scholarly journals Multiattribute Response of Maize Genotypes Tested in Different Coastal Regions of Brazil

2011 ◽  
Vol 2011 ◽  
pp. 1-6
Author(s):  
Lúcio Borges de Araújo ◽  
Mario Varela Nualles ◽  
Mirian Fernandes Carvalho Araújo ◽  
Carlos Tadeu dos Santos Dias
Keyword(s):  
Principal Components ◽  
Environment Interaction ◽  
Coastal Regions ◽  
Multivariate Response ◽  
Genotype Environment Interaction ◽  
Maize Genotypes ◽  
The Mean ◽  
Components Analysis ◽  
And Performance ◽  
Main Effects

This work applies the three mode principal components analysis to analyze simultaneously the multiple attributes; to fit of models with additive main effects and multiplicative interaction effects (AMMI models) and the regressions models on sites (SREG models); to evaluate, respectively, the multivariate response of the genotype × environment interaction and the mean response of 36 genotypes of corn tested in 4 locations in Brazil. The results were presented by joint plots to identify the best genotypes for their adaptability and performance in the set of attributes.

scholarly journals Genotype × Environment Interaction of Yield and Grain Quality Traits of Maize Hybrids in Greece

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 357
Author(s):  
Nikolaos Katsenios ◽  
Panagiotis Sparangis ◽  
Sofia Chanioti ◽  
Marianna Giannoglou ◽  
Dimitris Leonidakis ◽  
...  
Keyword(s):  
Principal Components ◽  
Mean Value ◽  
Quality Characteristic ◽  
Environment Interaction ◽  
High Concentration ◽  
Maize Hybrids ◽  
Genotype Environment Interaction ◽  
Maize Genotypes ◽  
Ammi Analysis ◽  
The Stability

The interaction of genotype by the environment is very common in multi-environment trials of maize hybrids. This study evaluates the quantity and the quality of grain production and the stability of four maize genotypes in a field experiment that was conducted in five different locations for two years. In order to make a reliable evaluation of the performance of genotypes in the environments, principal components analysis (PCA) was used to investigate the correlation of the yield, soil properties and quality characteristics, while the additive main effects and multiplicative interaction (AMMI) analysis detected the narrow adaptations of genotypes at specific mega-environments. For the yield, AMMI analysis indicated that a group of five environments (ENV1, ENV8, ENV6 ENV10 and ENV9) gave higher yields than the mean value and at the same time had low first interaction principal components axis (IPC1) scores, indicating small interactions. Regarding protein and fiber contents, ENV1 and ENV2, gave the highest values and this could be attributed to the high concentration rates of nutrients like Mg, Ca and the soil texture (C). Specifically for the protein, the results of the analysis indicated that certain environment would provide more protein content, so in order to obtain higher grain protein, growers should grow in certain locations in order to improve the content of this quality characteristic, certain genotypes should be used in certain environments.

scholarly journals Evaluation of promising maize hybrids in different Regions of Bangladesh

2018 ◽  
Vol 43 (2) ◽  
pp. 301-307
Author(s):  
ANMS Karim ◽  
S Ahmed ◽  
AH Akhi ◽  
MZA Talukder ◽  
TA Mujahidi
Keyword(s):  
Significant Variation ◽  
Interaction Model ◽  
Environment Interaction ◽  
Maize Hybrids ◽  
Genotype Environment Interaction ◽  
Single Cross ◽  
The Mean ◽  
Different Response ◽  
Main Effects ◽  
Selection Of

