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Birsa Agricultural University, Ranchi

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Subject: Genetics and Plant Breeding × Author: Kundan Kumar × End date: 2023 × Start date: 2023 × Thesis Type: M.Sc ×
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    ThesisItemOpen Access
    Genotype × Environment Interaction and Stability studies in Soybean [Glycine max (L.) Merrill)]
    (Birsa Agricultural University, Ranchi, 2023) Kundan Kumar; Nutan Verma
    Among the oilseed crops soybean [Glycine max. (L.) Merril.] is regarded globally as most important oilseed crop. It is cultivated throughout the world and in India with the coverage of 12.81 million hectare, production of 12.90 million tones and productivity around 1007 Kg/hectare (Annonymous, 2021). Soybean has occupied important place in agriculture and oil economy of India. The present investigation entitled “Genotype × Environment Interaction and Stability studies in Soybean [Glycine max (L.) Merr.]” was carried out at Department of Genetics and Plant Breeding, BAU Ranchi during 2021-22 and 2022-23 in four different environments by sowing at an interval of 20 days. Sowing was done on dated 28.06.2021 and 19.07.2021 during 2021-22 and in the following year 2022-23 the experiment was sown on 28.06.2022 and 19.07.2022. This way we have created four different four different environments(E1), (E2), (E3) and (E4). The mean performance of genotypes for the trait, grain yield across all environments showed that BSS-2, BAUS(M)-3, RSC-1046 and BAUS(M)-10 has highest yield in all the four environment (E1, E2, E3 and E4). The analysis of variance (ANOVA) revealed significant differences among genotypes for all the traits studied, indicating ample scope of selection for soybean improvement. The pooled analysis of variance based on four environments revealed that mean squares due to genotypes were highly significant for all traits under study. This significance shows the existence of variability among the genotypes investigated. The mean sum of squares due to environments and G×E interaction were also significant for all of the traits studied. For all the character studied the value of PCV was higher than GCV, this, suggested the role of environment in the expression of these characters. A higher GCV and PCV value for a character, plant height, no of pods per plant and grain yield shows high variability among characters, making these characters more suitable for selection procedures. Days to 50% flowering, days to maturity, 100 seed weight, plant height and no of pods per plant all had positive genotypic association with grain yield. These traits can be used for the indirect selection for grain yield. The mean squares for the IPCA1 and IPCA2 cumulatively contributed more than 90.19 % of the total G×E interaction for grain yield. All the environments exerted strong interactive forces. In AMMI I biplot for grain yield, BSS-2, BAUS-104, BAUS(M)-10, BAUS-102, BAUS-116, BAUS-40, BAUS-117 and RSC10-46 were among the stable genotypes with high mean values, and were thus recommended for all environments. The genotypes further away from the origin had high interaction with their environments, therefore they're better adapted to specific environments. E3 (Normal sowing 2022) and E4 (Late sowing2022) were favourable environments. Stable genotypes as per AMMI model for Days to 50% flowering were BS-1, BAUS-115, BAUS-106, JS97-52, for 100 seed weight(g) were NRC-149, BAUS(M)-10, BAUS-113, BAUS-106, for days to maturity were BAUS-115, BS-1, BAUS 31-17, for no. of pods per plant were BAUS-119, BAUS-117, RKS-18, BAUS- 104, for protein (%) were BAUS-104, BAUS-102, BAUS-118, for oil (%) were MACS-1460, BAUS-105, JS 20-116, BAUS-101, BAUS 31-17 for plant height (cm).