Study of the Genetic Analysis of Some Selected Okra Genotypes

Ashraf, Awais; Rahman, MM; Hossain, MM; Sarker, Umakanta

doi:10.21474/ijar01/10663

Cited by 12 publications

(7 citation statements)

References 1 publication

(1 reference statement)

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“…Thus, a greater potential is expected in selecting root length, root to shoot ratio shoot length and number of pods/plant among the studied progenies. Similar type of variations in genotypic and phenotypic coefficient was reported by several other researchers (Ashraf et al, 2020;Shwetha et al, 2020;Ranga et al, 2021). Using the coefficient of variation (CV%), crop plant attribute proportional degrees of variability are compared.…”

Section: Genotypic Variabilitysupporting

confidence: 66%

Study of Genotypic and Phenotypic Variation in F2 Segregating Generation of Okra [Abelmoschus esculentus (L.) Moench]

Shiri,

Gaurav,

Singh

et al. 2024

ASD

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Background: Hybrid breeding in Okra [Abelmoschus esculentus (L.) Moench] is important to improve productivity of this crop. Given that a breeder’s primary goal is production, it’s critical to understand the connections between the traits that have a direct and indirect impact on yield. Methods: 45 hybrids generated by crossing 10 diverse parents in structured mating design were evaluated to estimate the magnitude of their genetic variability and heritability in F2s. Mean performance across three replications were calculated for 21 traits and is assessed for their effect on yield. The recorded data were statistically analyzed at 5% level of significance by multivariate analysis using principal component and hierarchical cluster. Result: For all of the analyzed traits, the analysis of variance revealed a highly significant difference between population. A greater difference between PCV and GCV estimates for days to anthesis, days to 50% flowering and days to first picking indicates a greater degree of environmental control for these traits. Traits such as days to anthesis, days to 50% flowering and days to first picking shows low to moderate heritability. These findings show that there is enough genetic diversity in these parameters to support selection of better accessions. Using cluster analysis and principal component analysis (PCA), crosses were divided into groups based on their performance and the identification of the most discriminating attribute that accounted for the greatest variability. It revealed that yield per plant is highly associated with days to first picking, days to 50% flowering, days to anthesis, plant height and shoot length. Further, principal component shows 1000-seed weight and germination percentage followed by days to 50% flowering as the most discriminating trait. Hence, selection for any trait would favor future hybrids breeding programmes.

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Section: Genotypic Variabilitysupporting

confidence: 66%

Study of Genotypic and Phenotypic Variation in F2 Segregating Generation of Okra [Abelmoschus esculentus (L.) Moench]

Shiri,

Gaurav,

Singh

et al. 2024

ASD

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“…All examined traits exhibited noteworthy deviations for ANOVA which specified a wide divergence in the traits among accessions. Previous studies also recorded similar variations in VA [ [65] , [66] , [67] ], Zea mays [ [68] , [69] , [70] , [71] ], Oryza sativa [ [72] , [73] , [74] , [75] , [76] ], Cocos nucifera [ 77 , 78 ], Abelmoschus esculenthus [ 79 , 80 ], Mungbean [ 81 , 82 ] and broccoli [ 83 ] genotypes for yield, its related traits, and bioactive phytochemicals.…”

Section: Resultsmentioning

confidence: 61%

Evaluation of yield attributes and bioactive phytochemicals of twenty amaranth genotypes of Bengal floodplain

Jahan,

Sarker,

Hasan Saikat

et al. 2023

Heliyon

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“…In our study, ANOVA has shown a noteworthy difference among traits. The literature has also shown corroborative results in agronomic traits of mungbean [63], Zea mays [64][65][66][67], Oryza sativa [68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84], okra [85][86][87], A. spp. [88,89], broccoli [90], and Cocos nucifera [91,92].…”

Section: Discussionmentioning

confidence: 84%

Integrating BLUP, AMMI, and GGE Models to Explore GE Interactions for Adaptability and Stability of Winter Lentils (Lens culinaris Medik.)

Hossain

Sarker

Azam

et al. 2023

Plants

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Lentil yield is a complicated quantitative trait; it is significantly influenced by the environment. It is crucial for improving human health and nutritional security in the country as well as for a sustainable agricultural system. The study was laid out to determine the stable genotype through the collaboration of G × E by AMMI and GGE biplot and to identify the superior genotypes using 33 parametric and non-parametric stability statistics of 10 genotypes across four different conditions. The total G × E effect was divided into two primary components by the AMMI model. For days to flowering, days to maturity, plant height, pods per plant, and hundred seed weight, IPCA1 was significant and accounted for 83%, 75%, 100%, and 62%, respectively. Both IPCA1 and IPCA2 were non-significant for yield per plant and accounted for 62% of the overall G × E interaction. An estimated set of eight stability parameters showed strong positive correlations with mean seed yield, and these measurements can be utilized to choose stable genotypes. The productivity of lentils has varied greatly in the environment, ranging from 786 kg per ha in the MYM environment to 1658 kg per ha in the ISD environment, according to the AMMI biplot. Three genotypes (G8, G7, and G2) were shown to be the most stable based on non-parametric stability scores for grain yield. G8, G7, G2, and G5 were determined as the top lentil genotypes based on grain production using numerical stability metrics such as Francis’s coefficient of variation, Shukla stability value (σi2), and Wrick’s ecovalence (Wi). Genotypes G7, G10, and G4 were the most stable with the highest yield, according to BLUP-based simultaneous selection stability characteristics. The findings of graphic stability methods such as AMMI and GGE for identifying the high-yielding and stable lentil genotypes were very similar. While the GGE biplot indicated G2, G10, and G7 as the most stable and high-producing genotypes, AMMI analysis identified G2, G9, G10, and G7. These selected genotypes would be used to release a new variety. Considering all the stability models, such as Eberhart and Russell’s regression and deviation from regression, additive main effects, multiplicative interactions (AMMI) analysis, and GGE, the genotypes G2, G9, and G7 could be used as well-adapted genotypes with moderate grain yield in all tested environments.

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Study of the Genetic Analysis of Some Selected Okra Genotypes

Cited by 12 publications

References 1 publication

Study of Genotypic and Phenotypic Variation in F2 Segregating Generation of Okra [Abelmoschus esculentus (L.) Moench]

Study of Genotypic and Phenotypic Variation in F2 Segregating Generation of Okra [Abelmoschus esculentus (L.) Moench]

Evaluation of yield attributes and bioactive phytochemicals of twenty amaranth genotypes of Bengal floodplain

Integrating BLUP, AMMI, and GGE Models to Explore GE Interactions for Adaptability and Stability of Winter Lentils (Lens culinaris Medik.)

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