits revealed average intra-cluster distances of 2.06 for Cluster I and 2.02 for Cluster II, indicating close genetic relationships among genotypes within each cluster. The average inter-cluster distance between Cluster I and Cluster II was 4.61 * , which was considerably higher than the intra-cluster distances, signifying clear divergence between the two groups. A permutation test confirmed the significance of the inter-cluster separation with a p-value of 0.05 (Table 5 ), thus validating the cluster structure. Table 5 Shows the average intra-cluster and inter-cluster distances based on seven quantitative traits among six Amaranth genotypes Comparison Average distance Intra-cluster (Cluster 1) 2.06 Intra-cluster (Cluster 2) 2.02 Inter-cluster (Cluster 1 vs Cluster 2) 4.61 * 3.6. The relative contribution of different characters to genetic divergence To understand the influence of individual traits on genotype differentiation, the relative contribution of each trait to genetic divergence was evaluated using principal component loadings. Grain yield (GY) contributed the most to the clustering pattern, accounting for 19.77% of the observed variation, followed by inflorescence length (IF) at 18.90%, leaf length (LL) at 14.40%, and dry weight biomass (DWB) at 13.72%. Other traits such as leaf width (LW), plant height (PH), and stem girth (SG) contributed 11.94%, 11.40%, and 9.90%, respectively (Fig. 6 ). These results highlight the prominent role of yield-related traits, particularly GY and IF in the genetic differentiation of the amaranth genotypes. 4.0. Discussion This study provides a comprehensive evaluation of agro-morphological diversity and trait-based classification among six amaranth genotypes using a suite of multivariate analyses, including descriptive statistics, principal component analysis (PCA), correlation analysis, and hierarchical clustering. Building on prior work that focused primarily on stability assessment of these accessions [ 13 ], the present study offers a novel contribution by quantifying the relative importance of individual traits in driving genetic divergence and genotype grouping. Unlike earlier studies that treated traits with equal weight or relied on limited descriptors, this research applies a trait-weighted multivariate approach to dissect the structure of phenotypic variation and identify key drivers of differentiation. The integration of dimensionality reduction, clustering precision, and trait interrelationship analysis offers a robust framework that directly informs targeted selection strategies. Collectively, these insights enhance the efficiency of amaranth breeding by supporting data-driven parental selection, hybrid design, and dual-purpose genotype identification advancing both crop improvement efforts and the broader goals of climate-resilient agriculture. 4.1. Trait Variability and Selection Implications The wide phenotypic variation observed across traits with coefficients of variation exceeding 40% for most confirms a broad genetic base within the evaluated accessions. Notably, leaf length, inflorescence length, and plant height displayed particularly high variability, presenting opportunities to select contrasting parents for hybridization. Grain yield and dry biomass also varied substantially, reinforcing the crop’s potential for improvement in both productivity and biomass accumulation. The disproportionate contribution of grain yield and inflorescence length, accounting for nearly 39% of total variation driving cluster formation, underscores the predominance of reproductive traits in genetic differentiation. This aligns with findings in grain amaranth [ 15 ] and represents a novel quantification that sharpens trait prioritization in breeding programs. 4.2. Genotype Grouping and Trait-Based Associations PCA effectively distilled trait complexity, with the first two components explaining 63.7% of variance: PC1 capturing vegetative vigor (stem girth, leaf length, plant height, and Leaf width) and PC2 reflecting reproductive output (grain yield and biomass). This partitioning echoes patterns reported in amaranth and other crops [ 9 ], [ 27 ], providing a robust functional classification of genotypes. The positioning of CK-BH-01 and LL-BH-04 along reproductive traits marks them as prime candidates for yield improvement, while MN-BH-01’s alignment with vegetative traits highlights its suitability for biomass or dual-purpose uses. Such trait-based groupings refine parental selection by enabling breeders to strategically combine complementary genotypes for heterosis or to focus directly on reproductive trait enhancement, thereby surpassing traditional, less-targeted selection approaches. 4.3. Genetic Relationships and Clustering Precision Cluster analysis confirmed the PCA-based grouping into two statistically validated clusters, with Cluster I (PE-UP-BH-01, PE-LO-BH-01) showing marginally superior trait means compared to Cluster II (LL-BH -04, NU-BH-01, MN-BH-01, and CK-BH-01). The significantly lower intra-cluster distances relative to inter-cluster distances, supported by permutation testing (p = 0.05), substantiate the robustness of cluster demarcation. Importantly, integrating relative trait contributions into clustering reveals that grain yield and inflorescence length predominantly drive genetic structure, a refinement over previous studies that treated all traits equally [ 28 ]. This approach enhances the precision of genotype selection, facilitating more efficient breeding. 4.4. Trait Interrelationships and Breeding Potential Strong positive correlations among grain yield, leaf length, stem girth, and inflorescence length affirm the interconnectedness of yield components, consistent with reports in Amaranthus hybridus and A. hypochondriacus [ 16 ], [ 17 ]. These relationships support indirect selection strategies leveraging vegetative traits to boost yield. The association between dry biomass and traits like inflorescence weight and plant height further enables the identification of dual-purpose genotypes suitable for food and fodder, aligning with the crop’s growing role in climate-resilient farming systems [ 28 ]. The identification of accessions combining high yield and favourable morphological traits (e.g., CK-BH-01 and LL-BH-04) holds promise for varietal development adapted to diverse environments and stress conditions. Our findings build upon the earlier stability analyses of these accessions [ 13 ], which established performance consistency across environments. The current study complements that foundation by elucidating the trait-specific drivers of genetic differentiation and grouping, thereby enabling breeders to integrate both stability and trait-based precision in their selection criteria. To accelerate genetic gains in amaranth, future