Genotype by Environment Interaction Analysis for Grain Yield of Wheat (Triticum aestivum (L.) em.Thell) Genotypes

Gupta, Vijeta; Kumar, Mukesh; Singh, Vikram Jeet; Chaudhary, Lakshmi; Yashveer, Shikha; Sheoran, R. K.; Dalal, Mohinder Singh; Nain, Ashish; Lamba, Kavita; Gangadharaiah, Nikhil; Sharma, Rajat; Nagpal, Shreya

doi:10.3390/agriculture12071002

Cited by 23 publications

(16 citation statements)

References 34 publications

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“…AMMI and GGE biplots differed in their results for stability of genotypes and discriminating ability of test environments due to capturing variable proportion of GEI and the inner product property. These results are consistent with the previous studies on different crops (Enyew et al, 2021;Gupta et al, 2022;Mutari et al, 2022;Vaezi et al, 2017). When similar weather conditions prevail in the northwestern Himalayas, where oat is grown, farmers can practice an organic production system.…”

Section: Gge Biplot Analysissupporting

confidence: 94%

“…The first sector includes environment E1 and G89 was the winner genotype, environment E2 formed the second sector and G75 was the winner genotype, while environments E3, E4, and E5 formed the third sector and G64 was the winner genotype. This way of clustering environments and identifying best‐performing, specifically adapted genotypes has previously been used in oat (Sánchez‐Martín et al., 2014, 2017), wheat (Gupta et al., 2022), sorghum (Enyew et al., 2021), and barley (Maniruzzaman et al., 2019).…”

Section: Resultsmentioning

confidence: 99%

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Adaptation of oat genotypes in organic and conventionally managed fields in the northwestern Himalayas

Sanadya,

Sood,

Sharma

et al. 2024

Agronomy Journal

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Developing stable and high‐yielding crop varieties is crucial for sustainable agriculture. The present study investigated genotype × environment interactions (GEIs) in 96 oat (Avena sativa L.) genotypes across five environments (year–production system combinations) using an alpha lattice design. Additive main effect and multiplicative interaction (AMMI) analysis revealed significant genotype, environment, and GEI effects on the studied traits. The oat genotypes were grouped into four distinct clusters and even genotypes from the same source were grouped into different clusters, indicating a wide range of diversity. Stability indices have highlighted genotypes coded as G33, G4, and G5 as the most stable across environments. Specifically, concerning seed yield, the AMMI analysis identified G33, G4, G5, and G82 as highly stable genotypes. Moreover, the study observed specific adaptations, such as G29 in E1 (conventional system 2019–2020), G76 in E3 (conventional system 2020–2021), and G54 and G77 in E4 (organic system 2020–2021). The genotype and genotype × environment (GGE) biplot analysis unveiled that environment E3 (conventional system 2020–2021) emerged as the ideal environment, demonstrating the highest level of representativeness and discrimination. By utilizing the polygon view of the GGE biplot, the presence of two distinct mega‐environments (MEs; ME1 and ME2) was revealed in relation to seed yield. Within these MEs, genotypes coded as G2 and G38 in ME1 and G20, G72, G85, and G89 in ME2 exhibited specific adaptations. Furthermore, genotypes coded as G89, G95, and G82 exhibited both stability and high mean seed yield. By combining stability indices, AMMI, and GGE biplots, widely adapted genotypes have the potential to promote sustainable agricultural practices.

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Section: Gge Biplot Analysissupporting

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Adaptation of oat genotypes in organic and conventionally managed fields in the northwestern Himalayas

Sanadya,

Sood,

Sharma

et al. 2024

Agronomy Journal

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“…In the total GGE variance for grain yield, first principal component PC1 was covered 71 .57%, while the second principal component PC2 explained additional 17 .94% (89 .51% in total) . Similar findings of principal components values in the total GGE variance were obtained by Hagos and Abay (2013), Gupta et al . (2022) .…”

