RESUMEN
In order to find location-specific and broadly adapted genotypes for total root alkaloid content and dry root yield along with additive main effects and multiplicative interactions (AMMI) and genotype (G) main effects plus genotype × environment (E) interaction in Indian ginseng (Withania somnifera (L.) Dunal), (GGE) biplot analyses were used in the current study. Trials were carried out in a randomized complete block design (RCBD) over three succeeding years viz., 2016-2017, 2017-2018 and 2018-2019 at three different locations (S. K. Nagar, Bhiloda and Jagudan). Analysis of variance (ANOVA) for AMMI for dry root yield revealed that the environment, genotype, and GE interaction, respectively, accounted for significant sums of squares of 35.31%, 24.89%, and 32.96%. For total root alkaloid content, a significance of 27.59% of total sum of squares was justified by environment, 17.72% by genotype and 43.13% by GEI. Nine experimental trials in total were taken into consideration as contexts for the GEI analysis in 16 genotypes, including one check. AMMI analysis showed that genotypes, SKA-11, SKA-27, SKA-23 and SKA-10 were superior for mean dry root yield and SKA-11, SKA-27 and SKA-21 had better performance for total root alkaloid content across environment. The GGE biplot analysis showed genotypes SKA-11, SKA-27, SKA-10 desirable for dry root yield and SKA-26, SKA-27, SKA-11 for total root alkaloid content. As a result of the GGE and AMMI biplot techniques, SKA-11 and SKA-27 were determined to be the most desired genotypes for both total root alkaloid content and dry root yield. Further, simultaneous stability index or SSI statistics identified SKA-6, SKA-10, SKA-27, SKA-11 and AWS-1 for higher dry root yield, whilst SKA-25, SKA-6, SKA-11, SKA-12 and AWS-1 for total alkaloid content from root. Based on trait variation, GGE biplot analysis identified two mega-environments for dry root yield and a total of four for total root alkaloid content. Additionally, two representative and discriminating environments-one for dry root production and the other for total root alkaloid content were found. Location-specific and breeding for broad adaptation could be advocated for improvement and release of varieties for Indian ginseng.
RESUMEN
The present study was undertaken to delineate genotype-environment interactions and stability status of 16 genotypes of ashwagandha (Withania somnifera (L.) Dunal) in context to the 12 characters, namely plant height, number of primary branches, number of secondary branches, days to flowering, days to maturity, number of berries, number of seeds/berry, root length, root diameter, root branches, dry root yield and total alkaloid content (%). Experiment was carried out in a randomized complete block design with three replicationsover three different locations (S. K. Nagar, Jagudan and Bhiloda) in north Gujarat for three years (2016-17, 2017-18 and 2018-19). Pooled analysis of variance revealed that the mean squares due to genotypes and genotype 9 environment interaction along with linear and nonlinear components were highly significant (P<0.01) for most of the traits under study. Stability parameters for component traits through Eberhart and Russell model showed that genotypes that can be used directly in breeding programme are SKA-4 for early flowering, SKA-21 for early maturity and SKA-1, SKA-4, SKA-6 and SKA-17 for shorter plant height. Further, SKA-21 could be used for improving number of primary branches per plant, SKA-11 and SKA-17 for number of secondary branches per plant, SKA-19 for number of berries per plant, SKA-6, SKA-21, SKA-27 and AWS-1 for root branches and SKA-17 for root length as these genotypes were found to be moststable across the environments for mentioned traits. The result revealed that some reliable predictions about genotype 9 environment interaction and its unpredictable components were involved significantly in determining the stability of genotypes. Hence, the present investigation can be exploited for the identification of more productive genotypes in specific environments, leading to significant increase in root productivity of ashwagandha.
