Selection of environments using simultaneous clustering based on genotype × environment interaction

1994 ◽  
Vol 74 (2) ◽  
pp. 311-317 ◽  
Author(s):  
C. P. Baril ◽  
J-B. Denis ◽  
P. Brabant
Keyword(s):  
Cluster Analysis ◽  
Wheat Yield ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Yield Data ◽  
Cluster Procedure ◽  
Genotype Environment Interaction ◽  
Series Of Experiments ◽  
The Common ◽  
Selection Of

Cluster analysis is used to classify genotypes and environments to decompose and interpret genotype × environment (GE) interaction. A simultaneous clustering method is applied to wheat-yield data collected over 8 yr in seven locations, with two agronomic treatments per location. This approach evidenced redundancies among the used environments constituting the Institut National de la Recherche Agronomique series of experiments in northern France. The aim is to reduce the number of environments without losing GE interaction. A graphical method based on the decreasing mean square of GE interaction is proposed to provide a cutting criterion of the cluster procedure. The comparison of groupings made independently for successive years suggested the removal of some environments, hence providing rational savings in the breeding program. Lastly, the simultaneous two-way clustering procedure is compared with the common one-way clustering procedure. Key words: Cluster analysis, genotype × environment interaction, pattern analysis, series of experiments, wheat

scholarly journals AMMI and GGE Biplot for genotype × environment interaction: a medoid–based hierarchical cluster analysis approach for high–dimensional data

2018 ◽  
Vol 55 (2) ◽  
pp. 97-121 ◽  
Author(s):  
Anderson Cristiano Neisse ◽  
Jhessica Letícia Kirch ◽  
Kuang Hongyu
Keyword(s):  
Cluster Analysis ◽  
Wheat Yield ◽  
Hierarchical Cluster ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Spring Bread Wheat ◽  
Genotype Environment Interaction ◽  
Complex Process ◽  
Yield Trial ◽  
Selection Of

SummaryThe presence of genotype-environment interaction (GEI) influences production making the selection of cultivars in a complex process. The two most used methods to analyze GEI and evaluate genotypes are AMMI and GGE Biplot, being used for the analysis of multi environment trials data (MET). Despite their different approaches, both models complement each other in order to strengthen decision making. However, both models are based on biplots, consequently, biplot-based interpretation doesn’t scale well beyond two-dimensional plots, which happens whenever the first two components don’t capture enough variation. This paper proposes an approach to such cases based on cluster analysis combined with the concept of medoids. It also applies AMMI and GGE Biplot to the adjusted data in order to compare both models. The data is provided by the International Maize and Wheat Improvement Center (CIMMYT) and comes from the 14th Semi-Arid Wheat Yield Trial (SAWYT), an experiment concerning 50 genotypes of spring bread wheat (Triticum aestivum) germplasm adapted to low rainfall. It was performed in 36 environments across 14 countries. The analysis provided 25 genotypes clusters and 6 environments clusters. Both models were equivalent for the data’s evaluation, permitting increased reliability in the selection of superior cultivars and test environments.

scholarly journals Investigation of genotype-environment interaction by cluster analysis in animal experiments

1994 ◽  
Vol 74 (4) ◽  
pp. 607-612 ◽  
Author(s):  
C. Y. Lin ◽  
C. S. Lin
Keyword(s):  
Cluster Analysis ◽  
Key Words ◽  
Animal Experiment ◽  
Animal Experiments ◽  
Environment Interaction ◽  
Mean Square ◽  
Ge Interaction ◽  
Interaction Structure ◽  
Genotype Environment Interaction ◽  
Animal Data

