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Int.J.Curr.Microbiol.App.Sci (2020) 9(5): 1257-1270 International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 9 Number 5 (2020) Journal homepage: http://www.ijcmas.com Original Research Article https://doi.org/10.20546/ijcmas.2020.905.140 Wheat Genotypes Evaluated under North Eastern Plains Zone of the Country for Genotype X Environment Interaction Analysis Ajay Verma* and G. P. Singh ICAR-Indian Institute of Wheat & Barley Research, Karnal 132001 Haryana, India *Corresponding author ABSTRACT Keywords AMMI analysis, ASV, ASTAB, EV, D, MASV, Biplot graphs Article Info Accepted: 10 April 2020 Available Online: 10 May 2020 AMMI analysis observed highly significant values of environments, GxE interaction effects and genotypes effects for both the years of study. Lower values of EV1 during (2016-17) ranked genotypes as (G6, G2, G8); D1 for (G6, G2, G8); values of ASTAB1 for (G6, G2, G8) while SIPC1 for (G7, G1, G5) genotypes. EV2 measure pointed towards (G6, G2, G9) as desirable, D2 for (G6, G2, G8), whereas as per SIPC2 were (G5, G7, G8) & ASTAB2 opted for (G6, G2, G9). ASV and ASV1 recommended (G6, G2, G9) genotypes possessing stable performance as measures used 56.9% of GxE interaction. Ranked values of EV3 preferred G2, G6, G8; SIPC3 pointed towards G5, G8, G1; D3 identified G2, G6, G8 and ASTAB3 considered G2, G6, G8 wheat genotypes. Numerical values of D7 ranked G6, G2, G8; SIPC7 chosen G8, G1, G5. EV7 pointed towards G2, G6, G8 & ASTAB7 identified G9, G1, G4 as desirable over the studied environments. Composite measure MASV selected G6, G2, G9 & MASV1 cited as G6, G2, G8 would be desirable. Genotypes G8, G2 and G6 by Mean, GAI, HM, PRVG and MHPRVG measures would be of choice across environments of study. Biplot analysis of studied measures exhibited three major clusters and most of the measures clubbed in first cluster. Seven significant IPCA’s were used to calculate AMMI based measures during second year (2017-18) as accounted more than 96.6%. Minimum values of EV1 ranked (G9, G6, G1), D1 pointed (G9, G6, G1) , ASTAB1 for (G9, G6, G1) and for SIPC1 were (G7, G5, G10). EV2 pointed towards (G9, G1, G6) as desirable, for values of D2 genotypes were (G9, G1, G6) as per criterion of SIPC2 were (G7, G5, G10) & ASTAB2 favoured (G9, G1, G6). ASV and ASV1 recommended (G9, G1, G6) as of stable performance. D7 expressed minimum values G9, G1, G6; SIPC7 observed G5, G9, G10, measure EV7 pointed towards G1, G5, G4. ASTAB7 identified G9, G1, G4 as desirable. Composite measure MASV selected G9, G5, G1 and MASV1 as G9, G1, G5 for desirable performance. Genotypes G8 G9 by Mean, G7, G4 by GAI & HM, G4, G10 by PRVG and G7, G4 by MHPRVG measures based on yield of genotypes across environments of study. Association analysis among AMMI measures and yield based analytic measures by multivariate hierarchical Ward’s clustering approach grouped into four major clusters. Largest group I clubbed measures as MASV1, ASV, MASV, D2, D3, D7, with EV2, EV3, SIPC5, SIPC7, EV7. Group II contains ASTAB1, ASTAB2, ASTAB5, D1, D5, EV1, EV5, ASV1 whereas yield based measures exhibited close proximity and placed close to each other as in separate group. Introduction Quite large number of statistical methods are available for analysis of multi environment trials, aimed to subdivide the complex GxE interaction into simpler and more meaningful responses of genotypes among studied environments (Agahi et al., 2020). Multi Environment trials of cereal crops had been planned to have efficient estimation of main and interaction effects (Bocianowski et al., 2019). The procedures vary from univariate parametric, non-parametric and multivariate models. AMMI model ((Additive main effects and multiplicative interaction), incorporates both additive and multiplicative components with aim to summarize the genotypeenvironment interaction (Guilly et al., 2017). 