GENOTYPE × ENVIRONMENT INTERACTION IN CACTUS PEAR (OPUNTIA SPP.), ADDITIVE MAIN EFFECTS AND MULTIPLICATIVE INTERACTION ANALYSIS OF FRUIT YIELD

2006 ◽  
pp. 97-104 ◽  
Author(s):  
J. Potgieter ◽  
M. Smith
Keyword(s):  
Interaction Analysis ◽  
Fruit Yield ◽  
Environment Interaction ◽  
Cactus Pear ◽  
Multiplicative Interaction ◽  
Genotype Environment Interaction ◽  
Main Effects ◽  
Opuntia Spp

scholarly journals Evaluation of maize hybrids and environmental stratification by the methods AMMI and GGE biplot

2010 ◽  
Vol 10 (3) ◽  
pp. 247-253 ◽  
Author(s):  
Rogério Lunezzo de Oliveira ◽  
Renzo Garcia Von Pinho ◽  
Márcio Balestre ◽  
Denys Vitor Ferreira
Keyword(s):  
Interaction Analysis ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Multiplicative Interaction ◽  
Maize Hybrids ◽  
Growing Seasons ◽  
Single Cross ◽  
Predictive Correlation ◽  
Environmental Stratification ◽  
Main Effects

The purpose of this study was to evaluate yield stability, adaptability and environmental stratification by the methods AMMI (Additive Main Effects and Multiplicative Interaction Analysis) and GGE (Genotype and Genotypes by Environment Interaction) biplot and to compare the efficiency of these methods. Data from the evaluation of 20 experimental single-cross and three commercial hybrids and 11 locations, in two growing seasons, 2005/2006 and 2006/2007 were used. Analyses of variance, adaptability, stability and environmental stratification were performed. A better combination of adaptability and stability was observed in the hybrids 10 and 16, according to the graphics of AMMI and GGE biplot methods, respectively. The number of locations could be reduced by 28% based on stratification. The predictive correlation of the AMMI and GGE methods was 0.88 and 0.86, respectively. The results showed that it is possible to reduce the number of evaluation sites; AMMI tended to be more accurate than GGE analysis.

scholarly journals Genotype environment interaction analysis for fruit yield in okra (Abelmoschus esculentus L.) under alkaline environments

2021 ◽  
Vol 81 (01) ◽  
pp. 101-110
Author(s):  
Satish Kumar Sanwal ◽  
Anita Mann ◽  
Hari Kesh ◽  
Gurpreet Kaur ◽  
Raj Kumar ◽  
...  
Keyword(s):  
Interaction Analysis ◽  
Stability Index ◽  
Genotype By Environment Interaction ◽  
Fruit Yield ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Alkaline Environments ◽  
Prone Area ◽  
Marketable Fruit ◽  
Ph Range

Twenty four Okra genotypes were evaluated for marketable fruit yield and its related traits for genotype environment interaction during 2015-16 and 2016-17. The genotypes were exposed to alkaline environment with a pH range of 8.0±0.2, 8.5±0.2, 9.0±0.2 and 9.5±0.2. A significant level of deviation in expression of different traits was observed in all the genotypes with increasing pH. Based on Additive Main Effects and Multiplicative Interaction (AMMI), Genotype and Genotype Environment Interaction (GGE) biplot, Wrick’s ecovalence (Wi2 ), AMMI Stability Value (ASV) and Yield Stability Index (YSi) stable genotypes with high fruit yield were identified over the eight environments. The combined AMMI analysis of variance indicated that genotype main effect, environment and genotype-by-environment interaction effects showed variation of 19.83%, 63.07% and 17.10%, respectively for fruit yield. On the basis of different stability measures, VRO-112, VRO-110, Kashi Kranti, VROB178, AE-70 and VRO-108 were differentiated as high yielding and stable genotypes over the tested environments. This study will be helpful for selecting alkali tolerant okra parents for further breeding programme and recommending the suitable genotypes for alkalinity prone area

scholarly journals Evaluation of Stability of Field Pea genotypes in response to Mycosphaerella Pinodes Infection

2014 ◽  
Vol 65 (1) ◽  
pp. 79-85
Author(s):  
Lech Boros
Keyword(s):  
Quantitative Traits ◽  
Mixed Model ◽  
Field Experiments ◽  
Interaction Analysis ◽  
Regression Function ◽  
Mycosphaerella Pinodes ◽  
Environment Interaction ◽  
Linear Regression Function ◽  
Genotype Environment Interaction ◽  
Main Effects

Abstract Interaction of genotypes with environment for quantitative traits among them certain disease resistance makes difficult choice of proper genotypes for breeding proposes and may affects further cultivation effects. The aim of this study was assessment of stability of reaction to Mycosphaerella pinodes infection for the set of pea genotypes in four years field experiments with vary epidemic pressure. The Sheffé-Caliński mixed model and the Caliński-Kaczmarek joint regression model for genotype-environment interaction analysis was applied. Tested pea genotypes were grouped into two categories; responding stable to M. pinodes (reacting proportionally to changed environment) and unstable ones (showing significant interaction with environment). The unstable genotypes reacted irregularly to environments (not able to describe the reaction to M. pinodes by any linear regression function). Pea genotypes PI 142441, PI 142442, PI 404221, PI 413691, cv. Radley and Bohun were characterized by high negative main effects (most resistant) for disease severity and showed stable response to M. pinodes infection. Stability of mycospharealla blight reactions was not associated with the level of resistance in the cultivars tested.

scholarly journals Key locations for soybean genotype assessment in South Brazil region

