Investigation of Phenotypic Diversity and Genotype X Environment Interaction in Early Barnyard Millet Genotypes for Grain Yield Using Ammi Analysis and Gge Biplot

Investigation of Phenotypic Diversity and Genotype X Environment Interaction in Early Barnyard Millet Genotypes for Grain Yield Using Ammi Analysis and Gge Biplot | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigation of Phenotypic Diversity and Genotype X Environment Interaction in Early Barnyard Millet Genotypes for Grain Yield Using Ammi Analysis and Gge Biplot Sudhir Deepak M, Vanniarajan C, Chandirakala R, Renuka R, Kanchana S This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8899352/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Barnyard millet is a nutritious, small millet crop grown in rainfed condition, consumed just like rice. Reducing the maturity duration is an important breeding objective and assessing the phenotypic diversity help in identifying those genotypes with desirable traits. Twenty-seven genotypes were subjected to D 2 analysis which revealed the presence of eight different clusters. Cluster I had the maximum number of genotypes. Cluster II had five genotypes, cluster III had three genotypes and in cluster IV, V, VI, VII and VIII with one genotype each. Early maturing germplasm were in cluster III and the check MDU1 was in separate cluster VII. The percentage contribution of the traits towards divergence was maximum by grain yield per plant followed by plant height and net by days to maturity. Stability analysis of these genotypes in three different environments revealed the stable genotypes with high mean were ACM BM-20-019 and MDU1. E2 was the least interactive environment. Using What- Won- Where biplot, the three test environments were divided into two mega environments. In light of this, the genotype ACM BM-20-016 performed well in mega environment 1 (E1), while the genotypes ACM BM-20-019, MDU 1, and ACM BM-20-012 perform similarly in mega environment 2. (E2 and E3). Barnyard millet Mahalanobis D2 AMMI GGE biplot Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Barnyard millet [ Echinochloa frumentaceae (L.)] is a small millet widely grown in the areas of arid and semi-arid tropical regions of Asia and Africa (Bangar 2024). It can withstand water deficit and can be replaced in the place of water loving crops. It is an underutilized, nutritious crop with high micronutrients like Fe and Zn (Vijayprabha 2024) with range of 2.29 to 18 mg/ 100 g for Fe and 1.5 to 7.5 mg/ 100g for Zn (Renganathan 2017). It is the fourth most produced minor millet, ensuring food security for many impoverished people worldwide. India is the world’s largest producer of barnyard millet with an area pf 0.146 M ha − 1 and production (0.147 M t) with average productivity of 1034 kg ha − 1 (Sakkarai 2024). Its grains can be consumed just like rice. It is grown as rainfed crop in the following districts of Tamil Nadu such as Theni, Ramanathapuram, Madurai, Dindigul, Erode, Namakkal and Salem. It is used for both fodder and grain purposes (Nirmalakumari 2009). Though it has high nutrition and good agronomic performance, the crop still remains unexplored. At present, two cultivars MDU 1 and Co (Kv) 2 are the ruling varieties of barnyard millet in Tamil Nadu with the duration of 95–105 days (Vanniarajan 2018). Evolving genotypes with hastened maturity could help the farmers as a climate change mitigation strategy which is essential in the current scenario. Assessing the genetic diversity among the genotypes may help in grouping them, based on their differences in traits. Clustering them into different groups can help to discover the character responsible for variation and the variation among the genotypes (Supriya 2024; Singh et al. 2024). This will be useful for selecting the genotypes based on our objective in future breeding programme. Currently, barnyard millet is grown as Rabi crop. The early genotypes can be cultivated in all the seasons across environments, which could also find a place in the existing mono-cropping pattern (Monika 2021). With reference to the above statement, evaluating the performance of the genotypes across different environments using the stability analysis is needed, to find out the suitable genotype for specific environment and also across different environments. The performance of different genotypes in different environment is based on the interaction between environment and the genotype. Since, grain yield and its contributing traits are quantitative in nature, the influence of environment determines the expression of the phenotype. G x E interaction across several environments is analysed using AMMI model, which is a powerful tool which uses the ICPA (Interaction Principal Component Analysis). GGE biplot method is also used to interpret the complex G x E interaction (Yan and Hunt 2001 ). This study aims at studying the stability of the genotypes across three different environments for grain yield and its contributing traits, which would be helpful in further crop improvement programme. Materials and Methods The experiment was conducted using 27 barnyard millet genotypes, which includes check MDU 1. The list of the genotypes is mentioned in Table 1 . The experiment was carried out in Randomized Block Design with three replications. The crop was raised in rows with length of 7 m and the spacing adopted was 30 cm x10 cm. The crop management was done as per the package of practices developed by Tamil Nadu Agricultural University. Biometrical observations were taken down for seven genotypes were tested for yield contributing traits such as days to first flowering, days to 50% flowering, days to maturity, plant height, number of basal tillers, flag leaf length, flag leaf width, panicle length, single ear head weight, thousand grain weight, fodder yield per plant and grain yield per plant. Table 1 List of genotypes used in this study Code Genotypes Parentage Code Genotypes Parentage G1 ACM BM-20-001 MDU 1∗ IEC 108 G15 ACM BM-20-015 Co (Kv) ∗ IEC 385 G2 ACM BM-20-002 MDU 1 ∗ IEC 108 G16 ACM BM-20-016 MDU 1 ∗ IEC 109 G3 ACM BM-20-003 MDU 1 ∗ IEC 108 G17 ACM BM-20-017 MDU 1 ∗ IEC 109 G4 ACM BM-20-004 MDU 1 ∗ IEC 108 G18 ACM BM-20-018 MDU 1 ∗ IEC 107 G5 ACM BM-20-005 MDU 1 ∗ IEC 108 G19 ACM BM-20-019 MDU 1 ∗ IEC 107 G6 ACM BM-20-006 MDU 1 ∗ IEC 108 G20 ACM BM-20-020 MDU 1 ∗ IEC 107 G7 ACM BM-20-007 Co (Kv) ∗ IEC 385 G21 IEC 108 G8 ACM BM-20-008 Co (Kv) ∗ IEC 385 G22 IEC 109 G9 ACM BM-20-009 Co (Kv) ∗ IEC 385 G23 IEC 107 G10 ACM BM-20-010 Co (Kv) ∗ IEC 385 G24 IEC 350 G11 ACM BM-20-011 Co (Kv) ∗ IEC 385 G25 IEC 356 G12 ACM BM-20-012 Co (Kv) ∗ IEC 385 G26 IEC 385 G13 ACM BM-20-013 Co (Kv) ∗ IEC 385 G27 MDU 1 G14 ACM BM-20-014 Co (Kv) ∗ IEC 385 For, stability analysis, the experiment was carried out in randomized block design with three replications in three different environments for two years (2020 and 2021) as given in Table 2 . Table 2 Agro-ecological details of the experimental environments Code Location Year Latitude Longitude Altitude (MSL) Soil type Temperature Rainfall E1 AC &RI, Madurai 2021 9°96'N 78.21'E 166.00 Alfisol 24.6° C − 30.4° C 950 mm E2 Thirumangalam 2022 9°91' N 77°98'E 164.15 Vertisol 27° C − 39°C 857.6 mm E3 Aruppukottai 2022 9°30' N 78°5'E 123.00 Alfisol 21.1° C − 30.4°C 830 mm Statistical analysis: Statistical analyses include the combined ANOVA for testing the significance of the different sources of variation, D 2 analysis and the stability analysis, by AMMI and GGE biplots. Analysis of Variance (ANOVA) partitions the total variation and gives the contribution of the genotype, environment and genotype x environment to the total variance present. Mahalinobis (Pc 1936 ) D 2 Analysis was done and clustering was carried out as suggested by Rao ( 1952 ) using Tocher method. AMMI (Additive Main effect and Multiplicative Interaction) model separates the GE deviations into different ICPAs (Interaction Component Principal Axis). It takes into account both the ANOVA and the PCA. ANOVA gives the additive main effects and the PCA is the multiplicative part of the model, which explains about the interaction effects. The GGE biplot or the what-won -where biplots uses the genotype and the GE interaction to suggest the suitability of the genotypes across all the environments and also to particular environment. The AMMI analysis uses the formula: Y ij = µ + g i + e j + Ʃ λ k Y ik α jk + ε ij Meanwhile, the GGE model considers by the following formula: Y ij = µ + g i + e j + Ʃ λ k Y ik α jk + ε ij where, Y ij - mean of the trait of i th genotype in the j th environment; µ – grand mean; g i – genotype effect; e j - environment effect λ k - eigenvalue of the PCA axis k; Y ik – E-PCs scores for axis k; α jk - G-PCs scores for axis k; ε ij – error component (Abdelrahman et al. 2022 ) Classification of genotypes based on IPCA I scores Class I : Genotypes with high mean and positive IPCA I, Class II : Genotypes with high mean and negative IPCA I, Class III : Genotypes with low mean and positive IPCA I, Class IV : Genotypes with low mean and negative IPCA I The combined ANOVA analysis was performed using software TNAU STAT. The D 2 analysis as well as AMMI and GGE biplot analysis were conducted using PB Tools (PB Tools, 2014 ; Kuraloviya 2022) Results Analysis of variance showed that the grain yield showed significance for the genotype, environment and the genotype x environment interactions Table 3 . This indicates that the genotypes used in study are present with the significant amount of variation and they have the influence of the environment for their expression. But, the percentage contribution of the genotype to variation was high which indicates that, there is considerably low influence of environment on the trait. D analysis The D 2 analysis classifies the given genotypes into different groups based on the extent of variation present among them. The 27 genotypes studied were grouped into eight different clusters ( Table 4 ). Cluster I is the largest one with 14 genotypes followed by cluster II with five genotypes, cluster III with three genotypes. Clusters IV, V, VI, VII and VIII has solitary genotypes indicating that these genotypes are highly distinct from others. Table 3 ANOVA for grain yield source df sum of Square mean sum of square F value Environment 2 328.43 164.21* 37.25 Replication 6 26.45 4.4 0.57 Genotype 26 24923.71 968.60* 125.02 Genotype x Environment 52 2673.32 51.41* 6.71 Pooled error 156 1196.11 7.66 Total 242 2914.03 Table 4 Distribution of the genotypes into different clusters based on the different quantitative traits. Clusters Genotypes I ACM BM -20-010, ACM BM -20-020, ACM BM -20-008, ACM BM -20-004, ACM BM -20-005, ACM BM -20-007, ACM BM -20-006, ACM BM -20-001, ACM BM -20-018, ACM BM -20-014, ACM BM -20-003, ACM BM -20-015, ACM BM -20-009, ACM BM -20-012 II ACM BM -20-011, ACM BM -20-013, ACM BM -20-002, ACM BM -20-016, ACM BM -20-019 III IEC 109, IEC 107, IEC 108 IV ACM BM -20-017 V IEC 385 VI IEC 356 VII MDU 1 VIII IEC 350 Cluster I showed moderate mean for days to first flowering (54.07), days to 50% flowering (58.69), days to maturity (85.40), panicle length (131.63cm), 1000 grain weight (2.45 g) and grain yield per plant (29.46g), low for number of basal tillers (41.9), high for flag leaf length (29.49) and flag leaf breadth (3.37), single ear head weight (8.67g) and fodder yield per plant (42.75g). It has the genotypes with reduced duration than the check MDU1 but low yielding than it. Cluster II also showed moderate mean like cluster I for traits except plant height (140.93 cm), single ear head weight (9.31g), fodder yield per plant (56.38g) and grain yield per plant (35.24g) comparatively higher than the genotypes present in the cluster I. Cluster III has the IEC 109, IEC 107, IEC 108 genotypes which are early maturing with low mean for days to first flowering (33.44), 50% flowering (37.77) and maturity (65.11) and low mean for yield attributes like plant height (88.33 cm), single ear head weight (0.82g), fodder yield (4.54g), grain yield (3.33g) and high mean for number of tillers (8.44). Cluster IV has one genotype (ACM BM -20-017) which is late maturing with high mean for days to first flowering (61.33), days to 50% flowering (65.33) and days to maturity (94.33) and for plant height (151.34 cm), flag leaf length (32.13 cm) and breadth (3.89 cm) which is nearly similar to check MDU 1 except for grain yield which has low mean (34.11g) than MDU1. Cluster V (IEC 385) has one genotype with low days to first flowering (39.33), low grain yield (17.86) with high number of basal tillers (9.33) and low days to maturity (70.33) followed by cluster VI (IEC 356) comprises low in days to first flowering (40.33), low in days to maturing (71.33), grain yield per plant (19.13) and high in plant height (141.26) whereas Check MDU 1 has plant height of 147.43 cm. Cluster VII has the check MDU1 which is higher mean for maturity traits like days to first flowering (62.00), 50% flowering (67.00) and maturity (95.33) and yield traits including plant height (147.43 cm), number of tillers (6.00), panicle length (22.33 cm), single ear height (8.16g), 1000 grain weight (3.23g), fodder yield (59.68) and grain yield (42.63g) ( Table 5 ) . Table 5 Mean values for different yield attributes of different clusters Cluster DFF DFTF DM PH NBT FLL FLB PL SEW TGW FYD GYP I 54.07 58.69 85.40 131.63 4.19 29.49 3.37 18.66 8.67 2.45 42.75 29.46 II 52.20 57.26 84.40 140.93 4.46 28.97 3.48 20.63 9.31 2.45 56.38 35.24 III 33.44 37.77 65.11 88.83 8.44 16.00 1.43 12.81 0.82 2.73 4.54 3.33 IV 61.33 65.33 94.33 151.34 4.66 32.11 3.89 20.68 8.91 2.50 54.58 34.11 V 39.33 43.33 70.33 70.26 9.33 15.00 1.63 13.83 2.46 2.50 23.22 17.86 VI 40.33 45.33 71.33 141.26 5.33 26.46 3.23 21.46 4.46 2.76 30.61 19.13 VII 62.00 67.00 95.33 147.43 6.00 30.70 3.20 22.33 8.16 3.23 59.68 42.63 VIII 37.66 41.66 68.66 108.43 4.66 14.30 2.23 12.46 5.26 2.70 28.85 19.23 DFF - Days to first flowering; DFTF - Days to 50% flowering; DM - Days to maturity; PH - Plant height; NBT - Number of basal tillers; FLL - Flag leaf length; FLB - Flag leaf breadth; PL - Panicle length; SEW - Single ear head weight; TGW - Thousand grain weight; FYD - Fodder yield per plant; GYP - Grain yield per plant Intra and inter cluster distance The intra and inter cluster distance among eight clusters were computed and presented in Table 6 . The intra cluster distance ranged from 0.00 to 279.19. The cluster IV, V, VI, VII and VIII showed minimum intra cluster distance (0.00). Each contain single genotype and maximum intra cluster was exhibited by cluster I (279.19) followed by cluster II(235.25). The inter cluster distance varied from 350.63 and 5894.83 respectively. Maximum inter cluster distance was observed between clusters IV and V (5894.83). This was followed by clusters III and IV (5096.41). Whereas the lowest inter cluster distance was recorded between cluster II and IV (350.63). Table 6 Average intra and inter cluster distance Cluster Number I II III IV V VI VII VIII I 279.195 884.697 2561.45 1120.79 2743.76 1043.63 721.786 1331.49 II 235.258 3614.8 350.638 4575.63 936.377 1930.67 1943.89 III 95.5448 5096.41 525.445 1784.05 4483.99 565.838 IV 0 5894.83 1789.68 1603.49 3187.98 V 0 3099.31 4120.31 1243.09 VI 0 2506.52 621.21 VII 0 3055.6 VIII 0 Contribution of different characters to divergence The contribution of characters to total genetic divergence is important in deciding which characters to select. Table 7 and Fig. 1 show the estimated contribution of individual character to the expression of genetic diversity based on character wise D 2 values. Grain yield per plant (49.28 per cent) and plant height (31.33 per cent) were the top two major contributors towards the total genetic divergence among the genotypes. The contribution of other characters towards the expression of genetic diversity in percentage is presented in descending order viz; days to maturity (7.40 per cent), single earhead weight (2.27 per cent), days to first flowering (1.99 per cent), flag leaf breadth (1.99 per cent), days to 50% flowering (1.70 per cent), thousand grain weight (1.42 per cent), flag leaf length (1.13 per cent), panicle length (0.85 per cent), fodder yield per plant (0.56 per cent) and number of basal tillers (0.00 per cent).This made it abundantly clear that when genotypes are chosen from genetically diverged groups, the genotypes are classified according to the best divergence donating traits like grain yield per plant, plant height and days to maturity because these traits are associated with greater contributions to divergence. Table 7 Contribution of different characters to divergence Traits Number of times first ranks % Contribution to divergence DFF 7 1.99 DFTF 6 1.70 DM 26 7.40 PH 110 31.33 NBT 0 0 FLL 4 1.13 FLB 7 1.99 PL 3 0.85 SEW 8 2.27 TGW 5 1.42 FYD 2 0.56 GYP 173 49.28 DFF - Days to first flowering; DFTF - Days to 50% flowering; DM - Days to maturity; PH - Plant height; NBT - Number of basal tillers; FLL - Flag leaf length; FLB - Flag leaf breadth; PL - Panicle length; SEW - Single ear head weight; TGW - Thousand grain weight; FYD - Fodder yield per plant; GYP - Grain yield per plant Stability analysis The genotypes along with check MDU1 were tested for the stable performance for grain yield across three different environments for two years. The mean values of the genotypes for grain yield over the test environments were given in Table 8 . The mean values of the tested genotypes ranged from 3.28 g to 42.14 g. Among all the genotypes, the genotype, ACM BM-20-019 (G19) was identified with the highest grain yield mean (42.14g) than the check MDU 1 (G27) which was 41.54g. Out of three environments, the environment E1 showed the highest mean for the grain yield (27.37g) followed by E3 (27.10g) and E1 (24.78g). Additive Main Effects and Multiplicative Interaction (AMMI) analysis AMMI analysis partitions the existing G x E interactions into two significant PCAs. PC1 and PC2 contributed 69.2% and 30.8% of total GE interaction variation respectively. The ICPA scores obtained in the AMMI analysis displays the adaptability of the genotypes over environments and also their interaction with environments. Table 8 The mean, ICPA1 and ICPA2 values for the twenty-seven genotypes Genotype Mean IPCA 1 IPCA 2 Genotype Mean IPCA 1 IPCA 2 G1 28.57 0.03 -0.28 G15 31.47 -2.97 0.57 G2 34.46 1.97 -1.24 G16 38.88 -1.16 0.24 G3 24.28 -0.43 -0.67 G17 32.81 -0.18 -0.61 G4 32.04 2.09 -0.46 G18 27.65 0.89 0.23 G5 27.46 -1.09 -0.64 G19 42.14 0.10 0.90 G6 25.16 0.23 -0.71 G20 26.59 -0.91 -1.75 G7 25.88 -0.41 1.52 G21 3.53 -0.12 0.31 G8 29.52 0.86 0.38 G22 3.28 -0.05 0.36 G9 24.57 0.33 -0.11 G23 3.78 -0.03 0.32 G10 28.22 1.01 1.09 G24 19.44 0.29 0.91 G11 25.78 -0.79 -0.71 G25 19.17 0.42 1.04 G12 40.07 -0.20 -0.26 G26 18.42 0.12 0.81 G13 27.57 0.08 -0.85 G27 41.54 0.39 0.42 G14 30.94 -0.45 -0.81 Table 9 Mean yield of different environments Environment Mean Grain yield (g) IPCA I IPCA II E1 27.37 2.17 -1.64 E2 24.78 1.44 2.12 E3 27.10 -1.45 3.44 AMMI 1 biplot has two axes, mean yield on x axis, indicating the main effects and the interaction effect (ICPA 1) on Y axis (Fig. 2 ). The axis contributed from 69.2% to 30.8%. The mean values of genotypes, IPCA I and IPCA II was offered in Table 8 . The mean values of genotypes ranged from 3.28 to 42.14. The mean values of IPCA I and IPCA II was from − 2.97 to 2.09 and from − 1.75 to 1.572 respectively. Amongst the genotypes; the ACM BM-20-019 (42.14) expressed the highest in grain yield per plant followed by MDU 1(41.54) showed the highest mean value with positive IPCA values around zero. The mean for individual environment accounted for E1 (27.37), E2 (24.78) and E3 (27.10) respectively. Regarding AMMI biplot 1 ( Fig. 2 ) the ACM BM-20-001, ACM BM-20-002, ACM BM-20-004, ACM BM-20-006, ACM BM-20-009, ACM BM-20-008, ACM BM-20-010, ACM BM-20-013,ACM BM-20-018, ACM BM-20-019, MDU 1 had higher mean value with positive IPCA I scores were categorized under class 1. The genotypes, ACM BM-20-003, ACM BM-20-005, ACM BM-20-007, ACM BM-20-011, ACM BM-20-012, ACM BM-20-014, ACM BM-20-015, ACM BM-20-016, ACM BM-20-017, ACM BM-20-020 had high mean and negative IPCA I scores and fallen under class 2. Genotypes like, IEC 350, IEC 356 and IEC 385 had low mean with positive IPCA I scores fallen under class 3. Genotypes like IEC 107, IEC 108 and IEC 109 had low mean with negative IPCA I scores fallen under class 4. Over the environmental conditions, E1 had highest mean value of 27.37 with negative IPCA II values (-1.64) followed by E3 (mean = 27.10) had negative IPCA 1 values of -1.45. The environment E2 (mean = 24.78) alone had positive IPCA I and II values (1.44 & 2.12) respectively (Table. 9). With regard to AMMI biplot 2 ( Fig. 3 ) the environment E I had the longest spoke. The genotypes ACM BM-20-001, ACM BM-20-009, ACM BM-20-012, ACM BM-20-019, IEC 109, IEC 107 and MDU1 was nearer to origin indicated that minimal interaction of genotypes with environment. The remaining genotypes away from the origin indicated that they are more reactive to environment. Interaction of genotypes with specific environment are adjudged by the pointing of genotypes on the spoke. On this criteria, ACM BM-20-002 had interaction at E3, genotypes ACM BM-20-013, IEC 350, IEC 385, IEC 356 will be suitable to E2 and the genotype ACM BM-20-005 will be suitable to E1. The genotypes ACM BM-20-015 were away from the origin and indicate that the genotypes were more sensitive to environmental impacts. Genotype plus Genotype-by-Environment (GGE) Biplot analysis GGE Biplot gives comprehensive information about the environments, which are not available in AMMI analysis and the suitability of genotypes to the specific environment. It divides G × E interactions into three IPCAs. These components accounted for 97.1% of the total variation ( Table 10 ) . IPCA1, IPCA2, and IPCA3 accounted for 90.4%, 6.7%, and 2.9% of the variation in the GGE model, respectively. The following information are obtained from GGE biplot – environment view ( Fig. 4 ). The angle between the vectors indicating the environments confer the correlation between the environments. The environments, E2 and E3 are separated at an acute angle, less than that between E1 and E2. The angle between E1 and E3 is nearly 90 º , which indicates that they are not correlated. The length of the vector also helps to discriminate the environments. E1 is the most discriminating followed E3 and E2. Vector distance between the environments shows that E2 and E3 are in one group and E1 in another one. Thus, for future evaluations, either E2 or E3 may be chosen to represent that group and another environment chosen should be E1. From Fig. 5 , the GGE biplot environment view, the representative environments are chosen with respect to the Average Environment Co-ordination axis (AEC). The environment E2 is more representative because, it has less angle with respect to AEA. E1 and E3 are less representative due to wider angle separation between themselves. From Fig. 6 , the genotypes ACM BM-20-019, MDU1 and ACM BM-20-012 were found to be stable with higher yield, because they lie within the very first concentric circle. The genotype ACM BM-20-016 was also stable and high yield but on the later concentric circles. Table 10 Variation for grain yield per plant due to G x E partitioned with AMMI model and GGE model Source of variation Degrees of freedom Sum of squares Mean sum of squares % Variation AMMI MODEL Treatment ICPA1 27 1232.9544 45.66498 ** 69.2 ICPA 2 25 549.2615 21.97046** 30.8 ICPA 3 23 0.0000 0.00000 ns 0.0 GGE BIPLOT MODEL ICPA 1 27 16633.1057 616.04095** 90.4 ICPA 2 25 1232.7318 49.30927** 6.7 ICPA 3 23 532.1862 23.13853** 2.9 ** - p < .0.01 – probability level What – won- where biplot analysis The what-won-where biplot ( Fig. 7 ) generated by the GGE analysis clearly demonstrated genotype adaption patterns throughout the three test environments (E1, E2, and E3). The biplot's polygon view splits the graph into distinct sectors by creating perpendicular lines (equality lines) from the origin to each polygon's sides. The genotypes at the polygon's vertices were determined as the winning genotypes for the environments in their respective sectors. The biplot showed the existence of two independently mega-environments, Mega-environment I (Consists entirely of E1) and Mega-environment II (E2 and E3). This grouping reveals that E2 and E3 had similar genotype responses and were positively associated, whereas E1 responded differently and created an isolated testing zone. The partitioning of environments into two mega-environments shows variable genotype performance across locations, highlighting the presence of considerable genotype × environment interaction. In mega environment 1, the genotype ACM BM-20-016 was located near the vertex of the polygon encompassing E1, indicating that it was the most productive genotype in this environment. As a result, ACM BM-20-016 can be deemed specifically adapted to E1. In mega environment 2, The environments E2 and E3 were in the sector described by the vertex genotypes: ACM BM-20-019; MDU 1; ACM BM-20-012. These genotypes outperformed in both E2 and E3, indicating that they are more suitable and adaptable to this mega-environment 2. Their identical positioning suggests that yield performance is equivalent between the two environments. Discussion D 2 analysis for