scholarly journals Genotype × Environment Interaction for Resistance to Spider Mites in Fragaria

1999 ◽  
Vol 124 (4) ◽  
pp. 353-357 ◽  
Author(s):  
José López Medina ◽  
Patrick P. Moore ◽  
Carl H. Shanks ◽  
Fernando Flores Gil ◽  
Craig K. Chandler
Keyword(s):  
Spider Mite ◽  
The United States ◽  
Spider Mites ◽  
Environment Interaction ◽  
Interaction Patterns ◽  
First Order ◽  
Genotype Environment Interaction ◽  
Main Effect ◽  
Ammi Analysis ◽  
Main Effects

Genotype × environment interaction for resistance to the twospotted spider mite (Tetranychus urticae Koch) of eleven clones of Fragaria L. sp. (strawberries) grown in six environments throughout the United States was examined using two multivariate analysis techniques, principal coordinate analysis (PCA) and additive main effect and multiplicative interaction (AMMI). Both techniques provided useful and interesting ways of investigating genotype × environment interaction. PCA analysis indicated that clones X-11 and E-15 were stable across both low and high environments for the number of spider mites per leaflet. The initial AMMI analysis showed that the main effects of genotype, environment, and their first-order interaction were highly significant, with genotype × environment interaction due mainly to cultivar `Totem' and environment FL94. A second AMMI analysis, which excluded `Totem' and FL94, showed that the main effects of the remaining genotypes, environments, and genotype × environment interaction were also highly significant. AMMI biplot analysis revealed that FL93 and GH93 were unstable environments, but with opposite interaction patterns; and GCL-8 and WSU2198 were unstable genotypes with similar interactions that were opposite those of WSU 2202.

Analysis of Indian post-rainy sorghum multi-location trial data reveals complexity of genotype × environment interaction

2016 ◽  
Vol 155 (1) ◽  
pp. 44-59 ◽  
Author(s):  
S. RAKSHIT ◽  
K. N. GANAPATHY ◽  
S. S. GOMASHE ◽  
A. DHANDAPANI ◽  
M. SWAPNA ◽  
...  
Keyword(s):  
Trial Data ◽  
Conservation Practices ◽  
Environment Interaction ◽  
Improvement Project ◽  
Genotype Environment Interaction ◽  
Main Effect ◽  
Highly Correlated ◽  
Main Effects ◽  
Major Emphasis ◽  
New Cultivars

SUMMARYSorghum [Sorghum bicolor (L.) Moench] grown in India is of two adaptive types: rainy and post-rainy. The post-rainy sorghum is predominantly consumed by humans. While releasing new cultivars through multi-location testing, major emphasis is given to the superiority of new cultivars over existing cultivars, with very little emphasis on the genotype × environment interaction (GEI). To understand the complexity of GEI in post-rainy sorghum testing location trials, the multi-location evaluation data of two post-rainy seasons (2009/10 and 2010/11) under the All India Coordinated Sorghum Improvement Project were analysed. In both years, location explained the highest proportion of total sum of squares followed by the GEI effect and main effect of genotype. Additive main effects and multiplicative interaction (AMMI), stability values (ASV) and genotype + genotype × environment interaction (GGE) instability values recorded high correlation resulting in identification of the best performing cultivars. However, the rank correlations were lower, though still significant. A mixture of crossover and non-crossover GEI was a common occurrence in both years. ‘Which-won-where’ analysis suggested the existence of four possible mega-environments (ME) among post-rainy testing locations, with a few non-informative locations within ME. Mega-environments are characterized by soil type, rainfall pattern and moisture conservation practices. The present study indicated the possibility of reducing the number of test locations by eliminating non-representative highly correlated locations and suggested the need to breed for location-specific genotypes rather than genotypes with wider adaptability.

