Dimensionamento amostral para estimação de coeficientes de correlação em híbridos de milho, safras e níveis de precisão

Toebe, Marcos; Filho, Alberto Cargnelutti; Lopes, Sidinei José; Burin, Cláudia; Silveira, Tatiani Reis da; Casarotto, Gabriele

doi:10.1590/1678-4499.0324

Cited by 12 publications

(16 citation statements)

References 6 publications

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“…The sample size used to estimate the mean of each trait, with semi-amplitude of the confidence interval equal to 5% of the mean estimate (greater precision, in this study), and a 95% confidence level, ranged from four tassels for TL of the 30A68 genotype to 6251 tassels for NSB of the P1630 genotype (Tables 4, 5, and 6). These results confirm the presence of sample size variability among traits of maize genotypes, as verified among traits (Storck et al, 2007;Toebe et al, 2014) and between pairs of traits in maize (Cargnelutti Filho et al, 2010;Toebe et al, 2015). In line with this, Storck et al (2007) suggested using the sample size determined for the most important trait for the experiment.…”

Section: Resultssupporting

confidence: 77%

“…Thus, four sample size groups were formed for PDM, three groups for CSDM and PL, and two for TDM, BSL, CSL, TL, NPB, NSB, and TBN by the Scott-Knott test, confirming that different sample sizes among genotypes are required to estimate the mean of these traits with the same precision (Tables 4, 5, and 6). Therefore, it can be inferred that there is sample size variability among genotypes, as verified in maize (Martin et al, 2005;Storck et al, 2007;Toebe et al, 2015). In this context, Martin et al (2005) suggested sampling each genotype with its respective sample size or using the largest sample size determined in order to cover all genotypes.…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, Cargnelutti established the sample dimension to measure Pearson correlation coefficients among pairs of traits in maize hybrids. Also, the sample size was determined to estimate the coefficient of variation of the mean (Toebe et al, 2014) and Pearson correlation coefficients for different maize hybrids (Toebe et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Sample sizes to estimate mean values for tassel traits in maize genotypes

et al. 2016

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Tassel traits are important in maize breeding programs aiming to reduce the size and number of branches and maintain satisfactory pollen production in order to increase grain yield. The objectives of this study were to determine the sample size (number of tassels) required to estimate the mean values for tassel traits in maize genotypes and to verify the variability of sample size among genotypes. Twenty maize genotypes were evaluated in an experiment carried out in a randomized block design with three replicates. Twenty tassels were randomly collected in each plot, for a total of 1200 tassels. In each tassel, the following traits were measured: peduncle dry matter, branching space dry matter, central spike dry matter, tassel dry matter, peduncle length, branching space length, central spike length, tassel length, tassel dry matter to tassel length ratio, number of primary branches, number of secondary branches, and tassel branch number. Measures of central tendency and variability were calculated, analysis of variance and mean comparison tests were performed, normality was verified, and the sample size was determined. In order to estimate the means with the same precision, the sample size for weight traits was greater than that for length traits. For tassel traits, 11, 20, and 43 tassels are sufficient to estimate the mean with a precision of 40, 30, and 20%, respectively, of the estimated mean at a 95% confidence level. These data show that there is sample size variability among maize genotypes.

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Section: Resultssupporting

confidence: 77%

Section: Resultsmentioning

confidence: 99%

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Sample sizes to estimate mean values for tassel traits in maize genotypes

et al. 2016

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“…The use of an adequate sample size is necessary for the results generated by path analysis studies to be reliable. Sample sizing studies have previously been conducted using corn to estimate the mean values (Wolkowski et al, 1988;Magari et al, 1996;Costa et al, 2003;Vasić et al, 2003;Storck et al, 2007;Brera et al, 2010;Modolo et al, 2013;Toebe et al, 2014) and Pearson correlation coefficients between traits (Cargnelutti Filho et al, 2010;Toebe et al, 2015). The sample sizes recommended in those studies range from 10 to 674 plants depending on the genetic basis, harvest, variable, site, management, and level of accuracy considered.…”

Section: Introductionmentioning

confidence: 99%

“…The sample sizes recommended in those studies range from 10 to 674 plants depending on the genetic basis, harvest, variable, site, management, and level of accuracy considered. Resampling has been used for sample sizing in agricultural crops (Cargnelutti Filho et al, 2010Toebe et al, 2014Toebe et al, , 2015, especially in cases where the probability distribution of the data is unknown (Ferreira, 2009).…”

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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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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