Integrating univariate and multivariate statistical models to investigate genotype × environment interaction in durum wheat

Mohammadi, Reza; Sadeghzadeh, Behzad; Poursiahbidi, Mohammad Mehdi; Ahmadi, Mehdi

doi:10.1111/aab.12648

Cited by 22 publications

(18 citation statements)

References 52 publications

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“…For bread wheat data set, (Kaya et al, 2006) stated that the environment produced roughly 81% of total variation. The same trend was detected by (Mohammadi et al 2021) for durum wheat yield trials in Iran. (Mohammadi et al, 2015) reported about 81% of the total variation attributed by environments, whereas, genotype main effect contributed 2.5% of the total variation.…”

Section: Discussionsupporting

confidence: 80%

Estimation of AMMI and GGE Biplots for some Bread and Durum Wheat Genotypes

Mohamed

Darwish

El-Rady

et al. 2022

Journal of Plant Production

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Wheat breeders face environmental conditions and factors when developing and releasing a new cultivar from a breeding program, which considering a challenge especially with climate change. Genotype-byenvironment interaction analysis is key for selection and cultivar release, and to identify suitable production and test environments. Therefore, the grain yield of 12 bread and 12 durum wheat genotypes were evaluated in six different locations from Nile valley to newly reclaimed land in the west desert of Egypt, in two successive 2018/19 and 2019/20 seasons. The mega-environment was tested with the winner genotypes, the stable genotypes, and the individual ideal genotypes by using additive main effect and multiplicative interaction (AMMI), and genotype main effect plus genotype × environment interaction (GGE) biplot methods. The results of AMMI and GGE biplot revealed four mega-environments, one of them had several environments with winner G7 as well as a stable genotype. But the ideal genotype was G2 for the bread wheat genotypes set. On the other hand, the durum wheat genotypes set is divided into two mega-environments with winners G16 and G17 (breeding lines) of grain yield. But the stable genotype and ideal genotype was G18 according to AMMI and GGE biplot analyses across tested environments.

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Section: Discussionsupporting

confidence: 80%

Estimation of AMMI and GGE Biplots for some Bread and Durum Wheat Genotypes

Mohamed

Darwish

El-Rady

et al. 2022

Journal of Plant Production

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“…The low percentage of variance explained by genotype may be due to the fact that the genetic materials used in this study already are selected as top-yielding materials through wheat breeding program in previous years. Similar results also reported by other studies (Bocianowski et al, 2019;Chairi et al, 2020;Mohammadi et al, 2021) that the genotype portion in MET has been known to be the smallest among all sources of variation. The AMMI model further partitioned the interaction sum of square into eight significant IPCAs (Table 3), which captured 85.6% of the total GE interaction.…”

Section: Anova and Variation In Genotype's Performancesupporting

confidence: 91%

“…However, in this area rainfall is the most constraint of crop growth and productivity, leading to failure crops in some years (Mohammadi et al, 2018). The crop performance was significantly (p < 0.01) affected by environmental condition, which accounted for 83.5% of total variation (Table 3); a near value to the 84.3% and 85% obtained from previous studies in durum wheat in Iran (Mohammadi et al 2018(Mohammadi et al , 2021. However, selection for grain yield improvement is a very complex phenomenon and should be considered in accounting genotypes along with the environmental interactions.…”

Section: Discussionmentioning

confidence: 71%

“…Integrating univariate stability methods with multivariate methods allows breeders to select stable genotypes and help with improving recommendations to the growers (Mohammadi et al, 2021). The lowest values of superiority index (Pi) for genotypes G3, G11, G1, G24, and G22 revealed a minimum distance between the performance of these genotypes and the maximum response observed across environments.…”

