Introduction

Chickpea is one of the most important legumes crops due to its high protein content and substantial nutritional value. Multi-trait stability-based selection approaches enable a more comprehensive evaluation of plant genotypes and lead to more reliable selection outcomes. Therefore, the aim of this study was to identify superior chickpea genotypes based on multiple morpho-phenological traits using different selection indices derived from the REML/BLUP framework, along with the SIIG index, and to compare the efficiency of these indices.



Materials and Methods

In this study, 14 advanced chickpea genotypes, along with the cultivar Kavian as the control, were evaluated over three cropping seasons (2022–2025) at two Agricultural and Natural Resources Research Stations: Sarab Changai (Khorramabad, Lorestan Province) and Cherdavel (Ilam Province). The experiments were conducted using a randomized complete block design with three replications at each environment. Based on soil test results, 100 kg.ha⁻¹ ammonium phosphate and 35 kg.ha⁻¹ urea were uniformly applied during land preparation. Prior to sowing, seeds of all genotypes and the control cultivar were treated with the fungicide carboxin–thiram to prevent seed-borne fungal diseases. Sowing was carried out using a Wintersteiger research seeder at a density of 50 seeds m⁻². Each plot consisted of four rows, 4.0 m in length, with 30 cm row spacing, resulting in a total plot area of 4.8 m². At harvest, the two outer rows and 0.5 m from each end of the two central rows were discarded, and the remaining plot area (1.8 m²) was harvested for data collection.



Results and Discussion

A combined analysis of variance revealed highly significant differences among environments and genotypes for most agronomic traits, indicating strong environmental effects and substantial genetic diversity among chickpea genotypes. Genotype × environment (G×E) interactions were also significant for nearly all traits, reflecting differential genotypic responses across test environments. Restricted maximum likelihood (REML) estimates showed that genotypic, G×E, and residual variances accounted for 7.72%, 30.35%, and 61.93% of the total phenotypic variance for grain yield, respectively. High broad-sense heritability estimates for grain yield, 100-seed weight, and single-seed weight indicated that these traits are reliable targets for selection. Multi-trait selection indices, including MTSI, MGIDI, FAI-BLUP, and SIIG, consistently identified genotypes G4, G8, and G1 as superior across six environments. Principal component analysis revealed that grain yield, rainfall-use efficiency, and grain-filling rate were the main contributors to genetic differentiation among genotypes. Cluster and heatmap analyses further confirmed that genotypes G1, G8, and G10 exhibited high performance and stability, whereas G5, G12, and G15 showed poor stability across environments. Overall, the integration of multi-trait selection indices with stability analysis proved to be an effective strategy for identifying high-yielding and stable chickpea genotypes under rainfed conditions, providing valuable insights for breeding programs aimed at drought adaptation and yield improvement.



Conclusion

Overall, comparison of the results obtained from different selection indices indicated that the genotypes identified by the SIIG index (G1, G4, and G10), owing to their superior grain yield, rainfall-use efficiency, number of seeds per square meter, and grain yield formation rate, were the most promising candidates for further advanced evaluation. In addition, genotypes G8, G9, and G7, selected using the FAI-BLUP and MGIDI indices, were distinguished by their higher grain yield compared with the control cultivar. The results of this study confirmed that environmental variability and genotype × environment interactions play a critical role in determining chickpea performance under rainfed conditions. These findings emphasize the importance of integrating multi-trait selection indices with stability analyses in chickpea breeding programs to improve drought tolerance and achieve sustainable yield gains in dryland agroecosystems.