Genotype × environment interactions for two-row barley grain yield and implications for selection of genotypes

1993 ◽  
Vol 73 (4) ◽  
pp. 939-946
Author(s):  
K. W. May ◽  
G. C. Kozub
Keyword(s):  
Grain Yield ◽  
Barley Grain ◽  
Data Sets ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Hordeum Vulgare L ◽  
Canadian Prairie ◽  
Interaction Structure ◽  
Genotype Environment Interaction ◽  
Key Words Barley

The response of barley grain yield to Canadian prairie environments was studied to evaluate genotype × environment interactions with respect to barley genotype selection. Information from nine test sites and 11 entries over two 3-yr spans was used. Genotype × location × year interactions from analysis of variance were significant for grain yield in both data sets. The nature of these interactions was studied by considering the genotype mean performance, superiority and stability measures for each location, and joint regression and cluster analyses within each year. No single genotype was superior over all locations, and the groupings of genotypes for similarity of response at locations were not consistent for year. This indicated that genotypes selected on the basis of main effect means may not be those selected from a detailed consideration of the GE interaction structure. In the presence of sufficient genetic variability, examination of mean yield in conjunction with between-year variance at each location provides vital information on adaptation at specific locations, and is an appropriate selection tool for genotype registration and recommendation. Consideration of GE interaction, using joint regression and clustering, may indicate genotypes equivalent or marginally superior to the check. Key words: Barley, Hordeum vulgare L., genotype-environment interaction, grain yield

Genotype × environment interactions for two-row barley grain yield and implications for selection of test locations

1995 ◽  
Vol 75 (3) ◽  
pp. 571-575 ◽  
Author(s):  
K. W. May ◽  
G. C. Kozub
Keyword(s):  
Cluster Analysis ◽  
Grain Yield ◽  
Barley Grain ◽  
Significant Loss ◽  
Data Sets ◽  
Environment Interaction ◽  
Hordeum Vulgare L ◽  
Canadian Prairie ◽  
Genotype Environment Interaction ◽  
Yield Responses

The response of barley grain yield to Canadian prairie environments was studied to evaluate genotype × environment interactions, and to group locations according to genotype response, which identifies locations whose removal would not significantly affect the validity of conclusions. The data were also used to illustrate a method for handling a large genotype × location × year data base with few common entries. Information from 20 test locations with 11–19 annual entries over 7 yr was used. Analyses of variance of data sets with three to seven common entries in adjacent years indicated significant genotype × location × year interactions for grain yield. The structure of the genotype × location interaction was studied using cluster analysis within each year and summarized over years. Cluster analysis using individual years allowed more test entries and should increase the reliability of the conclusions compared to that using average over years with few entries. Clustering identified six locations with dissimilar genotype yield responses. In the 7 yr, 19 pairs of locations usually clustered together. The 19 pairs involved eight of the 20 locations and most were in the same geographical region. Some of the eight locations could be eliminated without significant loss of reliability. Rankings of test entries for grain yield at locations within a cluster were generally similar when genotype effects were larger. Key words:Barley, Hordeum vulgare L, genotype-environment interaction, grain yield

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.

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 Genotype-environment interaction in common bean cultivars with carioca grain, recommended for cultivation in Brazil in the last 40 years

2015 ◽  
Vol 15 (4) ◽  
pp. 244-250 ◽  
Author(s):  
Leiri Daiane Barili ◽  
Naine Martins do Vale ◽  
Adalgisa Lelis do Prado ◽  
José Eustáquio de Souza Carneiro ◽  
Fabyano Fonseca e Silva ◽  
...  
Keyword(s):  
Grain Yield ◽  
Common Bean ◽  
Block Design ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Breeding Programs ◽  
The Past ◽  
Genotype Environment Interaction ◽  
Randomized Block Design ◽  
New Cultivars

Abstract The purpose of this study was to analyze the effect of genotype-environment interaction (GE) on common bean cultivars with carioca grain (cream-colored beans with light brown stripes), recommended for cultivation by different Brazilian research institutions in the last 40 years. The experiments were carried out with 40 cultivars in four different environments (Coimbra and Viçosa, in the dry and winter seasons of 2013) using a randomized block design with three replications. The results showed an effective increase in grain yield resulting from the use of new cultivars from different Brazilian breeding programs in the past four decades. In addition, the analysis of the GE interaction indicated that the cultivars recommended after 2005 combined high mean grain yield, wide adaptability and high or stability.

Success of a selection program for increasing grain yield of two-row barley lines and evaluation of the modified augmented design (type 2)

1995 ◽  
Vol 75 (4) ◽  
pp. 795-799 ◽  
Author(s):  
K. W. May ◽  
G. C. Kozub
Keyword(s):  
Hordeum Vulgare ◽  
Grain Yield ◽  
Research Centre ◽  
Environment Interaction ◽  
Hordeum Vulgare L ◽  
Genotype Environment Interaction ◽  
Large Numbers ◽  
Augmented Design ◽  
Design Type

The modified augmented design (MAD) (type 2), which adjusts for environmental heterogeneity when large numbers of test lines are being evaluated in non-replicated plots, has been used in the barley (Hordeum vulgare L.) breeding program at the Lethbridge Research Centre. The superiority of test lines selected from two series of F7 test lines selected using the MAD were evaluated in replicated MAD tests (F8) and superior selections were further evaluated in standard replicated designs (F9). When adjustment was indicated for individual MAD tests, the generally high relative efficiencies (RE) confirmed the effectiveness of adjustment in reducing the error variation for yield. The need for adjustment and the adjustment method differed among replicates of the MAD conducted on adjacent blocks of land. The RE from replicated MAD tests indicated the effectiveness of the MAD for reducing the variability in the tests of F8 material. Correlations of the ranks of test lines in different replicates of MAD indicated that more of the same lines in each replicate would be designated as superior with adjustment for heterogenity. The lack of significant positive rank correlations among environments and clearly identifiable improvement in the mean yield was probably the result of the narrowing range in grain yield of the test lines as well as a genotype × environment interaction. Key words: Modified augmented design, Hordeum vulgare, barley, screening

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.

