{"paper_id":"98c92d47-f422-4c7e-9799-80a2085b17d0","body_text":"Determination of the effect of Genotype × Environment interaction on the trait seed cotton yield in upland cotton (G. hirsutum) genotypes using AMMI and GGE biplot methods | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Determination of the effect of Genotype × Environment interaction on the trait seed cotton yield in upland cotton (G. hirsutum) genotypes using AMMI and GGE biplot methods KALAPATI MOHAN VISHNUVARDHAN, Bana Venkata Ravi Prakash Reddy, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4122954/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 Background Cotton is an important natural fiber crop worldwide that demands the attention of the textile industry worldwide. Seed cotton yield is a complex polygenic trait that is influenced by many genetic and environmental factors across locations and years. Results The present investigation was conducted in three consecutive environments to delineate the genotype × environment interaction and to assess the stability of seven cotton genotypes at the Regional Agricultural Research Station, Nandyal, during 2018-19, 2019-20 and 2020-21. Multivariate stability tests such as additive main effect and multiplicative interaction (AMMI) and genotype and genotype-by-environment interaction (GGE) models were employed to investigate the stability among cotton genotypes. The AMMI results revealed that the majority of the variation was explained by the sum of the squares of the environmental variables, followed by the sum of the squares of the genotypic variables and the sum of the GEIs for the majority of the traits studied. The first two interaction principal components explained the majority of the GEI in all traits under study. A two-dimensional GGE biplot generated using the first two principal components revealed that the GGE biplot explained 97.14% of the total variation, which was distributed as 83.73% and 13.41% of the sum of squares between principal components PC1 and PC2, respectively, for biometric trait seed cotton yield. Conclusions Based on which-won-where polygon, ideal genotype ranking of AMMI and GGE biplot analysis, genotype, G3 (NDLH 2035-5) was identified as having the highest yield and was most stable in all the test environments studied. However, low yielding but stable genotypes such as G4 (BGDS 1033) and G7 (Sivanandi) were also identified. Among the three environments studied, environment E1 (2018-19) was identified as the most discriminating and representative. Gossypium hirsutum L. Genotype × Environment interaction Stability AMMI GGE biplot analysis Seed cotton yield Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Upland cotton ( Gossypium hirsutum spp.) is the primary source of natural fiber worldwide. In India, it is an important cash crop and lifeline of the textile industry. Millions of farmers are directly associated with the cultivation and harvesting of cotton crops and the sale of lint. Many others are indirectly linked with the cotton value chain. Cotton production in India is lower than that in many other cotton producing countries. Yield is a dependent attribute that arises from the interplay of many related traits. The influence of the environment usually results in low heritability, especially in locations prone to abiotic stresses. As a result, direct selection for yield is not sufficient for increasing crop productivity. Hence, understanding the degree of correlation that exists between yield and yield components is crucial for selection. Therefore, sustainable cotton production requires the identification and cultivation of stable cultivars. Several methods for estimating phenotypic stability across environments by determining GE interaction effects are available [Eberhart and Russel, 1966; Crossa, 1990; and Gauch, 1992 ]. Stability refers to a variety's or hybrid's ability to adapt to a wide range of growing conditions and maintain the same level of production efficiency as predicted. Understanding trait stability across environments is a vital prerequisite for attaining sustainable crop production and productivity. The estimates of genetic parameters obtained in one environment are biased due to the confounding of G × E. Therefore, it is necessary to consider the G × E interaction when determining the estimates of various genetic parameters to obtain realistic estimates. The presence of considerable genotype and genotype by environment interactions (GEIs) complicates the selection process and warrants the use of multienvironment trials (METs) to evaluate the relative performance of genotypes across environments. Among these statistical techniques, two frequently used multivariate analysis models are additive main effects and multiplicative interaction (AMMI) and genotype main effects and genotype by environment interaction (GGE) models. For the accurate analysis of METs, the AMMI model is a valuable tool due to its accuracy in GE interaction studies [Li et al., 2006 ]. AMMI analysis combines the additive parameter of traditional ANOVA with the multiplicative parameters of principal component analysis (PCA). Another method, GGE biplot, uses a biplot to show the factors (G and GE) that are important in genotype evaluation and sources of variation in GE interactions [Yan et al., 2000 ]. The GGE biplot is constructed by the first two principal components (PC1 and PC2) derived from subjecting environment centered yield data to singular value decomposition. It clearly shows which genotype won in which environments and thus facilitates mega-environment (ME) identification [Yan et al., 2000 ]. The AMMI and GGE biplot models are powerful tools for the effective analysis and interpretation of multienvironment data structures in breeding programs [Ebdon and Gauch, 2002 and Samonte et al., 2005 ]. The AMMI and GGE biplots have frequently been used for explaining GE interactions and to identify high yielding and high-adapted cultivars [Riaz et al., 2019 ]. The aim of the present research was to determine the G×E interaction and stability of newly developed cotton hybrids using GGE and AMMI biplot analyses of seed cotton yield, to identify stable hybrids across environments. The objectives of this study were (i) to identify stable genotypes by determining GE interaction effects obtained by AMMI analysis of seven metric traits in three years, (ii) to visually assess how to vary yield and other yield attributing trait performances across environments based on biplots, and (iii) to determine genotypes with high yield and yield attributing characteristics depending on differential genotypic responses to environments. Materials and Methods Field experiments were conducted in three consecutive years, i.e.., Kharif , 2018-19, 2019-20 and 2020-21, at the Regional Agricultural Research Station, Nandyal (15.46 o latitude and 78.48 o longitude). The experimental material was composed of seven upland cotton varieties (GBHV-193, RAH 1075, NDLH 2035-5, BGDS 1033, SURAJ, SAHANA and SIVANANDI) evaluated for seven yield attributing traits, viz ., days to 50% flowering (DFF), boll number (BN), boll weight (BW), plant height (PH), seed cotton yield (SCY), lint yield (LY) and ginning percentage (GP). Basic seasonal data for three consecutive years (2018-19, 2019-20 and 2020-21) during the crop growth period are presented in Fig. 1. The trial involved the use of black cotton soils in a randomized block design replicated three times each year. Details of the pedigree and source of the experimental material are presented in Table 1 . The ANGRAU recommended packages of practices were followed to establish healthy crops for three years. The plot size was maintained by four rows of 6 m long with 10 dibbles spaced 90 cm from row to row and 45 cm between plant to plant. The data were recorded for five randomly selected tagged plants for all the traits except DFF, which was recorded on a plot basis. The recorded data were analyzed for various stability models, viz. , AMMI and GGE biplots, using the software GEA-R version 4.1 [Angela et al., 2015 ] with the following model equation: Table 1 Pedigree and Source of experimental material S. No. Name of the genotype Code Pedigree Source 1 GBHV-193 G1 Pure line selection derived variety RCRS, Bharuch, Gujarat 2 RAH 1075 G2 GSHV 99/307 x PUSA 9127 UAS, Raichur, Karnataka 3 NDLH 2035-5 G3 NDLH – 1905 × MCU 5 RARS, Nandyal, Andhra Pradesh (ANGRAU) 4 BGDS 1033 G4 GSHV 99/307 x PUSA 9127 UAS, Raichur, Karnataka 5 SURAJ G5 LRA 5166 (CCH526612X HLS 329) CICR, Nagpur, Maharashtra 6 SAHANA G6 -- UAS, Karnataka 7 SIVANANDI G7 NA 1290 x Krishna RARS, Nandyal, Andhra Pradesh (ANGRAU) Y ij = µ + gi + ej + Σ n λ n γ in δ ij + Є ij where Y ij is the yield of the i th genotype in the j th environment; µ is the grand mean; gi and ej represent the genotype and environment deviations from the grand mean, respectively. λn is the eigenvalue of the principal component (PC) axis n γ in and δ ij are the genotype and environment principal component scores for axes n and Є represents the error term [3]. Furthermore, the AMMI stability value (ASV) was calculated to rank genotypes in terms of stability using the formula suggested by Purchase et al. 2020, as shown below: where SS represents the sum of squares of the first (IPCA1) and second (IPCA2) interaction principal component axes; and IPCA1 and IPCA2 are the genotypic scores obtained from the AMMI model. The yield selection index (YSI) was obtained by following the method devised by Farshadfar et al . (2011). YSIi = RYi + RASVi where YSIi denotes the genotype selection index for the i th genotype, RYi is the rank of the mean grain yield for the i th genotype, RASVi represents the rank of the AMMI stability value for the i th genotype. GGE analysis was performed using the software GEA-R version 4.1 [Angela et al., 2015 ] with the following model equation: Y ij − µ + Gi + Ej + Σλkαikγjk + e ij Where Y ij is the yield of the i th genotype in the j th environment; Gi and Ej represent the genotype and environment deviations from the grand mean, respectively; µ denotes the grand mean λk is the eigenvalue of the PCA axis k; αik and γjk indicate the genotype and environment PC scores, respectively, for the axis k; and e ij denotes the error term. Results AMMI Analysis of variance The AMMI analysis of variance for the pooled means of five yield traits across three environments (E1, E2 and E3) of seven cotton genotypes is presented in Table 2 . The results revealed that the greatest proportion of variation was explained by the environmental sum of squares, followed by the genotype sum of squares and the GEI sum of squares for traits such as the number of bolls per plant (56.9), seed cotton yield (48.39) and lint yield (40.40). However, for the trait ginning percentage, the GEI contribution to the sum of squares is greater than the environmental sum of squares and the genotypic sum of squares. This clearly indicated the varied response of genotypes across environments. The presence of a considerable amount of GEI sum of squares necessitates studying the stability of cotton genotypes in different environments. The partitioning of the GEI by the AMMI revealed two multiplicative axes, namely, PC1 and PC2. These two principal components cumulatively accounted for a cent percent of the GEI interaction sum of squares for all the five traits studied. Table 2 AMMI Analysis of variance for seed cotton yield and related traits across three environments Source DF SCY LY BN SS MS % V SS MS % V SS MS % V ENV 2 4717992 2358996 *** 48.39 462766.9 231383 *** 40.40 393.47 196.67 *** 56.9 GEN 6 2750548 458424.6 *** 28.21 344510.6 57418 *** 30.07 170.98 28.50 *** 24.73 GEN*ENV 12 2281099 190091.6 *** 23.39 338043.6 28170 *** 29.51 126.92 10.58 ** 18.36 PC1 7 2049790 292827.1 *** 89.85 289289 41327 *** 85.57 81.74 11.68 ** 64.40 PC2 5 231308.8 46261.76 10.14 48754.6 9751 ** 14.42 45.17 9.04 * 35.59 Residuals 42 1742857 41496.6 82862 1973 137.44 3.27 Contd.. AMMI-I and AMMI-II The AMMI model adopts a unified strategy that combines principal component analysis of G×E interactions with analysis of variance for genotype and environment main effects. The results are illustrated graphically in a biplot that allows concurrent visualization of genotype stability and mean performance by plotting the main effect means on the abscissa and PCA 1 values (interaction effects) on the ordinates. The horizontal line shows the interaction PC1 score of zero, and the vertical line indicates the mean of the genotype effect. The abscissa (X-axis) is divided into two parts by the ordinate (Y-axis), thereby separating the genotypes with below-average means from those with above-average means. Analysis of variance revealed that E, G and G × E explained 48.39%, 28.21% and 23.39%, respectively, of the total sum of squares respectively for the trait seed cotton yield (Table 2 ). AMMI analysis revealed an interaction component IPCAI, with 89.86% of the total interaction sum of squares. Genotypes, G3 and G2 were the top yielders, and exhibited above average performance with positive interactions in quadrant I with environment E1 ( Fig. 2A ). However, genotype G1, with above average performance but with negative interactions, was present in quadrant IV with environment E2, and the remaining genotypes, G4, G5, G6 and G7, with yields less than the grand mean and negative interactions, were confined to quadrant III with E3. Genotypes with IPC scores close to zero exhibit less GEI and better adaptation to all environments. The G7 and G4 scores of the genotypes were close to zero, and the plants were better adapted to all the environments. However, neither of the two entries had a yield higher than the average yield (1352.68 kg/ha) ( Fig. 2B , Table 3 ). This indicates that an adapted and stable variety might not always be a high yielder. A similar result was observed in the AMMI 2 biplot, where genotypes G7 and G4 were close to the center of the biplot, indicating their stability over other genotypes. The AMMI 2 biplot also revealed that E1 was a highly interactive environment where the G3 genotype exhibited a high yield ability; similarly, G1 and G4 performed well in E2 ( Fig. 2B ). There is no provision for quantitative stability measurement in the AMMI model. A measure of this kind is necessary to quantify and rank genotypes according to the stability of their traits. Accordingly, the AMMI stability value (ASV) is used to study the stability of traits. The variety with the lowest ASV determined by the IPCA axis and IPCA scores is considered the most stable. The lowest ASVs were recorded for G7, G4 and G6, which are considered to be stable. However, the parameter yield stability index blends yield and stability across environments, suggesting that the genotypes that exhibit lower YSI (G7, G3 and G4) are preferred because they have high and stable yield performance (Table 3 ). Table 3 IPCA components of genotypes along with ASV (AMMI Stability Value) ranks for different characters S.No. Entry Code Seed Cotton Yield (Kg/ha -1 ) E1 E2 E3 Mean RY i IPC1 IPC2 ASV ASV rank (RASV i ) YSI 1 GBHV-193 G1 1411.0 1805.8 1059.4 1425.4 3 -0.33 -0.24 2.99 4 7 2 RAH 1075 G2 2261.7 1472.6 1024 1586.1 2 1.00 0.25 8.87 7 9 3 NDLH 2035-5 G3 2000.4 1895.0 1084.1 1659.8 1 0.34 -0.40 3.07 5 6 4 BGDS 1033 G4 1383.6 1600.3 823.3 1269.1 5 -0.09 -0.31 0.86 2 7 5 SURAJ G5 1028.0 1483.6 1016 1175.9 6 -0.58 0.31 5.16 6 12 6 SAHANA G6 1005.8 1305.7 738.3 1016.6 7 -0.32 0.11 2.85 3 10 7 SIVANANDI G7 1483.6 1495.0 1029.1 1335.9 4 -0.01 0.28 0.31 1 5 1352.68 E1 = Environment 1 (2018-19); E2 = Environment 2 (2019-20) and E3 = Environment 3 (2020-21) contd.. The results showed that E, as the main component, explained 56.90% of the variability in the number of bolls per plant, whereas G and GE explained 24.73% and 18.36%, respectively (Table 3 ). AMMI analysis revealed that IPC A1 interacted with 64.41% of the total interaction sum of squares ( Fig. 3A ). Genotypes G2 and G1, with above average performance and negative interactions, were in quadrant IV, while G7 and G5, with below average numbers of bolls per plant and positive interactions, were in quadrant II. The IPC1 values of four genotypes, viz ., G4, G7, G2 and G1were closer to zero and hence stable across all the environments. However, G2 and G1 exhibited means above the overall mean, therefore, these genotypes were found to be stable combined with an increased number of bolls (Fig. 3B , Table 3 ). AMMI II revealed that G4 and G7 were close to origin indicating that they were less responsive to the environment had greater stability and general adaptability to all the other environments. Genotypes G5 and G3, which were scattered from biplot origin exhibited specific adaptations to E2 and E3, respectively. Among the three environments, E1 was found to be highly interactive with longer vectors compared to the others for the trait number of bolls per plant. In the present study, genotypes G4 (0.28) and G7 (0.40) exhibited the lowest ASVs and were considered as highly stable for the trait number of bolls per plant. The environment explained 35.50% of the variation in boll weight, the genotype genotype explained 30.44% and the contribution of the GE interaction to the total treatment variation was 34.04% (Table 3 ).The first two principal components (IPCA1: 73.49% and IPCA2: 26.51%) explained the maximum portion of the GEI. Single genotype G3 exhibited above average performance, and a positive interaction was detected in quadrant I with environments E1 and E2. Similarly, genotypes G1 and G4 which exhibited below average average performance coupled with negative interactions, were present in quadrant III with environment E3 ( Fig. 4A ). Genotypes G3 (NDLH 2035-5) and G5 (SURAJ) exhibited IPC1 values closer to zero and a mean above the overall mean indicating greater stability for BW across all the test environments ( Fig. 4B ). Genotypes G5, G3 and G4 had the lowest ASvs and were therefore considered stable for this trait (Table 3 ). For the trait lint yield, the effect of E explained 40.40%, while G and G × E explained 30.07% and 29.51%, respectively, of the total sum of squares Table 3 ). Furthermore, the first two IPCs were highly significant and explained a major portion of the genotype and environment interactions. The genotypes with above average performance coupled with positive interactions, viz., G2 and G3, were present in quadrant