1257 Int.J.Curr.Microbiol.App.Sci (2020) 9(5): 1257-1270 Although, analysis of variance (ANOVA) provided an interaction sum of squares that would be difficult to interpret and the prediction of yields in different environments is not easy (Kamila et al., 2016). AMMI separates the additive variance from the multiplicative variance and then applies principal component analysis (PCA) to the interaction portion to a new set of coordinate axes that explains in more detail the interaction pattern (Tekdal & Kendal, 2018). AMMI analysis has been shown to be effective because it captures a large portion of the GxE sum of squares, clearly separating main and interaction effects that provide researchers with different opportunities (Gauch 2013; Tena et al., 2019). Prime objectives of this study are (i) AMMI based measures depend on utilization of significant principal components (ii) explore the association among AMMI along with yield and adaptability measures. Materials and Methods North Eastern Plains Zone of India comprises eastern Uttar Pradesh, Bihar, Jharkhand, Assam and plains of West Bengal. Nine advanced wheat genotypes twelve locations and eleven genotypes at fifteen locations were evaluated under field trials at of north eastern plains zone during 2017-18 and 2018-19 cropping seasons respectively. Field trials were conducted at research centers in randomized complete block designs with three replications. Recommended agronomic practices were followed to harvest good yield. Details of genotype parentage along with environmental conditions were reflected in tables 1 & 2 for ready reference. AMMI first calculate genotype and environment additive effect using analysis of variance (ANOVA) and then analyse residual from these model using principal components analysis (PCA). AMMI stability value (ASV) was initially proposed by Purchase (1997) to quantify the stability measure by considering relative weight of IPCA1 and IPCA2 scores. In certain cases where more than two IPCAs were significant, ASV failed to encompass all the variability explained by GEI. In order to overcome this difficulty, Zali et al., (2012) attempted to present a modified version ASV i.e., Modified ASV which would cover all available IPCAs. But in doing so, Zali et al., (2012) interpreted the formula of ASV incorrectly compared to the original formula of Purchase (1997). In the present study the MASV of Zali et al., (2012) and a revised version of MASV (Ajay et al., 2019) were compared with other AMMI based measures. The description of widely used measures based on AMMI analysis was mentioned for completeness. Zobel 1994 EV1 EVF Sneller et al., 1997 SIPC1 SIPCF Purchase 1997 ASV ASV = [ Annicchiarico 1997 D D= Rao and Prabhakaran 2005 ASTAB 1258 Int.J.Curr.Microbiol.App.Sci (2020) 9(5): 1257-1270 Zali et al., 2012 Zali et al., 2012 Ajay et al., 2019 ASV1 ASV1 = [ AMMI analysis was performed using AMMISOFT version 1.0, available at