quantitative traits In the present study, twelve biometrical traits were studied in 27 genotypes and subjected to D 2 analysis. Based on the D 2 analysis, all the genotypes were grouped into eight different clusters. Formation of large number of clusters indicated more diversity among the genotypes. The clustering pattern in the present study revealed that the genotypes from different sources clustered together showed that there was little association between eco-geographical distribution of genotypes and genetic diversity. The different clusters indicated that, in general, selections have been towards the same goal in the different centres of origin of those genotypes and yet there is sufficient genetic variability distinctly differentiates them. Hence, the chosen genotypes used in the present study could be considered as a valid material. Genetic drift and selection in different environments may cause greater diversity than geographical diversity. The D 2 analysis classifies the given genotypes into different groups based on the extent of variation present among them. The 27 genotypes studied were grouped into eight different clusters. Similar results were obtained by Kuraloviya et al., ( 2022 ) in barnyard millet followed by Sharma et al. ( 2025 ) in pearl millet. Cluster I is the largest one with 14 genotypes. Similar results has been widely studied (Nireekshitha 2024). Followed by cluster II with five genotypes, cluster III with three genotypes. Clusters IV (Ray et al., 2025 ), V, VI, VII and VIII with one each indicating that these genotypes are highly distinct from others. Cluster VI and VII are in accordance with Sharma et al. ( 2025 ) in pearl millet and (Sathyaraj 2024) in finger millet. Similar results for cluster I with maximum genotypes were reported in barnyard millet (Prabu 2020; Kuraloviya 2022). Genotypes belonging to a common cluster were the least different. It is unlikely that a cross between genotypes from the same cluster will result in transgressive segregants. The parents of the various clusters with extreme divergence may therefore be used to construct a suitable transgressive segregant. This genotype grouping pattern revealed no correlation between genetic divergence and genotype distribution by geography. Similar results has been reported in pearl millet (Kavita 2024) showed that genotypes from various eco-geographical regions were randomly distributed into clusters, suggesting that geographic dispersion does not always show genetic divergence. Intra and inter cluster distance The primary goal of cluster creation is to measure inter- and intra-cluster distances, which serves as an index for choosing genetically diverse parents for a hybridization programme. According to Sharma et al. ( 2025 ), the cluster with the most genotypes (Cluster I) had a large intra cluster distance implying that genotypes within these clusters are monomorphic for the attributes analysed. The heterogeneity of genotypes within a cluster accounts for the intra-cluster distance. The low intra-cluster distance indicated that the genotypes in the clusters were tightly connected (Sharma 2020). on the other hand, compared to other clusters on the diagonal, Cluster I (279.195) and Cluster II (235.258) have comparatively larger intra-cluster distances, suggesting substantial genetic heterogeneity within these clusters. Similar findings on cluster II were reported in (Kanwer 2025) finger millet. This implies that the genotypes in Cluster I are not uniform and include valuable variation that may be utilized for parent selection or within-cluster selection to achieve even greater improvement. The maximum inter cluster distance was noted between cluster V and cluster VI followed by cluster III and IV. These cluster pairs are ideal candidates for hybridization to optimize genetic recombination and heterosis because of their high D 2 values, which show significant genetic difference between them. Conversely, cluster II and IV recorded minimum inter cluster distance implying closer genetic similarities in these groups. Hence large inter cluster distance groups are desirable for parent selection in breeding programmes. This findings are in accordance with Patel et al. ( 2023 ) in finger millet. Mean performance Cluster I showed moderate mean for days to first flowering, days to 50% flowering, days to maturity, panicle length, 1000 grain weight and grain yield per plant followed by low for number of basal tillers, high for flag leaf length and breadth, single ear head weight and fodder yield per plant. These results are on par with in finger millets(Kanwer 2025). Cluster I has the genotypes with reduced duration than the check MDU1 but low yielding than check. Cluster II also showed moderate mean like cluster I for traits except plant height, single ear head weight, fodder yield and for grain yield it is comparatively higher than the genotypes present in the cluster I and possessed moderate yield compared to check MDU 1. Hence selection of plants in cluster II favours low days to maturity compared to check MDU 1 with moderate grain yield per plant. Cluster III has IEC 109, IEC 107, IEC 108 genotypes which are early maturing with low mean for days to first flowering, 50% flowering and maturity and low mean for yield attributes like plant height, single ear head weight, fodder yield, grain yield and high mean for number of tillers. Cluster IV has one genotype which is late maturing with high mean for days to first flowering, days to 50% flowering and days to maturity and for plant height, flag leaf length and breadth which is nearly similar to check MDU 1 except for grain yield which has low mean than MDU1. Cluster VII has the check MDU1 which is higher mean for maturity traits like days to first flowering, 50% flowering and maturity and yield traits including plant height, number of tillers, panicle length, single ear height, 1000 grain weight, fodder yield and grain yield. From these results, it indicates that the late maturing ones are high yielding (Kuraloviya 2022). In hybridization programmes that try to improve numerous features at once, this knowledge is essential for choosing parents. Similar findings has been reported (Pali 2022) in finger millet. . Individual attributes received a rank according to how much they contributed to the overall genetic difference. Among the traits studied, the trait grain yield per plant contributed the highest to divergence (49.28%) with the highest rank. Similar findings were reported (Ray 2025) in finger millet and (Kuraloviya (2022); Prabu et al. ( 2020 ); Dhanalakshmi et al., ( 2019 ) barnyard millet; The next highest contribution was by plant height (31.33%). The lowest contribution was from number of basal tillers (0%) (Patel 2023), fodder yield per plant (0.5%), panicle length (0.85%), days to first flowering (1.99%), flag leaf length (1.99%), days to 50% flowering (1.70%), 1000 grain weight (1.42%), flag leaf breadth (1.13%). Likely results were reported for panicle length (Suthediya 2021) in kodo millet. Other traits like days to maturity contributed moderately (7.40%) followed by single ear head weight (2.27%). This made it abundantly clear that when genotypes are chosen from genetically diverged groups, the genotypes are classified according to the best divergence donating traits like grain yield per plant, plant height and days to maturity because these traits are associated with greater contributions to divergence. From these findings, it indicates that the late maturing ones are high yielding. Additive Main Effects and Multiplicative Interaction (AMMI) The AMMI analysis revealed that the first two interaction principal component axes (IPCA I and IPCA II) accounted for 69.2% and 30.8% of the overall G×E interaction, respectively. The higher proportion explained by IPCA I implies that the majority of the interaction was captured by the first multiplicative component, which is consistent with findings in proso millet crops where the first IPCA often accounts for the majority of interaction variance (Zhang 2025). This shows that contextual variations had a significant impact on genotype performance, and AMMI effectively partitioned the interaction effects. The wide range in per se grain yield per plant demonstrates significant genetic diversity between genotypes. The higher performance of ACM BM-20-019, followed by MDU 1 along with IPCA scores near zero, indicating both were in terms of high productivity and stability across environments. Genotypes with IPCA scores close to zero are considered widely adapted due to minimal interaction with environmental fluctuations. In finger millet, where stable genotypes were identified based on lower IPCA values (Patel 2023). The per se of environments indicated that E1 and E3 were more favourable environments than E2 (Ishwarya 2025). The negative IPCA values for E1 and E3, as well as the positive IPCA I and II scores for E2, indicate that E2 constituted a unique environment with diverse genotype responses. Such contrasting environmental interactions have previously been demonstrated in fox tail millet multi-environment trials, where some test locations had a better discriminating capacity and interaction effects (Rao et al. 2025). AMMI biplot I categorized genotypes into four groups based on their mean performance and IPCA I scores. Genotypes in class I (high mean and positive IPCA I) and class II (high mean and negative IPCA I) had high productivity but differed in their interaction patterns. This classification denotes the presence of both broadly and specifically adapted high-yielding genotypes. Similar genotype clustering into adaptation groups was seen in finger millet studies using AMMI analysis (Patel et al., 2023 ). In contrast, genotypes with low mean performance (classes III and IV) demonstrated weak adaptability and limited breeding value for yield development. AMMI biplot II explained genotype stability and specific adaptation. The larger spoke length of E1 indicates a strong impact on G×E interaction and discriminating capacity. Genotypes near the origin (ACM BM-20-019, MDU 1, ACM BM-20-001, ACM BM-20-009, ACM BM-20-012, IEC 107, and IEC 109) had limited contact and hence greater adaptability (Kuraloviya et al., 2022 ). In contrast, genotypes located further from the origin, such as ACM BM-20-015, were more responsive to environmental changes, indicating particular adaptability rather than overall stability. This finding is consistent with AMMI interpretations reported in recent millet improvement experiments (Zhang et al. 2025 ) The analysis reveals that G×E interaction has a considerable impact on grain yield per plant. The AMMI model successfully found stable and high-yielding genotypes, particularly ACM BM-20-019 and MDU 1, which had superior mean performance while remaining stable across environments. These genotypes may be excellent prospects for widespread cultivation and future breeding operations aimed at increasing millet yields. Genotype plus Genotype-by-Environment interaction (GGE) GGE biplot analysis effectively dissects genotype × environment (G × E) interactions and identifies stable and precisely suited genotypes for grain yield. In the current investigation, the first three interaction main component axes (ICPA1, ICPA2, and ICPA3) of the GGE model explained 97.1% of the total variation, with ICPA1 accounting for 90.4%. This high proportion of explained variation suggests that the GGE biplot well captured the genotype-environment interaction pattern. whereas it was 72.05% in pearl millet (Solomon and Yohans 2021), 98.55% in groundnut (Kona 2024) and 100% in finger millet (Ishwarya 2025). The environmental perspective of the biplot indicated unique correlations between the three test conditions. The sharp angle between E2 and E3 indicated a positive correlation, whereas the almost right angle between E1 and E3 implied a lack of correlation. This pattern clearly segregated the test locations into two groups (Ishwarya 2025), with E2 and E3 constituting one cluster and E1 forming the other. E1's greater vector length suggested that it was the most discriminating environment, followed by E3 and E2. The principles of GGE biplot methodology state that an ideal test environment should be both discriminating and representative. In the current investigation, E2 had a smaller angle (Zi 2026) with the Average Environment Coordination (AEC) axis, making it more representative, whereas E1, while highly discriminating, was less representative. The genotype perspective helped to identify stable and high-yielding genotypes. ACM BM-20-019, MDU 1, and ACM BM-20-012 were found at the centre of the concentric circles and the AEC abscissa, indicating high