I with environment E1 ( Fig. 5A ). In the AMMI 2 biplot, genotypes G1 and G6 were found close to the origin, indicating their stability and adaptability. However, genotype G2, which was highly scattered from biplot origin and had an IPCA1 value equal to one, is considered a highly unstable genotype and sensitive to environmental conditions for trait lint yield ( Fig. 5B) . The ASVs suggests that genotypes G1, G6 and G3 have ASVs close to zero; hence, they are stable (Table 3 ). The environment as the main effect explained 32.58% of the total sum of squares, whereas G × E and G explained only 39.74% and 27.66% respectively of the variation in the percentage of the trait ginning (Table 3 ). The first two IPCs explained most of the GE interaction. The single genotype G3 present in quadrant I with environment E3 exhibited above average performance and positive interaction effect. Similarly, genotypes G5, G6, G1 and G2 which are present in quadrants IV with E1, also showed above average performance but negative interaction effects. However, the remaining two genotypes, G4 and G7 which had below average performance and positive interaction effects were present in quadrant II with E2. The genotype G5 had an IPCA 1 score equal to zero and hence was highly stable ( Fig. 6A) . Analysis of the AMMI 2 biplot also revealed that only G5 was close to its origin and therefore was less sensitive to the environments and stable. G2 is considered to be highly unstable because it is far from the center of the biplot, while entries G3 and G5 performed well in E2, and E3 was beneficial for G4 and G7; likewise, genotypes G1 and G6 interacted favorably with E1 ( Fig. 6B) . Based on the ASV values, the lowest values were recorded for G5 and G7, indicating that these two varieties exhibit stable trait ginning percentages across environments (Table 3 ). GGE biplot (‘whichwonwhere’ pattern) The ability of a GGE biplot to display the which-won-where/what-won-where pattern of a genotype by utilizing an environment dataset is one of its most fascinating features. In this approach, a polygon is first constructed by joining the genotypes far from the origin consisting of all the other genotypes inside the polygon. Then, starting from the biplot origin, perpendicular lines are drawn to each side of the polygon. The G + GXE variation was 96.55%, 89.48%, 97.14%, 97.92% and 89.59% for the number of bolls per plant, boll weight, seed yield (kg/ha), lint yield (kg/ha) and ginning percentage, respectively, ( Fig. 7, Patterns a,b,c,d and e ). The environmental indicators were positioned into two, two, two, two and three segments or sections of the biplot for BN, BW, SCY, LY and GP, respectively, with different genotypes winning in each segment. The results confirmed the presence of distinct interactions between genotype and environment for all the traits evaluated. Based on seven genotypes and three environments, the generated GGE biplot was divided into four, seven, five, five and four sections in the clockwise direction for BN, BW, SCY, LY and GP, respectively. For trait BN, genotype G1 in ENV 1 and genotype G3 in ENV 2 and ENV 3 were highly stable, with a greater number of bolls. For the trait BW, genotype G7 in ENV 3 and genotype G3 in ENV 1 and ENV 2 were the best performers with high boll weight. Compared with those of other genotypes, the G2 genotype in ENV 1 and the G3 genotype in ENV 2 and ENV 3 exhibited greater SCY with high stability. For the trait LY, the G3 genotype in ENV 2 and ENV 3 and the G2 genotype in ENV 1 exhibited greater lint yields. In the case of GPs, the G3 genotype in ENV2, the G6 genotype in ENV 1 and the G2 genotype in ENV 2, the G6 genotype in ENV 1 and G2 genotype in ENV3 exhibited increased ginning percentages and stability. GGE biplot pattern of ‘mean vs. stability’ analysis The mean vs. stability' view is often referred to as the AEC view with SVP = one, since it aids in genotype evaluations based on mean performance and stability across environments feasible. Genotypes are ranked based on their performance in an environment by drawing a line that passes through the biplot called the “average environment axis,” or the axis of the AEC abscissa. The AEC ordinate passed through the biplot origin and was perpendicular to the AEC abscissa. In this study, the average principal component will be used in all environments and is presented with a circle and single arrow pointing in the direction of higher mean performance for each trait. Stable genotypes are those that fall on the AEC abscissa (horizontal axis) and have almost zero projection onto the AEC ordinate (vertical axis). The mean vs. stability pattern of the GGE biplot revealed 96.55% for BN, 89.48% for BW, 97.14% for SCY, 97.92% for LY and 89.59% for GP ( Fig. 8, Pattern, a,b,c,d and e ). For the trait BN, genotype G3 was found to be a good performer with a greater number of bolls in ENV 3 and ENV 2, followed by G2 in ENV1. Genotype G5 had the lowest number of bolls per plant. In the case of the trait boll weight, genotypes G3 followed by G5 exhibited the greatest boll weight. The entry 5 reported increased boll weight combined with high stability. Genotypes G2, G6, G1 and G4 were unstable and had lower boll weights. For economic traits such as seed cotton yield (kg/ha) and lint yield, genotype G3 had a greater yield. However, genotypes G4 and G7 exhibited relatively low yields and high stability. For the trait GP, genotype G6 exhibited a higher ginning percentage coupled with increased stability, while genotypes G4 and G7 showed lower GP% with high stability. Genotype ranking: best and ideal genotype assessment: An ideal genotype is the one with the highest mean performance and maximal stability. The genotype ranking or comparison with the deal genotype is a biplot view with concentric circles. The ideal genotype is always located in the inner circle, and the head of the arrow is at the center of the circle. In certain instances, such as for trait LY in the present study, if none of the genotypes were located inside the inner circle, genotypes that were located next to or closer to the inner circle were considered ideal. Therefore, genotypes G2, and G1 for BP, genotypes G5,G3 for BW, genotype G3 for LY, genotype G6 for GP were considered as ideal. Genotypes close to the ideal genotype were also more promising and appropriate. Therefore, the ranking of genotypes for the SCY trait was as follows: G3 > G7 > G4 > G1 > G2 > G5 > G6 ( Fig. 9, Patterns a, b, c, d and e ). ‘Descriminativeness vs. representativeness’ pattern of the GGE biplot The identification of the best ideal test environment is crucial for a successful breeding technique that eventually helps in the selection of superior genotypes. Two aspects namely, discrimitiveness (the ability of an environment to distinguish genotypes and representativeness (the ability of an environment to represent all other evaluated environments) denote the idealness of the tested environments. The average environmental coordinates (AECs) and test environments are capable of visualizing type-1 environments, type-2 environments and type-3 environments. The type-1 environments are represented by short vectors with average discriminative power indicating the average performance of genotypes. The type-2 environments are shown as the longest vectors with the highest discriminative powers, and are capable of discriminating the performance of genotypes. Type-3 environments are represented as the longest vector with large angles, and are suitable for the negative effects of environments. The ideal environments are those having the longest genotypic vector and located on or at acute angles to the AEC. Based on this classification, as suggested by Yan et al., 2007 , the results of the present study inferred that ENV 2 for BP and BW, and ENV 3 for SCY, LY and GP have short vectors representing the average or similar performance of the genotypes; hence, not much information is available about the genotype differences. The environments with a long vector that forms a shorter angle with the AEC abscissa line are ENV 2 for BP, ENV 1 for BW SCY, LY and GP indicating that the test environments were more representative and discriminative ( Fig. 10, patterns a,b,c,d and e ). Relationships among environments: To illustrate the interactions among environments, lines are drawn from the test environment to the center of the biplot; these lines are otherwise called “ environment vectors”.The cosine of the angle between the vectors of two environments reflects their correlation. In the present study, acute angles (< 90º) were reported for ENV2 and ENV 3 for BN, ENV 2 and ENV 1 for BW, ENV 2 and ENV 3 for SCY, and ENV 1 and ENV 3 for LY and ENV 1 and ENV 3 for GP, indicating the presence of a positive correlation between these environments. This demonstrates that genotypes exhibiting the best performance at ENV 2 can also exhibit the same performance at ENV 3 and vice versa for trait SCY. If the angle between two environmental vectors is an obtuse angle (> 90º), then these environments exhibit negative correlations, such as ENV3 and ENV2 for BW and GP, indicating that there is a negative association; genotypes that performed well in ENV3 do not perform similarly at ENV2 and vice-versa. The third situation, where the angle between two environmental vectors is right angle (90º) or closer to 90º exhibits no correlation or is unrelated, suggesting that each environment has unique genotypic performance. ENV3 and ENV1 for BW, ENV3 and ENV2 for LY and ENV1 and ENV2 for GP exhibited zero correlation for their respective traits ( Fig. 11, Patterns a, b, c, d and e ). Ranking of environments: Based on the ranking of environments, in the present experiment, the environments are ranked ENV 1 and ENV 3 for BP, ENV 1 for BW, and ENV 1 for SCY, LY and GP are considered ideal environments. The center of the concentric rings is the \"ideal test environment\". Conversely, ENV 2 for BP, ENV 3 for BW, ENV 3 and ENV 2 for SCY and LY, ENV 2 for GP were regarded as the poorest environments for selecting the genotypes across the environment ( Fig. 12, Patterns a,b,c,d and e ). Discussion AMMI Analysis of variance In the present study, AMMI analysis of variance revealed highly significant variations for all the five traits studied. The environment, genotype and genotype × environment interaction effects were highly significant (p < 0.001) for the SCY and LY. The environment had a major contribution to the treatment sum of squares, followed by the genotype and GEI for the number of bolls per plant, seed cotton yield and lint yield, indicating that varying environmental conditions prevailed during the experimental seasons and hence were suggested for multiple environmental trails. The environment was found to be a major contributor in studies [Riaz et al., 2013 , Orawu et al., 2017 , Shahzad et al., 2019 ] on cotton crops and Habtegebriel 2022 (soybean), and Enyew et al., 2022 (sorghum) crops]. The GEI sum of squares was greater for traits of boll weight and ginning percentage than for the genotype. The significant GEI suggested that the cotton genotypes responded differently to various environments indicating the need to identify and select location/environment specific genotypes.The variation in genotype performance across environments reduces the association between genotypes and their corresponding phenotypes [Gerrano et al., 2020 ]. This poor correlation between genotype and phenotype caused by the presence of GE interactions hinders the identification and selection of superior genotypes based on their genetic potential. Therefore, plant breeders are most concerned with genotype-environment interactions (GEIs) while creating better cultivars. A cultivar must perform well in all of the environments in which it is permitted to grow to be economically successful. The present study also revealed that the first two IPCs were sufficient for partitioning the GEI, which is in accordance with [Zobel et al., 1988 , Gauch and Zobel, ( 1996 ), Yan and Rajcan, ( 2002 ) and Sadabadi et al., 2018 ]. However, [Shahzad et al., 2019 , Habtegebriel, 2022 and Riaz et al., ( 2019 )] recorded three, four and six IPCs, respectively, in their studies. AMMI I and AMMI II: The AMMI model combines the analysis of variance of the genotype and environment main effects with the principal component analysis of the G × E interaction. The additive main effect and multiplicative interaction (AMMI) model is frequently employed in yield experiments to examine GE interactions. To ascertain adaptation and stability, one must comprehend how GE interacts with cultivars. In the AMMI I model, displacement along the abscissa depicts differences in main effects, whereas displacement along the ordinate indicates differences in interaction effects. Genotypes and environments on the same parallel line, relative to the ordinate, have similar yields, and genotypes or environment on the right side of the center of the axis have higher yields than those on the left-hand side. A similar result was observed in the current study, where genotype G3 (NDLH 2035-5) was found in quadrant I (right hand side with means above the grand mean and positive interaction) for all the traits studied and exhibited above average performance compared with the overall mean, indicating that this variety is the best performer for all the traits evaluated. Genotype G7 exhibited low IPCA 1 scores, while genotypes G3 and G5 had IPCA scores close to zero for the remaining traits, namely, BW, LY and GP, suggesting that these genotypes were stable for their respective traits. The AMMI II model biplot is created by using genotypic and environmental scores of the first two AMMI components [Vargas and Crossa, 2000 ). Furthermore, in the present experiment the first two PCA interactions with the AMMI model 2 biplot presented 100% of the G + G E interaction variance for all five traits studied possibly because only the two IPCAs were responsible for the entire variation. This result is in accordance with [Esan et al., 2023 ]. The closer the genotypes are to the origin, the less interactive they are, and the farther the genotypes are to the origin, the more interactive they are. Genotypes G7, G4, G6 and G1 were found close to their origin, indicating their general adaptability in all three environments for trait seed cotton yield. Genotype G3, the top yielder, exhibited specific adaptability for E1, while other genotypes, G5 and G6, exhibited favorable interactions with E3, and genotypes G1 and G4 exhibited favourable interactions with E2 for trait SCY. Genotype G5 displayed general adaptability for the traits of boll weight and ginning percentage. Environment E1 was highly interactive for all the traits except for boll weight. Genotypes with low ASVs and GSIs are considered to be stable. Accordingly, genotypes G4, and G7 for BN; G5, and G3 for BW; G1 and G3 for LY; and G5, G3 and G6 for GP had lower ASVs. However, for SCY, genotypes G7, G4 and G6 had lower ASVs and means lower than the average mean; therefore, the selection of these genotypes for further breeding programs might not be helpful for yielding fruitful results. Hence, another stability parameter, GSI employed in the present study for selecting the top-ranking genotypes in terms of both mean performance and stability revealed that genotype G3 (NDLH 2035-5) had a low GSI for SCY and LY, indicating the stability of the genotype across environments. Intriguingly, genotype G3 (NDLH 2035-5) also exhibited low ASV values for yield enhancing traits such as BW, LY and GP. Therefore, prioritizing the selection of this genotype in cotton breeding programs is beneficial. Previous researchers [Lin and Binns 1994 , Farshdafar, 2008 and Enewy et al. , 2021] have suggested that for the selection of superior cultivars, average yield and stability are the prerequisites. GGE biplot GGE biplot was divided into four, seven, five, five and four sections for BN, BW, SCY, LY and GP, respectively. Similarly, the GGE biplot was divided into eight, seven, six and four sections according to previous researches findings by [Regmi et al., 2021 , Abro et al., 2022 , Daemo et al., 2023 , and Yasar et al. , 2023]. The grouping of all the environmental indicators in one section of the biplot revealed that a particular genotype performed well in all the tested environments. In contrast, if the environmental indicators were placed in a different portion of the biplot, different genotypes acquired different environments. The vertex genotypes, which have the furthest distance from the origin in their direction, were considered to be the most responsive genotypes with superior performance in that particular environment, as suggested by [Yan and Tinker ( 2006 )]. In contrast, the genotypes positioned inside the polygon are less environmentally friend than the genotypes at the vertex. In the present study, there are biplots where a genotype is situated at a polygon vertex with no environmental vector in that section, suggesting that a particular genotype exhibited poor performance across the test environments. This result conforms with [Ali et al., 2017 ], where four genotypes were present at the vertex of the polygon with no environmental vectors for trait seed cotton yield. GGE biplot pattern of ‘mean vs stability’ analysis : The visualization of genotypes that combine high mean performance and stability is a notable feature of the GGE biplot graph. Stable genotypes are those that fall on the AEC abscissa (horizontal axis) and have almost zero projection onto the AEC ordinate (vertical axis). Similarly, genotypes G3 and G5 were stable and had high mean performances with respect to BN, BW and LY. However, for trait SCY, genotypes G4 and G7 exhibited comparatively lower yields than G3 but with high stability. In the same manner, several authors have also identified high yielding and stable genotypes of crops such as cotton [Farias et al., 2016 , Mustafa McPherson 2022 and Yasar 2023 ] and oats [Yan and Reid 2018 ] using GGE biplots. Genotype ranking: best and ideal genotype assessment: The genotypes found in the inner circle are the most desirable among the genotypes present in the outer circle [Ali et al., 2017 , Firew et al., 2019 and Habtegebriel 2022 ]. If no genotype was located inside the inner circle, genotypes that were located next to or closer to the inner circle were regarded as ideal [Khan et al., 