Nonparametric stability methods for interpreting genotype by environment interaction of bread wheat genotypes (Triticum aestivum L.)

Akçura, Mevlüt; Kaya, Yüksel

doi:10.1590/s1415-47572008005000004

Cited by 38 publications

(30 citation statements)

References 21 publications

(30 reference statements)

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“…The presence of significant G x E interaction indicated the inconsistency in performance of maize genotypes across environments. Similar results recorded by other authors (Akcura et al, 2005;Acura and Kaya, 2008;Asfaw, 2008;Dagne, 2008;Solomon et al, 2008;Abdurhaman, 2009 andMuluken, 2009).…”

Section: Discussionsupporting

confidence: 89%

Genotype X environment interaction of maize (Zea mays L.) across North Western Ethiopia

Anley¹,

Zeleke²,

Dessalegn³

2013

J. Plant Breed. Crop Sci.

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A study was conducted at Northwestern Ethiopia, during 2010 main cropping season. Fifteen maize genotypes were evaluated at four locations that differ in soil type, altitude and mean annual rainfall. The experiment was laid out in a randomized complete block design with three replications. Stability parameters that are useful tools for identification of genotypes with specific and wide adaptations, and contrasting the role played by genotype, environment and G x E interaction in multilocational variety trials were considered and analyzed. The highly significant G x E interactions indicated that genotypes performance was inconsistent across testing locations and need to be tested in several locations in order to select stable genotypes. Jibat-851, Wonchi and BHQPY-545 exhibited high mean grain yield across environments and average responsiveness with high degree of stability indicating general adaptability and thus can be recommended for north western Amhara region and for areas with similar environments. The best genotype with respect to location Adet was Gibe-1 while Wonchi was the best genotype for Merawi area. Phb-3253 performed well at Motta, while Phb-30G19 and Jibat-851 performed well at Finoteselam. Therefore, these genotypes can be recommended according to their specific adaptation area.

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Section: Discussionsupporting

confidence: 89%

Genotype X environment interaction of maize (Zea mays L.) across North Western Ethiopia

Anley¹,

Zeleke²,

Dessalegn³

2013

J. Plant Breed. Crop Sci.

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“…The following stability measurements were performed on grain yield under the six environments (three seasons and two locations which compose six environments), i.e., coefficient of variability (CV i ) [31], regression coefficient (b i ) [22], Wricke's ecovalance (W i ) [32], superiority measure (P i ) [21], Perkins and Jinks (D i ) [33], and average absolute rank difference of genotype on the environment (S i (1)) [24] . Moreover, the additive main effects and multiplicative interaction model (AMMI) [23] was applied on the grain yield variable for each sowing date separately, and after combing them.…”

Section: Stability Analysis and Genotype × Interaction (G × E)mentioning

confidence: 99%

“…The most frequently utilized two parametric stability methods are partitioning of G × E interaction [21] and the regression model [22]. The additive main effects and multiplicative interaction model (AMMI) [23] and the genotype main effect plus G × E interaction (GGE) [24] are the most frequently utilized non-parametric methods. Both AMMI and GGE biplot analyses are based on the principal component analysis (PCA).…”

Section: Introductionmentioning

confidence: 99%

Performance and Stability of Commercial Wheat Cultivars under Terminal Heat Stress

Elbasyoni

2018

Agronomy

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Egypt, the fifteenth most populated country and the largest wheat importer worldwide, is vulnerable to global warming. Ten of the commercial and widely grown wheat cultivars were planted in two locations, i.e., Elbostan and Elkhazan for three successive seasons 2014/2015, 2015/2016, and 2016/2017 under two sowing dates (recommended and late). Elbostan and Elkhazan are the two locations used in this study because they represent newly reclaimed sandy soil and the Nile delta soil (clay), respectively. A split-plot, with main plots arranged as a randomized complete block design and three replicates, was used. The overall objective of this study was to identify the ideal cultivar for recommended conditions and heat stressed conditions. The results revealed that heat stress had a significant adverse impact on all traits while it raised the prevalence and severity of leaf and stem rust which contributed to overall yield losses of about 40%. Stability measurements, the additive main effects and multiplicative interaction model (AMMI) and genotype main effect plus genotype × environment interaction (GGE), were useful to determine the ideal genotypes for recommended and late sowing conditions (heat stressed). However, inconsistency was observed among some of these measurements. Cultivar "Sids12" was stable and outperformed other tested cultivars under combined sowing dates across environments. However, cultivar "Gemmeiza9" was more stable and outperformed other cultivars across environments under the recommended sowing date. Moreover, cultivar "Gemmeiza12" was the ideal cultivar for the late sown condition. Based on our findings, importing and evaluating heat stress tolerant wheat genotypes under late sown conditions or heat stressed conditions in Egypt is required to boost heat stress tolerance in the adapted wheat cultivars.

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“…Huehn in Akcura and Kaya (2008) suggests that the nonparametric procedure has several advantages compared to the parametric stability. These benefits are the reduction of bias caused by outliers, no assumptions needed of the observed values, and ease of use in interpreting, adding, or deleting unsuitable genotypes.…”

Section: Introductionmentioning

confidence: 99%

NONPARAMETRIC STABILITY ANALYSIS OF YIELD FOR NINE CHILI PEPPER (Capsicum annuum L.) GENOTYPES IN EIGHT ENVIRONMENTS

Rahadi¹,

Syukur²,

Sujiprihati³

et al. 2013

Agrivita.J.Agr.Sci

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The objectives of this study were to compare nonparametric stability measures, and to identify promising high yield and stability of chili pepper (Capsicum annuum L.) genotypes in eight environments. In every environment, a Randomized Complete Block Design was used with three replications. The method of Nassar and Huehn, Kang, Fox, and Thennarasu was used to analyze the stability and high yield. Spearman's correlation and Principal Component analysis distinguishes the methods based on two different concepts of stability: the static (biological) and dynamic (agronomic) concepts. The top method was found to be the dynamic stability. Meanwhile, the methods of Si 1 , Si 2 , Si 3 , Si 6 , Npi 1 , NPi 2 , NPi 3 and NPi 4 were found to be the static stability. Based on the ranking frequency stability of the nonparametric method, the genotypes with the highest frequency of static stability ranking were genotypes IPB002003, IPB002046, IPB009019 and Tit Super, whereas IPB009002 and Tombak were categorized as those of dynamic stability. Genotype IPB120005 and IPB019015 were less adaptable in the multiple environments tested. It shows that the genotypes were specific in certain environments. IPB120005 had high yield and specific location in Boyolali in dry season and IPB019015 genotype was specific in Bogor in wet season.

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Nonparametric stability methods for interpreting genotype by environment interaction of bread wheat genotypes (Triticum aestivum L.)

Cited by 38 publications

References 21 publications

Genotype X environment interaction of maize (Zea mays L.) across North Western Ethiopia

Genotype X environment interaction of maize (Zea mays L.) across North Western Ethiopia

Performance and Stability of Commercial Wheat Cultivars under Terminal Heat Stress

NONPARAMETRIC STABILITY ANALYSIS OF YIELD FOR NINE CHILI PEPPER (Capsicum annuum L.) GENOTYPES IN EIGHT ENVIRONMENTS

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