Yield stability of rainfed durum wheat and GGE biplot analysis of multi-environment trials

2010 ◽  
Vol 61 (1) ◽  
pp. 92 ◽  
Author(s):  
Reza Mohammadi ◽  
Reza Haghparast ◽  
Ahmed Amri ◽  
Salvatore Ceccarelli
Keyword(s):  
Durum Wheat ◽  
Good Alternative ◽  
Yield Stability ◽  
Gge Biplot ◽  
Ge Interaction ◽  
Breeding Lines ◽  
Biplot Analysis ◽  
Stable Performance ◽  
Superior Genotypes ◽  
Highly Correlated

Integrating yield and stability of genotypes tested in unpredictable environments is a common breeding objective. The main goals of this research were to identify superior durum wheat genotypes for the rainfed areas of Iran and to determine the existence of different mega-environments in the growing areas of Iran by testing 20 genotypes in 4 locations for 3 years via GGE (genotype + genotype-by-environment) biplot analysis. Stability of performance was assessed by the Kang’s yield-stability statistic (YSi) and 2 new methods of yield-regression statistic (Ybi) and yield-distance statistic (Ydi).The combined analysis of variance showed that environments were the most important source of yield variability, and accounted for 76% of total variation. The magnitude of the GE interaction was ~10 times the magnitude of the G effect. The GGE biplot suggested the existence of 2 durum wheat mega-environments in Iran. The first mega-environment consisted of environments corresponding to ‘cold’ locations (Maragheh and Shirvan) and a moderately cold location (Kermanshah), where ‘Sardari’ was the best adapted cultivar; the second mega-environment comprised ‘warm’ environments, including the Ilam and Kermanshah locations, where the recommended breeding lines G16 (Gcn//Stj/Mrb3), G17 (Ch1/Brach//Mra-i), and G18 (Lgt3/4/Bcr/3/Ch1//Gta/Stk) produced the highest yields. Ranking of genotypes based on GGE was found to be highly correlated with that based on the statistics YSi and Ybi. The discriminating power v. the representative view of the GGE biplot identified Kermanshah as the location with the least discriminating ability but greater representation, suggesting the possible of testing genotypes adapted to both warm and cold locations at the Kermanshah site. The results verified that the statistics YSi and Ybi were highly correlated (r = 0.94**) and could be a good alternative for GGE biplot analysis for selecting superior genotypes with high-yielding and stable performance.

scholarly journals Genotype/Genotype × Environment Biplot Analysis for Cultivar Evaluation and Mega-environment Investigation in Primocane-fruiting Red Raspberry

2002 ◽  
Vol 127 (5) ◽  
pp. 776-780 ◽  
Author(s):  
J.A. Sullivan ◽  
Weikai Yan ◽  
J.P. Privé
Keyword(s):  
Environmental Variables ◽  
Trial Data ◽  
Rubus Idaeus ◽  
Gge Biplot ◽  
Red Raspberry ◽  
Biplot Analysis ◽  
Yield Trial ◽  
Superior Genotypes ◽  
Effective Analysis ◽  
Discriminating Ability

Primocane-fruiting (PF) red raspberry (Rubus idaeus L.) cultivars are being grown in many regions as their popularity increases. However, testing of this perennial fruit crop is expensive and requires many years. Large genotype (G) × environment (E) interactions can make identification of superior genotypes difficult. The G/G × E (GGE) biplot can be used to measure cultivar performance and group locations into mega-environments. The GGE biplot was applied to yield trial data of three PF red raspberry cultivars Autumn Bliss, Heritage, and Redwing grown in 17 environments (year-location combinations). The 17 environments encompassed six locations in Ontario and Quebec, Canada between 1989 and 1996. `Autumn Bliss' produced the highest yields in 11 of 17 environments. `Heritage' was usually the lowest yielding cultivar. Two mega-environments were identified based on the performance of `Autumn Bliss' and `Redwing'. Some environmental variables were likely to be responsible for the discriminating ability of the test environments as they were correlated with the primary effects. The GGE biplot was an effective analysis to determine mega-environments and the cultivars best adapted to each.

scholarly journals Grain yield stability analysis using AMMI and GGE biplot models in different breeding zones of Bangladesh

2021 ◽  
Author(s):  
Marium Khatun ◽  
A. K. M. Aminul Islam ◽  
M. Rafiqul Islam ◽  
M. A. Rahman Khan ◽  
M. Kamal Hossain
Keyword(s):  
Stability Analysis ◽  
Grain Yield ◽  
Primary Component ◽  
Yield Stability ◽  
Second Primary ◽  
Gge Biplot ◽  
Environment Analysis ◽  
Biplot Analysis ◽  
Ammi Analysis ◽  
Breeding Zone

