Genetic variability, correlation and path analysis for yield and yield components in promising rice (Oryza sativa L.) genotypes

Kishore, N.; Srinivas, T.; Nagabhushanam, U.; Pallavi, M.; Sameera, S. K.

doi:10.3329/sja.v13i1.24184

Cited by 44 publications

(50 citation statements)

References 5 publications

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“…Plant height showed positive and significant correlation with culm length (r p =0.995**), flag leaf length (r p =0.687**), panicle length (r p =0.403*), above ground biomass yield (r p =0.337*) and thousand grains weight (r p =0.464**) and negative and significant correlation with harvest index (r p =-0.566**). The finding is in conformity with Ghosal et al (2010) and Kishore et al (2015) for panicle length. Moreover, panicle length showed significant and positive association with culm length (r p =0.333*), flag leaf length (r p =0.539**), filled grains per panicle (r p =0.426**), total grains per panicle (r p =0.387*) and biomass yield (r p =0.539**) but non-significant association with unfilled grain per panicle (rp=0.218), fertile tillers per plant (r p =0.106), thousand grains weight (r p =0.084) and harvest index (r p =-0.281).…”

Section: Phenotypic Correlation Between the Traitssupporting

confidence: 86%

“…Likewise, Iftekharuddaula et al (2001) reported highily significant and positive correlation of plant height with panicle length and negative correlation for harvest index. Similarly, Ghosal et al (2010), Babu et al (2012) and Kishore et al (2015) reported positive correlation of plant height with panicle length. Panicle length was positively and significantly associated with culm length (r g =0.369*), flag leaf length (r g =0.511**), filled grain per panicle (r g =0.503**), thousand grains weight (r g =0.405*), unfilled grain per panicle (r g =0.500**), total grain per panicle (r g =0.524**) and biomass yield (r g =0.729**).…”

Section: Genotypic Correlation Coefficientmentioning

confidence: 75%

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Traits association and path coefficient analysis of yield and related traits in rainfed lowland rice (Oryza sativa L.) genotypes in North Western Ethiopia

Abebe¹,

Alamerew²,

Tulu³

2019

Afr. J. Plant Sci.

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The current study was conducted to assess the trait association and path analyses in thirty-six rain fed lowland rice genotypes at two locations. The aim of this present study was to estimate the extent of association between pairs of characters at phenotypic and genotypic levels and thereby compare the direct and indirect effects of the characters. The field experiment was laid out in 6x6 simple lattice design with two replications during 2015/16 cropping season. Thirteen characters were measured including yield and related traits. The extent of phenotypic correlation coefficient values indicated presence of fairly strong inherent association among the studied traits. Moreover, association analyses studies indicated yield had significantly (p<0.05) positive genotypic correlation with days to heading, days to maturity, filled grains per panicle, fertile tillers per plant harvest index, total spikelets per panicle and biomass yield. Path coefficient analyses revealed that biomass yield followed by harvest index, total spikelets per panicle and plant height exhibited the highest direct effects on grain yield; these traits can be considered for direct selection for grain yield of rice.

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Section: Phenotypic Correlation Between the Traitssupporting

confidence: 86%

Section: Genotypic Correlation Coefficientmentioning

confidence: 75%

Traits association and path coefficient analysis of yield and related traits in rainfed lowland rice (Oryza sativa L.) genotypes in North Western Ethiopia

Abebe¹,

Alamerew²,

Tulu³

2019

Afr. J. Plant Sci.

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“…Furthermore, in contrast to our results, they reported a significant positive correlation between PL and GP. Similar to the results of current work, a significant positive correlation between PH and PL was found by Hossain et al, (2015) and Kishore et al, (2015) while, Rai et al, (2015) reported it significant, but negative. According to our observations and in agreement with Hossain et al, (2015), the correlation between GW and GP was significant and positive while, Kishore et al, (2015) reported it significantly negative.…”

Section: Conventional and Sequential Path Analysissupporting

confidence: 81%

“…Similar to the results of current work, a significant positive correlation between PH and PL was found by Hossain et al, (2015) and Kishore et al, (2015) while, Rai et al, (2015) reported it significant, but negative. According to our observations and in agreement with Hossain et al, (2015), the correlation between GW and GP was significant and positive while, Kishore et al, (2015) reported it significantly negative. A significant positive correlation between GW and GL was reported by Haider et al, (2014) and Kumar (2015) while, like current study, Rafii et al, (2014) found no such correlation.…”

Section: Conventional and Sequential Path Analysissupporting

confidence: 81%

Correlation and Path Coefficient Analysis of Morphological Traits Affecting Grain Shape in Rice Genotypes (Oryza Sativa L.)

