Genetic Variability and Correlation Coefficients of Major Traits in Cold Tolerance Rice (Oriza sativa L.) Under Mountain Environment of Nepal

Ghimire, Netra Hari; Mahat, Paras Mani

doi:10.3126/ijasbt.v7i4.26922

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…Phenotypic coefficient of variation was consistently higher than the genotypic coefficient of variation for all the traits under investigation (Table 4). Similar results were reported Similar results were reported by Ghimire and Mahat, (2019); Kumar et al (2022) and Williams et al (2021). This disparity suggests that environmental factors significantly influence the expression of these traits (Adhikari et al, 2018;Gyawali et al, 2018).…”

Section: Phenotypic Coefficient Of Variation (Pcv) and Genotypic Coef...supporting

confidence: 87%

“…Plant height was positive and significant association with panicle length and 1000 grain weight. Similar association between them was found byGhimire and Mahat (2019) andShrestha et al (2021). However, contrasting result was reported byMandal et al (2023) for panicle length.Panicle length showed negative and significant association with effective tiller number per meter square at both levels (r p = -0.4128**, r g = -0.5212*).…”

supporting

confidence: 88%

“…Phenological traits (days to heading and maturity) exhibited a strong and significantly positive correlation with each other at both levels (r p = 0.852**, r g = 0.9847**). Similar result was reported byTiwari et al (2019) andGhimire and Mahat (2019).Days to heading showed positive association with thousand grain weight which was also reported byGhimire & Mahat, (2019);Gyawali et al (2018) andShrestha et al (2018). There was negative and significant association between days to maturity and filled grain whereas contrasting result was reported byKumar et al (2022) andSingh et al (2023).…”

supporting

confidence: 81%

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Genetic variation and trait association of fine rice genotypes

Bastola,

Adhikari,

Poudel

et al. 2023

Arch. Agric. Environ. Sci.

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To explore high yielding genotype and significant yield contributing trait, field experiment was conducted using alpha lattice design replicated three times at the National Wheat Research Program (NWRP), Bhairahawa, Nepal in 2022. Eighteen fine rice genotypes were evaluated based on nine quantitative traits (Heading days, maturity days, plant height, panicle length, effective tillers, filled grains per panicle, unfilled grains per panicle, thousand grain weight and grain yield per hectare). Variance analysis, correlation and estimation of genetic parameters were conducted to comprehend genetic variations and interrelationships among traits within genotypes. The results showed most of traits except unfilled grains had statistical significance differences. Tarahara-107 (3940 kg/ha) had the highest grain yield followed by Tarahara-2 (3700 kg/ha) and NR 2381-RGA1-RGA2 (3665 kg/ha). Similarly, grain yield showed positive and significant association with filled grains at both phenotypic and genotypic level (rp = 0.7074 ** and rg= 0.9482 **). Elevated values of PCV and GCV were recorded for filled grains (22.05 and 17.57). The high value GAM coupled with high heritability was recorded for filled grains (28.83%), plant height (23.10%), number of effective tillers (21.57%) and thousand grain weight (19.37%). Thus, identified genotypes NR 2380-RGA1-RGA2 and NR 2381-RGA1-RGA2 exhibited early and potential high yielding genotypes while Tarhara-107 and Tarhara-2 identified as high yield, medium maturity and good filled grain number per panicle. In addition, filled grains trait displayed tightly linked with grain yield and possess high GCV, PCV, GAM with moderate heritability indicated direct selection could be employed for trait enhancement in breeding program.

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Section: Phenotypic Coefficient Of Variation (Pcv) and Genotypic Coef...supporting

confidence: 87%

supporting

confidence: 88%

supporting

confidence: 81%

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Genetic variation and trait association of fine rice genotypes

Bastola,

Adhikari,

Poudel

et al. 2023

Arch. Agric. Environ. Sci.