research should incorporate molecular markers such as SSRs and SNPs to dissect the genetic architecture of key traits, improving marker-assisted selection. Exploring gene-by-environment interactions is essential for breeding genotypes that combine high yield potential with stability across heterogeneous agroecological zones. Expanding germplasm collections to include diverse landraces and wild relatives will enrich the breeding pool, potentially uncovering novel alleles for stress resilience and productivity. Finally, adopting a trait-based breeding framework focused on high-impact reproductive traits like grain yield and inflorescence length will optimize parental selection and hybrid development, supporting broader goals of food security, climate resilience, and agrobiodiversity conservation. Conclusion This study highlights key agro-morphological traits, particularly grain yield and inflorescence length, as major drivers of genetic differentiation among amaranth genotypes. The integration of PCA, correlation analysis, and clustering provided a robust framework for trait-based genotype classification. Accessions like CK-BH-01 and LL-BH-04 showed strong potential for yield improvement. These findings offer valuable guidance for targeted breeding and lay the groundwork for future work involving molecular tools and broader germplasm evaluation to enhance amaranth improvement strategies. Abbreviations PCA Principal Component Analysis DWB Dry Weight Biomass LL Leaf Length LW Leaf Width IF Inflorescence Length PH Plant Height SG Stem Girth Declarations Ethics approval and consent to participate: Not Applicable Consent for publication: Not Applicable Competing interests: The authors declare no competing interests. Funding: This study was funded by Sustainable Food Systems in Malawi (FoodMA) Programme at LUANAR. Author Contribution Conceptualization: A. S. and M.N., Data collection and cleaning: A.S., M. N., and R.M.K. Data analysis: M. N and A.S. Experimental layout: A. S and M.N. Funding acquisitions: A. S and M.N. Original manuscript draft: M. N. and A.S. Writing review, editing and final approval: A.S., M.N., R.M.K., L.Y., S.K., M.M., C.M., and K.M. Acknowledgements: The authors acknowledges Sustainable Food Systems in Malawi (FoodMA) Programme at LUANAR for financially supporting the study. 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Morphology and Genetic Analysis of Vegetative Characterization of Four Grain Amaranth Accessions, 2022, vol. 25, no. 8, pp. 1–5, 2022. 10.22186/jyi.25.8.1.1 Yeshitila M, Gedebo A, Degu HD, Olango TM, Tesfaye B. Study on characters associations and path coefficient analysis for quantitative traits of amaranth genotypes from Ethiopia. Sci Rep. 2023;13(1):1–18. 10.1038/s41598-023-47869-0 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Aug, 2025 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 22 Jul, 2025 Reviews received at journal 22 Jul, 2025 Reviewers agreed at journal 17 Jul, 2025 Reviews received at journal 15 Jul, 2025 Reviewers agreed at journal 12 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers invited by journal 09 Jul, 2025 Editor assigned by journal 09 Jul, 2025 Editor invited by journal 07 Jul, 2025 Submission checks completed at journal 07 Jul, 2025 First submitted to journal 07 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7029686","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":484489819,"identity":"1f040803-2be7-4d4b-baf1-c0b723159e89","order_by":0,"name":"Abel Sefasi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYJCCDwwMEnJ8pOhgnAHUYsxGqhaGxDai1fP3Hz7Y8OOPRXqbRPKzBz/3MMjzN3CnSeDTInEjLbGxt00it00izdyw5xmD4YwDvNvwamG4wWP+gLcBpCXBTILnAAPjBgbebTfw6ZA/f/5j458/EulsEunfJP8cYLAnqMXgQA5jMw+bRAKbRI6ZNNCWRIJaDG+kGTbLtkkYtvG8KZOWOSCRPOMw7/Yf+LTInT/8sPHNnzp5fvb0bZJvDtjY9rf3bjbApwUdAMOKmRT1o2AUjIJRMAqwAgD9T0Z8HnAIBgAAAABJRU5ErkJggg==","orcid":"","institution":"Lilongwe University of Agriculture and Natural Resources (LUANAR)","correspondingAuthor":true,"prefix":"","firstName":"Abel","middleName":"","lastName":"Sefasi","suffix":""},{"id":484489821,"identity":"0a603385-54ce-4b84-bb56-4b86b937af7e","order_by":1,"name":"Mvuyeni Nyasulu","email":"","orcid":"","institution":"Lilongwe University of Agriculture and Natural Resources (LUANAR)","correspondingAuthor":false,"prefix":"","firstName":"Mvuyeni","middleName":"","lastName":"Nyasulu","suffix":""},{"id":484489824,"identity":"ec1fdf91-4698-4c56-be43-571322d92e80","order_by":2,"name":"Rowland Maganizo Kamanga","email":"","orcid":"","institution":"Lilongwe University of Agriculture and Natural Resources (LUANAR)","correspondingAuthor":false,"prefix":"","firstName":"Rowland","middleName":"Maganizo","lastName":"Kamanga","suffix":""},{"id":484489826,"identity":"4015d73e-a59c-4e26-96ce-e80133b63c62","order_by":3,"name":"Louis Yalaukani","email":"","orcid":"","institution":"Chitedze Agricultural Research Station","correspondingAuthor":false,"prefix":"","firstName":"Louis","middleName":"","lastName":"Yalaukani","suffix":""},{"id":484489827,"identity":"cbec55ff-e3b4-4880-948b-b025b4816ee7","order_by":4,"name":"Samson Katengeza","email":"","orcid":"","institution":"Lilongwe University of Agriculture and Natural Resources (LUANAR)","correspondingAuthor":false,"prefix":"","firstName":"Samson","middleName":"","lastName":"Katengeza","suffix":""},{"id":484489828,"identity":"ebddca7c-5bad-4dae-bc6c-bede0376f7e5","order_by":5,"name":"Maurice Monjerezi","email":"","orcid":"","institution":"University of Malawi (UNIMA)","correspondingAuthor":false,"prefix":"","firstName":"Maurice","middleName":"","lastName":"Monjerezi","suffix":""},{"id":484489829,"identity":"15ae3888-68e1-410c-b2b8-5708ad7c123e","order_by":6,"name":"Charles Malidadi","email":"","orcid":"","institution":"Bvumbwe Agricultural Research Station","correspondingAuthor":false,"prefix":"","firstName":"Charles","middleName":"","lastName":"Malidadi","suffix":""},{"id":484489830,"identity":"dfc55df0-dafb-45f1-bd05-a945d61e87d4","order_by":7,"name":"Kingsley Masamba","email":"","orcid":"","institution":"Lilongwe University of Agriculture and Natural Resources (LUANAR)","correspondingAuthor":false,"prefix":"","firstName":"Kingsley","middleName":"","lastName":"Masamba","suffix":""}],"badges":[],"createdAt":"2025-07-02 13:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7029686/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7029686/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-025-07190-6","type":"published","date":"2025-08-27T15:57:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86664000,"identity":"9a1f0c35-c271-479d-b0ff-64b6b32f5dd3","added_by":"auto","created_at":"2025-07-14 10:53:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38583,"visible":true,"origin":"","legend":"\u003cp\u003eScree plot of principal components (PCs) for 7 quantitative traits in 6 Amaranths genotypes\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7029686/v1/f4a11ae5298823163c136793.png"},{"id":86664014,"identity":"636d304c-3ad6-408c-8468-5ad088861f5e","added_by":"auto","created_at":"2025-07-14 10:53:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52565,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal components analysis biplot showing quantitative variables and individuals on Dimensions 1 \u0026amp; 2\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7029686/v1/2656fbdbb4fcdbabfecefbc6.png"},{"id":86663973,"identity":"4f839648-0a15-4119-90f4-9db5eb2dcb99","added_by":"auto","created_at":"2025-07-14 10:53:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56694,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation matrix of seven quantitative traits measured across six Amaranth genotypes.