GGE-BIPLOT Analysis of Durum Wheat Yield Trials

Kaya, Yüksel

doi:10.47115/bsagriculture.1069462

Cited by 20 publications

(29 citation statements)

References 33 publications

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“…In early breeding cycles, genotypes that are far from the ideal genotype can be ruled out, while genotypes that are close to it can be considered in subsequent tests [55]. A genotype is more desirable if it is closer to the "ideal" genotype, which is located in the frst concentric circle of the GGE biplot graphic [64,66].…”

Section: Evaluation Of Genotypes Based On the Ideal Genotypementioning

confidence: 99%

AMMI and GGE Biplot Analyses for Mega Environment Identification and Selection of Some High-Yielding Cassava Genotypes for Multiple Environments

Daemo

Yohannes

Beyene

et al. 2023

International Journal of Agronomy

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Cassava (Manihot esculenta Crantz) is a staple food and generates income for smallholder farmers in southern Ethiopia. The performance of cassava genotypes varies in different growing environments; thus, the evaluation of genotypes tested in various environments plays an essential role in developing strategies to delineate environments, explore unstable genotypes in target environments, and identify stable genotypes for multiple environments. In this regard, there needs to be more information on the identification of mega-environments and stable genotypes with high yields for wide adaptation. Thus, this study aimed to identify mega-environment and high-yielding cassava genotypes for multiple environments using AMMI and GGE biplots. A total of 25 genotypes were evaluated in six environments using a RCBD during the 2020–2021 cropping season. The AMMI analysis of variances revealed that environments, genotypes, and genotype-environment interaction had a significant ( P ≤ 0.001 ) influence on cassava fresh storage root yield (t·ha−1), showing genetic variability among genotypes by changing environments. The genotype-by-environment interaction showed a 61.36% contribution to the total treatment SS variation, while the environment and genotype effects explained 28.16% and 10.48% of the total treatment SS, respectively. IPCA1 and IPCA2 accounted for 33.42% and 23.5% of the GE interactions SS, respectively. The GGE biplot showed that the six environments used in this study were delineated into three mega-environments, namely, the first (Tarcha and Disa), the second (Wara and Areka), and the third (Jimma and Bonbe). Those mega-environments could be helpful for genotype evaluation and effective breeding. The GGE biplot indicated that the vertex genotypes were G16, G17, and G25. They are regarded as specifically adapted genotypes since they are more responsive to environmental change. The GGE biplot also revealed that Tarcha was ideal, having the most discriminating and representative environment, while G10 was the ideal and the overall winning genotype for the current study. Moreover, the genotypes G10 and G14 were identified as being the most stable, with a higher fresh storage root yield than the grand mean. Thus, G10 and G14 were selected as superior genotypes that could be promoted to advanced yield trials to develop stable cultivars with better storage root yield of cassava.

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Section: Evaluation Of Genotypes Based On the Ideal Genotypementioning

confidence: 99%

AMMI and GGE Biplot Analyses for Mega Environment Identification and Selection of Some High-Yielding Cassava Genotypes for Multiple Environments

Daemo

Yohannes

Beyene

et al. 2023

International Journal of Agronomy

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“…GGE-biplot analyses for comparison of genotypes were performed to detect the ideal and desirable (Figure 1). An ideal genotype should have both high mean yield performance and high suitability across environments (Kaya et al, 2006;Yan and Tinker, 2006). The ideal genotypes should be in the center of concentric circles.…”

Section: Iv-gge-biplotmentioning

confidence: 99%

Influence of heat stress on yield and its components of some early maturing bread wheat genotypes under Middle Egypt conditions.