average performance and stability across contexts. ACM BM-20-016, while significantly far from the innermost circle, likewise demonstrated good yield and reasonable stability. The presence of stable genotypes identified by both AMMI and GGE studies (MDU 1 and ACM BM-20-019) increases the dependability of these selections. Kuraloviya et al. ( 2022 ) reported similar findings, highlighting that genotype MDU 1 was stable across multiple environments. The what-won-where biplot provides additional information into specific adaptation. The polygon view divided the environments into two mega-environments: Mega-environment I (E1) and Mega-environment II (E2, 3). The vertex genotype for E1 was ACM BM-20-016, signifying greater performance and specialized adaption to the environment. In contrast, ACM BM-20-019, MDU 1, and ACM BM-20-012 were the successful genotypes in E2 and E3. The separation of two mega-environments indicates the occurrence of crossover interaction, in which distinct genotypes function better in different environments. Similarly findings has been reported in barley (Ghazvini et al. 2025 ) and Ataei and foxtail millet (Ataei 2020 ). The two mega-environments emphasizing the practical importance of mega-environment identification in varietal prescription. Conclusion In this study, twenty-seven genotypes were subjected to D 2 analysis taking twelve yield attributes into account. Eight different clusters were obtained. The traits grain yield/ plant contributed more to the divergence present, followed by plant height and days to maturity. Thus, each cluster has its own characteristics and can be helpful in selecting genotypes. Thus, the cluster II showed the presence of early maturing, high yielding genotypes which can be chosen for further improvement programme. Stability for grain yield covering three different environments was analysed using AMMI and GGE biplot analyses. Both revealed the stable genotypes, MDU1 (G27) and ACM BM-20-019 (G19) in three environments and high mean for grain yield. From what-won-where biplot, genotypes suitable for specific environments were identified. Thus, the genotype ACM BM-20-016 perform well in mega environment 1 (E1) and the genotypes ACM BM-20-019, MDU 1 and ACM BM-20-012 perform similarly in the mega environment 2 (E2 and E3). Declarations Author Contribution Sudhir Deepak M - wrote the main manuscript textVanniarajan C, Chandirakala R, Renuka R and Kanchana S - Supported in writing the manuscript Acknowledgement The authors would like to express his sincere gratitude to all individuals, colleagues and Agricultural College and Research Institute, Tamil Nadu Agricultural University, Madurai. who supported this work. We also appreciate the valuable feedback provided by reviewers for the constructive suggestions. References Abdelrahman M, Alharbi K, El-Denary ME, Abd El-Megeed T, Naeem ES, Monir S, Al-Shaye NA, Ammar MH, Attia K, Dora SA, Draz ASE (2022) Detection of superior rice genotypes and yield stability under different nitrogen levels using AMMI model and stability statistics. 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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-8899352","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595068305,"identity":"8c787523-96dd-4970-b0e6-90d1a33bfb83","order_by":0,"name":"Sudhir Deepak 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18:25:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":30455,"visible":true,"origin":"","legend":"\u003cp\u003eAMMI II biplot for grain yield\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8899352/v1/4a54b3cf26db4022d88b6ae4.jpg"},{"id":104261321,"identity":"e410a909-d87d-4a89-a8e9-9320f0e3c867","added_by":"auto","created_at":"2026-03-09 18:25:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43113,"visible":true,"origin":"","legend":"\u003cp\u003eGrain yield – GGE biplot: environment view\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8899352/v1/94d2b2aa474b1aa5d96aaa43.jpg"},{"id":104261319,"identity":"332dbeed-1b2d-432f-a6ed-adf97177a425","added_by":"auto","created_at":"2026-03-09 18:25:49","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":31640,"visible":true,"origin":"","legend":"\u003cp\u003eGrain yield – GGE biplot: environment view\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8899352/v1/08b330d2fdc4bfcc5be5334c.jpg"},{"id":104261322,"identity":"33ae0b34-af41-4ec7-b50c-1bae8dee3762","added_by":"auto","created_at":"2026-03-09 18:25:49","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":30189,"visible":true,"origin":"","legend":"\u003cp\u003eGrain yield – GGE biplot: genotype view\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8899352/v1/62c5525246da4fb5a2ed66df.jpg"},{"id":104261324,"identity":"19a910c6-4b76-4f41-9fe4-d7a44b2042c0","added_by":"auto","created_at":"2026-03-09 18:25:49","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":34575,"visible":true,"origin":"","legend":"\u003cp\u003eWhat- won- where biplot for grain yield\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8899352/v1/746e31ee4ef6c4435f87e887.jpg"},{"id":104405289,"identity":"74a68d8c-e024-4b69-8e13-049d388fe9e0","added_by":"auto","created_at":"2026-03-11 12:22:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1555513,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8899352/v1/46538b46-07b2-461c-90a7-cd94801757b7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eInvestigation of Phenotypic Diversity and Genotype X Environment Interaction in Early Barnyard Millet Genotypes for Grain Yield Using Ammi Analysis and Gge Biplot\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBarnyard millet [\u003cem\u003eEchinochloa frumentaceae\u003c/em\u003e (L.)] is a small millet widely grown in the areas of arid and semi-arid tropical regions of Asia and Africa (Bangar 2024). It can withstand water deficit and can be replaced in the place of water loving crops. It is an underutilized, nutritious crop with high micronutrients like Fe and Zn (Vijayprabha 2024) with range of 2.29 to 18 mg/ 100 g for Fe and 1.5 to 7.5 mg/ 100g for Zn (Renganathan 2017). It is the fourth most produced minor millet, ensuring food security for many impoverished people worldwide. India is the world\u0026rsquo;s largest producer of barnyard millet with an area pf 0.146 M ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and production (0.147 M t) with average productivity of 1034 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Sakkarai 2024). Its grains can be consumed just like rice. It is grown as rainfed crop in the following districts of Tamil Nadu such as Theni, Ramanathapuram, Madurai, Dindigul, Erode, Namakkal and Salem. It is used for both fodder and grain purposes (Nirmalakumari 2009). Though it has high nutrition and good agronomic performance, the crop still remains unexplored.\u003c/p\u003e \u003cp\u003eAt present, two cultivars MDU 1 and Co (Kv) 2 are the ruling varieties of barnyard millet in Tamil Nadu with the duration of 95\u0026ndash;105 days (Vanniarajan 2018). Evolving genotypes with hastened maturity could help the farmers as a climate change mitigation strategy which is essential in the current scenario. Assessing the genetic diversity among the genotypes may help in grouping them, based on their differences in traits. Clustering them into different groups can help to discover the character responsible for variation and the variation among the genotypes (Supriya 2024; Singh et al. 2024). This will be useful for selecting the genotypes based on our objective in future breeding programme.\u003c/p\u003e \u003cp\u003eCurrently, barnyard millet is grown as Rabi crop. The early genotypes can be cultivated in all the seasons across environments, which could also find a place in the existing mono-cropping pattern (Monika 2021). With reference to the above statement, evaluating the performance of the genotypes across different environments using the stability analysis is needed, to find out the suitable genotype for specific environment and also across different environments. The performance of different genotypes in different environment is based on the interaction between environment and the genotype. Since, grain yield and its contributing traits are quantitative in nature, the influence of environment determines the expression of the phenotype. G x E interaction across several environments is analysed using AMMI model, which is a powerful tool which uses the ICPA (Interaction Principal Component Analysis). GGE biplot method is also used to interpret the complex G x E interaction (Yan and Hunt \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). This study aims at studying the stability of the genotypes across three different environments for grain yield and its contributing traits, which would be helpful in further crop improvement programme.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThe experiment was conducted using 27 barnyard millet genotypes, which includes check MDU 1. The list of the genotypes is mentioned in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The experiment was carried out in Randomized Block Design with three replications. The crop was raised in rows with length of 7 m and the spacing adopted was 30 cm x10 cm. The crop management was done as per the package of practices developed by Tamil Nadu Agricultural University. Biometrical observations were taken down for seven genotypes were tested for yield contributing traits such as days to first flowering, days to 50% flowering, days to maturity, plant height, number of basal tillers, flag leaf length, flag leaf width, panicle length, single ear head weight, thousand grain weight, fodder yield per plant and grain yield per plant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of genotypes used in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParentage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eParentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACM BM-20-001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMDU 1\u0026lowast; IEC 108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eACM BM-20-015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCo (Kv) \u0026lowast; IEC 385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACM BM-20-002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMDU 1 \u0026lowast; IEC 108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eACM BM-20-016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMDU 1 \u0026lowast; IEC 109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACM BM-20-003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMDU 1 \u0026lowast; IEC 108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eACM BM-20-017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMDU 1 \u0026lowast; IEC 109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACM BM-20-004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMDU 1 \u0026lowast; IEC 108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eACM BM-20-018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMDU 1 \u0026lowast; IEC 107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACM BM-20-005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMDU 1 \u0026lowast; IEC 108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eACM BM-20-019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMDU 1 \u0026lowast; IEC 107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACM BM-20-006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMDU 1 \u0026lowast; IEC 108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eACM BM-20-020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMDU 1 \u0026lowast; IEC 107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACM BM-20-007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCo (Kv) \u0026lowast; IEC 385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIEC 108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACM BM-20-008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCo (Kv) \u0026lowast; IEC 385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIEC 109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACM BM-20-009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCo (Kv) \u0026lowast; IEC 385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIEC 107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACM BM-20-010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCo (Kv) \u0026lowast; IEC 385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIEC 350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACM BM-20-011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCo (Kv) \u0026lowast; IEC 385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIEC 356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACM BM-20-012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCo (Kv) \u0026lowast; IEC 385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIEC 385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACM BM-20-013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCo (Kv) \u0026lowast; IEC 385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMDU 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACM BM-20-014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCo (Kv) \u0026lowast; IEC 385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor, stability analysis, the experiment was carried out in randomized block design with three replications in three different environments for two years (2020 and 2021) as given in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eAgro-ecological details of the experimental environments\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=\"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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"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\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAltitude (MSL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSoil type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAC \u0026amp;RI, Madurai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u0026deg;96'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.21'E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e166.