Using AMMI approach to delineate genotype by environment interaction and stability of barley (Hordeum vulgare L.) genotypes under northern Indian shivalik hill conditions

2020 ◽  
Vol 80 (03) ◽  
Author(s):  
Om Prakash Yadav ◽  
A. K. Razdan ◽  
Bupesh Kumar ◽  
Praveen Singh ◽  
Anjani K. Singh
Keyword(s):  
Hordeum Vulgare ◽  
Grain Yield ◽  
Principal Component ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Hordeum Vulgare L ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Principal Component Axis ◽  
Ammi Analysis

Genotype by environment interaction (GEI) of 18 barley varieties was assessed during two successive rabi crop seasons so as to identify high yielding and stable barley varieties. AMMI analysis showed that genotypes (G), environment (E) and GEI accounted for 1672.35, 78.25 and 20.51 of total variance, respectively. Partitioning of sum of squares due to GEI revealed significance of interaction principal component axis IPCA1 only On the basis of AMMI biplot analysis DWRB 137 (41.03qha–1), RD 2715 (32.54qha–1), BH 902 (37.53qha–1) and RD 2907 (33.29qha–1) exhibited grain yield superiority of 64.45, 30.42, 50.42 and 33.42 per cent, respectively over farmers’ recycled variety (24.43qha–1).

scholarly journals Genotype x Environment Interaction and Yield Stability of Several Yield Components Among Adapted Rice Cultivars in West Sumatra

Zuriat ◽  
2015 ◽  
Vol 18 (2) ◽  
Author(s):  
Aslim Rasyad ◽  
Azwir Anhar
Keyword(s):  
Grain Yield ◽  
Yield Components ◽  
Oryza Sativa L ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Genotype By Environment ◽  
West Sumatra ◽  
Commercial Purpose ◽  
1000 Grain Weight ◽  
Interaction Variance

Genotype by environment (GE) interaction and genotype stability of a trait in rice (Oryza sativa L.) are very important for plant breeders in making decision regarding the development and evaluation of new cultivars as well as for farmers in selecting suitable cultivars to be planted for commercial purpose. Yield components including panicles number plant–1, number of grains panicle–1, 1000-grain weight, and grain yield of five locally adapted cultivars of rice were evaluated at three locations in West Sumatera. The data were used to determine GE interaction variance components and stability of the traits. There were significant effects of locations on yield and some yield components except number of panicles plant–1. The cultivars differed significantly in all yield components but not in grain yield. The influence of GE interaction was highly significant on all yield components and grain yield. The magnitude of GE interaction variance component was greater than that of location for all traits. These data suggested that genotypes performed differently among the locations and were not stable with respect to the locations, so that farmers should select a suitable cultivar to be grown in the area of production.

scholarly journals Delineation of Genotype × Environment Interaction for Identification of Stable Genotypes to Grain Yield in Mungbean

2020 ◽  
Vol 2 ◽  
Author(s):  
Santhi Madhavan Samyuktha ◽  
Devarajan Malarvizhi ◽  
Adhimoolam Karthikeyan ◽  
Manickam Dhasarathan ◽  
Arumugam Thanga Hemavathy ◽  
...  
Keyword(s):  
Grain Yield ◽  
Tamil Nadu ◽  
Stability Index ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype Environment Interaction ◽  
South Indian ◽  
Indian State ◽  
Wide Adaptation ◽  
High Yields

In the present study, fifty-two mungbean (Vigna radiata) genotypes were evaluated for seven morphological traits at three different environments in South Indian state Tamil Nadu, namely Virinjipuram (E1), Eachangkottai (E2), and Bhavanisagar (E3) during Kharif 2017, 2018, and 2019, respectively. The data collected were subjected to variability and correlation analyses, followed by stability analysis using additive main effects and multiplicative interaction (AMMI) model, genotype and genotype × environment interaction effects (GGE) biplot. Variablility was observed among the genotypes for the following traits viz., plant height, days to fifty per cent flowering, number of pods per plant, pod length, number of seeds per pod, hundred seed weight and grain yield. Correlation analysis showed that the trait number of pods per plant was significantly associated with grain yield. The G × E was smaller than the genetic variation of grain yield as it portrayed the maximum contribution of genotypic effects (61.07%). GGE biplot showed E3 as a highly discriminating and representative environment. It also identified environment-specific genotypes viz., EC 396111 for E1, EC 396125 for E2 and EC 396101 for E3 environments. The genotypes with minimum genotype stability index (GSI) viz., V2802BG (7), HG 22 (13), and EC 396098 (13) were observed with wide adaptation and high yields across all the three environments. In summary, we identified stable genotypes adapted across environments for grain yield. These genotypes can be used as parent/pre-breeding materials in future mungbean breeding programs.

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