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In the present study, additive main effects and multiplicative interactions (AMMI) biplot analyses was used to dissect genotype x environment interaction (GEI) and to identify location specific and widely adapted genotypes for root branches, diameter and length in ashwagandha [Withania somnifera (L.) Dunal]. Trials were conducted in randomized complete block design (RCBD) with two replications over three consecutive years at three different locations. ANOVA analysis revealed environment, G×E interaction and genotype effects to contribute significantly (p less than 0.001) towards total sum of squares for root branches (61.00%, 22.18% and 14.00%); root diameter (51.06%, 24.26% and 15.34%) and root length (65.67%, 20.82% and 11.39%). Further, the GEI for these traits was mostly explained by the first, second and third principal component axis (IPCA1, IPCA2 and IPCA3). AMMI1 and AMMI2 biplot analyses showed differential stability of genotypes for root branches, diameter and length with few exceptions. Environmental contribution towards the genotypic performance from AMMI1 and AMMI2 analysis for root traits except environment Bhi16 contribution for root diameter and root length. AMMI1 biplots and simultaneous selection index (SSI) statistics identified SKA-11 as the most desirable genotype for root branches and length while SKA-26 and SKA-27 for root diameter. The ashwagandha genotypes identified for root attributes could be advocated either for varietal recommendation or in varietal development program.

Present investigation was undertaken to evaluate 50 linseed genotypes for three consecutive years for seed yield, oil content and agro-morphological traits using multivariate approach. Higher range, large value of Shannon-weaver diversity index for both traits and genotypes and large differences in mean values for most of the characters showed that a wide and significant variation existed among the genotypes and traits. Pooled analysis of variance revealed highly significant differences (p<0.001) among the genotypes for all the characters studied. The magnitude of the phenotypic coefficient of variation was somewhat higher than the genotypic coefficient of variation, indicating that the environment had little impact on the expression of these traits. Cluster analysis for yield and agro-morphological traits using unweighted pair group method of arithmetic averages (UPGMA) grouped the genotypes into nine clusters with varied number. Clustering of linseed genotypes from different geographical locations or source/origin into same cluster has confirmed that they are genetically related, and possibly from the same progenitor. The principal component analysis (PCA) revealed that most of the variation (76.41%) was accounted by first four PCA and indicated role of traits that contributed significantly towards a wide variation among the genotypes. The positive associations of seed yield per plant and oil content with component trait implies that improving one or more component traits could result in genetic enhancement of seed yield and oil content in linseed. The significant negative association of seed yield per plant and oil content with days to flowering and days to maturity has great advantages in breeding short duration linseed cultivars for hot and water stress climatic conditions of semi-arid regions. Trait specific genotypes namely, Shival, Sharda, IC54970, Mukta, IC56363, T-397, IC53281 and RLC-92 were identified for the development of short duration and dwarf cultivars with higher omega-3-fatty acid content.

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