Research Article Adaptability and stability of cotton cultivars (Gossypium hirsutum L. race latifolium H.) using factor analytic model

Nuvunga, Joel Jorge; Oliveira, Luiz Antonio Andrade de; Silva, C. P.; Pamplona, Andrezza Kéllen Alves; Silva, A. Q.; Moura, Emanoel Gomes de; Maleia, M. P.; Balestre, Márcio

doi:10.4238/gmr16039890

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…Full multivariate BLUP model utilizes all information along with variance heterogeneity (Kleinknecht et al, 2011). Factor analytic (FA) model with sufficient multiplicative terms is computationally robust and superiority of the Factor Analytic model in a breeding program had been demonstrated by Nuvunga et al (2018). North eastern plains zone of India comprises eastern Uttar Pradesh, Bihar, Jharkhand, Assam and plains of West Bengal.…”

Section: Introductionmentioning

confidence: 99%

Adaptability of Wheat Genotypes Under Mixed Model Methodology for North Eastern Plains Zone of the Country

Crossa¹,

Vargas²,

Joshi³

et al. 2019

JEBAS

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Best linear unbiased predictors of wheat genotypes were analysed as per analytic measures of adaptability for the North Eastern Plains zone of the country under mixed model framework. Among the tested genotypes, genotype K1006 had better adaptation while HD2733 & HD2967 were of specific adaptability during the cropping season of 2016-17. Biplot analysis based on first two significant principal components, accounted for 72.2 % of total GxE interaction sum of squares, observed stable performance of HD2967 and DBW187 genotypes while K1007 and HD2733 would be suitable for specific adaptations. Further, genotypes HD2967, DBW187 and HD3219 had expressed specific adaptation to Varanasi, Faizabad and Dumka, whereas K0307 and DBW39 were better adapted to conditions of Coochbehar & Chianki. Bikramganj with Manichak, Coochbehar with Chianki, Varanasi with Faizabad, Banka with Sabour and Bardwan maintained acute angles therefore would show similar performance of genotypes. Better adaptable genotypes with higher yield were HD2733, PBW762, HD3249 & DBW223 as per year cropping season of 2017-18. Biplot analysis identified HD2967, DBW233, HD3254 along with PBW762 and DBW39 genotypes of stable performance. Genotypes WH1218, DBW223, HD2733, HD3249 and DBW221 expressed unstable performance. Harmonic means of the relative performance of predicted genetic values by BLUP is suitable to identify the better adaptive genotypes with high yield.

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Section: Introductionmentioning

confidence: 99%

Adaptability of Wheat Genotypes Under Mixed Model Methodology for North Eastern Plains Zone of the Country

Crossa¹,

Vargas²,

Joshi³

et al. 2019

JEBAS

View full text Add to dashboard Cite

show abstract

“…These assumptions are often inappropriate and unreliable (Friesen et al 2016). The mixed model method considers fixed and random effects in commonly used experimental designs and correctly estimates Print ISSN : 1974ISSN : -1712 Online ISSN : 2230-732X genotype effect (G) and genotype × environment (G × E) interaction effects using an appropriate variance-covariance structure (Nuvunga et al 2018).…”

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confidence: 99%

Adaptability Measures for Wheat Genotypes Evaluated under Northern Hills Zone of Country for Irrigated Timely Sown Conditions

Verma¹

2020

IJAEB

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Wheat genotypes were evaluated under multi environment trials for Northern Hills Zone of India to study the adaptability performance. Genotypes HS612, HS507 and HPW430 were of high yield and better adaptability by analytic measures of adaptability based on BLUP values during 2015-16. Two interaction principal components, accounted for 89.9 % of total GxE interaction sum of squares in biplot analysis. HPW428, HS613, VL2020, VL2024 had specific adaptations to Almora and Malan while HS616, HPW423, HPW430, VL2021, HPW426 expressed for Shimla and Khudwani locations. Wheat genotypes HS612, HS507 and HPW430 were cited by analytic measures as per BLUE values. HPW429, HS613, VL2020, VL2024 had specific adaptations to Almora and Malan while HS616, HS618, HPW425, HPW426, HPW430, VL2023, VL2021, HPW426 observed for Shimla and Khudwani. Second year (2017-18) had seen high yield and better adaptability of HS631, HS632, VL2030, VL2025 genotypes as per BLUP values. Biplot analysis expressed specific adaptations of HPW429, HS613, VL2020, VL2024 to Almora and Malan locations. BLUE values based measures showed high yield and better adaptability of HS631, HS632, VL2030, VL2025 genotypes. Biplot analysis while utilizing 79.5 % of total GxE interaction sum of squares exhibited specific adaptations of HPW446, VL907, HS632, VL2025, VL2030 to Almora and Shimla. Stratification of wheat genotypes as per BLUP values was more efficient than that by BLUE. Biplot analysis exhibited more of GxE interactions sum of squares by first two significant principal components based on BLUP as compared to BLUE values. Highlights m Adaptability of wheat genotypes were studied by analytic measures based on random and fixed effects of genotypes. Further clustering pattern were studied among measures based on biplot analysis.

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Analytic Measures for Adaptability of Wheat Genotypes for Northern Hills Zone of Country by Mixed Model Approach

Verma¹,

Chatrath²,

Singh³

2019

Int.J.Curr.Microbiol.App.Sci

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Adaptability of wheat genotypes studied by mixed model methodology under rainfed sown trials for the Northern Hills Zone of the country. Analytic measures marked HS612, HPW430, VL2023 & HS507 as of high yield and better adaptability across major locations of this zone while HS615 & HS617 for low degree of adaptation as per year 2015-16. Biplot analysis expressed stable yield of HPW349 and HPW441 genotypes. Majhera, Ranichauri and Khudwani, would be suitable environments for stable yield of genotypes. HPW442 had specific adaptations to Dhaulakuan and Berthin while HP441 for Almora and VL907 for Malan and Ranichauri, whereas HPW349 and HS634 identified for Khudwani. Genotypes HS631, HS632, VL2030 & VL 2025 were of high yield and better adaptability across major locations of this zone while HS 635 & VL 2028 with lower level of adaptation during 2016-17. Biplot analysis considered 86.1 % of total GxE interaction sum of squares marked HS507, HS634, HS636 and UP2991 genotypes of stable yield. HPW447 had specific adaptations to Wadhura, and Khudwani while VL2030 & VL2025 for Almora, whereas VL2027, UP2990 & VL2028 identified for Bajaura. Third year of study 2017-18 identified HS562 & VL907 with yield and better adaptability. Biplot analysis observed UP2953, HPW428 and HS613 as desirable genotypes for yield and adaptability. VL2021, HS616, HS507, HPW425 and HPW426 had specific adaptations to Shimla and VL2020, VL2024, HS613 would be for Almora and Malan, whereas HPW426 identified for Khudwani. Analytic measures based on Harmonic means showed suitability to identify the better adaptive genotypes with high yield.

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Research Article Adaptability and stability of cotton cultivars (Gossypium hirsutum L. race latifolium H.) using factor analytic model

Cited by 3 publications

References 32 publications

Adaptability of Wheat Genotypes Under Mixed Model Methodology for North Eastern Plains Zone of the Country

Adaptability of Wheat Genotypes Under Mixed Model Methodology for North Eastern Plains Zone of the Country

Adaptability Measures for Wheat Genotypes Evaluated under Northern Hills Zone of Country for Irrigated Timely Sown Conditions

Analytic Measures for Adaptability of Wheat Genotypes for Northern Hills Zone of Country by Mixed Model Approach

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