https://scs.cals.cornell.edu/people/ hughgauch/ and SAS software version 9.3. AMMI based measures were compared with recent analytic measures of adaptability calculated as the relative performance of genetic values (PRVG) and MHVG (Harmonic mean of Genetic Values), based on the harmonic mean of the genotypic values across different environments. Another harmonic mean based measure of the relative performance of the genotypic values (MHPRVG) for the simultaneous analysis of stability, adaptability and yield (Resende & Durate, 2007). genotype across environments and n is number of environments. Genotypes with higher values of GAI are desirable Results and Discussion AMMI analysis provided a better understanding of the GxE interaction through analysis of variance, facilitated discriminating environments and adaptability of the genotypes to specific environments. Actually AMMI fits a family of models with retaining 0, 1, 2, or more significant interaction principal components (IPCs). First year (2017-18) AMMI analysis PRVGij = VGij / VGi MHVGi = Number of environments / MHPRVGi. = Number of environments / VGij is the genotypic value of the i genotype, in the j environment, expressed as a proportion of the average in this environment. Geometric adaptability index (GAI) (Mohammadi & Amri, 2008) was calculated as ; in which 1, 2, 3, … m are the mean yields of the first, second and mth Model diagnosis is required to determine the suitable AMMI model for a given dataset while satisfying statistical and practical considerations. FR-tests at the 0.01 level diagnose AMMI6. Sums of squares for GxE signal and noise were 85.47% and 14.53% respectively of total GxE sum of squares. Sum of Squares for GxE signal is 3.53 times of genotypes main effects depicts narrow adaptations are important for this dataset. Even just IPC1 alone is 1.56 times the genotypes main effects. Also note that GxE noise is 0.60 times the genotypes main 1259 Int.J.Curr.Microbiol.App.Sci (2020) 9(5): 1257-1270 effects. Highly significant environments, GxE interactions and genotypes effects were depicted in table 3. Large magnitude of GxE interactions for yield found in this investigation are similar to those found in other crops (Nowosad et al., 2018). AMMI derived measures based on the use of significant IPCA’s were calculated as EV1, ASTAB1, SIPC1, D1 measures (only first significant IPCA), while ASV, EV2, ASTAB2, SIPC2, ASV1, D2 considered IPCA1 & IPCA2, measures EV3, ASTAB3, SIPC3 and D3 used three IPCAs, measures EV5, ASTAB5, SIPC5 & D5 (based on five IPCAs), and finally EV7, ASTAB7, SIPC7 and D7 measures utilized all significant IPCAs. Significant GxE interactions sum of squares was further divided into seven significant interaction principal component axes (IPCAs) mentioned in table 3. Explained variation of GxE interaction accounted by each of IPCA exploited by defined measures, as type-1 AMMI based measures benefited 37.7%, type-2 measures utilized 60.4%, type 3 measures used up to 73.8%, type 5 measures benefited up to 92.6%, while type 7 measures accounted for most of variation and utilized to the extent of benefits 99.6% (Table 3). This justifies the use of AMMI derived measures based on the large numbers of IPCAs results in the most usage of GxE interaction variations (Mohammadi et al., 2015; Kendal &Tekdal, 2016). Lower and maximum values of EV1 ranked genotypes as (G6, G2, G8) and (G7, G1) whereas for D1 were (G6, G2, G8) and (G7, G1) values of ASTAB1 for (G6, G2, G8) and (G7, G1) and for SIPC1 were (G7, G1, G5) & (G3, G4), of low yield performance (Tables 5 & 6). EV2 measure pointed towards (G6, G2, G9) as desirable and (G1, G5) vice versa, D2 for genotypes (G6, G2, G8) & (G1, G3), whereas as per SIPC2 were (G5, G7, G8) & (G3, G4) and of ASTAB2 were (G6, G2, G9) & (G1, G3). In recent studies, agronomic concept of stability would be more preferred instead of static concept of stability (Nowosad et al., 2016). Using first two IPCAs in stability analysis could benefits dynamic concept of stability in identification of the stable high yielder genotypes. ASV and ASV1 recommended (G6, G2, G9) genotypes possessing stable performance and unsuitable ones were G1, G7 wheat genotypes for this zone. First two IPCAs in ASV and ASV1 measures used 56.9% of GxE interaction. The two IPCAs have different values and meanings and the ASV parameter using the Pythagoras theorem and to get estimated values between IPCA1 and IPCA2 scores to produce a balanced measure between the two IPCA scores (Purchase, 1997). ASV and ASV1 measures had used advantages of cross validation as computations are based on first two significant IPCAs. Ranked values EV3 preferred G2, G6, G8 and unstable performance of G1, G5 while SIPC3 pointed towards G5, G8, G1 and G4, G3 D3 identified G2, G6, G8 & G1, G7 genotypes; ASTAB3 values considered G2, G6, G8 & G1, G7 (Table 4). Genotypes G6, G9, G8 preferred by least values EV5 along with higher values found for G5, G1, SIPC5 values found G8, G5, G1 and G4, G3 whereas D5 considered G6, G8, G2 as suitable & G1, G5 as unsuitable ones; ASTAB5 selected G6, G8, G2 for stable performance & G1, G5 would be of unsuitable choice. D7 ranked genotypes G6, G2, G8 as of stable yield while G1 and G7would be undesirable; SIPC7 observed G8, G1, G5 of choice & G3, G2 of unstable yield (Tables 4 and 5). EV7 pointed towards G2, G6, G8 & G5, G1 as 1260 Int.J.Curr.Microbiol.App.Sci (2020) 9(5): 1257-1270 suitable and unsuitable respectively. Measure ASTAB7 identified G9, G1, and G4 as desirable and G4, G3 would be unstable over the studied environments. Composite measure MASV selected G6, G2, G9 genotypes as of stable performance and G1, G5 not recommended for cultivation due to unstable yield behavior. More over MASV1 cited as G6, G2, G8 would be desirable and G1, G5 vice versa. Genotypes G8, G2 and G6 along with G1 & G7 by Mean, GAI, HM, PRVG and MHPRVG measures would be of choice across environments of study. Biplot analysis First two significant principal components were considered for biplot analysis among the considered measures to understand the association among measures if any. The relationship among these is depicted in graph as number clusters of considered measures (Ndhlela et al., 2014). First two significant principal components of considered measures accounted for variation 85.4 % of total variations. Three clusters of studied measures were observed in Figure 1. Most of the measures clubbed in first cluster as of D1, D2, D3, D5, D7, , EV1, EV2, EV3, EV5, EV7, MASV1, ASV, MASV, ASV1, SIPC1, SIPC2, SIPC3, SIPC5, SIPC7,PRVG, MHPRVG. Second cluster comprised of Mean, HA and GAI while ASTAB1, ASTAB2, ASTAB3, ASTAB5 and ASTAB7 were in separate cluster. Second year (2018-19) AMMI analysis Model diagnosis based on statistical and practical considerations observed suitability of AMMI7 as also confirmed