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 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

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).

Genotype × Environment Interaction in Psychopathology: Fact or Artifact?

2006 ◽  
Vol 9 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Lindon J. Eaves
Keyword(s):  
Candidate Genes ◽  
Biological Significance ◽  
Simulated Data ◽  
Threshold Value ◽  
Environment Interaction ◽  
Additive Effects ◽  
Mixture Of Distributions ◽  
Genotype Environment Interaction ◽  
Genes And Environment ◽  
Main Effects

AbstractRecent studies have claimed to detect interaction between candidate genes and specific environmental factors (Genotype × Environment interaction, G × E) in susceptibility to psychiatric disorder. The objective of the present study was to examine possible artifacts that could explain widely publicized findings. The additive effects of candidate genes and measured environment on liability to disorder were simulated under a model that allowed for mixture of distributions in liability conditional on genotype and environment. Simulated liabilities were dichotomized at a threshold value to reflect diagnosis of disorder. Multiple blocks of simulated data were analyzed by standard statistical methods to test for the main effects and interactions of genes and environment on outcome. The main outcome of this study was simulated liabilities and diagnoses of major depression and antisocial behavior. Analysis of the dichotomized data by logistic regression frequently detected significant G × E interaction even though none was present for liability. There is therefore reason to question the biological significance of published findings.

Genetic main effect and genotype×environment interaction for cooking quality traits in a diallel set of Indica rice (Oryza sativa L.) varieties

2010 ◽  
Vol 61 (6) ◽  
pp. 475 ◽  
Author(s):  
Peyman Sharifi ◽  
Hamid Dehghani ◽  
Ali Moumeni ◽  
Mohammad Moghaddam
Keyword(s):  
Oryza Sativa ◽  
Field Experiments ◽  
Cooking Quality ◽  
Oryza Sativa L ◽  
Block Design ◽  
Environment Interaction ◽  
Quality Traits ◽  
Narrow Sense ◽  
Ge Interaction ◽  
Main Effects

Genetic main effects and genotype × environment (GE) interactions were determined for cooking quality traits of rice (Oryza sativa L.) using a complete diallel cross of seven. The field experiments were carried out over 2 years as a randomised complete block design with two replications. Amylose content (AC), gel consistency (GC) and gelatinisation temperature (GT) were affected by both genetic effects and GE interaction. Grain elongation (GEL) was found to be controlled by genetic main effects and general combining ability (GCA) × environment interaction. The high magnitude of GCA variances for all traits indicated that additive effects were more prominent in the determination of these characteristics. Narrow-sense heritabilities for AC, GT, GC and GEL were 61.21, 60.83, 29.98 and 52.29%, respectively. Among the genetic and GE interaction effects, GCA and GCA × environment were the main components for all traits. Relatively large narrow-sense heritabilities for AC, GT and GEL indicated that selection for these traits could be possible. However, due to the significance of genotype × year effects for AC, GT, and GEL genetic materials should be evaluated over several years in breeding programs.

scholarly journals Genotype by environment interaction for seeds yield in pea (Pisum sativum L.) using additive main effects and multiplicative interaction model

Euphytica ◽  
2019 ◽  
Vol 215 (11) ◽  
Author(s):  
Jan Bocianowski ◽  
Jerzy Księżak ◽  
Kamila Nowosad
Keyword(s):  
Pisum Sativum ◽  
Block Design ◽  
Interaction Model ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Multiplicative Interaction ◽  
Genotype By Environment ◽  
Randomized Complete Block Design ◽  
Main Effects