Section: Genotypesupporting

confidence: 90%

Stability of grain yield performance of winter wheat genotypes

Perišić¹,

Perišić²,

Luković³

et al. 2022

Selekcija i semenarstvo

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Bread wheat is one of the most represented field crops whose level and stability of yield is very important for the food security in Republic Serbia. In the paper was investigated stability of yield expression of 15 winter bread wheat genotypes in different agroecological conditions of Serbia, using the Additive main effects and multiplicative interaction (AMMI) model and GGE-biplot method of analysis. Aim of investigation was to determine which of applied analysis is superior in identification of the most desirable genotypes for cultivation in given environments. Analysis of variance showed that genotype and genotype-environment (G×E) interaction represent highly significant sources of variability in expression of grain yield. AMMI and GGE analyses were point out similar results and an indisputable conclusion is that multienvironment trials, besides routine usage of analysis of variance, must be analized with one of this two models, which combine analysis of variance and PCA analysis. AMMI analysis is simpler for interpretation and closer to the concept of view of the agronomical trial, while GGE analysis is more complex and gives more precise interpretation of "which-won-where", i .e. for defining of narrowly adapted genotypes in given environments. Thus, G11 as the genotype with highest average yield is narrow adapted to the environment Sombor and can be recommended, as well as in the environment 2 (Kruševac), while genotypes with modest requests (G2) rather can be recommended for cultivation in the environment 1 (Kragujevac), which is characterized by less fertile soil and a smaller amount and uneven distribution of precipitation.

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“…GGE (genotype-by-environment interaction) biplot analysis is a widely used method to study the stability of yield and quality of wheat grown in multiple locations and years (Bishwas et al, 2021;Bosi et al, 2022;Mulugeta et al, 2022). This analysis is based on a PCA where the effects of the genotype (G) and environmental interaction (GXE) are taken into account (Gupta et al, 2022). In order to evaluate the stability of the genotypes grown under several stress conditions, we conducted GGE biplot analysis (paper III).…”

Section: Resultsmentioning

confidence: 99%

Wheat quality under a climate spell : a focus on protein, physico-chemical and growth characteristics evaluated by innovatively combined approaches

Lama¹

2023

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The quality of bread is largely determined by the gluten protein concentration and composition, both greatly influenced by environmental factors such as heat and drought. Future climate in Sweden is expected to fluctuate severely, affecting gluten proteins and the production of bread wheat, as well as future availability of food. The thesis aimed to enhance knowledge of the effect of varying climates on the gluten protein quality in Swedish wheat and to evaluate new methods for yield and gluten protein screening in order to assist in future wheat breeding programs. In this thesis, plant growth-yield traits and gluten protein quality in flour and dough were studied in Swedish wheat of varying genetic backgrounds and imported varieties, all grown in diverse environments in Sweden. Red-green-blue (RGB) imaging and analytical chromatography tools, such as size exclusion high performance liquid chromatography (SE-HPLC) and mass spectrometry (LC-MS/MS), as well as near infrared spectroscopy (NIR) were used to study wheat plants and flour materials. A mixograph was used to prepare dough in this study. Robust flour sedimentation methods, such as swelling index of glutenins (SIG) and solvent retention capacity (SRC), were used to examine the gluten protein characteristics of wheat flour from varying growing environments and were compared to industrial flour screening methods. The results show that the combined heat-drought stresses negatively affected biomass, yield and thousand-kernel weight (TKW) in the wheat studied. During extreme heat and prolonged drought, higher amounts of large polymeric gluten proteins (%UPP) were observed in the spring wheat flours in both field and controlled growth environments. Total extractable gluten protein (TOTE) was higher in the wheat genotypes grown in the cool climate in the field and combined heat-drought stress in the greenhouse. No difference in optimum dough mixing time in wheat from different years was observed. Dough mixing time, together with the gluten protein parameters (%UPP and TOTE) could be promising traits for gluten stability evaluation in varying climates. RGB imaging in combination with SE-HPLC can be useful in screening stable wheat genotypes for yield and gluten quality in varying climates. A combination of robust small-scale sedimentation tests to assess wheat flour suitability for bread-making, SIG in diluted lactic acid, SRC and SE-HPLC can be effectively used for efficient screening of wheat resilient to climate change. The new set of combined methods that include plant imaging, flour sedimentation, analytical chromatography and NIR, is of the greatest interest for both breeding and breadbaking industries to evaluate wheat in a changing climate.

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Genotype by Environment Interaction Analysis for Grain Yield of Wheat (Triticum aestivum (L.) em.Thell) Genotypes

Cited by 23 publications

References 34 publications

Adaptation of oat genotypes in organic and conventionally managed fields in the northwestern Himalayas

Adaptation of oat genotypes in organic and conventionally managed fields in the northwestern Himalayas

Stability of grain yield performance of winter wheat genotypes

Wheat quality under a climate spell : a focus on protein, physico-chemical and growth characteristics evaluated by innovatively combined approaches

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