The highly significant effects of environments, genotypes and interactions were observed for forage and grain yield. The environmental effects explained the major portion of the total variance as of 82.3% and 58.8% respectively. Indicated that the environments were diverse and a major part of variation in yield resulted from environmental changes. The highly significant interaction effects partitioned into IPCA1, IPCA2 and IPCA3, IPCA4; which explained 30.4, 19.4, 14.8 & 13.2% for forage and 37.0, 17.2, 16.1 and 12.5% for harvested grain yield. AMMI stability value(ASV) identified promising genotypes G12(UPB 1035), G6(UPB 1034), G7(BH 971) and G13(RD 2857), G7 (BH 971) & G11(NDB 1570) for forage and grain respectively. AMMI distance (D) marked G3(RD 2035) G9(BH 970) & G13(RD 2857) for former while genotypes G15(RD 2856) G11(NDB 1570) & G7(BH 971) for grain yield. GSI score advocated G13(RD 2857), G11(NDB 1570) G3(RD 2035),G5(RD 2715 ) and G7(BH 971), G2(RD 2552) G14(AZAD) desirable genotypes for selection with forage and grain yield. Genotypes with IPCA-1 scores close to zero identified G4(UPB 1036), G7(BH 971), G16(NDB 1566) and G11(NDB 1570), G2(RD 2552) for forage and yield respectively would have wider adaptation to the tested environments as per AMMI graphical plots.

AMMI analysis explained the highly significant effects of the environment, GxE interaction, and genotypes for wheat genotypes evaluated under restricted irriation timely sown multi-location trials in the Central zone of the country during 2018-19 and 2019-20. About 77.1%, 12.2% & 2.3% had been contributed by environments, interactions and genotypes of the total sum of squares due to treatments respectively in the first year.

Background: Efficient estimation of main and interaction effects under multi environment trials had been carried out by AMMI. Studies with low proportion of the variance explained by first interaction principal component IPCA1 under AMMI analysis had been reported biased interpretation for the stability of the genotypes. Methods: Weighted Average of Absolute scores (WAASB), quantitative stability measure had been recommended for selection of productive genotypes with broad adaptation. The selection of promising genotypes had been assisted by use of superiority index (WAASBY) that considered simultaneous use of yield and stability by allowing variable weighting mechanism for yield and stability. Result: Wheat GW509 and HI1633 genotypes had been identified by stability measure WAASB for the first year. While Superiority index considered stability and high yield settled for HI1633 and Raj4083 genotypes. Analytic measures of adaptability PRVG and MHPRVG observed suitability of HI1633 and Raj 4083. SI expressed positive linear behaviour with yield, MHPRVG and PRVG measures. While stability measure WAASB maintained moderate negative correlation with yield, SI, MHPRVG and PRVG. Wheat genotypes HI1633, HI1641 and HI1646 identified by stability measure for the next year of study. Superiority index selected HI1641, HI1633 and MACS6752 genotypes. SI expressed direct linear relation with yield, MHPRVG and PRVG. Stability measure exhibited indirect relationships with SI, MHPRVG, PRVG and yield Wheat genotypes HI1633, Raj 4083 for first year and HI1641, MACS6752 for the second year of study.

GxE interaction to know adaptability of 19 salt salinity tolerant barley genotypes was studied by parametric and non-parametric measures. Genotypes KB1516, RD2907 and RD2794 showed minimum environmental variance over different environments. Superiority index identified genotypes RD2907 and NDB1445 with lowest value accompanied with higher. Wricke’s measure exhibited lower values of DWRB168_,  DWRB165 and NDB1445. Higher values of GAI showed consistent performance of RD2907_, NDB1445 and RD2552. Non-parametric measures S_i⁽¹⁾, S_i⁽³⁾ and S_i⁽⁶⁾ the considered DWRB165 and DWRB168  as desirable genotypes. Thennarasu’s first measure NP_i⁽¹⁾ found DWRB168 and NDB1445 as desirable adaptable and KB1546, RD2907 and NDB1173 were unstable genotypes. Wricke’s parameter was positively correlated with NP_i^(1), NP_i⁽³⁾ and Kang. GAI had significant positive with P_i and Kang while negative with S_i(6), NP_i(2) & NP_i(4). Worth to mention the negative association of P_i with S_i(6), NP_i^(2), NP_i^(4). Non parametric measures S_i ⁽³⁾ S_i ⁽⁶⁾ NP_i ⁽²⁾ & NP_i ⁽⁴⁾ clubbed together while Kang, W_i ², s²_{i ,}S_i ⁽¹⁾,S_i ^{(2) ,}NP_i ⁽¹⁾ & NP_i ⁽³⁾ joined in another cluster. Left over parametric measures were grouped in two separate clusters i.e. (b_i, S²_xi ,CV_i),(Yield, GAI Pi) respectively. Biplot analysis based on first two principal components showed three groups among the measures.

AMMI analysis of 21 malt barley genotypes evaluated at nine locations of north western plains zone revealed highly significant variation due to environments (61.8%), G x E interactions (19.5%) and genotypes (8.2%). Further, interaction effects were partitioned into seven interactions principal components. AMMI stability Value measures (ASV1 and ASV) had considered first two interaction principal components and based on 54.1% of total interaction variations had recommended (PL926, PL931, RD2849) and (BH1036, PL931, RD2849) malt barley genotypes respectively. Modified AMMI Stability Values measures (MASV1 and MASV) had exploited nearly 97.6% of total interaction variations, based on which BH1036, DWRB219, RD2849 and BH1036, DWRB219, PL926 malt barley genotypes were identified for stable yield performance. Geometric Mean (GM) based on BLUP effects of genotypes yield was in favour of DWRB219, BH1036 and DWRB221 while corresponding to Harmonic Mean of yield values, DWRB219, BH1036, DWRB221 genotypes would be of choice. Relative Performance of Genotypic Values (RPGV) favored DWRB219, BH1036 , DWRB221 barley genotypes and Harmonic Mean of Relative Performance of Genotypic Value (HMRPGV) measure favoured DWRB219, BH1036, DWRB220 malt genotypes. Non parametric composite measure, NPi (1) , suggested that UPB1097, RD3029, DWRB218 were suitable, whereas DWRB219, RD3029, G4 genotypes would be preferable as per values NPi(2) while NPi(3) identified DWRB219, BH1036, DWRB160. The composite measure NPi(4) found DWRB219, BH1036, DWRB160 as genotypes of choice for this zone. Measures MASV, MASV1, Si1, Si3, Si4, Si5,Si6 ,Si7, Si2, NPi (1), ASV and ASV1 accounted more in first principal component, whereas NPi (2), NPi (3), NPi (4), Average, GM, HM, Mean, PRVG, HMPRVG were major contributors for second principal component. Small cluster of standard deviation (Stdev) with CV, IPC4 and IPC7 were placed in second quadrant. Smallest cluster of IPC1 and IPC6 was also observed in this quadrant. Large cluster comprised of NPi(1), Si2 ,Si5 Si2 ,Si5 ,Si6 with ASV and ASV1. Measures GM, HM, PRVG and HMPRVG considered random effects of genotypes grouped with average and placed in last quadrant while adjacent cluster of NPi(2), NPi(3), and NPi(4) observed in same quadrant. Close association among the AMMI analysis based measures had been observed with adaptability measures based on BLUP effects of malt barley genotypes in the present study based on the Biplot analysis while considering first two principal components.