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“…Data were recorded on 13 characters viz; Days to 50% flowering, Days to maturity, plant height (cm), flag leaf length (cm), flag leaf width (cm), number of tillers per plant, number of panicles per hill, panicle length (cm), number of spikelets per panicle, biological yield (g), harvest index (%), test weight (g), and grain yield per hill (g). The observations were recorded as per the standard procedure and subjected to statistical analysis using mean values of five randomly selected plants [7][8][9][10][11][12].…”

Section: Methodsmentioning

confidence: 99%

Genetic Variability and Path Analysis for Yield and Grain Yield Component Characters in Rice (Oryza sativa L.) Genotypes

Jyotsna

Lavanya

2023

IJECC

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The present investigation consists of 21 genotypes of Rice including one check, which were grown under the Field Experiment Centre of the Department of GPB, SHUATS, Prayagraj during Kharif 2021 following Randomized Block Design with three replications. Analysis of variance among 21 genotypes showed significant difference for all the characters studied. Based on the mean performance, genotypes NUMALI followed by KALINGA and MTU-1155 were identified as suitable genotypes with highest grain yield per hill. High estimates of PCV, GCV, heritability coupled with high genetic advance as percent of mean were recorded for grain yield per hill, biological yield and flag leaf length indicating that these characters could be used as selection for crop improvement. Biological yield and harvest index showed positive significant association and recorded high positive direct effect on grain yield per hill at both the genotypic and phenotypic levels. These characters such as biological yield, and harvest index should be given prior consideration during selection for grain yield improvement in rice.

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“…For example, if σ 2 c is large and σ 2 gij is small, the breeder will invest more into crosses and less into the number of lines per cross. Also, the choice of crosses decides on which genes will be (re)combined to obtain superior progenies [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Breeding Parameters from Phenotypic Data of F4:5 RIL in Ethiopian Malt Barley (Hordeum distichum L.) Breeding Population

Tadesse

Wilde²,

Tesfaye

et al. 2022

International Journal of Agronomy

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The critical stage for any breeder is the selection of crossing parents to drive improved inbred for subsequent breeding cycles. In our study, we estimate breeding parameters such as mid-parent value (MPV), variances among and within crosses, the heritability of relevant traits and their correlations, the usefulness of crosses, and regression of cross means on MPV. 900 F4:5 Recombinant inbred lines (RILs) derived from 30 crosses were tested together with their parental lines in a modified split-plot p-rep design at two locations. The analysis revealed significant genetic variation among parents, crosses, and RIL for almost all traits. Heritability for parents ranged from 49.50% (malt extract) to 93.60% (plant height) and heritability for crosses ranged from 29.52% (grain protein concentration) to 87.0% (days to maturity), whereas heritability for RIL was the lowest with 27.40% for beta-glucan and the highest to 73.60% for thousand kernel weight, respectively. Significant ( P < 0 . 01 ) genotypic correlations with high impact for practical breeding were found between malting traits. Accordingly, the genotypic correlation ranged from −0.73 to 0.78 whereas the phenotypic correlation ranged from −0.60 to 0.65, respectively. Significant ( P < 0 . 01 ) regression of cross-mean on MPV where R2 ranges from 0.27 to 0.70 and is higher than 0.5 for most of the traits demonstrates that cross means can accurately be predicted from MPV and selection among crosses at an early stage is highly effective. Based on the usefulness criterion, 16 superior crosses were identified compared to the planet as the actual leading malt variety. Starting from a simple additive genetic model with random mating, we discuss deviations from the initial model and their impact on the actual estimates implying how to design a state-of-the-art cereal breeding program.

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Genetic Variability and Correlation Coefficients of Major Traits in Cold Tolerance Rice (Oriza sativa L.) Under Mountain Environment of Nepal

Cited by 7 publications

References 8 publications

Genetic variation and trait association of fine rice genotypes

Genetic variation and trait association of fine rice genotypes

Genetic Variability and Path Analysis for Yield and Grain Yield Component Characters in Rice (Oryza sativa L.) Genotypes

Estimation of Breeding Parameters from Phenotypic Data of F4:5 RIL in Ethiopian Malt Barley (Hordeum distichum L.) Breeding Population

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