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.

scholarly journals ADAPTABILITY AND STABILITY FOR IRON AND ZINC IN COWPEA BY AMMI ANALYSIS

2021 ◽  
Vol 34 (3) ◽  
pp. 590-598
Author(s):  
CARLOS ENRIQUE CARDONA-AYALA ◽  
HERMES ARAMENDIZ-TATIS ◽  
MIGUEL MARIANO ESPITIA CAMACHO
Keyword(s):  
Genetic Improvement ◽  
Block Design ◽  
Environment Interaction ◽  
Improvement Program ◽  
Genotype Environment Interaction ◽  
Randomized Complete Block Design ◽  
Ammi Analysis ◽  
Iron And Zinc ◽  
Ammi Model ◽  
Main Effects

ABSTRACT Iron and zinc deficiency is one of the main problems affecting vulnerable populations in the Colombian Caribbean, thereby generating malnutrition from the consumption of foods with low content of essential minerals. The objective of this study was to evaluate the genotype-environment interaction for iron and zinc accumulation in grains in 10 cowpea bean genotypes by additive main effects and multiplicative interaction (AMMI) model and to select the most stable ones to stimulate their planting or as parents in the genetic improvement program. Nine promising lines and a commercial control were evaluated using the randomized complete block design with 10 treatments and four replications in 10 environments of the northern Colombia in the second semester of 2017 and first of 2018. The adaptability and stability analysis was done using AMMI model. The results showed highly significant differences at the level of environments, genotypes, and genotype-environment interaction for iron and zinc, demostrating a differential adaptability of genotypes in the test environments. Genotypes 2 and 3 expressed greater adaptability and stability for iron contents in the seed; while genotype 1, recorded it for zinc contents. These three genotypes outperformed the commercial control and, therefore, can be recommended for planting or be used as parents in the genetic improvement program.

scholarly journals Genotype × Environment Interaction Patterns in Rangeland Variety Trials of Cool-Season Grasses in the Western United States

Agronomy ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 623 ◽  
Author(s):  
Joseph G. Robins ◽  
Craig W. Rigby ◽  
Kevin B. Jensen
Keyword(s):  
United States ◽  
Dry Matter ◽  
The United States ◽  
High Plains ◽  
Environment Interaction ◽  
Interaction Patterns ◽  
Dry Matter Yield ◽  
Forage Production ◽  
Cool Season ◽  
Genotype Environment Interaction

Rangeland revegetation is necessary to stabilize disturbed sites and increase forage production, but frequently fails due to a variety of environmental and biotic factors. Plant breeding efforts in perennial cool-season grasses result in the development of potential cultivars that must be evaluated in multi-environment trials to determine their level of adaptation. This study evaluated 49 cultivars for stand frequency and dry matter yield over five years at five environments in the Intermountain and High Plains regions of the United States. The results were significant differences among the included cultivars for both traits across and within environments. Yet, there was also crossover genotype × environment interaction. Thus, highest performing cultivars were to some extent dependent on the environment. Hycrest II crested wheatgrass and Vavilov II Siberian wheatgrass possessed high stand frequency (>80 %) and dry matter yield (>800 kg·ha−1) across environments and within environments except at the Eureka, UT environment where they possessed low stand frequency. These cultivars, and species, also possessed high productivity and stability for both traits. Thus, breeding efforts in the species resulted in widely adapted cultivars that may lack specific adaptation to some environments.

Analysis of a three-way interaction including multi-attributes

2006 ◽  
Vol 57 (11) ◽  
pp. 1185 ◽  
Author(s):  
Mario Varela ◽  
Jose Crossa ◽  
Jagdish Rane ◽  
Arun Kumar Joshi ◽  
Richard Trethowan
Keyword(s):  
Sowing Date ◽  
Environment Interaction ◽  
Data Array ◽  
Sowing Time ◽  
Genotype Environment Interaction ◽  
Sowing Dates ◽  
Main Effect ◽  
Ammi Analysis ◽  
Plant Densities ◽  
Mode 3

The additive main effect and multiplicative interaction (AMMI) has been widely used for studying and interpreting genotype × environment interaction (GEI) in agricultural experiments using multi-environment trials (METs). When METs are performed across several years the interaction is referred to as a 3-mode (3-way) data array, in which the modes are genotypes, environments, and years. The 3-way array can be applied to other conditions or factors artificially created by the researcher, such as different sowing dates or plant densities, etc. Three-way interaction data can be studied using the AMMI analysis. The objective of this study is to apply the 3-mode AMMI to 2 datasets. Dataset 1 comprises genotype (25) × location (4) × sowing time (4) interaction; 8 traits were measured. The structure of dataset 2 is genotype (20) × irrigation regimes (4) × year (3) on grain yield. Results of the 3-way AMMI on dataset 1 show that several important 3-way interactions were not detected when condensing location (4) × sowing time (4) into environments (16). An alternative 3-way array, genotype × attribute × locations for the early sowing date in Year 1, is considered. Results of the 3-way AMMI on dataset 2 show that different patterns of response of genotypes can be found at different irrigation methods and years.

