Chickpea’s sowing window has been extended from October to December in major crop growing districts of Karnataka due to climatic changes in recent years. Due to the prolonged cropping season, crops are exposed to variations in temperature, relative humidity and moisture levels
(Richards et al., 2020). These environmental fluctuations influence crop phenology, yield-contributing traits and quality characteristics. Therefore, it is crucial to identify chickpea genotypes that can consistently produce stable yields under changing environmental conditions.
The ANOVA analysis across five environments (ENV-1 to ENV-5) reveals significant differences among the genotypes, indicating variability among the genetic materials being studied (Table 2). The mean values varied, with ENV-1 recording the highest average of 2316.89, while ENV-5 had the lowest at 705.16. The coefficient of variation (CV) indicated that ENV-4 exhibited the greatest relative variability at 10.74%, whereas ENV-1 showed the least variability at 6.96%. Broad-sense heritability (h²) is high across all environments, ranging from 0.74 to 0.96, indicating that genetic factors significantly influence the phenotypic traits. This suggests a strong genetic control over the traits studied, with relatively stable genetic influence despite environmental differences.
The Pooled ANOVA (Table 3) across five environments revealed highly significant differences in the G x E interactions, indicating that the grain yield of the 14 genotypes varied significantly across the different environments. Environment mean squares were highly significant across the five environments with 82.90% contribution to the total variance. The interaction between genotype and environment mean squares were also significant with contribution 6.55% to the total variance. The replication within environments mean squares were non-significant with a minimal contribution of 0.25%. The residual mean squares contribute 2.55% to the total variance. These results registered the predominant influence of environmental factors on the yield attributing traits of chickpea.
AMMI analysis of 14 genotypes evaluated across five environments
The analysis of variance for grain yield of 14 chickpea genotypes evaluated across five environments (Table 4) through AMMI model revealed that chickpea genotypes were significantly (Pd£ 0.01) affected by different environments. The genotype by environment interaction also showed highly significant, suggesting that the performance of genotypes varies significantly across different environments. This interaction is crucial for identifying genotypes that perform well under specific conditions.
The principal components (PCs) derived from the genotype by environment interaction are highly significant. The first principal component (PC1) explains 51.1% of the interaction variation, while PC2, PC3 and PC4 explain an additional 20.2%, 19.2% and 9.5% respectively, cumulatively explaining 100% of the interaction variation. This reveals that these components effectively capture the variation due to genotype by environment interactions at different environments. According to the AMMI model, the genotypes which are characterized by means greater than grand mean and the IPCA score nearly zero are considered as adaptable genotypes across the environment
(Rashidi et al., 2013). However, the genotypes with high mean performance and large value of IPCA score suggests that these genotypes were adaptable to the specific environments.
The AMMI 1 biplot for grain yield of the 14 chickpea genotypes at five environmental conditions is shown in Fig 1. The main effects (genotypes and environments) accounted for 90.65% of the total variation and IPCA 1 accounted for 51.1% of the total variation due to genotype by environment interaction alone. For instance, ENV-1 and ENV-2, located on the right side with higher yield values revealed that G1, G7 and G9 may perform well in these environments. Conversely, ENV-5, positioned on the upper left, suggests that G12 and G13 are more adapted to this environment. ENV-3 and ENV-4, near the plot centre, show moderate interactions with several genotypes like G3, G4 and G10. This biplot effectively illustrates which genotypes are best suited to specific environments, aiding in targeted breeding and selection efforts. The AMMI1 biplot shows that genotypes G1, G7 and G9 perform best in high-yield environments ENV-1 and ENV-2, while G12 and G13 are better suited for ENV-5.
The AMMI 2 biplot provides a visual representation of the interactions between genotypes (G1 to G14) and environments (ENV-1 to ENV-5) based on the first two principal components (PC1 and PC2), which together accounts 71.3% of the total interaction variation (51.1% by PC1 and 20.2% by PC2). ENV-3 is unique and highly interactive, with genotypes G6 and G10 thriving in this environment. In contrast, ENV-2 is more compatible with genotypes G5 and G7.While, ENV-1, ENV-4 and ENV-5 cluster near the centre, indicating a moderate interaction with multiple genotypes, such as G3, G4, G8 and G13 shown in Fig 2. These results highlight the specific adaptation of certain genotypes to particular environments, which is crucial for targeted breeding programs to enhance yield stability and performance across varying conditions.
Test environment evaluation
The persistence of test-environment evaluation is to identify environments that are effective in distinguishing superior genotypes within a mega-environment. An “ideal” test environment should be both discriminating of the genotypes and representative of the mega-environment and it is based on environment-focused scaling (
Yan, 2002), that is, the singular values were entirely partitions the environment scores (“SVP = 2”) so that it is appropriate for studying the relationships among test environments. This type of AEC can be referred to as the “Discriminating power vs. Representativeness” view of the GGE biplot.
