Peanut antioxidants: Part 1. Genotypic variation and genotype-by-environment interaction in antioxidant capacity of raw kernels

LWT ◽  
2014 ◽  
Vol 57 (1) ◽  
pp. 306-311 ◽  
Author(s):  
Kim-Yen Phan-Thien ◽  
Graeme C. Wright ◽  
Barry L. Tillman ◽  
N. Alice Lee
Keyword(s):  
Antioxidant Capacity ◽  
Genotypic Variation ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Genotype By Environment

scholarly journals Genotype by environment interaction, correlation, AMMI, GGE biplot and cluster analysis for grain yield and other agronomic traits in sorghum (Sorghum bicolor L. Moench)

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258211
Author(s):  
Muluken Enyew ◽  
Tileye Feyissa ◽  
Mulatu Geleta ◽  
Kassahun Tesfaye ◽  
Cecilia Hammenhag ◽  
...  
Keyword(s):  
Grain Yield ◽  
Plant Height ◽  
Agronomic Traits ◽  
Genotypic Variation ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Phenotypic Data ◽  
Breeding Programs ◽  
Genotype By Environment ◽  
And Cluster Analysis

Genotype by environment (G×E) interaction is a major factor limiting the success of germplasm selection and identification of superior genotypes for use in plant breeding programs. Similar to the case in other crops, G×E complicates the improvement of sorghum, and hence it should be determined and used in decision-making programs. The present study aimed at assessing the G×E interaction, and the correlation between traits for superior sorghum genotypes. Three hundred twenty sorghum landraces and four improved varieties were used in alpha lattice experimental design-based field trial across three environments (Melkassa, Mieso and Mehoni) in Ethiopia. Phenotypic data were collected for days to flowering (DTF), plant height (PH), panicle length (PALH), panicle width (PAWD), panicle weight (PAWT) and grain yield (GY). The results revealed that the variance due to genotype, environment and G×E interaction were highly significant (P < 0.001) for all traits. GY and PAWT were highly affected by environments and G×E whereas DTF, PALH, PAWD and PH were mainly affected by genotypic variation. Therefore, multi-environment testing is needed for taking care of G × E interaction to identify high yielding and stable sorghum landraces. GY and PAWT revealed highly significant positive correlations indicating the possibility of effective selection of the two traits simultaneously. Among the studied populations, South Wello, West Hararghe and Shewa zones had highly diverse genotypes that were distributed across all clusters. Hence, these areas can be considered as hotspots for identifying divergent sorghum landraces that could be used in breeding programs. Melkassa was the most representative environment whereas Mieso was the most discriminating. Five genotypes (G148, G123, G110, G203 and G73) were identified as superior across the test environments for grain yield with farmer-preferred trait, such as plant height. The identified stable and high yielding genotypes are valuable genetic resources that should be used in sorghum breeding programs.

Using AMMI approach to delineate genotype by environment interaction and stability of barley (Hordeum vulgare L.) genotypes under northern Indian shivalik hill conditions

2020 ◽  
Vol 80 (03) ◽  
Author(s):  
Om Prakash Yadav ◽  
A. K. Razdan ◽  
Bupesh Kumar ◽  
Praveen Singh ◽  
Anjani K. Singh
Keyword(s):  
Hordeum Vulgare ◽  
Grain Yield ◽  
Principal Component ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Hordeum Vulgare L ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Principal Component Axis ◽  
Ammi Analysis

Genotype by environment interaction (GEI) of 18 barley varieties was assessed during two successive rabi crop seasons so as to identify high yielding and stable barley varieties. AMMI analysis showed that genotypes (G), environment (E) and GEI accounted for 1672.35, 78.25 and 20.51 of total variance, respectively. Partitioning of sum of squares due to GEI revealed significance of interaction principal component axis IPCA1 only On the basis of AMMI biplot analysis DWRB 137 (41.03qha–1), RD 2715 (32.54qha–1), BH 902 (37.53qha–1) and RD 2907 (33.29qha–1) exhibited grain yield superiority of 64.45, 30.42, 50.42 and 33.42 per cent, respectively over farmers’ recycled variety (24.43qha–1).

Exploring genotype‐by‐environment interaction in sweet sorghum under tropical environments

2021 ◽  
Author(s):  
Vander Fillipe Souza ◽  
Pedro César de Oliveira Ribeiro ◽  
Indalécio Cunha Vieira Júnior ◽  
Isadora Cristina Martins Oliveira ◽  
Cynthia Maria Borges Damasceno ◽  
...  
Keyword(s):  
Sweet Sorghum ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Tropical Environments

Genotype by environment interaction on phenology and synchronous maturity of mungbean

