Mixed-Model Analysis of Crossover Genotype-Environment Interactions

Yang, Rong‐Cai

doi:10.2135/cropsci2006.09.0611

Cited by 52 publications

(65 citation statements)

References 34 publications

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“…BLUP predictors and REML estimators allow inferences regarding the additive and non-additive genetic effects related to the GCA and SCA (Piepho et al, 2008;Möhring et al, 2011). Moreover, the great advantage of this method is considering the genotype effects as random (Yang, 2007).…”

Section: Introductionmentioning

confidence: 99%

Identifying superior spring wheat genotypes through diallel approaches

Pagliosa¹,

Benin²,

Beche³

et al. 2017

Aust J Crop Sci

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Diallel crosses enable plant breeders taking decisions concerning the parental choice for developing new varieties. Recently, a large number of methodological models have been reported. The objective of this study was to investigate the relationship between different methods of diallel analysis and their associations with grain yield performance in wheat (Triticum aestivum L.) segregating populations. In this study, four methods of diallel analysis were used to estimate the general (GCA) and specific (SCA) combining abilities: (a) Griffing, (b) Multivariate analyses of diallel crosses, (c) Restricted Maximum Likelihood / Best Linear Unbiased Prediction (REML/BLUP) and d) Genotypes and Genotypes x Environments biplot (GGE biplot). Six wheat cultivars were hybridized in a partial diallel scheme and later evaluated in the along with 15 F 2 segregating populations. Griffing and REML/BLUP analysis produced equivalent results for GCA and SCA and are meaningful for identifying superior inbred lines. Multivariate diallel analysis suggested the best crosses for simultaneous improvement of traits. GGE biplot method is an effective method for visual comparisons of GCA and SCA effects. Conventional and innovative methods of diallel analysis are useful, complementary and should be applied in the selection of parents and superior crosses.

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Section: Introductionmentioning

confidence: 99%

Identifying superior spring wheat genotypes through diallel approaches

Pagliosa¹,

Benin²,

Beche³

et al. 2017

Aust J Crop Sci

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“…A aleatoriedade dos efeitos genotípicos é uma vantagem dessa metodologia (Resende et al, 2001), pois, ao admitir o efeito aleatório, não subestima a interação genótipo x ambiente (Yang, 2007).…”

Section: Introductionunclassified

Adaptabilidade e estabilidade de cultivares de trigo em diferentes épocas de semeadura, no Paraná

Silva

Benin

Silva

et al. 2011

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi identificar as melhores épocas de semeadura e avaliar a adaptabilidade e a estabilidade de cultivares de trigo, em duas regiões tritícolas do Paraná. Avaliou-se a produtividade de grãos de sete cultivares, em Guarapuava, e de nove, em Palotina, em quatro épocas de semeadura, em 2006, 2007 e 2008. Utilizou-se o delineamento experimental de blocos ao acaso, com quatro repetições em Guarapuava, e três em Palotina. Foram utilizadas a metodologia REML/BLUP e a dos efeitos principais dos genótipos e da interação genótipo x ambiente (GGE biplot) para a avaliação da adaptabilidade e da estabilidade das cultivares, e o método AMMI para a identificação das melhores épocas de semeadura. Semeaduras em julho, em Guarapuava, e em abril, em Palotina, maximizam a produtividade de grãos. As cultivares Safira, em Guarapuava, e CD 113, em Palotina, são estáveis, amplamente adaptadas e apresentam alta produtividade de grãos.Termos para indexação: Triticum aestivum, AMMI, GGE biplot, interação genótipo x ambiente, REML/BLUP. Adaptability and stability of wheat cultivars at different sowing dates in the state of Paraná, BrazilAbstract -The objective of this work was to identify the best sowing dates and to evaluate the adaptability and stability of wheat cultivars in two wheat growing regions of the state of Paraná, Brazil. Seven cultivars were evaluated at Guarapuava and nine at Palotina as to grain yield, at four sowing seasons, in 2006, 2007 and 2008. The experimental design was a randomized complete block design, with four and three replicates, for Guarapuava and Palotina, respectively. The methodologies REML/BLUP and genotype main effect and genotype x environment interaction (GGE biplot) were used for adaptability and stability analysis, and the AMMI model was used to identify the best sowing dates. Sowing in July, at Guarapuava, and in April, at Palotina, maximized grain yield. The cultivars Safira, at Guarapuava, and CD 113, at Palotina, are stable, highly adapted and show high grain yield.Index terms: Triticum aestivum, AMMI, GGE biplot, genotype x environment interaction, REML/BLUP. IntroduçãoO trigo (Triticum aestivum L.) é o cereal de maior importância econômica mundial, com mais de 600 milhões de toneladas produzidas anualmente. O Brasil contribui com cerca de seis milhões de toneladas, com destaque para a região Sul, responsável por 90% da produção nacional (Companhia Nacional de Abastecimento, 2010), e para o estado do Paraná, o maior produtor do país.A expressão do potencial de produtividade de grãos depende de fatores genéticos e ambientais, bem como da interação entre ambos, o que resulta em expressivas diferenças no desempenho das cultivares quando cultivadas em diferentes condições ambientais (Yan & Holland, 2010). O termo ambiente (época de semeadura, ano e práticas culturais) pode ser definido como o resultado dos componentes biofísicos que influenciam o desenvolvimento e o crescimento das plantas.O potencial de produtividade de grãos pode ser maximizado pela escolha adequa...

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“…Three data sets from G×E trials were considered, which had been previously used by Lavoranti et al (2007), Yang (2007) and Rad et al (2013), respectively. The first data set (Rad et al 2013) consists of a 36×6 matrix for 36 wheat genotypes assessed in six environments under normal and drought stress conditions, at an experimental farm of the University of Putra, Malaysia.…”

Section: Methodsmentioning

confidence: 99%

“…The second data set (Lavoranti et al 2007) comprises a 20×7 matrix, for 20 Eucalyptus grandis progenies assessed in seven locations in the south and southeast regions of Brazil, and the studied variable was mean tree height (m). Lastly, the third data set (Yang 2007) is a 6×18 matrix, corresponding to six barley genotypes assessed in 18 environments in Alberta, Canada. The studied variable was yield (Mg ha -1 ).…”

Section: Methodsmentioning

confidence: 99%