Evaluation of Performance and Yield Stability Analysis Based on AMMI and GGE-Biplot in Promising Pigeonpea [Cajanus cajan (L.) Millspaugh] Genotypes

2020 ◽  
Author(s):  
N. SandhyaKishore ◽  
P. Jagan Mohan Rao ◽  
S. Sandeep ◽  
G. Neelima ◽  
P. Madhukar Rao ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Block Design ◽  
Crop Improvement ◽  
Interaction Model ◽  
Pigeon Pea ◽  
High Yield ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Wide Range ◽  
Minimum Interaction

Background: Pigeon pea is considered an excellent and affordable source of plant-based protein, essential amino and fatty acids, fibers, minerals and vitamins with consistent source of income and employment to small and marginal farmers and thus holds premier position in the world agriculture. Shifts in rainfall patterns and seasons due to climatic change require the development of varieties with stable and high yield over a wide range of environmental conditions became major objective of crop improvement. Methods: The present study was carried out to ascertain the stable genotypes, environments discrimination and genotype by environment crossovers using different stable models by conducting Multi-location pigeon pea trial in five environments during Kharif, 2018 in Randomized Complete Block Design. Stability analysis for grain yield was performed by deploying the AMMI (Additive Main Effects and Multiplicative Interaction) model and GGE (Genotype and Genotype by Environment) biplot method. The pigeon pea genotype WRG-330 was found superior among all the genotypes over checks over locations, while, WRG-327 exhibited almost minimum interaction with the environments convincing the reliability of the performance. The test environments at Adilabad and Tandur were observed representative with better discriminating ability. Conclusion: It is concluded that there is no large difference between the AMMI and GGE biplot analyses in evaluation of experimental pigeon pea genotypes in different locations and both methods revealed similar results convincing that both methods can be used equally.

scholarly journals Identification of pigeonpea genotypes with wider adaptability to rainfed environments through AMMI and GGE biplot analyses

2021 ◽  
Vol 81 (01) ◽  
pp. 63-73
Author(s):  
M. V. Nagesh Kumar ◽  
V. Ramya ◽  
C. V. Sameer Kumar ◽  
T. Raju ◽  
N. M. Sunil Kumar ◽  
...  
Keyword(s):  
Cajanus Cajan ◽  
Large Scale ◽  
Interaction Model ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Rainfed Agriculture ◽  
Genotype By Environment ◽  
Genotype Environment Interaction ◽  
Wide Range ◽  
Main Effects

Pigeonpea [Cajanus cajan (L.) Millspaugh] is an important pulse crop grown under Indian rainfed agriculture. Twenty eight pigeonpea genotypes were tested for stability and adaptability across ten rainfed locations in the States of Telangana and Karnataka, India using AMMI (additive main effects and multiplicative interaction) model and GGE (genotype and genotype by environment) biplot method. The grain yields were significantly affected by environment (56.8%) followed by genotype × environment interaction (27.6%) and genotype (18.6%) variances. Two mega environments were identified with several winning genotypes viz., ICPH 2740 (G15), TS 3R (G10), PRG 176 (G8) and ICPL 96058 (G22). E2 (Gulbarga, Karnataka), E3 (Bidar, Karnataka) and E6 (Vikarabad, Telangana) were the most discriminating environments. Genotypes, ICPH 2740, PRG 176 and TS 3R were the best cultivars in all the environments whereas PRG 158 (G9), ICPL 87119 (G12), ICPL 20098 (G19) and ICPL 96058 (G22) were suitable across a wide range of environments. Genotypes, ICPH 2740 and PRG 176 can be recommended on a large scale to the farmers with small holdings to enhance pigeonpea productivity and improve the food security

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.

