Genotype × Environment Interaction and Seed Yield, Oil and Protein Contents Stability of Soybean Genotypes

2005 ◽  
Vol 16 (2) ◽  
pp. 41-51
Author(s):  
F. EL-NAKHLAWY
Keyword(s):  
Seed Yield ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Soybean Genotypes

scholarly journals Yield Stability of Soybean Genotypes in Tropical Environments based on Genotype and Genotype-by-Environment Biplot

2019 ◽  
Vol 46 (3) ◽  
pp. 231-239 ◽  
Author(s):  
Ayda Krisnawati ◽  
And Mochammad Muchlish Adie
Keyword(s):  
Seed Yield ◽  
Yield Stability ◽  
High Yield ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Ge Interaction ◽  
Tropical Environments ◽  
Genotype Environment Interaction ◽  
Soybean Genotypes ◽  
Ideal Genotype

Genotype × environment interaction is universal phenomenon when different genotypes are tested in a number of environments. The objective of this experiment was to determine the seed yield stability of soybean genotypes. Seven soybean genotypes and two check cultivars were evaluated at eight soybean production centers during the dry season 2015. Stability analysis on seed yield was based on the GGE biplot method. The combined analysis showed that yield and yield components were significantly affected by genotype (G), environments (E), and genotype × environment interaction (GEI), except for number of filled pods. The highest yield was G6 (3.07 ton ha-1), followed by G7 (2.93 ton ha-1). The “which-won-where” polygon mapping resulted two mega-environments. The best genotype for the first mega-environment was G1 (G511H/Anjasmoro//Anjasmoro-2-8) at E5 (Pasuruan2); and the second one was G6 (G511 H/Anj//Anj///Anj////Anjs-6-7) at E1 (Nganjuk), E2 (Mojokerto), E3 (Blitar), E4 (Pasuruan1), E6 (Jembrana), E7 (Tabanan), and E8 (Central Lombok). The G7 (G511 H/Anjasmoro-1-4-2) was closest to ideal genotype as indicated by relatively stable and produced high yield across environments. The analysis of multi-environment trials data using GGE is useful for determining mega-environment analysis and stability of genotype which focusing on overall performance to identify superior genotypes.Keywords: GE interaction, GGE biplot, Glycine max, seed yield

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.

An analysis of variability and genotype × environment interaction in mung bean (Vigna radiata) in south-eastern Queensland

1982 ◽  
Vol 33 (3) ◽  
pp. 523 ◽  
Author(s):  
BC Imrie ◽  
KL Butler
Keyword(s):  
Plant Height ◽  
Seed Yield ◽  
Vigna Radiata ◽  
Mung Bean ◽  
Rainfall Variability ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Sowing Dates ◽  
Photoperiod Sensitive ◽  
Main Effects

Thirty mung bean (Vigna radiata) accessions were grown in replicated hill plots at two sites in each of 2 years. Plant height, days to flower and to harvest, and seed yield were recorded for each plot. In an analysis of variance most main effects and interactions were statistically significant. Estimated variance due to environment exceeded that due to genotypes for all characters. Broad sense heritability of seed yield was 0.07. The linear regression of seed yield on plant height was statistically significant, and there was a significant quadratic effect of yield on days to flower. It was concluded that environmental effects on the duration of growth phases were likely to be mainly due to the effect of different sowing dates on photoperiod-sensitive accessions, while rainfall variability most likely contributed to yield variance. Emphasis in an evaluation program should be on sampling years rather than locations.

scholarly journals Phenotypic and Quality Traits of Chickpea Genotypes under Rainfed Conditions in South Italy

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 962
Author(s):  
Mohamed Houssemeddine Sellami ◽  
Antonella Lavini ◽  
Cataldo Pulvento
Keyword(s):  
Seed Yield ◽  
Seed Quality ◽  
Ratio Test ◽  
Environment Interaction ◽  
South Italy ◽  
Legume Crop ◽  
Genotype Environment Interaction ◽  
The Stability ◽  
Total Seed