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7029686/v1/5e6145fea1b41dff9c10a3cc.png"},{"id":86663969,"identity":"03e6e84e-3dad-4dd3-aea3-0815c32d3698","added_by":"auto","created_at":"2025-07-14 10:53:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":27744,"visible":true,"origin":"","legend":"\u003cp\u003eSilhouette width to determine the number of clusters (K) for quantitative data\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7029686/v1/57e0b967612c02c0e422e4b8.png"},{"id":86663974,"identity":"a9ca0deb-3290-466d-bcba-013726d3a371","added_by":"auto","created_at":"2025-07-14 10:53:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":29862,"visible":true,"origin":"","legend":"\u003cp\u003eCluster analysis showing the relationship among 6 Amaranths genotypes based on 7 quantitative traits\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7029686/v1/10d774ac9e0c4375eb5ac29f.png"},{"id":86663989,"identity":"9cc77dc6-247d-4745-b51e-fb8ecf04a77c","added_by":"auto","created_at":"2025-07-14 10:53:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":14680,"visible":true,"origin":"","legend":"\u003cp\u003eRelative contribution (%) of seven quantitative traits to genetic divergence among six Amaranth genotypes based on principal component analysis. Traits with higher contributions influence the genetic differentiation more strongly\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7029686/v1/4ca2f22948ff952aada7715a.png"},{"id":90345025,"identity":"9cbadd80-d29a-49dd-a190-ff8a50982f6a","added_by":"auto","created_at":"2025-09-01 16:09:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1232272,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7029686/v1/f7e6bc8e-f233-43a2-ad68-8bedcee70f74.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multivariate analysis for yield and yield-related traits of amaranth Accessions from Malawi","fulltext":[{"header":"1.0. Introduction","content":"\u003cp\u003eAmaranth (\u003cem\u003eAmaranthus\u003c/em\u003e spp.) is gaining global recognition as a promising underutilized crop due to its exceptional nutritional profile, adaptability to diverse environments, and potential to enhance food and nutritional security amid climate change [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Rich in high-quality protein, lysine, iron, calcium, and other micronutrients, amaranth offers significant advantages over conventional cereals, particularly in smallholder and low-input farming systems [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In Africa, especially sub-Saharan regions, amaranth is increasingly valued for its dual-purpose use as both a leafy vegetable and a grain crop [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite its potential, amaranth remains genetically underexplored, with breeding efforts hindered by limited germplasm characterization, insufficient trait documentation, and the absence of robust trait-based selection frameworks [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While prior research has addressed agronomic performance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], stress tolerance [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and phenotypic stability [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], few studies have leveraged multivariate trait analysis to dissect genetic variation and inform breeding.\u003c/p\u003e\u003cp\u003eMultivariate statistical tools such as principal component analysis (PCA) and hierarchical clustering effectively simplify trait complexity and identify key contributors to genetic divergence [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These methods enable trait-based genotype grouping and targeted parental selection, revealing critical relationships between vegetative and reproductive traits that may otherwise remain hidden [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUnderstanding correlations and the relative contributions of traits like plant height, leaf size, stem girth, inflorescence structure, and grain yield is essential for effective varietal development [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Grain yield and inflorescence characteristics particularly influence genetic differentiation in \u003cem\u003eAmaranthus hypochondriacus\u003c/em\u003e and \u003cem\u003eA. cruentus\u003c/em\u003e, highlighting their value as key selection indices [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eModern breeding increasingly combines phenotypic data with molecular markers such as SSRs and SNPs to unravel genetic architectures and accelerate marker-assisted selection [\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Nonetheless, trait-based classification remains a foundational step for identifying elite germplasm before molecular investigations [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor the first time in this germplasm set, we integrate field-based agro-morphological trait data with multivariate statistical methods to quantify the hierarchical contributions of individual traits to genotype divergence, uncovering patterns of trait-based clustering. This addresses a critical gap in amaranth breeding research, where trait interrelationships and their impact on genetic structure remain poorly understood. Specifically, this study aims to: (i) assess agro-morphological diversity among six amaranth accessions, (ii) identify traits most strongly driving genetic divergence, and (iii) establish a trait-based classification framework to support efficient breeding. By combining traditional agronomic evaluation with advanced analytics, our work offers a practical blueprint to accelerate genetic improvement in amaranth and enhance its contribution to sustainable agriculture.\u003c/p\u003e"},{"header":"2.0. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e2\u003c/b\u003e.1. Plant Materials and Field Data\u003c/h2\u003e\u003cp\u003eThis study builds upon the experimental work detailed in Sefasi et al.