2021 ]. In the present study, genotype G3 was found to be ideal for BW, SCY and LY. However, genotype G6 was considered ideal for GP because a stable and high performing genotype for one trait does not necessarily indicate that it combines stability and superior performance for other related traits. This is mainly because different traits are governed by various genes, and because of the differential expression of genes among the genotypes in response to various environmental conditions, such as temperature variation and moisture stress. Descriminativeness vs. representativeness pattern of the GGE biplot : The GE biplot's discriminating power vs. representativeness view is a useful tool for evaluating test environments and can help identify a minimal set of representative and discriminating test environments. Identifying test environments, precisely defining genotype differences, and providing the necessary insights required for selection by plant breeders are essential. The ability of an environment to discriminate between genotypes is represented by the length of its vector, whereas its representativeness is depicted with a shorter angle formed with the abscissa [Yan and Tinker ( 2006 ) and Yan et al. 2007 ]. In the present study, the long vectors included ENV1 and ENV 3 for BN; ENV 3 for BW; and ENV1 for SCY, LY and GP, indicating the discriminating nature of the environments for the respective traits, while the shortest angle was formed for ENV 2 for BN and ENV 1 for the remaining traits BW, SCY, LY and GP. Test environments that are both discriminating and representative are ideal environments for selecting generally adapted superior genotypes [Yan and Tinker, 2006 ]. Relationships among environments GGE biplot analysis enables the understanding of the value of various test environments in terms of relative discrimination between genotypes and the associations between them for various traits. An ideal environment should have a high PC1 score and zero scores for PC 2 [Habtegebriel 2022 ]. The present study also obtained the same result. Furthermore [Farias et al., 2016 ] identified the Sapezal environment as the most discriminating of the eight environments studied, and [Ali et al. 2017 ] reported that E3 (CCIR Multan) was suitable for six of the environments studied. Ranking of environments The centre of the concentric rings is the \"ideal test environment\" [Balestre et al ., 2010]. Based on the ranking of environments, in the present experiment, the environments are ranked ENV 1 and ENV 3 for BP, ENV 1 for BW, and ENV 1 for SCY, LY and GP are considered ideal environments. Conversely, ENV 2 for BP, ENV 3 for BW, ENV 3 and ENV 2 for SCY&LY, and ENV 2 for GP are regarded as the poorest environments for selecting the genotypes across the environment. Conclusion The present study evaluated the genotype × environment interaction, genotype stability, Descriminativeness and representativeness of the test environments. AMMI model analysis revealed that the majority of the total variance in the yield component was explained by environment E. The GGE biplot model was effective for graphical visualization of the G × E interaction and for identifying stable and superior performing genotypes. Among the seven genotypes studied across three environments, genotypes G3 (NDLH 2035-5) and G1(GBHV-193) were high yielding and most stable in all the test environments studied. The yields of genotypes G7 (SIVANANDI) and G4 (BGDS 1033) were below average but were stable. These genotypes can also be used in breeding programmes when breeders intend to enhance certain phenotypes. There are other sets of genotypes, G5 (SURAJ) and G6 (SAHANA) whose yields are low and unstable. Genotype G3 (NDLH 2035-5), the top yielder compared with the check G7 variety (SIVANANDI), had recorded lower ASVs and GSIs, and the ideal genotype for GGE analysis was preferred over the other varieties studied because it can be used as a parent in hybridization programs. Environment E1 was identified as the most discriminating and representative and therefore may be utilised to detect promising genotypes with attractive yields and adaptability. Declarations Ethics approval and consent to participate “Not applicable” Consent for Publication All the authors have agreed to submit this review article to the Journal of Cotton Research. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests Non-financial competing interests include political, personal, religious, ideological, academic, and intellectual competing interests. All the authors does not have any interest regarding this. Funding All sources of funding for the research supported by the Acharya N.G. Ranga Agricultural University and AICRP on Cotton Scheme. Authors' contributions Kalapati Mohan Vishnuvardhan (corresponding author), performed the experiments along with Yettapu Rama Reddy and Bana Venkata Ravi Prakash Reddy. Kalapati Mohan Vishnuvardhan is responsible to analysis the data and preparation of tables and figures. Konuku Sudeepthi and Kolimigundla Amarnath supporting the tabulation of data and review the manuscript. Nayakanti Chinna Venkateswarlu supported in overall scrutiny of the manuscript. Acknowledgements The authors are immensely thankful to the AICRP on cotton and Acharya N.G. Agricultural University for providing the necessary facilities to carry out the research program successfully. References Abro, S., Rizwan, M., Rajput, M,T., Sial, M.A and Deh, Z.A. 2022. 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7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":263424,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSee image above for figure legend\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4122954/v1/46240e8a0e450fbf22073b35.png\"},{\"id\":54751185,\"identity\":\"239e99f4-2756-44e8-b2fb-f64d0de22a43\",\"added_by\":\"auto\",\"created_at\":\"2024-04-16 08:39:22\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":241136,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSee image above for figure legend\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4122954/v1/774d2a3507d48a2e4c1556a3.png\"},{\"id\":54751186,\"identity\":\"1bcbd804-7ec6-4b2a-9d10-4e3eab7bce76\",\"added_by\":\"auto\",\"created_at\":\"2024-04-16 08:39:22\",\"extension\":\"png\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":417367,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSee image above for figure legend\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4122954/v1/e9aa4d36ba2a8722e8ec9f45.png\"},{\"id\":54751184,\"identity\":\"947e04a1-3ac4-442e-ae0a-2b6c3ee39938\",\"added_by\":\"auto\",\"created_at\":\"2024-04-16 08:39:21\",\"extension\":\"png\",\"order_by\":10,\"title\":\"Figure 10\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":402478,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSee image above for figure legend\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"10.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4122954/v1/db5775bbb70340d3762d2b1f.png\"},{\"id\":54751778,\"identity\":\"6c5145c7-9b14-4a17-b2ab-5e98610c5a80\",\"added_by\":\"auto\",\"created_at\":\"2024-04-16 08:47:22\",\"extension\":\"png\",\"order_by\":11,\"title\":\"Figure 11\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":371129,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSee image above for figure legend\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"11.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4122954/v1/268e7e99b1be7b7f0a59640e.png\"},{\"id\":54751189,\"identity\":\"3824caf6-b5c3-4efc-a9bc-b92497318d18\",\"added_by\":\"auto\",\"created_at\":\"2024-04-16 08:39:22\",\"extension\":\"png\",\"order_by\":12,\"title\":\"Figure 12\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1068642,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSee image above for figure legend\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"12.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4122954/v1/2f0cf715116f11967f278ea8.png\"},{\"id\":59106354,\"identity\":\"c4a66c80-5bc3-4db3-a407-f7d133268a78\",\"added_by\":\"auto\",\"created_at\":\"2024-06-26 12:14:17\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":5648147,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4122954/v1/4577a2bd-f6cc-49ff-a147-dfa99db60871.pdf\"}],\"financialInterests\":\"\",\"formattedTitle\":\"Determination of the effect of Genotype × Environment interaction on the trait seed cotton yield in upland cotton (G. hirsutum) genotypes using AMMI and GGE biplot methods\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eUpland cotton (\\u003cem\\u003eGossypium hirsutum\\u003c/em\\u003e spp.) is the primary source of natural fiber worldwide. In India, it is an important cash crop and lifeline of the textile industry. Millions of farmers are directly associated with the cultivation and harvesting of cotton crops and the sale of lint. Many others are indirectly linked with the cotton value chain. Cotton production in India is lower than that in many other cotton producing countries. Yield is a dependent attribute that arises from the interplay of many related traits. The influence of the environment usually results in low heritability, especially in locations prone to abiotic stresses. As a result, direct selection for yield is not sufficient for increasing crop productivity. Hence, understanding the degree of correlation that exists between yield and yield components is crucial for selection.\\u003c/p\\u003e \\u003cp\\u003eTherefore, sustainable cotton production requires the identification and cultivation of stable cultivars. Several methods for estimating phenotypic stability across environments by determining GE interaction effects are available [Eberhart and Russel, 1966; Crossa, 1990; and Gauch, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e1992\\u003c/span\\u003e]. Stability refers to a variety's or hybrid's ability to adapt to a wide range of growing conditions and maintain the same level of production efficiency as predicted. Understanding trait stability across environments is a vital prerequisite for attaining sustainable crop production and productivity. The estimates of genetic parameters obtained in one environment are biased due to the confounding of G \\u0026times; E. Therefore, it is necessary to consider the G \\u0026times; E interaction when determining the estimates of various genetic parameters to obtain realistic estimates. The presence of considerable genotype and genotype by environment interactions (GEIs) complicates the selection process and warrants the use of multienvironment trials (METs) to evaluate the relative performance of genotypes across environments.\\u003c/p\\u003e \\u003cp\\u003eAmong these statistical techniques, two frequently used multivariate analysis models are additive main effects and multiplicative interaction (AMMI) and genotype main effects and genotype by environment interaction (GGE) models. For the accurate analysis of METs, the AMMI model is a valuable tool due to its accuracy in GE interaction studies [Li et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e]. AMMI analysis combines the additive parameter of traditional ANOVA with the multiplicative parameters of principal component analysis (PCA). Another method, GGE biplot, uses a biplot to show the factors (G and GE) that are important in genotype evaluation and sources of variation in GE interactions [Yan et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e]. The GGE biplot is constructed by the first two principal components (PC1 and PC2) derived from subjecting environment centered yield data to singular value decomposition. It clearly shows which genotype won in which environments and thus facilitates mega-environment (ME) identification [Yan et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e]. The AMMI and GGE biplot models are powerful tools for the effective analysis and interpretation of multienvironment data structures in breeding programs [Ebdon and Gauch, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e and Samonte et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e]. The AMMI and GGE biplots have frequently been used for explaining GE interactions and to identify high yielding and high-adapted cultivars [Riaz et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe aim of the present research was to determine the G\\u0026times;E interaction and stability of newly developed cotton hybrids using GGE and AMMI biplot analyses of seed cotton yield, to identify stable hybrids across environments.\\u003c/p\\u003e \\u003cp\\u003eThe objectives of this study were (i) to identify stable genotypes by determining GE interaction effects obtained by AMMI analysis of seven metric traits in three years, (ii) to visually assess how to vary yield and other yield attributing trait performances across environments based on biplots, and (iii) to determine genotypes with high yield and yield attributing characteristics depending on differential genotypic responses to environments.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cp\\u003eField experiments were conducted in three consecutive years, \\u003cem\\u003ei.e.., Kharif\\u003c/em\\u003e, 2018-19, 2019-20 and 2020-21, at the Regional Agricultural Research Station, Nandyal (15.46\\u003csup\\u003eo\\u003c/sup\\u003e latitude and 78.48\\u003csup\\u003eo\\u003c/sup\\u003e longitude). The experimental material was composed of seven upland cotton varieties (GBHV-193, RAH 1075, NDLH 2035-5, BGDS 1033, SURAJ, SAHANA and SIVANANDI) evaluated for seven yield attributing traits, \\u003cem\\u003eviz\\u003c/em\\u003e., days to 50% flowering (DFF), boll number (BN), boll weight (BW), plant height (PH), seed cotton yield (SCY), lint yield (LY) and ginning percentage (GP). Basic seasonal data for three consecutive years (2018-19, 2019-20 and 2020-21) during the crop growth period are presented in Fig.\\u0026nbsp;1.\\u003c/p\\u003e \\u003cp\\u003eThe trial involved the use of black cotton soils in a randomized block design replicated three times each year. Details of the pedigree and source of the experimental material are presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. The ANGRAU recommended packages of practices were followed to establish healthy crops for three years. The plot size was maintained by four rows of 6 m long with 10 dibbles spaced 90 cm from row to row and 45 cm between plant to plant. The data were recorded for five randomly selected tagged plants for all the traits except DFF, which was recorded on a plot basis. The recorded data were analyzed for various stability models, \\u003cem\\u003eviz.\\u003c/em\\u003e, AMMI and GGE biplots, using the software GEA-R version 4.1 [Angela et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e] with the following model equation:\\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\\u003ePedigree and Source of experimental material\\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\\u003eS. No.\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eName of the genotype\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCode\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePedigree\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eSource\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGBHV-193\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePure line selection derived variety\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eRCRS, Bharuch, Gujarat\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRAH 1075\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eGSHV 99/307 x PUSA 9127\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eUAS, Raichur, Karnataka\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNDLH 2035-5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNDLH \\u0026ndash; 1905 \\u0026times; MCU 5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eRARS, Nandyal, Andhra Pradesh (ANGRAU)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBGDS 1033\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eGSHV 99/307 x PUSA 9127\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eUAS, Raichur, Karnataka\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSURAJ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLRA 5166 (CCH526612X HLS 329)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eCICR, Nagpur, Maharashtra\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSAHANA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e--\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eUAS, Karnataka\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSIVANANDI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eG7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNA 1290 x Krishna\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eRARS, Nandyal, Andhra Pradesh (ANGRAU)\\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 \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eY\\u003csub\\u003eij\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;\\u0026micro;\\u0026thinsp;+\\u0026thinsp;gi\\u0026thinsp;+\\u0026thinsp;ej\\u0026thinsp;+\\u0026thinsp;Σ\\u003csub\\u003en\\u003c/sub\\u003eλ\\u003csub\\u003en\\u003c/sub\\u003eγ\\u003csub\\u003ein\\u003c/sub\\u003eδ\\u003csub\\u003eij\\u003c/sub\\u003e+ Є\\u003csub\\u003eij\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003ewhere Y\\u003csub\\u003eij\\u003c/sub\\u003e is the yield of the i\\u003csup\\u003eth\\u003c/sup\\u003e genotype in the j\\u003csup\\u003eth\\u003c/sup\\u003e environment;\\u003c/p\\u003e \\u003cp\\u003e\\u0026micro; is the grand mean;\\u003c/p\\u003e \\u003cp\\u003egi and ej represent the genotype and environment deviations from the grand mean, respectively.\\u003c/p\\u003e \\u003cp\\u003eλn is the eigenvalue of the principal component (PC) axis n\\u003c/p\\u003e \\u003cp\\u003eγ\\u003csub\\u003ein\\u003c/sub\\u003e and δ\\u003csub\\u003eij\\u003c/sub\\u003e are the genotype and environment principal component scores for axes n and\\u003c/p\\u003e \\u003cp\\u003eЄ represents the error term [3].\\u003c/p\\u003e \\u003cp\\u003eFurthermore, the AMMI stability value (ASV) was calculated to rank genotypes in terms of stability using the formula suggested by Purchase \\u003cem\\u003eet al.\\u003c/em\\u003e 2020, as shown\\u003c/p\\u003e \\u003cp\\u003ebelow:\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003ewhere SS represents the sum of squares of the first (IPCA1) and second (IPCA2) interaction principal component axes; and IPCA1 and IPCA2 are the genotypic scores obtained from the AMMI model.