Genotype × environmental interaction by AMMI and GGE biplot analysis for the provenances of Michelia chapensis in South China

2015 ◽  
Vol 27 (3) ◽  
pp. 659-664 ◽  
Author(s):  
Runhui Wang ◽  
Dehuo Hu ◽  
Huiquan Zheng ◽  
Shu Yan ◽  
Ruping Wei
Keyword(s):  
South China ◽  
Gge Biplot ◽  
Environmental Interaction ◽  
Biplot Analysis

Evaluation of sugarcane genotypes with respect to sucrose yield across three crop cycles using GGE biplot analysis

2021 ◽  
pp. 1-13
Author(s):  
Aliya Momotaz ◽  
Per H. McCord ◽  
R. Wayne Davidson ◽  
Duli Zhao ◽  
Miguel Baltazar ◽  
...  
Keyword(s):  
Block Design ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Genotype Environment Interaction ◽  
Area 5 ◽  
Randomized Complete Block Design ◽  
Main Effects

Summary The experiment was carried out in three crop cycles as plant cane, first ratoon, and second ratoon at five locations on Florida muck soils (histosols) to evaluate the genotypes, test locations, and identify the superior and stable sugarcane genotypes. There were 13 sugarcane genotypes along with three commercial cultivars as checks included in this study. Five locations were considered as environments to analyze genotype-by-environment interaction (GEI) in 13 genotypes in three crop cycles. The sugarcane genotypes were planted in a randomized complete block design with six replications at each location. Performance was measured by the traits of sucrose yield tons per hectare (SY) and commercial recoverable sugar (CRS) in kilograms of sugar per ton of cane. The data were subjected to genotype main effects and genotype × environment interaction (GGE) analyses. The results showed significant effects for genotype (G), locations (E), and G × E (genotype × environment interaction) with respect to both traits. The GGE biplot analysis showed that the sugarcane genotype CP 12-1417 was high yielding and stable in terms of sucrose yield. The most discriminating and non-representative locations were Knight Farm (KN) for both SY and CRS. For sucrose yield only, the most discriminating and non-representative locations were Knight Farm (KN), Duda and Sons, Inc. USSC, Area 5 (A5), and Okeelanta (OK).

GGE Biplot Analysis of Yield Associations with Root Traits in a Mesoamerican Bean Diversity Panel

Crop Science ◽  
2016 ◽  
Vol 56 (3) ◽  
pp. 1081-1094 ◽  
Author(s):  
V. Hoyos-Villegas ◽  
E.M. Wright ◽  
J.D. Kelly
Keyword(s):  
Root Traits ◽  
Gge Biplot ◽  
Biplot Analysis ◽  
Diversity Panel

scholarly journals GGE biplot analysis of multi-location yield trial of ginger (Zingiber Officinale Rosc.) Genotypes in South-Eastern, Nigeria

2020 ◽  
Vol 10 (1) ◽  
pp. 485-503
Author(s):  
Abua Mary Njei ◽  
Iwo Godfery Akpan ◽  
Ittah Macauley Asim ◽  
Obok Ekemini Edet ◽  
Edugbo Richmond Emu
Keyword(s):  
Zingiber Officinale ◽  
Gge Biplot ◽  
Biplot Analysis ◽  
South Eastern ◽  
Yield Trial

scholarly journals GENOTYPE BY ENVIRONMENT INTERACTION IN COWPEA LINES USING GGE BIPLOT METHOD

2018 ◽  
Vol 31 (1) ◽  
pp. 64-71 ◽  
Author(s):  
MASSAINE BANDEIRA E SOUSA ◽  
KAESEL JACKSON DAMASCENO-SILVA ◽  
MAURISRAEL DE MOURA ROCHA ◽  
JOSÉ ÂNGELO NOGUEIRA DE MENEZES JÚNIOR ◽  
LAÍZE RAPHAELLE LEMOS LIMA
Keyword(s):  
Maximum Yield ◽  
Genotype By Environment Interaction ◽  
Superior Performance ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Brazilian Cerrado ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Adjusted Means ◽  
Selection Of

