Path analysis under multicollinearity in soybean

Bizeti, Henrique Stoco; Carvalho, C. G. P. de; Souza, José Roberto Pinto de; Destro, Deonisio

doi:10.1590/s1516-89132004000500001

Cited by 57 publications

(56 citation statements)

References 14 publications

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“…Ridge path analysis (k = 0.00) overestimated the path coefficient values (direct and indirect effects), with wide variability and, therefore, it was not possible to make appropriate inferences in early maturing and super-early maturing genotypes. Studies based on ridge path analysis (k = 0.00) in maize crops by Carvalho et al (2001) and El-Taweel et al (2012), and in soybean by Bizeti et al (2004) also reported that ridge path analysis correlations were violated.…”

Section: Resultsmentioning

confidence: 99%

“…Studies conducted by Carvalho et al (1999Carvalho et al ( , 2001, Bizeti et al (2004), El-Taweel et al (2012), and Moreira et al (2013) have confirmed the effectiveness of ridge path analysis in reducing the adverse effects of multicollinearity.…”

Section: Introductionmentioning

confidence: 88%

“…Ridge path analysis has been used in studies on canola (Coimbra et al, 2005), maize (Carvalho et al, 2001;El-Taweel et al, 2012;Toebe and Cargnelutti Filho, 2013a,b), peppers (Moreira et al, 2013;Silva et al, 2013), bell peppers (Carvalho et al, 1999), and soybean (Bizeti et al, 2004). Studies conducted by Carvalho et al (1999Carvalho et al ( , 2001, Bizeti et al (2004), El-Taweel et al (2012), and Moreira et al (2013) have confirmed the effectiveness of ridge path analysis in reducing the adverse effects of multicollinearity.…”

Section: Introductionmentioning

confidence: 96%

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Genotypic correlation and path analysis in early and super-early maize genotypes

Alves¹,

Filho

2017

Genet. Mol. Res.

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ABSTRACT. The aims of this study were to assess the linear relationships between agronomic and nutritional traits and to identify promising traits for indirect selection in early and super-early maturing maize genotypes. Two trials were run in the 2009/2010 agricultural year, each consisting of a randomized block design with three replications. One trial was run on 36 early maturing maize genotypes and the other on 22 super-early maturing genotypes. Six agronomic traits, 11 protein-nutritional traits, and four energeticnutritional traits were measured. Error normality assumptions were verified using the Kolmogorov-Smirnov test and residual variance homogeneity assumptions using the Levene test. Analysis of variance and the F-test were run for each of the 21 traits. Next, the genotype correlation coefficient matrix was estimated for the 21 traits and each trial. Ridge path analysis was based on values of k = 0.00 and k = 0.10 on the diagonal of X'X correlation matrix, taking the nutritional traits as principal variables and agronomic traits as explanatory variables. The number of days from sowing to male flowering, the number of days from sowing to female flowering, plant height, ear insertion

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 96%

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Genotypic correlation and path analysis in early and super-early maize genotypes

Alves¹,

Filho

2017

Genet. Mol. Res.

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“…It demonstrated that the differences between the genotype traits and their interactions with the environments revealed the NP III G trait significant contribution to the principal parameter. The phenotypic correlation partitioning in direct and indirect effects allows one to quantify the influence of independent variables on soybean yield (Bizeti et al, 2004). For IGH in Tenente Portela-RS, indirect effects via NP II G and NP IV G with low and positive correlation coefficients were observed.…”

Section: Direct and Indirect Effects Between Traitsmentioning

confidence: 99%

Relations among phenotypic traits of soybean pods and growth habit

Carvalho¹,

Nardino²,

Demari³

et al. 2017

Afr. J. Agric. Res.

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This study aims to identify the phenotypic effects of traits associated with soybean pods and their growth habit. The experiments were carried out during the 2012 -2013 agricultural year. It was done in randomized blocks arranged in a 2×20 factorial design (environments × soybean cultivars) with three replications. Data were subjected to individual analysis of variance for each environment and growth habit to verify the homogeneity of variances. Phenotypic path analysis among agronomic traits was performed for each environment within the soybean growth habits. The results showed considerable influences of growth habit via traits associated with soybean pods. The traits number of pods with two, three, and four grains directly influenced the grain yield of genotypes with indeterminate growth habit regardless of the environment. The soybean genotypes with determinate growth habit contributed greatly to grain yield through the traits number of pods with one, two, and three grains and thousand grain weight via pods with two or four grains. Indirect selection of superior genotypes for pods per plant and grain weight could provide satisfactory results for soybean yield in relation to the growth habit. The interrelationships obtained between growth habits and environments, can be applied to superior genotypes selection strategies in breeding soybean programs.

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“…Correlation coefficients, although, very useful in quantifying the size and direction of trait associations can be misleading if the high correlation between two traits is a consequence of the indirect effect of other traits (Bizeti et al, 2004). Path coefficient is an excellent means of studying direct and indirect effects of inter-related components of a complex trait which measures the direct influence of one variable on another.…”

Section: Introductionmentioning

confidence: 99%