Yield performance and GGE biplot analysis of wheat genotypes under two irrigation treatments at El Bajío, Mexico

Abstract It is important to identify and develop stable wheat varieties that can grow under heat stress. This important issue was addressed in Bangladesh using six wheat genotypes, including three existing elite cultivars (‘BARI Gom 26’, ‘BARI Gom 27’, ‘BARI Gom 28’) and three advanced lines (‘BAW 1130’, ‘BAW 1138’, ‘BAW 1140’). Six sowing dates, namely early sowing (ES) (10 November), optimum sowing (OS) (20 November), slightly late sowing (SLS) (30 November), late sowing (LS) (10 December), very late sowing (VLS) (20 December) and extremely late sowing (ELS) (30 December) were assessed over two years in four locations, representative of the diversity in Bangladesh’s agro-ecological zones. In a split plot design, sowing dates were allocated as main plots and genotypes as subplots. A GGE biplot analysis was applied to identify heat tolerance and to select and recommend genotypes for cultivation in heat-prone zones. All tested genotypes gave greatest grain yield (GY) after OS, followed by SLS, ES and LS, while VLS and ELS gave smallest GY. When GY and the correlations between GY and stress tolerance indices were considered, ‘BAW 1140’, ‘BARI Gom 28’ and ‘BARI Gom26’ performed best under heat stress, regardless of location or sowing date. In contrast, ‘BARI Gom 27’ and ‘BAW 1130’ were susceptible to heat stress in all locations in both years. Ranking of genotypes and environments using GGE biplot analysis for yield stability showed ‘BAW1140’ to be most stable, followed by ‘BARI Gom 28’ and ‘BARI Gom 26’. Wheat sown on November 20 resulted in highest GY but that sown on December 30 resulted in lowest GY in both years. In conclusion, ‘BAW 1140’, ‘BARI Gom 28’ and ‘BARI Gom 26’ are the recommended wheat genotypes for use under prevailing conditions in Bangladesh.

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Multi-Locational Evaluation of Medium-Staple Cotton Genotypes for Seed-Cotton Yield under the Middleveld Agro-Ecological Zones of Zimbabwe

10.21203/rs.3.rs-43613/v2 ◽

2020 ◽

Author(s):

Marco Mare ◽

Blessing Chapepa ◽

Washington Mubvekeri

Keyword(s):

Analysis Of Variance ◽

Environment Interaction ◽

Gge Biplot ◽

Cotton Yield ◽

Cotton Production ◽

Seed Cotton ◽

Yield Performance ◽

Seed Cotton Yield ◽

Biplot Analysis ◽

The Ideal

Abstract BackgroundThe Zimbabwean national cotton breeding programme has the mandate to develop superior cotton (Gossypium Hirsutum) varieties with good field performance and high fibre properties. Cotton productivity in Zimbabwe has remained very low, with national average seed cotton yield record of 650kg ha-1 (AMA Report, 2019) compared to the potential 2000kg ha-1. Since this is a result of many biotic and abiotic factors, field experiments laid in a Randomized Complete Block Design were conducted on ten genotypes (seven test genotypes and three check varieties) from 2012 to 2019 across 13 diverse locations in Zimbabwe to evaluate cotton yield performance, stability and adaptability by Analysis of Variance (ANOVA) and Genotype and Genotype by Environment Interaction (GGE) Biplot methods. ResultsThe Analysis of Variance indicated significant (P< 0.001) effects of Genotype (G), Environment (E) and their Interaction (GE). The highest percentage of variation was explained by E/G/GE (60.34%) while G/E+GE together explained the rest of the variation (<40%). Joint effects of G and GE were partitioned using the GGE biplot analysis explaining total of 59.08% (PC1 = 36.96% and PC2 =22.12%) of the GGE sum of squares. The biplot analysis revealed that candidates 917-05-7, TN96-05-9, 912-05-1 and GN 96 (b)-05-8 were the ideal and stable genotypes. The candidate variety 917-05-7 significantly (P< 0.001) showed superior yield performance over checks CRI-MS1 and CRI-MS2 recording 5% and 5.5% yield increase respectively. Candidate 917-05-7 recorded a higher earliness index (78.11%) over checks CRI-MS1 and CRI-MS2 (77 and 76% respectively) thus indicating potential attributes for good cotton production with more pick-able bolls earlier than the current commercial varieties.ConclusionCandidate 917-05-7 has been identified as the ideal genotype in terms of high yielding potential, and stability hence recommended for commercial release and use as breeding parent for future breeding programs.

