Introduction: Environmental factors significantly influence crop yield and other quantitative traits, posing a challenge for scaling up agricultural production. Additionally, climate change and the variability of environmental conditions in experimental settings necessitate the development of resilient cultivars capable of adapting to unpredictable changes. To effectively identify the best-performing cultivars for specific environments, conducting multi-environment trials is essential. This process requires evaluating genotype-by-environment interactions (GEI) to select cultivars with optimal stability and performance. This study aimed to develop high-yielding chickpea cultivars adapted to the tropical and subtropical rainfed regions of Iran, utilizing indicators derived from the AMMI (Additive Main Effects and Multiplicative Interaction) analysis method.
Material and Methods: In this study, 13 chickpea genotypes along with two check genotypes (cultivar “Mansour” and local landrace “Bivanij”) were grown for three cropping years (2019-2022) in a three-replicated randomized block design at Sarab-Changaie Agricultural Research Station, Khoramabad, Lorestan. The experimental plots consisted of four four-meter planting lines with a row spacing of 30 cm and a density of 60 seeds per square meter. The annual rainfall in the first, second, and third cropping years was 523.6, 304.9, and 0.307 mm, respectively. Stability analysis was performed using the AMMI multivariate method. For statistical analyses, the multi-environment trial analysis package (Metan) and GGE were used in the R software environment.
Results and Discussion: In this study, the contribution of environment, genotype, and genotype × environment interaction to the total sum of squares was 79.18, 6.93, and 4.81 percent, respectively. AMMI analysis of variance showed that the effects of the environment, genotype, and their interaction, as well as the first two main components, were significant. Genotype 12 (FLIP07-125C) had the highest grain yield with 1602 kg/ha. The ASV index selected genotypes 14, 6, and 1, the SIPC and EV indices selected genotypes 14 and 1, and the ZA and WAAS indices selected genotypes 14, 1, and 6 as the most stable genotypes. Based on the ssiASV index, genotypes 1, 10, 8, and 12, based on the ssiSIPC and ssiZA indices, genotypes 1, 10, 8, and 14, and based on the ssiEV and ssiWAAS indices, genotypes 1, 10, and 8 were the best genotypes in terms of grain yield and stability. Based on the AMMI1 biplot, genotypes 10, 1, and 8 with grain yield higher than the total average yield and the lowest IPCA1 values were identified as stable genotypes with high general compatibility. According to this biplot, the second and third environments (E2 and E3) had the lowest IPCA1 content and the lowest genotype × environment interaction, and therefore, these environments had better yield stability. The first and third environments (E1 and E3) had higher grain yield than the overall average. In the AMMI2 biplot, genotypes 12, 10, 5, 11, 8, and 1, in addition to high general stability, had grain yield higher than the total average. Using the AMMI distance parameter, genotypes 14, 1, and 2 were identified as genotypes with stable performance. Based on the ssiDist index, genotypes 10, 1, and 14 were the best. The most stable genotypes based on the Lin and Binns superiority index were genotypes 12, 10, 5, and 8.
Conclusion: Since all significant principal components with different weights are used in calculating the WAAS index, this index better reflects yield stability, and the genotypes selected with this index have more reliable stability. In this regard, this method can be further investigated and used in future research. In general, based on different indices, genotypes 12 (FLIP07-125C), 1 (FLIP09-319C), 10 (X010TH163K2), and 8 (X010TH121K1) had favorable stability and could be a suitable option for further research.