AMMI and GGE biplot analysis of multi environment yield trial of soybeanin North Western Himalayan state Uttarakhand of India

2016 ◽  
Author(s):  
Anuradha Bhartiya ◽  
J. P. Aditya ◽  
Kamendra Singh ◽  
Pushpendra Pushpendra ◽  
J. P. Purwar ◽  
...  
Keyword(s):  
Seed Yield ◽  
Block Design ◽  
Principal Component ◽  
High Yield ◽  
Gge Biplot ◽  
Soybean Genotype ◽  
Genotype By Environment ◽  
Yield Trial ◽  
Soybean Genotypes ◽  
Ideal Genotype

The investigation was carried out to study Genotype × Environment (G×E) interaction for seed yield in 36 soybean genotypes including check PS1092 over 3 diverse environments represented by different altitudes in Uttarakhand. Grain yield performances of soybean genotypes were evaluated during Kharif 2013 season using a randomized complete block design. The AMMI analysis indicated that environment, genotypes and genotype by environment interactions had significantly affected seed yield and accounted for 9.76, 28.97 and 47.55% of the total variation, respectively. GGE biplot clearly displayed interrelationships between test locations as well as genotypes and facilitated visual comparisons based on Principal Component Analysis (PCA). The first two principal components PCI and PCII were used to create a two-dimensional GGE biplot that accounted for 45.68 and 38.88% variations respectively and based on discriminating and representative ability, E2 (Majhera) was most suitable location for selecting generally adapted genotypes. Soybean genotype C1 (PS1539) was identified as ideal genotype with high yield and low G×E interaction i.e. high stability.

scholarly journals Genotype by environment interaction for seed yield per plant in rapeseed using AMMI model

2011 ◽  
Vol 46 (2) ◽  
pp. 174-181 ◽  
Author(s):  
Ana Marjanović-Jeromela ◽  
Nevena Nagl ◽  
Jelica Gvozdanović-Varga ◽  
Nikola Hristov ◽  
Ankica Kondić-Špika ◽  
...  
Keyword(s):  
Seed Yield ◽  
Block Design ◽  
Total Yield ◽  
Interaction Model ◽  
Field Trials ◽  
Genotype By Environment Interaction ◽  
Yield Potential ◽  
High Yield ◽  
Environment Interaction ◽  
Genotype By Environment

The objective of this study was to assess genotype by environment interaction for seed yield per plant in rapeseed cultivars grown in Northern Serbia by the AMMI (additive main effects and multiplicative interaction) model. The study comprised 19 rapeseed genotypes, analyzed in seven years through field trials arranged in a randomized complete block design, with three replicates. Seed yield per plant of the tested cultivars varied from 1.82 to 19.47 g throughout the seven seasons, with an average of 7.41 g. In the variance analysis, 72.49% of the total yield variation was explained by environment, 7.71% by differences between genotypes, and 19.09% by genotype by environment interaction. On the biplot, cultivars with high yield genetic potential had positive correlation with the seasons with optimal growing conditions, while the cultivars with lower yield potential were correlated to the years with unfavorable conditions. Seed yield per plant is highly influenced by environmental factors, which indicates the adaptability of specific genotypes to specific seasons.

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 Stability Study of Selected Sweet Sorghum Genotypes for Ethanol Production under Different Environments Using AMMI Analysis and GGE Biplots

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Justice Kipkorir Rono ◽  
Erick Kimutai Cheruiyot ◽  
Jacktone Odongo Othira ◽  
Virginia Wanjiku Njuguna ◽  
Joseph Kinyoro Macharia ◽  
...  
Keyword(s):  
Sweet Sorghum ◽  
Ethanol Yield ◽  
Block Design ◽  
Cane Yield ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Cane Juice ◽  
Juice Yield ◽  
Sorghum Genotypes

The genotype and environment interaction influences the selection criteria of sorghum (Sorghum bicolor) genotypes. Eight sweet sorghum genotypes were evaluated at five different locations in two growing seasons of 2014. The aim was to determine the interaction between genotype and environment on cane, juice, and ethanol yield and to identify best genotypes for bioethanol production in Kenya. The experiments were conducted in a randomized complete block design replicated three times. Sorghum canes were harvested at hard dough stage of grain development and passed through rollers to obtain juice that was then fermented to obtain ethanol. Cane, juice, and ethanol yield was analyzed using the additive main effect and multiplication interaction model (AMMI) and genotype plus genotype by environment (GGE) biplot. The combined analysis of variance of cane and juice yield of sorghum genotypes showed that sweet sorghum genotypes were significantly (P<0.05) affected by environments (E), genotypes (G) and genotype by environment interaction (GEI). GGE biplot showed high yielding genotypes EUSS10, ACFC003/12, SS14, and EUSS11 for cane yield; EUSS10, EUSS11, and SS14 for juice yield; and EUSS10, SS04, SS14, and ACFC003/12 for ethanol yield. Genotype SS14 showed high general adaptability for cane, juice, and ethanol yield.

