Character Association and Path Coefficient Analysis of Grain Yield and Yield Components Maize (Zea mays L.)

Alvi, Muhammad Bashir; Rafique, Muhammad; Tariq, Muhammad; Hussain, Amer; Mahmood, Tariq; Sarwar, Muhammad

doi:10.3923/pjbs.2003.136.138

Cited by 28 publications

(16 citation statements)

References 2 publications

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“…However, the largest sample size found among the explanatory variables of a given scenario should be chosen because the path analysis of each scenario is performed by partitioning the correlation coefficients into direct and indirect effects, considering all explanatory variables jointly. Alvi et al (2003) measured 10 plants per plot with two replicates to estimate the direct and indirect effects of explanatory variables on corn YIELD. Thus, based on 20 plants, in each hybrid or line, the direct effects of PH, EIH, EL, ED, NR, number of grains per row, and onethousand-grain weight on YIELD were estimated.…”

Section: Discussionmentioning

confidence: 99%

“…Indirect selection can be performed when there are relations of cause and effect between variables, with high scores of correlation and direct effects of the same intensity and direction (Cruz and Regazzi, 1997;Cruz and Carneiro, 2006). Thus, path analysis has been widely used in studies of linear relationships between traits and for indirect selection of corn plants (Alvi et al, 2003;Jayakumar et al, 2007;Lopes et al, 2007;Selvaraj and Nagarajan, 2011;Langade et al, 2013) and contrasting coefficients have been reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Sample size for estimation of direct effects in path analysis of corn

Toebe

Filho²,

Storck³

et al. 2017

Genet. Mol. Res.

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The objective of this study was to assess the sample size required for estimating the direct effects of explanatory variables on the grain yield of corn in the combinations formed by three types of hybrids x two harvests x nine scenarios of explanatory variables x two types of path analyses x four levels of accuracy. A total of 6340 plants were evaluated (361, 373, and 416 plants from single-, triple-, and double-cross hybrids in the 2008/2009 harvest, respectively, and 1777, 1693, and 1720 plants from single-, triple-, and double-cross hybrids in the 2009/2010 harvest, respectively). Eleven explanatory variables were measured in each plant: plant height, ear insertion height, ear weight, number of grain rows per ear, ear length and diameter, cob weight and diameter, 100-grain mass, number of grains per ear, grain length and, the main variable, grain yield. Thus, nine scenarios were planned (scenarios 1, 2, 3, 4, 5, 6, 7, 8, and 9 with 0, 1, 2, 3, 4, 5, 6, 7, and 8 explanatory variables excluded, respectively) and two types of path analyses conducted (traditional and ridge path analyses). The sample size required for estimating the direct effect of each explanatory variable on grain yield was assessed using resampling with replacement at four accuracy levels [95% confidence interval ranges (95%CI) of 0.15, 0.25, 0.35, and 0.45]. The use of the ninth scenario of path analysis is recommended regardless of hybrid and harvest given the smaller sample size required compared to the previous scenarios. To estimate direct effects with the maximum 95%CI of 0.25, 10 to 530 plants are required depending on the type of hybrid, harvest, scenario, type of path analyses, and explanatory variables. To estimate the direct effects in the ninth scenario with a maximum 95%CI of 0.25, 120 plants suffice regardless of the type of hybrid, harvest, or type of path analyses.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sample size for estimation of direct effects in path analysis of corn

Toebe

Filho²,

Storck³

et al. 2017

Genet. Mol. Res.

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“…Selection for yield based on multiple traits is always better than selection based on yield alone (Muhammad et al, 2003;Bello et al, 2010). Yield is a quantitative character controlled by many genes (Lungu, 1978).…”

Section: Introductionmentioning

confidence: 99%

Genetic Associations Analysis for Fruit Yield and Its Contributing Traits of Indeterminate Tomato (Solanum lycopersicum L.) Germplasm under Open Field Condition

Meena¹,

Bahadur²

2015

JAS

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The study was initiated to generate genetic information on characters associations for tomato germplasm under open field condition. Nineteen indeterminate tomato germplasm were evaluated to estimate the nature and magnitude of associations of different characters with fruit yield and among themselves at Vegetable Research Farm, Department of Horticulture, SHIATS, Allahabad (India) during 2012-2013. The experiment was conducted using a Randomized Complete Block Design (RCBD) with three replications. Estimates of genetic parameters revealed that fruit yield was significantly and positively correlated with number of flowers per plant (0.2894 and 0.2891) followed by number of fruits per plant (0.4480 and 0.4486) and fruit weight (0.6223 and 0.6230) at genotypic and phenotypic level, respectively, strong association of these traits revealed that the selection based on these traits would ultimately improve the fruit yield and it is also suggested that hybridization of genotypes possessing combination of above characters is most useful for obtaining desirable high yielding segregation. In order to obtain a clear picture of the inter relationship between fruit yield per plant and its components, direct and indirect effects were measured using path coefficient analysis. Fruit weight had a very high positive direct genotypic and phenotypic effect 0.9566 and 0.9442, respectively on fruit yield per plant followed by number of flowers per plant, fruit set per cent, number of fruits per plant, TSS o Brix, plant height, radial diameter of fruit, leaf curl incidence per cent and days to 50% flowering. The characters showed high direct effect on yield per plant indicated that direct selection for these traits might be effective and there is a possibility of improving yield per plant through selection based on these characters. Residual effect was considerably low (0.0611 and 0.0751) which indicated that characters included in this study explained almost all variability towards yield.