\\u003c/p\\u003e \\u003cp\\u003eThe yield selection index (YSI) was obtained by following the method devised by Farshadfar \\u003cem\\u003eet al\\u003c/em\\u003e. (2011).\\u003c/p\\u003e \\u003cp\\u003eYSIi\\u0026thinsp;=\\u0026thinsp;RYi\\u0026thinsp;+\\u0026thinsp;RASVi\\u003c/p\\u003e \\u003cp\\u003ewhere YSIi denotes the genotype selection index for the i\\u003csup\\u003eth\\u003c/sup\\u003e genotype,\\u003c/p\\u003e \\u003cp\\u003eRYi is the rank of the mean grain yield for the i\\u003csup\\u003eth\\u003c/sup\\u003e genotype,\\u003c/p\\u003e \\u003cp\\u003eRASVi represents the rank of the AMMI stability value for the i\\u003csup\\u003eth\\u003c/sup\\u003e genotype.\\u003c/p\\u003e \\u003cp\\u003eGGE analysis was performed using the software GEA-R version 4.1 [Angela et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e] with the following model equation:\\u003c/p\\u003e \\u003cp\\u003eY\\u003csub\\u003eij\\u003c/sub\\u003e\\u0026thinsp;\\u0026minus;\\u0026thinsp;\\u0026micro;\\u0026thinsp;+\\u0026thinsp;Gi\\u0026thinsp;+\\u0026thinsp;Ej\\u0026thinsp;+\\u0026thinsp;Σλkαikγjk\\u0026thinsp;+\\u0026thinsp;e\\u003csub\\u003eij\\u003c/sub\\u003e\\u003c/p\\u003e \\u003cp\\u003eWhere Y\\u003csub\\u003eij\\u003c/sub\\u003e is the yield of the i\\u003csup\\u003eth\\u003c/sup\\u003e genotype in the j\\u003csup\\u003eth\\u003c/sup\\u003e environment; Gi and Ej represent the genotype and environment deviations from the grand mean, respectively; \\u0026micro; denotes the grand mean λk is the eigenvalue of the PCA axis k; αik and γjk indicate the genotype and environment PC scores, respectively, for the axis k; and e\\u003csub\\u003eij\\u003c/sub\\u003e denotes the error term.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec4\\\"\\u003e\\n \\u003ch2\\u003eAMMI Analysis of variance\\u003c/h2\\u003e\\n \\u003cp\\u003eThe AMMI analysis of variance for the pooled means of five yield traits across three environments (E1, E2 and E3) of seven cotton genotypes is presented in Table\\u0026nbsp;\\u003cspan\\u003e2\\u003c/span\\u003e. The results revealed that the greatest proportion of variation was explained by the environmental sum of squares, followed by the genotype sum of squares and the GEI sum of squares for traits such as the number of bolls per plant (56.9), seed cotton yield (48.39) and lint yield (40.40). However, for the trait ginning percentage, the GEI contribution to the sum of squares is greater than the environmental sum of squares and the genotypic sum of squares. This clearly indicated the varied response of genotypes across environments. The presence of a considerable amount of GEI sum of squares necessitates studying the stability of cotton genotypes in different environments. The partitioning of the GEI by the AMMI revealed two multiplicative axes, namely, PC1 and PC2. These two principal components cumulatively accounted for a cent percent of the GEI interaction sum of squares for all the five traits studied.\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 2\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eAMMI Analysis of variance for seed cotton yield and related traits across three environments\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSource\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDF\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eSCY\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eLY\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eBN\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSS\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMS\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e% V\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSS\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMS\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e% V\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSS\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMS\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e% V\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eENV\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4717992\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2358996\\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e48.39\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e462766.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e231383\\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e40.40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e393.47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e196.67\\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e56.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGEN\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2750548\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e458424.6\\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e28.21\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e344510.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e57418\\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e30.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e170.98\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e28.50\\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e24.73\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGEN*ENV\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2281099\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e190091.6\\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e23.39\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e338043.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e28170\\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e29.51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e126.92\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e10.58\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e18.36\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePC1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2049790\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e292827.1\\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e89.85\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e289289\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e41327\\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e85.57\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e81.74\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e11.68\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e64.40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePC2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e231308.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e46261.76\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e10.14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e48754.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9751\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e14.42\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e45.17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e9.04\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e35.59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eResiduals\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e42\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1742857\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e41496.6\\u003c/p\\u003e\\n 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\\\"\\u003e\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec5\\\"\\u003e\\n \\u003ch2\\u003eAMMI-I and AMMI-II\\u003c/h2\\u003e\\n \\u003cp\\u003eThe AMMI model adopts a unified strategy that combines principal component analysis of G\\u0026times;E interactions with analysis of variance for genotype and environment main effects. The results are illustrated graphically in a biplot that allows concurrent visualization of genotype stability and mean performance by plotting the main effect means on the abscissa and PCA 1 values (interaction effects) on the ordinates. The horizontal line shows the interaction PC1 score of zero, and the vertical line indicates the mean of the genotype effect. The abscissa (X-axis) is divided into two parts by the ordinate (Y-axis), thereby separating the genotypes with below-average means from those with above-average means.\\u003c/p\\u003e\\n \\u003cp\\u003eAnalysis of variance revealed that E, G and G \\u0026times; E explained 48.39%, 28.21% and 23.39%, respectively, of the total sum of squares respectively for the trait seed cotton yield (Table\\u0026nbsp;\\u003cspan\\u003e2\\u003c/span\\u003e). AMMI analysis revealed an interaction component IPCAI, with 89.86% of the total interaction sum of squares. Genotypes, G3 and G2 were the top yielders, and exhibited above average performance with positive interactions in quadrant I with environment E1 (\\u003cstrong\\u003eFig.\\u0026nbsp;2A\\u003c/strong\\u003e). However, genotype G1, with above average performance but with negative interactions, was present in quadrant IV with environment E2, and the remaining genotypes, G4, G5, G6 and G7, with yields less than the grand mean and negative interactions, were confined to quadrant III with E3. Genotypes with IPC scores close to zero exhibit less GEI and better adaptation to all environments. The G7 and G4 scores of the genotypes were close to zero, and the plants were better adapted to all the environments. However, neither of the two entries had a yield higher than the average yield (1352.68 kg/ha) (\\u003cstrong\\u003eFig.\\u0026nbsp;2B\\u003c/strong\\u003e, Table\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003e). This indicates that an adapted and stable variety might not always be a high yielder. A similar result was observed in the AMMI 2 biplot, where genotypes G7 and G4 were close to the center of the biplot, indicating their stability over other genotypes. The AMMI 2 biplot also revealed that E1 was a highly interactive environment where the G3 genotype exhibited a high yield ability; similarly, G1 and G4 performed well in E2 (\\u003cstrong\\u003eFig.\\u0026nbsp;2B\\u003c/strong\\u003e). There is no provision for quantitative stability measurement in the AMMI model. A measure of this kind is necessary to quantify and rank genotypes according to the stability of their traits. Accordingly, the AMMI stability value (ASV) is used to study the stability of traits. The variety with the lowest ASV determined by the IPCA axis and IPCA scores is considered the most stable. The lowest ASVs were recorded for G7, G4 and G6, which are considered to be stable. However, the parameter yield stability index blends yield and stability across environments, suggesting that the genotypes that exhibit lower YSI (G7, G3 and G4) are preferred because they have high and stable yield performance (Table\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eIPCA components of genotypes along with ASV (AMMI Stability Value) ranks for different characters\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eS.No.\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eEntry\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eCode\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"10\\\"\\u003e\\n \\u003cp\\u003eSeed Cotton Yield (Kg/ha\\u003csup\\u003e-1\\u003c/sup\\u003e)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eE1\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eE2\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eE3\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMean\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRY\\u003csub\\u003ei\\u003c/sub\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIPC1\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIPC2\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eASV\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eASV rank (RASV\\u003csub\\u003ei\\u003c/sub\\u003e)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYSI\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGBHV-193\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eG1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1411.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1805.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1059.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1425.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-0.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-0.24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.99\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRAH 1075\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eG2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2261.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1472.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1024\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1586.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8.87\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNDLH 2035-5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eG3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2000.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1895.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1084.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1659.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-0.40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBGDS 1033\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eG4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1383.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1600.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e823.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1269.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-0.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-0.31\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.86\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSURAJ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eG5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1028.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1483.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1016\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1175.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-0.58\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.31\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5.16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSAHANA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eG6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1005.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1305.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e738.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1016.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-0.32\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.85\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSIVANANDI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eG7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1483.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1495.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1029.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1335.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e-0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.31\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e1352.68\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"13\\\"\\u003eE1\\u0026thinsp;=\\u0026thinsp;Environment 1 (2018-19); 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\\\"\\u003e\\u003c/p\\u003e\\n 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B98kKfz9rjWns2KDt1O4eaMeyx6AdtntNGZVqXwLUEdi8VJqAJSu0h5QFGn8nvMxuKPjcS/W46NzN9/CizzzV0ItdiTia9c7PPefS5jnX+13jsU37Wab+y2pZskKPsyafutMu5jK/6J1vtoE4baFPss6Y/10gbZTb55prKyNn1mZNy+ZxrKs981k676FsVD0/mIk+ZNmpXznetrLfy7lr70y+eLcnDPmr0arP9+pO+b+nCdnRcw0q9zEk7/9//+3+/5T36KMlF9tOmKZM56Vjaro/UrIOMus01ZZIZchR9wA7n5zo+z3g7j3HtmfMZ4784hWzK078qztf+lQx9yuEfJdmzDmX6qh/WzsUv56xYK0/Nuq6Nfp9TZvU885G5l5hoi3aqF12OGTPq9HcrHvwPQtWDjuTmHHW77k+Yf51r+HtNwTf0zJK85tr4oG0zHujSLvWmv8xL22jrT9qAXtdQD+OpK+1KeXU6nzrHsR9mOY6uaXfak+syDx2U1JN2qsu1U1c+T7u0KeNP32ynvc6Ba8aNfuyetif/lfy0aeWLfTP39C1jw/NcJ8fRleM5xnO25T7Zp4w2ZR/PFnnBhbLik3FF3kJ/5r86HG9dArcQ+DuzbplV2cMS8HB4xoMhD1wP2Tw83yJovEh8AbieLzTqsxVtT5484+czF3KND4eWtyFwlr3jh9vbUOkqJVACJXAsAr1UHCsef2zNs14quETNjzw+fGffHwPeUMA6fGiuPn78MF+Nbag7RLf/pm0a89YXtbn+UdrE/GwxPQq7a+xw36wu6tfMf2sZ98v8lwpvbUfXK4ESKIH3ItBLxXuRf4V1/bdk/lvlZ/vTCi9U+v+WH7+uPT8y/dDQpjn+CmlwV5WrP/3Bp5b/ECDuz7bP3ir2nmdv9S8G/tQv7Oze+FOKnV8CJXBmAr1UnDl6tb0ESqAESqAESqAESqAEDkCgl4oDBKEmlEAJlEAJlEAJlEAJlMCZCfRScebo1fYSKIESKIESKIESKIESOACBXioOEISaUAIlUAIlUAIlUAIlUAJnJtBLxZmjV9tLoARKoARKoARKoARK4AAEeqk4QBBqQgmUQAmUQAmUQAmUQAmcmUAvFWeOXm0vgRIogRIogRIogRIogQMQ6KXiAEGoCSVQAiVQAiVQAiVQAiVwZgK9VJw5erW9BEqgBEqgBEqgBEqgBA5AoJeKAwShJpRACZRACZRACZRACZTAmQn0UnHm6NX2EiiBEiiBEiiBEiiBEjgAgV4qDhCEmlACJVACJVACJVACJVACZybQS8WZo1fbS6AESqAESqAESqAESuAABD58+PDhR39l0BxoDjQHmgPNgeZAc6A50BxoDrw4Bw5wsakJJVACJVACJVACJVACJVACJybQv/504uDV9BIogRIogRIogRIogRI4AoFeKo4QhdpQAiVQAiVwGALfv3/f/GvBl4z88uXLz7mfPn36kXp4vlf5+vXrzzX2dGrHx48fX7wsc7fWwIbPnz+/WHcnlkAJPB6BXioeL6b1qARKoARK4A4E+HvFXA4ofqTb3lOf8/jwpr31cb6nZ2vsmksFc7kU7F0q8GmrMI919gq+9WKxR6hjJfBcBHqpeK5419sSKIESKIErCeTlgEsB7b2PdNXmvNe4VLjOpXrvUsGFYuuChM1bY3NNfP327dvsbrsESuAJCfRS8YRBr8slUAIlUAKXCeTlwD+pyH+774UBufy3+jlPmfknFXzwI0fNBzzP/PhAp3Yd/1SCPnR4ubGNFzznzwuBlwr1a2PqRGYWdLk+Y/qea3iRQLfrTT1tl0AJPBeBXiqeK971tgRKoARK4EoC+RE9P7T9MOcjP59Rjawf2luXCi8HjDvHD3w//r1gUPNhv9KpfnTkM230YQvzeXa+em3/NOCvf2iXNthm/Xx2Dmtqt32tS6AEnpNALxXPGfd6XQIlUAIlcIFAXg68OPgBnR/wfqT7IZ7zlOODfBY+6tHH/JRTz+pPCNCRsj7Pftro1l7qvESkjWnX9CUvEj6zpiXXt691CZTAcxLopeI5416vS6AESqAELhCYH958mNPHx3V+TM8P8Zyn3OpS4aWBWh3IK+s4Y1mmTu1iXS8kyNPPbz7TTht/Cvz1Dy8OqYdn5PmpzznYMvsca10CJfBcBHqpeK5419sSKIESKIErCeSHtx/99FH80OYjPJ8Zy3nzApBLqxMZ5+UH+hxXLnWyPpePVUGX+vLZtfJPLnI+9qdO100Zn9GxpUeZ1iVQAs9BoJeK54hzvSyBEiiBEriSgP+2no/r+WPM4sc9Mv6bff90gb7/+Z//+TV/68Obj33nIpMf86yTa3DJSNuUnzYil3akjlyLeSu7mJv9U/8cY72WEiiBEuilojlQAiVQAiVQAiclkJcMP/73/mThWjfzspOXEtdgXfrvsda1NlWuBErg2AR6qTh2fGpdCZRACZRACWwS4KPeP31AiD81uNeHPheLf//737/+CpVG8CcV/CnMvdZRb+sSKIFzE+il4tzxq/WvQMB/E9f6n3/1o0zKpDnQHGgONAeaA82BZQ68wjdZVZZACZRACZRACZRACZRACTwRgf5JxRMFu66WQAmUQAmUQAmUQAmUwGsQuOpSkf8VCf/HWdMY/o6lfxTi3++ktm/+Fy3m/LO10zd9tO5/CWM7mjKihiG/a3nx93edv71CR0qgBEqgBEqgBErg7Qjwjcv/BmlV6Ofbhe9kvnf89vF7htpv5PzeXul6aZ/fT/lfbnuprr15Fy8VAMBhC3C2jNJo5Ll8UATo/EeqZWMy4BvP9F9TkLtW9hp9R5aRVeaO+XILA+ZnPh7Z55Vt2o8P+cvDyAMo99FK15H7rvHTnMDP3ENH8+saX/K/wIN8SwmUQAmUwPMQ8L09v2f4zuFH8Z3HpYJCzfvP8Z+df/1npP2Gtu8eNWu99vvp79vChsU6ncNbRvFhwC8NB6IAU8cjPJsgL/0ggtNMwEfgsvLBDTfHYHcLAz/wpp4ztb1M5aEx99RK5kw+YuvKh/ST/M9f8jiar5d88VLo+XdLTh/N19pTAiVQAiVwPQHOe79N5vcg/bw/LHwP5zfx6tto6nDun9b5bf6nurbmX7xU5L+BSzArhYJIuMBOgL6ccS77V/qO3odv+IHfcJp8/GDSZz888MtEkoNtmMCPn33IUOR6dC7TPjlh/16Bo8zMJeRhQj88JgN1M87zGYr5sPcRfY3M0X3d84GYGi9javuIfu35knHUlyP6UJtKoARKoATuT8BvQL9TcoX8rlm94xz3exiZfKf4TUQ/30C8iyi+axj3e0ld+a2kXuYgyzy/qWjneNr90ueLlwoU44yO7RmhYwBRnj6N1hHG8/mlxr/3vMnFYGuXPmYQTRg/UmhT1EUywIyaAkfmK6P8z46T/EMOk0+ar//U+YwMDJibeUW/bfTzk1nqPeKzscd+Yr2Kacoc0YdrbEoftvxED2PE+MjlGl/IP/w4Sx4emXdtK4ESKIGzEOAdRvFDf77TfX/wfpjvOr93/M7jWyaL30+8V1gHOedQu6ZzfA8xxrN6GWdt23Md5/9pfdOb3Jf/hKIRgqUtCGQ1XrCMC8UxdZyp1gf85gMR/7LIgNpn5lBkaTt1pY6UOysrfTOZ0z+f9ROOXhbocy7PlNxAMiXH/DH36MV9oM3mQNqtzBn8SbvzWR/2/EQeuaPn9rW+mJ/ma/LocwmUQAmUwGMRWH2H8L5YFT/457j9zJnvDvWv3pG+b3jHWtDFj0Kd313IOWeu4/w/rf+2ZEMTjqQzfuStPnamkb6InW+bpdTj2Mbyh+7Wh+m3bPCNIFLnM04xhzE/KNW14oEc7FZjhwYUxuEDv1n0Xx6w44csfXKRsRsCPZPp1H3UtvsAP/Vx2poyc+ws7fRhy098Qe7o5Vpf8IOD/Aw+HZ157SuBEiiBoxOY32XzWyc/6vGFd8Oqj3n0+/2o337n+K1kP+8ZxvKbiDH6+c1n2trm++yn0J3/8c+vvLGADumobcT4UMBIx/zwSxWMM4fiXOTz2Q9H5XL+kZ+1e/rtB4U+UuczPsnORFHXisFrJsBb8dV/2bAueeDm0n/qfEaOHHIem4W283lWhzI/Bw/8D+PpvtHUbG/JKHuGesuH9NPY4c8q94/i5zW+aCuy/N6rsCf8rWxwD/niWcmcsU+fZ+357LniuDHyLD4zD/NT39hLfmzYZz1jm3Pdj7LJsSPvz+nTS9rmgZxoX8sw55pHMsQWdcr3JfYddQ4+pa/ayTmv3/DhR1/mFOPsS4qsz8QI2/Eri/7Zjz/pE8+Tl2dTyqnTbyc50Z99nufK0zYH85lxeLsGtc/OvUd91aUCBzQco/woAJpJgXE888sCiDyMBI6c/QI1CDn/qM8GVZ+z1g/7YCM/g63PyPDsOO1ZGJ9JOGXO0E6f8VMW2m4+MSZDxpK1nBzPOeaT+o5auwfcR/oIH8tKxrGz1CsfiJF+ZuyI+ZHjt+eLfhhPctTn94qV+2QydQ++xsvkvXzNdfGbn8W4mXPwWJ09yL13zLT5pbV5qK/o0X98M/bJxzzRd2WYZ5HZzCXHH6le+XqJod8+coclOeYec5y+o59zt8ZSX/FrFnLLnDE3zTOZOu5cmdk+cp2+4x9FPzPW+OS+on/Lx+SVfqsr923qkyVr5/r2Mx/O2c75qTfXfenzPzPhpZo679UIkLweWK+2SBW/CYH5gvHAoPbAZZNn/zx438TQP1zkkp95+OnrUXP8ki95QGcc/xDhH003h+YLTO6z/48WO9Bk/M6XJHuHmOQeMp6Zb3A5ezG26ZcfEvNswVflkw39yDLPsmLo2KPVK1/3GCq/yh/yDJaOydv2I7DDl9V+wre57+BoHlIznucQeZu5+wh8ntGHXioOHHU37CMdQgfGXdNK4GEIcGbw0ubnixzn7M+XOf3K5lnjxYQxPzydz5gfE44dAd68VPhBmLbx4YJPMkifU+5sz8YmP8z0nxyYfhvfzI+Vz344HynOKzvv0bfydY+heyCZb9kh/2tkt3QcrZ+ckxmcssiGvbYqjpt/z5BfKw6P1reO9qN5eVJ/fEl0s500gDW7BN6JAGeH5wc1xXMkP6jp5+OSDwJe7oxR+6HA8/xQ92OUjyOe/Th/J1d/W1bb8IOfPv8mFH9/m3H5KIM/W/OUOWJtvPOj1Q9iecDHYp/trdpcOCOTLZ+2+le+7jE03/ww3tJLP7rnh/ee/NHHyDNzbSuX5MP4PCfMV/ffM+TX0WN6D/t6qbgHxeoogRIogQMR4EXtJcEPSV/e+YJXxo8C6ny5+1GgDlzk2Tb1/Fh4TwzaxseOPq3scVw/VjJn6/MjzQ897PeDePXRi+8wWo2l76sP7Rx/pOeVr3sMyX0YJvMtHo90ocBHubjPqPPskEOeMTluPwzhdw1DdbY+LoFeKo4bm1pWAiVQAi8i4AXCjx7avrR5+XsR8MW++uDxY8kPdQ3Jdj47/p512uNH9so3bEwO2iyP/Phx7Oi1/hpn7PXDD79mcWzla+pgfOuDceo8e3vlq5xWDGVOPUsyZNz2ivece4Z2+oFvcz95xugLe3Nyoo95W3vUua3PQ6CXivPEqpaWQAmUwFUEfHn7kcTL2zJf/r7YGUeeDwTmI+dHds7n2XY+q/8962mPl6r8ANK+ycF+5qzkHT9qbcz8eMXOvQ9ixmHALwv+5we0OXRGJunXNc8rXy8xJOfcK67BHOPgB7esH+EDmlzTP32Wg/346zmEDO2ZQ+ZsyqnvrDU+EmN5GHfrrfjDgDmr4jnGOHsT2a28ModZz31MvaV7td6f9P1+mvyJps4tgRIogRJ4dwK+vHgRUWjzouHF4hi1L5l8OfnCyxcTferwI4C2/Y69t+PYkT/sSZ/1LfuQl4P2n/FSkbEwHn6IyMSPPf20xn9lqFNu6pWhcx+pztyGA+1rGU459g8l95aM0XvmkmeDuTL9Z4/Rl0xXfrsXqR+hwCbPE7nomzzMD/up3YcydYw56KHIS5bml+M5Z69qA+wAACAASURBVOqZtil777qXinsTrb4SKIESKIFTEvClPV/Sp3SmRpdACbwZAc+O/JiflwovAV4KNI5+ZecYF/k8j5BNGeflxSzHXYMa2a2xlPuT514q/oRe55ZACZRACZRACZRACTw1AT7W+dOoLH7w26dMXgAYo99LSf5JB2P5J0OrP+Fw3MsCevJi49quM23M8Xs8/07gHhqrowRKoARKoARKoARKoASehAB/ojAvBF4qLv3VNy8Eys9LgZcR9eSlZF5G5sUDnfZ5Acn59w5PLxX3Jlp9JVACJVACJVACJVACT0OAj/etS4Uf/lwK5ge9H/peGKi3/rdLXjqos9iPbi8oOe6za81Li+P3qHupuAfF6iiBEiiBEiiBEiiBEnhKAnt/UgEQP+jnhYD+LF4u7ENvXkS4NMzLi3+Sge68MHiZcQ1tSH2uc6+6l4p7kayeEiiBEiiBEiiBEiiBpyPgh3067p8g2McFgUuDf5rABcDnLZl5WaE9LyZeHuZlA53IeqlY2ei696p7qbgXyeopgRIogRIogRIogRJ4SgJcGPyTAj7w/VMHav90wL7//b//969xLxZ+9CvDZYBLBDrtW10cgM3lQT0JPy8VWzIp/6fPvVT8KcHOL4ESKIESKIESKIESeGoCXAK2PvrfA4x/guHF4i1s66XiPSLdNUugBEqgBEqgBEqgBB6KgH+6cCSnuFy8xYUCnz/4Ryqtf/9/Yy2P8mgONAeaA82B5kBzoDnQHGgOXJkDR7pN1ZYSKIESKIESKIESKIESKIHzEehffzpfzGpxCZRACZRACZRACZRACRyKwO6lgv/VOb+WEnhNAvlfNuCPGCnzv2JwtP8B1GvyqO4SKIESKIESKIESOBuBzUsFlwk+8J71UuH/at6/R0fbD13ZOGZt8PkAts//cUz2MZb6lWF+/ifF1Ddr51JnSbuu6U+Z93r2QuF/hg074JN5J7vk9F723mNdcyN9TL0yUe6sfmv/lp/4zH+VArkj++h+w07PgIzXfNbvuT+n3Hu1r/Un8zBjyLM+vpcP91iXnHsEP25hYewynnO+MrDJknlz1NxOe1/r2Xc0Z9deMbfm2faoeed7Gr953iqPfq5s+f2n/b4r4Xvk/ff7qTG8ZvPsHT5D/GGaHp65MTgI8hCBTX5g8JyHBxsn5YFD35SZBzdyMEd2VbRtJha2GiueXRs9+sF42rzS/9Z98FjZpC/aM9nZf7Y6/Zox1BfjZdtY2j5Dfa2f5jk+Tr+P4qe+uPe27MIXYnpUP7T7Wn9W+/Lo54k+XqqJ0ZFfzJfsf8k4Pht76lWepgzPefZwVsOMX77HXmLLWeeQ/zJhf6z2CL5t9cP0EfMOn8wtz8EtP1dsHuVcea28hg+/M5ReKhZR4tBwg+Rw9q0OlPxIJAE8fNRB3zyMkZnJstp06rDOtejDttSTLwDncKDxO0rB3unHlm0rdluyR+7Pg3bmx8pu5K/Jh9Xc9+y7xs+UwcfM3/e0fa6ddu7FjFw+qg/p0zX+uDfneZVzj3aepI+XnvHrLPG65Mu14/muIY6rXN56j5APKT/lrrXh7HL4ne/QuT/0j9xavdueJe/IleQkl0c/V/Tz3jU8yacV03uv9af6eqkYBDlsr3nZzEsF7Txg5iHMMvSlDH0kCQeVhfWv+YicB9bcxLRZL0uuk/3v9ewBMz9U8I1f2rti915232Nd4j7js9J7rdxq7hH6rrH/2px/b3/Yl5mraY/72NylffSy549+IoNPq5L7czV+5D7844dvZ4jVPVjOOM42a2y9R8gDfpbZtv/Ra97f7g18nW36HPf9luf8s+QdZ0P6bV7I5lHPFf28dy039mzuw3uvcw9967fFX5ox/swvjpcAIngELjeEhwP9HLoUNwV9/OZlIecos5JDF/0W9JpA9q1q5qQc87QN+Tzs0pYjvUC1izoL7fSFMfom45xzpmf2lDlxye7J4ZL8kcav8ZN8lMXMgyP5QhywcysH8dVY4QeyRy6X/EnbOVv4WfQPH490nmjfLTVn6FZMb9FzBtmZk7OND1vvkZkDs30G/+9hY75X0Tfbcw32iudCjj163q18Tv95njn0SOfK9PWe7dW+vaf+P9W1++Yj6M96qZgvy3kIzA3hx5Ef+qvDhL7VCwzGrofeawqJ5VrKo5t+flvrXLPZ1fcWNbbOHLuF3VvY+Fpr4Ltx31pjstmSO3L/NX6Sl0f3lf2GL+TnLNif/ey/bE/5I7T3/En7kFvFhr6jnSdp97XPj+DDNb6Su1lm27HVe2S+72bbuY9ewybfu7O98n8rv7b6VzrO1Mc77Zqz79HPldeKGedu5uBrrfNSvb+fMkMLB8fqZTLEHq7JQTE3PEGk37I6VBmnn8KmmjroSx3qUpY1nK8ODn5+cx59W4m1sg19yE+btOG9ai9j0+9p5xa797L7Huvi896lAp/3xu9hw1vouOQnNuDrGc4abMTWWejPWJG/W/tzzn3P9pY/adM8lxw74nmibbfUZ8i7W/xBlhyd7w5y0tylnmfsXCPfI1M+dc15j9Imv2VITcl9zvh8L69838qvrf6VjrP0weRav5DN974+0n8pN5V9xvpavu/FZvNSQbDdUEd34t7wPJDT73mA5IHL+n4c+2ExD2Fk6Ns6hOhnPda5phCblSx2bW1IbFtt4mvWe00Z/DDXrNM348HYFr/XtO+1dBOn9HOuc0s+zLlHal/yE1vJS/fOkWyftmzlX+5tYrolN/W9d/saO892ntzClJw7Q97d4tOWLH76TqPe83v1HiFXyO0z5fcWi5f253sdRpfep4wzZ5ZHzbs8T8y16bvtRz5X9PHeNbl0Kefuveat+jYvFbcqekR5P3CtPRzYLPZlbbA5MOx3k2UfY7MwV9k5NtvqpvajVJu0wTn2I3tpkzun9esRyNiZT6y2iv+Z43WNn7xUlJt5+3oRuE0z+0sbqd1vq5jhg7Ipd9uKryt9rT8Zm8zTRzhP8iw+8x57SaYY1/Q7zx7ju9qP5MHR8/slTG6dI6P8KDan0JXnQHJUBobJ/9b1jyifuWGO6Du13zbmHzKPdq68VlySbebca633p3r/+XX7pxo7vwRKoARKoARKoARKoARK4KkI9FLxVOGusyVQAiVQAiVQAiVQAiVwfwK9VNyfaTWWQAmUQAmUQAmUQAmUwFMR6KXiqcJdZ0ugBEqgBEqgBEqgBErg/gR6qbg/02osgRIogRIogRIogRIogaci0EvFU4W7zpZACZRACZRACZRACZTA/Qn0UnF/ptVYAiVQAiVQAiVQAiVQAk9FoJeKpwp3nS2BEiiBEiiBEiiBEiiB+xPopeL+TKuxBEqgBEqgBEqgBEqgBJ6KQC8VTxXuOlsCJVACJVACJVACJVAC9yfQS8X9mVZjCZRACZRACZRACZRACTwVgV4qnircdbYESqAESqAESqAESqAE7k+gl4r7M63GEiiBEiiBEiiBEiiBEngqAr1UPFW462wJlEAJlEAJlEAJlEAJ3J9ALxX3Z1qNJVACJVACJVACJVACJfBUBHqpeKpw19kSKIESKIESKIESKIESuD+BXiruz7QaS6AESqAESqAESqAESuCpCPRS8VThrrMlUAIlUAIlUAIlUAIlcH8Cm5eKT58+/fjw4cPP3/2XrcYS+JvAt2/ffuUaOUf58uXLLwGezUVkW0qgBEqgBEqgBEqgBI5FYHmp4CPOj7fPnz//4ILxbAW//ZC13uOAzNevX3/DlB/Dzs2+jx8//iZv4/v37z/Xps7iXHUxpqw2UmdhDcey/yjPXijMN+zCXvhT6MdvCnynfz8HTvqPPV/0dStHzuTynp+M8cucPppvxgI75x5PW/PMmHs35Y72bAzcc9M+96hyj5CT+sjZ4vli3yPWxnDPN/N8xte5xP+Zi/t7a5/AhnMMTpNhvqfPdDbcEm/zB/+3zsmUQW517sOOMfKu5T8EZAKXo7O5eEq4GZ4xuPOFQzBXLyCSn4NmtUHYRPMQom8lC2N5s1YePqzhnLRrbl5l0MVY6jhiDNksK6aTmbYj/wiHDT4T41UhZvhJWeXPas5R+/b8zFzdyoP39otYmIvk3dyX2sdYxix9U+aItb5h25Zve2fMEX26xSZ8Xp0/t+g4uqx5u3XeYP/emeM89Jwlr+8dkzyH2TNzT7AefTCiwCllOBtgnJzvbeN76rv2nEwmctJudDzDftTfa2tyCjYWzivzzL4j1euvmmGhL8vR/fBNgpcvHA6TfAkLwD4PX/up2USO20/fpcMZXZlI6MgNuRWTXAsZ9Bw1AbFr+imjrRpuyWVL7sj92E9erfIFu2febcX6yD5i2yU/0/5r9kTKv9fz/FjQjtzn+H2WmOVeunQm4asx1e8z15yVc6+d2Z8924nb1nnDvMnB/M28Ru4Rzt89Tltj+O17FJarvZJ7CZ7KU6c8eefY1npn78ff/F5Z+QODlCHnsr2a074f//iePBqTi5cKAs8GecaSB62H8irp/ZCnnuO0HZchfXnI2J/1/Nhmw+WhNdvMnZsU+T27c733eMbe6Sds6OM3uWHjJW7v4ceta+oXPq4KPsLGMtv2H72+5Gfaj7/KZ//RnrExY6N97jPGyGF+ZyrYu/Jr+nCt3Jx3tLZxyjP+aDbe0x7zc0vnPGNsk++Zy7O9pe/R+udZPdvpL6zzm2nm2Gzn3Ed53jon0z84wIrCfiTn+K6BbfLLOX1+gEsFyfGshcQmwf25AZIHfR66bowcZ8z5WbOB9gqyud68RMw2uohVzlE/fcgfrcALP6mzrDgynodQyp/pOT/K8H1VfKE7Ntv2H7m+xs+0n9jOPMjxozzv7Vv3+tnOTOz1bLrEec//S3OPNG6MyDt+j154B2ydN/g+zxjbcCKvLbNt/6PXk91s679nAOOeZzPHZtu5j1Rfc06kDExok6fmKnXL7wTIr9yPv48eo7X+qvnLNgLtxjiGuW9rRW7+rY9yZDhA8pdWkgC+wOynLzeU/VmjLzfVvETMNnPnOqnv0nop+5bP+DntXl0q6IP12Uv6gO+rQqxy3832as7R+q7xM22eOZBjR3lm32Zcpl34wDhxPfrBP22nfY3dZ4jTyrfsIzaereRp5mrKPdKzH2pbPs0zxjbxzlye7S19j9Y/z+rZnv7Cz70yc2y259yzty+dk/hHPua+m3kFv8y7szO5l/3m1L30vYae9VfNX/9bAIM+E+A1DDmiztz8JPgqoDLS/rk5VvPoY9PsFQ4tX3zIpV765yXnUoxWtu+t/1ZjsMDX5MiHWfLBt2wf1ZdLzPzgxN/8ZZzRkXlHG9kzlWv91KeMrX1Hq4nRXt6Rx+YwsmeLGbyxHz+2CnHdG9+ad7R+8i33H897sT2a/S+x51JObp05xDvZrP5l1kvsOdsccob8p1BfOrOQkduUT11n43DJ3kvnpPPJK3nSN/MPdjnuvGevzakjc1h+rWD4PHRJlmcrM9HnYQATZLLMA2QeysjSd+lQgn8yT73TLnTSt7UJWY/fUQt+buUbds+xI/tyC2P8WhV4eGnE1zMcJCs/7Nvyk/F8eeDrVg6r671q46HN0w72n3vafJ4yR29jf545015itTc+5c/QXp2lZ7D7Vhsv5STj5vg8c9y/+Q66df2zyycT9gHtvUJepYyXseS8N/+sY+YQ9u+9tzwr9XPmpznneOvf//P6R+ax/qo5ssVvZBsbgsTm5+Yw8en717/+9WucfgubynkcLD67ibIvN6DzqZ1Dnbq1SV05Rxvt40BTzxxTpvX7EiA+FvIi88E8yT5lz1Zv+Zl7BZkj+sqHlPvImthQZszYl8rkB8WR46W91Hmhm77hwyOeI/hpPI8cpz+xLd9bxNkyY0yb8bkP813i3Ges3d+5D2QDD8dhOHMqz5F8pz8Kx/QP/5PBzDN8ToYykCVz8yxy/NlrOJ4hd/4+YZ49YvW/BEqgBEqgBEqgBEqgBErgRQR6qXgRtk4qgRIogRIogRIogRIogRKQQC8VkmhdAiVQAiVQAiVQAiVQAiXwIgK9VLwIWyeVQAmUQAmUQAmUQAmUQAlIoJcKSbQugRIogRIogRIogRIogRJ4EYFeKl6ErZNKoARKoARKoARKoARKoAQk0EuFJFqXQAmUQAmUQAmUQAmUQAm8iEAvFS/C1kklUAIlUAIlUAIlUAIlUAIS6KVCEq1LoARKoARKoARKoARKoAReRKCXihdh66QSKIESKIESKIESKIESKAEJ9FIhidYlUAIlUAIlUAIlUAIlUAIvItBLxYuwdVIJlEAJlEAJlEAJlEAJlIAEeqmQROsSKIESKIESKIESKIESKIEXEeil4kXYOqkESqAESqAESqAESqAESkACvVRIonUJlEAJlEAJlEAJlEAJlMCLCPRS8SJsnVQCJVACJVACJVACJVACJSCBXiok0boESqAESqAESqAESqAESuBFBDYvFV++fPnx4cOHn7/v37+/SHknlcA1BL59+/Yr18g5CvlnyfGvX7/a3boESqAESqAESqAESuAgBJaXCi4RftRRf/78+SDm/rkZ+OZlKT9Q88MVf5WZdV6wcoz5WfJS9unTp59D2ffx48cU//mMPepM2xh0rrqcTJs5K330qU/5VZ3+Tj9W8vfsk3uui82Zcz4js/Lznva8pS783CqZpzPmW3OO2r/npzl/5LhmLNiHl4p7Ls+KS3OOMH72ON3C0HNzz+db9B1dVn8v5S/7cOvdA6uz5fRrxGXrXexaeV54FjhmbTwma8fPWuvXXp4pM8982vCa/WdlcU+78xvt6Htw+VWTRvMht5cg9wT3lroMUvrKc/pKcue4myE/gKdM+sCB4Qex/fShZxbWURb9eYDTdg72aSO6tIXxPKB4TtvneraRyXn2v1UNP/3JNWVBX/ohh5Q94zM+E+OtsmKyJXvk/j0/iasvEHIwY34kn7QLe/di5r59z/30Um6PEKdrfcdXz01i+yhnypb/+MuPgq/6PuXZi+R35i+y8uHZ/TrnPks7eSTX9D/50S8/ZWDsmWLfI9TJYyvPUoZnOVD7nmeu/Y/A5U99IJ/MKZgdfQ9uf9X8ReJRg4tfM0AkNX0Wgmei20fC5yGxklGWRJj86Mv5ys4aGRMJHT4jZ1KlbdidLwtkOLyyb65BG93Ipd8rudfowzbWTj/21pks92SPPGae4fuqMM7Y1vhqzhH7LvlJzmXemddH8yXzc2/vEq9L++1ovmHPo8TpJWzday+Ze5Y5+Y4iP/fO0Xzv4B/7M989jJ8xx+8Vq6138Z7+5A2/PPP25p1t7Jo8w/88T1fvOPLrqO+CI8QEZsnwCDalDeuvmr9eNBjP7xE3gRs9N7kvVwHlJrGPhM+NsJJRlsPYdbKPNS8V5nl4zzVme9qNbvr4YWu+FOa6yFDQOW2dsvduy1Ib0I+t5l3ag330X8Pu3nbeW59+ZR7lGvKQhe2UOcPzJT+JpTmOP7N9NB85B7di4V43d/f23NH8erQ43cqXs+VRC/ma/s329Js9mLlLbuT7n3aOz/mP3oZlngGzPf3nfJOX7GHMOeG+m3PO2NY3bZ9t++GVZ/6KH3Nh1LImALMjl81LhUZzoBzdCW29pXZDk8BscBKd5zxAVwmPHPLIUlYy2sFh4kdG1tdsmJSZa2Q718jNqg3Yifw1JfVeI/+nMrKcdtNO/11Hednbf6aaeOkvOXGpIG+uXpI90vg1fhJjWWD7bB/JH2wjXlt7iRiZs+bpkezfsuXR4rTl51Y/scozf0vurP3z/J/t6Rc5TE5YeM6cP/Ie1ebXrOc7crbn2pwLvq9gmfw4T/L8m3PP1J55Ndv6Ao98n63egXDKHHRu6//8y+Lkd0Qml79q4q/bHNGBl9qUgSGBSW42Qr5gVgfG/GBYyWgTenMd+unz40O5WSOTh81cY7aZj865lnovraccvue69r9mDfdpNzZs2Uy/h/Rr2vVaujO/Vgfqat0tFivZo/Rd4yd+Zb7N9lF80Q7yjpilzY5N29mjKznlj1I/YpxuYTvPnlvmnkF2ftzN9vSBPOb9k4U+8t5fjj3b83z3zvbkkfnFXsv9xli259wztWdezXb6Yh5Rk1uzJLM59uztM7C5eKngxXgGR25NtukTbRI8N/nqwEAmN8JKRltecqlgM65s86Df2qx7cVIfvrmh7dNWasbR/5YFv7ApueNLMk574H3Wgl/yz/oS81WsjszgWj+JecYdJkcvxAL/ZqHfPcoY+XsprlPHW7cfOU7XsCT3VrG8Zu6ZZPIdtXonpS/kbeZxjl2am7KP8Mz+zXMan3KfM773PmI8z7fJj7Et1mfkd0ueTZb6C9+jn5va+tY1uXKGfFm+xUl2N9PWx91bA733eiTvLGyKPARykyALC7hk0k8ZXlLqmIcIOujbY4o+izai0znoVr9y1PStEo6+VX/O5RmfXG+OvXabtc03axnPD5/XtuUt9ePrpfIIh+yWn8TYfCdH3yv/LsUgx7U3+3gmTx1Lv6bckduPFKdLnMk3z1Hi5fOleWccxzf94z1Crm4VxlfvC/rM7625z9B/zbtYDjCfrNljvtvg6bNzzlzfkmec9fO8Z765Bzefz8zkXrbDI3nl873WuJeey18191rpQHo4ONncucExj8B5+Do+63Rjjtn2JWU7LwT2zQOatR2z1hbWJInoVxd96UfKshnVsZd82KnctCf97PPrEIC9hfgZA2PN+CMcrFt+4jt+M67v8jhKnXsEO/MjIGOWvky5o/hyyY4zx+mSbzme+wufzxqv9OnSM/sLP/M9MfM33yeeO+7PvffIpbUfbdz8yXex79z0dcUs3/Myzjlnf76UZ+bY9N1+9yN1y38IuAeTDXl01NLIHTUytasESqAESqAESqAESqAETkKgl4qTBKpmlkAJlEAJlEAJlEAJlMBRCfRScdTI1K4SKIESKIESKIESKIESOAmBXipOEqiaWQIlUAIlUAIlUAIlUAJHJdBLxVEjU7tKoARKoARKoARKoARK4CQEeqk4SaBqZgmUQAmUQAmUQAmUQAkclUAvFUeNTO0qgRIogRIogRIogRIogZMQ6KXiJIGqmSVQAiVQAiVQAiVQAiVwVAK9VBw1MrWrBEqgBEqgBEqgBEqgBE5CoJeKkwSqZpZACZRACZRACZRACZTAUQn0UnHUyNSuEiiBEiiBEiiBEiiBEjgJgV4qThKomlkCJVACJVACJVACJVACRyXQS8VRI1O7SqAESqAESqAESqAESuAkBHqpOEmgamYJlEAJlEAJlEAJlEAJHJVALxVHjUztKoESKIESKIESKIESKIGTEOil4iSBqpklUAIlUAIlUAIlUAIlcFQCvVQcNTK1qwRKoARKoARKoARKoAROQqCXipMEqmaWQAmUQAmUQAmUQAmUwFEJXLxUfPny5Qe/lhJ4LQLfvn378eHDh18/1lnlHHKfPn16LTOqtwRKoARKoARKoARK4IUELl4q+NhbfeC9cL3DTvv+/fuvj9r8wF35Tp8yfOTyscuP8vHjx19jylA7LoDPnz//lEOesa9fvzr0j5o1VnaoP3XzbP/UaT9rH6Vob/qAnSsb4fAIl4pr4oCfyJEfZy2X/DT2yh3V1zwbVvvQ+OjH2XJUu1d7Tt+s8S33qv1nq/XZmhg/Q/G9sxfrrbOH94m85rul7P5DQL5wWuWUbB+Vn2f6Xj74jbR1TsLwUfnscdkby7139PN391JBcHmJ7r1I90CccYzDIAvtTHBYpIybKGXmi9eDRhnm+AHlB4tjuTbPHkIzBrlG2sNalFyDtv08I7868H5OfON/wGH6Nu2lDR/ktg6iNzb7xctdEwd89eDA363ceLERbzDxWj/TlKPGVl/cq2mzz2n7Vk4re6Ra37Dp0rngWWduHsmPW2yZ+wkGRzkPb/HjVln8Nt7UkwP66DO+8+wxP/b2wa02nUX+GnZw872OfJ4J+Ak/+Z/F71vs9HzAz63CO9y9BitzTXlza5Wbyjxjbd6s8upoPDajT7BxgCRYffQdzZF72TM3BL4bUBN+bgRZaQOHyZRBx9aBM+erx5q5GQPsUBcyjLkJ3bD056G21e8a71Hjty+qS+vDAPn06dKcI45fE4eUIbYzl47o17QpfbgmZshnjk9979m+1ZczHPzyvMU39uDqbFPXWWv8eoaSsSPuq32Z+ZBnD/35zuE5ZR+d3zXs2Pfm0uTF/KOeb/eMHX7Pb6jUnzkDq2wjRx8/v2dy7rM+JyOezbGj8ti8VGg4G+EZNoMBmhuCtgkOhzxYnTPrPIAcIxnyA5rna3Qxn1hkDPLwYny2nePaWSN7lI/U1aUC+2DDzxy0foRLhbG4Jg7kTMbduWeqr/ETf66Ve0/fiUUe8Fu2kKfm7JbM0fov8df31dl2NF9usecR9ti1/nKmZpntHFtx4X1lHpwtv9O3lzxPVrONTpjRz/5nP/GzH3bsHcYfmZ0MLjGWUcp5BsFHdjne59//xslRefx+yvxlJQElOSgcIvyepbDp85fJDYd5EWADKK/s6sXrZmMzWZx36ZBhPGPAOjlntrER3diRJW3N/vd6hgV2JhNsoa3tcJNr9r+XzfdY95o44LP5Mfncw4a30HGNn9phvG0frcY+4jH3/8pO9uqZYnYpTuxBzx84nMm3VXyy72yxSttvfSZ/s8y2Y1tnj++wa/aAuh6lnqxmWz9lx56y0Jf7hrmPtIf0k9ocyb75zJ6DAT/kLTKjhlnL7wRkJqffR4/T+v2U+csuX6A6QX10R+6FFF+zcIDqu5shx3mmn58lDxD73Gy5iRizP+c7x5r1c5wNp03IzDZ9HFr4MtdjjP6jbNpVbmE7DCn4jUz+HuWldk0c4JCx/gnlZP+4xs8z+OhevfRBcAZfVim0Fac8e1Zn20rXWfrOGquX8CW+WWY7x3ieZw/vDN8rz8QNFpPVbMsOLjLyHcv+yT2ETLad+wi1Z+Q1FsS4+AAAIABJREFUvvAel4M18+Aju2v0PJMMXLZy7ygcfj9lFlYR7Az4QuShumbAMojzsNDxyWj14mWj+DGMzvww4Zk5W2UeQmxcdTFnaxOic3WpwN6jbFr5Zo5t8djq3+J29P5r4oDPxPfM5ZKf+HiUfLzEmVhg71bZ28dbc47SvxUnzsT5O0u8LrE9+97a8o8cNWa+K/K9xPilXEVGPrxHUh6de/tgy66z9l/Djj3heyw/rumXI/5v7bOzskm70+/sXz0nL/LJfLWW5WruM/e5n4/KoJeKERkSOgsBzCByOCCTByrJnxsgDyB0zTlsptTJ3Dx0cn3np376co1ps/NzDfucu7pspMxbPnsQeZhQr+y75kX4lnb/6VrEcOVn6iXu5MuZyyU/yf1LHI7i/9aewj788FwgZj4fxfZLdlyKE/Pz3Lmk7+jjxOjse+sWxvjqe4b6ku959pDL+Z5hH5wtv29hNWWvYQcv9gfFd5p68p0Gu7Ocd9p/bT393puX52XKXZObKf9Mz/A1x47q9+9f0Ee18g3scjPkh63Pc3kOGMeo80OD5xzzOQ8R5k8dcw3bbDB15MUi7fVw9+BXXh3U9lErn+N9fhsCW3EgtuYRh4ZyGfO3sfA+q2j/zLf005XI8aOW3Gf4kvs4fZn73lge1S/tuiVOzHmkS8WR88743Lv2bEnfM48dJy/m2UPbfMn597bxqPpkk777HtdmZeDEmCXfzdnv+CPU86zUp8yvPCe3OMB3a0ydz1TDwn13hvdKLxXPlJ31tQRKoARKoARKoARKoARegUAvFa8AtSpLoARKoARKoARKoARK4JkI9FLxTNGuryVQAiVQAiVQAiVQAiXwCgR6qXgFqFVZAiVQAiVQAiVQAiVQAs9EoJeKZ4p2fS2BEiiBEiiBEiiBEiiBVyDQS8UrQK3KEiiBEiiBEiiBEiiBEngmAr1UPFO062sJlEAJlEAJlEAJlEAJvAKBXipeAWpVlkAJlEAJlEAJlEAJlMAzEeil4pmiXV9LoARKoARKoARKoARK4BUI9FLxClCrsgRKoARKoARKoARKoASeiUAvFc8U7fpaAiVQAiVQAiVQAiVQAq9AoJeKV4BalSVQAiVQAiVQAiVQAiXwTAR6qXimaNfXEiiBEiiBEiiBEiiBEngFAr1UvALUqiyBEiiBEiiBEiiBEiiBZyLQS8UzRbu+lkAJlEAJlEAJlEAJlMArEOil4hWgVmUJlEAJlEAJlEAJlEAJPBOBXiqeKdr1tQRKoARKoARKoARKoARegUAvFa8AtSpLoARKoARKoARKoARK4JkI7F4qPn78+OPDhw8/f88Epb6+LYFv3779yjPyjfLly5ffjPj8+fMvGeRbSqAESqAESqAESqAEjkNg81Lx9evXH9+/fz+OpQe0JD+GeebDl/Lp06dfH8B+HMPSCxrjKWO/86erzl19TBMn53MJPFuRYfqGP8kC//Hz0Qp+XirkSbK5JH/E8Ut+krdHjq/7Dz/cz5OzeYwMvzPuxbPHacbkUpszJs+ZS/KPMI6/xHnPb98pWznsu+vIe/Y1Y8UZAEM4XCpwTk55lvD8aMXc2Don01/yKxmYm5fOodTxTM8wvSbn3pvJ5lcNASe4Z/+geU3AMJIPwc6Az8NEO/IwTxkPmzyAnEMfsjnXMWpsyLVz7OjPMFwdQOkrz+TiSu7o/m3Zhy+XDk8/VM2xLV1H7r/kp+fMKu+P4pe56B5d2TXtP9t+fIQ4reKy1eeZYmy35B6p3/cIPuH3zFn6yXH2JCXlf3b8+PHzzHomZvptne9a9gy/reJ5kZxhS39y3pp/tv7kwfm3996CCe8/OFCQlRP5tcf1bFzuYS884HWG98rmpcLExxGDfQ84j6QjNwV+5WG7dWjvyZAwq83EnL1DKA+6M/HF7slwZb8HDwdy8lvJnqEPfzwk9uzF10uH89789x671k98PPIZY/7B85pDXb/fm/+162sve3GvHD1Oe7avxtiDj3CerHxb9eVZQsxXuQyTfAd5wUAf8jm2WuPR+8iXPKuSz/Qd2ZSf72nG6HuU4oUJf/AL/7YKYymfZyx8k/GWjmfrh8lqzx6Nw/5bZPybi6MZ/972sDF4Ea8OBsZWGyM3WsqgY08XvuZLIX1n7hmSLW3mWZ/ngQIHfsnKuXkQ2Xe2Wr/wcavw8vbFv8qvrXlH6r/GT+wld1d75Ui+YIsxuWQXvpwpZo8Wp0vxcZx46rt9j1zP82a28X2+Y2xzFnH20t46mx+Znb7N989sK+cZQH55tpFv/Cyzbf8Za/ND22fbfmr33BY7x3NOn//zJ4fsv6OX7a+asPwMjoS5b/rIBuCQnQd0HiZpUG6YnDvnO4ePEz9QOJxyfsqcMUb4hd/6d40/HMRT3nlnqH3ZYOtWzDmQffkQ1zP6e42fxgsfffHad7QaG4kXL8JL5Ux78dHidCk2Oc4eW52nKfNIz/O8mW18neeNbfLEZ+SYe8Zz6U/jOT+EZ1v95hW1Z9u8RMy2c89Yz0vEbOsTOSOPyY4x8oqfMs5r/WCXCjdIA7tNgAM3PzjyMMlZfizSpwwbkPmrgowbjTrXUJ7NuDVfmaPW+DTza88f+MHrrCXjj++rkjL5Il/JHrUvfdjyU9vx8QwvEfIOX8jPvTLzeU/2vcceMU7XMsX3M8XqWr+25OY+nG3mzfPGNqwyV+CW7a01H61/fgjPNv4mFzh5tk2Gs31mVvMSMdv6lmxW7JCDGXnX8jsB8ugMXNZfNeELjrgporuP8cd4wshNsjow2Gi5qfYOHHWmPH0e8o5T732Ep9wRn8ktXm7p55Y/8IPZWQt+4ev84VeWOU77THvwWj/1mZw+i3/kH/5tFcbO4ssjx2krPtnPmXPm8yR9mc8ZW95LlHx3ML76QIFJnsWcPRRyOlkhc5Y8/+nAC/7BuZxnMSpgoN+MyzbV05fzeIbXZJ7xyPlnfc7vn5kv+DR5ygguWZBb5WbKPOMzTM/AZXmpwHgDngfJMwZyz2cYuSHcMMrTzzj9ltx09OUBRXuOo8MDTB25WTmoPJjyRaDsWWrZmXPJLcfg80gFPy8V43tJ7sjjl/zEx5nnR/XnUg6yp3PPH9WPlV2PFKeVf7OPM/OZ3m/57pjvHtmQu+Z4yjOe5/J8Vzn/0WveyX7YkT+X3ruTs9yS86MwSx7XvLdkMf0n7y5xnXOeoQ0Xc+/I/l7+qjmy9e9sGwcGQeaw5UfQs9B2bI7nPDeQFxFk/8//+T+/5qZO9bEhlee55XwEiKWFHFjF8ZrDWR1Hrff8zH0w988R/MlLLX7khWEVszN/pJ45TrfmCrHDX35njtmtfrvf0ueZx7KZ55HvG5gdca/eyuKl8rCDASwtvuttWyObrJJhniXKn70mZ2BDDllmftmPrAxk+mz7URaXavNr5t2lee8x/vdXzXus3jVLoARKoARKoARKoARKoAROT6CXitOHsA6UQAmUQAmUQAmUQAmUwPsS6KXiffl39RIogRIogRIogRIogRI4PYFeKk4fwjpQAiVQAiVQAiVQAiVQAu9LoJeK9+Xf1UugBEqgBEqgBEqgBErg9AR6qTh9COtACZRACZRACZRACZRACbwvgV4q3pd/Vy+BEiiBEiiBEiiBEiiB0xPopeL0IawDJVACJVACJVACJVACJfC+BHqpeF/+Xb0ESqAESqAESqAESqAETk+gl4rTh7AOlEAJlEAJlEAJlEAJlMD7Euil4n35d/USKIESKIESKIESKIESOD2BXipOH8I6UAIlUAIlUAIlUAIlUALvS6CXivfl39VLoARKoARKoARKoARK4PQEeqk4fQjrQAmUQAmUQAmUQAmUQAm8L4FeKt6Xf1cvgRIogRIogRIogRIogdMT6KXi9CGsAyVQAiVQAiVQAiVQAiXwvgR6qXhf/l29BEqgBEqgBEqgBEqgBE5P4OKl4tOnTz8+fPjw4+vXr6d3tg4ck8C3b99+5hh5xo/y5cuXfxhLH+OfP3/+x1g7SqAESqAESqAESqAE3o/A7qWiH3A/fnz//v3XBy8Xq9UHrR/FyGZB1g9l52UfF7Ys6PejOvvVkR/aadfUg9zHjx9TxWGfZUdtWeUdPp7FJ/3Yqo2nObGSyzxZjZ+pb5XT2m/OHzm22ogfPK+KeWxsj+xPnh15pky/0qfM1UfKzWf5l2b6uRfvvbieIa9n/t67LZ/cC3MNz4rV/qdPjnPeI7T1fe+c1M88g+yTz4qdMs9em4N7+/i9GW1eKjiEjmz4W4GDgx8SBHSV8HBibMWLvtmv/PQBHfxcL8eZw2bNj2/GkZ+FPuyeslPuCG14Tj7YlX4xjj+PUNIv4jkvovhI/JXj+cy+m7er2OG7+yl9Xsm+Vx82Ggs/srdiljYeOWb6gx/k4FZZ7UsYeD6hZyWzpe9o/fgui6PZdk97iJFx2nov5P6bZ45z72nT2XQlH3LGPZB+7J1nyK/OjZx/5md8cy/tnZP4CIv5LcNc+ZCj6jozk3vbzrvyyO8V/V2+UdwcOPAsB69AZk0g8+N8lez0IbMKeB7o6qZvS08eXspTq2d+BEw9xE5Z6iOXS4ePthMD/MR3P