Genotype × environment interaction of winter wheat (<i>Triticum aestivum</i> L.) in South Africa: II. Stability analysis of yield performance

Purchase, J. L.; Hatting, H.; Deventer, C. S. van

doi:10.1080/02571862.2000.10634878

Cited by 525 publications

(570 citation statements)

References 13 publications

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“…The AMMI model combines ANOVA for the genotype and environment main effects with principal components analysis of GEI (Zobel et al, 1988;Gauch and Zobel, 1996). Therefore, based on the AMMI model (IPCA1 and IPCA2) the AMMI stability value (ASV) has been used (Purchase et al, 2000). This ASV is comparable with the methods used by Shukla, Eberhart, and Russell for genotype stability (Purchase et al, 2000).…”

Section: Introductionmentioning

confidence: 56%

“…Since the IPCA1 score contributes more to GE sum of squares (Table 2), it has to be weighted by the proportional difference between IPCA1 and IPCA2 scores to compensate for the relative contribution of IPCA1 and IPCA2 to the total GE sum of squares. The distance from zero is then determined using the Pythagorean Theorem (Purchase et al, 2000). In the ASV method, a genotype with the lowest ASV score is the most stable; accordingly, genotype 12 was the most stable followed by genotype 5.…”

Section: Ammi Stability Valuementioning

confidence: 99%

“…AMMI stability value (ASV) was proposed by Purchase et al (2000) to quantify and rank genotypes according to their yield stability. The ASV is the distance from zero in a two-dimensional scattergram of IPCA1 (interaction principal component analysis axis 1) scores against IPCA2 scores.…”

Section: Ammi Stability Valuementioning

confidence: 99%

“…Therefore, based on the AMMI model (IPCA1 and IPCA2) the AMMI stability value (ASV) has been used (Purchase et al, 2000). This ASV is comparable with the methods used by Shukla, Eberhart, and Russell for genotype stability (Purchase et al, 2000). The development and use of the yield-stability statistic (YSi) was also used to recommend varieties (Kang, 1993;Pazdernik et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

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Use of AMMI and other stability statistics in the simultaneous selection of rice genotypes for yield and stability under direct-seeded conditions

Bose

Jambhulkar

Pande

et al. 2014

Chilean J. Agric. Res.

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Rice (Oryza sativa L.) is one of the most important cereal crops in the world. India is one of the largest rice-producing countries. Although more than 900 rice varieties have been released in India, many of them are no longer cultivated within a few years of release due to inconsistent performance in diverse environments; only a few varieties with stable performance continue to be under cultivation after 15 to 20 yr of their release. Development and adaptability of rice cultivars in a wide range of target environments are the eventual goals of plant breeders. An attempt has been made to estimate the level of genotype-environment interaction (GEI) and eliminate as much as possible the unexplainable and extraneous variability contained in the data. Therefore, several statistical techniques have been used to describe GEI and measure genotype stability. Field experiments were conducted with 12 genotypes under direct-seeded conditions (irrigated and rainfed) for three consecutive years (2009 to 2012) in a randomized complete block design with three replicates. The GEI was analyzed using additive main effects and multiplicative interaction (AMMI). Results of AMMI analysis indicated that the first three AMMI (AMMI1 to AMMI3) were highly significant (P < 0.05). The partitioning of TSS (total sum of squares) exhibited that the genotype effect was a predominant source of variation followed by GEI and environment, which suggests the possible existence of different environment groups. The AMMI stability value discriminated genotypes 11 and 12 as stable genotypes based on the yield stability index (YSI) and sustainability index (SI).

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Section: Introductionmentioning

confidence: 56%

Section: Ammi Stability Valuementioning

confidence: 99%

Section: Ammi Stability Valuementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Use of AMMI and other stability statistics in the simultaneous selection of rice genotypes for yield and stability under direct-seeded conditions

Bose

Jambhulkar

Pande

et al. 2014

Chilean J. Agric. Res.