Test environments with longer vectors are more effective at distinguishing between genotypes, while test environments with smaller angles are more representative of the mega-environment compared to those with larger angles. ENV-2 is having the longest vector, hence discriminating all the 14 genotypes and ENV-4 exhibits small angle among the all-test environment indicating ENV-4 is representative of all the 14 genotypes as in Fig 3.
Hence, ENV-4 may be regarded as an ideal test location. Hence, this biplot elucidates the differential performance of genotypes across various environments, highlighting which environments are most effective for distinguishing genotypic performance and which genotypes show specific adaptation to particular environmental conditions.
Genotype evaluation
An ideal stable genotype should have high mean performance coupled with high stability within a mega environment. Genotype evaluation is only useful for a particular mega-environment.
Yan (2002) defined an “ideal” genotype on the basis of both mean performance and stability and the genotypes can be ranked based on their biplot distance from the ideal genotype. The Fig 4 reveals that the biplot involves five environments with the “Average Environment Coordination” (AEC) axis. The genotype-focused singular value partitioning (SVP), which divides the singular values completely into genotype scores is the basis of this AEC axis. The AEC view with SVP = 1 is commonly known as the “Mean vs. Stability” view due to its ability to compare genotypes between environments based on mean performance and stability within a mega-environment.
The genotype on the extreme left of AEC axis indicates the higher mean performance and thus, the genotypes are ranked as G9 = G1 > G7 > G5 > G6 > G2 > G14 > G11 > G10 > G12 > G3 > G4 > G13. The genotypes which closure to the AEC ordinate indicates most stable genotypes. Among 14 genotypes, G4 and G10 are closure to the AEC ordinate compared to other genotypes. Hence, G4 and G10 are more stable genotypes.
Mega-environment analysis
The first principal component (PC1) scores of the genotypes and the environments are plotted against the corresponding scores for the second principal component (PC2) that derived from the SVD of environment-centred GGE biplot. This mega-environment analysis exhibits the which-won-where view of the biplot (
Yan and Kang, 2002).
The biplot is divided into sectors by the perpendicular lines to the polygon sides, each of which has a distinct winning cultivar. The genotypes at the vertex of the polygon sides in the sector’s boundary is the winning cultivar for that sector. In which-won-where view of the biplot shown in Fig 5, the five environments fell into two sectors with different winning cultivars. Specifically, G2, G9 and G1 were the highest yielding cultivar in first mega-environment (E1, E3 and E5). Whereas, G7 and G5 recorded highest yielding cultivar in the second E2 and E4 mega-environments. Therefore, identification of specific genotypes that perform well in certain environments can help in developing targeted breeding programs
(Das et al., 2022).
The comprehensive evaluation of 14 genotypes (G1 to G14) based on various metrics such as yield (Y), coefficient of variation (CV), adjusted coefficient of variation (ACV), Shukla’s stability variance (Shukla), Wricke’s ecovalence (Wi_g, Wi_f, Wi_u) and other stability parameters (Table 5) reveals significant variations in yield performance, stability and adaptability across different environments. Genotypes G1 and G7 emerge as the top performers with the highest yield (1724 and 1731) and stability, as indicated by low coefficients of variation (CV), Wricke’s ecovalence (Wi_g, Wi_f, Wi_u) and Shukla’s stability variance. These genotypes also show excellent adaptability and predictability, with low Pi_a and Pi_u values, making them ideal candidates for diverse and variable growing conditions. Conversely, genotypes G13 and G4 exhibit the lowest yields and highest variability, marked by high CV and Shukla’s variance, indicating their poor performance and instability. G3 stands out for its exceptional stability, with the lowest CV and ACV values, despite its lower yield, suggesting its suitability for environments where stability is prioritized. Overall, this analysis highlights G1 and G7 as the most promising genotypes for both high yield and stability, while G13 and G4 are less favourable due to their lower performance and higher variability similar finding was reported by
(Tiwari et al., 2018).
By integrating all the models, genotypes G1 and G7 consistently recorded high yield, low variability and constant stability across different environments. Genotypes G13 and G4, despite showing stability, generally have low yields and high variability, making them less favourable. While, Genotype G3 shows excellent stability (low CV, ACV) but lower yield, suggesting it could be suitable in environments where stability is prioritized over yield and Genotype G5 is high-yielding but shows high variability and instability suggests that it might perform well in favourable coupled with consistent environments
(Laxuman et al., 2022).
Overall, this comprehensive analysis provides a detailed performance and stability profile of each genotype, assisting in selecting the best-performing and most stable genotypes for particular environmental conditions.
