Rank correlation among different statistical models in ranking of winter wheat genotypes

Roostaei, Mozaffar; Mohammadi, Reza; Amri, Ahmed

doi:10.1016/j.cj.2014.02.002

Cited by 46 publications

(37 citation statements)

References 31 publications

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“…In the cultivar selection and recommendation process, the cultivar ranking (Roostaei et al, 2014) and its compatibility across locations (Hu, 2015), are very important. This is why in this research we analyzed the Spearman rank correlation.…”

Section: Multi Environmental Trial Datasetmentioning

confidence: 99%

Yield response of winter wheat cultivars to environments modeled by different variance-covariance structures in linear mixed models

Studnicki

Mądry

Noras

et al. 2016

Span. j. agric. res.

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The main objectives of multi-environmental trials (METs) are to assess cultivar adaptation patterns under different environmental conditions and to investigate genotype by environment (G×E) interactions. Linear mixed models (LMMs) with more complex variance-covariance structures have become recognized and widely used for analyzing METs data. Best practice in METs analysis is to carry out a comparison of competing models with different variance-covariance structures. Improperly chosen variance-covariance structures may lead to biased estimation of means resulting in incorrect conclusions. In this work we focused on adaptive response of cultivars on the environments modeled by the LMMs with different variance-covariance structures. We identified possible limitations of inference when using an inadequate variance-covariance structure. In the presented study we used the dataset on grain yield for 63 winter wheat cultivars, evaluated across 18 locations, during three growing seasons (2008/2009-2010/2011) from the Polish Post-registration Variety Testing System. For the evaluation of variance-covariance structures and the description of cultivars adaptation to environments, we calculated adjusted means for the combination of cultivar and location in models with different variance-covariance structures. We concluded that in order to fully describe cultivars adaptive patterns modelers should use the unrestricted variance-covariance structure. The restricted compound symmetry structure may interfere with proper interpretation of cultivars adaptive patterns. We found, that the factor-analytic structure is also a good tool to describe cultivars reaction on environments, and it can be successfully used in METs data after determining the optimal component number for each dataset.

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Section: Multi Environmental Trial Datasetmentioning

confidence: 99%

Yield response of winter wheat cultivars to environments modeled by different variance-covariance structures in linear mixed models

Studnicki

Mądry

Noras

et al. 2016

Span. j. agric. res.

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“…Mohammadi et al (2010) found positive associations, and with high repeatability between years, for the WRW, AMMI, and ERd methods. Roostaei et al (2014) observed an association of medium magnitude (0.56) between the concepts of stability for AMMI and GGE. Cargnelutti Filho et al (2009), with competition trials of corn genotypes, verified total concordance (r s = 1.00) among the estimates of the PPW and WRW stability parameters, a result also obtained in this study (r s = 1.00).…”

Section: Resultsmentioning

confidence: 94%

“…Comparisons between methodologies of adaptability and stability analysis have been performed for several crops, such as corn (Bujak et al, 2014), sugarcane (Paula et al, 2014), soybean (Freitas Monteiro et al, 2015), and wheat (Roostaei et al, 2014). However, comparisons between methodologies based on linear bissegmented regression and AMMI and GGE biplot are poorly understood and require further studies.…”

Section: Introductionmentioning

confidence: 99%

Repeatability of associations between analytical methods of adaptability, stability, and productivity in soybean

Woyann

Milioli

Bozi

et al. 2018

Pesq. agropec. bras.

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-The objective of this work was to evaluate the association between estimates of adaptability, stability, and productivity in soybean (Glycine max), and to estimate the repeatability coefficient of these associations between years. A total of 22 genotypes were evaluated in 27 environments in the 2012/2013 crop season and in 19 environments in 2013/2014. In the next crop seasons, 28 genotypes were evaluated in 26 environments, in 2014/2015, and in 25 environments in 2015/2016, totalizing 97 trials. Fourteen methods were evaluated; Spearman correlation coefficients were obtained for the parameters of stability, adaptability, and productivity; and the repeatability coefficients, as well as the minimum number of required environments for a coefficient of determination of 80 and 90%, were

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“…It is visible that the genotype PKB Talas is the closest to the beginning of the coordinates and that it exhibits the highest stability, i. Although the AMMI and GGE biplot (AMMI 2 biplot) gave similar results in identifying superior genotypes, the AMMI 2 biplot was more versatile and flexible, and provided a better understanding (Roostaei et al, 2014 (Figure 2). The AMMI model does not provide a quantitative stability measure, such a measure is essential in order to quantify and rank genotypes according to their yield stability, which is why ASV is proposed.…”

Section: Asv =mentioning

confidence: 99%

Analysis of main additive effects and multiplicative interactions of components of yield of certain wheat genotypes

Đurić¹,

Matković²,

Cvijanović³

et al. 2016

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Summary:The effect of genotype, seed category and year on yield components (number of spikelets per spike, grain number per spike, grain weight per spike and thousand grain weight) for three wheat genotypes were studied during a two-year research. Genotype PKB Lepoklasa and seed category prebasic seed had the highest values for the largest number of the studied traits, while the genotype BG Merkur and the certified seed of the first generation had the lowest values. Higher values for the studied traits were found in 2010, compared to 2009. AMMI analysis established a significant effect of the interaction of factors and main components (PCA 1 and PCA 2) for all investigated traits except thousand grain weight. The genotype PKB Talas was the most stable, while the genotype PKB Lepoklasa was the least stable. Seed categories certified seed of the first generation and basic seed had similar reactions to the interaction between factors.

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Rank correlation among different statistical models in ranking of winter wheat genotypes

Cited by 46 publications

References 31 publications

Yield response of winter wheat cultivars to environments modeled by different variance-covariance structures in linear mixed models

Yield response of winter wheat cultivars to environments modeled by different variance-covariance structures in linear mixed models

Repeatability of associations between analytical methods of adaptability, stability, and productivity in soybean

Analysis of main additive effects and multiplicative interactions of components of yield of certain wheat genotypes

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