Chickpea (Cicer arietinum L.) is an important cool-season food legume crop that is mainly cultivated as a rainfed crop. This study was conducted in Italy between 2017 and 2019 to evaluate the stability of seed yield (SY), biomass (AGB) and 1000 seed weight (THS), and to assess the seed quality of 12 kabuli chickpea accessions under field conditions. The likelihood-ratio test revealed significant effects of genotype only for the SY and THS. The environment and genotype × environment interaction (GEI) effects were highly significant for all variables. We found that the environment (year) and GEI explain 55.72% and 20.87% of the total seed yield variation, respectively. Most chickpea accessions showed sensitivity to frost conditions in the third growing season. No relationship was observed between the yield and the protein content in Kabuli chickpea. Among the accessions, Ares and Reale showed the best performance under all environmental conditions, and the Reale was the most stable chickpea.

scholarly journals Assessment of Genotype × Environment Interaction of Safflower (Carthamus tinctorius L.) Genotypes by Parametric and Non-Parametric Methods

2021 ◽  
Vol 3 (1) ◽  
pp. 112-118
Author(s):  
İlhan Subaşı ◽  
Dilek Başalma
Keyword(s):  
Seed Yield ◽  
Positive Association ◽  
Stability Index ◽  
Oil Yield ◽  
Coefficient Of Determination ◽  
Mean Deviation ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Stability Parameters ◽  
Genotype Environment Interaction

Genotype-environment interaction is a significant factor for finding and selecting stable and productive varieties in safflower breeding programs. This study was conducted at three locations over two years (2016-2017) to determine the extent of genotype by environment (GE) interaction in seed and oil yield. 20 safflower lines and cultivars were evaluated in terms of stability in 3 environments. Considering the stability and performance, the most suitable genotypes were determined as Remzibey-05 and Genotype-125 in seed yield, Genotype-8 and Genotype-155 in oil yield. In terms of stability and performances of genotypes, the environment of Ikizce 2017 (E4) was prominent. Correlation analysis among parametric and nonparametric features was given only for seed yield. The following stability parameters were calculated: the coefficient of variation (CV), regression constant (ai), regression coefficient (bi), mean deviation squares from regression (S2di), coefficient of determination (Ri2), stability variance (σi2), ecovalance value (Wi), stability index (Pi) and as nonparametric stability measures Si(1) and Si(2) values. This analysis indicated that seed yield was significantly positively correlated only with Pi (P<0.01). CV showed a positively significant correlation with ai. S2di and ri2 had a positive association with Ri2, σi2, Wi, Pi, Si(1), Si(2), and between each other.

scholarly journals Stability Analysis for Seed Yield over Environments in Coriander

2016 ◽  
Vol 4 (11) ◽  
pp. 1014
Author(s):  
Sangeeta Yadav ◽  
Arun Kumar Barholia
Keyword(s):  
Stability Analysis ◽  
Seed Yield ◽  
High Fertility ◽  
Environment Interaction ◽  
Stability Parameters ◽  
Genotype Environment Interaction ◽  
Ammi Analysis ◽  
Back Ground ◽  
Ammi Model ◽  
Main Effects

Thirty five genotypes of coriander (Coriandrum sativum L.) were tested in four artificially created environments to judge their stability in performance of seed yield. The differences among genotypes and environments were significant for seed yield. Stability parameters varied considerably among the tested genotypes in all the methods used. The variation in result in different methods was due to non-fulfillment of assumption of different models. However, AMMI analysis provides the information on main effects as well as interaction effects and depiction of PCA score gives better understanding of the pattern of genotype – environment interaction. The sum of squares due to PCAs was also used for the computation of AMMI stability values for better understanding of the adaptability behavior of genotypes hence, additive main effects and multiplicative interaction (AMMI) model was most appropriate for the analysis of G x E interactions for seed yield in coriander. Genotypes RVC 15, RVC 19, RVC 22, RVC 25 and Panipat local showed wider adaptability while, Simpo S 33 exhibited specific adaptability to favourable conditions of high fertility. These genotypes could be utilized in breeding programmers to transfer the adaptability genes into high yielding genetic back ground of coriander.

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