Genotype x environment interaction and yield stability analysis of mung bean (<i>Vigna radiata</i> (L.) Wilczek) genotypes in Northern Ethiopia

Baraki, Fiseha; Gebregergis, Zenawi; Belay, Yirga; Berhe, Muez; Zibelo, Haile

doi:10.1080/23311932.2020.1729581

Cited by 26 publications

(28 citation statements)

References 16 publications

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“…A vertical line to the AEC axis indicated that G36, G5, G15, G2, and G9 were stable and good-to low-yielding genotypes. Similar results were reported for cowpea [51] and mungbean [50][51][52][53]. The projection on the AEA was nearer to zero for the genotypes G18 and G29 followed by G31 and G36, indicating the limited interaction between these genotypes and environments.…”

Section: Discussionsupporting

confidence: 83%

Development of Novel Blackgram (Vigna mungo (L.) Hepper) Mutants and Deciphering Genotype × Environment Interaction for Yield-Related Traits of Mutants

et al. 2021

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Blackgram (Vigna mungo (L.) Hepper) yields are noticeably poor due to a shortage of improved varieties and an aggravated narrow genetic base. An attempt was made to isolate novel blackgram mutants by selecting for yield-related traits derived through gamma irradiation and testing the mutant genotype’s stability across the different environments. The irradiated blackgram populations M1-M5 were established in the background of cultivars ADT 3, Co 6, and TU 17-9. Desirable mutants were selected from M3 to M5 generations. It was observed in M2 and M3 that gamma rays showed higher mutagenic efficacy and generated good inherited variance for the yield-related traits. M4 established three divergent groups in each blackgram cultivar revealed by clustering analysis. The number of pods per plant, number of clusters per plant, and number of pods per cluster showed a strong direct association with single plant yield and could be considered as selection traits. G × E interactions were higher than the variation due to genotype for single plant yield. Limited environmental interaction was observed for the genotypes G24, G16, G36, G30, and G17, as revealed by AMMI, and the genotypes G18 and G29, as revealed by GGE. GGE biplot revealed the environment-specific genotypes G13 for E1 (Aduthurai), G7 for E2 (Kattuthottam), and G34 for E3 (Vamban) and also portrayed the highly discriminating (E3) and representative (E2) environments. Selected novel blackgram genotypes from this research are useful genetic stocks for genetic improvement and breeding.

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Section: Discussionsupporting

confidence: 83%

Development of Novel Blackgram (Vigna mungo (L.) Hepper) Mutants and Deciphering Genotype × Environment Interaction for Yield-Related Traits of Mutants

et al. 2021

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“…In order to interpret GEI effect in METs, numerous statistical approaches and model have been developed. The additive main effect and multiplicative interaction (AMMI) analysis is one of the best model which widely used for choose of the superior genotypes in barley and other crop plants (Khalili and Pour-Aboughadareh 2016;Paderewski et al 2016;Vaezi et al 2017;Vaezi et al 2018;Baraki et al 2020;Ahakpaz et al 2021). Although this analysis have many advantages in explain GEI, disability of this model in analyzing the structure of the linear mixed-effect model (LMM) caused to arrival new models.…”

Section: Introductionmentioning

confidence: 99%

MGIDI and WAASB indices: The useful approaches for selection of salt-tolerant barley genotype at the early growth and maturity stages

Pour-Aboughadareh¹,

Sanjani²,

Chaman-Abad³

et al. 2021

Preprint

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Salinity stress is one of the major limiting abiotic stresses that decrease crop production worldwide. To recommend genotypes for cultivation under saline stress conditions, comprehensive understanding of genetic basis and plant responses to this stress is need. In the present study, a total of 20 barley genotypes were investigated to isolate potential salt-tolerant genotypes at the early growth stage using hydroponic system, and adult plant under field conditions. Different growth and physiological traits including root fresh and dry weights (RFW and RDW), shoot fresh and dry weights (SFW and SDW), relative water content (RWC), membrane stability index (MSI), relative chlorophyll content (SPAD index), root and shoot Na+ (RN and SN), root and shoot K+ (RK and SK), root and shoot K+:Na+ ratios (RKN and SKN), root-to-shoot Na+ translocation (RTSN), root-to-shoot K+ translocation (RTSK), stomatal conductance (GS), transpiration rate (TE), and photosynthesis rate (PN) were measured. Barley seedling were treated with two salinity levels (0 mM NaCl (as control conditions) and 200 mM NaCl (as stress conditions)) for 30 days. Moreover, the yield performance and stability of investigated barley genotypes were evaluated across five environments during the 2018–2020 cropping seasons. Salinity stress significantly decreased growth and physiological traits in all seedling plants; however, some salt-tolerant genotypes showed the lowest reduction in measured traits. Multivariate analysis grouped measured traits and tested genotypes into different clusters. The multi-trait genotype–ideotype distance index (MGIDI) selected genotypes G12, G14, G6, G7, and G16 as the salt-tolerant barley genotypes. Considering the results of the AMMI analysis showed that grain yields of tested barley genotypes were influenced by environment (E), genotype (G) and GE interaction effects. Based on the weighted average of absolute scores of the genotype index (WAASB) and other stability statistics, G7, G8, G14, and G16 were selected as superior genotypes. Considering the outputs of MGIDI and WAASB indices revealed that three genotypes G7, G14 and G16 can be recommended as new genetic resources for improving and stabilizing grain yield in barley programs for the moderate climate and saline regions of Iran. In conclusion, our results suggest that the using MGIDI index in the early growth stage can accelerate screening nurseries in barley breeding programs.

