scholarly journals GGE-BIPLOT OF MULTIVARIATE INDEX TO SELECT MAIZE PROGENIES FOR EFFICIENT ASSOCIATION WITH Azospirillum brasiliense1

2021 ◽  
Vol 34 (4) ◽  
pp. 739-751
Author(s):  
FELIPE CECCON ◽  
LIVIA MARIA CHAMMA DAVIDE ◽  
MANOEL CARLOS GONÇALVES ◽  
ADRIANO DOS SANTOS ◽  
ELAINE PINHEIRO REIS LOURENTE
Keyword(s):  
Selection Index ◽  
High Yield ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Mato Grosso ◽  
Genotype Environment Interaction ◽  
Two Cities ◽  
The Stability ◽  
Mato Grosso Do Sul ◽  
Selection Of

ABSTRACT Maize is widely cultivated in Brazil, and nitrogen is a major nutrient for its yield. Azospirillum brasiliense bacteria help in plant nutrient supply; however, maize-Azospirillum symbiosis is not very efficient and requires selection of genotypes with a more efficient association. Multivariate indexes facilitate selection using a single value, and GGE-biplot analysis enables the visualization of the genotype-environment interaction from this value. The present study aimed to select progenies that effectively associate with the bacteria and study the efficiency of progeny selection using a multivariate index observed in the GGE-biplot method. The experiments were conducted in two cities in the state of Mato Grosso do Sul. In a simple 16 × 16 lattice, 256 genotypes were evaluated in the presence and absence of diazotrophic bacteria. PH, SL, SD, FI, HGM, SS, and GY were measured for the construction of a selection index. Genotypes exhibited significant genotype–environment interactions for all evaluated traits, allowing their use in the selection index. High-yield genotypes were not those with the highest selection index values. The traits GY, SD, HGM, SS, SL, and PH contributed the most to the construction of the index. The no-till system may have contributed to the weaker response of maize inoculated with Azospirillum brasiliense. Genotype 96 had the highest values of the characteristics used to calculate the GISI, along with the stability between environments.

scholarly journals Selection of corn inbred lines, testers, and hybrids adapted to second-crop environments

2020 ◽  
Vol 55 ◽  
Author(s):  
Rodrigo Gomes ◽  
Marcos Ventura Faria ◽  
Jocimar Costa Rosa
Keyword(s):  
Inbred Lines ◽  
The State ◽  
High Yield ◽  
Crop Season ◽  
Mato Grosso ◽  
The Stability ◽  
Corn Inbred ◽  
Main Effects ◽  
Selection Of ◽  
Single Cross Hybrids

Abstract: The objective of this work was to evaluate the yield performance, adaptability, and stability of corn (Zea mays) topcross hybrids in nine environments in Brazil, in order to select inbred lines, testers, and promising hybrid combinations for these environments in the second crop season. The trials were carried out during the second crop season of 2017, in six sites in the state of Paraná and three in the state of Mato Grosso do Sul, Brazil. Sixty-six single-cross hybrids, made up of 33 inbred lines crossed with two testers, were evaluated. The stability of the genotypes was assessed by the Schmidt & Cruz method and by the additive main effects and multiplicative interaction (AMMI) methodology. In addition, the genetic parameters and the general combining ability of the testers were estimated. Tester 2 is the most adapted to discriminate inbred lines in the second-crop environments. Inbred lines 12, 21, 23, and 32 are recommended for obtaining hybrids to be cultivated in the second crop season due to their good adaptability and stability and high yield in the crosses in which they participated. The HS123, HS223, and HS232 hybrids present the best performance and are the most recommended for the second-crop environments.

scholarly journals AMMI and GGE Biplot Analysis for Stability of Yield in Mid-early Pigeonpea [Cajanus cajan (L.) Millspaugh] Genotypes

2022 ◽  
Author(s):  
P. Jagan Mohan Rao ◽  
N. Sandhyakishore ◽  
S. Sandeep ◽  
G. Neelima ◽  
A. Saritha ◽  
...  
Keyword(s):  
Block Design ◽  
High Yield ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Genotype Environment Interaction ◽  
Single Location ◽  
Randomized Complete Block Design ◽  
Advanced Stages ◽  
Superior Genotypes

Background: The genotype × environment interaction greatly influences the success of breeding and in multi-location trials complicates the identification of superior genotypes for a single location, due to magnitude of genotype by location interaction are often greater than genotype by year interaction. This necessitates genotype evaluation in multi environments trials in the advanced stages of selection. Methods: Nine elite pigeonpea genotypes of mid-early duration were evaluated in six diverse locations in randomized complete block design with three replications during kharif, 2019 to ascertain the stable genotypes, environments discrimination and genotype by environment crossovers using AMMI and GGE biplot stability models. Result: The results in the present investigation revealed that first two principal components explained 73.4% of variation interaction, while, 80.50% in GGE biplot. Both the models identified WRGE-126 (G6) as stable performer with high yield (1733 kg ha-1) and among the locations Tandur (E1) measured as the ideal environment. Whereas, the environments, Adilabad (E3) and Warangal (E4) were observed representative with better discriminating ability.

