scholarly journals Understanding and utilization of genotype-by-environment interaction in maize breeding

Genetika ◽  
2010 ◽  
Vol 42 (1) ◽  
pp. 79-90 ◽  
Author(s):  
Vojka Babic ◽  
Milosav Babic ◽  
Mile Ivanovic ◽  
Marija Kraljevic-Balalic ◽  
Miodrag Dimitrijevic
Keyword(s):  
Cluster Analysis ◽  
Principal Components ◽  
Maximum Yield ◽  
Growing Season ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Maize Breeding ◽  
Good Guide ◽  
Components Analysis ◽  
Selection Of

Due to the interaction and noise in the experiments, yield trails for studying varieties are carried out in numerous locations and in the course of several years. Data of such trials have three principle tasks: to evaluate precisely and to predict the yield on the basis of limited experimental data; to determine stability and explain variability in the response of genotypes across locations; and to be a good guide for the selection of the best genotype for sowing under new agroecological conditions. The yield prediction without the inclusion of the interaction with the environments is incomplete and imprecise. Therefore, a great deal of breeding and agronomic studies are devoted to observing of the interaction via multilocation trials with replicates with the aim to use the interaction to obtain the maximum yield in any environment. Fifteen maize hybrids were analyzed in 24 environments. As the interaction participates in the total sum of squares with 6%, and genotypes with 2%, the interaction deserves observations more detailed than the classical analysis of variance (ANOVA) provides it. With a view to observe the interaction effect in detail in order to prove better understanding of genotypes, environments and their interactions AMMI (Additive Main Effect and Multiplicative Interaction) and the cluster analysis were applied. The partition of the interaction into the principal components by the PCA analysis (Principal Components Analysis) revealed a part of systematic variations in the interaction. These variations are attributed to the length of the growing season in genotypes and to the precipitation sum during the growing season in environments. Results of grouping by the cluster analysis are in high accordance with grouping observed in the biplot of the AMMI1 model.

scholarly journals GENOTYPE BY ENVIRONMENT INTERACTION IN COWPEA LINES USING GGE BIPLOT METHOD

2018 ◽  
Vol 31 (1) ◽  
pp. 64-71 ◽  
Author(s):  
MASSAINE BANDEIRA E SOUSA ◽  
KAESEL JACKSON DAMASCENO-SILVA ◽  
MAURISRAEL DE MOURA ROCHA ◽  
JOSÉ ÂNGELO NOGUEIRA DE MENEZES JÚNIOR ◽  
LAÍZE RAPHAELLE LEMOS LIMA
Keyword(s):  
Maximum Yield ◽  
Genotype By Environment Interaction ◽  
Superior Performance ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Brazilian Cerrado ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Adjusted Means ◽  
Selection Of

ABSTRACT The GGE Biplot method is efficien to identify favorable genotypes and ideal environments for evaluation. Therefore, the objective of this work was to evaluate the genotype by environment interaction (G×E) and select elite lines of cowpea from genotypes, which are part of the cultivation and use value tests of the Embrapa Meio-Norte Breeding Program, for regions of the Brazilian Cerrado, by the GGE-Biplot method. The grain yield of 40 cowpea genotypes, 30 lines and 10 cultivars, was evaluated during three years (2010, 2011 and 2012) in three locations: Balsas (BAL), São Raimundo das Mangabeiras (SRM) and Primavera do Leste (PRL). The data were subjected to analysis of variance, and adjusted means were obtained to perform the GGE-Biplot analysis. The graphic results showed variation in the performance of the genotypes in the locations evaluated over the years. The performance of the lines MNC02-675F-4-9 and MNC02-675F-4-10 were considered ideal, with maximum yield and good stability in the locations evaluated. There mega-environments were formed, encompassing environments correlated positively. The lines MNC02-675F-4-9, MNC02-675F-9-3 and MNC02-701F-2 had the best performance within each mega-environment. The environment PRL10 and lines near this environment, such as MNC02-677F-2, MNC02-677F-5 and the control cultivar (BRS-Marataoã) could be classified as those of greater reliability, determined basically by the genotypic effects, with reduced G×E. Most of the environments evaluated were ideal for evaluation of G×E, since the genotypes were well discriminated on them. Therefore, the selection of genotypes with adaptability and superior performance for specific environments through the GGE-Biplot analysis was possible.

The Golf Players' Motivations: The Algarve Case

2006 ◽  
Vol 6 (3) ◽  
pp. 227-238 ◽  
Author(s):  
Antonia Correia ◽  
Pedro Pintassilgo
Keyword(s):  
Cluster Analysis ◽  
Principal Components Analysis ◽  
Social Environment ◽  
Principal Components ◽  
Golf Courses ◽  
The Sun ◽  
Market Segments ◽  
The Third ◽  
Components Analysis ◽  
Selection Of

The purpose of this article is to investigate the motivations behind golf demand in the Algarve — one of Europe's most popular golf destinations. The research is based on the results of a survey on the golf demand of Algarve's golf courses, held in 2002. In order to identify the main motives behind golf demand in the region, a principal components analysis was performed. Four main choice factors were identified to explain the selection of Algarve's golf courses. The first was designated social environment and is associated with motives such as events and beaches. The second, leisure, is related to restaurants and bars, landscape, weather and accommodation. The third, entitled golf, is directly related to characteristics of courses. The fourth, logistics, is associated with variables such as price and accessibility. It is also found, through a cluster analysis that the choice factors can be associated with three market segments: the tourist golfer, who is mostly concerned with the golf courses and the game; the householder golfer, essentially centred on accommodation, gastronomy, landscape, weather, price and accessibility; and finally, the sun-beach tourist, who is mostly interested in tourist opportunities.

