One compound approach combining factor-analytic model with AMMI and GGE biplot to improve multi-environment trials analysis
暂无分享,去创建一个
[1] Harry X. Wu,et al. Patterns of additive genotype-by-environment interaction in tree height of Norway spruce in southern and central Sweden , 2017, Tree Genetics & Genomes.
[2] Runhui Wang,et al. Genotype × environmental interaction by AMMI and GGE biplot analysis for the provenances of Michelia chapensis in South China , 2016, Journal of Forestry Research.
[3] Harry X. Wu,et al. Pattern of genotype by environment interaction for radiata pine in southern Australia , 2015, Annals of Forest Science.
[4] Víctor Leiva,et al. An interactive biplot implementation in R for modeling genotype-by-environment interaction , 2014, Stochastic Environmental Research and Risk Assessment.
[5] Robin Thompson,et al. Factor analytic and reduced animal models for the investigation of additive genotype-by-environment interaction in outcrossing plant species with application to a Pinus radiata breeding programme , 2014, Theoretical and Applied Genetics.
[6] Hortensia Sixto,et al. Genetic variation and genotype-environment interactions in short rotation Populus plantations in southern Europe , 2011, New Forests.
[7] L. Graudal,et al. Evaluation of an international series of Pinus kesiya provenance trials for growth and wood quality traits , 2008 .
[8] Hugh G. Gauch,et al. Statistical Analysis of Yield Trials by AMMI and GGE: Further Considerations , 2008 .
[9] K. Jayawickrama,et al. Efficiency of using spatial analysis in first-generation coastal Douglas-fir progeny tests in the US Pacific Northwest , 2008, Tree Genetics & Genomes.
[10] B. Cullis,et al. The accuracy of varietal selection using factor analytic models for multi-environment plant breeding trials , 2007 .
[11] A. Gilmour,et al. Spatial analysis enhances modelling of a wide variety of traits in forest genetic trials , 2006 .
[12] B. Potts,et al. Genotype by environment interaction for growth of Eucalyptus globulus in Australia , 2006, Tree Genetics & Genomes.
[13] G. Dutkowski. Improved models for the prediction of breeding values in trees , 2005 .
[14] A. Gilmour,et al. Spatial analysis methods for forest genetic trials , 2002 .
[15] Robin Thompson,et al. Analyzing Variety by Environment Data Using Multiplicative Mixed Models and Adjustments for Spatial Field Trend , 2001, Biometrics.
[16] Gw Dutkowski,et al. Analysis of early tree height in forest genetic trials is enhanced by including a spatially correlated residual , 2001 .
[17] Weikai Yan. GGEbiplot—A Windows Application for Graphical Analysis of Multienvironment Trial Data and Other Types of Two-Way Data , 2001 .
[18] Hugh G. Gauch,et al. Identifying mega-environments and targeting genotypes , 1997 .
[19] K. A. Gomez,et al. Statistical Procedures for Agricultural Research. , 1984 .
[20] Yan Wei,et al. Optimal Use of Biplots in Analysis of Multi-Location Variety Test Data , 2010 .
[21] Yan Weikai. Optimal use of biplots in analysis of multi-location variety test data. , 2010 .
[22] T. McrAe,et al. ApplicAtion of GGE biplot AnAlysis to EvAluAtE GEnotypE (G), EnvironmEnt (E), And G×E intErAction on Pinus radiata: A cAsE study* , 2008 .
[23] Robin Thompson,et al. ASREML user guide release 1.0 , 2002 .
[24] José Crossa,et al. Statistical analyses of multilocation trials , 1990 .
[25] K. W. Finlay,et al. The analysis of adaptation in a plant-breeding programme , 1963 .