The Application of GGE Biplot Analysis for Evaluat ng Test Locations and Mega-Environment Investigation of Cotton Regional Trials

Xu, Naiyin; Fok, Michel; Zhang, Guowei; Li, Jian; Zhou, Zhiguo

doi:10.1016/s2095-3119(13)60656-5

Cited by 46 publications

(35 citation statements)

References 29 publications

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“…Contudo, isto ocorreu apenas em um dos anos avaliados, o que foi compensado pelo reconhecimento do padrão de agrupamento dos locais. Segundo Xu et al (2013), quando são considerados diferentes anos de avaliação, nem sempre ocorrerá o agrupamento consistente dos mesmos locais, em razão das flutuações climáticas, variações de características de solo, tratos culturais, além da substituição natural dos genótipos avaliados. Logo, esses autores defendem o artifício da substituição de genótipos vencedores, dada a possibilidade de se reconhecer o padrão de agrupamento entre alguns locais de avaliação.…”

Section: Resultsunclassified

Estratificação ambiental e otimização de rede de ensaios de genótipos de soja no Cerrado

Branquinho

Duarte

Souza

et al. 2014

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi estabelecer uma estratificação ambiental consistente, para a recomendação e a avaliação de linhagens experimentais e cultivares de soja na região do Cerrado, a partir de análises da interação entre genótipos e ambientes (GxA) quanto à produtividade de grãos, além de avaliar a atual rede de ensaios de valor de cultivo e uso (VCU) para sua otimização. Os dados provieram de 559 ensaios de competição de linhagens de soja, realizados em 57 localidades, durante sete safras agrícolas (2002/2003 a 2008/2009). Realizaram-se análises conjuntas de variância, pelo modelo AMMI ("additive main effects and multiplicative interaction"), e de estratificação ambiental, pela abordagem correlata de "genótipos vencedores". A interação GxA foi sempre significativa, como resultado da resposta diferencial dos genótipos à variação ambiental. Os locais de teste se agruparam de modo diferente de acordo com os grupos de maturação. Observou-se redundância em 20% dos locais, o que indica a possibilidade de otimização da rede de ensaios, via eliminação ou substituição dessas localidades. A região-alvo deve receber estratificações distintas, congêneres a cada grupo de maturação, e pode ser dividida em 22 (ciclo precoce), 23 (ciclo médio) e 21 (ciclo tardio) estratos ambientais.Termos para indexação: Glycine max, análise AMMI, genótipo vencedor, interação genótipo x ambiente, locais-chave, mega-ambiente. Environmental stratification and optimization of a multi-environment trial net for soybean genotypes in CerradoAbstract -The objective of this work was to establish a consistent environmental stratification for recommendation and evaluation of experimental soybean lines and cultivars in the Cerrado region, from the analyses of genotype by environment interaction (GxE) for grain yield, besides evaluating the current trial network on the value for cultivation and use (VCU), for its optimization. Data were obtained from 559 competition trials of soybean lines, performed in 57 locations, during seven harvest periods (2002/2003 to 2008/2009). Joint analyses of variance were carried out by the AMMI (additive main effects and multiplicative interaction) model, and the environmental stratification by the related statistical approach of "winning genotypes". The GxE interaction was always significant as a result of the genotype differential response to environmental change. Test locations clustered differently according to maturity groups. Redundancy was observed in 20% of the locations, which indicates the possibility of optimizing the trial network by the elimination or replacement of these locations. The target region should receive different stratifications, congenerous to each maturity group, and may be divided into 22 (early cycle), 23 (medium cycle), and 21 (long cycle) environmental strata.Index terms: Glycine max, AMMI analysis, winner genotype, genotype x environment interaction, key locations, mega-environment. IntroduçãoNo Brasil, a soja é amplamente cultivada, com grande variação nas condições de cultivo...

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Section: Resultsunclassified

Estratificação ambiental e otimização de rede de ensaios de genótipos de soja no Cerrado

Branquinho

Duarte

Souza

et al. 2014

Pesq. agropec. bras.