by FR-tests at the 0.01 level of significance. The sums of squares for GxE signal and noise were 83.31% and 16.69% of total GxE interaction sum of squares respectively. Accordingly, this much signal suggests AMMI7 also merits consideration. Sum of squares for GxE signal is 4.13 times that for genotypes main effects. Hence, narrow adaptations are important for this dataset. Even just IPC1 alone is 1.73 times the genotype main effects. Also note that GxE noise is 0.83 times the genotype main effects. Accuracy may be improved by discarding noise, as this increases repeatability helps to simplifies conclusions and accelerates progress from selection process. First four IPCA’s contributed more than 80%. IPCA1 explained 34.8% of the variation affected by interaction, while IPCA2, IPCA3 and IPCA4 accounted for 22.1, 13.8 and 10%, respectively. Explained variation of GxE interaction accounted by each of IPCA exploited by defined measures, as type-1 AMMI based measures benefited 34.8%, type-2 measures utilized 56.9%, type 3 measures used up to 70.8%, type 5 measures benefited up to 87.5%, while type 7 measures accounted for most of variation and utilized to the extent of benefits 96.6% (Table 4). This justifies the use of AMMI derived measures based on the large numbers of IPCAs results in the most usage of GxE interaction variations. Minimum and maximum values of EV1 observed for (G9, G6, G1) and (G7, G5) while corresponding to D1 were (G9, G6, G1) and (G7, G5) absolute values of ASTAB1 for (G9, G6, G1) and (G7, G5) and for SIPC1 were (G7, G5, G10) & (G11, G2), of low yield performance (Tables 3 & 4). 1261 Int.J.Curr.Microbiol.App.Sci (2020) 9(5): 1257-1270 Table.1 Parentage details of genotypes along with environmental conditions (2017-18) Code Genotype Parentage Code Locations Latitude Longitude G1 G2 G3 G4 G5 G6 G7 G8 G9 HD2888 HI 1612 WH 1235 BRW3806 K 1317 DBW2 52 K 8027 HD3171 HI1628 (C 306/T.SPHAEROCOCCUM//HW2004) (KAUZ//ALTAR84/AOS/3/MILAN/KAUZ/4/HUITES) (METSO/ER2000/5/2*SERI*3//RL6010/4*YR/3/PASTOR/4/BAV92) (NI 5439/MACS 2496) (K0307/K9162) (PFAU/MILAN/5/CHEN/AE.SQ(TAUS)//BCN/3/VEE#7/BOW/4/PASTOR) (HD 1696/2*K852) (PBW343/HD2879 (FRET2*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/PFAU/WEAVER//BRAMBLING) E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 Varanasi Burdwan Coochbehar Chianki Deegh Ghaghraghat Kalyani Kanpur Purnea Pusa Ranchi Sabour 25° 19' N 23° 13'N 26° 34' N 24° 01' N 26° 02' N 26° 54' N 22° 58 ' N 26° 26'N 25° 46' N 25° 98' N 23° 20'N 25° 23' N 82° 59’E 87° 51’E 89° 44’E 84° 10’E 80° 54’E 81° 56’E 88° 26’E 80° 19’E 87° 28’E 85° 67’E 85° 18’E 87° 04’E Altitud e (m) 84 38 42 241 121 100 16 133 43 56 644 42 Table.2 Parentage details of genotypes along with environmental conditions (2018-19) Code G1 G2 G3 Genotype HD 3249 HD 2733 PBW 781 G4 G5 G6 G7 G8 G9 G10 G11 DBW 257 DBW 39 HD 3277 RAJ 4529 DBW 187 WH 1239 K0307 HD 2967 Parentage PBW343*2/KUKUNA//SRTU/3/PBW343*2/KHVAKI ATTILA/3/TUI/CARC//CHEN/CHTO/4/ATTILA PBW621/4/BW9250*3//Yr10/6* Avocet/3/ BW9250*3//Yr15/6* Avocet/5/2*PBW 621 HUW640/HD3055 ATTILA/HUI CHEN/AEG.SQUARROSA//BCN/3/BAV92/4/BERKUT PHS 0624/WR1136 NAC/TH.AC//3*PVN/3/MIRLO/BUC/4/2*PASTOR/5/KACHU/6/KACHU TAM200/PASTOR//TOBA97 K8321/UP2003 ALD/CUC//URES/HD2160M/HD2278 1262 Code E1 E2 E3 Location Kanpur Faizabad Varanasi Latitude 26° 26' N 26° 46' N 25° 19' N Longitude 80° 19' E 82° 9' E 82° 59' E Altitude 126 97 81 