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], where six amaranth accessions (MN-BH-01, LL-BH-04, NU-BH-01, PE-UP-BH-01, PE-LO-BH-01, and CK-BH-01) were evaluated for nutritional traits. The plant materials, field conditions, and experimental design are described in detail in that publication and are not repeated here. In the current extension, we focused on agro-morphological traits related to vegetative growth and yield performance to further examine genetic diversity and trait associations among the same accessions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Growth Measurement\u003c/h2\u003e\u003cp\u003eAgro-morphological data collected for the current study included: leaf length (cm), leaf width (cm), stem girth (cm), inflorescence length (cm), plant height (cm), grain yield (kg/ha), and dry weight biomass (kg/ha). All measurements were taken at the blooming stage, following standardized procedures as outlined by Nyasulu et al.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], with the exception of dry weight biomass.\u003c/p\u003e\u003cp\u003eDry weight biomass was determined by harvesting the above-ground biomass from a 2.25 m\u0026sup2;. The harvested biomass was weighed fresh in the field, then oven-dried at 70\u0026deg;C for 72 hours to a constant weight. The final dry weight was converted to kg/ha using the appropriate plot-to-hectare conversion factor.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Data analysis\u003c/h2\u003e\u003cp\u003eData were subjected to multivariate and descriptive statistical analyses to assess the agro-morphological diversity among the six amaranth accessions. All statistical computations, including principal component analysis (PCA), correlation analysis, hierarchical clustering, and silhouette width analysis, were performed using R version 4.2.2 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. PCA was conducted to reduce dimensionality and identify traits contributing most to phenotypic variation, while clustering analyses were used to group genotypes based on trait similarity.\u003c/p\u003e\u003cp\u003eThe average silhouette width method was used to determine the optimal number of clusters. Trait contributions to clustering were calculated to identify variables most responsible for genotype separation.\u003c/p\u003e\u003cp\u003eBar graphs representing trait means and variability were generated using GraphPad Prism version 10.4.1 for better visual presentation.\u003c/p\u003e\u003c/div\u003e"},{"header":"3.0. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Descriptive Statistics\u003c/h2\u003e\u003cp\u003eDescriptive statistics revealed substantial variation among the seven quantitative traits evaluated across the amaranth accessions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Leaf length ranged from 3.6 to 30.1 cm, with a mean of 16.9 cm and a high coefficient of variation (CV) of 59.5%, indicating wide genetic variability. Leaf width showed a mean of 6.5 cm, ranging from 0.5 to 20.1 cm (CV\u0026thinsp;=\u0026thinsp;44.5%), while stem girth had a mean of 1.4 cm with values between 0.1 and 4.1 cm (CV\u0026thinsp;=\u0026thinsp;45.4%). Inflorescence length varied from 2.5 to 70.5 cm with a mean of 20.9 cm (CV\u0026thinsp;=\u0026thinsp;56.2%). Plant height exhibited the highest mean (140.4 cm), ranging from 13 to 368 cm (CV\u0026thinsp;=\u0026thinsp;44.1%). Grain yield per plant ranged from 31.5 to 1217.8 g, with a mean of 365.3 g (CV\u0026thinsp;=\u0026thinsp;40.2%), while dry weight biomass ranged from 513.6 to 15,466.7 g, with a mean of 3212.9 g (CV\u0026thinsp;=\u0026thinsp;35.3%). Most traits showed positive skewness, suggesting a tendency toward lower values, except for plant height and biomass, which were slightly negatively skewed. Kurtosis values were generally close to zero, indicating near-normal distributions, although inflorescence length and grain yield showed more peaked distributions (kurtosis\u0026thinsp;=\u0026thinsp;2.0 and 1.2, respectively).\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\u003eDescriptive statistics for seven quantitative traits measured across amaranth accessions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoefficient of variation (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eKurtosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSkewness\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeaf Length (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeaf Width (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStem Girth (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInflorescence Length (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e70.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlant Height (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e140.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrain yield/kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e365.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1217.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDry weight Biomass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3212.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e513.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15466.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.1\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\u003e3.2. Analysis of Variance (ANOVA)\u003c/h2\u003e\u003cp\u003eAnalysis of variance showed significant variation among amaranth accessions, locations, and their interactions for most measured traits (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with the values presented representing mean squares from the ANOVA. Accession had a highly significant effect (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on leaf length, leaf width, stem girth, inflorescence length, and plant height, and a significant effect on grain yield (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), but differences among accessions for dry weight biomass were not significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Location significantly influenced all traits, showing highly significant effects (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) except for dry weight biomass, which was significantly affected at a lower level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The accession \u0026times; location interaction was significant for all traits except dry weight biomass, which showed no significant interaction effect (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Residual mean squares were comparatively low, indicating reliable experimental precision. Overall, these results highlight the genetic variability and environmental effects on amaranth growth and yield traits, while dry biomass remained relatively stable across genotypes and locations.