Abstract The objective of this study was to evaluate the genotype by environment interaction using the additive main effects and multiplicative interaction model for seeds yield of pea cultivars grown in Poland. Twelve pea (Pisum sativum L.) cultivars: Bohun, Boruta, Cysterski, Ezop, Kavalir, Lasso, Medal, Santana, Tarchalska, Terno, Wenus and Zekon were evaluated in 20 environments (ten locations in 2 years). The experiment was laid out as randomized complete block design with three replicates. Seeds yield ranged from 26.10 dt ha−1 (for Wenus in Radostowo 2011) to 79.73 dt ha−1 (for Lasso in Słupia 2010), with an average of 50.70 dt ha−1. AMMI analyses revealed significant genotype and environmental effects as well as genotype-by-environment interaction with respect to seeds yield. In the analysis of variance, 89.19% of the total seeds yield variation was explained by environment, 1.65% by differences between genotypes, and 8.33% by GE interaction. The cultivar Terno is the highest stability. The cultivar Tarchalska is recommended for further inclusion in the breeding program because its stability and the highest averages of seeds yield.

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

EFFECTS OF GENOTYPE-ENVIRONMENT INTERACTION ON BREAD WHEAT QUALITY IN WESTERN CANADA

1977 ◽  
Vol 57 (1) ◽  
pp. 185-191 ◽  
Author(s):  
R. J. BAKER ◽  
F. G. KOSMOLAK
Keyword(s):  
Triticum Aestivum L ◽  
Western Canada ◽  
Loaf Volume ◽  
Environment Interaction ◽  
Hard Red Spring Wheat ◽  
Flour Protein ◽  
Genotype Environment Interaction ◽  
Protein Flour ◽  
Flour Yield ◽  
Composite Samples

Means, variances and correlations were used in the assessment of the importance of environment and of genotype–environment interaction in determining eight traits which relate to milling and baking quality in hard red spring wheat (Triticum aestivum L.). The study included two composite samples, representing different geographic areas within Western Canada, of 20–30 lines in each of four trials. Differences between environments were found in all traits. It was concluded that genotype–environment interaction was most important in determining mixograph development time, falling number and remix loaf volume, less important in determining farinograph absorption, and least important in determining flour protein, flour yield, grinding time and sedimentation value.

Performance of tropical early-maturing maize cultivars in multiple stress environments

2010 ◽  
Vol 90 (6) ◽  
pp. 831-852 ◽  
Author(s):  
B. Badu-Apraku ◽  
A. Menkir ◽  
S. Ajala ◽  
R. Akinwale ◽  
M. Oyekunle ◽  
...  
Keyword(s):  
Drought Stress ◽  
Striga Hermonthica ◽  
Environment Interaction ◽  
Maize Productivity ◽  
Maize Cultivars ◽  
Genotype Environment Interaction ◽  
Maize Varieties ◽  
Early Maturing ◽  
Use Efficiency ◽  
Growing Conditions

Maize (Zea mays L.) production in west Africa (WA) is constrained by drought, Striga hermonthica infestation and low soil nitrogen (N). Maize varieties resistant to Striga, drought, and low N are ideal for WA, but genotype × environment interaction on these traits are usually significant due to differential responses of cultivars to growing conditions. Three studies were conducted from 2007 to 2009 at five locations in Nigeria to evaluate the performance of selected early-maturing cultivars under drought stress versus well-watered, Striga-infested versus Striga-free, and in low- versus high-N environments. Drought stress reduced grain yield by 44%, Striga infestation by 65%, and low N by 40%. GGE biplot analysis showed that the genotypes TZE-W DT STR C4, Tillering Early DT, TZE-W DT STR QPM C0 and TZE-Y DT STR C4 performed relatively well in all study environments. TZE-W DT STR C4 and TZE Comp3 C1F2 were outstanding under drought, TZE-W DT STR C4, EVDT-W 99 STR QPM C0 and TZE-W DT STR QPMC0 under Striga infestation and Tillering Early DT, EVDT 97 STRC1, TZE-W DT STR C4, and TZE Comp3 C3 under N deficiency. Maize productivity in WA can be significantly improved by promoting cultivation of genotypes that combine high resistance/tolerance to Striga and drought with improved N-use efficiency.

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