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Genotypes x environment interaction (GEI) effects are of special interest for plant breeders to identify stable genotypes. Present experiment was conducted for two years and two seasons from 2016–17 to 2017–18 at National Pulses Research Centre, TNAU, Vamban to assess the stability of 28 greengram genotypes for seed yield. In AMMI1 biplot for seed yield, the genotypes viz., VGG 16–003, VGG 16–016, VGG 16–054 and VGG 16–055 had IPCA 1 score close to zero with high main effects indicating that these genotypes were less influenced by environments and high yielders. VGG 16–054 and VGG 16–055 with high main effect and positive IPCA 1 score away from zero were identified as highly interacting genotypes with high yield. Genotypes viz., VGG 16–026, VGG 16–048, VGG 16–052, VGG 16–058, VBN (Gg) 3 and CO 8 were less interacting genotypes with high seed yield. These genotypes may be recommended for both the seasons viz., Kharif and Rabi seasons. Among environments, Kharif and Rabi seasons are highly interacting environments.

A investigation was carried out to ascertain the GEI, the yield stability and adaptability of 12 advanced spine gourd genotypes (G) in one environments over three crop years (2013, 2014 and 2015). The AMMI and GGE biplot model were used to study the nature of GEI on the fruit yield. First and second component of AMMI model totally explained more than 99% of GEI variations. G7 having maximum trait value with specific adaptation while G2 and G3 were having general adoptability. AMMI 2 biplot revealed high stability of G5 and G2 across environments. Results of GGE biplot model showed that the G4 with the environment of E3 and G7 with the environment of E1 and E2, respectively showed a special adaptability. G7 could be recommended for Northern hill zone of Chhattisgarh. Considering both graphical analysis models of AMMI and GGE biplot could be recommended. The ideal environment, according to both the models, was E2. The results indicated that AMMI and GGE biplot are facilitated visual comparison and informative methods to detect genotypes stability and in the preferential genotypes recommendations.

This study was carried out to evaluate the performance of rice cultivars for grain yield stability performance analysis and wide adaptation by GGE biplot method. An experiment was conducted to evaluate 8 rice (Oryza sativa L.) hybrids, 2 check varieties for their stability at 6 different locations, viz Patna, Purnia, Lucknow, Gosaiganj, Barabanki and Prayagraj during rainy 2018 season representing different agroclimatic zones of Uttar Pradesh and Bihar. GGE biplot methodology was utilised to find out the grain yield performance and stability of rice cultivars examined over six environments. GGE biplot analysis considers both genotype (G) and GE interaction effects and graphically displays Genotype Environment interaction in a two-way table. GGE biplot is an effective method based on principal component analysis (PCA) to fully explore multi environment data. The significant Genotype by Environment interaction effects for yield infers that genotypes reaction was different over different environments, indicating that the genotype selection must be specific to the growing conditions. Based on the analyses, genotypes H2, H3 and H5 were high yielding and highly stable genotypes. Hybrid H6 at Environment 4 and hybrid H4 at Environment 3 performed well. Environments E1, E2, E5 and E6 were suitable evaluating environments for this set of rice cultivars.

The study of G×E Interaction (GEI) is critical for evaluating the mean performance and stability of cultivars across wide range of environmental conditions. An experiment was conducted during kharif 2014-2015 for discriminating 22 basmati rice genotypes for days to 50% flowering and days to 75% maturity using AMMI and GGE stability models under four different environments viz. direct (DSR-wet) and (DSR-dry) and indirect seeding transplanted rice (TPR) and system of rice intensification (SRI) conditions at CCS HAU farm, Kaul. Estimates of G×E interaction following Eberhart and Russell (1966) and AMMI biplot analysis as per Gauch and Zobel model (1989) were computed. For days to 50% flowering, genotypes like Pusa Basmati 6, Pusa Sugandh 3, Haryana Basmati-1 and Pusa RH 10 were identified under SRI, CSR-30 under DSR (dry) and DSR (wet) and HKR 98-476, Pusa Sugandh 2 and Pusa Sugandh 5 under TPR conditions. However, for days to 75% maturity, Pusa Sugandh 3, Pusa Basmati 1121, Pusa Basmati 1 and HKR 06-434 were adapted best to SRI, Traori Basmati, Basmati-370, HKR 98-476 and HKR 06-443 to TPR and DSR (dry) and HKR 06-487 and Pusa RH 10 to DSR (wet).

Fourteen blackgram genotypes (comprising 10 advanced generations and four parents) were evaluated for three different season viz., Kharif, Rabi and summer both in open conditions as well as inter crop in coconut garden under six environments. Analysis of variance on the data pooled over seasons showed significant difference between genotypes for days to flowering, number of pod bearing branches, number of pods, length of pods, 100-seed weight and yield per plant. Environmental effect was significant for all the traits except plant height whereas genotype x environmental interaction were significant for all the traits. Analysis of genotype x environment interaction by AMMI model showed blackgram culture T6 (4.5.9; T9 x Rusami) as the best with respect to days to flowering, number of branches per plant, pod bearing branches per plant and number of pods per plant for all environments. The genotype T5 (4.5.8; T9 x Rusami) with high mean yield and stability can be selected for cultivation under all environments.

