scholarly journals Genotype x Environment interaction in the yield and fruit quality of passion fruit germplasm grown in the Ecuadorian Littoral

2020 ◽  
Vol 20 (sup3) ◽  
pp. S1829-S1844
Author(s):  
William Viera ◽  
Beatriz Brito ◽  
Eddie Zambrano ◽  
Lenin Ron ◽  
Jorge Merino ◽  
...  
Keyword(s):  
Fruit Quality ◽  
Genotype X Environment Interaction ◽  
Passion Fruit ◽  
Environment Interaction ◽  
Genotype X Environment

scholarly journals Technological quality of common bean grains obtained in different growing seasons

Bragantia ◽  
2014 ◽  
Vol 73 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Eliana Francischinelli Perina ◽  
Cássia Regina Limonta Carvalho ◽  
Alisson Fernando Chiorato ◽  
Rodrigo Lorencetti Tunes Lopes ◽  
João Guilherme Ribeiro Gonçalves ◽  
...  
Keyword(s):  
Common Bean ◽  
Winter Season ◽  
Cooking Time ◽  
Genotype X Environment Interaction ◽  
Soluble Solids ◽  
Environment Interaction ◽  
Genotype X Environment ◽  
Growing Seasons ◽  
Technological Quality

The traits that provide technological quality to common bean grains exhibit genetic and environmental variation and variation in the genotype x environment interaction. In this context, the aim of this study was to assess the effect of different periods of the growing season on the technological quality of common bean grains. The experiment was conducted with 25 bean genotypes (carioca [beige with brown stripes] and black commercial group) that are part of the Value for Cultivation and Use (Valor de Cultivo e Uso - VCU) trials in three growing seasons, namely, the 2009/2010 rainy season, the 2010/2011 dry season and the 2010/2011 winter season, in a randomized block experimental design with three replications in which the following items were assessed: cooking time (CT), water absorption capacity before cooking (Peanc) and after cooking (Peapc), percentage of whole grains (PWG), total soluble solids in the broth (TSSb), volume expansion before cooking (EXPVbc) and after cooking (EXPVac), and dry grain density (DD), grain density after maceration (SD) and grain density after cooking (CD). Assessments showed that the different growing seasons for obtaining grains for the purpose of analysis of technological quality have an effect on the results and on differentiation among genotypes, indicating genotype x environment interaction. They also showed that the genotypes C2-1-6-1, C4-8-1-1, LP04-03, IAC-Imperador, P5-4-4-1 and Pr11-6-4-1-2 had the best results in relation to cooking time in the mean values of the three growing seasons. The use of early selection based on phenotypic correlations that exist among the technological features is not expressive, due to the variation of magnitude among the different growing seasons.

scholarly journals Genotype X Environment Interaction of Food Barley

2015 ◽  
Vol 21 ◽  
pp. 41-48
Author(s):  
Gebremedhin Welu
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Genotype X Environment Interaction ◽  
Environment Interaction ◽  
Genotype X Environment ◽  
Genotype Effect ◽  
Environment Effects ◽  
Randomized Complete Block Design ◽  
Barley Genotypes

The objective of this experiment was to estimate the magnitude of genotype X environment interaction on grain yield and yield related traits. Twelve varieties of food barley were included in the study planted in randomized complete block design with three replications. The ANOVA of combined and individual location revealed significant differences among the food barley genotypes for grain yield and other traits. The results of ANOVA for grain yield showed highly significant (p≤0.01) differences among genotypes evaluated for grain yield at Maychew and significant (p≤0.05) differences in Korem, Alage and Mugulat. The ANOVA over locations showed a highly significant (p≤0.01) variation for the genotype effect, environment effects, genotype X environment interaction (GEI) effect and significant (p≤0.05) variation for GEI effect of yield and for most of the yield related traits of food barley genotypes. Haftysene, Yidogit, Estayish and Basso were the genotypes with relatively high mean grain yield across all locations and they are highly performing genotypes to the area. Among locations, the highest mean grain yield was recorded at Korem and it was a suited environment to all the genotypes whereas Mugulat is unfavoured one. ECOPRINT 21: 41-48, 2014DOI: http://dx.doi.org/10.3126/eco.v21i0.11903

scholarly journals Genotype x Environment Interaction and Yield-Stability Analyses of Rice Grown in Tropical Inland Swamp

2011 ◽  
Vol 39 (1) ◽  
pp. 220 ◽  
Author(s):  
Adesola L. NASSIR ◽  
Omolayo J. ARIYO
Keyword(s):  
Total Variation ◽  
Yield Stability ◽  
Genotype X Environment Interaction ◽  
Environment Interaction ◽  
Rice Varieties ◽  
Environmental Indices ◽  
Genotype X Environment ◽  
Additional Information ◽  
Gxe Interaction ◽  
The Stability

Twelve rice varieties were cultivated in inland hydromorphic lowland over a four year-season period in tropical rainforest ecology to study the genotype x environment (GxE) interaction and yield stability and to determine the agronomic and environmental factors responsible for the interaction. Data on yield and agronomic characters and environmental variables were analyzed using the Additive Main Effect and Multiplicative Interaction (AMMI), Genotype and Genotype x Environment Interaction, GGE and the yield stability using the modified rank-sum statistic (YSi). AMMI analysis revealed environmental differences as accounting for 47.6% of the total variation. The genotype and GxE interaction accounted for 28.5% and 24% respectively. The first and second interaction axes captured 57% and 30% of the total variation due to GXE interaction. The analysis identified ‘TOX 3107’ as having a combination of stable and average yield. The GGE captured 85.8%of the total GxE. ‘TOX 3226-53-2-2-2’ and ‘ITA 230’ were high yielding but adjudged unstable by AMMI. These two varieties along with ‘WITA 1’ and ‘TOX 3180-32-2-1-3-5’ were identified with good inland swamp environment, which is essentially moisture based. The two varieties (‘TOX 3226-53-2-2-2’ and ‘ITA 230’), which were equally considered unstable in yield by the stability variance, ?2i, were selected by YSi in addition to ‘TOX 3107’, ‘WITA 1’, ‘IR 8’ and ‘M 55’. The statistic may positively complement AMMI and GGE in selecting varieties suited to specific locations with peculiar fluctuations in environmental indices. Correlation of PC scores with environmental and agronomic variables identified total rainfall up to the reproductive stage, variation in tillering ability and plant height as the most important factors underlying the GxE interaction. Additional information from the models can be positively utilized in varietal development for different ecologies.

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