Selecting stable and high-yielding sorghum cultivars for the semi-arid tropics

1994 ◽  
Vol 74 (4) ◽  
pp. 759-762
Author(s):  
O. P. Dangi ◽  
R. I. Hamilton ◽  
C. S. Lin ◽  
D. Andre ◽  
J. J. Johnson
Keyword(s):  
High Yield ◽  
Annual Rainfall ◽  
Environment Interaction ◽  
Mean Square ◽  
Stability Parameters ◽  
The North ◽  
Genotype Environment Interaction ◽  
Northern Cameroon ◽  
The Stability ◽  
Extreme North

A sorghum breeding program was reactivated in 1981 and selected cultivars, along with local checks, were evaluated in two experiments in the sorghum growing region of northern Cameroon. Experiment 1 was conducted in the Extreme North Province where annual rainfall ranges from 450 to 850 mm. Experiment 2 was conducted in the North Province where annual rainfall exceeds 850 mm. The objective of the study was to select a high yield and high stability sorghum cultivar for each region. The cultivar's responses were investigated using two analyses: the adaptability analysis and the stability analysis. The former used the method of superiority measure, defined by distance mean square between the test cultivar and the maximum (the highest yield in the location), and the latter used type 4 stability parameter, defined by the years within location mean square averaged over all locations. The conceptual separation of adaptability and stability facilitated the cultivars assessment. The results showed that in exp. 1, three cultivars S–35, CS–54 and CS–61 had similar adaptability and stability, while in the exp. 2, S–34 was best in terms of yield but was unstable due to susceptibility to grain mold. In contrast, the second best cultivar CS–63 was poorer in the high-yielding environments but was more stable than S–34. Key words: Sorghum, genotype-environment interaction, adaptability, stability parameters

scholarly journals Selection Index in the Study of Adaptability and Stability in Maize

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Rogério Lunezzo de Oliveira ◽  
Renzo Garcia Von Pinho ◽  
Daniel Furtado Ferreira ◽  
Luiz Paulo Miranda Pires ◽  
Wagner Mateus Costa Melo
Keyword(s):  
Principal Components ◽  
Predictive Accuracy ◽  
Selection Index ◽  
The Other ◽  
Gge Biplot ◽  
Southern Brazil ◽  
Maize Hybrids ◽  
Biplot Analysis ◽  
The Stability ◽  
Selection Of

This paper proposes an alternative method for evaluating the stability and adaptability of maize hybrids using a genotype-ideotype distance index (GIDI) for selection. Data from seven variables were used, obtained through evaluation of 25 maize hybrids at six sites in southern Brazil. The GIDI was estimated by means of the generalized Mahalanobis distance for each plot of the test. We then proceeded to GGE biplot analysis in order to compare the predictive accuracy of the GGE models and the grouping of environments and to select the best five hybrids. The G × E interaction was significant for both variables assessed. The GGE model with two principal components obtained a predictive accuracy (PRECORR) of 0.8913 for the GIDI and 0.8709 for yield (t ha−1). Two groups of environments were obtained upon analyzing the GIDI, whereas all the environments remained in the same group upon analyzing yield. Coincidence occurred in only two hybrids considering evaluation of the two features. The GIDI assessment provided for selection of hybrids that combine adaptability and stability in most of the variables assessed, making its use more highly recommended than analyzing each variable separately. Not all the higher-yielding hybrids were the best in the other variables assessed.

scholarly journals AMMI and GGE Biplot for genotype × environment interaction: a medoid–based hierarchical cluster analysis approach for high–dimensional data

2018 ◽  
Vol 55 (2) ◽  
pp. 97-121 ◽  
Author(s):  
Anderson Cristiano Neisse ◽  
Jhessica Letícia Kirch ◽  
Kuang Hongyu
Keyword(s):  
Cluster Analysis ◽  
Wheat Yield ◽  
Hierarchical Cluster ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Spring Bread Wheat ◽  
Genotype Environment Interaction ◽  
Complex Process ◽  
Yield Trial ◽  
Selection Of

