How many test locations and replications are needed in crop variety trials for a target region?

Yan, Weikai; Frégeau‐Reid, Judith; Martin, Richard A.; Pageau, Denis; Mitchell‐Fetch, Jennifer

doi:10.1007/s10681-014-1253-7

Cited by 39 publications

(78 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the biplot using the first two principal components, the variables disease severity, yield, and oil content were located on three different sectors (Figure 2), within the third and fourth concentric circles (Figure 3). The concentric circles on the biplot help to visualize the vector length (the distance from a marker to the biplot origin) (Yan et al, 2015), and also show the discriminating abilities of the variables (Jalata, 2011). Treatments with longer vectors indicate higher contributions and also higher variances.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot

Leite

Oliveira

2015

Pesq. agropec. bras.

View full text Add to dashboard Cite

-The objective of this work was to evaluate the suitability of the multivariate method of principal component analysis (PCA) using the GGE biplot software for grouping sunflower genotypes for their reaction to Alternaria leaf spot disease (Alternariaster helianthi), and for their yield and oil content. Sixty-nine genotypes were evaluated for disease severity in the field, at the R3 growth stage, in seven growing seasons, in Londrina, in the state of Paraná, Brazil, using a diagrammatic scale developed for this disease. Yield and oil content were also evaluated. Data were standardized using the software Statistica, and GGE biplot was used for PCA and graphical display of data. The first two principal components explained 77.9% of the total variation. According to the polygonal biplot using the first two principal components and three response variables, the genotypes were divided into seven sectors. Genotypes located on sectors 1 and 2 showed high yield and high oil content, respectively, and those located on sector 7 showed tolerance to the disease and high yield, despite the high disease severity. The principal component analysis using GGE biplot is an efficient method for grouping sunflower genotypes based on the studied variables.Index terms: Alternariaster helianthi, Helianthus annuus, disease resistance, principal component analysis. Agrupamento de genótipos de girassol quanto à produtividade, ao teor de óleo e à reação à mancha de alternária por GGE biplotResumo -O objetivo deste trabalho foi avaliar a adequação do uso do método multivariado de análise de componentes principais (ACP), com uso do programa GGE biplot, para o agrupamento de genótipos de girassol quanto à sua reação à mancha de alternária (Alternariaster helianthi), e quanto à produtividade e ao teor de óleo. Sessenta e nove genótipos foram avaliados quanto à severidade da doença em campo, na fase de desenvolvimento R3, em sete safras, em Londrina, PR, com uso de uma escala diagramática própria, desenvolvida para esta doença. A produtividade e o teor de óleo também foram avaliados. Os dados foram padronizados com o programa Statistica, e o GGE biplot foi utilizado para ACP e exibição gráfica dos dados. Os dois primeiros componentes principais explicaram 77,9% da variação total. De acordo com o biplot poligonal, obtido com os dois primeiros componentes principais e as três variáveis resposta, os genótipos foram divididos em sete setores. Os genótipos alocados nos setores 1 e 2 apresentaram alta produtividade e alto teor de óleo, respectivamente, e os agrupados no setor 7 apresentaram tolerância à doença e alta produtividade, apesar de elevada severidade da doença. O método de análise de componentes principais com uso do GGE biplot é eficiente para agrupar genótipos de girassol com base nas variáveis estudadas.Termos para indexação: Alternariaster helianthi, Helianthus annuus, resistência a doenças, análise de componentes principais.

show abstract

Section: Resultsmentioning

confidence: 99%

“…Eigenvalues and eigenvectors of PCA were calculated for yield, Alternaria leaf spot severity, and oil content, using GGE biplot (Yan & Kang, 2003;Yan et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot

Leite

Oliveira

2015

Pesq. agropec. bras.