scholarly journals Genotype by environment interaction for seeds yield in pea (Pisum sativum L.) using additive main effects and multiplicative interaction model

Euphytica ◽  
2019 ◽  
Vol 215 (11) ◽  
Author(s):  
Jan Bocianowski ◽  
Jerzy Księżak ◽  
Kamila Nowosad
Keyword(s):  
Pisum Sativum ◽  
Block Design ◽  
Interaction Model ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Multiplicative Interaction ◽  
Genotype By Environment ◽  
Randomized Complete Block Design ◽  
Main Effects

Abstract The objective of this study was to evaluate the genotype by environment interaction using the additive main effects and multiplicative interaction model for seeds yield of pea cultivars grown in Poland. Twelve pea (Pisum sativum L.) cultivars: Bohun, Boruta, Cysterski, Ezop, Kavalir, Lasso, Medal, Santana, Tarchalska, Terno, Wenus and Zekon were evaluated in 20 environments (ten locations in 2 years). The experiment was laid out as randomized complete block design with three replicates. Seeds yield ranged from 26.10 dt ha−1 (for Wenus in Radostowo 2011) to 79.73 dt ha−1 (for Lasso in Słupia 2010), with an average of 50.70 dt ha−1. AMMI analyses revealed significant genotype and environmental effects as well as genotype-by-environment interaction with respect to seeds yield. In the analysis of variance, 89.19% of the total seeds yield variation was explained by environment, 1.65% by differences between genotypes, and 8.33% by GE interaction. The cultivar Terno is the highest stability. The cultivar Tarchalska is recommended for further inclusion in the breeding program because its stability and the highest averages of seeds yield.

The use of an AMMI model and its parameters to analyse yield stability in multi-environment trials

2008 ◽  
Vol 146 (5) ◽  
pp. 571-581 ◽  
Author(s):  
N. SABAGHNIA ◽  
S. H. SABAGHPOUR ◽  
H. DEHGHANI
Keyword(s):  
Stability Analysis ◽  
Crop Improvement ◽  
Principal Component ◽  
Environmental Variance ◽  
Combined Analysis ◽  
Genotype By Environment ◽  
Breeding Programmes ◽  
Ammi Model ◽  
Index Measure ◽  
Stability Statistics

SUMMARYGenotype by environment (G×E) interaction effects are of special interest for breeding programmes to identify adaptation targets and test locations. Their assessment by additive main effect and multiplicative interaction (AMMI) model analysis is currently defined for this situation. A combined analysis of two former parametric measures and seven AMMI stability statistics was undertaken to assess G×E interactions and stability analysis to identify stable genotypes of 11 lentil genotypes across 20 environments. G×E interaction introduces inconsistency in the relative rating of genotypes across environments and plays a key role in formulating strategies for crop improvement. The combined analysis of variance for environments (E), genotypes (G) and G×E interaction was highly significant (P<0·01), suggesting differential responses of the genotypes and the need for stability analysis. The parametric stability measures of environmental variance showed that genotype ILL 6037 was the most stable genotype, whereas the priority index measure indicated genotype FLIP 82-1L to be the most stable genotype. The first seven principal component (PC) axes (PC1–PC7) were significant (P<0·01), but the first two PC axes cumulatively accounted for 71% of the total G×E interaction. In contrast, the AMMI stability statistics suggested different genotypes to be the most stable. Most of the AMMI stability statistics showed biological stability, but the SIPCF statistics of AMMI model had agronomical concept stability. The AMMI stability value (ASV) identified genotype FLIP 92-12L as a more stable genotype, which also had high mean performance. Such an outcome could be regularly employed in the future to delineate predictive, more rigorous recommendation strategies as well as to help define stability concepts for recommendations for lentil and other crops in the Middle East and other areas of the world.