The conventional ANOVA (F ratio of GE interaction mean squares to error mean square) provides a means to test if GE interaction is significant, but it does not tell us which factor levels are significantly different or how they are interacting. To answer the latter question, plant researchers developed a technique to group genotypes for similarity of GE interactions and through the resulting groups to explore the GE interaction structure. The basic idea of the technique is to stratify genotypes (or environments) into subgroups such that GE interactions among genotypes (or environments) are homogeneous within groups but heterogeneous among groups. This technique is introduced in this paper using an animal experiment as an example for illustration. The possibilities and limitations of applying this technique to animal data are also discussed. Key words: Genotype-environment interaction, cluster analysis

scholarly journals THE ANALYSIS OF GENOTYPE × ENVIRONMENT INTERACTION USING RAPESEED (BRASSICA NAPUS L.) BY GGE BIPLOT METHOD

2020 ◽  
Vol 51 (5) ◽  
pp. 1337-1349
Author(s):  
Motahhari & et al.
Keyword(s):  
Seed Yield ◽  
Agricultural Research ◽  
Environment Interaction ◽  
Block Designs ◽  
Gge Biplot ◽  
Ge Interaction ◽  
Brassica Napus L ◽  
Biplot Analysis ◽  
Genotype Environment Interaction ◽  
Selection Of

This study was aimed to asses seed yield performances of 16 rapeseed genotypes  in randomized complete block designs (RCBD) with three replications at four Agricultural Research Stations of cold and mid-cold regions over two years in Iran (2015-2017). GGE biplot analysis indicated that the first two components explained 83% of seed yield variations. Genotype, location and their interaction explained 18%, 52% and 30%of the total GE variation, respectively. In this research, a graphically represented GGE biplot analysis enabled selection of stable and high-yielding genotypes for all investigated locations, as well as genotypes with specific adaptability. The GGE biplot analysis was adequate in explaining GE interaction for seed yield in rapeseed. It can be concluded that genotypes G2, G4 and G13 had the highest mean seed yield and stability in four investigated locations. For specific adaptability, G13 was recommended for Isfahan, Karaj and Kermanshah and G4 for Mashhad.

Simultaneous selection of yield and yield stability in chickpea genotypes using the GGE biplot technique

2013 ◽  
Vol 61 (3) ◽  
pp. 185-194 ◽  
Author(s):  
E. Farshadfar
Keyword(s):  
Grain Yield ◽  
Principal Component ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Yield Data ◽  
Genotype By Environment ◽  
Growing Seasons ◽  
Genotype Environment Interaction ◽  
The Mean ◽  
Selection Of

GGE biplot analysis is an effective method, based on principal component analysis (PCA), to fully explore multi-environment trials (METs). It allows visual examination of the relationships among the test environments, genotypes and the genotype-by-environment interactions (G×E interaction). The objective of this study was to explore the effect of genotype (G) and the genotype × environment interaction (GEI) on the grain yield of 20 chickpea genotypes under two different rainfed and irrigated environments for 4 consecutive growing seasons (2008–2011). The yield data were analysed using the GGE biplot method. The first mega-environment contained environments E1, E3, E4 and E6, with genotype G17 (X96TH41K4) being the winner; the second mega-environment contained environments E5, E7 and E8, with genotype G12 (X96TH46) being the winner. The E2 environment made up another mega-environment, with G19 (FLIP-82-115) the winner. The mean performance and stability of the genotypes indicated that genotypes G4, G16 and G20 were highly stable with high grain yield.

scholarly journals Analysis of genotype × environment interaction in rain-fed durum wheat of Iran using GGE-biplot and non-parametric methods

2012 ◽  
Vol 92 (4) ◽  
pp. 757-770 ◽  
Author(s):  
Reza Mohammadi ◽  
Ahmed Amri
Keyword(s):  
Grain Yield ◽  
Durum Wheat ◽  
Environment Interaction ◽  
Parametric Methods ◽  
Gge Biplot ◽  
Ge Interaction ◽  
Yield Data ◽  
Stability Performance ◽  
Genotype Environment Interaction ◽  
Non Parametric

