Correlation and Path Coefficient Analyses in Sugarcane

Chaudhary, Rewati R; Joshi, Bal Krishna

doi:10.3126/narj.v6i0.3340

Cited by 28 publications

(29 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But the negative association of stalk diameter with stalk count 10 months after planting and millable cane number and its positive significant association with cane yield should be treated with caution. The significant negative association of stalk diameter with millable cane number was reported [1] [17] [29]. Millable cane number was also negatively and significantly correlated with leaf width and leaf area which had positive significant association with cane yield.…”

Section: Correlationmentioning

confidence: 59%

“…In this case selecting for the character millable cane number would produce maximum yield as compared with any of the other characters. [1] [28] also indicated the importance of millable cane number in selection for higher cane yield. In their study [7] [9], found that cane yield was more closely correlated with stalk population than weight per stalk or stalk diameter.…”

Section: Correlationmentioning

confidence: 99%

“…In sugarcane, complex traits like cane yield and quality are influenced by a number of characters. These characters directly and indirectly contribute to the yield [1]. [2] emphasized that genetic diversity and location specific varieties are essential for achieving sustainable advances in productivity.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Correlation and Path Coefficient Analyses in Sugarcane Genotypes of Ethiopia

Tena¹,

Mekbib²,

Ayana³

2016

AJPS

View full text Add to dashboard Cite

To study the relations of certain morphological and sugar quality characters with sugarcane yield, an experiment was conducted at Wonji and Metehara Sugar Estates, Ethiopia between March 2012 and October 2013. The experiment, comprising of 400 sugarcane genotypes of which 174 were local that were collected from different regional states of Ethiopia and 226 introduced, was laid out in partial balanced lattice design with two replications. Data was collected on cane yield and its components, sugar yield and sugar quality traits. ANOVA, correlation and path coefficient analysis were done. Analysis of variance revealed significant differences (P < 0.01) for all the characters studied. Cane yield showed strong positive and highly significant (P < 0.01) correlation with millable cane number (r = 0.832), single cane weight (r = 0.528), stalk height (r = 0.517) and sugar yield (r = 0.987). There was also positive significant (P = 0.05) correlation of tiller count and cane diameter with cane yield. Path analysis revealed the highest positive direct effect of millable cane number (0.812) on cane yield followed by single cane weight (0.682) and pol percent (0.550). However, stalk diameter and brix percent had considerable negative direct effects and indirect positive effects through single cane weight on cane yield. Therefore, in view of their significant positive association with cane yield, indirect effects of stalk diameter and brix percent via single cane weight should be considered during selection. Genotypes should be selected on the basis of millable cane number, single cane weight and pol percent for getting higher cane and sugar yield.

show abstract

Section: Correlationmentioning

confidence: 59%

Section: Correlationmentioning

confidence: 99%

See 1 more Smart Citation

Correlation and Path Coefficient Analyses in Sugarcane Genotypes of Ethiopia

Tena¹,

Mekbib²,

Ayana³

2016

AJPS

View full text Add to dashboard Cite

show abstract

“…lationship between various characters and also the type and extent of the contribution to yield (Panhwar et al, 2003;Chaudhary and Joshi, 2005). Genotypic and phenotypic correlations were observed between ten characters under four different moisture levels and are presented in Tables 7 to 10, respectively.…”

Section: Genotypic and Phenotypic Correlationmentioning

confidence: 99%

Relationship between genetic parameters in maize (Zea mays) with seedling growth parameters under 40-100% soil moisture conditions

Muhammad

Qayyum

2013

Genet. Mol. Res.

View full text Add to dashboard Cite

ABSTRACT. We estimated the association of genetic parameters with production characters in 64 maize (Zea mays) genotypes in a green house in soil with 40-100% moisture levels (percent of soil moisture capacity). To identify the major parameters that account for variation among the genotypes, we used single linkage cluster analysis and principle component analysis. Ten plant characters were measured. The first two, four, three, and again three components, with eigen values > 1 contributed 75.05, 80.11, 68.67, and 75.87% of the variability among the genotypes under the different moisture levels, i.e., 40, 60, 80, and 100%, respectively. Other principal components (3-10, 5-10, and 4-10) had eigen values less than 1. The highest estimates of heritability were found for root fresh weight, root volume (0.99), and shoot fresh weight (0.995) in 40% soil moisture. Values of genetic advance ranged from 23.4024 for SR at 40% soil moisture to 0.2538 for shoot dry weight in 60% soil moisture. The high magnitude of broad sense heritability provides evidence that these plant characters are under the control of additive 4781 ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 12 (4): 4780-4796 (2013) Genetic and seedling growth parameters in Zea mays genetic effects. This indicates that selection should lead to fast genetic improvement of the material. The superior agronomic types that we identified may be exploited for genetic potential to improve yield potential of the maize crop.

show abstract

“…Each correlation coefficient between a predictor variable and the response variable is partitioned into its component parts: the direct effect or path coefficient (a standardized partial regression coefficient) for the predictor variable and indirect effects which involve the product of a correlation coefficient between two predictor variables with the appropriate path coefficient in the path diagram (Dewey and Lu, 1959). By determining the interrelationships among seed yield components, a better understanding of both the direct and indirect effects of the specific components can be attained (Chaudhary and Joshi, 2005).…”

Section: Introductionmentioning

confidence: 99%