scholarly journals Genotype × Environment Interaction for Resistance to Early Leaf Spot of Groundnut Mini Core Collections in the Savannas of Nigeria

2021 ◽  
Author(s):  
Abdulwahab S. Shaibu ◽  
Zainab L. Miko ◽  
Sanusi G. Mohammed ◽  
Hakeem A. Ajeigbe ◽  
Alhassan Usman ◽  
...  
Keyword(s):  
Leaf Spot ◽  
Mixed Model ◽  
Yield Potential ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Core Collections ◽  
Mixed Model Analysis ◽  
Genotype Environment Interaction ◽  
Arachis Hypogaea L ◽  
The Stability

Background: The genetic yield potential of groundnut (Arachis hypogaea L.) has been continuously challenged by several diseases including early leaf spot (ELS).Methods: In the current study, we evaluated groundnut mini core collections under artificial and natural disease epiphytotics in six environments to identify stable elite sources for ELS resistance and pod yield. Mixed model analysis was done to adequately capture the variance component as a result of genotype (G), environment (E) and G × E interaction (GEI).Result: Highly significant (p less than 0.001) effects for G and GEI on ELS and pod weight were observed. The parametric and non-parametric stability models ranked the genotype differently for their stability to ELS. The GGE biplot identified ICG 1519 as a stable genotype for the ELS resistance. For pod weight, ICG 8896 and ICG 7897 were consistently stable from all the stability models including the GGE biplot. ICG 9449 and ICG 4540 were identified as stable genotypes for both ELS and pod weight. These elite sources of ELS resistance identified in the current study will be useful in the development and deployment of groundnut varieties with resistance to ELS and high pod yielding potentials.

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 Delineation of Genotype × Environment Interaction for Identification of Stable Genotypes to Grain Yield in Mungbean

2020 ◽  
Vol 2 ◽  
Author(s):  
Santhi Madhavan Samyuktha ◽  
Devarajan Malarvizhi ◽  
Adhimoolam Karthikeyan ◽  
Manickam Dhasarathan ◽  
Arumugam Thanga Hemavathy ◽  
...  
Keyword(s):  
Grain Yield ◽  
Tamil Nadu ◽  
Stability Index ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype Environment Interaction ◽  
South Indian ◽  
Indian State ◽  
Wide Adaptation ◽  
High Yields

In the present study, fifty-two mungbean (Vigna radiata) genotypes were evaluated for seven morphological traits at three different environments in South Indian state Tamil Nadu, namely Virinjipuram (E1), Eachangkottai (E2), and Bhavanisagar (E3) during Kharif 2017, 2018, and 2019, respectively. The data collected were subjected to variability and correlation analyses, followed by stability analysis using additive main effects and multiplicative interaction (AMMI) model, genotype and genotype × environment interaction effects (GGE) biplot. Variablility was observed among the genotypes for the following traits viz., plant height, days to fifty per cent flowering, number of pods per plant, pod length, number of seeds per pod, hundred seed weight and grain yield. Correlation analysis showed that the trait number of pods per plant was significantly associated with grain yield. The G × E was smaller than the genetic variation of grain yield as it portrayed the maximum contribution of genotypic effects (61.07%). GGE biplot showed E3 as a highly discriminating and representative environment. It also identified environment-specific genotypes viz., EC 396111 for E1, EC 396125 for E2 and EC 396101 for E3 environments. The genotypes with minimum genotype stability index (GSI) viz., V2802BG (7), HG 22 (13), and EC 396098 (13) were observed with wide adaptation and high yields across all the three environments. In summary, we identified stable genotypes adapted across environments for grain yield. These genotypes can be used as parent/pre-breeding materials in future mungbean breeding programs.

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 Genotype Environment Interaction in Grass Pea (Lathyrus sativusL.) Lines

2009 ◽  
Vol 2009 ◽  
pp. 1-7 ◽  
Author(s):  
G. B. Polignano ◽  
V. Bisignano ◽  
V. Tomaselli ◽  
P. Uggenti ◽  
V. Alba ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Plant Height ◽  
Seed Yield ◽  
Principal Components ◽  
Environment Interaction ◽  
Grass Pea ◽  
Genotype Environment Interaction ◽  
Marginal Areas ◽  
Different Seasons ◽  
The Stability

Eight grass pea lines grown in three different seasons were evaluated for the stability of seed yield, 100 seeds weight, flowering time, plant height, and biomass. Significant differences existed among years, lines, and lines years interaction for all traits except for 100 seeds weight. Two methods of multivariate analysis cluster and principal components were utilized to determine: firstly, whether a pattern existed among lines in their response across years and secondly to examine the relationships among them. In both analyses, each line was presented as a vector whose elements were given by the performance of lines in each year. The analyses used arranged the lines into groups that were differentiable in terms of performances and stability. Our results provide useful information to aid the choice of grass pea lines in the Mediterranean marginal areas.

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 Genotype environment interaction and stability for yield and its components in advanced breeding lines of red rice (Oryza sativa L.)

2020 ◽  
Vol 49 (3) ◽  
pp. 425-435
Author(s):  
BM Dushyantha Kumar ◽  
AP Purushottam ◽  
P Raghavendra ◽  
T Vittal ◽  
KN Shubha ◽  
...  
Keyword(s):  
Grain Yield ◽  
Oryza Sativa L ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Red Rice ◽  
Yield Performance ◽  
Breeding Lines ◽  
Genotype Environment Interaction ◽  
Ammi Analysis ◽  
Total Variability

Effects of genotype, environment and their interaction for grain yield and yield attributing characters in 20 advanced breeding lines of rice across six environments was investigated. Yield stability and adaptability of yield performance were analyzed by Eberhart and Russel model and (GGE) bi-plot. The AMMI analysis of variance indicated that mean squares due to genotypes, location and genotype location contributed per cent 59.08, 5.79 and 21.63, respectively for total variability in grain yield per hectare. Estimates of GGE bi-plot revealed that the lines G1, G3, G11, G13, G15, G12, G16, G7 and G10 were positioned near GGL bi-plot origin indicating wider adaptation for the trait grain yield per hectare. Eberhart and Russel Model and GGE biplot model showed the advanced breeding lines viz., JB 1-11-7 (G1) and JA 6-2 (G15) exhibited wider adaptability across the tested environments for number of productive tillers per plant and yield per hectare.

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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