View full text Add to dashboard Cite

show abstract

“…A number of procedures for assessing the efficiency of plant breeding and variety testing activities have been proposed, with varying levels of sophistication and complexity, but most involve the quantification of the relative magnitude of sources of variation (i.e., variance components) (Cullis et al, 2000; Fehr, 1987; Brennan et al, 1998; Acquaah, 2007; Yan et al, 2015; Kleinknecht et al, 2016). The heritability of a trait is a quantitative and comprehensive measure of the ability of a variety testing program to reveal any differences among tested genotypes (Yan, 2014; Yan et al, 2015). Heritability has a curvilinear relationship with the number of test environments used in a MET and can be increased by increasing the number of test locations when it is small, but cannot be effectively improved when it becomes larger (Yan et al, 2015).…”

mentioning

confidence: 99%

“…The heritability of a trait is a quantitative and comprehensive measure of the ability of a variety testing program to reveal any differences among tested genotypes (Yan, 2014; Yan et al, 2015). Heritability has a curvilinear relationship with the number of test environments used in a MET and can be increased by increasing the number of test locations when it is small, but cannot be effectively improved when it becomes larger (Yan et al, 2015). Yan et al (2015) therefore propose a method whereby, given a pre‐specified target heritability, the optimum number of locations for a specific trait in a mega‐environment needed to achieve that heritability can be estimated.…”

mentioning

confidence: 99%

Quantifying Genotype × Environment Effects in Long‐Term Common Wheat Yield Trials from an Agroecologically Diverse Production Region

George

Lundy

2019

Crop Science

View full text Add to dashboard Cite

Multienvironment trials (METs) are used to investigate the performance of crop genotypes. To efficiently generate reliable performance estimates, the magnitude and patterns of genotype × environment interaction (G×E) in MET data must be known. We quantified G×E in fall‐planted common wheat (Triticum aestivum L.) in California, with the goal of increasing the reliability and efficiency of statewide variety testing activities. Linear mixed models and the genotype main effects plus G×E interaction effects (GGE) biplot method were used to analyze MET data for 211 common wheat genotypes, the MET consisted of 9 locations and 14 seasons. The representativeness and discriminating power of the MET locations were tested, and estimates of the optimum number of test locations were made. The analyses did not find evidence for significant, repeatable, crossover G×E. The genotype and G×E effects were of a similar magnitude, and the G×E effects were relatively strong compared with other sources of variance but were dominated by seasonal effects, with potentially repeatable genotype‐by‐location (G×L) effects being relatively weak. The GGE analyses did not detect repeatable G×L patterns across seasons. As a result, we conclude that the cereal production regions of California consist of a single, but unstable, mega‐environment for common wheat grain yield. The test location evaluation found few significant differences between test locations in terms of how well they represent the target production environment. We estimate that the number of test locations could be reduced while maintaining trial accuracy, which would improve the resource use efficiency of statewide trial activities without sacrificing information about variety‐specific common wheat yield performance.

show abstract

“…Furthermore, Yan et al (2015) developed a function to calculate the optimum number of locations. Results of the present study showed wide variations for locations for both total and marketable root yields (Figs.…”

Section: Discussionmentioning

confidence: 99%

Performance of informal market sweet potato cultivars in on-farm trials in South Africa

et al. 2017

View full text Add to dashboard Cite

Several new sweet potato cultivars have been released in South Africa from 2004 until 2011 with varietal traits suitable for the informal market. This paper reports on results of on-farm trials conducted to evaluate the cultivars in broader agro-ecologies in the country. The trials were conducted at 15 localities across the 2011/12 to 2014/15 planting seasons including six cultivars with varying flesh colours. Data collection included marketable and unmarketable storage root yield classes, dry mass, and taste acceptability. The sites were representative of cool subtropical and warm temperate regions. Statistical analysis included ANOVA, AMMI, Discriminant analysis (DA) and Principal component analysis (PCA). Significant effects were detected for locality*cultivar and region*cultivar interactions for total and marketable yield, and for cultivar only for marketable yield. The factor region was not significant. The mean marketable yield ranged from 13.1 to 19.0 t/ha, being highest for 199062.1 and Ndou, while Bophelo had average stable yield over a broad range of environments. The most prevalent unmarketable class was weevil damage (15.1%). The study demonstrated PCA and DA as quick analytical tools to associate quality traits with cultivars to facilitate cultivar recommendations. Bophelo and Ndou had been sold with success on local informal markets and have large potential to be promoted wider.

show abstract

How many test locations and replications are needed in crop variety trials for a target region?

Cited by 39 publications

References 15 publications

Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot

Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot

Quantifying Genotype × Environment Effects in Long‐Term Common Wheat Yield Trials from an Agroecologically Diverse Production Region

Performance of informal market sweet potato cultivars in on-farm trials in South Africa

Contact Info

Product

Resources

About