AMMI biplot analysis for stability in basmati rice (<i>Oryza sativa</i> L.) in different production systems

Jain, Bharat Taindu; Sarial, A. K.; Saharan, R. P.; Harikesh,; Anuragi, Hirdayesh

doi:10.5958/0975-928x.2018.00061.3

Cited by 3 publications

(1 citation statement)

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“…The genotype, G4 (WGL 1367), was found superior among all the genotypes as well as over the checks and across all the environments under study.These findings were in agreement with Rukmini Devi et al [26], Kesh et al [23], Mohan et al [17], Siddi et al [27].The remaining genotypes had less than the mean grain yield and found specific adaptation to few tested environments. Likewise, Jain et al [28] reported that "genotypes with PC1 scores close to zero are usually widely adapted and they were considered more stable in their performance across test environments".…”

Section: Ammi 1 Biplotmentioning

confidence: 99%

Evaluation of Phenotypic Stability for Grain Yield in Rice (Oryza sativa L.) Genotypes Using AMMI and GGE Biplot Models

Chandra

Devi²,

Lingaiah³

et al. 2022

IJECC

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The experimens were carried out under four seasons with 9 rice genotypes at Regional Agricultural Research Station, Warangal, Telangana State, India during kharif season (July to November) 2019 (E1), rabi season (December to April) 2019-20 (E2), kharif season (July to November) 2020 (E3) and rabi season (December to April) 2020-21 (E4). The objective of the study was to assess the stability and adaptability of 9 rice genotypes over four seasons. Analysis of variance clearly showed that environments contributed the highest (69.66%) in the total sum of squares followed by genotypes×environments (12.66%) indicating a very greater role played by environments and their interactions in realizing final grain yield. AMMI 1 analysis revealed that rice genotypes viz., G4 (WGL 1367), G3 (WGL 1362), and G6 (WGL 1370) recorded higher mean grain yield with positive IPCA1 scores. AMMI 2 revealed that the genotypes, G6 (WGL 1370) and G3 (WGL 1362) were plotted near to zero IPCA1 axis indicating that these genotypes are relatively more stable across locations. GGE bi-plot genotype view depicts that the genotypes G4 (WGL 1367) and G3 (WGL 1362) were fell in the second concentric circle and found to be more stable across environments. GGE bi-plot environment view showed that rabi season of 2020-21 (E4) was the most ideal environment. However, rabi season of 2019-20 (E2) and kharif season 2020 (E3) were poor and most discriminating. What-won-where biplot indicated that four environments fell into two mega environments. Hence the genotype G4 (WGL 1367) was the winning genotype in the mega environment 1 viz., rabi season of 2019-20 (E2), rabi season of 2020-21 (E4) and kharif season of 2019 (E1). Whereas the genotype G9 (KNM 118) was the winner in the mega environment 2 i.e., Kharif, season of 2020 (E3).

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Section: Ammi 1 Biplotmentioning

confidence: 99%

Evaluation of Phenotypic Stability for Grain Yield in Rice (Oryza sativa L.) Genotypes Using AMMI and GGE Biplot Models

Chandra

Devi²,

Lingaiah³

et al. 2022

IJECC

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Genotype by Environment Interaction Effects on Nutraceutical Lipid Compounds of Pigmented Rice (Oryza sativa L. ssp. indica)

Harakotr

Prompoh

Suriharn

et al. 2021

International Journal of Agronomy

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To identify a rice genotype with a wider or specific adaptability, the magnitude and patterns of genotype interaction with the environment are valuable clues. However, such information on the nutraceutical lipid compound in rice is limited. In this study, concentrations of five pigmented rice genotypes with different lipid compound levels were evaluated for α-tocopherol, γ-oryzanol, phytosterols, octacosanol, and squalene contents. The research was conducted in eight environments (four locations and two years). Combined analysis of variance showed that levels of α-tocopherol, γ-oryzanol, octacosanol, and squalene were mainly affected by genotype, accounting for more than 42.57% of the total variance. This suggests that improvement of these traits may be achieved by using conventional rice breeding methods. A large proportion of the variation in campesterol and β-sitosterol contents was contributed by the environment. The effect of genotype by environment was significant for all observed traits, especially for γ-oryzanol, β-sitosterol, and squalene. This complicates identification of superior genotypes for these compounds across all environments. Stability parameters showed rice genotypes with high levels of nutraceutical lipid compounds to be sensitive to changes in environmental conditions; thus, specific adaptation is suitable. Our findings suggest that rice breeders and producers should be testing and selecting rice genotypes possessing high added-value lipid concentrations in specific growing conditions or regions.

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The Characterization of 10 Spring Camelina Genotypes Grown in Environmental Conditions in North-Eastern Poland

et al. 2020

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Camelina (Camelina sativa (L.) Crantz) is an alternative oilseed crop that is garnering increasing popularity due to its multiple applications and greater tolerance to adverse environmental conditions than oilseed rape. The study analyzed selected traits of 10 Canadian and Polish spring camelina genotypes grown in a field experiment in north-eastern Poland in 2015–2018. The greatest differences were observed in seed yield where the effect of weather and environmental conditions explained 72.7% of variance, the effect of genotype explained 5.9% of variance, and the effect of the genotype-by-environment interaction explained 5.7% of total variance. In contrast, 1000-seed weight was not affected by environmental conditions, and it was differentiated only by genotype which explained 73.3% of variance. Genotype was responsible for 4.5%–25.3% of the variance in the remaining traits. The genotype-by-environment interaction explained 2.0%–18.8% of variance in the examined traits. The additive main effects and multiplicative interaction model (AMMI) revealed that genotype 13CS0787-15 was potentially most suited for cultivation in the temperate climate of north-eastern Poland, Central Europe. This genotype was characterized by the highest seed yields and straw yields, as well as the greatest yield stability.

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AMMI biplot analysis for stability in basmati rice (Oryza sativa L.) in different production systems

Cited by 3 publications

References 0 publications

Evaluation of Phenotypic Stability for Grain Yield in Rice (Oryza sativa L.) Genotypes Using AMMI and GGE Biplot Models

Evaluation of Phenotypic Stability for Grain Yield in Rice (Oryza sativa L.) Genotypes Using AMMI and GGE Biplot Models

Genotype by Environment Interaction Effects on Nutraceutical Lipid Compounds of Pigmented Rice (Oryza sativa L. ssp. indica)

The Characterization of 10 Spring Camelina Genotypes Grown in Environmental Conditions in North-Eastern Poland

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