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 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 Drought Tolerant Maize Hybrids Using Path Coefficient Analysis and Selection Index

2017 ◽  
Vol 20 (3) ◽  
pp. 132-139 ◽  
Author(s):  
Abdalla Dao ◽  
Jacob Sanou ◽  
Edgar V.S. Traor ◽  
Vernon Gracen ◽  
Eric Y. Danquah
Keyword(s):  
Selection Index ◽  
Path Coefficient ◽  
Path Coefficient Analysis ◽  
Maize Hybrids ◽  
Drought Tolerant ◽  
Selection Of

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.

scholarly journals GGE biplot analysis of multi-environment trials of NS maize hybrids

2011 ◽  
Vol 48 (1) ◽  
pp. 77-82 ◽  
Author(s):  
Bojan Mitrovic ◽  
Dusan Stanisavljevic ◽  
Sanja Treskic ◽  
Milisav Stojakovic ◽  
Goran Bekavac ◽  
...  
Keyword(s):  
Gge Biplot ◽  
Maize Hybrids ◽  
Biplot Analysis

scholarly journals On the Stability of Resonant Asteroid Orbits

1983 ◽  
Vol 74 ◽  
pp. 141-152 ◽  
Author(s):  
J.D. Hadjidemetriou ◽  
S. Ichtiaroglou
Keyword(s):  
Phase Space ◽  
Periodic Orbits ◽  
Energy Levels ◽  
The Other ◽  
Stable Region ◽  
Surface Of Section ◽  
Resonant Orbits ◽  
Symmetric Periodic Orbits ◽  
The Stability ◽  
Selection Of

AbstractThe stability of the asteroid orbits has been studied by the method of surface of section. Families of simple symmetric periodic orbits of the asteroid and their stability have been computed and this served as a guide for the selection of the energy levels for the surface of section. In this way all possible cases for the structure of phase space have been obtained. It was found that the region in phase space around the resonant orbits at the resonances 1/3, 3/5, 5/7,.... is unstable, but small stability regions of doubly symmetric periodic orbits near the above resonances are also present. At the resonances 1/2, 2/3, 3/4, .... it was found that there exist two separate regions in phase space at about the same resonance 1/2, 2/3, 3/4,...., respectively, one being stable and the other unstable. At certain energy levels only the stable region appears. The above results are consistent with the observed distribution of the asteroids.

scholarly journals A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Andrea Bommert ◽  
Jörg Rahnenführer ◽  
Michel Lang
Keyword(s):  
Feature Selection ◽  
Predictive Model ◽  
Predictive Accuracy ◽  
Pearson Correlation ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Sparse Models ◽  
Data Set ◽  
The Stability ◽  
Selection Of

Finding a good predictive model for a high-dimensional data set can be challenging. For genetic data, it is not only important to find a model with high predictive accuracy, but it is also important that this model uses only few features and that the selection of these features is stable. This is because, in bioinformatics, the models are used not only for prediction but also for drawing biological conclusions which makes the interpretability and reliability of the model crucial. We suggest using three target criteria when fitting a predictive model to a high-dimensional data set: the classification accuracy, the stability of the feature selection, and the number of chosen features. As it is unclear which measure is best for evaluating the stability, we first compare a variety of stability measures. We conclude that the Pearson correlation has the best theoretical and empirical properties. Also, we find that for the stability assessment behaviour it is most important that a measure contains a correction for chance or large numbers of chosen features. Then, we analyse Pareto fronts and conclude that it is possible to find models with a stable selection of few features without losing much predictive accuracy.

scholarly journals Evaluation of genotype x environment interactions in maize hybrids using GGE biplot analysis

2011 ◽  
Vol 11 (1) ◽  
pp. 01-09 ◽  
Author(s):  
Fatma Aykut Tonk ◽  
Emre Ilker ◽  
Muzaffer Tosun
Keyword(s):  
Grain Yield ◽  
Gge Biplot ◽  
Maize Breeding ◽  
Private Companies ◽  
Breeding Programs ◽  
Maize Hybrids ◽  
Biplot Analysis ◽  
Maize Genotypes ◽  
Cropping Seasons ◽  
Hybrid Maize

Seventeen hybrid maize genotypes were evaluated at four different locations in 2005 and 2006 cropping seasons under irrigated conditions in Turkey. The analysis of variance showed that mean squares of environments (E), genotypes (G) and GE interactions (GEI) were highly significant and accounted for 74, 7 and 19 % of treatment combination sum squares, respectively. To determine the effects of GEI on grain yield, the data were subjected to the GGE biplot analysis. Maize hybrid G16 can be proposed as reliably growing in test locations for high grain yield. Also, only the Yenisehir location could be best representative of overall, locations for deciding about which experimental hybrids can be recommended for grain yield in this study. Consequently, using of grain yield per plant instead of grain yield per plot in hybrid maize breeding programs could be preferred by private companies due to some advantages.

scholarly journals Assessment of Early-Maturing Maize Hybrids and Testing Sites Using GGE Biplot Analysis

Crop Science ◽  
2017 ◽  
Vol 57 (6) ◽  
pp. 2942-2950 ◽  
Author(s):  
M. Oyekunle ◽  
A. Haruna ◽  
B. Badu-Apraku ◽  
I. S. Usman ◽  
H. Mani ◽  
...  
Keyword(s):  
Gge Biplot ◽  
Maize Hybrids ◽  
Biplot Analysis ◽  
Early Maturing

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 GGE Biplot Analysis for Stability and Adaptability of Maize Hybrids in Western Region of Indonesia

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Dedi Ruswandi ◽  
Muhammad Syafii ◽  
Haris Maulana ◽  
Mira Ariyanti ◽  
Nyimas Poppy Indriani ◽  
...  
Keyword(s):  
Western Region ◽  
Gge Biplot ◽  
Specific Location ◽  
Maize Hybrids ◽  
Biplot Analysis ◽  
Small Holder Farmers ◽  
The Western Region ◽  
On Farm ◽  
Small Holder

Hybrids that are stable or adaptable in a specific location for the western region of Indonesia are required to increase production of maize in Indonesia. The objectives of the study were (i) to select maize hybrids which are stable or adaptable in the western region of Indonesia and (ii) to determine the discriminant location for evaluating superior hybrids in the western region. Therefore, twelve maize hybrids were planted in different locations and seasons in the western region. Hybrids were selected based on GGE biplot analysis. The results showed that G9 and G10 were stable maize hybrids. G6 was the selected hybrid for the first megaenvironment; whereas, G3 was selected as the hybrid for the second megaenvironment. The L8 and L17 were the discriminant environment for evaluating hybrids in the western region of Indonesia. The high-yielding hybrids selected in this study should be broadly evaluated on-farm in order to disseminate for small holder farmers in Sumatera and Java islands.

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