Mohammadi, R. and Amri, A. 2012. Analysis of genotype × environment interaction in rain-fed durum wheat of Iran using GGE-biplot and non-parametric methods. Can. J. Plant Sci. 92: 757–770. Multi-environment trials (MET) are conducted annually throughout the world in order to use the information contained in MET data for genotype evaluation and mega-environment identification. In this study, grain yield data of 13 durum and one bread wheat genotypes grown in 16 diversified environments (differing in winter temperatures and water regimes) were used to analyze genotype by environment (GE) interactions in rain-fed durum MET data in Iran. The main objectives were (i) to investigate the possibility of dividing the test locations representative for rain-fed durum production in Iran into mega-environments using the genotype main effect plus GE interaction (GGE) biplot model and (ii) to compare the effectiveness of the GGE-biplot and several non-parametric stability measures (NPSM), which are not well-documented, for evaluating the stability performance of genotypes tested and the possibility of recommending the best genotype(s) for commercial release in the rain-fed areas of Iran. The results indicate that the grain yield of different genotypes was significantly influenced by environmental effect. The greater GE interaction relative to genotype effect suggested significant environmental groups with different top-yielding genotypes. Warm environments differed from cold environments in the ranking of genotypes, while moderate environments were highly divergent and correlated with both cold and warm environments. Cold and warm environments were better than moderate environments in both discriminating and representativeness, suggesting the efficiency and accuracy of genotype selection would be greatly enhanced in such environments. According to the NPSM, genotypes tend to be classified into groups related to the static and dynamic concepts of stability. Both the GGE-biplot and NPSM methods were found to be useful, and generally gave similar results in identifying high-yielding and stable genotypes. In contrast to NPSM, the GGE-biplot analysis would serve as a better platform to analyze MET data, because it always explicitly indicates the average yield and stability of the genotypes and the discriminating ability and representativeness of the test environments.

GENOTYPE-ENVIRONMENT INTERACTION OF YIELD IN CEREAL CROPS IN NORTHWESTERN CANADA

1981 ◽  
Vol 61 (2) ◽  
pp. 255-263 ◽  
Author(s):  
R. M. De PAUW ◽  
D. G. FARIS ◽  
C. J. WILLIAMS
Keyword(s):  
Triticum Aestivum L ◽  
Regression Coefficients ◽  
Cereal Crops ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Hordeum Vulgare L ◽  
Genotype Environment Interaction ◽  
Early Maturing ◽  
Frost Free Period ◽  
Main Effects

Three cultivars of each crop, wheat (Triticum aestivum L.), oats (Avena sativa L.), and barley (Hordeum vulgare L.), were grown for 4 yr at five locations north of the 55th parallel in northwestern Canada. There were highly significant differences among all main effects and interactions. Galt barley produced the highest seed yield followed by Centennial barley, Random oats and Harmon oats. Victory oats, Olli barley, Neepawa wheat and Pitic 62 wheat yielded similarly to each other while Thatcher wheat was significantly lower yielding. Mean environment yields ranged from 2080 to 5610 kg/ha. The genotype-environment (GE) interaction of species and cultivars was sufficiently complicated that it could not be characterized by one or two statistics (e.g., stability variances or regression coefficients). However, variability in frost-free period among years and locations contributed to the GE interaction because, for example, some cultivars yielded well (e.g., Pitic 62) only in those year-location environments with a relatively long frost-free period while other early maturing cultivars (e.g., Olli) performed well even in a short frost-free period environment.

GENOTYPE-ENVIRONMENT INTERACTIONS IN YIELD OF WHEAT

1969 ◽  
Vol 49 (6) ◽  
pp. 743-751 ◽  
Author(s):  
R. J. Baker
Keyword(s):  
Detailed Analysis ◽  
Spring Wheat ◽  
Variance Components ◽  
Wheat Yield ◽  
Yield Stability ◽  
Western Canada ◽  
Environment Interaction ◽  
Hard Red Spring Wheat ◽  
Genotype Environment Interaction ◽  
Different Types

