Evaluation of sugarcane genotypes and production environments in Paraná by GGE biplot and AMMI analysis

The purpose of this study was to evaluate sugarcane genotypes for the trait tons of sugar per hectare (TSH), stratifying five production environments in the state of Paraná. The performance of 20 genotypes and 2 standard cultivars was analyzed in three consecutive growing seasons by the statistical methods AMMI and GGE Biplot. The GGE Biplot grouped the locations into two mega-environments and indicated the best-performing genotypes for each one, facilitating the selection of superior genotypes. Another advantage of GGEBiplot is the definition of an ideal genotype (G) and environment (E), serving as reference for the evaluation of genotypes and choice of environments with greater GE interaction. Both models indicated RB006970, RB855156 and RB855453 as the genotypes with highest TSH and São Pedro do Ivai as the environment with the greatest GE interaction. Both approaches explained a high percentage of the sum of squares, with a slight advantage of AMMI over GGE Biplot analysis.

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Stability and G × E Analysis of Oil Yield of Sunflower Single Cross Hybrids In Diverse Environments

10.21203/rs.3.rs-648764/v1 ◽

2021 ◽

Author(s):

Mehdi Ghaffari ◽

Amir Gholizadeh ◽

Seyyed Abbasali Andarkhor ◽

َAsadolah Zareei Siahbidi ◽

Seyed Ahmad Kalantar Ahmadi ◽

...

Keyword(s):

Graphical Method ◽

Block Design ◽

Oil Yield ◽

Gge Biplot ◽

Biplot Analysis ◽

Growing Seasons ◽

Superior Genotypes ◽

Ideal Genotype ◽

Selection Of ◽

The Ideal

Abstract Multi-environment trials have a fundamental role in selection of the best genotypes across different environments before its commercial release. This study was carried out to identify high-yielding stable sunflower genotypes using the graphical method of the GGE biplot. For this purpose, 11 new hybrids along with four cultivars were evaluated in a randomized complete block design with four replications across 8 environments (combination of years and locations) during 2018–2020 growing seasons. The results indicated that genotype (G), environment (E) and genotype × environment (G×E) effects were significant for oil yield. The G, E and G×E interaction effects accounted for 51.94, 9.50 and 18.67% of the total variation, respectively. Results of biplot analysis showed that the first and second principle components accounted 45.9% and 20.4%, respectively, and in total 66.3% of oil yield variance. GGE biplot analysis indicated two major mega-environments of sunflower testing locations in Iran. Based on the hypothetical ideal genotype biplot, the genotypes G3 and G5 were better than the other genotypes for oil yield and stability, and had the high general adaptation to all environments. Ranking of genotypes based on the ideal genotype from the most appropriate to most inappropriate genotypes is as follows: G5 ˃ G3 ˃ G8 ˃ G14 ˃ G6 ˃ G2 ˃ G13 ˃ G12 ˃ G10 ˃ G11 ˃ G1 ˃ G7 ˃ G4 ˃ G15 ˃ G9. Furthermore, ranking the environments based on the ideal environment introduced Sari location as the best environment. Therefore, the Sari location can be used as suitable test location for selecting superior genotypes of sunflower in Iran. Generally, our results showed the efficiency of the graphical method of the GGE biplot for selection of the genotypes that are stable, high yielding, and responsive.

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Identification of the Optimum Environments for the High Yield and Quality Traits of Lentil Genotypes Evaluated in Multi-Location Trials

Sustainability ◽

10.3390/su13158247 ◽

2021 ◽

Vol 13 (15) ◽

pp. 8247

Author(s):

Dimitrios N. Vlachostergios ◽

Christos Noulas ◽

Anastasia Kargiotidou ◽

Dimitrios Baxevanos ◽

Evangelia Tigka ◽

...

Keyword(s):

Crude Protein ◽

Agronomic Traits ◽

High Yield ◽

Breeding Programs ◽

Growing Seasons ◽

Yield And Quality ◽

Food And Feed ◽

Superior Genotypes ◽

Selection Of ◽

The Ideal

Lentil is a versatile and profitable pulse crop with high nutritional food and feed values. The objectives of the study were to determine suitable locations for high yield and quality in terms of production and/or breeding, and to identify promising genotypes. For this reason, five lentil genotypes were evaluated in a multi-location network consisting of ten diverse sites for two consecutive growing seasons, for seed yield (SY), other agronomic traits, crude protein (CP), cooking time (CT) and crude protein yield (CPY). A significant diversification and specialization of the locations was identified with regards to SY, CP, CT and CPY. Different locations showed optimal values for each trait. Locations E4 and E3, followed by E10, were “ideal” for SY; locations E1, E3 and E7 were ideal for high CP; and the “ideal” locations for CT were E3 and E5, followed by E2. Therefore, the scope of the cultivation determined the optimum locations for lentil cultivation. The GGE-biplot analysis revealed different discriminating abilities and representativeness among the locations for the identification of the most productive and stable genotypes. Location E3 (Orestiada, Region of Thrace) was recognized as being optimal for lentil breeding, as it was the “ideal” or close to “ideal” for the selection of superior genotypes for SY, CP, CT and CPY. Adaptable genotypes (cv. Dimitra, Samos) showed a high SY along with excellent values for CP, CT and CPY, and are suggested either for cultivation in many regions or to be exploited in breeding programs.

