Selection of Kabuli chickpea genotypes using multiple trait-based selection indices (MGIDI and SIIG)

Introduction: Chickpea (Cicer arietinum L.) a premier pulse crop that thrives on residual soil moisture, is a rich source of dietary proteins, vitamins, fiber, and minerals. In breeding programs aimed at identifying superior genotypes, careful selection of genotypes is of paramount importance. For traits such as seed yield, indirect selection based on traits that exhibit a strong correlation with yield has proven to be highly effective. Therefore, the primary objective of this study was to identify elite chickpea genotypes with desirable agronomic traits by employing combined selection indices, as well as to compare the performance of these indices with one another.The use of advanced selection indices, such as MGIDI and SIIG, enables more accurate assessment of genotypic performance and facilitates the selection of superior cultivars. To select the most efficient and superior genotypes across multiple traits, various indices were employed, including the Multi-Trait Genotype-Ideotype Distance Index (MGIDI). This innovative method does not require weighting coefficients, prevents issues related to multicollinearity, and provides a clear and independent selection criterion. The objective of this study was to identify promising chickpea genotypes using both the MGIDI and SIIG selection indices.
Materials and Methods: The objective of this study was to select promising chickpea genotypes using the MGIDI and SIIG selection indices. To achieve this goal, 13 selected genotypes along with three check cultivars (Kasra, Yadgar, and the local Biounij landrace) were evaluated at the Sarab Changayi Research Station over two cropping seasons (2023–2025). The experiment was conducted in a randomized complete block design with three replications. The seeding rate was established at 50 seeds per square meter. Sowing was conducted using a Wintersteiger research drill across four rows, each four meters in length, with a row spacing of 30 cm, covering a total area of 4 m². Prior to harvest, the two outermost rows and 0.5 m from both the beginning and end of the two central rows were excluded. The remaining portion of each plot (0.125 m²) was harvested manually, and seed yield was determined as the primary performance trait. Outlier detection and normality of data distribution were assessed using the Shapiro–Wilk test. Statistical analyses were performed using SAS and R software.
Results: Analysis of variance revealed significant differences among genotypes for all evaluated traits. The highest seed yield was observed in genotypes G9, G5, G12, and G4, with mean yields of 1131, 1108, 1059, and 1048 kg ha⁻¹, respectively. Based on the two-year mean, highly significant positive correlations were observed between seed yield and traits including precipitation use efficiency (r = 0.96**), number of seeds per square meter (r = 0.97**), and seed yield formation rate (r = 0.99**), highlighting the importance of these traits in improving genotype performance. Eleven traits were included in the MGIDI and SIIG models. According to the MGIDI selection index, genotypes G5, G3, and G4 exhibiting the lowest index values and higher seed yields than the check and overall mean were identified as desirable genotypes, while G9 ranked fourth. Evaluation based on the SIIG index also indicated that genotypes G9, G5, G12, and G4, with the highest SIIG values, were among the top-performing genotypes. In the cluster analysis, the genotypes were classified into three distinct groups. The first, second, and third groups contained 11, 4, and 1 genotypes, respectively. The results of the factor analysis led to the identification of four factors with eigenvalues greater than one (3.37, 2.90, 1.93, and 1.88, respectively). Collectively, these four factors accounted for 91.9% of the total variation among the studied traits.
Conclusion: Accordingly, genotypes G5, G9, and G4 are proposed as promising candidates for future cultivar release programs.