\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\u003eMean squares from two-way analysis of variance for growth and yield traits of amaranth accessions evaluated across multiple locations\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource of Variation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLeaf Length\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLeaf Width\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStem Girth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInflorescence Length\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePlant Height\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGrain yield\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDry weight biomass\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccession\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e567.1\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e412.0\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.3\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2416\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e81592\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e113746\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7114767\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1106.6\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e203.7\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22.3\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4157\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e374422\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e582501\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e14858203\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccession*Location\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e114.5\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.8\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.9\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e870\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20933\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e93378\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e14858203\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e25334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4003708\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eNote\u003c/b\u003e: * = P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** = P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** = P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ns\u0026thinsp;=\u0026thinsp;not significant.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Principal Component Analysis (PCA)\u003c/h2\u003e\u003cp\u003ePrincipal component analysis (PCA) extracted six principal components (PC1 to PC6) with eigenvalues\u0026thinsp;\u0026ge;\u0026thinsp;0.24, cumulatively accounting for 97.42% of the total variance (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). PC1 explained 46.24% of the variance, followed by PC2 (17.41%), PC3 (12.90%), PC4 (10.78%), PC5 (6.59%), and PC6 (3.50%). High loadings on PC1 were observed for stem girth (0.50), leaf length (0.48), plant height (0.47), and leaf width (0.43). Grain yield (0.65) and dry weight biomass (0.45) loaded strongly on PC2, while inflorescence length (\u0026minus;\u0026thinsp;0.83) contributed most to PC3. Dry weight biomass (\u0026minus;\u0026thinsp;0.79) dominated PC4, leaf width (\u0026minus;\u0026thinsp;0.85) PC5, and leaf length (\u0026minus;\u0026thinsp;0.63) and plant height (0.71) PC6.\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\u003eEigenvectors and eigenvalues of the first six principal components (PCs) for 7 quantitative characters of 6 genotypes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eTraits Principal Components\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePC3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePC4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePC5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePC6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEigenvalue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariance. Percent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCumulative. Variance. Percent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e87.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e93.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e97.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeigenvalues\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeaf Length\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeaf Width\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStem Girth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInflorescence Length\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlant Height\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrain Yield\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDry Weight Biomass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.04\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=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Scree plot\u003c/h2\u003e\u003cp\u003eIn order to determine the optimal number of principal components to retain for further analysis, a scree plot was used to visualize the eigenvalues and identify the point at which additional components contribute minimally to the total variance. The first two components were considered significant, as their eigenvalues were greater than or equal to 1. Beyond these, eigenvalues decreased sharply, indicating reduced importance, and thus these components were excluded from subsequent analyses. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, PC1 explained 46.2% of the variance, followed by PC2 (17.4%), PC3 (12.9%), PC4 (10.8%), PC5 (6.6%), and PC6 (3.5%), with corresponding eigenvalues of 3.24, 1.22, 0.91, 0.76, 0.46, and 0.24, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Trait-Based Grouping of Genotypes Using Principal Component Analysis\u003c/h2\u003e\u003cp\u003eThe principal component analysis (PCA) revealed distinct trait-based grouping among the six amaranth genotypes based on their performance across seven agronomic traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The first two principal components (Dim1 and Dim2) accounted for a cumulative 63.6% of the total variation, with Dim1 explaining 46.2% and Dim2 contributing 17.4%. The genotypes grouped according to their associated traits, highlighting key differentiating characteristics. CK-BH-01 and LL-BH-04 were positioned on the positive side of Dim1 and strongly associated with inflorescence length (IF) and grain yield (GY), indicating their superior yield potential. NU-BH-01 was distinct on the positive axis of Dim2, reflecting a unique trait profile compared to the