scholarly journals Simultaneous Selection Index as a Tool for Identification of Stable High Yielding Maydis Leaf Blight Resistant Maize Prebreeding Lines

2021 ◽  
Author(s):  
Anjali Joshi ◽  
Sneha Adhikari ◽  
Narendra Kumar Singh ◽  
Amarjeet Kumar ◽  
Jai Prakash Jaiswal ◽  
...  
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Selection Index ◽  
Leaf Blight ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype X Environment ◽  
Biplot Analysis ◽  
Breeding Programmes ◽  
Simultaneous Selection

Abstract Maize is a crop possessing high adaptability however, large differential genotypic responses have been reported when evaluated under multiple environments. Using randomized complete block design with two replications a total of 169 teosinte derived maize backcross inbred lines (BILs) were evaluated in three different environments namely, E2, E4 and E6 for maydis leaf blight (MLB) resistance and grain yield. Out of these, 73 BILs were identified displaying resistance to MLB in at least one of the environments and were subjected to additive main effect and multiplicative interaction (AMMI) analysis and genotype and genotype X environment (GGE) biplot analysis for identification of lines showing stable and high MLB resistance and grain yield. Highly significant effects of genotype, environment and genotype X environment interaction (GEI) were observed for both the traits studied. AMMI ANOVA for percent disease index (PDI) revealed that highest percentage of total sum of squares (SS) was attributed to GEI (40.55%) while 32.86% and 26.59% was contributed by genotype and environment, respectively. For grain yield largest contribution of 68.02% towards SS was done by genotype component followed by GEI (17.50%) and E (14.48%). GGE biplot analysis identified two mega environments for both PDI (E2, E4/E6) and grain yield (E2/E4, E6). Based on AMMI stability value (ASV), genotype MT-90 (32) was observed to be most stable for PDI. While for grain yield highest stability was displayed by genotype MT-83 (28). Simultaneous selection index (SSI) helped in identification of ten stable high yielding MLB resistant genotypes namely, MT-120 (45), MT-14 (2), MT-166 (62), MT-148 (55), MT-190 (72), MT-37 (9), MT-19 (3), MT-114 (42), MT-77 (27) and MT-94 (35) which could be used in future breeding programmes either as donor of MLB resistance and grain yield or after combining ability analysis these genotypes could be used as parents for development of superior yielding MLB resistant hybrids.

scholarly journals GGE-Biplot analysis of multienvironments yield trials of Egyptian cotton (Gossypium barbadense L.,)

2021 ◽  
Vol 2 (1) ◽  
pp. 61
Author(s):  
Ahmed Mohamed Abdelmoghny ◽  
Reham Helmy Gibely ◽  
Mariz Sobhy Max ◽  
Emad Abdelazeim Amer ◽  
Salah Saber Hassan
Keyword(s):  
Block Design ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Cotton Yield ◽  
Comparative Performance ◽  
Genotype X Environment ◽  
Biplot Analysis ◽  
Cotton Genotypes ◽  
Egyptian Cotton ◽  
The Ideal

GGE biplot technique is one of the most appropriate methods for investigating the genotype x environment interaction. A total of twenty one Egyptian cotton genotypes were tested to evaluate stability and adaptability during two agricultural years at four environments using randomized complete block design with six replications. The analysis of variance showed that the effect of environments, years, environment x years, genotypes, and genotype x environment was highly significant for lint cotton yield / plot. Also, the interaction effect due to G x Y x E was also significant. The variation of sum of squares was divided for genotypes, years, environments, and GEI to 25.261 %, 0.574 %, 36.660 % and 3.396 % respectively of total variance for lint cotton yield / plot. The analysis of environments revealed that the cotton genotypes showed maximum mean values for lint cotton yield in El-Gharbiya then Kafr El-Sheikh. Comparative performance of genotypes through genotype by environment interaction (GEI) revealed that genotypes produced maximum lint cotton yield during 2019 at El-Gharbiya followed by Kafr El-Sheikh. The results of biplot analysis showed that the first and second principle components accounted 87.96 % and 5.86 %, respectively, and in total of 93.82 % lint cotton yield variance. The polygon view led to the identification of top six genotypes. G6, G7 and G15 were the ideal genotypes which has the highest mean performance coupled with maximum stability. The ideal genotype could be used as a benchmark for selection. While, the desirable genotypes was (G2, G4 and G5) characterized by high mean yield but less ideal genotypes. E2 was the ideal environment across four environments which have the highest ability to discriminate the genotypes. Four environments had long vectors with small angles (acute) are highly correlated and clustered as one mega-environment. The cotton breeder should evaluate the genotypes under new environments to reduce the costs.     

scholarly journals Genotype X Environment Interaction of Food Barley

2015 ◽  
Vol 21 ◽  
pp. 41-48
Author(s):  
Gebremedhin Welu
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Genotype X Environment Interaction ◽  
Environment Interaction ◽  
Genotype X Environment ◽  
Genotype Effect ◽  
Environment Effects ◽  
Randomized Complete Block Design ◽  
Barley Genotypes