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAlfisol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.6\u0026deg; C\u0026thinsp;\u0026minus;\u0026thinsp;30.4\u0026deg; C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e950 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThirumangalam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u0026deg;91' N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77\u0026deg;98'E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e164.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVertisol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27\u0026deg; C\u0026thinsp;\u0026minus;\u0026thinsp;39\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e857.6 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAruppukottai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u0026deg;30' N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78\u0026deg;5'E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e123.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAlfisol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21.1\u0026deg; C\u0026thinsp;\u0026minus;\u0026thinsp;30.4\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e830 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis:\u003c/h2\u003e \u003cp\u003eStatistical analyses include the combined ANOVA for testing the significance of the different sources of variation, D\u003csup\u003e2\u003c/sup\u003e analysis and the stability analysis, by AMMI and GGE biplots. Analysis of Variance (ANOVA) partitions the total variation and gives the contribution of the genotype, environment and genotype x environment to the total variance present. Mahalinobis (Pc \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1936\u003c/span\u003e) D\u003csup\u003e2\u003c/sup\u003e Analysis was done and clustering was carried out as suggested by Rao (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1952\u003c/span\u003e) using Tocher method. AMMI (Additive Main effect and Multiplicative Interaction) model separates the GE deviations into different ICPAs (Interaction Component Principal Axis). It takes into account both the ANOVA and the PCA. ANOVA gives the additive main effects and the PCA is the multiplicative part of the model, which explains about the interaction effects. The GGE biplot or the what-won -where biplots uses the genotype and the GE interaction to suggest the suitability of the genotypes across all the environments and also to particular environment.\u003c/p\u003e \u003cp\u003eThe AMMI analysis uses the formula:\u003c/p\u003e \u003cp\u003eY\u003csub\u003eij\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026micro;\u0026thinsp;+\u0026thinsp;g\u003csub\u003ei\u003c/sub\u003e + e\u003csub\u003ej\u003c/sub\u003e + Ʃ λ\u003csub\u003ek\u003c/sub\u003eY\u003csub\u003eik\u003c/sub\u003eα\u003csub\u003ejk\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ε\u003csub\u003eij\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eMeanwhile, the GGE model considers by the following formula:\u003c/p\u003e \u003cp\u003eY\u003csub\u003eij\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026micro;\u0026thinsp;+\u0026thinsp;g\u003csub\u003ei\u003c/sub\u003e + e\u003csub\u003ej\u003c/sub\u003e + Ʃ λ\u003csub\u003ek\u003c/sub\u003eY\u003csub\u003eik\u003c/sub\u003eα\u003csub\u003ejk\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ε\u003csub\u003eij\u003c/sub\u003e\u003c/p\u003e \u003cp\u003ewhere, Y\u003csub\u003eij\u003c/sub\u003e - mean of the trait of i\u003csup\u003eth\u003c/sup\u003e genotype in the j\u003csup\u003eth\u003c/sup\u003e environment;\u003c/p\u003e \u003cp\u003e\u0026micro; \u0026ndash; grand mean; g\u003csub\u003ei\u003c/sub\u003e \u0026ndash; genotype effect; e\u003csub\u003ej\u003c/sub\u003e - environment effect\u003c/p\u003e \u003cp\u003eλ\u003csub\u003ek\u003c/sub\u003e - eigenvalue of the PCA axis k; Y\u003csub\u003eik\u003c/sub\u003e \u0026ndash; E-PCs scores for axis k;\u003c/p\u003e \u003cp\u003eα\u003csub\u003ejk\u003c/sub\u003e - G-PCs scores for axis k; ε\u003csub\u003eij\u003c/sub\u003e \u0026ndash; error component (Abdelrahman et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClassification of genotypes based on IPCA I scores\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eClass I\u003c/b\u003e: Genotypes with high mean and positive IPCA I, \u003cb\u003eClass II\u003c/b\u003e: Genotypes with high mean and negative IPCA I, \u003cb\u003eClass III\u003c/b\u003e: Genotypes with low mean and positive IPCA I, \u003cb\u003eClass IV\u003c/b\u003e: Genotypes with low mean and negative IPCA I\u003c/p\u003e \u003cp\u003eThe combined ANOVA analysis was performed using software TNAU STAT. The D\u003csup\u003e2\u003c/sup\u003e analysis as well as AMMI and GGE biplot analysis were conducted using PB Tools (PB Tools, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kuraloviya 2022)\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAnalysis of variance showed that the grain yield showed significance for the genotype, environment and the genotype x environment interactions Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. This indicates that the genotypes used in study are present with the significant amount of variation and they have the influence of the environment for their expression. But, the percentage contribution of the genotype to variation was high which indicates that, there is considerably low influence of environment on the trait.\u003c/p\u003e\n\u003ch3\u003eD analysis\u003c/h3\u003e\n\u003cp\u003eThe D\u003csup\u003e2\u003c/sup\u003e analysis classifies the given genotypes into different groups based on the extent of variation present among them. The 27 genotypes studied were grouped into eight different clusters \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Cluster I is the largest one with 14 genotypes followed by cluster II with five genotypes, cluster III with three genotypes. Clusters IV, V, VI, VII and VIII has solitary genotypes indicating that these genotypes are highly distinct from others.\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\u003eANOVA for grain yield\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003esource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003esum of Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emean sum of square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnvironment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e328.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e164.21*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReplication\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGenotype\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24923.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e968.60*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e125.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGenotype x Environment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2673.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.41*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePooled error\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1196.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2914.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\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\u003eDistribution of the genotypes into different clusters based on the different quantitative traits.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClusters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACM BM -20-010, ACM BM -20-020, ACM BM -20-008, ACM BM -20-004, ACM BM -20-005, ACM BM -20-007, ACM BM -20-006, ACM BM -20-001, ACM BM -20-018, ACM BM -20-014, ACM BM -20-003, ACM BM -20-015, ACM BM -20-009, ACM BM -20-012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACM BM -20-011, ACM BM -20-013, ACM BM -20-002, ACM BM -20-016, ACM BM -20-019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIEC 109, IEC 107, IEC 108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACM BM -20-017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIEC 385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIEC 356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMDU 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIEC 350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCluster I showed moderate mean for days to first flowering (54.07), days to 50% flowering (58.69), days to maturity (85.40), panicle length (131.63cm), 1000 grain weight (2.45 g) and grain yield per plant (29.46g), low for number of basal tillers (41.9), high for flag leaf length (29.49) and flag leaf breadth (3.37), single ear head weight (8.67g) and fodder yield per plant (42.75g). It has the genotypes with reduced duration than the check MDU1 but low yielding than it. Cluster II also showed moderate mean like cluster I for traits except plant height (140.93 cm), single ear head weight (9.31g), fodder yield per plant (56.38g) and grain yield per plant (35.24g) comparatively higher than the genotypes present in the cluster I. Cluster III has the IEC 109, IEC 107, IEC 108 genotypes which are early maturing with low mean for days to first flowering (33.44), 50% flowering (37.77) and maturity (65.11) and low mean for yield attributes like plant height (88.33 cm), single ear head weight (0.82g), fodder yield (4.54g), grain yield (3.33g) and high mean for number of tillers (8.44). Cluster IV has one genotype (ACM BM -20-017) which is late maturing with high mean for days to first flowering (61.33), days to 50% flowering (65.33) and days to maturity (94.33) and for plant height (151.34 cm), flag leaf length (32.13 cm) and breadth (3.89 cm) which is nearly similar to check MDU 1 except for grain yield which has low mean (34.11g) than MDU1. Cluster V (IEC 385) has one genotype with low days to first flowering (39.33), low grain yield (17.86) with high number of basal tillers (9.33) and low days to maturity (70.33) followed by cluster VI (IEC 356) comprises low in days to first flowering (40.33), low in days to maturing (71.33), grain yield per plant (19.13) and high in plant height (141.26) whereas Check MDU 1 has plant height of 147.43 cm. Cluster VII has the check MDU1 which is higher mean for maturity traits like days to first flowering (62.00), 50% flowering (67.00) and maturity (95.33) and yield traits including plant height (147.43 cm), number of tillers (6.00), panicle length (22.33 cm), single ear height (8.16g), 1000 grain weight (3.23g), fodder yield (59.68) and grain yield (42.63g) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean values for different yield attributes of different clusters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDFF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDFTF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNBT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFLL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFLB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSEW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTGW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eFYD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eGYP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e131.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e42.