other accessions. In contrast, MN-BH-01 was grouped based on its strong alignment with vegetative traits such as plant height (PH), stem girth (SG), and leaf length (LL). Meanwhile, PE-LO-BH-01 and PE-UP-BH-01 clustered nearer to the center, showing moderate associations with leaf width (LW) and dry weight biomass (DWB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo complement the PCA-based grouping, a Pearson correlation analysis was conducted to assess the strength and direction of relationships among the seven agronomic traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The resulting correlation matrix revealed consistently strong positive associations, indicating that improvements in one trait are likely to enhance others. Notably, grain yield (GY) showed a high correlation with leaf length (LL, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.94), stem girth (SG, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.89), and inflorescence fresh weight (IF, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.76), suggesting these traits are major contributors to yield performance. Similarly, LL was strongly correlated with SG (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.98), IF (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.90), and dry biomass weight (DWB, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.90), reinforcing its central role in biomass accumulation. Plant height (PH) was most closely associated with leaf width (LW, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.96), and DWB also showed significant positive correlations with IF (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.90) and PH (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.87). These findings support the PCA-based trait groupings and highlight key trait linkages that can be targeted in selection strategies aimed at improving yield and biomass productivity in amaranth.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Cluster Analysis\u003c/h2\u003e\u003cp\u003eTo determine the optimal number of clusters for grouping the amaranth genotypes, a silhouette width analysis was conducted (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The silhouette plot indicated that the highest average silhouette width occurred at \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2, suggesting that partitioning the genotypes into two distinct clusters provides the most appropriate structure for the dataset. Beyond \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2, the average silhouette width declined, with slight fluctuations observed at higher values of \u003cem\u003eK\u003c/em\u003e, indicating less distinct separation and reduced clustering quality.\u003c/p\u003e\u003cp\u003eTherefore, two clusters were considered optimal for subsequent genotype classification, reflecting underlying patterns in trait performance and supporting the trait-based differentiation observed in the PCA and correlation analyses. Cluster I comprised two genotypes (PE-UP-BH-01 and PE-LO-BH-01), while Cluster II included four genotypes (LL-BH-04, NU-BH-01, CK-BH-01, and MN-BH-01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe analysis of morphological and yield-related traits across the two clusters revealed minimal differences (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Cluster I exhibited slightly higher mean values in leaf length (16.18 cm), leaf width (6.48 cm), stem girth (1.40 cm), inflorescence length (21.14 cm), plant height (138.51 cm), grain yield (371.12 kg/ha), and dry biomass weight (3240 kg/ha) compared to Cluster II. The grand means for these traits were generally consistent across clusters, reflecting overall homogeneity within the population. Independent t-tests showed no statistically significant differences between clusters for any of the traits (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean performance of different quantitative traits in each cluster. .\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTraits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eClusters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGrand Mean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT-test (P value)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeaf Length (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeaf Width (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStem Girth (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInflorescence Length (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlant Height (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e137.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e137.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrain Yield (kg/ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e371.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e362.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e366.735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDry Weight Biomass (kg/ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3199.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3219.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.70\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=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Intra and inter-cluster distances\u003c/h2\u003e\u003cp\u003eCluster analysis based on the seven quantitative traits revealed average intra-cluster distances of 2.06 for Cluster I and 2.02 for Cluster II, indicating close genetic relationships among genotypes within each cluster. The average inter-cluster distance between Cluster I and Cluster II was 4.61\u003csup\u003e*\u003c/sup\u003e, which was considerably higher than the intra-cluster distances, signifying clear divergence between the two groups. A permutation test confirmed the significance of the inter-cluster separation with a p-value of 0.05 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), thus validating the cluster structure.