scholarly journals Genetic and Environmental Variation of Glucosinolate Content in Chinese Cabbage

HortScience ◽  
2006 ◽  
Vol 41 (6) ◽  
pp. 1382-1385 ◽  
Author(s):  
Ji Yeon Kang ◽  
Khalid E. Ibrahim ◽  
John A. Juvik ◽  
Doo Hwan Kim ◽  
Wha Jeung Kang
Keyword(s):  
Health Promotion ◽  
Chinese Cabbage ◽  
Phenotypic Variation ◽  
Genetic Manipulation ◽  
Genotype By Environment Interaction ◽  
Glucosinolate Content ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Total Head ◽  
Risk Of Cancer

Strong evidence exists to suggest that increased consumption of glucosinolates from Brassica vegetables is associated with reduced risk of cancer induction and development. Development of elite germplasm of these vegetables with enhanced levels of glucosinolates will putatively enhance health promotion among the consuming public. To evaluate levels of glucosinolate phenotypic variation in Chinese cabbage tissue and partition the total phenotypic variation into component sources (genotype, environment, and genotype-by-environment interaction), a set of 23 Brassica rapa L. var. pekinensis genotypes were grown in two different environments (field plots and greenhouse ground beds). Gluconasturtiin and glucobrassicin were found to account for ≈80% of total head glucosinolate content. Significant differences were found in glucosinolate concentrations between the lowest and highest genotypes for glucobrassicin (6-fold) and for gluconasturtiin (2.5-fold). Analysis of variance showed that for the three major glucosinolates (gluconasturtiin, glucobrassicin, and progoitrin), the genotypic effects described most of the phenotypic variation (62% averaged over the three compounds). The next most important factor was genotype × environment interaction (29%), whereas variation affiliated with the environment was found to be relatively minor (8%). These results suggest that genetic manipulation and selection can be conducted to increase glucosinolate content and the putative health promotion associated with consumption of Chinese cabbage.

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.

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).

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

Effect of genotype-environment interaction on grain yield of exotic rice (Oryza sativa L.) hybrids

2018 ◽  
Vol 44 (4) ◽  
pp. 507-514
Author(s):  
MU Kulsum ◽  
MJ Hasan ◽  
MN Haque ◽  
M Shalim Uddin ◽  
KM Iftekharduddaula
Keyword(s):  
Oryza Sativa L ◽  
Major Complication ◽  
Genotype By Environment Interaction ◽  
Yield Potential ◽  
High Yield ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Wide Range ◽  
Main Effects ◽  
Moderate Yield

Genotype by environment interaction (GEI) is a major complication in plant breeding. Authors used additive main effects and multiplicative interaction (AMMI) to evaluate the effects of GEI in hybrid rice genotype and their adaptation in three years at four locations. Among rice hybrid genotypes ACI93024 was stable in all environments with high yield potential. Using AMMI analysis AMMI 1 biplot showed the genotypes HS-273, Heera-2, ACI-2 and HRM-02 were highly stable with moderate yield potential but the genotype ACI93024 was more adapted to a wide range of environment than the rest of the genotypes, while BRRI dhan28 indices the lowest stability. ACI-2, LP-70 and Mayna were specifically adapted to the environment of Rangpur, Jessore and Gazipur, respectively. Comilla was identified as stable environment for all the genotypes.

GENOTYPE-ENVIRONMENT INTERACTION VARIANCES IN YIELD TRIALS OF FALL RYE

1970 ◽  
Vol 50 (1) ◽  
pp. 77-80 ◽  
Author(s):  
P. J. KALTSIKES
Keyword(s):  
Genotype By Environment Interaction ◽  
Western Canada ◽  
Environment Interaction ◽  
First Order ◽  
Genotype By Environment ◽  
Genotype Environment Interaction ◽  
Yield Difference ◽  
The Mean ◽  
Yield Trials ◽  
Interaction Variance

Estimates of genotype by environment interaction variances were obtained from the western Canada Co-operative fall rye tests grown in 1963–1967. All first-order interactions and the second-order interactions were significantly greater than zero at the 0.05 level of probability. Although the estimate of cultivar by year interaction variance was relatively small, it accounted for 40% of the variance of a cultivar mean when only three years of testing were considered. However, testing in 20 locations for three years with four replicates could detect yield differences of approximately 10% of the mean of the highest yielding cultivar. If further reduction of the yield difference detectable is desired, more locations should be included in the test.