A detailed analysis of genotype-environment interactions was carried out among yields of six cultivars of hard red spring wheat grown at each of nine locations in five different years. Subdividing the sum of squares for genotype-environment interactions into components due to each cultivar indicated that the Finlay-Wilkinson method of measuring yield stability is of little value for wheat yield in western Canada. Conventional estimates of variance components due to the different types of genotype-environment interaction indicated that all except the genotype-year interaction were significant and important.

scholarly journals Analysis of yield stability of strawberry varieties and hybrids

2020 ◽  
Vol 25 ◽  
pp. 02014
Author(s):  
Vadim Lapshin ◽  
Valentina Yakovenko ◽  
Sergey Shcheglov
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster ◽  
Yield Stability ◽  
Environment Interaction ◽  
Multivariate Statistical ◽  
Genotype Environment Interaction ◽  
Yield Structure ◽  
Biological Yield ◽  
Factor Of Influence

The profitability of strawberry cultivation is largely determined by the capacity and quality of the yield, depended on the features of the variety genotype. The aim of this work was to estimate the yield stability of varieties and hybrids by the methods of multivariate statistical analysis and identify the best genotypes. To solve this problem, we have used the two-factor analysis of variance and hierarchical cluster analysis according to the Ward’s method as well as the integral estimate of the differences between the values of yield. The results of the studies have shown that the genotype of the variety (hybrid) are makes a decisive factor of influence for variability of the yield structure signs from 17,1% (number of inflorescences) to 32,2% (number of berries). The «genotype × environment» interaction is comparable with the genotype influence, the share of influence of the year conditions of the year is insignificant. Cluster analysis according to complex of economic valuable signs allows us to identify the eight forms that the most adapted to the conditions of the Krasnodar Territory as 13-1-15, Florence, Roxana, 18-1-15, Asia, Onda, Kemia, Nelli from which the Roxana, Florence, 18-1-15, 13-1-15 have a high and steadily rising biological yield.

scholarly journals Grouping genotypes and test environments by some cluster methods regarding genotype × environment interaction in multi-environment trials

Genetika ◽  
2012 ◽  
Vol 44 (3) ◽  
pp. 457-473 ◽  
Author(s):  
Naser Sabaghnia ◽  
Rahmatollah Karimizadeh ◽  
Mohtasham Mohammadi
Keyword(s):  
Lens Culinaris ◽  
Developed Countries ◽  
Environment Interaction ◽  
Stopping Criterion ◽  
Ge Interaction ◽  
Regression Methods ◽  
Genotype By Environment ◽  
Genotype Environment Interaction ◽  
Main Effect ◽  
Cluster Methods

Lentil (Lens culinaris Medik.) is an important source of protein and carbohydrate food for people of developing countries and is popular in some developed countries where they are perceived as a healthy component of the diet. Ten lentil genotypes were tested for grain yield in five different environmental conditions, over two consecutive years to classify thes genotypes for yield stability. Seed yield of lentil genotypes ranged from 989.3 to 1.367 kg ha-1 and the linear regression coefficient ranged from 0.75 to 1.18. The combined analysis of variance showed that the effect of environment (E) and genotype by environment (GE) interaction were highly significant while the main effect of genotype (G) was significant at 0.05 probability level. Four different cluster procedures were used for grouping genotypes and environments. According to dendograms of regression methods for lentil genotypes there were two different genotypic groups based on G plus GE or GE sources. Also, the dendograms of ANOVA methods indicated 5 groups based on G and GE sources and 4 groups based on GE sources. According to dendograms of regression methods for environments there were 5 different groups based on G plus GE sources while the dendograms of ANOVA methods indicated 9 groups based on G and GE sources and 3 groups based on GE sources. The mentioned groups were determined via F-test as an empirical stopping criterion for clustering. The most responsive genotypes with high mean yield genotypes are G2 (1145.3 kg ha-1), G8 (1200.2 kg ha-1) and G9 (1267.9 kg ha-1) and could be recommended as the most favorable genotypes for farmers.

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