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Identification of stable lentil (Lens culinaris Medik) genotypes through GGE biplotand AMMI analysis for North Hill Zone of India

Legume Research - An International Journal ◽

10.18805/lr-3901 ◽

2018 ◽

Author(s):

Muniyandi Samuel Jeberson ◽

Kadanamari Sankarappa Shashidhar ◽

Shabir Hussain Wani ◽

Amit Kumar Singh ◽

Sher Ahmad Dar

Keyword(s):

Principal Components ◽

Lens Culinaris ◽

Graphical Representation ◽

Environment Interaction ◽

Gge Biplot ◽

Genotype X Environment ◽

Ammi Analysis ◽

Total Variability ◽

Wide Adaptation ◽

Superior Genotypes

In the present investigation with 24 lentil genotypes, first two Principal components revealed more than 90 per cent of the variability for the yield which indicates that G and GE together accounted for more than 10 per cent of total variability. Based on the present analysis of using GGE biplot models, considering simultaneous mean yield and stability, the genotypes G4, G12, G6, G13 and G2 were relatively stable in all the environments.The environment E1(Berthin) was discriminative (informative). This environment contributed most to the variability in grain yield. Hence, GGE biplot method is suitable to discriminate the genotypes based on their stable and instability nature across the environments.The AMMI analysis revealed that G13, G14, G12, G2, G23, G16 and G9 had wide adaptation and not be affected by the Genotype x environment interaction (GxE); hence mayyieldedgood across the environments. E2 and E3 could be considered as good selection sites for identifying broad based and most adaptable lentil genotypes. This study has clearly and by far aided in identification of stable and superior genotypes in graphical representation.

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

Crop and Pasture Science ◽

10.1071/cp09151 ◽

2010 ◽

Vol 61 (1) ◽

pp. 92 ◽

Cited By ~ 35

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.

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

Indian Journal of Agricultural Research ◽

10.18805/ijare.a-290 ◽

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.

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Stability analysis of soybean [Glycine max (L.) Merrill] genotypes under multi-environments rainfed condition of North Western Himalayan hills

Indian Journal of Genetics and Plant Breeding (The) ◽

10.31742/ijgpb.78.3.6 ◽

2018 ◽

Vol 78 (3) ◽

Author(s):

Anuradha Bhartiya ◽

J. P. Aditya ◽

Vedna Kumari ◽

Naval Kishore ◽

J. P. Purwar ◽

...

Keyword(s):

Grain Yield ◽

Seed Weight ◽

Gge Biplot ◽

Rainfed Condition ◽

Soybean Genotypes ◽

Glycine Max L ◽

Superior Genotypes ◽

Ideal Genotype ◽

100 Seed Weight ◽

Location Interaction

In the present study, performance of five promising soybean genotypes over 4 locations during kharif 2013, 2014 and 2015 were investigated using GGE biplot analysis. Location attributed the highest proportion of the variation for all the traits except 100 seed weight ranging from 26.97-86.81% whereas, genotype contributed only 3.01-60.51% and genotype x location interaction contributed 6.01-31.42% of total variation. For 100 seed weight genotype has contributed major proportion of variation (66.26%) than location (31.08%) and genotype x location interaction (2.65%). Superior genotypes for key traits viz., grain yield (VLS 86) and 100 seed weight (Himso 1685) were effectively identified using GGE biplot graphical approach. It may be stated from present study that, VLS 86 was the closest to ideal genotype with stability for high grain yield as well as earliness. ‘Which-won-where’ study partitioned the testing locations into two mega-environments: first with three locations with VLS 86 as the winning genotype; second mega environment encompassed only one location with Himso 1685 as the winning genotype. Existence mega environments was found correlated with the rainfall pattern and clearly suggested that different entries need to be selected and deployed for realising maximum grain yield in hill zone.

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GGE Biplot Analysis of Yield Stability in Multi-environment Trials of Lentil Genotypes under Rainfed Condition

Notulae Scientia Biologicae ◽

10.15835/nsb529067 ◽

2013 ◽

Vol 5 (2) ◽

pp. 256-262 ◽

Cited By ~ 16

Author(s):

Rahmatollah KARIMIZADEH ◽

Mohtasham MOHAMMADI ◽

Naser SABAGHNI ◽

Ali Akbar MAHMOODI ◽

Barzo ROUSTAMI ◽

...