0IdO1vtwYndq3yh33zSN3zOefYfvTYXZ85qt3lq+wyxJWarDwp9oNbv7DvSc+bSVg66N2dMci4cLrE4kt9pC34f/XxMe//kOc8P4jrfGejeO3PYv/wy9n9izxnnki+wo8BhtW/2zjNiAEN1nJHBLTbDZ3U2wO5SLsFonju3rP2IsuzP1b49oq/LSwXJkJvomTbDDBIHBf6vNgiybBJfTnl4q2ceNPTTNxOEjeQarDcLc1hrJtfUgw50IXv0jYmd84DBfvr44Zt+yIa8lPdkdKa2cVrZPA/k3Isr+aP2mZurfMbm6ddsH9EvfCJv98pebPfmvfWYZ8pqXfYdBZmt+Bnf1fwj93mmkG+eM0e2909s01d1zLb9sPCMpc+9aB54Vl/KffU9Wj33wGwnM32XIW048mNeclb20eqtc5LzhDE48FvlE5xg1/I3AVjJ7ehs/vn1Gv9WXJdw5hE+5PTn1toXK4H1kFUHB4R9yE1OOdeNZIKogzrnwXsePIy7DvMdRzZL6slDLWWO8rz1oqLfjZPP2I3fjh3Fj1vt8HAgjqtCDNPH1WV1Ne9IfcTJF8aWnzM/Z/tI/mhLxsW+WV8jM+e8dRsbics1/+KBfMxzxX2b59Bb2/8n63mGZH76/Cd6jziXd0bGeLa1+ZozB0ZnyG19umc9z7DZZi3YZB7NNjJb/O9p6xF0beUJ/X6zsA8zN7Wbfn4t/yHgvpMJzHw+IqPlVw0GG3iM5sA5shNvBdaPwVyPTcIB429upvlClmfypY9EUQf1HEePlwqSDBnaUy518My8I5eVr24i7J6HMGPT5yP7t2cbvm/tq8yH1cG7p/cIY5l3+Lkq7BXiaZlt+49SE6u0d8uus+Qne4vYXPJpdc7gO37O826LyZH655mMH5mvR7L1T22Z5+dsp/5rzpwzxjt9fOnzPMNmG73z/Jpt1350hnvnJDlGDlrgmG36z3J+6sNr1/BMJpxV2X7t9W/Vv37b//jx66MVhTMRbl3kzPIzePOgmC+jyWq+wGAxk4JNNT8u56HFnNx8tLEl7ePjID8QkMeeIxf8xlf8seBDHrw8ywd/fVb+rDU+X/IFmWRzBl+JHzGdv8xf/Ji+zZw/kq/YnnttyzZ8vxTTrbnv0Y9PeWasbMD3VQ7Sn/t0NfeIfcQnY4lvZ4rZrUzznTR9X+ma+zJlklv2P/ozee4+oV7l/eS2dZ49MsNL52RyJGfm9wls0NHyN4GZb+zh1Xn894z3fdq8VOCIHwWPfOBewk/Sp/8wMenp96BRzzxYZhs5+vJgoa1O9TCe665k2KCpJ5/VMzex/Ueq8d1cs5487F/5eCRfbrGF2Ew/cz4xR+bshditCr77UiHXjxxb7cSPPTsZ24vpisN79qVfW3Zs5SAxI0fPWMhJ4wQDn8/oyyWbiZFxuvQ+2DtzGJvvu0trP8p4nk/s8Xw36+M15xnzVnPVcfY6z5PVOYnvnifzY5n8kg1jPp+dyT3sh6t779Ievsd6f6Jj/bb/E40PNpeNwc+PWg9n+3ITcagoR+CVoY9nSvYhw49xagvJox7kc46JhSzrqRc7ch3G2ZTq0W7XaP0+BIwHdcaS+JhLxvtRYoavlvSTPtqM67tyR6lzLxo74zJ9wWb341Hsn3bkGYU/tC3pj+cSMpmn5ib9R/dVv1Z1xvUZPl58P5i7MMl4G9ccV2bm/YrnM/S5JzLvfcfqP/zgleeZMmffM/q4qnM/zXzJPGOuuYacRbbOzTFlnrnOc3vu0aNx+TuqR7Os9pRACZRACZRACZRACZRACZyCQC8VpwhTjSyBEiiBEiiBEiiBEiiB4xLopeK4sallJVACJVACJVACJVACJXAKAr1UnCJMNbIESqAESqAESqAESqAEjkugl4rjxqaWlUAJlEAJlEAJlEAJlMApCPRScYow1cgSKIESKIESKIESKIESOC6BXiqOG5taVgIlUAIlUAIlUAIlUAKnINBLxSnCVCNLoARKoARKoARKoARK4LgEeqk4bmxqWQmUQAmUQAmUQAmUQAmcgkAvFacIU40sgRIogRIogRIogRIogeMS6KXiuLGpZSVQAiVQAiVQAiVQAiVwCgK9VJwiTDWyBEqgBEqgBEqgBEqgBI5LoJeK48amlpVACZRACZRACZRACZTAKQj0UnGKMNXIEiiBEiiBEiiBEiiBEjgugV4qjhubWlYCJVACJVACJVACJVACpyDQS8UpwlQjS6AESqAESqAESqAESuC4BHqpOG5salkJlEAJlEAJlEAJlEAJnIJALxWnCFONLIESKIESKIESKIESKIHjEti8VHz48OFH/r5//35cL2rZqQl8+/btt1zDmS9fvvzy6dOnT7+Nf/369ddYH0qgBEqgBEqgBEqgBN6fwPJSMT/aPn/+/ONRLxX4xuXp48ePP/i41fe8UNGXH7bIUWCinPMMKfLozoKM8smTD2j7px7syg9s9KV86tcXdP3rX//6pVPd1NhlmR/zaZMyr11rg0xZDztlh005Bp/J6LVtvLd+46GPK/0Zy/eIy8qml/bh714hx88aU+zei+Oe30cb24sTfjJOrB6h5Bl69v11Szw8V/ZyVhninWx4pm/237L+mWXlsscuGeW7NvuT6Zl5TNs9I8iPrfM8OSTH7DfHpv62f/zkmtyOyGT/bf+XxUd34qVg+Vj1JWlS52ZgLD9oVx8/jK/40MfmWBX0uK7jrJtr0Y9N6JmyzkF/Hlz087JkHgV9Oa6Pqc8+db51jS3z0oQNK6Z7/W9t90vXS7+In7FKfcTNGJEXGcOUO8OzH29btuInHHLfbckesR/7M6ZHtPEam/biRI5mPp7dX/zxzKE+uz/XxBcZ9pi+Uq/23N7ZQw7ALvPh2rXPLncNO3w0r6a/j86OnDC3yKGtd1u+y/Lcn7mYcpPlM7fP8L5Zf/VG1PIAju6HeCSRM3nZDJncBJA+Sx4s2QejLMgxD905Xxk2H2NuQvqdoww1B5QH+FyDceZjYx5kzmFcG1InenIz206Zt3rGvq3DZ8uGZLYlc+T+jGPmXtqceYY8MT5jwXbykRjvFTjg89kKvvF7hJzci5N+Gp+z5qP25x7kDMK/Zyj5PoLB6vzZOnvmu4Scp+9ZyjXsYMpZN8+7Z2R3zZm+d3ae/Ux9jX3hOXx0Nvtv+78+bB/58OAA2HpJ0j99nwfGKsD25QGdSeY4utSPrM/KKmcy2W/NuAeZL0pkfUbf6sXBPHU7X51vWWMfDLSXteFAHz9t1Cbk8e8Ryire+mVM8Bc5fmcsxo9Y7pVrXkB7899jjBiRi8RGP9/Djnusqf1bccoPKtab7XvY8F469P291n/LdWd8Zxtbts6e+Q6a7bf04z3WmqxmW3bUnAmM+16brGb7Pfx57TXZV7y/9gqcYDGL773Z/8ztM71v9t/2O38N5ZECzAHAb75gVpcKZPzIM9CTRW6U1eHjOh4+zOd5bkL1sM7q4qMe5Bzn2cMMfVuXCvuRXdk4fXqNNvax9vR7y+707TXseSudxA2/97ibG8b4rWy71zqZz3t+sh65iPyZinHBbp/PZL+2XhOneYmYbXWdqfbcIzc9Z89k/0tsnftwttVJTjCWeQ2j5DTbzn3UerKa7el3nguT1WzPuY/Q9vtizxfyy2+VlNvqT5lne3YvZl4dlUEvFX9FxpdMHpyrS0V+8BLguSn8UObQ8Tc/mk0QlubZj6qU82BXB/VcK/Wggzb2K5e2ZgIi51z9zvG3fMYvbXHdPbuVeYQa34nzqsAEDnsyq3lH6ct9hA97xfzfkznSGHHhRznDIb/H7po4ER/9Rdds7+k/+hj++y9kjm7rn9o39+Fsq3919sApc2W2nfuo9WQ12yu/2SeUyWq2V3PP3MeZmOfFyhe+OzKfUmZ+D+TYMz6f7X2z+7YnObY+eh4huDP5CZ4HAf6tLhX2sylWyT83CmtMudlmnfminnpWB9HUw0GHnr1LhZcI42r7veKJHdid/s44YNsW7/ey+x7r4rNxSH30yeO945N2XftM/Ijp/OHLqpCzKw4r2SP0se+mb3luHMHGa2y4Nk7z7MH3RyqPeKnI2OpfvmNWZywx3Tp7pnzqeqRc2PIl/Z0stub4fp7yqWtr7ln7r31Pw2BVmO+7bzX+jH1ne9/svh3cFI8aSA5QD1x8JJnTZ8Y4EGZRbpX8Od958yU8ZfxwzLVWMmkruqcM81nLj7d5mLlObmj7tPU9am3IDzV90B5ilXzsP3NNHKaf+ENeGSPZnNnPmf/TF3w906Ui7cfuuQ9z/EzPW3EiBz17HslfYsOZ8ijxu5RrGTt8Xu25vbOHHCAXMh8urfko49ewS1/hCyfLs7DznMDvrX2V7z3yLTnRfrT3vDlwjzrz8B76XkPH7qXiNRY8kk4CxC8/ZrUv+5DJwiZgPJPfPvpTno2lLuR9zs2HbjdTyqR+NqJz//3vf/969uNT+zzMpl/OzY2eazGem1t9re9LwDhQZ3yJf+ZExjvz6b7WvI02fLU8mp/EJveUfp6x3osTcWM8c/SMPmKzvuDPPD/P6tO1dnuuZM5euyfzffGM74oVO9+z8IcpOcVvntmPzi79kwF5Rcn8csx67r/My2tz+pnkzvC++ftt/0yRqa8lUAIlUAIlUAIlUAIlUAJ3I9BLxd1QVlEJlEAJlEAJlEAJlEAJPCeBXiqeM+71ugRKoARKoARKoARKoATuRqCXiruhrKISKIESKIESKIESKIESeE4CvVQ8Z9zrdQmUQAmUQAmUQAmUQAncjUAvFXdDWUUlUAIlUAIlUAIlUAIl8JwEeql4zrjX6xIogRIogRIogRIogRK4G4FeKu6GsopKoARKoARKoARKoARK4DkJ9FLxnHGv1yVQAiVQAiVQAiVQAiVwNwK9VNwNZRWVQAmUQAmUQAmUQAmUwHMS6KXiOeNer0ugBEqgBEqgBEqgBErgbgR6qbgbyioqgRIogRIogRIogRIogeck0EvFc8a9XpdACZRACZRACZRACZTA3Qj0UnE3lFVUAiVQAiVQAiVQAiVQAs9JoJeK54x7vS6BEiiBEiiBEiiBEiiBuxH48OHDhx/9lUFzoDnQHGgONAeaA82B5kBzoDnw4hy42/WkikqgBEqgBEqgBEqgBEqgBJ6SQP/601OGvU6XQAmUQAmUQAmUQAmUwP0I9FJxP5bVVAIlUAKHJPD169dff831y5cvSxs/ffr0S8Y/+v727dtP2Tm2VNDOEiiBEiiBpybQS8VTh7/Ol0AJPDoBLgafP3/+6aaXg5XPHz9+/O1SQdviJYNaXY61LoESKIESKAEI9FLRPCiBEiiBBybw/fv3X95xKeBiMQsy/GmGhT/N8E806PdPLBxvXQIlUAIlUAKTQC8Vk0jbJVACJfBgBLg0+KcN1/xJAxcPLxLIOzcvHg+GqO6UQAmUQAn8IYFeKv4QYKeXQAmUwFkIeDnwwrCymwtI/tWn+deirrmUrPS2rwRKoARK4LEJ9FLx2PGtdyVQAiXwi4B/YrH3Jw6M5cWBORYvJbZbl0AJlEAJlIAEeqmQROsSKIESeAIC/MnD3qUi/+rTCgcXi5YSKIESKIESmAT6dphE2i6BEiiBByLAJcG/zpR/tck/tfB/kK3L89JA2/9xN3P2LiTqaF0CJVACJfB8BHqpeL6Y1+MSKIEnIsClwb+25OUC971U5F91mn/1CTkuFKv5T4SwrpZACZRACVxBoJeKKyBVpARKoARKoARKoARKoARKYJtALxXbbDpSAiVQAiVQAiVQAiVQAiVwBYFeKq6AVJESKIESKIESKIESKIESKIFtAr1UbLPpSAmUQAmUQAmUQAmUQAmUwBUEeqm4AlJFSqAESqAESqAESqAESqAEtgn0UrHNpiMlUAIlUAIlUAIlUAIlUAJXEOil4gpIFSmBEiiBEiiBEiiBEiiBEtgm0EvFNpuOlEAJlEAJlEAJlEAJlEAJXEGgl4orIFWkBEqgBEqgBEqgBEqgBEpgm0AvFdtsOlICJVACJVACJVACJVACJXAFgV4qroBUkRIogRIogRIogRIogRIogW0CvVRss+lICZRACZRACZRACZRACZTAFQR6qbgCUkVKoARKoARKoARKoARKoAS2CfRSsc2mIyVQAiVQAiVQAiVQAiVQAlcQ6KXiCkgVKYESKIESKIESKIESKIES2CbQS8U2m46UQAmUQAmUQAmUQAmUQAlcQeDD//pf/+tHf2XQHGgONAeaA82B5kBzoDnQHGgOvDQH+icVV9y8KlICJVACJVACJVACJVACJbBNoJeKbTYdKYESKIESKIESKIESKIESuILA8lLx8ePHHx8+fFj+vn79+pta5L58+fJb37WNW9a5Vqdynz+n3KBTAAAX9ElEQVR//sHvkQrs4f39+/eLbhETY5Wc51wYGeup9Nu3bz/HqLNoB/PQfals6bEfPcaKPp8v6WX806dPv+zXD+vMS/uoX1KuXeclurET/Y9S8EfeGYMt/5B5rVxd5diWHZnXMwfZN9OnW3N1a929ftfd27faZZ22Z95OHXvrOoYu9c4611H+lhre15wft+h8T1ljBadr9nPmT3J2L+hL5iVzLLneiqPn/iVbLulxD+Xaq/W0q3UJlMDzEtj8wlp96HCo+JGQB5F9L8F4aZ2X6DzrnL2XNJx8qcN+r6DHmPCsvC8u5/Ky8oVDbPNFcelF5gsGnfy2yiU9zmNtbV7lhHKrmjXSdmTwWbv0kX6ep+xK56rv0jqrOY/aJ9vpX54RPJOzcNsq6DHuPL9Grrp25ph91pkvPGO3diHDXHOeHNKnW3PV9a6t9/a8fKcu8x2e2oydL837S+vM9R+5TdxlOv1Mvnu5xjz0GCeezSdqYu4a5qJrMeYecT5jrJft3FfEnvZWyXlTj/akTejZyomtNdpfAiXwHARuulSskHAg5ct3JbPX99ov5b21jzQ2Xx4r25TxpbKS4SWQLxBfTsrmy4EXSI7PNnNSnvZ8mVwbv6mHeZk32M36ljlu/6qec6dM+ngNwznf9qV1lHv0WoYrP5M14+Ri5mPOee1cnTm0F79pN3P92GKez9g/23Od9PFPn2W92vNzL861cg6y6cOU3WtfWmdv7qONrc5IfEzWtMl58mRVkM2zbuZevlPRQ35ZbKMj18wY0c95a5lt+6n39KTcPL8Z+5OcSt19LoESeBwCf588w6d8qTLEAbI6JPMAHCqual5aR/0cphxsvhipafPzYOTZw9p5GOEazvEQd7469NO248qrBw6OMWfalnqQ0yb6aaOPPp7RSVt99iE7iy8H6q2C3kvjrKeu1OMLK/u0d/ZhNwWm8kmZ+Tz1zLW0J21njgW/WGtViEcyRof2reTRm+usZFZ9l9bBJ/1iDW2ijzY/WdlmHefx7BrOoU1RnlrbzRvajqv/vXP1p9F/7T18WZXXzlXWzTyQk/xWNtkHd/NtS4+y1PBfFfQYm4wdOtVLv7miDtr0Y3/Oc5za+GcfOlflEuvVHPsurbOVa9qO/eaxfhED57GOaziHmuKZnTrop80c9SmvHmOtXMbhp+K//sG4P/OCNdEHS8bMA+WyL3X5jA3OsS9r9Moj+302L2jjH/oszFvp1l/kVjJTj/pmnXpyDJ/TDsewZdXveOsSKIHnIrB+E/71MZCHKM+rg5BDxQMddLTnPNurFx5zHbd2HQ91+unzwPMQYy1lWVsZ56GbPvU6D1n6bGvDv//971+yHMIUdLmOevQDGX6umzqdjw4Z0acOfNE2ZLCFsb2i/9Srgo5cdyXj+Go9/NI356ZP9jl3NabMrKfsXGvlm9ynrtmWI2v4y5xMedZBLwUblJ+1Mjl3bx1zCD2uzTNzXMt+2jDk5zxsSa7OQxY9jFGUP3qu/jT2L8babh81feZi9uez43LJsZk/jCUn2lNmlWOpM5/hbAzcv46v9GzlKj7oPzLYZAy3ciXXQx451pxFLoz7Qz5L5qx25PrOs5Z56thbJ/W7NjpYg0JtP238YI7z8FWe2OA8ZJODNjAP/dqrPtqpRz9Ym2dtSNtcWztpI6du1lKn7BjzmXmzoAMZ15/jtLV1NUZf2jXX0/85F5v0EYboyII9zL1UUk/KTjscm/G1v3UJlMBzEtj8ip0HE4fN6lDi8MoXwa0YL60z9efhhj0epKybduS8ucbqYPZlg5+sYcm5+cx4rkFb29DPc/6QTRmec618/im4+IcvOOpVYV3XWY1jPzKU1XqwTJ7I6dPUp2976+WcqUdG2EG51p7U6TO68iUOH3xdFfxzzdX4Xt+ldSa/fJGzZrJK+3LeXGO2se8MuYqdxCF9Trb4tTWG3D1ylTUy71Y5ljblc+YTdma8VvswY5h68jll8hkZcwUbc+3VWupc+YPeVaEfFi8pl9aBTcYyeWF/+pMcc95cY+U3PrjOVlynnlwD37VN/ejJX8rIKtfKZ8dXNXKrWLjuag592J/z5nqr84B5+IVuyvSZPvPrp8DOP1JPik07HNuyx/HWJVACz0Vg8y2zOphWaDykHaPNAbT65WGp/KV1pv55uNGmcKBywFly3lxjtpmj/N5Lac5zjmtq25RznFoZnnOtfE75fPaF5Msjx3jeW5c5jFvUZZua+KQMfWmvsjJ33LjS78++nIOPWZBRnnrOoT37cr7P177YWD9zZK6fthDbWS6tM+2dL3Laxi4557y5xiqm5t3MmZTNZ/xwjj7hK/OnnOPUyvCca+Vzys/nvdjtrXvPXJ0x1qaMtX3av2pjr2Vr78x5ylO7njLUPjNursz4u5Z5kzqvjYNzjCe+aM+ssWOWS+vMWM5co40OSnLMeXON2XYuuij6wnPK5vOcQ1vbphxjFmVs51r57PiqnnFUZm9dZDInaBMP5ljQK4PsuySjHuYa8xlrdKce9VNv+b3lZ87tcwmUwPMQuPul4lZ0+WJZzd074JHnEOZg45cv3pw310CWQzIL8vTPQz/n5jNzcw3aHrzomQe2aylDO9fKZ2VnvfeBgSzrYtOqrOzxRaP8bNOf9tKevmH3Src6race+61XOuaHl7KznjbNcdqw22Kzkl/1XVpn2otPzLGQP/xgxs+S8+YatGGX5Qy5ik/pY9rPM35txWOVC/SlvtlG50tybNpFfDJmK1tXtmcMUyc+Gr+UyWfkzZUZ7709Dw9155pbz6yRZ+SW3Oy/tM6lcxGf8Je1k23Om2vod8YcefRQMtY5N5+RyzVoEw/61L/ioczPhcZaua7jqxo7Vvntuqs5yE978Bd7LbPNOjJRZq4x28plvdKT41t+E0/yqqUESqAEIPD710owmYexQ/PgmwewctfWl9aZ+ufh5uGdBy9r57zVGujxMM5DF32MWXJuPs81aGub+tIm11IG+VzLOa67qpWhXhX6Vwf87NMuau3aejmkvaypzdSUyWRlF31Tj3J7PhHD/Ahxzqy3bMc37dxiMHXttS+tw3ryRA9rpv3GR/6ulfNWa8BOvfJirrFQT8YinxnP/UDbeKgvbXItZeZaznHdWaMr/eaZOVloz5gwPvu0i1q7VoyYm/bOtRibNqQMz+h1PdrwNX9y/owrsqtclZPrZpzzmfmpk7XSV9r8Zpnxd3zypx/fsPEl5dI6rJe6Z66xJvYnW/py3moN9GQ+8Jzx8Dnn5vNcg3bahr60W+Ypo+2ulTYwtlXQAfNVQYc54ThruwZ9sqIPecrMJ9ppf87L9Weu/VQW/9jToxjxS/vsx0652de6BErgeQn880311wvOF9ms8wDJsey/FieHZerIZw9C+zi8Uj4POGTyAOdAdd5///d//3rOA9gDWjnasw+djmeNHuyzb2Ub9jlO7cvBvqkbZvq3Yjnl0//kjQ7Hpj+5tnPSD/uop/3JF3/VRb1XtvSoA3u3SurmeSWbsU6beGZsrq+MjLbWnv2X1tEf9MM05ZMdPiBrSf75nL7OOB45V9NvWdO3KvhoHKaPzk12ySf1zRg7x5gky5yXz6nbtXNe2pfxUwdzViV5oE/d1qybMtie/jjG+llW9qpTu3O9rRikztXzpXWw13VZI+WTk2OuYWyY+1//9V+/dNDOkj4Y19nn+rO+xracg03T/lyLuDhOf5aMGTq1NWV8RkeyyTW0BxlL+sE6lOxzDnXmiXr3Yn9JzyW/0K1N2tu6BErgeQn8foI/L4eH8ZyXxN5L5CyO8tLNFy92z/ZZfKmdawKPnKtrj9tbAn//icPZWXCZeIR3zdnjUPtL4EgEeqk4UjTuZAv/liv/Tded1L6ZGj42578JpN1LxZuF4M0WesRcfTN4Xei0BFZn3Jmc8U/u8k9GzmR/bS2BEngdAr1UvA7Xd9fKBzgvrrOV/tuvs0Xsz+1trv45w2o4HwHOurP+yx/+qlVLCZRACUwCPRkmkbZLoARKoARKoARKoARKoARuItBLxU24KlwCJVACJVACJVACJVACJTAJ9FIxibRdAiVQAiVQAiVQAiVQAiVwE4FeKm7CVeESKIESKIESKIESKIESKIFJoJeKSaTtEiiBEiiBEiiBEiiBEiiBmwj0UnETrgqXQAmUQAmUQAmUQAmUQAlMAr1UTCJtl0AJlEAJlEAJlEAJlEAJ3ESgl4qbcFW4BEqgBEqgBEqgBEqgBEpgEuilYhJpuwRKoARKoARKoARKoARK4CYCvVTchKvCJVACJVACJVACJVACJVACk0AvFZNI2yVQAiVQAiVQAiVQAiVQAjcR6KXiJlwVLoESKIESKIESKIESKIESmAR6qZhE2i6BEiiBEiiBEiiBEiiBEriJQC8VN+GqcAmUQAmUQAmUQAmUQAmUwCTQS8Uk0nYJlEAJlEAJlEAJlEAJlMBNBHqpuAlXhUugBEqgBEqgBEqgBEqgBCaBXiomkbZLoARKoARKoARKoARKoARuItBLxU24KlwCJVACJVACJVACJVACJTAJ9FIxibRdAiVQAiVQAiVQAiVQAiVwE4FeKm7CVeESKIESKIESKIESKIESKIFJoJeKSaTtEiiBEiiBEiiBEiiBEiiBmwj0UnETrgqXQAmUQAmUQAmUQAmUQAlMAr1UTCJtl0AJlEAJlEAJlEAJlEAJ3ESgl4qbcFW4BEqgBEqgBEqgBEqgBEpgEuilYhJpuwRKoARKoARKoARKoARK4CYCvVTchKvCJVACJVACJVACJVACJVACk0AvFZNI2yVQAiVQAiVQAiVQAiVQAjcR6KXiJlwVLoESKIESKIESKIESKIESmAR6qZhE2i6BEiiBEiiBEiiBEiiBEriJQC8VN+GqcAmUQAmUQAmUQAmUQAmUwCTQS8Uk0nYJlEAJlEAJlEAJlEAJlMBNBHqpuAlXhUugBEqgBEqgBEqgBEqgBCaBXiomkbZLoARKoARKoARKoARKoARuItBLxU24KlwCJVACJVACJVACJVACJTAJ9FIxibRdAiVQAiVQAiVQAiVQAiVwE4FeKm7CVeESKIESKIESKIESKIESKIFJoJeKSaTtEiiBEiiBEiiBEiiBEiiBmwj0UnETrgqXQAmUQAmUQAmUQAmUQAlMAr1UTCJtl0AJlEAJlEAJlEAJlEAJ3ESgl4qbcFW4BEqgBEqgBEqgBEqgBEpgEuilYhJpuwRKoARKoARKoARKoARK4CYCvVTchKvCJVACJVACJVACJVACJVACk0AvFZNI2yVQAiVQAiVQAiVQAiVQAjcR6KXiJlwVLoESKIESKIESKIESKIESmAR6qZhE2i6BEiiBEiiBEiiBEiiBEriJQC8VN+GqcAmUQAmUQAmUQAmUQAmUwCTQS8Uk0nYJlEAJlEAJlEAJlEAJlMBNBHqpuAlXhUugBEqgBEqgBEqgBEqgBCaBXiomkbZLoARKoARKoARKoARKoARuItBLxU24KlwCJVACJVACJVACJVACJTAJ9FIxibRdAiVQAiVQAiVQAiVQAiVwE4FeKm7CVeESKIESKIESKIESKIESKIFJoJeKSaTtEvj/7dvhTeRmFAVQeqEYaqEUKqEQ6qCWjV6kF1lX3p1cYpJofUZC8Oxre+b4j68+hgABAgQIECBAgEAloFRUXMIECBAgQIAAAQIECKSAUpEiZgIECBAgQIAAAQIEKgGlouISJkCAAAECBAgQIEAgBZSKFDETIECAAAECBAgQIFAJKBUVlzABAgQIECBAgAABAimgVKSImQABAgQIECBAgACBSkCpqLiECRAgQIAAAQIECBBIAaUiRcwECBAgQIAAAQIECFQCSkXFJUyAAAECBAgQIECAQAooFSliJkCAAAECBAgQIECgElAqKi5hAgQIECBAgAABAgRSQKlIETMBAgQIECBAgAABApWAUlFxCRMgQIAAAQIECBAgkAJKRYqYCRAgQIAAAQIECBCoBJSKikuYAAECBAgQIECAAIEUUCpSxEyAAAECBAgQIECAQCWgVFRcwgQIECBAgAABAgQIpIBSkSJmAgQIECBAgAABAgQqAaWi4hImQIAAAQIECBAgQCAFlIoUMRMgQIAAAQIECBAgUAkoFRWXMAECBAgQIECAAAECKaBUpIiZAAECBAgQIECAAIFKQKmouIQJECBAgAABAgQIEEgBpSJFzAQIECBAgAABAgQIVAJKRcUlTIAAAQIECBAgQIBACigVKWImQIAAAQIECBAgQKASUCoqLmECBAgQIECAAAECBFJAqUgRMwECBAgQIECAAAEClYBSUXEJEyBAgAABAgQIECCQAkpFipgJECBAgAABAgQIEKgElIqKS5gAAQIECBAgQIAAgRRQKlLETIAAAQIECBAgQIBAJaBUVFzCBAgQIECAAAECBAikgFKRImYCBAgQIECAAAECBCoBpaLiEiZAgAABAgQIECBAIAWUihQxEyBAgAABAgQIECBQCSgVFZcwAQIECBAgQIAAAQIpoFSkiJkAAQIECBAgQIAAgUpAqai4hAkQIECAAAECBAgQSAGlIkXMBAgQIECAAAECBAhUAkpFxSVMgAABAgQIECBAgEAKKBUpYiZAgAABAgQIECBAoBJQKiouYQIECBAgQIAAAQIEUkCpSBEzAQIECBAgQIAAAQKVgFJRcQkTIECAAAECBAgQIJACSkWKmAkQIECAAAECBAgQqASUiopLmAABAgQIECBAgACBFFAqUsRMgAABAgQIECBAgEAloFRUXMIECBAgQIAAAQIECKSAUpEiZgIECBAgQIAAAQIEKgGlouISJkCAAAECBAgQIEAgBZSKFDETIECAAAECBAgQIFAJKBUVlzABAgQIECBAgAABAimgVKSImQABAgQIECBAgACBSkCpqLiECRAgQIAAAQIECBBIAaUiRcwECBAgQIAAAQIECFQCSkXFJUyAAAECBAgQIECAQAooFSliJkCAAAECBAgQIECgElAqKi5hAgQIECBAgAABAgRSQKlIETMBAgQIECBAgAABApWAUlFxCRMgQIAAAQIECBAgkAJKRYqYCRAgQIAAAQIECBCoBJSKikuYAAECBAgQIECAAIEUUCpSxE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\\\"\\u003e\\u003cbr\\u003e\\u003c/div\\u003e \\u0026nbsp;\\n \\u003c/div\\u003e\\n \\u003cp\\u003eThe results showed that E, as the main component, explained 56.90% of the variability in the number of bolls per plant, whereas G and GE explained 24.73% and 18.36%, respectively (Table\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003e). AMMI analysis revealed that IPC A1 interacted with 64.41% of the total interaction sum of squares (\\u003cstrong\\u003eFig.\\u0026nbsp;3A\\u003c/strong\\u003e). Genotypes G2 and G1, with above average performance and negative interactions, were in quadrant IV, while G7 and G5, with below average numbers of bolls per plant and positive interactions, were in quadrant II. The IPC1 values of four genotypes, \\u003cem\\u003eviz\\u003c/em\\u003e., G4, G7, G2 and G1were closer to zero and hence stable across all the environments. However, G2 and G1 exhibited means above the overall mean, therefore, these genotypes were found to be stable combined with an increased number of bolls \\u003cstrong\\u003e(Fig.\\u0026nbsp;3B\\u003c/strong\\u003e, Table\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003e\\u003cstrong\\u003e).\\u003c/strong\\u003e AMMI II revealed that G4 and G7 were close to origin indicating that they were less responsive to the environment had greater stability and general adaptability to all the other environments. Genotypes G5 and G3, which were scattered from biplot origin exhibited specific adaptations to E2 and E3, respectively. Among the three environments, E1 was found to be highly interactive with longer vectors compared to the others for the trait number of bolls per plant. In the present study, genotypes G4 (0.28) and G7 (0.40) exhibited the lowest ASVs and were considered as highly stable for the trait number of bolls per plant.\\u003c/p\\u003e\\n \\u003cp\\u003eThe environment explained 35.50% of the variation in boll weight, the genotype genotype explained 30.44% and the contribution of the GE interaction to the total treatment variation was 34.04% (Table\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003e).The first two principal components (IPCA1: 73.49% and IPCA2: 26.51%) explained the maximum portion of the GEI. Single genotype G3 exhibited above average performance, and a positive interaction was detected in quadrant I with environments E1 and E2. Similarly, genotypes G1 and G4 which exhibited below average average performance coupled with negative interactions, were present in quadrant III with environment E3 (\\u003cstrong\\u003eFig.\\u0026nbsp;4A\\u003c/strong\\u003e). Genotypes G3 (NDLH 2035-5) and G5 (SURAJ) exhibited IPC1 values closer to zero and a mean above the overall mean indicating greater stability for BW across all the test environments (\\u003cstrong\\u003eFig.\\u0026nbsp;4B\\u003c/strong\\u003e). Genotypes G5, G3 and G4 had the lowest ASvs and were therefore considered stable for this trait (Table\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cp\\u003eFor the trait lint yield, the effect of E explained 40.40%, while G and G \\u0026times; E explained 30.07% and 29.51%, respectively, of the total sum of squares Table\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003e). Furthermore, the first two IPCs were highly significant and explained a major portion of the genotype and environment interactions. The genotypes with above average performance coupled with positive interactions, viz., G2 and G3, were present in quadrant I with environment E1 (\\u003cstrong\\u003eFig.\\u0026nbsp;5A\\u003c/strong\\u003e). In the AMMI 2 biplot, genotypes G1 and G6 were found close to the origin, indicating their stability and adaptability. However, genotype G2, which was highly scattered from biplot origin and had an IPCA1 value equal to one, is considered a highly unstable genotype and sensitive to environmental conditions for trait lint yield (\\u003cstrong\\u003eFig.\\u0026nbsp;5B)\\u003c/strong\\u003e. The ASVs suggests that genotypes G1, G6 and G3 have ASVs close to zero; hence, they are stable (Table\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cp\\u003eThe environment as the main effect explained 32.58% of the total sum of squares, whereas G \\u0026times; E and G explained only 39.74% and 27.66% respectively of the variation in the percentage of the trait ginning (Table\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003e). The first two IPCs explained most of the GE interaction. The single genotype G3 present in quadrant I with environment E3 exhibited above average performance and positive interaction effect. Similarly, genotypes G5, G6, G1 and G2 which are present in quadrants IV with E1, also showed above average performance but negative interaction effects. However, the remaining two genotypes, G4 and G7 which had below average performance and positive interaction effects were present in quadrant II with E2. The genotype G5 had an IPCA 1 score equal to zero and hence was highly stable (\\u003cstrong\\u003eFig.