Abstract
Chickpea productivity remains low due to limited genetic variability and susceptibility to biotic and abiotic stresses. To address these challenges, the introgression of novel genes from wild relatives and multi-environment evaluations are essential for identifying high-yielding, stable interspecific derivatives (ISDs). Despite the availability of several statistical tools, few chickpea studies have used a comprehensive multi-model approach for stability analysis. This study fills the void by applying four models- AMMI, GGE, WAASB, and MTSI for the first time in chickpea to evaluate multi-trait stability across diverse environments. 19 ISDs developed from four interspecific crosses (BGD 72 × ILWC 229, PBG 5 × ILWC 229, BGD 72 × ILWC 246, and PBG 5 × ILWC 246), along with two checks (PBG 7 and PBG 8), were evaluated across six environments. Trials were conducted over three consecutive rabi seasons (2020-2023) at Ludhiana (ENV1-ENV3), followed by multi-location testing during rabi 2023-24 at Ludhiana (ENV4), Karnal (ENV5), and Kanpur (ENV6). Key yield traits, including number of pods per plant (NPP), 100-seed weight (HSW), and seed yield per plot (SYPP), were analyzed using all four models. Significant effects of genotype, environment, and genotype × environment interactions (GEIs) were detected through pooled ANOVA, and positive correlations were observed among all key traits. Genotypes GL21-1 (G1) and GL21-54 (G8) were superior performers with high trait values and stable performance across all environments, with yield variability up to 20% under stress-prone conditions. Among the models, GGE and WAASB were found effective for individual traits, while MTSI was superior for multi-trait selection. These promising ISDs can be released as high-yielding varieties or used as donor parents in climate-resilient chickpea breeding programs, addressing the rising global demand for chickpea production.