GGE and AMMI biplot for interpreting interaction of genotype X environments of cassava promising genotypes

Sholihin,

doi:10.1063/5.0041787

Cited by 6 publications

(7 citation statements)

References 13 publications

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“…On the other hand, the vertex genotypes G3, G5, G13, and G22 were the poorest genotypes in almost the entire test environments because they were the furthest from the biplot's origin on the opposite side of the environments. Similar results were reported by Agyeman et al [24], Akinwale et al [30], Noerwijati and Prajitno [40], and Sholihin [57], who characterized genotypes' which-won-where patterns. Tey found that some genotypes performed better in a specifc environment than others and that some genotypes performed worst in some environments.…”

Section: Gge Biplotsupporting

confidence: 87%

“…Tus, the G8, G11, G14, G21, and G24 genotypes were close to the biplot origin, indicating roughly average performance, and their GEI variation was lower than that of the vertex genotypes. Tis fnding was similar to that of Noerwijati and Prajitno [40], Esuma et al [45], Sholihin [57], Akter et al [58], and Bakare et al [59], who reported that the testing environment was delineated into diferent megaenvironments with winning genotypes and sectors containing various numbers of genotypes.…”

Section: Gge Biplotsupporting

confidence: 81%

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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: Gge Biplotsupporting

confidence: 87%

Section: Gge Biplotsupporting

confidence: 81%

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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“…On the other hand, because they were located on the other side of the test environments and the furthest from the biplot's origin, the vertex genotypes-except G6, the rest: G2, G3, and G7-were the ones that performed the poorest over practically the entire set of test settings. Similar findings reported on genotypes' which-won-where patterns [26,[44][45][46] agree with this study's finding. They discovered that certain genotypes performed better than others in a particular environment and that certain genotypes performed worse in certain contexts.…”

Section: Which-won-where View Of Gge Biplotsupporting

confidence: 92%

“…It follows that G6 was the preferred genotype position, followed by G5, making it the preferred genotype among those of oat. This is comparable to the findings of [35,44,54] who showed that the first and second concentric circles, respectively, include one optimal genotype and a few other suitable genotypes. Similar to this, Ref.…”

Section: Evaluation Of Genotypes Based On the Ideal Genotypesupporting

confidence: 89%

AMMI and GGE Biplot Analyses for Mega-Environment Identification and Selection of Some High-Yielding Oat (Avena sativa L.) Genotypes for Multiple Environments

Wodebo,

Tolemariam,

Demeke

et al. 2023

Plants

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This paper reports an evaluation of eleven oat genotypes in four environments for two consecutive years to identify high-biomass-yielding, stable, and broadly adapted genotypes in selected parts of Ethiopia. Genotypes were planted and evaluated with a randomized complete block design, which was repeated three times. The additive main effect and multiplicative interaction analysis of variances revealed that the environment, genotype, and genotype–environment interaction had a significant (p ≤ 0.001) influence on the biomass yield in the dry matter base (t ha−1). The interaction of the first and second principal component analysis accounted for 73.43% and 14.97% of the genotype according to the environment interaction sum of squares, respectively. G6 and G5 were the most stable and widely adapted genotypes and were selected as superior genotypes. The genotype-by-environment interaction showed a 49.46% contribution to the total treatment of sum-of-squares variation, while genotype and environment effects explained 34.94% and 15.60%, respectively. The highest mean yield was obtained from G6 (12.52 kg/ha), and the lowest mean yield was obtained from G7 (8.65 kg/ha). According to the additive main effect and multiplicative interaction biplot, G6 and G5 were high-yielding genotypes, whereas G7 was a low-yielding genotype. Furthermore, according to the genotype and genotype–environment interaction biplot, G6 was the winning genotype in all environments. However, G7 was a low-yielding genotype in all environments. Finally, G6 was an ideal genotype with a higher mean yield and relatively good stability. However, G7 was a poor-yielding and unstable genotype. The genotype, environment, and genotype x environment interaction had extremely important effects on the biomass yield of oats. The findings of the graphic stability methods (additive main effect and multiplicative interaction and the genotype and genotype–environment interaction) for identifying high-yielding and stable oat genotypes were very similar.

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“…There was positive correlation between starch yield and starch content in nine months [9]. Starch is affected by genetic factor and interaction between genetics and environments [15,16,17]. Vamas 1 with mean of starch yield of 7.21 t ha -1 in seven months is significantly out-yielded all the check varieties UJ3 and UJ5, 34% and 23%, respectively higher than check (Table 3), it was significantly higher (34%) than the existing cultivar (UJ3).…”

Section: The Starch Yieldmentioning

confidence: 99%

Vamas 1, a new early root bulking, high-yielding, high-starch content cassava variety

Sholihin¹

2022

E3S Web Conf.

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A good variety of cassava is important factor in increasing the productivity. If the productivity of cassava is increase, the competitiveness of cassava will increase. Vamas 1 is new variety released 2020 by Indonesian Government. Based on multi-location trial during 2015-2018 in 8 location in Lampung, the fresh tuber yield in 7 months of Vamas 1 was 25% higher that UJ3, equal to IDR 64,600,00,-/ha if the price of cassava IDR 1000,-/kg and 15% higher than UJ5, equal to IDR 6,460,000,-/ha. Starch yield of Vamas 1 was 39% higher than UJ3, equal to IDR 16,056,000,-/ha and 23% higher than UJ5, equal to 10,736,000,-/ha. Plant height of Vamas 1 was lower than UJ5. Starch content of Vamas 1 was higher than UJ3 and UJ5. Vamas 1 was shown moderately resistant against mite insect under artificial screening which is similar to UJ3 and UJ5. Vamas 1 has shown moderately resistant to cassava root rot desease under artificial screening, while the checks showed the susceptible. HCN content of fresh tuber of Vamas 1 was 19.68 ppm less than UJ3 (48.9 ppm) and UJ5 (40.56 ppm) as checks.

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GGE and AMMI biplot for interpreting interaction of genotype X environments of cassava promising genotypes

Cited by 6 publications

References 13 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

AMMI and GGE Biplot Analyses for Mega-Environment Identification and Selection of Some High-Yielding Oat (Avena sativa L.) Genotypes for Multiple Environments

Vamas 1, a new early root bulking, high-yielding, high-starch content cassava variety

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