2020 ◽  
Vol 41 (3) ◽  
pp. 767
Author(s):  
Samuel Cristian Dalló ◽  
Andrei Daniel Zdziarski ◽  
Leomar Guilherme Woyann ◽  
Anderson Simionato Milioli ◽  
Rodrigo Zanella ◽  
...  
Keyword(s):  
Interaction Analysis ◽  
Block Design ◽  
Environment Interaction ◽  
Santa Barbara ◽  
Breeding Programs ◽  
Soybean Genotype ◽  
Discriminating Power ◽  
Genotype Environment Interaction ◽  
Program Costs ◽  
Main Effects

Mitigating the high costs of soybean breeding programs requires constant improvement of all the involved processes. Identifying representative and discriminating test locations, as well as excluding redundant and/or non-representative locations, makes it possible to select genotypes with more accuracy while reducing the costs of the multi-environment trials (MET). Therefore, this study had three objectives: to evaluate the representativeness and discriminating power of test locations; to identify similar test locations for each Edaphoclimatic Region (ECR) and locations that did not contribute to genotype evaluation; and to recommend the best locations for evaluating MET in order to reduce breeding program costs in the soybean macro regions 1 (M1) and 2 (M2). Grain yield (GY) data from ‘Value-for-Cultivation-and-Use’ (VCU) trials obtained during the 2012-2016 crop seasons were used, totaling 132 environments (location x year) and 43 genotypes. The experiments were arranged in a randomized complete block design with three replications. Representative and discriminant locations were identified by GGL (genotype main effects plus genotype × location interaction) + GGE (genotype main effects plus genotype × environment interaction) analysis, using GGEbiplot software. Representative and discriminant locations were identified for each ECR and can be used as core locations for breeding programs. Similarly, locations that were not representative and discriminant, or that present redundancy in the results, should be excluded from or replaced in MET. The most recommended locations for conducting VCU trials in M1 are: Cachoeira do Sul (ECR 101); Ronda Alta, Passo Fundo, Santa Bárbara do Sul, and Ciríaco (ECR 102); and Castro (ECR 103). For M2, the most suitable locations are Rolândia, Marechal Cândido Rondon, Campo Mourão, Santa Terezinha de Itaipu, Palotina, Floresta, and Londrina (ECR 201); Naviraí (ECR 202); and Ponta Porã and Maracajú (ECR 204).

scholarly journals Análises Biplot: conceitos, interpretações e aplicações

2012 ◽  
Vol 42 (8) ◽  
pp. 1404-1412 ◽  
Author(s):  
Raphael Rossi Silva ◽  
Giovani Benin
Keyword(s):  
Environment Interaction ◽  
Gge Biplot ◽  
Multiplicative Interaction ◽  
Genotype Environment Interaction ◽  
Main Effects

A seleção e recomendação de genótipos superiores são dificultadas devido à ocorrência da interação genótipo e ambiente. Nesse contexto, as análises biplot têm sido cada vez mais utilizadas na análise de dados agronômicos, com interações de natureza complexa. Entretanto, as particularidades existentes no gráfico biplot dificultam sua interpretação, podendo induzir o pesquisador a erros. Assim, este artigo de revisão discute a aplicabilidade e a interpretação gráfica dos modelos AMMI (Additive Main effects and Multiplicative Interaction) e GGE biplot (genotype main effects + genotype environment interaction) destas análises no gráfico biplot. Também, visa a desmistificar a necessidade de comparação entre ambas as metodologias. Discute-se quanto à escolha da metodologia mais adequada, levando em consideração a informação requerida e os objetivos do pesquisador.

Genotype-environment interaction and stability analysis in hybrid rice: an application of additive main effects and multiplicative interaction

2013 ◽  
Vol 42 (1) ◽  
pp. 73-82 ◽  
Author(s):  
M Umma Kulsum ◽  
M Jamil Hasan ◽  
Anowara Akter ◽  
Hafizar Rahman ◽  
Priyalal Biswas
Keyword(s):  
Grain Yield ◽  
Hybrid Rice ◽  
Amylose Content ◽  
Environment Interaction ◽  
Multiplicative Interaction ◽  
Hybrid Combination ◽  
Stability Performance ◽  
Genotype Environment Interaction ◽  
Wide Range ◽  
Main Effects

Genotype-environment interaction and stability performance were investigated on amylase, protein and grain yield with 13 hybrid rice promising combinations in five environments. The combined ANOVA showed that the mean sum of square due to genotype (G), environment (E) and G × E interaction were significant for amylose content, protein content and grain yield. This suggests a number of variabilities among the genotypes and environments as and the indicated genotypes interacted significantly with environments. The Additive Main Effects and Multiplicative Interaction (AMMI) biplot for yield clearly indicated that the hybrids BR10A/BR12R, II32A/BR15R, II32A/BR16R, II32A/BR10R, BR9A/BR15R, BRRI hybrid dhan2 and BRRI hybrid dhan3 were high yielding, stable and had general adaptability at all locations. The AMMI estimation had a profound effect in producing sharp and stratified ranking patterns and on this basis BRRI hybrid dhan2 would be considered more adapted to a wide range of environments than the rest of the genotypes. The biplot technique was used to identify appropriate genotype to special locations. This consideration on the basis of average yield for specific genotype to the specific location. The hybrid combination II32A/BR12R was more suitable for Gazipur location and the hybrid combination BR10A/BR13R was considered for Comilla region. Barisal was more stable site than other location for grain yield due to IPCA score near zero which had no interaction effect. DOI: http://dx.doi.org/10.3329/bjb.v42i1.15867 Bangladesh J. Bot. 42(1): 73-81, 2013 (June)

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