2016
DOI: 10.1016/s2095-3119(15)61157-1
|View full text |Cite
|
Sign up to set email alerts
|

GGE biplot analysis of yield stability and test location representativeness in proso millet (Panicum miliaceum L.) genotypes

Abstract: The experiments were conducted for three consecutive years across 14 locations using nine non-waxy proso millet genotypes and 16 locations using seven waxy proso millet genotypes in China. The objectives of this study were to analyze yield stability and adaptability of proso millets and to evaluate the discrimination and representativeness of locations by Analysis of variance (ANOVA) and GGE biplot methods. Grain yields of proso millet genotypes were significantly influenced by environment (E), genotype (G), a… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
2

Citation Types

4
30
0
4

Year Published

2017
2017
2024
2024

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 44 publications
(38 citation statements)
references
References 20 publications
4
30
0
4
Order By: Relevance
“…A biplot can be used to visualize three types of patterns: (1) relationships among the testers (treatments, traits, or their combination), (2) similarities or differences among genotypes, and (3) the discriminating power of the treatment or testing location. Crop breeders have used GGE to evaluate the performance of cultivars, and the discrimination and representativeness of locations ( Zhang et al, 2016 ). For example, based on yield data from 13 durum wheat genotypes tested at four locations in northwestern Ethiopia, Abate et al (2015) identified that the test location of Debretabor was the most discriminating environment for maximizing the variance among candidate cultivars compared to the locations of Adet and Simada.…”
Section: Discussionmentioning
confidence: 99%
“…A biplot can be used to visualize three types of patterns: (1) relationships among the testers (treatments, traits, or their combination), (2) similarities or differences among genotypes, and (3) the discriminating power of the treatment or testing location. Crop breeders have used GGE to evaluate the performance of cultivars, and the discrimination and representativeness of locations ( Zhang et al, 2016 ). For example, based on yield data from 13 durum wheat genotypes tested at four locations in northwestern Ethiopia, Abate et al (2015) identified that the test location of Debretabor was the most discriminating environment for maximizing the variance among candidate cultivars compared to the locations of Adet and Simada.…”
Section: Discussionmentioning
confidence: 99%
“…The environments within the same sector share the same winning genotype and environments in different sectors share different winning genotypes. The identification of best cultivar for each location using "Which-won-where" was previously reported in proso millet (Zhang et al, 2016) and cotton (Xu et al, 2014).…”
Section: Discussionmentioning
confidence: 99%
“…In order to deal with G×E interaction in breeding programs and separating yield potential and yield stability in multi-environment trials, numerous stability analysis methods including variation coefficient, linear regression, Wrick's equivalence (Wi), stability variance of Shukla ( 2 i  ), additive main effect and multiplicative interaction (AMMI) and genotype and genotype by environment interaction (GGE) biplot have been proposed (GAUCH and ZOBEL, 1988;ZHANG and KONG, 2002). In the recent decade, AMMI and GGE statistical analysis have been widely used in different studies (RAKSHIT et al, 2012;BOSE et al, 2014;HONGYU et al, 2014;TEMESGEN et al, 2015;ZHANG et al, 2016). Although AMMI analysis provides information on main and interaction effects, interaction effects have been only taken in to account in AMMI analysis and genotype effects have been ignored (ZHANG et al, 2016).…”
Section: Introductionmentioning
confidence: 99%
“…In the recent decade, AMMI and GGE statistical analysis have been widely used in different studies (RAKSHIT et al, 2012;BOSE et al, 2014;HONGYU et al, 2014;TEMESGEN et al, 2015;ZHANG et al, 2016). Although AMMI analysis provides information on main and interaction effects, interaction effects have been only taken in to account in AMMI analysis and genotype effects have been ignored (ZHANG et al, 2016). In addition, AMMI analysis requires a greater number of genotypes and several years for evaluation in comparison to GGE Biplot, moreover, it makes misleading in identifying whichwon-where (YAN et al, 2007;FASAHAT et al, 2015).…”
Section: Introductionmentioning
confidence: 99%