Section: Discussionmentioning

confidence: 99%

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Genotype × environment interaction and stability analyses of grain yield in rainfed winter bread wheat

et al. 2022

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The genotype × environment (GE) interaction analysis is fundamental in crop breeding programs to guide selection and for recommendation of high performing and stable genotypes for breeding objectives. This study aimed at quantifying the GE interaction effects and determines grain yield stability among winter bread wheat genotypes under rainfed conditions of Iran. Twenty-four winter wheat genotypes were evaluated under nine test locations using a randomized complete blocks design with four replications during three cropping seasons (2019–21). The additive main effects and multiplicative interaction (AMMI) model and several parametric and nonparametric stability statistics were applied for analysis of grain yield data collected from the experiments. AMMI analysis of variance for grain yield revealed significant effects (p < 0.01) for genotype, environment, and GE interaction. The environment was the main source of variation and accounted for 83.5% of the total yield variation, followed by GE (6.5%) and genotype (1.0%) effects. The AMMI biplot analysis indicated the genotypes G3, G23, G22, G10, and G19 as high yielding with stability performance across environments. Genotypes G14, G13, G20, and G9 showed large positive interaction with the environments featuring the highest rainfall during growing season, while genotypes G7, G6, and G21 had a large positive interaction with environments with low rainfall. Spearman’s rank correlation analysis revealed that the AMMI stability value, Shukla’s stability variance (σ2i), Wricke’s ecovalence (W2i), coefficient of determination (R2i), variance in regression deviations (S2di), and nonparametric statistic of S2(i) were not correlated with mean yield in tested genotypes, showing they are related to static/biological concept of stability. In contrast, the genotypic superiority index (Pi) and regression coefficient (bi) were significantly correlated (p < 0.01) with mean yield and corresponded to dynamic/agronomic concept of stability. These findings suggest that selection of genotypes should be considered based on selection objectives of using the various stability parameters described here. In conclusion, the selected genotypes in this study should be recommended as new cultivars or parental lines for grain yield and stability improvement under rainfed conditions of Iran or similar agro-ecologies.

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“…So far, GGE-biplot analysis has been applied to many crops such as soybean (Yan and Rajcan, 2002), rice (Samonte et al, 2005), bread wheat (Kaya et al, 2006;Roozeboom et al, 2008;Akcura et al, 2011;Nehe et al, 2019), barley (Dehghani et al, 2006;Mohammadi et al, 2009), peanuts (Putto et al, 2008), lentils (Sabaghnia et al, ), corn (İlker et al, 2009, oats (Yan et al, 2010) and sorghum (Rao et al, 2011). However, despite reports on GGE-biplot analysis in selecting superior genotypes or test environments in such crops, its application to durum wheat METs in Turkey is insufficient (Tekdal et al, 2017;Kendal, 2019;Mohammadi et al, 2021). Genotypes (i.e., breeding lines) are routinely tested to select ones adaptable to Turkey's Winter Durum Wheat Zone.…”

Section: Introductionmentioning

confidence: 99%

GGE-BIPLOT Analysis of Durum Wheat Yield Trials

Kaya

2022

Black Sea Journal of Agriculture

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Durum wheat (Triticum durum L.) is a vital crop in the rain-fed areas of Turkey. In this study, the grain yield of twenty durum wheat genotypes (sixteen advanced lines with four checks) tested across 8 rain-fed environments during the 2008-2009 cropping season was evaluated using GGE (Genotype (G) Main Effect + Genotype by Environment Interaction (GEI)) Biplot Approach. Environment (E) captured most (83 %) of the grain yield (GY) variation, while the portions attributed to G and GEI were only 6 and 11 %, respectively. In addition, most of the testing Es were highly correlated. According to GGE-Biplot analysis, 'Which won where' pattern partitioned the testing Es into three mega-environments (ME): the first ME with six Es with G13 (the highest yielder) as the winning genotype; the second ME encompassed one environment (E1, Konya) with G4 (the lowest yielder) as the winning genotype, and the last ME represented by one location (E6, Altintas) with G10 (the higher yielder) as the winning genotype. GGE-Biplot analysis showed that although the Durum Wheat Yield Trials were conducted in many environments, outcomes alike can be obtained from one or two representatives of each ME. On the other hand, no correlation of these MEs with their geographic location was observed. In conclusion, the presence of cross-over GEI underscores that efforts should be given to identify specifically adapted genotypes rather than broadly adapted ones tested on multi-environment trials (METs).

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Integrating univariate and multivariate statistical models to investigate genotype × environment interaction in durum wheat

Cited by 22 publications

References 52 publications

Estimation of AMMI and GGE Biplots for some Bread and Durum Wheat Genotypes

Estimation of AMMI and GGE Biplots for some Bread and Durum Wheat Genotypes

Genotype × environment interaction and stability analyses of grain yield in rainfed winter bread wheat

GGE-BIPLOT Analysis of Durum Wheat Yield Trials

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