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AMMI and GGE Biplot Analysis for G×E Interaction of Wheat Genotypes under Different Irrigation and Sowing Condition

Indian Journal of Agricultural Research ◽

10.18805/ijare.a-5603 ◽

2021 ◽

Author(s):

Ragini Dolhey ◽

V.S. Kandalkar

Keyword(s):

Concentric Circle ◽

Environment Interaction ◽

Gge Biplot ◽

Wheat Genotypes ◽

Biplot Analysis ◽

Genotype Environment Interaction ◽

Significant Difference ◽

Ammi Model ◽

Ideal Genotype ◽

Main Effects

Background: AMMI analysis showed that genotype, environment and genotype-environment interaction had a highly significant variation for 20 wheat genotypes analyzed over four environments. ASV ranking revealed G15 (RVW-4275) as a stable genotype while G3 (RVW-4263) and G9 (RVW-4269) as unstable genotypes. GGE biplot analysis for environment interrelationship revealed that E1 (Irrigated timely sown), E2 (Restricted irrigation timely sown) were correlated forming one group and E3 (Irrigated late sown), E4 (Restricted irrigation late sown) were correlated forming another group. Polygon view showed that G9 (RVW-4269) was found stable and better performing in E1, G12 (RVW-4272) was stable under the E2 environment and G3 (RVW-4263) was stable in E3. Ideal genotype graph with concentric circles having ideal genotype at the center and genotypes G12(RVW-4272), G18(RVW-4278), G13(RVW-4273), G11(RVW-4271), G10 (RVW4270) present in a concentric circle close to the center can to considered as stable and desirable genotypes.Methods: In the present study the plant material comprised of 20 wheat genotypes. These genotypes were randomly allocated in different replication under different environmental condition. The field trial was evaluated at four different environments viz., E1- Irrigated timely sown, E2- Restricted irrigation is timely sown (RI- 2 irrigation), E3- Irrigated late sown, E4- Restricted irrigation late sown during Rabi season of 2016-2017 at research farms, college of agriculture, Gwalior, MP. The genotype main effects and genotype × environment interaction effects (GGE) model and additive main effects and multiplicative interaction (AMMI) model were two statistical approaches used to determine stable genotype in R software.Result: Highly significant difference was seen for genotype and G×E interaction in our study, revealing that genotype yield output was highly impacted by G×E. In all four environments and G3, G9 as unstable genotypes in all four environments, ASV ranking revealed G15 as a stable genotype. For further breeding, these genotypes G12, G18, G13, G11, G2, G10 and G15 may be used to grow genotypes adapted to conditions of partial irrigation or drought stress.

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AMMI AND GGE BIPLOT ANALYSIS OF YIELD PERFORMANCE OF SPRING BARLEY (Hordeum vulgare L.) VARIETIES IN MULTI ENVIRONMENT TRIALS

The Journal Agriculture and Forestry ◽

10.17707/agricultforest.64.1.15 ◽

2018 ◽

Vol 64 (1) ◽

Author(s):

Pavel SOLONECHNYI ◽

Mikhail KOZACHENKO ◽

Nataliya VASKO ◽

Vladimir GUDZENKO ◽

Vitaliy ISHENKO ◽

...

Keyword(s):

Hordeum Vulgare ◽

Spring Barley ◽

Gge Biplot ◽

Hordeum Vulgare L ◽

Yield Performance ◽

Biplot Analysis

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AMMI and GGE Biplot Analysis for Yield Stability of Wheat Genotypes under Drought and High Temperature Stress

International Journal of Current Microbiology and Applied Sciences ◽

10.20546/ijcmas.2020.905.043 ◽

2020 ◽

Vol 9 (5) ◽

pp. 377-389

Author(s):

Kirpa Ram ◽

Renu Munjal ◽

Hari Kesh ◽

Su resh ◽

Anita Kumari

Keyword(s):

High Temperature ◽

Temperature Stress ◽

High Temperature Stress ◽

Yield Stability ◽

Gge Biplot ◽

Wheat Genotypes ◽

Biplot Analysis

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Multi-Locational Evaluation of Medium-Staple Cotton Genotypes for Seed-Cotton Yield under the Middleveld Agro-Ecological Zone of Zimbabwe.