\\u0026nbsp;6A)\\u003c/strong\\u003e. Analysis of the AMMI 2 biplot also revealed that only G5 was close to its origin and therefore was less sensitive to the environments and stable. G2 is considered to be highly unstable because it is far from the center of the biplot, while entries G3 and G5 performed well in E2, and E3 was beneficial for G4 and G7; likewise, genotypes G1 and G6 interacted favorably with E1 (\\u003cstrong\\u003eFig.\\u0026nbsp;6B)\\u003c/strong\\u003e. Based on the ASV values, the lowest values were recorded for G5 and G7, indicating that these two varieties exhibit stable trait ginning percentages across environments (Table\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec6\\\"\\u003e\\n \\u003ch2\\u003eGGE biplot (\\u0026lsquo;whichwonwhere\\u0026rsquo; pattern)\\u003c/h2\\u003e\\n \\u003cp\\u003eThe ability of a GGE biplot to display the which-won-where/what-won-where pattern of a genotype by utilizing an environment dataset is one of its most fascinating features. In this approach, a polygon is first constructed by joining the genotypes far from the origin consisting of all the other genotypes inside the polygon. Then, starting from the biplot origin, perpendicular lines are drawn to each side of the polygon. The G\\u0026thinsp;+\\u0026thinsp;GXE variation was 96.55%, 89.48%, 97.14%, 97.92% and 89.59% for the number of bolls per plant, boll weight, seed yield (kg/ha), lint yield (kg/ha) and ginning percentage, respectively, (\\u003cstrong\\u003eFig.\\u0026nbsp;7, Patterns a,b,c,d and e\\u003c/strong\\u003e). The environmental indicators were positioned into two, two, two, two and three segments or sections of the biplot for BN, BW, SCY, LY and GP, respectively, with different genotypes winning in each segment. The results confirmed the presence of distinct interactions between genotype and environment for all the traits evaluated. Based on seven genotypes and three environments, the generated GGE biplot was divided into four, seven, five, five and four sections in the clockwise direction for BN, BW, SCY, LY and GP, respectively. For trait BN, genotype G1 in ENV 1 and genotype G3 in ENV 2 and ENV 3 were highly stable, with a greater number of bolls. For the trait BW, genotype G7 in ENV 3 and genotype G3 in ENV 1 and ENV 2 were the best performers with high boll weight. Compared with those of other genotypes, the G2 genotype in ENV 1 and the G3 genotype in ENV 2 and ENV 3 exhibited greater SCY with high stability. For the trait LY, the G3 genotype in ENV 2 and ENV 3 and the G2 genotype in ENV 1 exhibited greater lint yields. In the case of GPs, the G3 genotype in ENV2, the G6 genotype in ENV 1 and the G2 genotype in ENV 2, the G6 genotype in ENV 1 and G2 genotype in ENV3 exhibited increased ginning percentages and stability.\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGGE biplot pattern of \\u0026lsquo;mean\\u003c/strong\\u003e \\u003cstrong\\u003evs.\\u003c/strong\\u003e \\u003cstrong\\u003estability\\u0026rsquo; analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eThe mean \\u003cem\\u003evs.\\u003c/em\\u003e stability\\u0026apos; view is often referred to as the AEC view with SVP\\u0026thinsp;=\\u0026thinsp;one, since it aids in genotype evaluations based on mean performance and stability across environments feasible. Genotypes are ranked based on their performance in an environment by drawing a line that passes through the biplot called the \\u0026ldquo;average environment axis,\\u0026rdquo; or the axis of the AEC abscissa. The AEC ordinate passed through the biplot origin and was perpendicular to the AEC abscissa. In this study, the average principal component will be used in all environments and is presented with a circle and single arrow pointing in the direction of higher mean performance for each trait. Stable genotypes are those that fall on the AEC abscissa (horizontal axis) and have almost zero projection onto the AEC ordinate (vertical axis). The mean \\u003cem\\u003evs.\\u003c/em\\u003e stability pattern of the GGE biplot revealed 96.55% for BN, 89.48% for BW, 97.14% for SCY, 97.92% for LY and 89.59% for GP (\\u003cstrong\\u003eFig.\\u0026nbsp;8, Pattern, a,b,c,d and e\\u003c/strong\\u003e). For the trait BN, genotype G3 was found to be a good performer with a greater number of bolls in ENV 3 and ENV 2, followed by G2 in ENV1. Genotype G5 had the lowest number of bolls per plant. In the case of the trait boll weight, genotypes G3 followed by G5 exhibited the greatest boll weight. The entry 5 reported increased boll weight combined with high stability. Genotypes G2, G6, G1 and G4 were unstable and had lower boll weights. For economic traits such as seed cotton yield (kg/ha) and lint yield, genotype G3 had a greater yield. However, genotypes G4 and G7 exhibited relatively low yields and high stability. For the trait GP, genotype G6 exhibited a higher ginning percentage coupled with increased stability, while genotypes G4 and G7 showed lower GP% with high stability.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec7\\\"\\u003e\\n \\u003ch2\\u003eGenotype ranking: best and ideal genotype assessment:\\u003c/h2\\u003e\\n \\u003cp\\u003eAn ideal genotype is the one with the highest mean performance and maximal stability. The genotype ranking or comparison with the deal genotype is a biplot view with concentric circles. The ideal genotype is always located in the inner circle, and the head of the arrow is at the center of the circle. In certain instances, such as for trait LY in the present study, if none of the genotypes were located inside the inner circle, genotypes that were located next to or closer to the inner circle were considered ideal. Therefore, genotypes G2, and G1 for BP, genotypes G5,G3 for BW, genotype G3 for LY, genotype G6 for GP were considered as ideal. Genotypes close to the ideal genotype were also more promising and appropriate. Therefore, the ranking of genotypes for the SCY trait was as follows: G3\\u0026thinsp;\\u0026gt;\\u0026thinsp;G7\\u0026thinsp;\\u0026gt;\\u0026thinsp;G4\\u0026thinsp;\\u0026gt;\\u0026thinsp;G1\\u0026thinsp;\\u0026gt;\\u0026thinsp;G2\\u0026thinsp;\\u0026gt;\\u0026thinsp;G5\\u0026thinsp;\\u0026gt;\\u0026thinsp;G6 (\\u003cstrong\\u003eFig.\\u0026nbsp;9, Patterns a, b, c, d and e\\u003c/strong\\u003e).\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lsquo;Descriminativeness\\u003c/strong\\u003e \\u003cstrong\\u003evs.\\u003c/strong\\u003e \\u003cstrong\\u003erepresentativeness\\u0026rsquo; pattern of the GGE biplot\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eThe identification of the best ideal test environment is crucial for a successful breeding technique that eventually helps in the selection of superior genotypes. Two aspects namely, discrimitiveness (the ability of an environment to distinguish genotypes and representativeness (the ability of an environment to represent all other evaluated environments) denote the idealness of the tested environments. The average environmental coordinates (AECs) and test environments are capable of visualizing type-1 environments, type-2 environments and type-3 environments. The type-1 environments are represented by short vectors with average discriminative power indicating the average performance of genotypes. The type-2 environments are shown as the longest vectors with the highest discriminative powers, and are capable of discriminating the performance of genotypes. Type-3 environments are represented as the longest vector with large angles, and are suitable for the negative effects of environments. The ideal environments are those having the longest genotypic vector and located on or at acute angles to the AEC.\\u003c/p\\u003e\\n \\u003cp\\u003eBased on this classification, as suggested by Yan et al., \\u003cspan\\u003e2007\\u003c/span\\u003e, the results of the present study inferred that ENV 2 for BP and BW, and ENV 3 for SCY, LY and GP have short vectors representing the average or similar performance of the genotypes; hence, not much information is available about the genotype differences. The environments with a long vector that forms a shorter angle with the AEC abscissa line are ENV 2 for BP, ENV 1 for BW SCY, LY and GP indicating that the test environments were more representative and discriminative (\\u003cstrong\\u003eFig.\\u0026nbsp;10, patterns a,b,c,d and e\\u003c/strong\\u003e).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec8\\\"\\u003e\\n \\u003ch2\\u003eRelationships among environments:\\u003c/h2\\u003e\\n \\u003cp\\u003eTo illustrate the interactions among environments, lines are drawn from the test environment to the center of the biplot; these lines are otherwise called \\u0026ldquo; environment vectors\\u0026rdquo;.The cosine of the angle between the vectors of two environments reflects their correlation. In the present study, acute angles (\\u0026lt;\\u0026thinsp;90\\u0026ordm;) were reported for ENV2 and ENV 3 for BN, ENV 2 and ENV 1 for BW, ENV 2 and ENV 3 for SCY, and ENV 1 and ENV 3 for LY and ENV 1 and ENV 3 for GP, indicating the presence of a positive correlation between these environments. This demonstrates that genotypes exhibiting the best performance at ENV 2 can also exhibit the same performance at ENV 3 and vice versa for trait SCY. If the angle between two environmental vectors is an obtuse angle (\\u0026gt;\\u0026thinsp;90\\u0026ordm;), then these environments exhibit negative correlations, such as ENV3 and ENV2 for BW and GP, indicating that there is a negative association; genotypes that performed well in ENV3 do not perform similarly at ENV2 and vice-versa. The third situation, where the angle between two environmental vectors is right angle (90\\u0026ordm;) or closer to 90\\u0026ordm; exhibits no correlation or is unrelated, suggesting that each environment has unique genotypic performance. ENV3 and ENV1 for BW, ENV3 and ENV2 for LY and ENV1 and ENV2 for GP exhibited zero correlation for their respective traits (\\u003cstrong\\u003eFig.\\u0026nbsp;11, Patterns a, b, c, d and e\\u003c/strong\\u003e).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec9\\\"\\u003e\\n \\u003ch2\\u003eRanking of environments:\\u003c/h2\\u003e\\n \\u003cp\\u003eBased on the ranking of environments, in the present experiment, the environments are ranked ENV 1 and ENV 3 for BP, ENV 1 for BW, and ENV 1 for SCY, LY and GP are considered ideal environments. The center of the concentric rings is the \\u0026quot;ideal test environment\\u0026quot;. Conversely, ENV 2 for BP, ENV 3 for BW, ENV 3 and ENV 2 for SCY and LY, ENV 2 for GP were regarded as the poorest environments for selecting the genotypes across the environment (\\u003cstrong\\u003eFig.\\u0026nbsp;12, Patterns a,b,c,d and e\\u003c/strong\\u003e).\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAMMI Analysis of variance\\u003c/h2\\u003e \\u003cp\\u003eIn the present study, AMMI analysis of variance revealed highly significant variations for all the five traits studied. The environment, genotype and genotype \\u0026times; environment interaction effects were highly significant (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) for the SCY and LY. The environment had a major contribution to the treatment sum of squares, followed by the genotype and GEI for the number of bolls per plant, seed cotton yield and lint yield, indicating that varying environmental conditions prevailed during the experimental seasons and hence were suggested for multiple environmental trails. The environment was found to be a major contributor in studies [Riaz et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e, Orawu et al., \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e, Shahzad et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e] on cotton crops and Habtegebriel \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e (soybean), and Enyew et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e (sorghum) crops]. The GEI sum of squares was greater for traits of boll weight and ginning percentage than for the genotype. The significant GEI suggested that the cotton genotypes responded differently to various environments indicating the need to identify and select location/environment specific genotypes.The variation in genotype performance across environments reduces the association between genotypes and their corresponding phenotypes [Gerrano et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e]. This poor correlation between genotype and phenotype caused by the presence of GE interactions hinders the identification and selection of superior genotypes based on their genetic potential. Therefore, plant breeders are most concerned with genotype-environment interactions (GEIs) while creating better cultivars. A cultivar must perform well in all of the environments in which it is permitted to grow to be economically successful. The present study also revealed that the first two IPCs were sufficient for partitioning the GEI, which is in accordance with [Zobel et al., \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e1988\\u003c/span\\u003e, Gauch and Zobel, (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e1996\\u003c/span\\u003e), Yan and Rajcan, (\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e) and Sadabadi et al., \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e]. However, [Shahzad et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e, Habtegebriel, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e and Riaz et al., (\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e)] recorded three, four and six IPCs, respectively, in their studies.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAMMI I and AMMI II:\\u003c/h2\\u003e \\u003cp\\u003eThe AMMI model combines the analysis of variance of the genotype and environment main effects with the principal component analysis of the G \\u0026times; E interaction. The additive main effect and multiplicative interaction (AMMI) model is frequently employed in yield experiments to examine GE interactions. To ascertain adaptation and stability, one must comprehend how GE interacts with cultivars. In the AMMI I model, displacement along the abscissa depicts differences in main effects, whereas displacement along the ordinate indicates differences in interaction effects. Genotypes and environments on the same parallel line, relative to the ordinate, have similar yields, and genotypes or environment on the right side of the center of the axis have higher yields than those on the left-hand side. A similar result was observed in the current study, where genotype G3 (NDLH 2035-5) was found in quadrant I (right hand side with means above the grand mean and positive interaction) for all the traits studied and exhibited above average performance compared with the overall mean, indicating that this variety is the best performer for all the traits evaluated. Genotype G7 exhibited low IPCA 1 scores, while genotypes G3 and G5 had IPCA scores close to zero for the remaining traits, namely, BW, LY and GP, suggesting that these genotypes were stable for their respective traits. The AMMI II model biplot is created by using genotypic and environmental scores of the first two AMMI components [Vargas and Crossa, \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e). Furthermore, in the present experiment the first two PCA interactions with the AMMI model 2 biplot presented 100% of the G\\u0026thinsp;+\\u0026thinsp;G E interaction variance for all five traits studied possibly because only the two IPCAs were responsible for the entire variation. This result is in accordance with [Esan et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe closer the genotypes are to the origin, the less interactive they are, and the farther the genotypes are to the origin, the more interactive they are. Genotypes G7, G4, G6 and G1 were found close to their origin, indicating their general adaptability in all three environments for trait seed cotton yield. Genotype G3, the top yielder, exhibited specific adaptability for E1, while other genotypes, G5 and G6, exhibited favorable interactions with E3, and genotypes G1 and G4 exhibited favourable interactions with E2 for trait SCY. Genotype G5 displayed general adaptability for the traits of boll weight and ginning percentage. Environment E1 was highly interactive for all the traits except for boll weight.\\u003c/p\\u003e \\u003cp\\u003eGenotypes with low ASVs and GSIs are considered to be stable. Accordingly, genotypes G4, and G7 for BN; G5, and G3 for BW; G1 and G3 for LY; and G5, G3 and G6 for GP had lower ASVs. However, for SCY, genotypes G7, G4 and G6 had lower ASVs and means lower than the average mean; therefore, the selection of these genotypes for further breeding programs might not be helpful for yielding fruitful results. Hence, another stability parameter, GSI employed in the present study for selecting the top-ranking genotypes in terms of both mean performance and stability revealed that genotype G3 (NDLH 2035-5) had a low GSI for SCY and LY, indicating the stability of the genotype across environments. Intriguingly, genotype G3 (NDLH 2035-5) also exhibited low ASV values for yield enhancing traits such as BW, LY and GP. Therefore, prioritizing the selection of this genotype in cotton breeding programs is beneficial. Previous researchers [Lin and Binns \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e1994\\u003c/span\\u003e, Farshdafar, 2008 and Enewy \\u003cem\\u003eet al.\\u003c/em\\u003e, 2021] have suggested that for the selection of superior cultivars, average yield and stability are the prerequisites.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eGGE biplot\\u003c/h2\\u003e \\u003cp\\u003eGGE biplot was divided into four, seven, five, five and four sections for BN, BW, SCY, LY and GP, respectively. Similarly, the GGE biplot was divided into eight, seven, six and four sections according to previous researches findings by [Regmi et al., \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e, Abro et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e, Daemo et al., \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e, and Yasar \\u003cem\\u003eet al.