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eShows the average intra-cluster and inter-cluster distances based on seven quantitative traits among six Amaranth genotypes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComparison\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage distance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntra-cluster (Cluster 1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntra-cluster (Cluster 2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInter-cluster (Cluster 1 vs Cluster 2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.61\u003csup\u003e*\u003c/sup\u003e\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=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.6. The relative contribution of different characters to genetic divergence\u003c/h2\u003e\u003cp\u003eTo understand the influence of individual traits on genotype differentiation, the relative contribution of each trait to genetic divergence was evaluated using principal component loadings. Grain yield (GY) contributed the most to the clustering pattern, accounting for 19.77% of the observed variation, followed by inflorescence length (IF) at 18.90%, leaf length (LL) at 14.40%, and dry weight biomass (DWB) at 13.72%. Other traits such as leaf width (LW), plant height (PH), and stem girth (SG) contributed 11.94%, 11.40%, and 9.90%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These results highlight the prominent role of yield-related traits, particularly GY and IF in the genetic differentiation of the amaranth genotypes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4.0. Discussion","content":"\u003cp\u003eThis study provides a comprehensive evaluation of agro-morphological diversity and trait-based classification among six amaranth genotypes using a suite of multivariate analyses, including descriptive statistics, principal component analysis (PCA), correlation analysis, and hierarchical clustering. Building on prior work that focused primarily on stability assessment of these accessions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], the present study offers a novel contribution by quantifying the relative importance of individual traits in driving genetic divergence and genotype grouping. Unlike earlier studies that treated traits with equal weight or relied on limited descriptors, this research applies a trait-weighted multivariate approach to dissect the structure of phenotypic variation and identify key drivers of differentiation. The integration of dimensionality reduction, clustering precision, and trait interrelationship analysis offers a robust framework that directly informs targeted selection strategies. Collectively, these insights enhance the efficiency of amaranth breeding by supporting data-driven parental selection, hybrid design, and dual-purpose genotype identification advancing both crop improvement efforts and the broader goals of climate-resilient agriculture.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Trait Variability and Selection Implications\u003c/h2\u003e\u003cp\u003eThe wide phenotypic variation observed across traits with coefficients of variation exceeding 40% for most confirms a broad genetic base within the evaluated accessions. Notably, leaf length, inflorescence length, and plant height displayed particularly high variability, presenting opportunities to select contrasting parents for hybridization. Grain yield and dry biomass also varied substantially, reinforcing the crop\u0026rsquo;s potential for improvement in both productivity and biomass accumulation. The disproportionate contribution of grain yield and inflorescence length, accounting for nearly 39% of total variation driving cluster formation, underscores the predominance of reproductive traits in genetic differentiation. This aligns with findings in grain amaranth [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and represents a novel quantification that sharpens trait prioritization in breeding programs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Genotype Grouping and Trait-Based Associations\u003c/h2\u003e\u003cp\u003ePCA effectively distilled trait complexity, with the first two components explaining 63.7% of variance: PC1 capturing vegetative vigor (stem girth, leaf length, plant height, and Leaf width) and PC2 reflecting reproductive output (grain yield and biomass). This partitioning echoes patterns reported in amaranth and other crops [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], providing a robust functional classification of genotypes. The positioning of CK-BH-01 and LL-BH-04 along reproductive traits marks them as prime candidates for yield improvement, while MN-BH-01\u0026rsquo;s alignment with vegetative traits highlights its suitability for biomass or dual-purpose uses. Such trait-based groupings refine parental selection by enabling breeders to strategically combine complementary genotypes for heterosis or to focus directly on reproductive trait enhancement, thereby surpassing traditional, less-targeted selection approaches.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Genetic Relationships and Clustering Precision\u003c/h2\u003e\u003cp\u003eCluster analysis confirmed the PCA-based grouping into two statistically validated clusters, with Cluster I (PE-UP-BH-01, PE-LO-BH-01) showing marginally superior trait means compared to Cluster II (LL-BH -04, NU-BH-01, MN-BH-01, and CK-BH-01). The significantly lower intra-cluster distances relative to inter-cluster distances, supported by permutation testing (p\u0026thinsp;=\u0026thinsp;0.05), substantiate the robustness of cluster demarcation. Importantly, integrating relative trait contributions into clustering reveals that grain yield and inflorescence length predominantly drive genetic structure, a refinement over previous studies that treated all traits equally [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This approach enhances the precision of genotype selection, facilitating more efficient breeding.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Trait Interrelationships and Breeding Potential\u003c/h2\u003e\u003cp\u003eStrong positive correlations among grain yield, leaf length, stem girth, and inflorescence length affirm the interconnectedness of yield components, consistent with reports in \u003cem\u003eAmaranthus hybridus\u003c/em\u003e and \u003cem\u003eA. hypochondriacus\u003c/em\u003e [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These relationships support indirect selection strategies leveraging vegetative traits to boost yield. The association between dry biomass and traits like inflorescence weight and plant height further enables the identification of dual-purpose genotypes suitable for food and fodder, aligning with the crop\u0026rsquo;s growing role in climate-resilient farming systems [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The identification of accessions combining high yield and favourable morphological traits (e.g., CK-BH-01 and LL-BH-04) holds promise for varietal development adapted to diverse environments and stress conditions.\u003c/p\u003e\u003cp\u003eOur findings build upon the earlier stability analyses of these accessions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], which established performance consistency across environments. The current study complements that foundation by elucidating the trait-specific drivers of genetic differentiation and grouping, thereby enabling breeders to integrate both stability and trait-based precision in their selection criteria.