10.21203/rs.3.rs-43613/v1 ◽

2020 ◽

Author(s):

Marco Mare ◽

Blessing Chapepa ◽

Washington Mubvekeri

Keyword(s):

Analysis Of Variance ◽

Environment Interaction ◽

Gge Biplot ◽

Cotton Yield ◽

Cotton Production ◽

Seed Cotton ◽

Yield Performance ◽

Seed Cotton Yield ◽

Biplot Analysis ◽

The Ideal

Abstract Background The Zimbabwe national cotton breeding programme has the mandate to develop superior cotton (Gossypium Hirsutum) varieties with good field performance and high fibre properties. Cotton productivity in Zimbabwe has remained very low, with national average seed cotton yield record of 650 kg/ha (AMA Report, 2019) compared to the potential 2000 kg/ha. Though this is a result of many biotic and abiotic factors, field experiments laid in a Randomized Complete Block Design were conducted on ten genotypes (seven test genotypes and three check varieties) from 2012 to 2019 across 13 diverse locations in Zimbabwe to evaluate cotton yield performance, stability and adaptability by Analysis of Variance (ANOVA) and Genotype and Genotype by Environment Interaction (GGE) Biplot methods.Results The Analysis of Variance indicated significant (P < .001) effects of Genotype (G), Environment (E) and their Interaction (GE). The highest percentage of variation was explained by E/G/GE (60.34%) while G/E + GE together explained the rest of the variation (< 40%). Joint effects of G and GE were partitioned using the GGE biplot analysis explaining total of 59.08% (PC1 = 36.96% and PC2 = 22.12%) of the GGE sum of squares. The biplot analysis revealed that candidates 917-05-7, TN96-05-9, 912-05-1 and GN 96 (b)-05-8 were the ideal and stable genotypes. The candidate variety 917-05-7 significantly (P < .001) showed superior yield performance over checks CRI-MS1 and CRI-MS2 recording 5% and 5.5% yield increase respectively. Candidate 917-05-7 recorded a higher earliness index (78.11%) over checks CRI-MS1 and CRI-MS2 (77 and 76% respectively) thus indicating potential attributes for good cotton production with more pick-able bolls earlier than the current commercial varieties.Conclusion Candidate 917-05-7 has been identified as the ideal genotype in terms of high yielding potential, and stability hence recommended for commercial release and use as breeding parent for future breeding programs.

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GGE biplot analysis of yield perfomance and stability of durum wheat genotypes in multi envi-ronment trials in Algeria

Plant Breeding and Seed Production ◽

10.30835/2413-7510.2018.152126 ◽

2018 ◽

Vol 0 (114) ◽

pp. 8-18

Author(s):

A. Bendjama ◽

M. Solonechnyi

Keyword(s):

Durum Wheat ◽

Gge Biplot ◽

Wheat Genotypes ◽

Biplot Analysis

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Genotype × environmental interaction by AMMI and GGE biplot analysis for the provenances of Michelia chapensis in South China

Journal of Forestry Research ◽

10.1007/s11676-015-0181-2 ◽

2015 ◽

Vol 27 (3) ◽

pp. 659-664 ◽

Cited By ~ 3

Author(s):

Runhui Wang ◽

Dehuo Hu ◽

Huiquan Zheng ◽

Shu Yan ◽

Ruping Wei

Keyword(s):

South China ◽

Gge Biplot ◽

Environmental Interaction ◽

Biplot Analysis

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Improving the precision of genotype selection in wheat performance trials

Cropp Breeding and Applied Biotechnology ◽

10.1590/s1984-70332013000400003 ◽

2013 ◽

Vol 13 (4) ◽

pp. 234-240 ◽

Cited By ~ 8

Author(s):

Giovani Benin ◽

Lindolfo Storck ◽

Volmir Sérgio Marchioro ◽

Francisco de Assis Franco ◽

Ivan Schuster ◽

...

Keyword(s):

Grain Yield ◽

Field Trials ◽

Adjustment Coefficient ◽

Experimental Accuracy ◽

Yield Data ◽

Yield Performance ◽

Wheat Genotypes

The aim of this study was to verify whether using the Papadakis method improves model assumptions and experimental accuracy in field trials used to determine grain yield for wheat lineages indifferent Value for Cultivation and Use (VCU) regions. Grain yield data from 572 field trials at 31 locations in the VCU Regions 1, 2, 3 and 4 in 2007-2011 were used. Each trial was run with and without the use of the Papadakis method. The Papadakis method improved the indices of experimental precision measures and reduced the number of experimental repetitions required to predict grain yield performance among the wheat genotypes. There were differences among the wheat adaptation regions in terms of the efficiency of the Papadakis method, the adjustment coefficient of the genotype averages and the increases in the selective accuracy of grain yield.

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