In the present investigation g x e interaction of twenty seven feed barley genotypes were evaluated at fifteen locations by non parametric measures. Results based on nonparametric measures do not require distributional assumptions for testing of effects. JB322 was high yielder followed by PL890 & HUB250 among studied genotypes. CMR and CSD measures pointed towards HUB113, NDB1634 and UPB1054, JB322 as desirable genotypes by respective measures. Si 1 and Si 2 measures identified JB322 and UPB1054 along with UPB1054 & HUB 113 as of stable yield performance. Values of the sum of Zi 1 and Zi 2 denoted significant differences among feed barley genotypes across 15 studied environments. Genotypes UPB1054, HUB113, BH1005 based on Si 3 and Si 6 were identified as the stable genotypes whereas KB1436 & RD2552 were unstable. First two NPs were very similar for unstable performance of RD2552 and last two NPs for similar behaviour of HUB250. Biplot analysis observed highly significant negative rank correlation of yield with corrected mean yield, SD and no significant correlation with MR.

Finger millet (Eleusine coracana (L.) Gaertn. subsp. coracana ) production has become stagnant over the years and one of the possible ways to increase the production can be spread of widely adaptable high yielding cultivars. Five national finger millet cultivars were grown in randomized complete block design at ICAR-Vivekananda Institute of Hill Agriculture for six consecutive years to evaluate the grain yield stability. The grain yield data were subjected to AMMI and GGE biplot techniques for assessing the stability and patterns of GE interaction in finger millet National cultivars. The combined ANOVA showed that finger millet grain yield was significantly affected by environment, which explained 54.67% of the total treatment (G+E+GE) variation, whereas the G and GEI accounted for 10.38% and 34.96%, respectively. The partitioning of GEI sum of squares using AMMI analysis indicated that the first two PCAs were highly significant. The first IPCA axis (IPCA1) accounted for 50.3% of the G×E interaction sum of squares. The second IPCA axis accounted for 38.2% of the interaction sum of squares. Both represented a total of 88.5% variation. AMMI 1 biplot indicated the general adaptation of genotype HR 374 across the environments, whereas the other genotypes showed specific adaptation to one or other environments. GGE-biplot graphical analysis further confirmed the results and revealed that HR 374 as an ideal genotype in terms of high yield and stability followed by RAU 8 as desirable genotype. In our research, both of AMMI and biplot models were successful in assessing the performance of genotypes and the selection of best genotype was identical in both of them.

White grained finger millet genotypes has become a thrust area in finger millet breeding due to increased demand of non-glutinous food products and lesser acceptability of brown grained finger millet. Sixteen white grain finger millet lines were developed by crossing extra early maturing brown grained finger millet genotypes including adapted varieties with late maturing white grained finger millet genotypes. The quantitative data of 16 lines along with parents were subjected to multivariate analysis. A wide range of variation was observed for all the studied traits. The parental lines of brown and white grained finger millet genotypes exhibited extreme values for grain yield and days to maturity whereas, the developed white grained genotypes showed moderate values. Projections of genotypes in PCA biplot showed close association of newly developed white grain genotypes VL 427, VL 360, VR 485, VR 443, VL 366, VR 425B, VR 425A and VL 356 with VL 201 (brown type) because of earliness and high yield potential. The cluster analysis further indicated that white grained genotypes from second cluster are probable candidates for further testing and release, and further refinement in breeding strategy by hybridizing white genotypes from second cluster with brown genotype in third cluster for incorporating earliness and high yield.

The present investigation was undertaken to evaluate thirty one mungbean genotypes for yield and drought related traits based on GT biplot analysis. The GT biplot analysis is a powerful statistical tool for studying relationship among traits, evaluating cultivars based on multiple traits and for identifying those that are superior in certain traits. GT biplot analysis showed positive relationship between yield and other traits viz., number of pods per plant, number of clusters per plant, days to maturity, plant height, chlorophyll content and chlorophyll stability index are identified as important traits for yield as well as drought tolerance improvement. Hence, these traits could be considered as key components during the selection process aiming towards the breeding of mungbean genotypes for high yield and drought tolerance. The genotypes LGG 450, PUSA 9531, LGG 528, Asha, EC 396117 and MH 565 were identified as ideal cultivars which could serve as a good genetic raw material for development of better cultivars for high seed yield coupled with drought tolerance.