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“…The environments within the same sector share the same winning genotype and environments in different sectors share different winning genotypes. The identification of best cultivar for each location using "Which-won-where" was previously reported in proso millet (Zhang et al, 2016) and cotton (Xu et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of genotype x environment interaction using GGE-biplot on dry beans(Phaseolus vulgaris L.) in Limpopo province of South Africa

Mathobo¹,

Marais²

2017

Aust J Crop Sci

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Dry bean is the second most important legume crop in South Africa due to its high protein content. GGE-Biplot was used to evaluate genotype x environment interaction (GEI) and stability performance of dry bean genotypes during 2011 and 2012 growing seasons. Eight dry bean genotypes were planted at different locations in the Limpopo province, South Africa with different agro-climatic characteristics. The genotypes used were OPS-RS1, Jenny, Kranskop, DBS310, OPS-RS4, DBS 360, OPS-RS2 and OPS-RS5. The ANOVA analysis results for grain yield, number of pods per plant and number of seeds per plant revealed that the environment contributed more to the total variation followed by GEI and finally genotype. According to GGE biplot for grain yield, OPS-RS1 was regarded as the more stable genotype and had the highest mean yield, DBS 360 had the highest mean number of pods per plant and DBS 310 produced the highest mean number of seeds per plant. The GGE biplot methodology clearly presented the GEI relationship among environments and genotypes.

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“…The formation of MEs has many consequences because the analysis of each ME must be performed separately. Furthermore, these results must be repeatable across years (i.e., repeatable location groups need to be found) (Gupta et al, 2013;Munaro et al, 2014;Xu et al, 2014). The first method used to verify the occurrence of MEs is through the strategy designated as "analyze yearly and summarize across years" (Yan, 2014).…”

Section: MVmentioning

confidence: 99%

Estimation of Missing Values Affects Important Aspects of GGE Biplot Analysis

et al. 2017

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RESEARCHT he presence of genotype-by-environment interaction (GEI) is common in multi-environment trials in wheat (Triticum aestivum L.) and other crops; it leads to changes in the performance of cultivars in different environments. A GEI demands that trials be conducted at multiple locations for several years to obtain reliable data for the possible release of a new cultivar.Trials conducted across several years and locations increase the possibility of obtaining datasets with unbalanced or incomplete data. This can occur because of planned or unplanned actions by the breeder. The planned actions might be the removal of genotypes that did not perform well in a given year, introducing new genotypes developed by the breeding program and the introduction of newly released cultivars. On the other hand, unplanned actions, which are unrelated to the breeder, might be attributable to human or environmental causes (Yan, 2013).There are three primary strategies for analyzing unbalanced or incomplete datasets. The first strategy consists in the removal of genotypes or environments with missing values. The second ABSTRACT Multi-environment trials often yield unbalanced datasets, thus necessitating the estimation of missing values. It is unknown whether this estimation affects the graphic characteristics of genotype plus genotype-by-environment interaction (GGE) biplots. Therefore, our objectives were to investigate the effects of different percentages of missing values on the number of significant principal components (pCs) and on mega environments, "winner" (highest-performing) genotypes, and the amount of variation explained by the pCs. Two complete sets of two-way data from wheat (Triticum aestivum L.) were used. The first set consisted of the original data (Data1, from which we created scenarios with 0, 30, and 60% missing data. For the second dataset (Data2), we removed 50% data from the original dataset, estimated missing values to make it a new complete dataset, and created scenarios like those for Data1. Missing values were estimated via expectation-maximization-GGE (EM-GGE) and EM-additive main effects and multiplicative interaction (EM-AMMI) methods. The percentage of variation explained by the pCs was affected by the percentage of missing data; a large percentage of missing values considerably increased the amount of variation explained by pC 1 and pC 2 and reduced the complexity of the genotype-by-environment interaction because two pCs accounted for more than 80% of the variation, instead of the three pCs that were required to explain the variation in the original dataset. The EM-GGE estimation method was able to maintain the original conformation of the 'which-won-where' biplot when £30% of estimated data were used. The EM-GGE was superior to the EM-AMMI method for estimating missing data. The estimation of more than 30% of the data should be avoided because it can lead to significant changes in mega environment conformation and the identification of "winner" genotypes.

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The Application of GGE Biplot Analysis for Evaluat ng Test Locations and Mega-Environment Investigation of Cotton Regional Trials

Cited by 46 publications

References 29 publications

Estratificação ambiental e otimização de rede de ensaios de genótipos de soja no Cerrado

Estratificação ambiental e otimização de rede de ensaios de genótipos de soja no Cerrado

Evaluation of genotype x environment interaction using GGE-biplot on dry beans(Phaseolus vulgaris L.) in Limpopo province of South Africa

Estimation of Missing Values Affects Important Aspects of GGE Biplot Analysis

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