Abstract During the 2018-2019 Boro season (dry season), 70 rice genotypes were examined with alpha lattice experimental design with the goal of measuring grain yield stability analysis. Results indicated that AMMI analysis explained 100% of the G×E variance, while captured 81.74% variance. Based on the GGE and AMMI analysis, the most stable and high yielding genotype was identified G41 followed by G22, G26, G58, G24 and G61. The AMMI 1 biplot analysis revealed that the first primary component of interaction (IPC1) factor was responsible for 64.2 % variation due to G × E interaction. On other hand, the second primary component (PC2) factor accounted for 35.8% variation of the G × E interaction. These two-primary component (PC1 and PC2), all together accounted for 100% variation of the G × E interaction. The contribution of G68 was highest to the interaction followed by G70, G58, G42, G61, G45, G38, G14, G33, G60, G53, and G9. Best environment analysis indicated that the ranking was Rajshahi < Gazipur < Cumilla. GGE biplot analysis accounted for 81.74% variation comprising two principal components PC1 and PC2 with 45.62% and 36.12% variations respectively. Rajshahi was more stable than Gazipur. Based on environment analysis genotypes, G22, G26, G58, and G44 can be recommended as best stable genotypes that breeding zone. However, the genotype G61 was identified adapted to Cumilla breeding zone.

Grain yield stability of barley genotypes in uniform regional yield trails in warm and semi warm dry land area

2017 ◽  
Author(s):  
Hassan Khanzadeh ◽  
Behroz Vaezi ◽  
Rahmatolah Mohammadi ◽  
Asghar Mehraban1 ◽  
Tahmaseb Hosseinpor ◽  
...  
Keyword(s):  
Grain Yield ◽  
Agricultural Research ◽  
Gge Biplot ◽  
Ge Interaction ◽  
Dry Land ◽  
Biplot Analysis ◽  
Ammi Model ◽  
Cropping Seasons ◽  
Ideal Genotype ◽  
Better Than

The aim of this study was to assess the effect of GEI on grain yield of barley advanced lines and exploit the positive GEI effect using AMMI and SREG GGE biplot analysis. Therefore, 18 lines were evaluated at five research stations (Ghachsaran, Mogan, Lorestan, Gonbad and Ilam) of Dryland Agricultural Research Institute (DARI), in the semi-warm regions in Iran, in 2012, 2013 and 2014 cropping seasons under rain-fed conditions. Analysis of variance showed that grain yield variation due to the environments, genotypes and GE interaction were highly significant (p>0.01), which accounted for 68.9%, 9.3% and 22.7% of the treatment combination sum of squares, respectively. To determine the effects of GEI on yields, the data were subjected to AMMI and GGE biplot analysis. The first five AMMI model terms were highly significant (p>0.01) and the first two terms explained 59.56% of the GEI. There were two mega-environments according to the SREG GGE model. The best genotype in one location was not always the best in other test locations. According to AMMI1 biplot, G2, G4, G5 and G6 were better than all other genotypes across environments. G2 was the ideal genotype to plant in Gachsaran. It seems that Ghachsaran is the stable environment between the environments studied and next in rank was Gonbad. In finally, the ATC method indicated that G1, G3, G4 and G6 were more stable as well as high yielding.

Targeting test environments and rust-resistant genotypes in lentils (Lens culinaris) by using heritability-adjusted biplot analysis

2018 ◽  
Vol 69 (11) ◽  
pp. 1113 ◽  
Author(s):  
A. K. Parihar ◽  
Ashwani K. Basandrai ◽  
K. P. S. Kushwaha ◽  
S. Chandra ◽  
K. D. Singh ◽  
...  
Keyword(s):  
Lens Culinaris ◽  
Rust Resistance ◽  
High Efficiency ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Rust Disease ◽  
Ge Interaction ◽  
Sources Of Resistance ◽  
Biplot Analysis ◽  
Resistant Genotypes