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“…As genotypes, environments and genotypes  environment interactions were significant, it was proceeded to calculate AMMI and GGE stability analysis. Asfaw et al (2012) and Baraki et al (2020) also reported significant GEI in grain yield of mungbean and cowpea genotypes evaluated in different environments.…”

Section: Combined Analysis Of Variancementioning

confidence: 91%

“…Furthermore, environments E2 and E4 are also with longer vector length and are considered as good test environments for selecting widely adapted genotypes. Asfaw et al (2012) and Baraki et al (2020) also used the discriminating representativeness view of the GGE bi-plot to evaluate the testing environments for mungbean and cowpea genotypes, respectively.…”

Section: Discriminating and Representativeness Of The Test Environmentsmentioning

confidence: 99%

“…The genotype G5 (IC-512946) was also the genotype with relatively higher yield and stability, while the remaining eight genotypes are the poor yielding genotypes which are too far from the ideal genotype and are relatively stable since, they are with short vector length from the AEA. Asfaw et al (2012) and Baraki et al (2020) also used the GGE bi-plot of the mean and stability to evaluate the performance and stability of mungbean and cowpea genotypes respectively against the ideal genotypes.…”

Section: Mean Performance and Stability Of Genotypesmentioning

confidence: 99%

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Understanding of Yield Stability in Jack Bean (Canavalia ensiformis L.) Genotypes using AMMI and GGE bi-plot Models

Saidaiah

Pandravada

Sivaraj

et al. 2021

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Background: Jack bean is an under-exploited legume species, a source of food, medicine and cover crop. By virtue of its adaptive nature to low fertility soils, it is one of the few pulses that grow well on highly leached, nutrient depleted, lowland tropical soils. But, in India, crop improvement work is very little done. Stability of yield is a major criterion for farmer’s acceptability of any variety and there are several methods to estimate the stability and G x E interaction effects of a genotype across seasons. Among these, AMMI analysis is the most recent and widely exploited in different crops for the identification of stable genotypes. In this context, yield stability of 10 accessions of jack bean is studied to identify the stable genotypes.Methods: The experiment was conducted with 10 Jack bean genotypes in RCBD with two replications under rain fed conditions during 2017-2020 in Kharif for four seasons. The data was subjected to analysis of variance and then taken for AMMI and GGE analysis for identification of stable genotypes.Result: The combined analysis of variance revealed that there was highly significant variation (p less than 0.01) in grain yield and environments and genotype interaction among the genotypes. The average bean yield of the genotypes was 533.1 grams per plant. The highest and the lowest mean yield was recorded in PSR-12202 and CHMJB-02 respectively which was corroborated by the AMMI bi-plot as well. Similar to the AMMI bi-plot, the GGE bi-plot also confirmed that PSR-12202 was the stable genotype across the environments, whereas, G1, G2, G3, G4, G6, G7 and G8 were the other genotypes with low yields in some or all the environments. Kharif, 2018 and Kharif, 2020 are discriminating environments and are declared as the most representative than Kharif, 2017 and Kharif, 2019. Generally, PSR-12202 was the ideal genotype with higher mean yield and relatively good stability; G5 was the moderately good yielding genotype and the most unstable genotype; Whereas, G1, G2, G3, G4, G6, G7 and G8 were the poorly yielding and unstable genotypes. Both AMMI and GGE bi-plot are able to establish the genotypic stability and these models can be exploited for judging the genotypes for their GEI in other crops as well.

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Genotype x environment interaction and yield stability analysis of mung bean (Vigna radiata (L.) Wilczek) genotypes in Northern Ethiopia

Cited by 26 publications

References 16 publications

Development of Novel Blackgram (Vigna mungo (L.) Hepper) Mutants and Deciphering Genotype × Environment Interaction for Yield-Related Traits of Mutants

Development of Novel Blackgram (Vigna mungo (L.) Hepper) Mutants and Deciphering Genotype × Environment Interaction for Yield-Related Traits of Mutants

MGIDI and WAASB indices: The useful approaches for selection of salt-tolerant barley genotype at the early growth and maturity stages

Understanding of Yield Stability in Jack Bean (Canavalia ensiformis L.) Genotypes using AMMI and GGE bi-plot Models

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