Assessing the Representativeness and Repeatability of Test Locations for Genotype Evaluation

Yan, Weikai; Pageau, Denis; Frégeau‐Reid, Judith; Durand, Julie

doi:10.2135/cropsci2011.01.0016

Cited by 86 publications

(112 citation statements)

References 15 publications

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“…Type III test locations are of limited use, but if used, can only be used to remove extremely defective genotypes. Type IV are least useful for genotype evaluation because they are not discriminating and not repeatable (Yan et al 2011). …”

Section: Multi-environment Analysismentioning

confidence: 99%

Assessing the usefulness of GGE biplot as a statistical tool for plant breeders and agronomists

Akinwale¹,

Fakorede²,

Badu‐Apraku³

et al. 2014

Cereal Research Communications

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Genotype main effect plus genotype-by-environment interaction (GGE) biplot produces a graphical display of results that facilitates a better understanding of complex genotype-by-environment interaction in multi-environment trials of breeding and agronomic experiments. However, the full potential and weaknesses of this powerful tool are not fully understood by breeders, agronomists, entomologists and pathologists. The objective of this paper was to review the usefulness of this statistical tool and enumerate some of its weaknesses. Its main application has so far been in the analysis of multi-environment data. It has been used to analyze the performance of crop cultivars under multiple stress environments, from which ideal cultivars, mega-environments, and core testing sites were identified. More recently, GGE biplot has been employed in genetic analysis of diallel data to estimate the combining abilities and identify heterotic groups among inbred parents. Genotype-by-trait biplot has also been utilized in trait profile analysis, and in identification of traits that are reliable for indirect selection of a target primary trait. Two major shortcomings of this tool are (i) failure to identify more than two distinct, contrasting groups in diallel studies and (ii) lack of statistical tests for most of its graphical displays. Other aspects of GGE biplot that need further study and development are (i) estimation of genetic variances, covariances, and heritability, including the analysis of data generated from North Carolina Designs I, II, and III as well as other genetic designs, considering their importance in plant breeding programs; (ii) analysis of Quantitative Trait Loci (QTL) data for proper understanding of the genetic constitution of each individual plant or line; and (iii) analysis of Genotype-by-pathogen or insect strain interaction data. Nevertheless, GGE biplot has helped greatly in the accurate analysis and interpretation of data from breeding and agronomic field evaluation experiments.

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Section: Multi-environment Analysismentioning

confidence: 99%

Assessing the usefulness of GGE biplot as a statistical tool for plant breeders and agronomists

Akinwale¹,

Fakorede²,

Badu‐Apraku³

et al. 2014

Cereal Research Communications

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show abstract

“…The fast and easy identification of genotypes exhibiting high average yield and low GE interaction, thus stability, is considered critical for a breeding program (Cecarelli, 1989) and usually involves multi-location tests and subsequent GGE biplot analysis (Yan and Holland, 2010;Yan et al, 2011). GGE biplots can reveal the ideal genotype, a ranking of cultivars on the basis of yield and stability and ideal location for better performance of genotypes (Yan and Kang, 2003;Fan et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Genotype by Environment Interactions on Cotton Fiber Traits and Their Implications on Variety Recommendation

Greveniotis

Sioki²

2017

JAS

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The objective of this study was to evaluate cotton fiber quality traits across diverse environments. This could lead to the selection of the best environment for cotton cultivations and the best adapted commercial cultivars. For this reason four main cotton regions were chosen as different environments: Thessaly, Thrace, Macedonia and Sterea Ellas. Five of the most commercial upland cotton cultivars (DP332, DP377, ST402, CELIA and ELSA) were used for evaluation of their fiber quality traits. Each cultivar was sown in 10 different fields (in order to exploit and evaluate different soil types) in each of the above mentioned regions and (in total) 200 fields were used in total. Four samples from each field were collected in order to analyze fiber quality traits: micronaire, maturity index (%), fiber length as the upper half mean length (mm), fiber strength (gram/tex), uniformity index (%), fiber elongation, Cultivation regions must also be selected in a way that they could promote fiber traits, in order to ensure the highest fiber quality. Cotton fiber quality traits were affected differently from environmental fluctuations, showing that the ranking of cultivars according to environmental fluctuations is important, if proper breeding methods should be applied. Each trait is affected differently by environmental fluctuations and requires precise knowledge of the degree of inheritance i.e., as to how much qualitative or quantitative is the trait, in order to choose and apply the proper breeding method.

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“…As seguintes alternativas possibilitariam a análise de experimentos incompletos sobre a interação GxE: extração de um subconjunto balanceado que elimine os genótipos ou os ambientes com dados faltantes (Ceccarelli et al, 2007;Yan et al, 2011); preenchimento das parcelas vazias com médias ambientais; e preenchimento dos dados faltantes com estimativas obtidas por métodos que envolvam, por exemplo, o uso de modelos multiplicativos ou de modelos lineares mistos (Arciniegas-Alarcón et al, 2011;Kumar et al, 2012). Essas estratégias podem resolver o problema de desbalanceamento, mas nenhuma delas é simples e efetiva (Yan, 2013).…”

Section: Introductionunclassified

Imputação múltipla livre de distribuição em tabelas incompletas de dupla entrada

Arciniegas-Alarcón

Dias

García-Peña

2014

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi propor um novo algoritmo de imputação múltipla livre de distribuição, por meio de modificações no método de imputação simples recentemente desenvolvido por Yan para contornar o problema de desbalanceamento de experimentos. O método utiliza a decomposição por valores singulares de uma matriz e foi testado por meio de simulações baseadas em duas matrizes de dados reais completos, provenientes de ensaios com eucalipto e cana-de-açúcar, com retiradas aleatórias de valores em diferentes percentagens. A qualidade das imputações foi avaliada por uma medida de acurácia geral que combina a variância entre imputações e o viés quadrático médio delas em relação aos valores retirados. A melhor alternativa para imputação múltipla é um modelo multiplicativo que inclui pesos próximos a 1 para os autovalores calculados com a decomposição. A metodologia proposta não depende de pressuposições distribucionais ou estruturais e não tem restrições quanto ao padrão ou ao mecanismo de ausência dos dados.Termos para indexação: dados ausentes, decomposição por valores singulares, ensaios multiambiente, experimentos desbalanceados, interação genótipo x ambiente, melhoramento de plantas. Distribution-free multiple imputation in incomplete two-way tablesAbstract -The objective of this work was to propose a new distribution-free multiple imputation algorithm, through modifications of the simple imputation method recently developed by Yan in order to circumvent the problem of unbalanced experiments. The method uses the singular value decomposition of a matrix and was tested using simulations based on two complete matrices of real data, obtained from eucalyptus and sugarcane trials, with values deleted randomly at different percentages. The quality of the imputations was evaluated by a measure of overall accuracy that combines the variance between imputations and their mean square deviations in relation to the deleted values. The best alternative for multiple imputation is a multiplicative model that includes weights near to 1 for the eigenvalues calculated with the decomposition. The proposed methodology does not depend on distributional or structural assumptions and does not have any restriction regarding the pattern or the mechanism of the missing data.

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Assessing the Representativeness and Repeatability of Test Locations for Genotype Evaluation

Cited by 86 publications

References 15 publications

Assessing the usefulness of GGE biplot as a statistical tool for plant breeders and agronomists

Assessing the usefulness of GGE biplot as a statistical tool for plant breeders and agronomists

Genotype by Environment Interactions on Cotton Fiber Traits and Their Implications on Variety Recommendation

Imputação múltipla livre de distribuição em tabelas incompletas de dupla entrada

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