Dissecting Genetic Variability and Multi-Trait Performance in Cucumber

Cucumber improvement requires the simultaneous enhancement of yield, earliness, fruit quality, and postharvest performance, yet many breeding studies rely on single-season or single-trait evaluations. This study therefore assessed 18 diverse cucumber genotypes across two consecutive Rabi seasons (2025 and 2026) using 23 agronomic, phenological, yield, fruit-quality, and postharvest traits, of which 12 non-redundant core traits were retained for the principal genetic and multivariate analyses. Genotypic variability, GCV, PCV, broad-sense heritability, genetic advance, interannual correlation, principal component analysis, hierarchical clustering, and multi-trait selection using MGIDI and MTSI were evaluated. Genotype effects were highly significant for nearly all estimable traits in both years (P<0.001). CU-10 produced the highest yield in both years (6.90 and 7.69 t ha-1), followed by CU-11 (4.38 and 4.89 t ha-1) and CU-09 (4.12 and 4.59 t ha-1). Yield showed high GCV (51.80%), PCV (52.21%), broad-sense heritability (98.46%), and GAM (106.04%). Fruit number plant-1 and fruit weight also exhibited high heritability (97.57% and 95.91%) and GAM (80.75% and 59.46%). Interannual repeatability was strongest for fruit number plant-1 (r=0.987), yield (r=0.986), and plant mortality (r=0.981), whereas shelf life was less stable (r=0.426, P=0.078). The first two principal components explained 57.29% of total variation, increasing to 70.92% with PC3. MGIDI selected CU-09, CU-11, and check variety Anuska, whereas MTSI selected CU-11, CU-01, and CU-06. Overall, CU-11 was the only genotype selected by both MGIDI and MTSI, indicating a favourable balance of multi-trait performance and relative interannual stability, while the other selected genotypes provided complementary breeding value for hybridization and further validation. These findings provide an integrated framework for improving productivity, stability, and postharvest performance in cucumber breeding programs.