Keyword(s):

Block Design ◽

Gge Biplot ◽

Average Yield ◽

Ge Interaction ◽

Rainfed Condition ◽

Yield Variation ◽

Randomized Complete Block Design ◽

Ideal Genotype ◽

Genotype G1 ◽

The Ideal

This investigation was done to study GE interaction over twelve environments for seed yield in 18 genetically diverse genotypes. Grain yield performances were evaluated for three years at four locations in Iran using a randomized complete block design. The first two principal components (IPC1 and IPC2) were used to create a two-dimensional GGE biplot that accounted percentages of 49% and 20% respectively of sums of squares of the GE interaction. The combined analysis of variance indicated that year and location were the most important sources affecting yield variation and these factors accounted for percentages of 50.0% and 33.3% respectively of total G+E+GE variation. The GGE biplot suggested the existence of three lentil mega-environments with wining genotypes G1, G11 and G14. According to the ideal-genotype biplot, genotype G1 was the better genotype demonstrating high mean yield and high stability of performance across test locations. The average tester coordinate view indicated that genotype G1 had the highest average yield, and genotypes G1 and G12 recorded the best stability. The study revealed that a GGE biplot graphically displays interrelationships between test locations as well as genotypes and facilitates visual comparisons.

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Application of AMMI model in zoning of FAO 400-500 maize hybrids

Selekcija i semenarstvo ◽

10.5937/selsem2101041d ◽

2021 ◽

Vol 27 (1) ◽

pp. 41-49

Author(s):

Bojan Drašković ◽

Veselinka Zečević ◽

Zdravko Hojka ◽

Milomir Filipović ◽

Jelena Srdić ◽

...

Keyword(s):

Positive Interactions ◽

Maize Breeding ◽

Ge Interaction ◽

Maize Hybrids ◽

Genotype X Environment ◽

Production Environments ◽

Ammi Analysis ◽

Negative Effect ◽

Breeding Programmes ◽

Ammi Model

Identification of high yielding and stable genotypes is one of the main goals in all breeding programmes. Estimation of hybrids is often aggravated due to the presence of genotype x environment (GE) interaction. One of the ways to eliminate negative effect of this interaction is the application of reliable statistical models such as AMMI model, which singles out high yielding and stable genotypes that have positive reaction to the improvement of production environments. This research aimed to establish specific maize hybrids interactions in different environments in two years by AMMI analysis. Twelve KWS maize hybrids belonging to FAO 400-500, were examined in two years over eight locations in Vojvodina. The highest yield in both years had the hybrid KWS2 (12.764 kg ha-1). Based on the AMMI1 model, hybrid KWS9 showed the highest stability and adaptability at all locations. According to AMMI2, hybrids KWS1, KWS3 and KWS12, had the highest stability and adaptability, while hybrids KWS2, KWS6, KWS8, KWS9 and KWS10 showed a satisfactory level of stability and it is necessary to pay attention to which locations they have positive interactions in order to be recommended in such regions. Nevertheless, based on the AMMI2, locations Temerin, Kikinda and Zrenjanin, showed similar interaction response, which points out that the number of trial locations could be reduced. Obtained results would contribute to the more precise decision in hybrids recommendation for the certain region, but also in defining further aims in maize breeding.

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THE ANALYSIS OF GENOTYPE × ENVIRONMENT INTERACTION USING RAPESEED (BRASSICA NAPUS L.) BY GGE BIPLOT METHOD

IRAQI JOURNAL OF AGRICULTURAL SCIENCES ◽

10.36103/ijas.v51i5.1143 ◽

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.

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Yield Stability of Soybean Genotypes in Tropical Environments based on Genotype and Genotype-by-Environment Biplot

Jurnal Agronomi Indonesia (Indonesian Journal of Agronomy) ◽

10.24831/jai.v46i3.18333 ◽

2019 ◽

Vol 46 (3) ◽

pp. 231-239 ◽

Cited By ~ 1

Author(s):

Ayda Krisnawati ◽

And Mochammad Muchlish Adie

Keyword(s):

Seed Yield ◽

Yield Stability ◽

High Yield ◽

Environment Interaction ◽

Gge Biplot ◽

Ge Interaction ◽

Tropical Environments ◽

Genotype Environment Interaction ◽

Soybean Genotypes ◽

Ideal Genotype

Genotype × environment interaction is universal phenomenon when different genotypes are tested in a number of environments. The objective of this experiment was to determine the seed yield stability of soybean genotypes. Seven soybean genotypes and two check cultivars were evaluated at eight soybean production centers during the dry season 2015. Stability analysis on seed yield was based on the GGE biplot method. The combined analysis showed that yield and yield components were significantly affected by genotype (G), environments (E), and genotype × environment interaction (GEI), except for number of filled pods. The highest yield was G6 (3.07 ton ha-1), followed by G7 (2.93 ton ha-1). The “which-won-where” polygon mapping resulted two mega-environments. The best genotype for the first mega-environment was G1 (G511H/Anjasmoro//Anjasmoro-2-8) at E5 (Pasuruan2); and the second one was G6 (G511 H/Anj//Anj///Anj////Anjs-6-7) at E1 (Nganjuk), E2 (Mojokerto), E3 (Blitar), E4 (Pasuruan1), E6 (Jembrana), E7 (Tabanan), and E8 (Central Lombok). The G7 (G511 H/Anjasmoro-1-4-2) was closest to ideal genotype as indicated by relatively stable and produced high yield across environments. The analysis of multi-environment trials data using GGE is useful for determining mega-environment analysis and stability of genotype which focusing on overall performance to identify superior genotypes.Keywords: GE interaction, GGE biplot, Glycine max, seed yield

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