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e29.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e140.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e56.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e35.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e151.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e54.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e34.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e23.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e17.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e141.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e21.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e30.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e19.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e147.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e22.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e59.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e42.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e108.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e28.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e19.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDFF\u003c/b\u003e- Days to first flowering; \u003cb\u003eDFTF\u003c/b\u003e- Days to 50% flowering; \u003cb\u003eDM\u003c/b\u003e- Days to maturity; \u003cb\u003ePH\u003c/b\u003e- Plant height; \u003cb\u003eNBT\u003c/b\u003e- Number of basal tillers; \u003cb\u003eFLL\u003c/b\u003e- Flag leaf length; \u003cb\u003eFLB\u003c/b\u003e- Flag leaf breadth; \u003cb\u003ePL\u003c/b\u003e- Panicle length; \u003cb\u003eSEW\u003c/b\u003e- Single ear head weight; \u003cb\u003eTGW\u003c/b\u003e- Thousand grain weight; \u003cb\u003eFYD\u003c/b\u003e- Fodder yield per plant; \u003cb\u003eGYP\u003c/b\u003e- Grain yield per plant\u003c/p\u003e\n\u003ch3\u003eIntra and inter cluster distance\u003c/h3\u003e\n\u003cp\u003eThe intra and inter cluster distance among eight clusters were computed and presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The intra cluster distance ranged from 0.00 to 279.19. The cluster IV, V, VI, VII and VIII showed minimum intra cluster distance (0.00). Each contain single genotype and maximum intra cluster was exhibited by cluster I (279.19) followed by cluster II(235.25).\u003c/p\u003e \u003cp\u003eThe inter cluster distance varied from 350.63 and 5894.83 respectively. Maximum inter cluster distance was observed between clusters IV and V (5894.83). This was followed by clusters III and IV (5096.41). Whereas the lowest inter cluster distance was recorded between cluster II and IV (350.63).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage intra and inter cluster distance\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=\"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 \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\u003eCluster Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eVIII\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e279.195\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e884.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2561.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1120.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2743.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1043.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e721.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1331.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e235.258\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3614.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e350.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4575.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e936.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1930.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1943.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIII\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e95.5448\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5096.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e525.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1784.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4483.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e565.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIV\u003c/b\u003e\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 \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5894.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1789.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1603.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3187.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eV\u003c/b\u003e\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 \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3099.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4120.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1243.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVI\u003c/b\u003e\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 \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2506.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e621.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVII\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3055.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVIII\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\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 \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eContribution of different characters to divergence\u003c/h2\u003e \u003cp\u003eThe contribution of characters to total genetic divergence is important in deciding which characters to select. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e show the estimated contribution of individual character to the expression of genetic diversity based on character wise D\u003csup\u003e2\u003c/sup\u003e values. Grain yield per plant (49.28 per cent) and plant height (31.33 per cent) were the top two major contributors towards the total genetic divergence among the genotypes.\u003c/p\u003e \u003cp\u003eThe contribution of other characters towards the expression of genetic diversity in percentage is presented in descending order \u003cem\u003eviz;\u003c/em\u003e days to maturity (7.40 per cent), single earhead weight (2.27 per cent), days to first flowering (1.99 per cent), flag leaf breadth (1.99 per cent), days to 50% flowering (1.70 per cent), thousand grain weight (1.42 per cent), flag leaf length (1.13 per cent), panicle length (0.85 per cent), fodder yield per plant (0.56 per cent) and number of basal tillers (0.00 per cent).This made it abundantly clear that when genotypes are chosen from genetically diverged groups, the genotypes are classified according to the best divergence donating traits like grain yield per plant, plant height and days to maturity because these traits are associated with greater contributions to divergence.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContribution of different characters to divergence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of times first ranks\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% Contribution to divergence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFTF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNBT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFYD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGYP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDFF\u003c/b\u003e- Days to first flowering; \u003cb\u003eDFTF\u003c/b\u003e- Days to 50% flowering; \u003cb\u003eDM\u003c/b\u003e- Days to maturity; \u003cb\u003ePH\u003c/b\u003e- Plant height; \u003cb\u003eNBT\u003c/b\u003e- Number of basal tillers; \u003cb\u003eFLL\u003c/b\u003e- Flag leaf length; \u003cb\u003eFLB\u003c/b\u003e- Flag leaf breadth; \u003cb\u003ePL\u003c/b\u003e- Panicle length; \u003cb\u003eSEW\u003c/b\u003e- Single ear head weight; \u003cb\u003eTGW\u003c/b\u003e- Thousand grain weight; \u003cb\u003eFYD\u003c/b\u003e- Fodder yield per plant; \u003cb\u003eGYP\u003c/b\u003e- Grain yield per plant\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStability analysis\u003c/h3\u003e\n\u003cp\u003eThe genotypes along with check MDU1 were tested for the stable performance for grain yield across three different environments for two years. The mean values of the genotypes for grain yield over the test environments were given in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The mean values of the tested genotypes ranged from 3.28 g to 42.14 g. Among all the genotypes, the genotype, ACM BM-20-019 (G19) was identified with the highest grain yield mean (42.14g) than the check MDU 1 (G27) which was 41.54g. Out of three environments, the environment E1 showed the highest mean for the grain yield (27.37g) followed by E3 (27.10g) and E1 (24.78g).\u003c/p\u003e\n\u003ch3\u003eAdditive Main Effects and Multiplicative Interaction (AMMI) analysis\u003c/h3\u003e\n\u003cp\u003eAMMI analysis partitions the existing G x E interactions into two significant PCAs. PC1 and PC2 contributed 69.2% and 30.8% of total GE interaction variation respectively. The ICPA scores obtained in the AMMI analysis displays the adaptability of the genotypes over environments and also their interaction with environments.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe mean, ICPA1 and ICPA2 values for the twenty-seven genotypes\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype\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\u003eIPCA 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIPCA 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIPCA 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIPCA 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eG15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eG16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eG17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eG18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eG19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eG20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eG21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eG22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eG23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eG24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eG25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eG26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eG27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean yield of different environments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Grain yield (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIPCA I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIPCA II\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAMMI 1 biplot has two axes, mean yield on x axis, indicating the main effects and the interaction effect (ICPA 1) on Y axis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The axis contributed from 69.2% to 30.8%. The mean values of genotypes, IPCA I and IPCA II was offered in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The mean values of genotypes ranged from 3.28 to 42.14. The mean values of IPCA I and IPCA II was from \u0026minus;\u0026thinsp;2.97 to 2.09 and from \u0026minus;\u0026thinsp;1.75 to 1.572 respectively. Amongst the genotypes; the ACM BM-20-019 (42.14) expressed the highest in grain yield per plant followed by\u003c/p\u003e \u003cp\u003eMDU 1(41.54) showed the highest mean value with positive IPCA values around zero. The mean for individual environment accounted for E1 (27.37), E2 (24.78) and E3 (27.10) respectively.\u003c/p\u003e \u003cp\u003eRegarding AMMI biplot 1\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e the ACM BM-20-001, ACM BM-20-002, ACM BM-20-004, ACM BM-20-006, ACM BM-20-009, ACM BM-20-008, ACM BM-20-010, ACM BM-20-013,ACM BM-20-018, ACM BM-20-019, MDU 1 had higher mean value with positive IPCA I scores were categorized under class 1. The genotypes, ACM BM-20-003, ACM BM-20-005, ACM BM-20-007, ACM BM-20-011, ACM BM-20-012, ACM BM-20-014, ACM BM-20-015, ACM BM-20-016, ACM BM-20-017, ACM BM-20-020 had high mean and negative IPCA I scores and fallen under class 2. Genotypes like, IEC 350, IEC 356 and IEC 385 had low mean with positive IPCA I scores fallen under class 3. Genotypes like IEC 107, IEC 108 and IEC 109 had low mean with negative IPCA I scores fallen under class 4. Over the environmental conditions, E1 had highest mean value of 27.37 with negative IPCA II values (-1.64) followed by E3 (mean\u0026thinsp;=\u0026thinsp;27.10) had negative IPCA 1 values of -1.45. The environment E2 (mean\u0026thinsp;=\u0026thinsp;24.78) alone had positive IPCA I and II values (1.44 \u0026amp; 2.12) respectively \u003cb\u003e(Table. 9).