Analyzing the genotype-by-environment interactions under a randomization-derived mixed model

Caliński, Tadeusz; Czajka, S.; Kaczmarek, Z.; Krajewski, Paweł; Pilarczyk, W.

doi:10.1198/jabes.2009.0014

Cited by 18 publications

(14 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the next step, the data from a series of experiments were processed using the computer program SERGEN (Caliński et al 1998) based on the methods developed by Kaczmarek (1986), and Caliński et al (1997Caliński et al ( , 2005Caliński et al ( , 2009. In these methods, GEI effect related to each genotype is measured by the value of the relevant F-statistic; a given genotype is considered as stable, if F-statistic value for GEI is not significant at P =0.05.…”

Section: Discussionmentioning

confidence: 99%

Effect of Genotype, Environment and Their Interaction on Quality Parameters of Wheat Breeding Lines of Diverse Grain Hardness

Surma

Adamski

Banaszak

et al. 2012

Plant Production Science

Self Cite

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Effect of Genotype, Environment and Their Interaction on Quality Parameters of Wheat Breeding Lines of Diverse Grain Hardness

Surma

Adamski

Banaszak

et al. 2012

Plant Production Science

Self Cite

View full text Add to dashboard Cite

“…It is a matrix of size 18×9, for 18 pea varieties evaluated in 9 different locations in Poland. The experiment was conducted by the Research Centre for Cultivar Testing, Słupia Wielka, and the variable of interest was mean yield in dt/ha (Caliński et al, 2009). …”

Section: The Datamentioning

confidence: 99%

An alternative methodology for imputing missing data in trials with genotype-by-environment interaction: some new aspects

Arciniegas-Alarcón

García-Peña

Krzanowski

et al. 2014

Biometrical Letters

View full text Add to dashboard Cite

SummaryA common problem in multi-environment trials arises when some genotypeby-environment combinations are missing. In Arciniegas-Alarcón et al. (2010) we outlined a method of data imputation to estimate the missing values, the computational algorithm for which was a mixture of regression and lower-rank approximation of a matrix based on its singular value decomposition (SVD). In the present paper we provide two extensions to this methodology, by including weights chosen by cross-validation and allowing multiple as well as simple imputation. The three methods are assessed and compared in a simulation study, using a complete set of real data in which values are deleted randomly at different rates. The quality of the imputations is evaluated using three measures: the Procrustes statistic, the squared correlation between matrices and the normalised root mean squared error between these estimates and the true observed values. None of the methods makes any distributional or structural assumptions, and all of them can be used for any pattern or mechanism of the missing values.

show abstract

“…Caliński et al [2][3][4] studied single stage analyses of heteroscedastic observations from complete I × J-tables without grouping of the experiments, whereas the present paper considers second stage analyses of homoscedastic observations from incomplete I × J-tables with possible grouping of environments. At the first stage of the two-stage analysis, experiments are analysed separately according to their specific experimental designs.…”

Section: Journal Of Applied Statisticsmentioning

confidence: 99%

The use of a reference variety for comparisons in incomplete series of crop variety trials

Forkman

2013

Journal of Applied Statistics

View full text Add to dashboard Cite

In series of crop variety trials, 'test varieties' are compared with one other and with a 'reference' variety that is included in all trials. The series is typically analyzed with a linear mixed model and the method of generalized least squares. Usually, the estimates of the expected differences between the test varieties and the reference variety are presented. When the series is incomplete, i.e. when all test varieties were not included in all trials, the method of generalized least squares may give estimates of expected differences to the reference variety that do not appear to accord with observed differences. The present paper draws attention to this phenomenon and explores the recurrent idea of comparing test varieties indirectly through the use of the reference. A new 'reference treatment method' was specified and compared with the method of generalized least squares when applied to a five-year series of 85 spring wheat trials. The reference treatment method provided estimates of differences to the reference variety that agreed with observed differences, but was considerably less efficient than the method of generalized least squares.

show abstract

Analyzing the genotype-by-environment interactions under a randomization-derived mixed model

Cited by 18 publications

References 11 publications

Effect of Genotype, Environment and Their Interaction on Quality Parameters of Wheat Breeding Lines of Diverse Grain Hardness

Effect of Genotype, Environment and Their Interaction on Quality Parameters of Wheat Breeding Lines of Diverse Grain Hardness

An alternative methodology for imputing missing data in trials with genotype-by-environment interaction: some new aspects

The use of a reference variety for comparisons in incomplete series of crop variety trials

Contact Info

Product

Resources

About