2021 ◽  
Author(s):  
Siti Marwiyah ◽  
Willy Bayuardi Suwarno ◽  
Desta Wirnas ◽  
Trikoesoemaningtyas xxx ◽  
Surjono Hadi Sutjahjo
Keyword(s):  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Genotype By Environment

scholarly journals Genotype and environment interaction of sweetpotato varieties

2019 ◽  
Vol 44 (3) ◽  
pp. 501-512
Author(s):  
S Sultana ◽  
HC Mohanta ◽  
Z Alam ◽  
S Naznin ◽  
S Begum
Keyword(s):  
Tuber Yield ◽  
Agricultural Research ◽  
Genotype By Environment Interaction ◽  
Yield Potential ◽  
Environment Interaction ◽  
Dry Matter Content ◽  
Genotype By Environment ◽  
Discriminating Power ◽  
Study Results ◽  
Main Effect

The article presents results of additive main effect and multiplicative interaction (AMMI) and genotype (G) main effect and genotype by environment (GE) interaction (G × GE) biplot analysis of a multi environmental trial (MET) data of 15 sweetpotato varieties released from Bangladesh Agricultural Research Institute conducted during 2015–2018. The objective of this study was to determine the effects of genotype, environment and their interaction on tuber yield and to identify stable sweetpotato genotypes over the years. The experimental layout was a randomized complete block design with three replications at Gazipur location. Combined analysis of variance (ANOVA) indicated that the main effects due to genotypes, environments and genotype by environment interaction were highly significant. The contribution of genotypes, environments and genotype by environment interaction to the total variation in tuber yield was about 60.16, 10.72 and 12.82%, respectively. The first two principal components obtained by singular value decomposition of the centred data of yield accounted for 100% of the total variability caused by G × GE. Out of these variations, PC1 and PC2 accounted for 71.5% and 28.5% of variability, respectively. The study results identified BARI Mistialu- 5, BARI Mistialu- 14 and BARI Mistialu- 15 as the closest to the “ideal” genotype in terms of yield potential and stability. Varieties ‘BARI Mistialu- 8, BARI Mistialu- 11 and BARI Mistialu- 12’ were also selected as superior genotypes. BARI Mistialu- 3 and BARI Mistialu- 13 was comparatively low yielder but was stable over the environment. Among them BARI Mistialu-12, BARI Mistialu-14 and BARI Mistialu-15 are rich in nutrient content while BARI Mistialu-8 and BARI Mistialu-11 are the best with dry matter content and organoleptic taste. Environments representing in 1st and 3rd year with comparatively short vectors had a low discriminating power and environment in 2nd year was characterized by a high discriminating power. Bangladesh J. Agril. Res. 44(3): 501-512, September 2019

EFFECTS OF GROWTH INHIBITION BY HYDROCORTISONE ACETATE ON VARIANCE IN CHICKENS

1970 ◽  
Vol 12 (3) ◽  
pp. 627-634
Author(s):  
J. S. Gavora ◽  
G. C. Hodgson
Keyword(s):  
Body Weight ◽  
External Environment ◽  
Hormonal Treatment ◽  
Genotype By Environment Interaction ◽  
Future Research ◽  
Environment Interaction ◽  
Internal Environment ◽  
Hydrocortisone Acetate ◽  
Genotype By Environment ◽  
Treatment Interaction

Traditionally genotype by environment interaction studies have dealt with changes in external environment. In this experiment an attempt was made to alter internal environment and keep external environment constant. Cockerels from each of six different commercial stocks were injected with 0,1,2 and 4 mgs hydrocortisone acetate per 100 gms body weight at 14 days of age. This type of hormonal treatment was shown to release additional variability in growth without producing any stock-treatment interaction at the level of means. The results indicate a possible new avenue for future research.

Genotype by environment interaction for yearling weight in Nellore cattle applying reaction norms models

2018 ◽  
Vol 58 (11) ◽  
pp. 1996
Author(s):  
S. Ribeiro ◽  
J. P. Eler ◽  
V. B. Pedrosa ◽  
G. J. M. Rosa ◽  
J. B. S. Ferraz ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Environmental Gradient ◽  
Genotype By Environment Interaction ◽  
Reaction Norms ◽  
Breeding Value ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Genotype Environment Interaction ◽  
Nellore Cattle ◽  
Gradient Variation

In the present study, a possible existence of genotype × environment interaction was verified for yearling weight in Nellore cattle, utilising a reaction norms model. Therefore, possible changes in the breeding value were evaluated for 46 032 animals, from three distinct herds, according to the environmental gradient variation of the different contemporary groups. Under a Bayesian approach, analyses were carried out utilising INTERGEN software resulting in solutions of contemporary groups dispersed in the environmental gradient from –90 to +100 kg. The estimates of heritability coefficients ranged from 0.19 to 0.63 through the environmental gradient and the genetic correlation between intercept and slope of the reaction norms was 0.76. The genetic correlation considering all animals of the herds in the environmental gradient ranged from 0.83 to 1.0, and the correlation between breeding values of bulls in different environments ranged from 0.79 to 1.0. The results showed no effect of genotype × environment interaction on yearling weight in the herds of this study. However, it is important to verify a possible influence of the genotype × environment in the genetic evaluation of beef cattle, as different environments might cause interference in gene expression and consequently difference in phenotypic response.

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