Lentil rust incited by the fungus Uromyces viciae-fabae is a major impedance to lentil (Lens culinaris Medik.) production globally. Host-plant resistance is the most reliable, efficient and viable strategy among the various approaches to control this disease. In this study, 26 lentil genotypes comprising advanced breeding lines and released varieties along with a susceptible check were evaluated consecutively for rust resistance under natural incidence for two years and at five test locations in India. A heritability-adjusted genotype main effect plus genotype × environment interaction (HA-GGE) biplot program was used to analyse disease-severity data. The results revealed that, among the interactive factors, the GE interaction had the greatest impact (27.81%), whereas environment and genotype showed lower effects of 17.2% and 20.98%, respectively. The high GE variation made possible the evaluation of the genotypes at different test locations. The HA-GGE biplot method identified two sites (Gurdaspur and Pantnagar) as the ideal test environments in this study, with high efficiency for selection of durable and rust-resistant genotypes, whereas two other sites (Kanpur and Faizabad) were the least desirable test environments. In addition, the HA-GGE biplot analysis identified three distinct mega-environments for rust severity in India. Furthermore, the analysis identified three genotypes, DPL 62, PL 165 and PL 157, as best performing and durable for rust resistance in this study. The HA-GGE biplot analysis recognised the best test environments, restructured the ecological zones for lentil-rust testing, and identified stable sources of resistance for lentil rust disease, under multi-location and multi-year trials.

Genotype by environment interaction effect on yield and quality of potatoes

2008 ◽  
Vol 88 (6) ◽  
pp. 1099-1107 ◽  
Author(s):  
Ian Affleck ◽  
J. Alan Sullivan ◽  
R. Tarn ◽  
D. E. Falk
Keyword(s):  
Sugar Content ◽  
Genotype By Environment Interaction ◽  
Yield Stability ◽  
High Yield ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Quality Factors ◽  
French Fry ◽  
Fry Colour ◽  
Biplot Analysis

Colour is an important character in the processing of potatoes for French fries. French fry colour is closely associated with sugar content in the tuber. This study examines the stability of yield, sugar content and French fry colour for eight potato cultivars and advanced selections in four environments. Stability was determined using three approaches based on the Eberhart-Russell, Tai and GGE Biplot analyses. The GGE Biplot analysis provided a better characterization of stability than the other two analyses. The most stable and best performing genotypes for both French fry colour and total sugars were Russet Burbank and Umatilla Russet. Cal White had high yield and yield stability but had average stability for poor (dark) French fry colour. The GGE biplot analysis was able to identify mega-environments and those environments which optimized differentiation between genotypes. Both factors are important for the optimization of resources for testing new genotypes. Stability for quality factors in potato can be as important or more important than yield for some processing uses. In this study, genotypes with stability for sugar content and French fry colour were identified and these may be used as parents in breeding for stability. Key words: Potato, yield stability, quality, French fry

scholarly journals THE USE OF AMMI MODEL FOR INTERPRETING GENOTYPE × ENVIRONMENT INTERACTION IN DURUM WHEAT

2017 ◽  
Vol 54 (5) ◽  
pp. 670-683 ◽  
Author(s):  
REZA MOHAMMADI ◽  
MOHAMMAD ARMION ◽  
ESMAEIL ZADHASAN ◽  
MALEK MASOUD AHMADI ◽  
AHMED AMRI
Keyword(s):  
Grain Yield ◽  
Durum Wheat ◽  
Air Temperature ◽  
Heading Date ◽  
Principal Component ◽  
High Yield ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Breeding Lines ◽  
Ammi Model

SUMMARYDurum wheat (Triticum durum) is one of the most important cereal crops in the Mediterranean region; however, its cultivation suffers from low yield due to environmental constrains. The main objectives of this study were to (i) assess genotype × environment (GE) interaction for grain yield in rainfed durum wheat and to (ii) analyse the relationships of GE interaction with genotypic/meteorological variables by the additive main effects and multiplicative interaction (AMMI) model. Grain yield and some related traits were evaluated in 25 durum wheat genotypes (landrace, breeding line, old and new varieties) in 12 rainfed environments differing in winter air temperature. The AMMI analysis of variance indicated that the environment had highest contribution (84.3% of total variation) to the variation in grain yield. The first interaction principal component axis (IPCA1) explained 77.5% of GE interaction sum of squares (SS), and its effect was 5.5 times greater than the genotype effect, indicating that the IPCA1 contributed remarkably to the total GE interaction. Large GE interaction for grain yield was detected, indicating specific adaptation of genotypes. While the postdictive success method indicated AMMI-4 as the best model, the predictive success one suggested AMMI-1. The AMMI biplot analysis confirmed a rank change interaction among the locations, indicating the presence of strong and unpredictable rank-change location-by-year interactions for locations. In contrast to landraces and old varieties, the breeding lines with high yield performance had high phenotypic plasticity under varying environmental conditions. Results indicated that the GE interaction was associated with the interaction of heading date, plant height, rainfall, air temperature and freezing days.

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.

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