The objective of this experiment was to estimate the magnitude of genotype X environment interaction on grain yield and yield related traits. Twelve varieties of food barley were included in the study planted in randomized complete block design with three replications. The ANOVA of combined and individual location revealed significant differences among the food barley genotypes for grain yield and other traits. The results of ANOVA for grain yield showed highly significant (p≤0.01) differences among genotypes evaluated for grain yield at Maychew and significant (p≤0.05) differences in Korem, Alage and Mugulat. The ANOVA over locations showed a highly significant (p≤0.01) variation for the genotype effect, environment effects, genotype X environment interaction (GEI) effect and significant (p≤0.05) variation for GEI effect of yield and for most of the yield related traits of food barley genotypes. Haftysene, Yidogit, Estayish and Basso were the genotypes with relatively high mean grain yield across all locations and they are highly performing genotypes to the area. Among locations, the highest mean grain yield was recorded at Korem and it was a suited environment to all the genotypes whereas Mugulat is unfavoured one. ECOPRINT 21: 41-48, 2014DOI: http://dx.doi.org/10.3126/eco.v21i0.11903

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 Adaptability and phenotypic stability of sugarcane clones

2018 ◽  
Vol 53 (1) ◽  
pp. 42-52 ◽  
Author(s):  
Jiuli Ani Vilas Boas Regis ◽  
João Antonio da Costa Andrade ◽  
Adriano dos Santos ◽  
Aparecido Moraes ◽  
Rafael William Romo Trindade ◽  
...  
Keyword(s):  
Block Design ◽  
Saccharum Officinarum ◽  
Genotype X Environment Interaction ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Phenotypic Stability ◽  
Genotype X Environment ◽  
Randomized Complete Block Design ◽  
Ideal Genotype ◽  
The Ideal

Abstract: The objective of this work was to select superior sugarcane (Saccharum officinarum) clones with good stability and adaptability, considering the genotype x environment interaction in two productive cycles. Twenty-five early clones plus five control clones were evaluated during two cuts (ratoon cane and plant cane) in 24 environments. A randomized complete block design was used, with three replicates. Tons of stems per hectare and tons of pol per hectare were evaluated. To verify adaptability and stability, the bisegmented regression and the multivariate (AMMI and GGE biplot) methods were used. According to the three methods, which are complementary regarding the desired information, the most promising clones in terms of stability and general adaptability are G5, G12, and G13; the last two are closest to the ideal genotype. The G13 clone is highly productive in favorable and unfavorable environments, presenting the highest averages for ton of stems and pol per hectare. The G3, G4, G10, G15, G17, G18, G22, G23, G25, G26, and G30 clones are not recommended for the 24 evaluated environments.

scholarly journals Identification of stable genotypes and genotype by environment interaction for grain yield in sorghum (Sorghum bicolor L. Moench)

2018 ◽  
Vol 17 (1) ◽  
pp. 81-86 ◽  
Author(s):  
Massaoudou Hamidou ◽  
Oumarou Souleymane ◽  
Malick N. Ba ◽  
Eric Yirenkyi Danquah ◽  
Issoufou Kapran ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genotype By Environment Interaction ◽  
Planting Date ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Food Crop ◽  
Planting Dates ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Sorghum Bicolor L

AbstractSorghum is a staple food crop in Niger and its production is constrained by sorghum midge and the use of low yielding, local sorghum varieties. To improve sorghum productivity, it is crucial to provide farmers with high yielding sorghum cultivars that are resistant to midge. We evaluated 282 genotypes in four environments of Niger Republic. Alpha (0.1) lattice with two replications was the experimental design. Genotype and genotype by environment (GGE) biplot analysis was used to study grain yield (GY) stability and G × E interactions. The results revealed that two distinct mega environments were present. Genotype L232 was the best genotype for GY in the first planting date at Konni and the first and second planting dates (PDs) at Maradi. Genotype L17 was the best for GY in the second PD at Konni. The second PD at Konni was the most discriminating environment while the first PD at Konni is suitable for selecting widely adapted genotypes for GY.

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 Estimation of adaptability and stability of early strains of ‘Carioca’ and black beans (Phaseolus vulgaris) in Pernambuco State, Brazil

2020 ◽  
pp. 263-270 ◽  
Author(s):  
Richardson Sales Rocha ◽  
Paulo Ricardo dos Santos ◽  
Tâmara Rebecca Albuquerque de Oliveira ◽  
Rogério Figueiredo Daher ◽  
Maxwel Rodrigues Nascimento ◽  
...  
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Yield Data ◽  
Black Beans ◽  
Black Bean ◽  
Randomized Block Design ◽  
Yield Trait ◽  
Selection For

Low yields of early ‘Carioca’ and black bean varieties are a consequence of instability and low yield adaptability to specific growing environments. The goal of this work was to evaluate the genotype x environment interaction of early ‘Carioca’ and black bean new lines aiming at obtaining genotypes with high grain yield and good adaptability and stability. In (2012), 15 genotypes of the black group were evaluated in Ibimirim, Belém do São Francisco, and Petrolina municipalities, in Pernambuco State, Brazil; and, in (2016), 11 genotypes of early ‘Carioca’ bean were evaluated in Caruaru, Arcoverde, and Araripina municipalities, also in Pernambuco State. The experiment was conducted in a randomized block design with three replicates, and grain yield data were submitted to analysis of variance, in which means were compared by the Tukey’s test and analysis via GGE Biplot. In Arcoverde, the grain yield was greater for all early ‘Carioca’ bean genotypes, whereas, in the environment of Petrolina, there was the worst grain yield in kg ha-1 for all black bean genotypes, individually. The GGE Biplot methodology was found to be efficient in identifying genotypes with good adaptability, stability, and yield. Differences presented by the genotypes for the grain yield trait give the basis to recommend the genotypes and express the possibility of selection for genetic improvement of the species.

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