SummaryThe presence of genotype-environment interaction (GEI) influences production making the selection of cultivars in a complex process. The two most used methods to analyze GEI and evaluate genotypes are AMMI and GGE Biplot, being used for the analysis of multi environment trials data (MET). Despite their different approaches, both models complement each other in order to strengthen decision making. However, both models are based on biplots, consequently, biplot-based interpretation doesn’t scale well beyond two-dimensional plots, which happens whenever the first two components don’t capture enough variation. This paper proposes an approach to such cases based on cluster analysis combined with the concept of medoids. It also applies AMMI and GGE Biplot to the adjusted data in order to compare both models. The data is provided by the International Maize and Wheat Improvement Center (CIMMYT) and comes from the 14th Semi-Arid Wheat Yield Trial (SAWYT), an experiment concerning 50 genotypes of spring bread wheat (Triticum aestivum) germplasm adapted to low rainfall. It was performed in 36 environments across 14 countries. The analysis provided 25 genotypes clusters and 6 environments clusters. Both models were equivalent for the data’s evaluation, permitting increased reliability in the selection of superior cultivars and test environments.

scholarly journals Evaluation of stability in maize hybrids using univariate parametric methods

2021 ◽  
Author(s):  
Seyed Habib Shojaei ◽  
Khodadad Mostafavi ◽  
Amirparviz Lak ◽  
Ali Omrani ◽  
Saeed Omrani ◽  
...  
Keyword(s):  
Grain Yield ◽  
Climatic Conditions ◽  
High Yield ◽  
Environment Interaction ◽  
Average Yield ◽  
Stability Parameters ◽  
Environmental Variance ◽  
Maize Hybrids ◽  
Genotype Environment Interaction ◽  
The Stability

AbstractGenotype × environment interaction is one of the complex issues of breeding programs to produce high-yielding and compatible cultivars. Interaction of genotype × environment and make the more accurate selection, the performance and stability of hybrids need to be considered simultaneously. This study aimed to investigate stable genotypes with yield using 12 maize hybrids in different climatic conditions of Iran. The experimental design used was a randomized complete blocks design in three replications in two cropping years in Karaj, Birjand, Shiraz, and Arak stations. The simple analysis of variance performed on grain yield of genotypes indicated that all hybrids studied each year and station were significantly different in grain yield. Also, the combined analysis results showed a significant effect on the environment, the effects of genotype, and the interaction of genotype × environment and t in the studied hybrids different. Comparing Duncan's mean on the data obtained from the research, KSC705 genotypes with an average yield of 7.21 and KSC704 genotype with an average yield of 7.04 were identified as high yield cultivars. In order to identify stable cultivars, six stability parameters were used. KSC260 and KSC707 genotypes had stability Based on the environmental variance, also had stability based KSC705, KSC707 genotype on environmental the coefficient of variation, and KSC260 genotypes had stability based methods of genotype and environment interaction. As well as based on Eberhart and Russell regression coefficient had the stability to KSC400 and SC647 genotypes. Also, they were identified as the most stable genotypes based on the detection coefficient method, KSC707, and KSC703 genotypes.

scholarly journals THE ANALYSIS OF GENOTYPE × ENVIRONMENT INTERACTION USING RAPESEED (BRASSICA NAPUS L.) BY GGE BIPLOT METHOD

2020 ◽  
Vol 51 (5) ◽  
pp. 1337-1349
Author(s):  
Motahhari & et al.
Keyword(s):  
Seed Yield ◽  
Agricultural Research ◽  
Environment Interaction ◽  
Block Designs ◽  
Gge Biplot ◽  
Ge Interaction ◽  
Brassica Napus L ◽  
Biplot Analysis ◽  
Genotype Environment Interaction ◽  
Selection Of

This study was aimed to asses seed yield performances of 16 rapeseed genotypes  in randomized complete block designs (RCBD) with three replications at four Agricultural Research Stations of cold and mid-cold regions over two years in Iran (2015-2017). GGE biplot analysis indicated that the first two components explained 83% of seed yield variations. Genotype, location and their interaction explained 18%, 52% and 30%of the total GE variation, respectively. In this research, a graphically represented GGE biplot analysis enabled selection of stable and high-yielding genotypes for all investigated locations, as well as genotypes with specific adaptability. The GGE biplot analysis was adequate in explaining GE interaction for seed yield in rapeseed. It can be concluded that genotypes G2, G4 and G13 had the highest mean seed yield and stability in four investigated locations. For specific adaptability, G13 was recommended for Isfahan, Karaj and Kermanshah and G4 for Mashhad.

scholarly journals l.240 ADAPTABILITY AND STABILITY OF Coffea arabica LINES IN THE WESTERN AMAZON

2019 ◽  
Vol 14 (2) ◽  
pp. 240
Author(s):  
Carolina Augusto de Souza ◽  
Alexsandro Lara Teixeira ◽  
Josemar Dávila Torres ◽  
Camila Andrade Silva ◽  
Marcelo Curitiba Espindula ◽  
...  
Keyword(s):  
Low Temperatures ◽  
Coffea Arabica ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Early Maturation ◽  
Genotype Environment Interaction ◽  
The Mean ◽  
Low Altitude ◽  
Set Up ◽  
Selection Of