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“…bras., Brasília, v.48, n.5, p.466-477, maio 2013 DOI: 10.1590/S0100-204X2013000500002 a verificada por Alvi et al (2003), Mohammadi et al (2003), Saidaiah et al (2008), Bello et al (2010) e Kumar et al (2011). As variáveis AIE e PRE também apresentaram reduzida associação com a produtividade de grãos, com valores de correlação semelhantes aos descritos por Alvi et al (2003), Saidaiah et al (2008) e Bello et al (2010). Em razão da ampla oscilação verificada nos 13 ensaios quanto ao sentido e à intensidade das associações das variáveis FM, EP, AIE e PRE com a produtividade de grãos, é provável que essas variáveis não apresentem relação de causa e efeito (Cruz & Regazzi, 1997).…”

Section: Resultsunclassified

“…Além do estudo das correlações fenotípicas, genotípicas e ambientais, trabalhos complementares com o uso de análise de trilha vêm sendo realizados (Carvalho et al, 2001;Ahmad & Saleem, 2003;Alvi et al, 2003;Mohammadi et al, 2003;Saidaiah et al, 2008;Bello et al, 2010;Kumar et al, 2011;Pavan et al, 2011;Selvaraj & Nagarajan, 2011;Toebe & Cargnelutti Filho, 2013), para identificar as verdadeiras relações de causa e efeito entre os caracteres (Cruz & Regazzi, 1997) .…”

Section: Introductionunclassified

Não normalidade multivariada e multicolinearidade na análise de trilha em milho

Toebe

Filho

2013

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi avaliar a interferência da não normalidade multivariada e da multicolinearidade na análise de trilha, em milho. Foram utilizados os dados de 13 ensaios de competição de cultivares de milho. Foram mensuradas a variável principal (produtividade de grãos) e sete variáveis explicativas (número de dias até o florescimento, estatura de plantas, altura de inserção da espiga, posição relativa da espiga, número de plantas, número de espigas e prolificidade), em cada cultivar. Procedeu-se, então, à transformação dos dados e ao diagnóstico de normalidade univariada e multivariada. Antes e após a transformação de dados, foram calculados os coeficientes de correlação e realizado o diagnóstico de multicolinearidade. A análise de trilha foi realizada por três métodos: tradicional; sob condições de multicolinearidade (análise de trilha em crista); e tradicional com eliminação de variáveis. A transformação de dados reduz o grau de multicolinearidade e a variabilidade das estimativas dos efeitos diretos, na análise de trilha tradicional com alto grau de multicolinearidade. A multicolinearidade exerce maior impacto sobre a estimativa dos efeitos diretos nas análises de trilha do que a não normalidade multivariada. A análise de trilha tradicional com eliminação de variáveis é mais adequada do que a análise de trilha em crista.Termos para indexação: Zea mays, análise em crista, eliminação de variáveis, transformações Box-Cox. Multivariate nonnormality and multicollinearity in path analysis in cornAbstract -The objective of this work was to evaluate the effect of multivariate nonnormality and multicollinearity in the path analysis of corn. We used data from 13 corn cultivar competition trials. The response variable (grain yield) and seven explanatory variables (number of days to tasseling, plant height, ear height, relative ear position, number of plants, number of ears and prolificity) were measured in each cultivar. Then, data transformation and the univariate and multivariate normality diagnosis were proceeded. The correlation coefficients were calculated and the diagnosis of multicollinearity was performed, before and after data transformation. The path analysis was done according to three methods: traditional; under multicollinearity (ridge path analysis); and traditional with variable elimination. Data transformation reduces the degree of multicollinearity and the variability of the direct effects, in the traditional path analysis with high multicollinearity. Multicollinearity exerts more impact on the estimation of the direct effects in path analysis than multivariate nonnormality. The traditional path analysis with elimination of variables is more appropriate than the ridge path analysis.

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Character Association and Path Coefficient Analysis of Grain Yield and Yield Components Maize (Zea mays L.)

Cited by 28 publications

References 2 publications

Sample size for estimation of direct effects in path analysis of corn

Sample size for estimation of direct effects in path analysis of corn

Genetic Associations Analysis for Fruit Yield and Its Contributing Traits of Indeterminate Tomato (Solanum lycopersicum L.) Germplasm under Open Field Condition

Não normalidade multivariada e multicolinearidade na análise de trilha em milho

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