Kiani¹,

Agahi²

2016

AgricultForest

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SUMMARYThis work was undertaken to look into the interrelationships among morphological traits and rice grain shape. For this purpose, a set of 25 rice genotypes was sown and subjected to a farm survey, based on the standard evaluation system for rice. Correlation coefficient analysis showed that the grain shape was positively correlated with grain length, panicle length, plant height and the number of tillers while, there were statistically significant and negative correlations between grain shape with maturity date, number of grains per panicle, grain breadth, 100-grain weight and flag leaf width. Sequential path analysis revealed that grain breadth, grain length and number of grains per panicle, as first-order variables, was responsible for about 98% of the variation in grain shape. Also, 100-grain weight, maturity date, number of tillers and flag leaf width were determined as second-order predictors.Amongst second-order predictors, 100-grain weight was a noteworthy trait regarding its high direct and indirect effects on grain breadth and grain length. Study of multicollinearity measures revealed that sequentializing of predictor variables reduced problems due to multicollinearity leading to a better understanding of the interrelationships among the various traits and their relative contribution. Also, the bootstrap analysis indicated that all direct effects were significant.The results suggested that grain breadth, grain length and number of grains per panicle, as first-order predictor variables had the highest direct effect on grain shape and could be used as a selection criterion to improve rice grain shape. Also, 100-grain weight, maturity date, number of tillers and flag leaf width, as second-order predictor variables affect the rice grain shape indirectly through their effects on first-order predictors. The authors recommend for the use of sequential equation modeling to conduct a proper sequential path analysis. Keywords INTRODUCTIONRice appearance is a character considered as one of the main quality attributes by consumers therefore, measuring and understanding factors influencing rice grain appearance is a great challenge for industries and breeders in meeting consumer preferences (Haider et al., 2014). In genetics, grain shape has been widely accepted as a complex trait controlled by multiple genes with small effects (Yin et al., 2015). Thus, understanding the complexities governing the relationships among traits leads to increased selection gain in breeding programs. In this regard, correlation coefficients, multiple linear regression and path analysis are some of the common statistical methods. Determination of correlation coefficients is an important statistical procedure to examine the relationship between traits. However, increase of the number of independent variables controlling a particular dependent variable can lead to increased interdependence. In such situation, correlations may be insufficient to explain the associations in a way that will enable breeders to decide on a direct or indir...

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“…A critical analysis of the genetic variability parameters, namely, genotypic variance coefficient (GVC), phenotypic of variance coefficient (PVC), heritability and genetic progress for various economically important traits is a major precondition for any breeders working in crop improvement programs [35]. The values of GVC, PVC and heritability (H 2 %) taken alone give no indication of the importance of the genetic progress that would result from the selection of the best individuals, but coupled with the genetic gain compared to the average EGG, this value is more useful [31].…”

Section: Discussionmentioning

confidence: 99%

Screening of Rice Accessions Resistant to Blast in Benin

Bello¹

2018

JDMP

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The objective of this screening is to identify genotypes with effective resistance genes against leaf blast. Two hundred rice accessions were collected in Benin's production areas and screened in upland ecology at Sowe (Glazoue, Benin). The experimental device used is an Alpha lattice 20 x 10 with 3 repetitions. The hierarchical ascending classification (HAC) allowed obtaining three large groups of accessions according to their behavior in relation to the populations of Magnaporthe oryzae present on the site: the C1 group composed of resistant genotypes (R), the group C2 composed of genotypes moderately sensitive (MS) and finally the group C3 that consists of sensitive genotypes (S). A highly significant difference was observed between genotypes based on recorded severity scores (five rating stages of disease and the AUDPC or Area Under the Disease Progress Curve). In general, the difference between the phenotypic variable (VP) and the genotypic variable (Vg) is relatively weak for all characters. All the traits studied had recorded high genotypic variation coefficients (GVC) and phenotypic variation coefficients (PVC) (> 20), with the exception of the first level of disease severity (Pyri1), thus justifying the high variability between genotypes with respect to resistance to disease. Heritability rate (H 2 %) coupled with high values of expected genetic gain compared to the mean (EGGM), indicated a low environmental influence in disease expression and a prevalence of the effect of additive genes in genetic determinism. The experiment has identified a pool of varieties with good behavior against blast disease that can be used as genetic control material in research and breeding programs in Benin.

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Genetic variability, correlation and path analysis for yield and yield components in promising rice (Oryza sativa L.) genotypes

Cited by 44 publications

References 5 publications

Traits association and path coefficient analysis of yield and related traits in rainfed lowland rice (Oryza sativa L.) genotypes in North Western Ethiopia

Traits association and path coefficient analysis of yield and related traits in rainfed lowland rice (Oryza sativa L.) genotypes in North Western Ethiopia

Correlation and Path Coefficient Analysis of Morphological Traits Affecting Grain Shape in Rice Genotypes (Oryza Sativa L.)

Screening of Rice Accessions Resistant to Blast in Benin

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