View full text Add to dashboard Cite

show abstract

“…The additive main effects and multiplicative interaction (AMMI) model is a combines the analysis of variance (ANOVA) with additive factors and the principal component analysis (PCA) with multiplicative factors in a single analysis. Based on the results of AMMI analysis, several stability parameters such as AMMI's stability measures (EVi and EVF), sums of the absolute value of the IPCA scores (SIPCi and SIPCF) and AMMI's stability value (ASV) have been proposed for test of genotypes (Zobel 1994;Purchase 1997;Purchase et al 2000).…”

Section: Introductionmentioning

confidence: 99%

GGE biplot and AMMI analysis of barley yield performance in Iran

Vaezi

Pour-Aboughadareh

Mohammadi

et al. 2017

Cereal Research Communications

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Successful production and development of stable and adaptable cultivars only depend on the positive results achieved from the interaction between genotype and environment that consequently has significant effect on breeding strategies. The objectives of this study were to evaluate genotype by environment interactions for grain yield in barley advanced lines and to determine their stability and general adaptability. For these purposes, 18 advanced lines along with two local cultivars were evaluated at five locations (Gachsaran, Lorestan, Ilam, Moghan and Gonbad) during three consecutive years (2012)(2013)(2014)(2015). The results of the AMMI analysis indicated that main effects due to genotype (G), environment (E) and GE interaction as well as four interaction principal component axes were significant, representing differential responses of the lines to the environments and the need for stability analysis. According to AMMI stability parameters, lines G5 and G7 were the most stable lines across environments. Biplot analysis determined two barley mega-environments in Iran. The first mega-environment contained of Ilam and Gonbad locations, where the recommended G13, G19 and G1 produced the highest yields. The second mega-environment comprised of Lorestan, Gachsarn and Moghan locations, where G2, G9, G5 and G7 were the best adapted lines. Our results revealed that lines G5, G7, G9 and G17 are suggested for further inclusion in the breeding program due to its high grain yield, and among them G5 recommended as the most stable lines for variable semi-warm and warm environments. In addition, our results indicated the efficiency of AMMI and GGE biplot techniques for selecting genotypes that are stable, high yielding, and responsive.

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Grain yield stability of soybean (Glycine Max (L.) Merrill) for different stability models across diverse environments of Ethiopia

Hailemariam

Tesfaye

2023

Agrosystems Geosci & Env

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For grain yield stability analysis, genotype by environment interactions are crucial in properly identifying and discriminating between varieties and locations. Hence, this experiment was conducted with the objectives to evaluate the stability of soybean using additive main effects and multiplicative interaction (AMMI), multi‐trait stability index (MTSI), weighted average absolute scores biplot (WAASB), Eberhart and Russell regression model, and genotype plus genotype by environment interaction (GGE) biplot analysis for grain yield of soybean genotypes and identified stable genotypes in the different soybean agroecologies of Ethiopia. Twenty‐four soybean genotypes were planted at six soybean environments with RCBD in three replications in the 2015/2016 cropping season. Stability measures, namely, AMMI, AMMI stability value, and GGE biplot analysis were used to identify the high‐yielding and stable genotypes across the testing environments. AMMI‐1 biplot showed Pawe as the ideal environment; Bako as a favorable environment; Asosa an average environment; and the rest namely, Dimtu, Jimma, and Metu as unfavorable environments. On the other hand, AMMI‐2 biplot analysis certain genotypes like Prichard, Spry, Delsoy 4710, and Croton 3.9 were identified as stable genotypes. Bako and Metu were identified as the most discriminating environments. Mega environments and the best yielding soybean genotypes on each mega environment were revealed by the GGE biplot analysis model. For other multivariate statistics used for this study, MTSI, WAASB, and regression models, stable and superior varieties for grain yield were revealed. Through the MTSI, the four genotypes, namely, Liu yue mang, SCS‐1, Clarck‐63k, and AFGAT, were found to be stable and superior over the rest tested genotypes. Overall, the genotypes SCS‐1 and AGS‐7‐1 were stable across soybean growing environments and are recommended for mega environment production.

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Genotype × environment interaction of winter wheat (Triticum aestivum L.) in South Africa: II. Stability analysis of yield performance

Cited by 525 publications

References 13 publications

Use of AMMI and other stability statistics in the simultaneous selection of rice genotypes for yield and stability under direct-seeded conditions

Use of AMMI and other stability statistics in the simultaneous selection of rice genotypes for yield and stability under direct-seeded conditions

GGE biplot and AMMI analysis of barley yield performance in Iran

Grain yield stability of soybean (Glycine Max (L.) Merrill) for different stability models across diverse environments of Ethiopia

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