\\u003c/em\\u003e, 2023]. The grouping of all the environmental indicators in one section of the biplot revealed that a particular genotype performed well in all the tested environments. In contrast, if the environmental indicators were placed in a different portion of the biplot, different genotypes acquired different environments. The vertex genotypes, which have the furthest distance from the origin in their direction, were considered to be the most responsive genotypes with superior performance in that particular environment, as suggested by [Yan and Tinker (\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e)]. In contrast, the genotypes positioned inside the polygon are less environmentally friend than the genotypes at the vertex. In the present study, there are biplots where a genotype is situated at a polygon vertex with no environmental vector in that section, suggesting that a particular genotype exhibited poor performance across the test environments. This result conforms with [Ali et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e], where four genotypes were present at the vertex of the polygon with no environmental vectors for trait seed cotton yield.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eGGE biplot pattern of \\u0026lsquo;mean\\u003c/b\\u003e \\u003cb\\u003evs\\u003c/b\\u003e \\u003cb\\u003estability\\u0026rsquo; analysis\\u003c/b\\u003e:\\u003c/p\\u003e \\u003cp\\u003eThe visualization of genotypes that combine high mean performance and stability is a notable feature of the GGE biplot graph. Stable genotypes are those that fall on the AEC abscissa (horizontal axis) and have almost zero projection onto the AEC ordinate (vertical axis). Similarly, genotypes G3 and G5 were stable and had high mean performances with respect to BN, BW and LY. However, for trait SCY, genotypes G4 and G7 exhibited comparatively lower yields than G3 but with high stability. In the same manner, several authors have also identified high yielding and stable genotypes of crops such as cotton [Farias et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e, Mustafa McPherson 2022 and Yasar \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e] and oats [Yan and Reid \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e] using GGE biplots.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eGenotype ranking: best and ideal genotype assessment:\\u003c/h2\\u003e \\u003cp\\u003eThe genotypes found in the inner circle are the most desirable among the genotypes present in the outer circle [Ali et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e, Firew et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e and Habtegebriel \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e]. If no genotype was located inside the inner circle, genotypes that were located next to or closer to the inner circle were regarded as ideal [Khan et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e]. In the present study, genotype G3 was found to be ideal for BW, SCY and LY. However, genotype G6 was considered ideal for GP because a stable and high performing genotype for one trait does not necessarily indicate that it combines stability and superior performance for other related traits. This is mainly because different traits are governed by various genes, and because of the differential expression of genes among the genotypes in response to various environmental conditions, such as temperature variation and moisture stress.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eDescriminativeness\\u003c/b\\u003e \\u003cb\\u003evs.\\u003c/b\\u003e \\u003cb\\u003erepresentativeness pattern of the GGE biplot\\u003c/b\\u003e:\\u003c/p\\u003e \\u003cp\\u003eThe GE biplot's discriminating power \\u003cem\\u003evs.\\u003c/em\\u003e representativeness view is a useful tool for evaluating test environments and can help identify a minimal set of representative and discriminating test environments. Identifying test environments, precisely defining genotype differences, and providing the necessary insights required for selection by plant breeders are essential. The ability of an environment to discriminate between genotypes is represented by the length of its vector, whereas its representativeness is depicted with a shorter angle formed with the abscissa [Yan and Tinker (\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e) and Yan et al. \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e]. In the present study, the long vectors included ENV1 and ENV 3 for BN; ENV 3 for BW; and ENV1 for SCY, LY and GP, indicating the discriminating nature of the environments for the respective traits, while the shortest angle was formed for ENV 2 for BN and ENV 1 for the remaining traits BW, SCY, LY and GP. Test environments that are both discriminating and representative are ideal environments for selecting generally adapted superior genotypes [Yan and Tinker, \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eRelationships among environments\\u003c/h2\\u003e \\u003cp\\u003eGGE biplot analysis enables the understanding of the value of various test environments in terms of relative discrimination between genotypes and the associations between them for various traits. An ideal environment should have a high PC1 score and zero scores for PC 2 [Habtegebriel \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e]. The present study also obtained the same result. Furthermore [Farias et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e] identified the Sapezal environment as the most discriminating of the eight environments studied, and [Ali et al. \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e] reported that E3 (CCIR Multan) was suitable for six of the environments studied.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eRanking of environments\\u003c/h2\\u003e \\u003cp\\u003eThe centre of the concentric rings is the \\\"ideal test environment\\\" [Balestre \\u003cem\\u003eet al\\u003c/em\\u003e., 2010]. Based on the ranking of environments, in the present experiment, the environments are ranked ENV 1 and ENV 3 for BP, ENV 1 for BW, and ENV 1 for SCY, LY and GP are considered ideal environments. Conversely, ENV 2 for BP, ENV 3 for BW, ENV 3 and ENV 2 for SCY\\u0026amp;LY, and ENV 2 for GP are regarded as the poorest environments for selecting the genotypes across the environment.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThe present study evaluated the genotype \\u0026times; environment interaction, genotype stability, Descriminativeness and representativeness of the test environments. AMMI model analysis revealed that the majority of the total variance in the yield component was explained by environment E. The GGE biplot model was effective for graphical visualization of the G \\u0026times; E interaction and for identifying stable and superior performing genotypes. Among the seven genotypes studied across three environments, genotypes G3 (NDLH 2035-5) and G1(GBHV-193) were high yielding and most stable in all the test environments studied. The yields of genotypes G7 (SIVANANDI) and G4 (BGDS 1033) were below average but were stable. These genotypes can also be used in breeding programmes when breeders intend to enhance certain phenotypes. There are other sets of genotypes, G5 (SURAJ) and G6 (SAHANA) whose yields are low and unstable. Genotype G3 (NDLH 2035-5), the top yielder compared with the check G7 variety (SIVANANDI), had recorded lower ASVs and GSIs, and the ideal genotype for GGE analysis was preferred over the other varieties studied because it can be used as a parent in hybridization programs. Environment E1 was identified as the most discriminating and representative and therefore may be utilised to detect promising genotypes with attractive yields and adaptability.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eEthics approval and consent to participate\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026ldquo;Not applicable\\u0026rdquo;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eConsent for Publication\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll the authors have agreed to submit this review article to the Journal of Cotton Research.\\u003c/p\\u003e\\n\\u003ch4\\u003e\\u003cem\\u003eAvailability of data and materials\\u003c/em\\u003e\\u003c/h4\\u003e\\n\\u003cp\\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003ch4\\u003e\\u003cem\\u003eCompeting interests\\u003c/em\\u003e\\u003c/h4\\u003e\\n\\u003cp\\u003eNon-financial competing interests include political, personal, religious, ideological, academic, and intellectual competing interests. \\u0026nbsp;All the authors does not have any interest regarding this.\\u003c/p\\u003e\\n\\u003ch4\\u003e\\u003cem\\u003eFunding\\u003c/em\\u003e\\u003c/h4\\u003e\\n\\u003cp\\u003eAll sources of funding for the research supported by the Acharya N.G. Ranga Agricultural University and AICRP on Cotton Scheme.\\u003c/p\\u003e\\n\\u003ch4\\u003e\\u003cem\\u003eAuthors\\u0026apos; contributions\\u003c/em\\u003e\\u003c/h4\\u003e\\n\\u003cp\\u003eKalapati Mohan Vishnuvardhan (corresponding author), performed the experiments along with Yettapu Rama Reddy \\u0026nbsp;and Bana Venkata Ravi Prakash Reddy. Kalapati Mohan Vishnuvardhan is responsible to analysis the data and preparation of tables and figures. Konuku Sudeepthi and Kolimigundla Amarnath supporting the tabulation of data and review the manuscript. Nayakanti Chinna Venkateswarlu supported in overall scrutiny of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eAcknowledgements\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors are immensely thankful to the AICRP on cotton and Acharya N.G. Agricultural University for providing the necessary facilities to carry out the research program successfully.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAbro, S., Rizwan, M., Rajput, M,T., Sial, M.A and Deh, Z.A. 2022. Evaluation of upland cotton genotypes for stability over different locations using AMMI and GGE biplot analysis. \\u003cem\\u003ePakistan Journal of Botany\\u003c/em\\u003e. 54(5): 1733-1739.\\u003c/li\\u003e\\n\\u003cli\\u003eAli, I., Khan, N.U., Mohammad, F., Iqbal, M.A., Abbas, A., Farhatullah, Bibi, Z., Ali, S., Khalil, A., Ahmad, S and Rahman, M.Ur. 2017. 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(Amsterdam, Netherlands: Elsevier Science Publishers).\\u003c/li\\u003e\\n\\u003cli\\u003eGerrano, A.S., Rensburg, W.S.J.V., Mathew, I., Shayanowako, A.I.T., Bairu, M.W., Venter, S.L., Swart,W., Mofokeng, A., Mellem, J and Labuschagne, M. 2020. Genotype and genotype x environment interaction effectson the grain yield performance of cowpea genotypesin dry land farming system in South Africa. \\u003cem\\u003eEuphytica.\\u003c/em\\u003e80(216):1-11.\\u003c/li\\u003e\\n\\u003cli\\u003eHabtegebriel, H.M. 2022. Adaptability and stability for soybean yield by AMMI and GGE models in Ethiopia. \\u003cem\\u003eFrontiers in Plant Science\\u003c/em\\u003e. 13 (950992): 1-19.\\u003c/li\\u003e\\n\\u003cli\\u003eKhan, M.H.Md., Rafii, M.Y., Ramlee, S.I., Jusoh, M and Mamun, Al.Md. 2021. AMMI and GGE biplot analysisfor yield performance and stabilityassessment of selectedbambara groundnut (\\u003cem\\u003eVignasubterranea\\u003c/em\\u003eL. Verdc.) genotypesunder the multi-environmentaltrials (METs). \\u003cem\\u003eScientific reports. \\u003c/em\\u003e11(2791): 1-17.\\u003c/li\\u003e\\n\\u003cli\\u003eLi, W., Yan, Z. H., Wei, Y. M., Lan, X. J., and Zheng, Y. L. 2006. Evaluation of genotype \\u0026times; environment interactions in Chinese spring wheat by the AMMI model, correlation and path analysis. \\u003cem\\u003eJ. Agron. Crop Sci\\u003c/em\\u003e, 192(3): 221\\u0026ndash;227. doi: 10.1111/j.1439-037X.2006. 00200.x\\u003c/li\\u003e\\n\\u003cli\\u003eLin, C.S and M.R., Binns. 1994. Concepts and methods for analysis regional trial data for cultivar and location selection\\u003cem\\u003e. \\u003c/em\\u003e\\u003cem\\u003ePlant Breeding Reviews\\u003c/em\\u003e. 12:271 \\u0026ndash; 297.\\u003c/li\\u003e\\n\\u003cli\\u003eMustafaMcPherson. 2022. An application of gge biplot to cotton varietydevelopment. \\u003cem\\u003eCrop Breeding Genetics and Genomics\\u003c/em\\u003e. 4(1): 1-8.\\u003c/li\\u003e\\n\\u003cli\\u003eOrawu, M., Amoding, G., Serunjogi, L., Ogwang, G and Ogwang, C. 2017. Yield stability of cotton genotypes at three diverse agro-ecologies of Uganda. \\u003cem\\u003eJournal of Plant Breeding and Genetics.\\u003c/em\\u003e 5(3): 101-114\\u003c/li\\u003e\\n\\u003cli\\u003ePurchase, J. L., Hatting, H., and Vandeventer, C. S. 2000. Genotype \\u0026times; environment interaction of winter wheat (\\u003cem\\u003eTriticum aestivum\\u003c/em\\u003e L.) in South Africa: II. Stability analysis of yield performance. \\u003cem\\u003eSouth Afric. J. Plant Soil\\u003c/em\\u003e. 17: 101\\u0026ndash;107. doi: 10.1080/02571862.2000.10634878.\\u003c/li\\u003e\\n\\u003cli\\u003eRegmi, D., Poudel, M.R., Bishwas, K.C., Poudel, P.B. 2021. Yield stability of different elite wheat lines under drought and irrigated environments using Ammi and GGE biplots. \\u003cem\\u003eInternational Journal of Applied Sciences and Biotechnology. \\u003c/em\\u003e9(2): 98-106.\\u003c/li\\u003e\\n\\u003cli\\u003eRiaz, M., Farooq, J., Ahmed, S., Amin, M., Chattha, W.S., Ayoub, M and Kainth, R.A. 2019. Stability analysis of different cotton genotypes under normal and water-deficit conditions. \\u003cem\\u003eJournal of Integrative Agriculture\\u003c/em\\u003e. 18 (6): 1257\\u0026ndash;1265.\\u003c/li\\u003e\\n\\u003cli\\u003eRiaz, M., Jehanzeb, F., Saghir, A., Muhammad, A., Waqas, S. C., Maria, A., and Riaz, A. K. 2019. Stability Analysis of Different Cotton Genotypes under Normal and Water-Deficit Conditions. \\u003cem\\u003eJournal of Integrative Agriculture\\u003c/em\\u003e. 18(6): 1257\\u0026ndash;1265.\\u003c/li\\u003e\\n\\u003cli\\u003eRiaz, M., Naveed, M., Farooq, A., Farooq, A., Mahmood, Ch., Rafiq, M., Nadeem, M and Sadiq, A. 2013. 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Cultivar evaluation and mega-environment investigation based on the GGE biplot. \\u003cem\\u003eCrop Sci\\u003c/em\\u003e., 40(3): 597\\u0026ndash;605.\\u003c/li\\u003e\\n\\u003cli\\u003eYan, W and Rajcan, I 2002. Biplot analysis of the test sites and trait relations of soybean in Ontario. \\u003cem\\u003eCrop Science\\u003c/em\\u003e. 42: 11-20.\\u003c/li\\u003e\\n\\u003cli\\u003eYan, W and Reid, J.F. 2018. Genotype by yield*trait (GYT)biplot: a novel approach forgenotype selection based onmultiple traits. \\u003cem\\u003eScientific reports.\\u003c/em\\u003e 8 (8242): 1-10. \\u003c/li\\u003e\\n\\u003cli\\u003eYan, W and Tinker, N. A. 2006. Biplot analysis of multi-environment trial data: Principles and applications. \\u003cem\\u003eCanadian Journal of Plant Science.\\u003c/em\\u003e 86 (3): 623\\u0026ndash;645. \\u003c/li\\u003e\\n\\u003cli\\u003eYan, W., Kang, M.S., Ma, B., Woods, S and Cornelius, P.L. 2007. GGE biplot \\u003cem\\u003evs.\\u003c/em\\u003e AMMI analysis ofgenotype-by-environment data. \\u003cem\\u003eCrop Science\\u003c/em\\u003e. 47:641\\u0026ndash;653.\\u003c/li\\u003e\\n\\u003cli\\u003eYasar, M. 2023. Yield and fiber quality traits of cotton (\\u003cem\\u003eGossypium hirsutum\\u003c/em\\u003e L.) cultivars analyzed by biplot method. \\u003cem\\u003eJournal of King Saud University \\u0026ndash; Science.\\u003c/em\\u003e 35:1-10.\\u003c/li\\u003e\\n\\u003cli\\u003eZobel, R.W., Wright, M.S and Gauch, H.G. 1988. Statistical analysis of a yield trial. \\u003cem\\u003eAgronomy Journal.\\u003c/em\\u003e 80: 388-393.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Gossypium hirsutum L., Genotype × Environment interaction, Stability, AMMI, GGE biplot analysis, Seed cotton yield\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4122954/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4122954/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eCotton is an important natural fiber crop worldwide that demands the attention of the textile industry worldwide. Seed cotton yield is a complex polygenic trait that is influenced by many genetic and environmental factors across locations and years.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eThe present investigation was conducted in three consecutive environments to delineate the genotype \\u0026times; environment interaction and to assess the stability of seven cotton genotypes at the Regional Agricultural Research Station, Nandyal, during 2018-19, 2019-20 and 2020-21. Multivariate stability tests such as additive main effect and multiplicative interaction (AMMI) and genotype and genotype-by-environment interaction (GGE) models were employed to investigate the stability among cotton genotypes. The AMMI results revealed that the majority of the variation was explained by the sum of the squares of the environmental variables, followed by the sum of the squares of the genotypic variables and the sum of the GEIs for the majority of the traits studied. The first two interaction principal components explained the majority of the GEI in all traits under study. A two-dimensional GGE biplot generated using the first two principal components revealed that the GGE biplot explained 97.14% of the total variation, which was distributed as 83.73% and 13.41% of the sum of squares between principal components PC1 and PC2, respectively, for biometric trait seed cotton yield.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eBased on which-won-where polygon, ideal genotype ranking of AMMI and GGE biplot analysis, genotype, G3 (NDLH 2035-5) was identified as having the highest yield and was most stable in all the test environments studied. However, low yielding but stable genotypes such as G4 (BGDS 1033) and G7 (Sivanandi) were also identified. Among the three environments studied, environment E1 (2018-19) was identified as the most discriminating and representative.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Determination of the effect of Genotype × Environment interaction on the trait seed cotton yield in upland cotton (G. hirsutum) genotypes using AMMI and GGE biplot methods\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-04-16 08:39:16\",\"doi\":\"10.21203/rs.3.rs-4122954/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"3598a734-b57e-4734-bcc7-f626884becc5\",\"owner\":[],\"postedDate\":\"April 16th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-06-26T12:06:05+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-04-16 08:39:16\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4122954\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4122954\",\"identity\":\"rs-4122954\",\"version\":[\"v1\"]},\"buildId\":\"re_ckhLnmML6MCF96OHNJ\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}