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 E15 Gorakhpur IARI-Pusa Sabour Purnea Banka RPCAU-Pusa Ranchi Chianki Dumka Kalyani Burdhwan Shillongani 26° 45' N 28°38 ' N 25°23' N 25° 46' N 24° 53' N 25°98' N 23°20'N 23°45'N 24°27' N 22° 58' N 23° 13' N 26° 8' N 83° 21' E 77°09' E 87°04' E 87° 28' E 86° 55 ' E 25°67 E 85°18'E 85°30'E 87°26' E 88° 26'E 87° 51' E 91° 43' E 84 52 46 36 79 52 651 215 137 11 30 86 Int.J.Curr.Microbiol.App.Sci (2020) 9(5): 1257-1270 Table.3 AMMI analysis of genotypes (2017-18) Source Treatments Genotypes Environments GxE interaction IPC1 IPC2 IPC3 IPC4 IPC5 IPC6 IPC7 Residual Error Total Degree of freedom 107 8 11 88 18 16 14 12 10 8 6 4 324 431 MS 231.71 166.47 1632.53 62.54 115.32 78.09 52.49 54.99 37.82 30.04 23.55 5.81 9.09 64.35 Level of significance *** *** *** *** *** *** *** *** *** *** *** % of Total SS % of GxE SS Cumulative % SS by PCA’s 89.39 4.80 64.74 19.84 37.72 37.72 22.71 60.43 13.35 73.78 11.99 85.77 6.87 92.64 4.37 97.01 2.57 99.58 Table.4 AMMI analysis of genotypes (2018-19) Source Treatments Genotypes Environments GxE interaction IPC1 IPC2 IPC3 IPC4 IPC5 IPC6 IPC7 Residual Error Total Degree of freedom 164 10 14 140 23 21 19 17 15 13 11 21 495 659 MS 226.6779 253.3109 1578.168 89.62649 190.0607 132.1767 91.42374 74.01184 55.89603 55.62916 38.39033 20.06844 14.9596 67.64822 Level of significance *** *** *** *** *** *** *** *** *** *** ** 1263 % of Total SS 83.39 5.68 49.56 28.15 % of GxE SS Cumulative % SS by PCA’s 34.84 22.12 13.84 10.03 6.68 5.76 3.37 34.84 56.96 70.80 80.83 87.51 93.28 96.64 Int.J.Curr.Microbiol.App.Sci (2020) 9(5): 1257-1270 Table.5 Principal components analysis of genotypes (2017-18) EV1 EV2 EV3 EV5 EV7 D1 D2 D3 D5 D7 SIPC1 SIPC2 SIPC3 SIPC5 SIPC7 G1 0.1314 0.1472 0.1210 0.0769 0.0630 6.3572 8.6421 9.1840 9.3368 9.4660 -2.4469 -0.0460 -1.4080 -2.0995 -3.1828 G2 0.0098 0.0076 0.0170 0.0537 0.0470 1.7340 2.0352 3.0346 5.5861 5.8546 0.6674 0.2305 -0.7560 -1.0334 -0.9600 G3 0.1192 0.0861 0.0610 0.0567 0.0550 6.0540 6.9147 7.0228 7.7961 8.0735 2.3302 3.7004 3.1625 2.7346 4.2003 G4 0.0449 0.0456 0.0336 0.0722 0.0540 3.7155 4.8503 4.9872 7.5772 7.6460 1.4301 2.7087 3.2174 6.4158 5.8597 G5 0.0137 0.0967 0.0866 0.0861 0.0634 2.0562 6.4801 7.1666 8.1295 8.1749 -0.7914 -3.3117 -4.6531 -2.6253 -2.4446 G6 0.0002 0.0001 0.0224 0.0205 0.0500 0.2365 0.2811 3.0888 3.6766 5.3456 -0.0910 -0.1533 1.1947 -0.0629 1.6934 G7 0.1542 0.0776 0.0792 0.0508 0.0575 6.8857 6.9029 7.6999 7.8331 8.2202 -2.6503 -2.8500 -1.3550 -0.8605 0.1483 G8 0.0110 0.0250 0.0281 0.0427 0.0503 1.8356 3.4029 4.0544 5.3494 6.0128 0.7065 -0.4687 -1.4346 -3.6609 -4.1156 G9 0.0157 0.0139 0.0510 0.0403 0.0597 2.1967 2.7167 5.0047 5.5894 6.4615 0.8455 0.1900 2.0321 1.1922 -1.1988 EV = Eigenvector, D = Parameter of Annicchiarico; SIPC1 = SIPC for first IPCA, SIPC 2 = SIPC for first two IPCAs, …, ASV = AMMI stability value; MASV = Modified AMMI stability value Table.6 AMMI based estimates of genotypes (2017-18) G1 G2 G3 G4 G5 G6 G7 G8 G9 ASTAB1 ASTAB2 ASTAB3 ASTAB5 ASTAB7 MASV1 MASV ASV1 ASV MEAN GAI PRVG MHPRVG HM 40.41 74.69 84.35 87.18 89.61 6.81 5.64 4.72 3.96 31.42 30.61 0.9206 0.8979 29.72 3.01 4.14 9.21 31.20 34.28 4.87 4.16 1.19 0.96 36.52 35.53 1.0575 1.0531 34.46 36.65 47.81 49.32 60.78 65.18 5.82 4.76 4.11 3.30 34.04 33.11 0.9908 0.9761 32.01 13.80 23.52 24.87 57.41 58.46 6.29 5.18 2.70 2.24 33.30 32.08 0.9618 0.9414 30.66 4.23 41.99 51.36 66.09 66.83 6.54 5.57 2.84 2.72 