Soybean [Glycine max (L.) Merr.] is the leading Indian oilseed crop grown under rainfed conditions. Meticulous understanding of genotype × environment interaction patterns is essential to develop superior and widely adaptable soybean varieties. In the current study, 32 soybean genotypes were evaluated at eight locations for two consecutive years. Additive main effect and multiplicative interaction ANOVA revealed that only 41.6% of variance was explained by the first two interaction principal component axes (IPCAs), leaving 58.4% to the remaining 13 IPCs. The weighted average of absolute scores (WAASB) stability index, a best linear unbiased prediction–based mixed model that takes in to account all the IPCAs, has been used in stability analysis. SL1171 (WAASB score, 4.09) was found to be highly stable among the genotypes under study, with grain yield (2,050.87 kg ha−1) lower than the grand mean (2,082.50 kg ha−1). A superiority index that allows weighting between mean performance and stability (WAASBY) was used to select stable and high yielding genotypes. MACS 1620 (WAASBY score, 74.47) was found to be high yielding (2,476.05 kg ha−1) and widely adaptable. A simultaneous selection index (i.e., multi‐trait stability index [MTSI]) has been used for selecting early‐maturing and high‐yielding genotypes. DSb 33 was found to have the lowest MTSI (0.001) and can be used as a parent for breeding for early maturity and higher yield. The 100‐seed weight was found to be positively correlated with grain yield and can be used in direct selection for grain yield. Through genotypic cluster analysis, NRC 146 was found to be more divergent, with the highest mean 100 seed weight (16.39 g), and therefore can be used as a parent for breeding solely for grain yield.
In the present study, performance of five promising soybean genotypes over 4 locations during kharif 2013, 2014 and 2015 were investigated using GGE biplot analysis. Location attributed the highest proportion of the variation for all the traits except 100 seed weight ranging from 26.97-86.81% whereas, genotype contributed only 3.01-60.51% and genotype x location interaction contributed 6.01-31.42% of total variation. For 100 seed weight genotype has
contributed major proportion of variation (66.26%) than location (31.08%) and genotype x location interaction (2.65%). Superior genotypes for key traits viz., grain yield (VLS 86) and 100 seed weight (Himso 1685) were effectively identified using GGE biplot graphical approach. It may be stated from present study that, VLS 86 was the closest to ideal genotype with stability for high grain yield as well as earliness. ‘Which-won-where’ study partitioned the testing locations into two mega-environments: first with three locations with VLS 86 as the winning genotype; second mega environment encompassed only one location with Himso 1685 as the winning genotype. Existence mega environments was found correlated with the rainfall pattern and clearly suggested that different entries need to be selected and deployed for realising maximum grain yield in hill zone.
The present investigation was undertaken to assess the genetic variability and character associations for seed yield and component characters in 307 soybean germplasm lines. The lines were raised in augmented block design in four blocks during kharif 2014. Out of these, twenty seven promising genotypes were selected and forwarded for evaluation along with four checks in randomized block design with three replications at CSKHPKV, Palampur (H.P) during kharif 2015. The analysis of variance revealed the presence of sufficient genetic variability in the breeding material. High PCV and moderate GCV were recorded for harvest index and biological yield/plant. High heritability coupled with high genetic advance were observed for harvest index followed by biological yield /plant, seed yield /plant, number of pods/plant and 100-seed weight indicating the predominance of additive gene action in controlling the trait. Number of branches /plant, number of seeds /pod, biological yield/plant and harvest index exhibited significantly positive correlation with seed yield /plant both at phenotypic and genotypic levels. Two traits viz., harvest index and biological yield /plant could be considered as direct selection indices for yield improvement in soybean.
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