ABSTRACT The GGE Biplot method is efficien to identify favorable genotypes and ideal environments for evaluation. Therefore, the objective of this work was to evaluate the genotype by environment interaction (G×E) and select elite lines of cowpea from genotypes, which are part of the cultivation and use value tests of the Embrapa Meio-Norte Breeding Program, for regions of the Brazilian Cerrado, by the GGE-Biplot method. The grain yield of 40 cowpea genotypes, 30 lines and 10 cultivars, was evaluated during three years (2010, 2011 and 2012) in three locations: Balsas (BAL), São Raimundo das Mangabeiras (SRM) and Primavera do Leste (PRL). The data were subjected to analysis of variance, and adjusted means were obtained to perform the GGE-Biplot analysis. The graphic results showed variation in the performance of the genotypes in the locations evaluated over the years. The performance of the lines MNC02-675F-4-9 and MNC02-675F-4-10 were considered ideal, with maximum yield and good stability in the locations evaluated. There mega-environments were formed, encompassing environments correlated positively. The lines MNC02-675F-4-9, MNC02-675F-9-3 and MNC02-701F-2 had the best performance within each mega-environment. The environment PRL10 and lines near this environment, such as MNC02-677F-2, MNC02-677F-5 and the control cultivar (BRS-Marataoã) could be classified as those of greater reliability, determined basically by the genotypic effects, with reduced G×E. Most of the environments evaluated were ideal for evaluation of G×E, since the genotypes were well discriminated on them. Therefore, the selection of genotypes with adaptability and superior performance for specific environments through the GGE-Biplot analysis was possible.

Yield stability of rainfed durum wheat and GGE biplot analysis of multi-environment trials

2010 ◽  
Vol 61 (1) ◽  
pp. 92 ◽  
Author(s):  
Reza Mohammadi ◽  
Reza Haghparast ◽  
Ahmed Amri ◽  
Salvatore Ceccarelli
Keyword(s):  
Durum Wheat ◽  
Good Alternative ◽  
Yield Stability ◽  
Gge Biplot ◽  
Ge Interaction ◽  
Breeding Lines ◽  
Biplot Analysis ◽  
Stable Performance ◽  
Superior Genotypes ◽  
Highly Correlated

Integrating yield and stability of genotypes tested in unpredictable environments is a common breeding objective. The main goals of this research were to identify superior durum wheat genotypes for the rainfed areas of Iran and to determine the existence of different mega-environments in the growing areas of Iran by testing 20 genotypes in 4 locations for 3 years via GGE (genotype + genotype-by-environment) biplot analysis. Stability of performance was assessed by the Kang’s yield-stability statistic (YSi) and 2 new methods of yield-regression statistic (Ybi) and yield-distance statistic (Ydi).The combined analysis of variance showed that environments were the most important source of yield variability, and accounted for 76% of total variation. The magnitude of the GE interaction was ~10 times the magnitude of the G effect. The GGE biplot suggested the existence of 2 durum wheat mega-environments in Iran. The first mega-environment consisted of environments corresponding to ‘cold’ locations (Maragheh and Shirvan) and a moderately cold location (Kermanshah), where ‘Sardari’ was the best adapted cultivar; the second mega-environment comprised ‘warm’ environments, including the Ilam and Kermanshah locations, where the recommended breeding lines G16 (Gcn//Stj/Mrb3), G17 (Ch1/Brach//Mra-i), and G18 (Lgt3/4/Bcr/3/Ch1//Gta/Stk) produced the highest yields. Ranking of genotypes based on GGE was found to be highly correlated with that based on the statistics YSi and Ybi. The discriminating power v. the representative view of the GGE biplot identified Kermanshah as the location with the least discriminating ability but greater representation, suggesting the possible of testing genotypes adapted to both warm and cold locations at the Kermanshah site. The results verified that the statistics YSi and Ybi were highly correlated (r = 0.94**) and could be a good alternative for GGE biplot analysis for selecting superior genotypes with high-yielding and stable performance.

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