AMMI Biplot Analysis in Cotton (Gossypium hirsutum L.) Genotypes for Genotype X Environment Interaction at Four Agro-ecologies in Telangana State

Lingaiah, N.; Sudharshanam, A.; Rao, V. R.; Prashant, Y.; Kumar, M. Vijay; Reddy, P. Indhudar; Prasad, B. Ram; Reddy, P. R.; Rao, P. Jagan Mohan

doi:10.9734/cjast/2020/v39i1530722

Cited by 4 publications

(3 citation statements)

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“…However, out of 57 hybrids, 18 Based on Biplot (Fig. 2 and Table 4), genotypes were grouped into 4 groups based on their distribution to quadrants [5,11,[20][21][22][23].…”

Section: High Yielding Genotypes and Environmentsmentioning

confidence: 99%

Additive Main Effects and Multiplicative Interaction for Seed Cotton Yield Performance and stability in Bt Cotton Hybrids (MON531 and MON15985)

Samak

Rajeswari²,

Ravikesavan

et al. 2022

IJPSS

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A study was conducted to understand the stability of seed cotton yield of 105 Bt cotton hybrids (BGI and BGII) using Additive Main effects and Multiplicative Interactions (AMMI) analysis across three diverse locations in India during rainy season 2018. This study holds importance as the hybrids were resistant to Helicoverpa bollworms due to Bt events (MON531 and MON15985) in them which were expected to nullify variation arising out of differential bollworm pressure in different location, unlike in non Bt hybrids where this variation also played role in the stability of the hybrids. The main effect differences among hybrids (41.23 %), environments (39.56 %) and the interaction effects (19.21%) were highly significant of the total variance of seed cotton yield indicating a large difference between the testing location causing different hybrids to perform differently across the testing environments. The first two principal components axes (IPCA I and IPCA II) were highly significant and contributed 52.12 % and 47.88% of total interaction respectively. The distance from the origin and the placement of locations in different quadrants of biplot reflected that the locations were substantially discriminatory as Aurangabad and Dharwad, which were high yielding and Raichur was low yielding. Results showed that hybrids IAHH-8096 BGII, IAHH-8103 BGI, IAHH-8061 BGII and IAHH-8007 BGII were having lowest interaction and stable across the location, whereas hybrids IAHH-8080 BGI, IAHH-8084 BGI, IAHH-8004 BGII and IAHH-8105 BGI were having more interaction and unstable genotypes. Large frequency of hybrids (57) were stable with IPCA scores nearing zero and low ASV values which is attributed resistance to bollworm due to Bt events.

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“…However, out of 57 hybrids, 18 Based on Biplot (Fig. 2 and Table 4), genotypes were grouped into 4 groups based on their distribution to quadrants [5,11,[20][21][22][23].…”

Section: High Yielding Genotypes and Environmentsmentioning

confidence: 99%

Additive Main Effects and Multiplicative Interaction for Seed Cotton Yield Performance and stability in Bt Cotton Hybrids (MON531 and MON15985)

Samak

Rajeswari²,

Ravikesavan

et al. 2022

IJPSS

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“…Stability studies in crop breeding particularly focus on yield and yield potential traits across multiple locations. Consequently, different cotton lines demonstrate varying yield potentials at different locations (Hasan et al 2016;Said, 2016;Alishah et al 2019;Lingaiah et al 2020). Such considerations contribute to the development of adaptable and highyielding cotton varieties for diverse agricultural environments.Testing genotypes across diverse environments allows assessment of cotton genotypes' adaptability to various natural factors, aiding in cultivar selection and eventual release (Hasan et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…(2019) andLingaiah et al (2020). The genotypes CNH1134, NDLH2057-1, and AKH2013-2 are classi ed as high performing and stable for ginning outturn.…”

mentioning

confidence: 99%

Deciphering genotype performance, genotype and environment interaction of seed cotton yield and ginning outturn using GGE biplot for selection of Gossypium hirsutum for rainfed ecosystem

Subbarayan,

et al. 2024

Preprint

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Background Cotton is mostly grown under rainfed condition by small and marginal farmers and provides economic livelihood. The assessment of adaptability of a suitable genotype is important and therefore, multilocation evaluation of genotypes was conducted at eight agro-ecologically diverse environments under rainfed ecosystems in India. The data was analyzed for key biometrical traits using pooled ANOVA, LSD test, correlation matrices and regression analysis over two years (2019-20). Additionally, we employed genotype plus genotype by environment interaction (GGE) biplot analysis to identify winning genotypes across different environments. Results The results showed that genotype, environment and their interaction (GEI) significantly (p < 0.01) influenced SCY and GOT%. The GEI showed a high significant impact on SCY (40.46%) and GOT% (48.86%) among genotypes. Correlation analysis revealed positive associations between SCY and major yielding traits such as boll weight, ginning outturn and lint yield. Regression analysis demonstrated that a combination of these traits contributed to high stable yield over the years across the rainfed conditions. The total variation in SCY was 96.61%, composed of PC1 and PC2 values at 92.06% and 4.55%, respectively. GGE biplot analysis identified the genotypes viz., NDLH2057-1, TVH007, and NH615 exhibited high yield potential and ginning outturn and high stability across multilocations. The average environment axis (AEA) of the biplot identified most representative location for the yield and GOT%, respectively. Conclusion The findings identified the use of optimal genotypes for cultivation and use of such genotypes for trait introgression in future breeding and crop improvement, which may facilitate genetic gain of yielding traits and ginning outturn in rainfed environments.

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Multi-environment Evaluations Across Ecological Regions Reveal That the Kernel Oil Content of Cottonseed Is Equally Determined by Genotype and Environment

Gōng

Kong

ChangWen³

et al. 2022

J. Agric. Food Chem.

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Cotton is the fifth-largest oil crop in the world. A high kernel oil content (KOC) and high stability are important cottonseed attributes for food security. In this study, the phenotype of KOC and the genotype-by-environment interaction factors were collectively dissected using 250 recombinant inbred lines, their parental cultivars sGK156 and 901-001, and CCRI70 across multi-environments. ANOVA and correlation analysis showed that both genotype and environment contributed significantly to KOC accumulation. Analyses of additive main effect multiplicative interaction and genotype-by-environment interaction biplot models presented the effects of genotype, environment, and genotype by environment on KOC performance and the stability of the experimental materials. Interaction network analysis revealed that meteorological and geographical factors explained 38% of the total KOC variance, with average daily rainfall contributing the largest positive impact and cumulative rainfall having the largest negative impact on KOC accumulation. This study provides insight into KOC accumulation and could direct selection strategies for improved KOC and field management of cottonseed in the future.

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AMMI Biplot Analysis in Cotton (Gossypium hirsutum L.) Genotypes for Genotype X Environment Interaction at Four Agro-ecologies in Telangana State

Cited by 4 publications

References 0 publications

Additive Main Effects and Multiplicative Interaction for Seed Cotton Yield Performance and stability in Bt Cotton Hybrids (MON531 and MON15985)

Additive Main Effects and Multiplicative Interaction for Seed Cotton Yield Performance and stability in Bt Cotton Hybrids (MON531 and MON15985)

Deciphering genotype performance, genotype and environment interaction of seed cotton yield and ginning outturn using GGE biplot for selection of Gossypium hirsutum for rainfed ecosystem

Multi-environment Evaluations Across Ecological Regions Reveal That the Kernel Oil Content of Cottonseed Is Equally Determined by Genotype and Environment

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