Hussein

2021

Egyptian Journal of Agricultural Research

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Nine bread wheat early maturing promising lines and three local cultivars were evaluated at Mallawy Agric. Res. Stations, ARC, Egypt, for the two successive growing seasons of 2018/2019 and 2019/2020 under the two sowing dates, the recommended (SD1:30 th November) and the late sowing date (SD2:30 th December). A split-plot arrangement with three replicates was used in each season. The aim of this study was to select the best genotypes for early maturity and higher grain yield. The results showed significant differences among genotypes for all studied traits in the optimum sowing date (SD1) compared to late sowing date (SD2). The optimum sowing date (S1) produced the highest values for all studied traits in both seasons, while the late sowing date (S2) was recorded the earlier plants for heading and maturity dates. The studied genotypes differences significantly in all estimated traits, lines 1 and 2 had the earliest genotypes for heading days, while Giza 171 and line 8 were the latest one for this trait. On the other hand, lines 9 and 4 were the earliest genotypes for maturity date, while the latest one was recorded for Giza 171. Line 5 surpassed all the other genotypes in grain yield (28.31 and 23.17 ard. fed -1 ) in the first and second seasons, respectively and it is appeared to be more tolerant to late sowing date as well as heat stress. The interaction (SD x G) had a significant effect on most studied traits. Lines 9 and 6 were the earliest genotypes for maturity when sown on 30 Nov. also, lines 9 and 4 were the earliest genotypes when sown on 30 Dec. The highest grain yield was obtained from line 4 followed by line 5 when sown on 30 Nov. and line 5 had the highest grain yield when sown on 30 Dec. in the first season. While, in the second season, lines 5 and 8 recorded the highest grain yield when sown on 30 Nov. and 30 Dec. respectively. Line 5 is suitable for planting in a wide range of planting dates (from 30 th Nov. to 30 th Dec.). Moreover, it is early maturing and can be recommended for late sowing (30 th Dec.). Therefore, the results of the present study confirmed that lines 4, 5, 7 and 6 were superior over the other genotypes in producing the earliest maturity date and higher grain yield and can be used, in future, in bread wheat breeding programs for the development of wheat cultivars for high temperature stress condition at the terminal growth stage.

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“…This method delivers an accurate and unbiased prediction of genotypic values, along with providing genetic correlations for the performance of the same genotypes in different environments (Piepho et al., 2008). The genotype and genotype × environment interaction (GGE) biplot analysis has been used to visualize GEI in many crop species, including rice ( Oryza sativa L.) (Samonte et al., 2005), wheat ( Triticum aestivum L.) (Kaya et al., 2006), barley ( Hordeum vulgare L.) (Mohammadi et al., 2009), sorghum [ Sorghum bicolor (L.) Moench] (Rakshit et al., 2012), and bermudagrasses ( Cynodon spp.) (Gouveia et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Multienvironment evaluation of St. Augustinegrass breeding lines for turf performance via a mixed‐models approach

Pereira

Paudel

Nunes

et al. 2021

Intl Turfgrass Soc Res J

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St. Augustinegrass [Stenotaphrum secundatum (Walter) Kuntze] is a turfgrass extensively used in managed landscapes in Florida and throughout the southeastern United States. To develop cultivars with broad adaptation, it is necessary to understand the effect of genotype × environment interactions. The objective of this study was to estimate genetic parameters in St. Augustinegrass for coverage, turf quality (TQ), and turf color (TC) in a multienvironment trial across five locations in Florida (Arcadia, Hague, Citra, Cocoa Beach, and Jay) evaluated over 5 yr (2011)(2012)(2013)(2014)(2015)(2016). The experiment consisted of 26 advanced breeding lines, and four cultivars: 'Floraverde', 'SS-100' (Palmetto ® ), 'NUF-76' (Captiva ® ), and 'Floratam'. Trials were arranged as randomized complete block designs with three replications. Linear mixed models with repeated measures were implemented in ASReml-R. Significant differences were observed among genotypes. The estimates of genotype-mean broad-sense heritabilities ranged from 0.37 to 0.96 for coverage, 0.00 to 0.37 for TC, and 0.27 to 0.93 for TQ. Genotypic correlations ranged from 0.05 to 0.90 for coverage, 0.48 to 0.69 for TC, and 0.35 to 0.83 for TQ, indicating stable genotypic performance for TQ and TC between locations. For coverage, most genotypes clustered together, whereas greater genotypic variability was observed for TQ and TC. Genotypes '04-4-1-4-6' and 'FSA1602' showed the best overall performance across the different traits analyzed for most locations and both genotypes are recommended for further use in future crosses in the breeding program. FSA1602 was recently released as FSA1602 and is being commercialized under the trademark CitraBlue ® . INTRODUCTIONSt. Augustinegrass [Stenotaphrum secundatum (Walter) Kuntze] is a warm-season (C 4 ) turfgrass species that is Abbreviations: GEI, genotype × environment interaction; GGE, genotype and genotype × environment interaction; H 2 , genotype mean-based heritability; MET, multienvironment trial; PC, principal component; TC, turf color; TQ, turf quality.

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GGE-BIPLOT Analysis of Durum Wheat Yield Trials

Cited by 20 publications

References 33 publications

AMMI and GGE Biplot Analyses for Mega Environment Identification and Selection of Some High-Yielding Cassava Genotypes for Multiple Environments

AMMI and GGE Biplot Analyses for Mega Environment Identification and Selection of Some High-Yielding Cassava Genotypes for Multiple Environments

Influence of heat stress on yield and its components of some early maturing bread wheat genotypes under Middle Egypt conditions.

Multienvironment evaluation of St. Augustinegrass breeding lines for turf performance via a mixed‐models approach

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