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWith regard to AMMI biplot 2 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e the environment E I had the longest spoke. The genotypes ACM BM-20-001, ACM BM-20-009, ACM BM-20-012, ACM BM-20-019, IEC 109, IEC 107 and MDU1 was nearer to origin indicated that minimal interaction of genotypes with environment. The remaining genotypes away from the origin indicated that they are more reactive to environment. Interaction of genotypes with specific environment are adjudged by the pointing of genotypes on the spoke. On this criteria, ACM BM-20-002 had interaction at E3, genotypes ACM BM-20-013, IEC 350, IEC 385, IEC 356 will be suitable to E2 and the genotype ACM BM-20-005 will be suitable to E1. The genotypes ACM BM-20-015 were away from the origin and indicate that the genotypes were more sensitive to environmental impacts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenotype plus Genotype-by-Environment (GGE) Biplot analysis\u003c/h2\u003e \u003cp\u003eGGE Biplot gives comprehensive information about the environments, which are not available in AMMI analysis and the suitability of genotypes to the specific environment. It divides G \u0026times; E interactions into three IPCAs. These components accounted for 97.1% of the total variation\u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. IPCA1, IPCA2, and IPCA3 accounted for 90.4%, 6.7%, and 2.9% of the variation in the GGE model, respectively. The following information are obtained from GGE biplot \u0026ndash; environment view \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e The angle between the vectors indicating the environments confer the correlation between the environments. The environments, E2 and E3 are separated at an acute angle, less than that between E1 and E2. The angle between E1 and E3 is nearly 90\u003csup\u003e\u0026ordm;\u003c/sup\u003e, which indicates that they are not correlated. The length of the vector also helps to discriminate the environments. E1 is the most discriminating followed E3 and E2. Vector distance between the environments shows that E2 and E3 are in one group and E1 in another one. Thus, for future evaluations, either E2 or E3 may be chosen to represent that group and another environment chosen should be E1. From Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the GGE biplot environment view, the representative environments are chosen with respect to the Average Environment Co-ordination axis (AEC). The environment E2 is more representative because, it has less angle with respect to AEA. E1 and E3 are less representative due to wider angle separation between themselves. From Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the genotypes ACM BM-20-019, MDU1 and ACM BM-20-012 were found to be stable with higher yield, because they lie within the very first concentric circle. The genotype ACM BM-20-016 was also stable and high yield but on the later concentric circles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariation for grain yield per plant due to G x E partitioned with AMMI model and GGE model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDegrees of freedom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean sum of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% Variation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAMMI MODEL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICPA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1232.9544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.66498 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICPA 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e549.2615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.97046**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICPA 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00000 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGGE BIPLOT MODEL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICPA 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16633.1057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e616.04095**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICPA 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1232.7318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.30927**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICPA 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e532.1862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.13853**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.9\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=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e** - p \u0026lt;\u0026thinsp;.0.01 \u0026ndash; probability level\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eWhat \u0026ndash; won- where biplot analysis\u003c/h2\u003e \u003cp\u003eThe what-won-where biplot \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e generated by the GGE analysis clearly demonstrated genotype adaption patterns throughout the three test environments (E1, E2, and E3). The biplot's polygon view splits the graph into distinct sectors by creating perpendicular lines (equality lines) from the origin to each polygon's sides. The genotypes at the polygon's vertices were determined as the winning genotypes for the environments in their respective sectors. The biplot showed the existence of two independently mega-environments, Mega-environment I (Consists entirely of E1) and Mega-environment II (E2 and E3). This grouping reveals that E2 and E3 had similar genotype responses and were positively associated, whereas E1 responded differently and created an isolated testing zone. The partitioning of environments into two mega-environments shows variable genotype performance across locations, highlighting the presence of considerable genotype \u0026times; environment interaction. In mega environment 1, the genotype ACM BM-20-016 was located near the vertex of the polygon encompassing E1, indicating that it was the most productive genotype in this environment. As a result, ACM BM-20-016 can be deemed specifically adapted to E1. In mega environment 2, The environments E2 and E3 were in the sector described by the vertex genotypes: ACM BM-20-019; MDU 1; ACM BM-20-012. These genotypes outperformed in both E2 and E3, indicating that they are more suitable and adaptable to this mega-environment 2. Their identical positioning suggests that yield performance is equivalent between the two environments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eD\u003csup\u003e2\u003c/sup\u003e analysis for quantitative traits\u003c/h2\u003e \u003cp\u003eIn the present study, twelve biometrical traits were studied in 27 genotypes and subjected to D\u003csup\u003e2\u003c/sup\u003e analysis. Based on the D\u003csup\u003e2\u003c/sup\u003e analysis, all the genotypes were grouped into eight different clusters. Formation of large number of clusters indicated more diversity among the genotypes. The clustering pattern in the present study revealed that the genotypes from different sources clustered together showed that there was little association between eco-geographical distribution of genotypes and genetic diversity. The different clusters indicated that, in general, selections have been towards the same goal in the different centres of origin of those genotypes and yet there is sufficient genetic variability distinctly differentiates them. Hence, the chosen genotypes used in the present study could be considered as a valid material. Genetic drift and selection in different environments may cause greater diversity than geographical diversity.\u003c/p\u003e \u003cp\u003eThe D\u003csup\u003e2\u003c/sup\u003e analysis classifies the given genotypes into different groups based on the extent of variation present among them. The 27 genotypes studied were grouped into eight different clusters. Similar results were obtained by Kuraloviya et al., (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in barnyard millet followed by Sharma et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) in pearl millet. Cluster I is the largest one with 14 genotypes. Similar results has been widely studied (Nireekshitha 2024). Followed by cluster II with five genotypes, cluster III with three genotypes. Clusters IV (Ray et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), V, VI, VII and VIII with one each indicating that these genotypes are highly distinct from others. Cluster VI and VII are in accordance with Sharma et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) in pearl millet and (Sathyaraj 2024) in finger millet. Similar results for cluster I with maximum genotypes were reported in barnyard millet (Prabu 2020; Kuraloviya 2022). Genotypes belonging to a common cluster were the least different. It is unlikely that a cross between genotypes from the same cluster will result in transgressive segregants. The parents of the various clusters with extreme divergence may therefore be used to construct a suitable transgressive segregant. This genotype grouping pattern revealed no correlation between genetic divergence and genotype distribution by geography. Similar results has been reported in pearl millet (Kavita 2024) showed that genotypes from various eco-geographical regions were randomly distributed into clusters, suggesting that geographic dispersion does not always show genetic divergence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eIntra and inter cluster distance\u003c/h2\u003e \u003cp\u003eThe primary goal of cluster creation is to measure inter- and intra-cluster distances, which serves as an index for choosing genetically diverse parents for a hybridization programme. According to Sharma et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), the cluster with the most genotypes (Cluster I) had a large intra cluster distance implying that genotypes within these clusters are monomorphic for the attributes analysed. The heterogeneity of genotypes within a cluster accounts for the intra-cluster distance. The low intra-cluster distance indicated that the genotypes in the clusters were tightly connected (Sharma 2020). on the other hand, compared to other clusters on the diagonal, Cluster I (279.195) and Cluster II (235.258) have comparatively larger intra-cluster distances, suggesting substantial genetic heterogeneity within these clusters. Similar findings on cluster II were reported in (Kanwer 2025) finger millet. This implies that the genotypes in Cluster I are not uniform and include valuable variation that may be utilized for parent selection or within-cluster selection to achieve even greater improvement.\u003c/p\u003e \u003cp\u003eThe maximum inter cluster distance was noted between cluster V and cluster VI followed by cluster III and IV. These cluster pairs are ideal candidates for hybridization to optimize genetic recombination and heterosis because of their high D\u003csup\u003e2\u003c/sup\u003e values, which show significant genetic difference between them. Conversely, cluster II and IV recorded minimum inter cluster distance implying closer genetic similarities in these groups. Hence large inter cluster distance groups are desirable for parent selection in breeding programmes. This findings are in accordance with Patel et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in finger millet.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMean performance\u003c/h2\u003e \u003cp\u003eCluster I showed moderate mean for days to first flowering, days to 50% flowering, days to maturity, panicle length, 1000 grain weight and grain yield per plant followed by low for number of basal tillers, high for flag leaf length and breadth, single ear head weight and fodder yield per plant. These results are on par with in finger millets(Kanwer 2025). Cluster I has the genotypes with reduced duration than the check MDU1 but low yielding than check. Cluster II also showed moderate mean like cluster I for traits except plant height, single ear head weight, fodder yield and for grain yield it is comparatively higher than the genotypes present in the cluster I and possessed moderate yield compared to check MDU 1. Hence selection of plants in cluster II favours low days to maturity compared to check MDU 1 with moderate grain yield per plant.