### Abstract

Genotype performance in multienvironment trials (METs) are usually analyzed by parametric analysis of variance (ANOVA) and stability models. The results of these models can give misleading inferences when some sensitive assumptions are not satisfied. In this paper, assumptions of combined ANOVA are scrutinized in detail to justify the validity of use of 5 nonparametric stability methods (S _{i} ^{(1)}, S _{i} ^{(2)}, S _{i} ^{(3)}, S _{i} ^{(6)} and YS _{i} ^{(1)}, YS _{i} ^{(2)}) applied to 20 genotypes grown in 40 hetroscedastic and nonnormal environments in Pakistan for the year 2004-05. There is a severe heterogeneity problem in the data because the ratio of the largest estimated mean squares error (MSE) for individual environments randomized complete block design (RCBD) to the smallest MSE is approximately (1.00/0.02=50). Out of 40 environments individual coefficients of determination (R ^{2}), 27 are less than 0.70. This leads to violation of linearity assumption in the model. Standardized residual plots vs. individual environments plots and normal probability plot are indicators of the violation of homogeneity, normality assumptions and absence of outliers. No linear relationship was established between the natural logs of the error variance and the natural log of environments' mean, which again violates coefficient of variation (CV) assumption. Remedial transformations as suggested in literature were not successful to stabilize environments MSEs and could not normalize the data, so as a last resort in this regard nonparametric stability methods seem to justify the analysis of genotype x environment interactions (GEI). The low values of modified rank-sum statistics YS _{i} ^{(1)} and YS _{i} ^{(2)} were positively and significantly associated with mean yield but the other nonparametric methods were not correlated with mean yield. The results of principal component analysis and correlation analysis of nonparametric stability methods indicate that the use of modified rank-sum method would be justifiable for simultaneous selection for high yield and stability. Using modified rank-sum method, the genotypes G7, G3, G15, G5 and G12 were found to be the most stable with yield, whereas G14 and G19 were the least stable genotypes.

Original language | English (US) |
---|---|

Pages (from-to) | 711-730 |

Number of pages | 20 |

Journal | Pakistan Journal of Botany |

Volume | 41 |

Issue number | 2 |

State | Published - Apr 1 2009 |

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### ASJC Scopus subject areas

- Plant Science

### Cite this

*Pakistan Journal of Botany*,

*41*(2), 711-730.

**Nonparametric methods in combined heteroscedastic experiments for assessing stability of wheat genotypes in Pakistan.** / Shah, Syed Haider; Shah, Syed Munawar; Khan, M. Inayat; Ahmed, Maqsood; Hussain, Imdad; Eskridge, K. M.

Research output: Contribution to journal › Article

*Pakistan Journal of Botany*, vol. 41, no. 2, pp. 711-730.

}

TY - JOUR

T1 - Nonparametric methods in combined heteroscedastic experiments for assessing stability of wheat genotypes in Pakistan

AU - Shah, Syed Haider

AU - Shah, Syed Munawar

AU - Khan, M. Inayat

AU - Ahmed, Maqsood

AU - Hussain, Imdad

AU - Eskridge, K. M.

PY - 2009/4/1

Y1 - 2009/4/1

N2 - Genotype performance in multienvironment trials (METs) are usually analyzed by parametric analysis of variance (ANOVA) and stability models. The results of these models can give misleading inferences when some sensitive assumptions are not satisfied. In this paper, assumptions of combined ANOVA are scrutinized in detail to justify the validity of use of 5 nonparametric stability methods (S i (1), S i (2), S i (3), S i (6) and YS i (1), YS i (2)) applied to 20 genotypes grown in 40 hetroscedastic and nonnormal environments in Pakistan for the year 2004-05. There is a severe heterogeneity problem in the data because the ratio of the largest estimated mean squares error (MSE) for individual environments randomized complete block design (RCBD) to the smallest MSE is approximately (1.00/0.02=50). Out of 40 environments individual coefficients of determination (R 2), 27 are less than 0.70. This leads to violation of linearity assumption in the model. Standardized residual plots vs. individual environments plots and normal probability plot are indicators of the violation of homogeneity, normality assumptions and absence of outliers. No linear relationship was established between the natural logs of the error variance and the natural log of environments' mean, which again violates coefficient of variation (CV) assumption. Remedial transformations as suggested in literature were not successful to stabilize environments MSEs and could not normalize the data, so as a last resort in this regard nonparametric stability methods seem to justify the analysis of genotype x environment interactions (GEI). The low values of modified rank-sum statistics YS i (1) and YS i (2) were positively and significantly associated with mean yield but the other nonparametric methods were not correlated with mean yield. The results of principal component analysis and correlation analysis of nonparametric stability methods indicate that the use of modified rank-sum method would be justifiable for simultaneous selection for high yield and stability. Using modified rank-sum method, the genotypes G7, G3, G15, G5 and G12 were found to be the most stable with yield, whereas G14 and G19 were the least stable genotypes.

AB - Genotype performance in multienvironment trials (METs) are usually analyzed by parametric analysis of variance (ANOVA) and stability models. The results of these models can give misleading inferences when some sensitive assumptions are not satisfied. In this paper, assumptions of combined ANOVA are scrutinized in detail to justify the validity of use of 5 nonparametric stability methods (S i (1), S i (2), S i (3), S i (6) and YS i (1), YS i (2)) applied to 20 genotypes grown in 40 hetroscedastic and nonnormal environments in Pakistan for the year 2004-05. There is a severe heterogeneity problem in the data because the ratio of the largest estimated mean squares error (MSE) for individual environments randomized complete block design (RCBD) to the smallest MSE is approximately (1.00/0.02=50). Out of 40 environments individual coefficients of determination (R 2), 27 are less than 0.70. This leads to violation of linearity assumption in the model. Standardized residual plots vs. individual environments plots and normal probability plot are indicators of the violation of homogeneity, normality assumptions and absence of outliers. No linear relationship was established between the natural logs of the error variance and the natural log of environments' mean, which again violates coefficient of variation (CV) assumption. Remedial transformations as suggested in literature were not successful to stabilize environments MSEs and could not normalize the data, so as a last resort in this regard nonparametric stability methods seem to justify the analysis of genotype x environment interactions (GEI). The low values of modified rank-sum statistics YS i (1) and YS i (2) were positively and significantly associated with mean yield but the other nonparametric methods were not correlated with mean yield. The results of principal component analysis and correlation analysis of nonparametric stability methods indicate that the use of modified rank-sum method would be justifiable for simultaneous selection for high yield and stability. Using modified rank-sum method, the genotypes G7, G3, G15, G5 and G12 were found to be the most stable with yield, whereas G14 and G19 were the least stable genotypes.

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M3 - Article

AN - SCOPUS:79960641533

VL - 41

SP - 711

EP - 730

JO - Pakistan Journal of Botany

JF - Pakistan Journal of Botany

SN - 0556-3321

IS - 2

ER -