scholarly journals Genetic Analysis and Selection Criteria in Bambara Groundnut Accessions Based Yield Performance

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1634
Author(s):  
Atiqullah Khaliqi ◽  
Mohd Y. Rafii ◽  
Norida Mazlan ◽  
Mashitah Jusoh ◽  
Yusuff Oladosu
Keyword(s):  
Genetic Variability ◽  
Large Scale ◽  
Block Design ◽  
Crop Improvement ◽  
Yield Component ◽  
Bambara Groundnut ◽  
Breeding Systems ◽  
High Yield ◽  
Improvement Programs ◽  
The Relationship

The knowledge of genetic variability and breeding techniques is crucial in crop improvement programs. This information is especially important in underutilized crops such as Bambara groundnut, which have limited breeding systems and genetic diversity information. Hence, this study evaluated the genetic variability and established the relationship between the yield and its components in Bambara groundnut based on seed weight using multivariate analysis. A field trial was conducted in a randomized complete block design with three replications on 28 lines. Data were collected on 12 agro-morphological traits, and a statistical analysis was conducted using SAS version 9.4 software, while the variance component, genotypic and phenotypic coefficient variation, heritability, and genetic advance values were estimated. A cluster analysis was performed using NT-SYS software to estimate the genetic relations among the accessions. The results showed significant variability among the accessions based on the yield and yield component characteristics. The evaluated lines were grouped into seven primary clusters based on the assessed traits using the UPGMA dendrogram. Based on the overall results, G5LR1P3, G1LR1P3, G4LR1P1, G2SR1P1 and G3SR1P4 performed the best for the yield and yield components. These improved lines are recommended for large-scale evaluation and utilization in future breeding programs to develop high-yield Bambara groundnut varieties.

scholarly journals Analysis of Genotype by Environment Interaction on Cocoa Hybrids (Theobroma cacao L.) Resistance to Phytophthora Pod Rot

2016 ◽  
Vol 32 (3) ◽  
pp. 162-170
Author(s):  
Agung Wahyu Soesilo ◽  
Indah Anita Sari ◽  
Bayu Setyawan
Keyword(s):  
Block Design ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Experimental Station ◽  
Stability Performance ◽  
Stable Performance ◽  
Wet Climate ◽  
The Stability

Phenomenon of genotype by environment interaction was able to influence the stability performance of cocoa resistance to Phytophthora pod rot (PPR). This research had an objective to evaluate the effect of genotype by environment interaction on resistance of cocoa hybrids to PPR. The tested hybrids were F1 crosses between selected clones of TSH 858, Sulawesi 1, Sulawesi 2, NIC 7, ICS 13, KEE 2 and KW 165. There were 14 tested hybrids and an open pollinated hybrid of ICS 60 x Sca 12 was used as control in multilocation trials at four different agroclimatic locations, namely Jatirono Estate ((highland-wet climate), Kalitelepak Estate (lowland-wet climate), Kaliwining Experimental Station (low land-dry climate) and Sumber Asin Experimental Station (highland-dry climate). Trials were established in the randomized complete block design with four replications. Resistance to PPR were evaluated based on the percentage of infected pod for the years during wet climate of 2010 in Jatirono, Kalitelepak and Kaliwining followed in dry climate of 2011–2015 in Kaliwining and Sumber Asin. Variance of data were analyzed for detecting the effect of genotype by environment interaction (GxE) then visualized with a graph of genotype main effect and genotype by environment interaction (a graph of GGE) biplot. There was consistently no interaction effect between hybrid and location to PPR incidence which was affected by single factor of hybrid, year, location and interaction between year and location. The effect of year indicated yearly change of weather was more important to PPR incidence than location difference. A graph of GGE biplot indicated a stable performance of the tested hybrids among locations.

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