\u003c/p\u003e\u003cp\u003eTo accelerate genetic gains in amaranth, future research should incorporate molecular markers such as SSRs and SNPs to dissect the genetic architecture of key traits, improving marker-assisted selection. Exploring gene-by-environment interactions is essential for breeding genotypes that combine high yield potential with stability across heterogeneous agroecological zones. Expanding germplasm collections to include diverse landraces and wild relatives will enrich the breeding pool, potentially uncovering novel alleles for stress resilience and productivity. Finally, adopting a trait-based breeding framework focused on high-impact reproductive traits like grain yield and inflorescence length will optimize parental selection and hybrid development, supporting broader goals of food security, climate resilience, and agrobiodiversity conservation.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights key agro-morphological traits, particularly grain yield and inflorescence length, as major drivers of genetic differentiation among amaranth genotypes. The integration of PCA, correlation analysis, and clustering provided a robust framework for trait-based genotype classification. Accessions like CK-BH-01 and LL-BH-04 showed strong potential for yield improvement. These findings offer valuable guidance for targeted breeding and lay the groundwork for future work involving molecular tools and broader germplasm evaluation to enhance amaranth improvement strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrincipal Component Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDWB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDry Weight Biomass\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLeaf Length\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLW\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLeaf Width\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInflorescence Length\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePlant Height\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStem Girth\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003cp\u003eNot Applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003cp\u003eNot Applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests:\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis study was funded by Sustainable Food Systems in Malawi (FoodMA) Programme at LUANAR.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: A. S. and M.N., Data collection and cleaning: A.S., M. N., and R.M.K. Data analysis: M. N and A.S. Experimental layout: A. S and M.N. Funding acquisitions: A. S and M.N. Original manuscript draft: M. N. and A.S. Writing review, editing and final approval: A.S., M.N., R.M.K., L.Y., S.K., M.M., C.M., and K.M.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\u003cp\u003eThe authors acknowledges Sustainable Food Systems in Malawi (FoodMA) Programme at LUANAR for financially supporting the study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData sets generated during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYadav A, Yadav K. From humble beginnings to nutritional powerhouse: the rise of Amaranth as a climate-resilient superfood. 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Sci Rep. 2023;13(1):1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-023-47869-0\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-47869-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Amaranthus spp., Genetic diversity, Multivariate analysis, Principal component analysis (PCA), Trait-based selection, Cluster analysis","lastPublishedDoi":"10.21203/rs.3.rs-7029686/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7029686/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Amaranth (\u003cem\u003eAmaranthus\u003c/em\u003e spp.) is an underutilized, climate-resilient crop with significant potential to enhance food and nutritional security. This study evaluated the genetic diversity and trait-based classification of six amaranth genotypes using multivariate analyses on seven quantitative traits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Principal component analysis revealed that the first two components explained 63.6% of the total variation, effectively distinguishing genotypes based on vegetative vigor and reproductive output. Cluster analysis identified two statistically distinct groups, with grain yield and inflorescence length contributing most to genetic divergence. Strong positive correlations among grain yield, stem girth, leaf length, and inflorescence traits suggest opportunities for indirect selection. Genotypes CK-BH-01 and LL-BH-04 exhibited superior performance in yield-related traits, making them as promising candidates for breeding improvement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e These findings provide a robust, data-driven framework for trait-based selection in amaranth breeding, supporting the development of high-yielding, stress-resilient varieties adapted to diverse agroecological zones.\u003c/p\u003e","manuscriptTitle":"Multivariate analysis for yield and yield-related traits of amaranth Accessions from Malawi","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 10:53:11","doi":"10.21203/rs.3.rs-7029686/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-22T13:06:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T08:18:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47309824090084630960835489123164439565","date":"2025-07-17T04:36:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-15T14:06:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299152937745634403843364712393258971830","date":"2025-07-12T16:12:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25786147957315324437969352372899085384","date":"2025-07-10T05:36:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-10T03:40:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-09T12:27:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-07T12:18:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-07T11:06:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-07-07T11:03:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"65c27e16-6389-444d-be0a-6694638e2255","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T16:04:54+00:00","versionOfRecord":{"articleIdentity":"rs-7029686","link":"https://doi.org/10.1186/s12870-025-07190-6","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2025-08-27 15:57:29","publishedOnDateReadable":"August 27th, 2025"},"versionCreatedAt":"2025-07-14 10:53:11","video":"","vorDoi":"10.1186/s12870-025-07190-6","vorDoiUrl":"https://doi.org/10.1186/s12870-025-07190-6","workflowStages":[]},"version":"v1","identity":"rs-7029686","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7029686","identity":"rs-7029686","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}