Diallel Analysis of Combining Ability for Seed Yield and Attributing Traits in Sesame (Sesamum indicum L.) Across Diverse Environments

To evaluate combining ability across 8 parental lines and their 56 F1 hybrids, an 8 × 8 diallel analysis was conducted focusing on seed yield and associated agronomic traits. Analysis of GCA effects revealed that TBS-105, V-29, TBS-10, TBS-3, TBS-7 and R-09 served as superior general combiners for the majority of characters, whereas V-29, TBS-12 and TBS-105 specifically excelled for oil content. Furthermore, several high-performing crosses were identified through SCA effects, stemming from various parental combinations (High × High, High × Low and Low × Low). Notably, the hybrids TBS-105 × R-09, TBS-7 × TBS-12 and TBS-12 × V-29 (among others) exhibited the most desirable SCA effects and mean values for seed yield per plant when pooled across environments.

The experimental material comprised eight sesame genotypes (TBS-03, TBS-07, TBS-10, TBS-12, TS-105, R-9, R-20 and V-29) and their 56 F1 hybrids, generated via an 8 × 8 full diallel mating design (including reciprocals) during Kharif 2019 at the Oilseed Research Station, Latur. For comparative evaluation, three standard checks (AKT-101, JLT-408 and GT-2) were included. The resulting hybrids were evaluated across four distinct environments: Parbhani and Ambajogai during both Summer 2020 (E1, E2) and Kharif 2020 (E3, E4). These trials were conducted under the aegis of Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani. Observations for ten agro-morphological traits were recorded from five randomly selected plants per treatment in each replication. Mean values were subsequently subjected to combining ability analysis for seed yield and its components following the Method I, Model I approach.

The pooled analysis of variance revealed highly significant differences among genotypes for the majority of the traits studied, underscoring substantial genetic variability within the experimental material. This wide-ranging diversity justified further partitioning of the variance into its components. Estimates of general combining ability (GCA), specific combining ability (SCA) and reciprocal combining ability (RCA) effects, calculated from the data pooled across all four environments.