Nine single cross promising maize hybrids and three check varieties (NK40, BHM9 and 900 MGold) were assessed for genotype environment interaction (GEI) and stability for the selection of promising one(s) in seven different locations of Bangladesh. The AMMI (additive main effects and multiplicative interaction) model was used to analyze the genotype-environment interaction over seven locations to select desired hybrid having higher yield and other potential attributes. Regarding genotypes (G), significant variation was found in all the characters except yield while environment (E) was found significant for all the characters. The environment of Gazipur was poor; Jamalpur, Hathazari, Ishurdi, Rahmatpur, Burirhat, and Jessore were positive environments for tested maize hybrids. Considering the mean, bi and S2di for all the parameters, it was evident that all the genotypes showed different response of adaptability under different environmental conditions. Among the hybrids E12 (900MG), E7(CML502 × CML491), E1(BIL95 × BIL28), E10 (NK40) and E2 (BIL95 × BIL31) were exhibited high yielder.Considering bi ̴1(regression co-efficient), S2di ̴ 0 (deviation from regression)and mean versace IPCA1 (fig1)indicated that hybrids E7and E1showed the higher yield as well as stable across locations.Bangladesh J. Agril. Res. 43(2): 301-307, June 2018

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 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 Selection of a suitable definition of environment for the estimation of genotype × environment interaction in the weaning weight of beef cattle

2008 ◽  
Vol 53 (No. 10) ◽  
pp. 407-417 ◽  
Author(s):  
L. Vostrý ◽  
J. Přibyl ◽  
V. Jakubec ◽  
Z. Veselá ◽  
I. Majzlík
Keyword(s):  
Beef Cattle ◽  
Evaluation Model ◽  
Economic Value ◽  
Information Criterion ◽  
Ratio Test ◽  
Residual Variance ◽  
Environment Interaction ◽  
Weaning Weight ◽  
Genotype Environment Interaction ◽  
And Performance

Genotype by environment interactions for weaning weight in beef cattle were tested using several definitions of environments. Four breeds of beef cattle (Hereford, Aberdeen Angus, Beef Simmental, and Charolais) were represented. The environments were defined according to five criteria: altitude, production areas, economic value of the land, less favourable areas, and performance levels of a breed within herds. Ten mixed models were compared including the effects of direct and maternal genetics, herd-year-season, maternal permanent environmental, breed, environment, genotype × environment interaction, sex of calf, and age of dam. The suitability of the models was tested by Akaike’s Information Criterion, likelihood ratio test, and magnitude of the residual variance. The most suitable definitions of environment were less favoured areas and herd levels of performance. Estimates of direct heritability ranged from 0.07 to 0.19. Genotype × environment interactions should be included in a genetic evaluation model for interbreed comparisons of beef cattle in the Czech Republic.

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.

scholarly journals Genotype-environment interaction, genetic trends, and performance dissimilarity of Nellore herds raised in three different environmental gradients

2018 ◽  
Vol 39 (1) ◽  
pp. 349
Author(s):  
Julio Cesar de Souza ◽  
Fabio Rafael Leão Fialho ◽  
Marcos Paulo Gonçalves Rezende ◽  
Carlos Henrique Cavallari Machado ◽  
Mariana Pereira Alencar ◽  
...  
Keyword(s):  
Weight Gain ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Environmental Gradients ◽  
Superior Performance ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
And Performance ◽  
Main Components ◽  
Genetic Trends

The objectives of this work were to evaluate the genotype-environment interaction, and estimate genetic parameters, genetic trends, and performance dissimilarity-weight gain from birth to weaning (WGBW), adjusted weight to 205 days (W205), weight gain from weaning to 18 months of age (WG18), and adjusted weight to 550 days (W550)-in Nellore animals born between 1986 and 2012, and raised in pasture-based system in three different environmental gradients in Brazil. Data of 62,001 animals-11,729 raised in the Alto Taquari/Bolsão region (ATBR), 21,143 raised in the Campo Grande/Dourados region (CGDR) and 29,129 raised in the western São Paulo/Paraná region (SPPR) in Brazil-were used. The contemporary groups were defined by sex, location, and birth year and season, with at least nine individuals, two different environments, and breeding bulls with at least five progenies. The statistical model contained the direct additive and residual genetic effects (random effects), and environmental and contemporary group effects (fixed effects). Genetic parameters, genotype-environment interaction and genetic trends were estimates using animal model (uni- and/or bi- traits). The level of similarity between regions was evaluated using principal components. The animals raised in the CGDR had superior performance regarding the traits evaluated. The direct heritability estimates ranged from 0.39 to 0.44 (WGBW), 0.41 to 0.45 (W205), 0.42 to 0.55 (WG18) and 0.60 to 0.62 (W550). The maternal heritability of the traits ranged from 0.20 (WGBW), 0.12 to 0.18 (W205), 0.00 to 0.06 (WG18) and 0.02 to 0.22 (W550). According to the Spearman correlation, the ranking of the breeding bulls in the regions evaluated were different. The mean of Euclidean distance indicated low similarity between ATBR and CGDR (43.20), and ATBR and SPPR (29.24). CGDR and SPPR presented similarity of 17.84. The breed values increased over the years in the traits evaluated. The cumulative variance percentage of the first two main components explained 99.99% variation among the regions, and the weight gains of the animals were the most important to differentiate the regions. A genotype-environment interaction was found for the traits evaluated, thus, the breeding bull selected with superior genetic merit for one region might not be the best for others.