Simultaneous selection for high yield and stability in common bean (Phaseolus vulgaris) genotypes

2002 ◽  
Vol 138 (3) ◽  
pp. 249-253 ◽  
Author(s):  
F. MEKBIB
Keyword(s):  
Common Bean ◽  
Genotype By Environment Interaction ◽  
Yield Stability ◽  
High Yield ◽  
Environment Interaction ◽  
Yield Performance ◽  
Simultaneous Selection ◽  
Selection For ◽  
Stability Statistics ◽  
Selection Of

Phenotypic yield stability is a trait of special interest for plant breeders and farmers. This value can be quantified if genotypes are evaluated in different environments. Common bean is the main cash crop and protein source of farmers in many lowland and mid-altitude areas of Ethiopia. An experiment was undertaken to evaluate common bean genotypes for yield performance at Alemaya, Bako and Nazreth in Ethiopia for 3 years. The yield performance of genotypes was subjected to stability analysis and yield-stability statistics were generated to aid the selection of genotypes that were high yielding and very stable. The significant genotype by environment interaction indicated that the relative performance of the varieties altered in the different environments. Genotype yield performance varied ranging from 1511–2216 kg/ha. Simultaneous selection for yield and yield-stability statistics using YS(i) statistics indicated that A 410, GLP x92, Mx-2500-19, G 2816, A-195, 997-CH-1173, Diacol calima, ICA 15541 and AND 635 were both high yielding and stable. Following this study, using farmers’ evaluation and other criteria, GLP x92 and G-2816 were identified as preferred genotypes and were released for further production.

Chemistry research in Europe: A publication analysis (2006–2016)

2019 ◽  
pp. 016555151986549
Author(s):  
Hakan Kaygusuz
Keyword(s):  
Cluster Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Polymer Science ◽  
Chemistry Research ◽  
Research Outcome ◽  
Components Analysis ◽  
Number Of Publications

In this article, chemistry research in 51 different European countries between years 2006 and 2016 was studied using statistical methods. This study consists of two parts: In the first part, different economical, institutional and citation parameters were correlated with the number of publications, citations and chemical industry numbers using principal components analysis and hierarchical cluster analysis. The results of the first part indicated that economical and geographical parameters directly affect the chemistry research outcome. In the second part, research in branches of chemistry and related disciplines such as analytical chemistry, polymer science and physical chemistry were analysed using principal components analysis and hierarchical cluster analysis for each country. Publication data were collected as the number of chemistry publications (in Science Citation Index–Expanded (SCI-E)) between years 2006 and 2016 in different chemistry subdisciplines and related scientific areas. Results of the second part of the study produced geographical and economical clusters of countries, interestingly, without addition of any geographical data.

Potential of random amplified microsatellites (RAMS) to typify and discriminate varieties of Physalis ixocarpa Brot. ex Hornem.

2018 ◽  
pp. 396
Author(s):  
Eli Amanda Delgado-Alvarado, Norma Almaraz-Abarca ◽  
Cirenio Escamirosa- Tinoco ◽  
Jose Natividad Uribe-Soto, Jose Antonio Avila-Reyes ◽  
Rene Torres-Ricario, Ana Isabel Chaidez-Ayala
Keyword(s):  
Cluster Analysis ◽  
Genetic Variability ◽  
South America ◽  
Principal Components ◽  
Genetic Relationships ◽  
Important Species ◽  
Physalis Peruviana ◽  
Edible Species ◽  
Components Analysis ◽  
Genetic Closeness

Physalis ixocarpa is an edible species of Solanaceae. This is one of the few cultivated and economically important species of the genus in Mesoamerica. In Mexico, several varieties and landraces have been developed, which have not been molecularly characterized. In the current study, five RAMS primers were used to characterize and assess the genetic variability of two varieties and three landraces of this species. The capacity of these markers to discriminate between them was also evaluated. With comparative aims, Physalis peruviana, the most economically important species of the genus in South America, was analyzed in the same manner. The results revealed that the varieties and landraces of P. ixocarpa conserve important levels of genetic variability (21.75% > Polymorphism < 42.75%), which were higher than that found for P. peruviana (10.75% Polymorphism). RAMS were useful specific markers, as P. peruviana and P. ixocarpa were clearly distinguished one from each other by both cluster analysis and principal components analysis. Close genetic relationships were found between the landraces San Isidro Chihuiro and Verde Puebla, and between the varieties Diamante and Rendidora. In spite of the genetic closeness, the RAMS amplification profiles had a clear varietal-specific tendency, in such a way that they may represent varietal fingerprints, which can be used as authentication tool for varieties and landraces of P. ixocarpa.

Sign in / Sign up

Export Citation Format

Share Document