\u003c/p\u003e \u003cp\u003eCluster III has IEC 109, IEC 107, IEC 108 genotypes which are early maturing with low mean for days to first flowering, 50% flowering and maturity and low mean for yield attributes like plant height, single ear head weight, fodder yield, grain yield and high mean for number of tillers. Cluster IV has one genotype which is late maturing with high mean for days to first flowering, days to 50% flowering and days to maturity and for plant height, flag leaf length and breadth which is nearly similar to check MDU 1 except for grain yield which has low mean than MDU1. Cluster VII has the check MDU1 which is higher mean for maturity traits like days to first flowering, 50% flowering and maturity and yield traits including plant height, number of tillers, panicle length, single ear height, 1000 grain weight, fodder yield and grain yield. From these results, it indicates that the late maturing ones are high yielding (Kuraloviya 2022). In hybridization programmes that try to improve numerous features at once, this knowledge is essential for choosing parents. Similar findings has been reported (Pali 2022) in finger millet.\u003c/p\u003e \u003cp\u003e. Individual attributes received a rank according to how much they contributed to the overall genetic difference. Among the traits studied, the trait grain yield per plant contributed the highest to divergence (49.28%) with the highest rank. Similar findings were reported (Ray 2025) in finger millet and (Kuraloviya (2022); Prabu et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Dhanalakshmi et al., (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) barnyard millet; The next highest contribution was by plant height (31.33%). The lowest contribution was from number of basal tillers (0%) (Patel 2023), fodder yield per plant (0.5%), panicle length (0.85%), days to first flowering (1.99%), flag leaf length (1.99%), days to 50% flowering (1.70%), 1000 grain weight (1.42%), flag leaf breadth (1.13%). Likely results were reported for panicle length (Suthediya 2021) in kodo millet. Other traits like days to maturity contributed moderately (7.40%) followed by single ear head weight (2.27%). This made it abundantly clear that when genotypes are chosen from genetically diverged groups, the genotypes are classified according to the best divergence donating traits like grain yield per plant, plant height and days to maturity because these traits are associated with greater contributions to divergence. From these findings, it indicates that the late maturing ones are high yielding.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAdditive Main Effects and Multiplicative Interaction (AMMI)\u003c/h2\u003e \u003cp\u003eThe AMMI analysis revealed that the first two interaction principal component axes (IPCA I and IPCA II) accounted for 69.2% and 30.8% of the overall G\u0026times;E interaction, respectively. The higher proportion explained by IPCA I implies that the majority of the interaction was captured by the first multiplicative component, which is consistent with findings in proso millet crops where the first IPCA often accounts for the majority of interaction variance (Zhang 2025). This shows that contextual variations had a significant impact on genotype performance, and AMMI effectively partitioned the interaction effects. The wide range in \u003cem\u003eper se\u003c/em\u003e grain yield per plant demonstrates significant genetic diversity between genotypes. The higher performance of ACM BM-20-019, followed by MDU 1 along with IPCA scores near zero, indicating both were in terms of high productivity and stability across environments. Genotypes with IPCA scores close to zero are considered widely adapted due to minimal interaction with environmental fluctuations. In finger millet, where stable genotypes were identified based on lower IPCA values (Patel 2023).\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eper se\u003c/em\u003e of environments indicated that E1 and E3 were more favourable environments than E2 (Ishwarya 2025). The negative IPCA values for E1 and E3, as well as the positive IPCA I and II scores for E2, indicate that E2 constituted a unique environment with diverse genotype responses. Such contrasting environmental interactions have previously been demonstrated in fox tail millet multi-environment trials, where some test locations had a better discriminating capacity and interaction effects (Rao et al. 2025).\u003c/p\u003e \u003cp\u003eAMMI biplot I categorized genotypes into four groups based on their mean performance and IPCA I scores. Genotypes in class I (high mean and positive IPCA I) and class II (high mean and negative IPCA I) had high productivity but differed in their interaction patterns. This classification denotes the presence of both broadly and specifically adapted high-yielding genotypes. Similar genotype clustering into adaptation groups was seen in finger millet studies using AMMI analysis (Patel et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In contrast, genotypes with low mean performance (classes III and IV) demonstrated weak adaptability and limited breeding value for yield development.\u003c/p\u003e \u003cp\u003eAMMI biplot II explained genotype stability and specific adaptation. The larger spoke length of E1 indicates a strong impact on G\u0026times;E interaction and discriminating capacity. Genotypes near the origin (ACM BM-20-019, MDU 1, ACM BM-20-001, ACM BM-20-009, ACM BM-20-012, IEC 107, and IEC 109) had limited contact and hence greater adaptability (Kuraloviya et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, genotypes located further from the origin, such as ACM BM-20-015, were more responsive to environmental changes, indicating particular adaptability rather than overall stability. This finding is consistent with AMMI interpretations reported in recent millet improvement experiments (Zhang et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe analysis reveals that G\u0026times;E interaction has a considerable impact on grain yield per plant. The AMMI model successfully found stable and high-yielding genotypes, particularly ACM BM-20-019 and MDU 1, which had superior mean performance while remaining stable across environments. These genotypes may be excellent prospects for widespread cultivation and future breeding operations aimed at increasing millet yields.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eGenotype plus Genotype-by-Environment interaction (GGE)\u003c/h2\u003e \u003cp\u003eGGE biplot analysis effectively dissects genotype \u0026times; environment (G \u0026times; E) interactions and identifies stable and precisely suited genotypes for grain yield. In the current investigation, the first three interaction main component axes (ICPA1, ICPA2, and ICPA3) of the GGE model explained 97.1% of the total variation, with ICPA1 accounting for 90.4%. This high proportion of explained variation suggests that the GGE biplot well captured the genotype-environment interaction pattern. whereas it was 72.05% in pearl millet (Solomon and Yohans 2021), 98.55% in groundnut (Kona 2024) and 100% in finger millet (Ishwarya 2025).\u003c/p\u003e \u003cp\u003eThe environmental perspective of the biplot indicated unique correlations between the three test conditions. The sharp angle between E2 and E3 indicated a positive correlation, whereas the almost right angle between E1 and E3 implied a lack of correlation. This pattern clearly segregated the test locations into two groups (Ishwarya 2025), with E2 and E3 constituting one cluster and E1 forming the other. E1's greater vector length suggested that it was the most discriminating environment, followed by E3 and E2. The principles of GGE biplot methodology state that an ideal test environment should be both discriminating and representative. In the current investigation, E2 had a smaller angle (Zi 2026) with the Average Environment Coordination (AEC) axis, making it more representative, whereas E1, while highly discriminating, was less representative. The genotype perspective helped to identify stable and high-yielding genotypes. ACM BM-20-019, MDU 1, and ACM BM-20-012 were found at the centre of the concentric circles and the AEC abscissa, indicating high average performance and stability across contexts. ACM BM-20-016, while significantly far from the innermost circle, likewise demonstrated good yield and reasonable stability. The presence of stable genotypes identified by both AMMI and GGE studies (MDU 1 and ACM BM-20-019) increases the dependability of these selections. Kuraloviya et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported similar findings, highlighting that genotype MDU 1 was stable across multiple environments.\u003c/p\u003e \u003cp\u003eThe what-won-where biplot provides additional information into specific adaptation. The polygon view divided the environments into two mega-environments: Mega-environment I (E1) and Mega-environment II (E2, 3). The vertex genotype for E1 was ACM BM-20-016, signifying greater performance and specialized adaption to the environment. In contrast, ACM BM-20-019, MDU 1, and ACM BM-20-012 were the successful genotypes in E2 and E3. The separation of two mega-environments indicates the occurrence of crossover interaction, in which distinct genotypes function better in different environments. Similarly findings has been reported in barley (Ghazvini et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Ataei and foxtail millet (Ataei \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The two mega-environments emphasizing the practical importance of mega-environment identification in varietal prescription.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, twenty-seven genotypes were subjected to D\u003csup\u003e2\u003c/sup\u003e analysis taking twelve yield attributes into account. Eight different clusters were obtained. The traits grain yield/ plant contributed more to the divergence present, followed by plant height and days to maturity. Thus, each cluster has its own characteristics and can be helpful in selecting genotypes. Thus, the cluster II showed the presence of early maturing, high yielding genotypes which can be chosen for further improvement programme. Stability for grain yield covering three different environments was analysed using AMMI and GGE biplot analyses. Both revealed the stable genotypes, MDU1 (G27) and ACM BM-20-019 (G19) in three environments and high mean for grain yield. From what-won-where biplot, genotypes suitable for specific environments were identified. Thus, the genotype ACM BM-20-016 perform well in mega environment 1 (E1) and the genotypes ACM BM-20-019, MDU 1 and ACM BM-20-012 perform similarly in the mega environment 2 (E2 and E3).\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSudhir Deepak M - wrote the main manuscript textVanniarajan C, Chandirakala R, Renuka R and Kanchana S - Supported in writing the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to express his sincere gratitude to all individuals, colleagues and Agricultural College and Research Institute, Tamil Nadu Agricultural University, Madurai. who supported this work. 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Reducing the maturity duration is an important breeding objective and assessing the phenotypic diversity help in identifying those genotypes with desirable traits. Twenty-seven genotypes were subjected to D\u003csup\u003e2\u003c/sup\u003e analysis which revealed the presence of eight different clusters. Cluster I had the maximum number of genotypes. Cluster II had five genotypes, cluster III had three genotypes and in cluster IV, V, VI, VII and VIII with one genotype each. Early maturing germplasm were in cluster III and the check MDU1 was in separate cluster VII. The percentage contribution of the traits towards divergence was maximum by grain yield per plant followed by plant height and net by days to maturity. Stability analysis of these genotypes in three different environments revealed the stable genotypes with high mean were ACM BM-20-019 and MDU1. E2 was the least interactive environment. Using What- Won- Where biplot, the three test environments were divided into two mega environments. In light of this, the genotype ACM BM-20-016 performed well in mega environment 1 (E1), while the genotypes ACM BM-20-019, MDU 1, and ACM BM-20-012 perform similarly in mega environment 2. (E2 and E3).\u003c/p\u003e","manuscriptTitle":"Investigation of Phenotypic Diversity and Genotype X Environment Interaction in Early Barnyard Millet Genotypes for Grain Yield Using Ammi Analysis and Gge Biplot","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-09 18:25:39","doi":"10.21203/rs.3.rs-8899352/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b8168d10-b45e-4210-bc3f-c043692ab63a","owner":[],"postedDate":"March 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-10T12:12:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-09 18:25:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8899352","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8899352","identity":"rs-8899352","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}