scholarly journals GGE Biplot Analysis of Multi-Environment Yield Trials in Soybean Promising Lines

2019 ◽  
Vol 3 (2) ◽  
pp. 72
Author(s):  
Ayda Krisnawati ◽  
M. Muchlish Adie
Keyword(s):  
Block Design ◽  
Genotype By Environment Interaction ◽  
Yield Stability ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Tropical Regions ◽  
Biplot Analysis ◽  
Genotype Environment Interaction ◽  
Promising Line ◽  
The Tropics

Soybean in Indonesia is grown in diverse agro-ecological environments. The performance of soybean yield often varies due to significant genotype × environment interaction (GEI), therefore the yield stability of performance is an important consideration in the breeding program. The aim of the research was to exploring the GEI pattern and yield stability of soybean promising lines in the tropics using GGE (Genotype and Genotype by Environment Interaction) biplot method. A total of 16 soybean promising lines were evaluated in ten environments during 2016 growing season. The experiment was arranged in a randomized completely block design with four replicates. The analysis of variance revealed that environments (E) explained the highest percentage of variation (51.45%), meanwhile the genotypes (G) and genotype × environment interactions (GEI) contributed for 3.24%, and 14.59% of the total variation, respectively. Seed yield of 16 soybean promising lines ranged from 2.41 to 2.83 t.ha-1 with an average of 2.74 t.ha-1. Joint effects of genotype and interaction (G+GE) which was partitioned using GGE biplot analysis showed that the first two components were significant, explaining 60.88% (37.89% PC1 and 22.98% PC2) of the GGE sum of squares. Indonesia can be divided into at least four putative mega environments for soybean production. The GGE biplot identified G10 as high yielding and stable promising line, thus recommended to be developed in multi-environment in tropical regions of Indonesia.

scholarly journals Genotype-by-Environment Interaction Effects under Heat Stress in Tropical Maize

Agronomy ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1998
Author(s):  
Vinayan Madhumal Thayil ◽  
Pervez H. Zaidi ◽  
Kaliyamoorthy Seetharam ◽  
Reshmi Rani Das ◽  
Sudarsanam Viswanadh ◽  
...  
Keyword(s):  
Heat Stress ◽  
South Asia ◽  
Grain Filling ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Maize Hybrids ◽  
Genotype Environment Interaction ◽  
Maize Genotypes ◽  
Spring Maize ◽  
Stable Performance

Spring maize area has emerged as a niche market in South Asia. Production of maize during this post-rainy season is often challenged due to heat stress. Therefore, incorporating heat stress resilience is an important trait for incorporation in maize hybrids selected for deployment in this season. However, due to the significant genotype × environment interaction (GEI) effects under heat stress, the major challenge lies in identifying maize genotypes with improved stable performance across locations and years. In the present study, we attempted to identify the key weather variables responsible for significant GEI effects, and identify maize hybrids with stable performance under heat stress across locations/years. The study details the evaluation of a set of prereleased advanced maize hybrids across heat stress vulnerable locations in South Asia during the spring seasons of 2015, 2016 and 2017. Using factorial regression, we identified that relative humidity (RH) and vapor pressure deficit (VPD) as the two most important environmental covariates contributing to the large GEI observed on grain yield under heat stress. The study also identified reproductive stage, starting from tassel emergence to early grain-filling stage, as the most critical crop stage highly susceptible to heat stress. Across-site/year evaluation resulted in identification of six high yielding heat stress resilient hybrids.

scholarly journals Genotype-by-environment interaction for seed glucosinolate content in winter oilseed rape (Brassica napus L.) using an additive main effects and multiplicative interaction model

2018 ◽  
Vol 55 (1) ◽  
pp. 85-96 ◽  
Author(s):  
Jan Bocianowski ◽  
Kamila Nowosad ◽  
Alina Liersch ◽  
Wiesława Popławska ◽  
Agnieszka Łącka
Keyword(s):  
Block Design ◽  
Interaction Model ◽  
Genotype By Environment Interaction ◽  
Glucosinolate Content ◽  
Environment Interaction ◽  
Brassica Napus L ◽  
Multiplicative Interaction ◽  
Genotype By Environment ◽  
Winter Rapeseed ◽  
Main Effects