Growing Coffea arabica in regions of the Western Amazon is limited by early maturation and by its limited adaptation to regions of low altitude and high temperature. The aim in this study was to quantify the genotype × environment interaction of C. arabica lines in four different environments of the Western Amazon, seeking to assist selection of new lines with greater adaptability and stability for the region. In the months of December 2012 and January 2013, four competitive trials were set up in municipalities of the states of Rondônia and Acre. Each trial was composed of 21 lines and 4 reference cultivars evaluated as controls recommended for planting in the southeast region. In combined analysis, significant differences were not detected between the cultivars and controls; the mean yield of hulled coffee was 12.05 bags ha-1. The Alta Floresta Do Oeste environment has higher yield and is the only environment favorable for growing C. arabica; that environment is differentiated from the others through its higher altitudes and low temperatures. Through GGE biplot analyses, lines 12 and 13, identified as H514-7-10-6-9 and H514-7-10-6-2-3-9, were found to have results superior to the controls in the municipality of Alta Floresta Do Oeste, RO. The gain from selection of 56% obtained from line G12 and the gain of 46% obtained from line G13 show performance superior to the best control. The germplasm studied does not have genetic variability that contributes to selection of plants for adaptation to the low altitude and high temperatures in the amazonic region.

scholarly journals Yield Stability of Soybean Genotypes in Tropical Environments based on Genotype and Genotype-by-Environment Biplot

2019 ◽  
Vol 46 (3) ◽  
pp. 231-239 ◽  
Author(s):  
Ayda Krisnawati ◽  
And Mochammad Muchlish Adie
Keyword(s):  
Seed Yield ◽  
Yield Stability ◽  
High Yield ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Ge Interaction ◽  
Tropical Environments ◽  
Genotype Environment Interaction ◽  
Soybean Genotypes ◽  
Ideal Genotype

Genotype × environment interaction is universal phenomenon when different genotypes are tested in a number of environments. The objective of this experiment was to determine the seed yield stability of soybean genotypes. Seven soybean genotypes and two check cultivars were evaluated at eight soybean production centers during the dry season 2015. Stability analysis on seed yield was based on the GGE biplot method. The combined analysis showed that yield and yield components were significantly affected by genotype (G), environments (E), and genotype × environment interaction (GEI), except for number of filled pods. The highest yield was G6 (3.07 ton ha-1), followed by G7 (2.93 ton ha-1). The “which-won-where” polygon mapping resulted two mega-environments. The best genotype for the first mega-environment was G1 (G511H/Anjasmoro//Anjasmoro-2-8) at E5 (Pasuruan2); and the second one was G6 (G511 H/Anj//Anj///Anj////Anjs-6-7) at E1 (Nganjuk), E2 (Mojokerto), E3 (Blitar), E4 (Pasuruan1), E6 (Jembrana), E7 (Tabanan), and E8 (Central Lombok). The G7 (G511 H/Anjasmoro-1-4-2) was closest to ideal genotype as indicated by relatively stable and produced high yield across environments. The analysis of multi-environment trials data using GGE is useful for determining mega-environment analysis and stability of genotype which focusing on overall performance to identify superior genotypes.Keywords: GE interaction, GGE biplot, Glycine max, seed yield

Simultaneous selection of yield and yield stability in chickpea genotypes using the GGE biplot technique

2013 ◽  
Vol 61 (3) ◽  
pp. 185-194 ◽  
Author(s):  
E. Farshadfar
Keyword(s):  
Grain Yield ◽  
Principal Component ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Yield Data ◽  
Genotype By Environment ◽  
Growing Seasons ◽  
Genotype Environment Interaction ◽  
The Mean ◽  
Selection Of

GGE biplot analysis is an effective method, based on principal component analysis (PCA), to fully explore multi-environment trials (METs). It allows visual examination of the relationships among the test environments, genotypes and the genotype-by-environment interactions (G×E interaction). The objective of this study was to explore the effect of genotype (G) and the genotype × environment interaction (GEI) on the grain yield of 20 chickpea genotypes under two different rainfed and irrigated environments for 4 consecutive growing seasons (2008–2011). The yield data were analysed using the GGE biplot method. The first mega-environment contained environments E1, E3, E4 and E6, with genotype G17 (X96TH41K4) being the winner; the second mega-environment contained environments E5, E7 and E8, with genotype G12 (X96TH46) being the winner. The E2 environment made up another mega-environment, with G19 (FLIP-82-115) the winner. The mean performance and stability of the genotypes indicated that genotypes G4, G16 and G20 were highly stable with high grain yield.

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