scholarly journals Ammi analysis of genotype × environment interaction and stability of sesame genotypes

2020 ◽  
Vol 49 (2) ◽  
pp. 215-221
Author(s):  
Bhagwat Singh ◽  
Rajani Bisen
Keyword(s):  
Significant Interaction ◽  
Degrees Of Freedom ◽  
Principal Component ◽  
Mean Value ◽  
Environment Interaction ◽  
Average Yield ◽  
Genotype Environment Interaction ◽  
Ammi Analysis ◽  
Sesame Genotypes ◽  
Main Effects

The additive main effects and multiplicative interaction (AMMT) model for seed yield of 60 varieties of sesame per plant detected significant effects of the genotypes (17.87% sum of squares (SS)), environments (14.15% SS) and genotype × environment interaction (67.99% SS). The model also extracted two significant interaction principal component analysis (IPCA) with a total of 79.9% SS and 67.8% corresponding degrees of freedom. Genotype TMV-7 (17.308 g/plant) followed by N-8 (13.994 g/plant) had the highest average yield which was much greater than the grand mean (10.976 g/plant) and declared as area specific adapted genotypes. Environments El, E2 and E4 were unfavorable while E3 is the most suitable as indicated by high mean value of IPCA 1 and low value of IPCA 2.

scholarly journals Why People Who Know Less Think They Know about COVID-19: Evidence from US and Singapore

2021 ◽  
pp. 107769902110494
Author(s):  
Sangwon Lee ◽  
Masahiro Yamamoto ◽  
Edson C. Tandoc
Keyword(s):  
Social Media ◽  
The United States ◽  
Factual Knowledge ◽  
Interaction Patterns ◽  
Traditional Media ◽  
Subjective Knowledge ◽  
Types Of Knowledge ◽  
Knowledge Based ◽  
Different Types ◽  
Main Effects

This study explores the effects of traditional media and social media on different types of knowledge about COVID-19. We also explore how surveillance motivation moderates the relationship between media use and different types of knowledge. Based on cross-national data from Singapore and the United States, we find that news seeking via social media is negatively related to factual knowledge and positively related to subjective knowledge and knowledge miscalibration. News seeking via traditional media is not significantly related to factual knowledge. Although the main effects are highly consistent across the two countries, we find some different interaction patterns across these countries.

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).

GENOTYPE-ENVIRONMENT INTERACTION OF YIELD IN CEREAL CROPS IN NORTHWESTERN CANADA

1981 ◽  
Vol 61 (2) ◽  
pp. 255-263 ◽  
Author(s):  
R. M. De PAUW ◽  
D. G. FARIS ◽  
C. J. WILLIAMS
Keyword(s):  
Triticum Aestivum L ◽  
Regression Coefficients ◽  
Cereal Crops ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Hordeum Vulgare L ◽  
Genotype Environment Interaction ◽  
Early Maturing ◽  
Frost Free Period ◽  
Main Effects

Three cultivars of each crop, wheat (Triticum aestivum L.), oats (Avena sativa L.), and barley (Hordeum vulgare L.), were grown for 4 yr at five locations north of the 55th parallel in northwestern Canada. There were highly significant differences among all main effects and interactions. Galt barley produced the highest seed yield followed by Centennial barley, Random oats and Harmon oats. Victory oats, Olli barley, Neepawa wheat and Pitic 62 wheat yielded similarly to each other while Thatcher wheat was significantly lower yielding. Mean environment yields ranged from 2080 to 5610 kg/ha. The genotype-environment (GE) interaction of species and cultivars was sufficiently complicated that it could not be characterized by one or two statistics (e.g., stability variances or regression coefficients). However, variability in frost-free period among years and locations contributed to the GE interaction because, for example, some cultivars yielded well (e.g., Pitic 62) only in those year-location environments with a relatively long frost-free period while other early maturing cultivars (e.g., Olli) performed well even in a short frost-free period environment.

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