Summary The objective of this study was to assess genotype-by-environment interaction for seed glucosinolate content in winter rapeseed cultivars grown in western Poland using the additive main effects and multiplicative interaction model. The study concerned 25 winter rapeseed genotypes (15 F1 CMS ogura hybrids, parental lines and two European cultivars: open pollinated Californium and F1 hybrid Hercules), evaluated at five locations in a randomized complete block design with four replicates. The seed glucosinolate content of the tested genotypes ranged from 5.53 to 16.80 μmol∙g-1 of seeds, with an average of 10.26 μmol∙g-1. In the AMMI analyses, 48.67% of the seed glucosinolate content variation was explained by environment, 13.07% by differences between genotypes, and 17.56% by genotype-by-environment interaction. The hybrid PN66×PN07 is recommended for further inclusion in the breeding program due to its low average seed glucosinolate content; the restorer line PN18, CMS ogura line PN66 and hybrids PN66×PN18 and PN66×PN21 are recommended because of their stability and low seed glucosinolate content.

scholarly journals The application of AMMI model for barley cultivars evaluation in multi-year trials

Genetika ◽  
2014 ◽  
Vol 46 (2) ◽  
pp. 445-454 ◽  
Author(s):  
Milan Mirosavljevic ◽  
Novo Przulj ◽  
Jan Bocanski ◽  
Dusan Stanisavljevic ◽  
Bojan Mitrovic
Keyword(s):  
Genotype By Environment Interaction ◽  
Barley Grain ◽  
Environment Interaction ◽  
Barley Cultivars ◽  
Growing Seasons ◽  
Genotype Environment Interaction ◽  
New Varieties ◽  
Stable Performance ◽  
Ammi Model ◽  
Barley Genotypes

The interpretation of new varieties performance is disturbed under the influence of genotype-by-environment interaction. Among several methods used for understanding this effect, one of the most frequently used methods is Additive Main Effects and Multiplicative Interaction (AMMI) analysis. In this study we used AMMI method with the aim to estimate the genotype - environment interaction of 14 barley genotypes, and to identify barley genotypes that have high and stable performance in different environments. The trials were conducted during 11 growing seasons (1995/96 - 2005/06), arranged in a randomized complete block (RCB) design with four replications in location Rimski Sancevi. The results showed that the influence of environment (seasons), genotypes and their interaction on barley grain yield were significant (p < 0.01). Based on AMMI method, two-rowed variety Novosadski 317 and the six-rowed variety Novosadski 331 can be distinguished due their high and stable yields.

scholarly journals Interpretation of Genotype-by-Environment Interaction for Carotenoid and Tocopherol Content in Broccoli

HortScience ◽  
2005 ◽  
Vol 40 (4) ◽  
pp. 1098D-1099
Author(s):  
Khalid E. Ibrahim ◽  
Kanta Kobira ◽  
John A. Juvik
Keyword(s):  
Stability Analysis ◽  
Beta Carotene ◽  
Genotype By Environment Interaction ◽  
Tocopherol Content ◽  
Alpha Tocopherol ◽  
Genetic Enhancement ◽  
Environment Interaction ◽  
Response Patterns ◽  
Genotype By Environment ◽  
Risk Of Cancer

Genotype-by-environment interaction (G×E) is a fundamental concern in plant breeding since it hinders developing genotypes with wide geographical usefulness. Analysis of variance (ANOVA) has been widely used to interpret G×E, but it does not elucidate the nature and causes of the interaction. Stability analysis provides a summary of the response patterns of genotypes to different growing environments. Two classes of phytochemicals with putative health promoting activity are carotenoids and tocopherols that are relatively abundant in broccoli. Growing clinical and epidemiological evidence suggests that vegetables with enhanced levels of these phytochemicals can reduce the risk of cancer, cardiovascular, and eye diseases. The objective of this study is to have better understanding of the genetic, environmental and G×E interaction effects of these phytochemicals in broccoli to determine the feasibility of the genetic enhancement. The ANOVA and Shukla's stability test were applied to a set of data generated by the HPLC analysis of different carotenoid and tocopherol forms for six broccoli accessions grown over three environments. The ANOVA results show a significant G×E for both phytochemicals that ranged from 22.6% of the total phenotypic variation for beta-carotene to 54.0% for delta-tocopherol while the environmental effects were nonsignificant. The genotypic effects ranged from as low as 1% for alpha-tocopherol to 31.5% and 36.0% for beta-carotene and gamma-tocopherol, respectively. Stability analysis illustrated that the most stable genotype for all phytochemicals is Brigadier. The results suggest that feasibility of the genetic enhancement for major carotenoids and tocopherols. A second experiment that includes a larger set of genotypes and environments was conducted to confirm the results of this study.

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