scholarly journals Genotype Environment Interaction in Grass Pea (Lathyrus sativusL.) Lines

2009 ◽  
Vol 2009 ◽  
pp. 1-7 ◽  
Author(s):  
G. B. Polignano ◽  
V. Bisignano ◽  
V. Tomaselli ◽  
P. Uggenti ◽  
V. Alba ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Plant Height ◽  
Seed Yield ◽  
Principal Components ◽  
Environment Interaction ◽  
Grass Pea ◽  
Genotype Environment Interaction ◽  
Marginal Areas ◽  
Different Seasons ◽  
The Stability

Eight grass pea lines grown in three different seasons were evaluated for the stability of seed yield, 100 seeds weight, flowering time, plant height, and biomass. Significant differences existed among years, lines, and lines years interaction for all traits except for 100 seeds weight. Two methods of multivariate analysis cluster and principal components were utilized to determine: firstly, whether a pattern existed among lines in their response across years and secondly to examine the relationships among them. In both analyses, each line was presented as a vector whose elements were given by the performance of lines in each year. The analyses used arranged the lines into groups that were differentiable in terms of performances and stability. Our results provide useful information to aid the choice of grass pea lines in the Mediterranean marginal areas.

scholarly journals Identification of pigeonpea genotypes with wider adaptability to rainfed environments through AMMI and GGE biplot analyses

2021 ◽  
Vol 81 (01) ◽  
pp. 63-73
Author(s):  
M. V. Nagesh Kumar ◽  
V. Ramya ◽  
C. V. Sameer Kumar ◽  
T. Raju ◽  
N. M. Sunil Kumar ◽  
...  
Keyword(s):  
Cajanus Cajan ◽  
Large Scale ◽  
Interaction Model ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Rainfed Agriculture ◽  
Genotype By Environment ◽  
Genotype Environment Interaction ◽  
Wide Range ◽  
Main Effects

Pigeonpea [Cajanus cajan (L.) Millspaugh] is an important pulse crop grown under Indian rainfed agriculture. Twenty eight pigeonpea genotypes were tested for stability and adaptability across ten rainfed locations in the States of Telangana and Karnataka, India using AMMI (additive main effects and multiplicative interaction) model and GGE (genotype and genotype by environment) biplot method. The grain yields were significantly affected by environment (56.8%) followed by genotype × environment interaction (27.6%) and genotype (18.6%) variances. Two mega environments were identified with several winning genotypes viz., ICPH 2740 (G15), TS 3R (G10), PRG 176 (G8) and ICPL 96058 (G22). E2 (Gulbarga, Karnataka), E3 (Bidar, Karnataka) and E6 (Vikarabad, Telangana) were the most discriminating environments. Genotypes, ICPH 2740, PRG 176 and TS 3R were the best cultivars in all the environments whereas PRG 158 (G9), ICPL 87119 (G12), ICPL 20098 (G19) and ICPL 96058 (G22) were suitable across a wide range of environments. Genotypes, ICPH 2740 and PRG 176 can be recommended on a large scale to the farmers with small holdings to enhance pigeonpea productivity and improve the food security

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