34.66 33.35 0.9988 0.9812 31.88 0.06 0.08 9.54 13.52 28.58 4.70 4.04 0.16 0.13 36.17 35.68 1.0623 1.0572 35.14 47.41 47.65 59.29 61.36 67.57 5.31 4.42 4.41 3.42 32.22 31.37 0.9385 0.9250 30.44 3.37 11.58 16.44 28.62 36.15 4.96 4.23 1.66 1.49 36.74 35.82 1.0675 1.0605 34.77 4.83 7.38 25.05 31.24 41.75 4.77 4.23 1.55 1.27 34.10 33.59 1.0021 0.9937 33.04 ASTAB = AMMI stability; PRVG = Relative performance of genetic values; MHPRVG= (Harmonic mean of relative performance of genetic values; GAI= Geometric adaptability measure 1264 Int.J.Curr.Microbiol.App.Sci (2020) 9(5): 1257-1270 Table.7 Principal components analysis of genotypes (2018-19) EV1 G1 0.0068 G2 0.0429 G3 0.0256 G4 0.0180 G5 0.0944 G6 0.0032 G7 0.2130 G8 0.0154 G9 0.0016 G10 0.0309 G11 0.0481 EV2 0.0063 0.0619 0.0387 0.0200 0.0472 0.0141 0.1074 0.0873 0.0029 0.0199 0.0943 EV3 0.0292 0.0604 0.0258 0.0669 0.0525 0.0359 0.0831 0.0587 0.0050 0.0134 0.0690 EV5 0.0440 0.0378 0.0238 0.0465 0.0381 0.0362 0.0735 0.0379 0.0553 0.0592 0.0478 EV7 0.0316 0.0606 0.0500 0.0336 0.0316 0.0527 0.0527 0.0461 0.0396 0.0542 0.0473 D1 5.47 13.70 10.58 8.87 20.31 3.76 30.52 8.20 2.62 11.62 14.51 D2 6.79 20.31 15.99 11.81 20.32 9.14 30.59 22.57 4.33 12.63 24.50 D3 13.28 22.62 16.00 20.46 22.86 14.89 31.56 22.63 5.88 12.66 25.15 D5 16.95 22.79 17.52 21.10 23.46 17.60 33.83 22.97 15.98 21.90 25.78 D7 16.98 26.24 21.17 21.13 23.86 20.36 33.84 24.21 16.00 22.69 26.57 SIPC1 0.6721 1.6843 1.3014 1.0907 -2.4984 0.4619 -3.7528 1.0088 -0.3228 -1.4292 1.7841 SIPC2 1.2277 3.7498 2.9536 2.1660 -2.5216 1.6103 -3.4548 -1.8875 -0.7966 -2.1115 -0.9353 SIPC3 -0.5408 5.2938 3.0151 -0.4221 -4.1444 3.4301 -2.2543 -2.1491 -0.1801 -1.9940 -0.0542 SIPC5 -2.4070 5.9807 4.4624 0.5340 -3.2038 2.4244 -0.1099 -1.2515 -3.2776 -4.5138 1.3620 SIPC7 -2.4964 3.2040 7.7698 0.3238 -4.3990 2.4621 0.1576 -2.4167 -3.4798 -3.3179 2.1925 Table.8 AMMI based estimates of genotypes (2018-19) ASTAB1 ASTAB2 ASTAB3 ASTAB5 ASTAB7 ASV ASV1 MASV MASV1 MEAN GAI HM PRVG MHPRVG 3.67 5.91 26.10 46.70 46.86 1.0100 1.1954 4.1596 4.5159 48.81 48.49 48.16 1.0358 1.0300 G1 23.07 54.03 69.42 70.78 103.42 2.9553 3.3619 6.2246 7.3708 48.16 47.53 46.86 1.0170 1.0076 G2 13.77 33.59 33.61 42.19 70.46 2.3232 2.6326 5.0702 5.9705 45.93 45.34 44.76 0.9690 0.9625 G3 9.67 18.07 61.31 66.25 66.52 1.7406 2.0265 4.7818 5.3608 45.33 44.88 44.40 0.9602 0.9512 G4 50.75 50.76 67.76 72.92 76.74 3.1354 3.9347 4.4604 5.2721 45.79 45.34 44.88 0.9710 0.9597 G5 1.73 11.31 32.69 47.63 69.04 1.2864 1.3594 5.1141 5.8188 47.06 46.54 45.98 0.9945 0.9881 G6 114.52 115.16 124.47 149.40 149.59 4.7190 5.9178 6.0329 7.2914 45.67 44.85 44.04 0.9646 0.9455 G7 8.27 69.16 69.60 72.26 84.98 3.1608 3.3034 5.2706 6.1060 51.17 50.61 50.07 1.0814 1.0747 G8 0.85 2.48 4.93 45.90 46.01 0.6234 0.6950 4.2612 4.4906 50.41 49.95 49.46 1.0665 1.0615 G9 16.61 19.99 20.08 73.80 81.54 1.9190 2.3520 5.3592 6.1410 45.50 44.97 44.45 0.9615 0.9543 G10 25.88 79.56 84.57 90.24 99.22 3.5225 3.9102 5.5448 6.4681 46.71 45.65 44.51 0.9785 0.9660 G11 1265 Int.J.Curr.Microbiol.App.Sci (2020) 9(5): 1257-1270 0.5 G6 PC1 = 70.5; PC2=14.9; TOTAL = 85.4% G2 0.4 G8 G9 0.3 GAI MEAN HM 0.2 G4 0.1 G5 0 -0.2 -0.1 SIPC1 PRVG EV1 0 0.1 0.2 0.3 D7 D5 -0.1 D3 D1D2 SIPC2 ASV1 SIPC3 MASV1 -0.2 EV7 SIPC7 ASV EV5 EV3 MHPRVG EV2 SIPC5 0.4 0.5 MASV ASTAB7 G3 G7 ASTAB5 -0.3 ASTAB1 ASTAB3 -0.4 G1 ASTAB2 -0.5 Figure.1 Biplot analysis of genotypes and AMMI based estimates (2017-18) 1266 0.6