Spring barley genotypes (cultivars and advanced breeding lines) have been tested in three environmental zones of Ukraine (Central Forest-Steppe, Northern Steppe and Polissia). The ANOVA has revealed reliable contributions from all three source of the variation: genotype, environment and genotype–environment interaction, but with their different ratio depending on the test conditions and studied genotypes. For spring barley cultivars the contribution of genotype–environment interaction was 22.55%, but for the advanced breeding lines it was only 10.56%. To establish patterns of genotype–environment interaction and genotypes ranking the GGE biplot model has been used. Both the change in the contribution of genotype to the total variation depending on environmental conditions and the dependence of characteristics of test environments on the genotypes studied have been revealed that in general, the combination of different ecological and year conditions of trial contributed to the identification of "the best of the best" genotypes in the final stage of breeding work. Spring barley cultivar MIP Bohun and breeding lines Deficiens 5005, Nutans 4855, and Nutans 4941 with optimal combination of yield performance and stability have been selected.

Twenty common bean (Phaseolus vulgaris L.) genotypes were evaluated based on drought tolerance indices through principal component analysis. All the indices were positively correlated with each other with the exception of drought susceptibility index, which was negatively correlated with all other indices used in the study. The first PC that accounted for 73.01 % of variation, and indices such as yield (non-stress), yield (stress), geometric mean (GM), harmonic mean (HM), drought resistance index(DRI), coefficient of drought resistance (CDR), drought tolerance index (DTI) and relative drought index (RDI) were related to tolerance and percent reduction and DSI were related to susceptibility. The genotypes with higher component scores of PC1 viz., WB-1634 (1.97), WB-341 (1.371), WB-185 (1.268) and WB-451 (1.253) are also tolerant to drought. The Biplot of PC1 and PC2 also revealed WB-1634 as tolerant, whereas genotypes WB-6, WB-1587 and SR-1 as susceptible to drought.

Finger millet (Eleusine coracana (L.) Gaertn. subsp. coracana) is an important food-grain in semi-arid, hilly tribal areas of India and Africa for subsistence farming. GGE biplot techniques were applied for the assessment of stability and patterns of Genotype by Environment Interaction (GEI) in elite finger millet genotypes grown in four different locations. The combined ANOVA for grain yield of thirteen finger millet cultivars at four environments showed that Environments (E), Genotypes (G) and GEI were highly significant. The partitioning of GEI sum of squares showed that first and second IPCA axis accounted for 64.1% and 28.1% of the interaction sum of squares for GGE analysis. The biplot analysis grouped the four environments into two mega environments with VL 368 and VR 988 as winning genotypes. The genotype VL 368 was found to be an ideal genotype in terms of high yield and stability followed by KOPN 942, PPR 2773, TNAU 1214, VR 988 and VL 369 as desirable genotype. Among environments, E1 and E3 were the most interactive environments while, E2 and E4 showed little variation in genotypes relative ranking.

Harvest index (HI) is directly proportional to grain yield and inversely to total biological yield. The HI of direct-seeded rice is often lower than that of transplanted crops. Cultivars able to maintain a high HI are preferred for direct seeding. Aimed so,22 basmati rice genotypes comprising released varieties and elite lines including an hybrid were evaluated under direct and indirect seeding conditions. In direct seeding wet (DSR-wet) and dry (DSR-dry) and under indirect seeding transplanted rice (TPR) and system of rice intensification (SRI) made the four environments of experiment. The experiment was conducted during kharif 2014-2015 season in RBD with three replications at experimental farm of CCSHAU, College of Agriculture, Kaul. Plot size consisted of 5 row of 2m length and 0.20m breadth. Standard agronomic practices of different production systems were followed. Data were recorded for HI and test grain weight. Stability parameter and AMMI biplot identified genotypes Pusa Basmati-1, HKR 08-425 and Haryana Basmati-1 with high HI adaptable to better environment SRI and DSR. Genotype Pusa Basmati 1509, PusaSugandh 5, HKR 06- 443, CSR-30 and Pusa RH 10 were identified to be stable for test grain weight. Environment wise genotype Traori Basmati and PusaSugandh 5 were adapted to DSR (dry) while HKR 06-487 and Pusa RH 10 were adapted to DSR (wet) for HI.

In this investigation 23 genotypes of wheat were tested for stability in 19 locations of North Western plains of the country, Yield data generated from the trials were analysed using AMMI analysis. The distribution of genotype by AMMI revealed that the genotypes 10,13, 20,12,15 and 14 scattered close to the origin, indicating minimal interaction of these genotypes with environments. Studied environments explained 57.2% of the total variation, whereas G and GxE captured 6.2% and 24.3%, respectively. First two principal components (PC1 and PC2) were used to create a 2-dimensional GGE biplot and explained 26.4% and 14.3% of GGE sum of squares (SS), respectively. Environments of Karnal, Ludhiana and Gurdaspur fall in same sector with genotypes 23 & 16. The spearman correlations calculated based on ranks by stability methods varied from positive value 0.97 to negative correlation of 0.759. The cultivar superiority estimate (Pi) maintained negative correlation with other estimates ranking.