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About this book
About this book
Research data is expensive and precious, yet it is seldom fully utilized due to our ability of comprehension. Graphical display is desirable, if not absolutely necessary, for fully understanding large data sets with complex interconnectedness and interactions. The newly developed GGE biplot methodology is a superior approach to the graphical analysis of research data and may revolutionize the way researchers analyze data. GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists introduces the theory of the GGE biplot methodology and describes its applications in visual analysis of multi-environment trial (MET) data and other types of research data.
The text includes three parts: I) Genotype by environment interaction and stability analysis, II) GGE biplot and multi-environment trial (MET) data analysis, and III) GGE biplot software and applications in analyzing other types of two-way data. Part I presents a comprehensive but succinct treatment of genotype-by-environment (G x E) interaction in order to provide an overall picture of the entire G x E issue and to show how GGE biplot methodology fits in. Part II describes and demonstrates the numerous utilities of a GGE biplot in visualizing MET data. Part III describes the "GGE biplot" software and extends its application to the analysis of genotype by trait data, QTL mapping data, diallel cross data, and host by pathogen data. Altogether, this book demonstrates that the GGE biplot methodology is a superior data-visualization tool and allows the researcher to graphically extract and utilize the information from MET data and other types of two-way data to the fullest extent.
GGE Biplot Analysis makes this useful technology accessible on a wider scale to plant and animal breeders, geneticists, agronomists, ecologists, and students in these and other related research areas. The information presented here will greatly enhance researchers' ability to understand their data and will make a significant contribution toward helping to meet the challenges of food production and food security that currently face the world. Readers will be amazed to see how much more they can extract from their data by implementing the new and easily understood GGE biplot methods presented here and will soon agree that any delay in using this technique is a loss to their research achievement.
GENOTYPE-BY-ENVIRONMENT INTERACTION AND STABILITY ANALYSISGenotype-by-Environment InteractionHeredity and EnvironmentGenotype-by-Environment InteractionImplications of GEI in Crop BreedingCauses of Genotype-by-Environment InteractionStability Analyses in Plant Breeding and Performance TrialsStability Analysis in Plant Breeding and Performance TrialsStability Concepts and StatisticsDealing with Genotype-by-Environment InteractionGGE Biplot: Genotype + GE InteractionGGE BIPLOT AND MULTI-ENVIRONMENTAL TRIAL ANALYSISTheory of BiplotThe Concept of BiplotThe Inner-Product Property of a BiplotVisualizing the BiplotRelationships among Columns and among RowsBiplot Analysis of Two-Way DataIntroduction to GGE BiplotThe Concept of GGE and GGE BiplotThe Basic Model for a GGE BiplotMethods of Singular Value PartitioningAn Alternative Model for GGE BiplotThree Types of Data TransformationGenerating a GGE Biplot Using Conventional MethodsBiplot Analysis of Multi-Environment Trial DataObjectives of Multi-Environment Trial Data AnalysisSimple Comparisons Using GGE BiplotMega-Environment InvestigationCultivar Evaluation for a Given Mega-EnvironmentEvaluation of Test EnvironmentsComparison with the AMMI BiplotInterpreting Genotype-by-Environment InteractionGGE BIPLOT SOFTWARE AND APPLICATIONS TO OTHER TYPES OF TWO-WAY DATAGGE Biplot Software-The Solution for GGE Biplot AnalysesThe Need for GGE Biplot SoftwareThe Terminology of Entries and TestersPreparing Data File for GGE BiplotOrganization of GGE Biplot SoftwareFunctions for a Genotype-by-Environment DatasetFunction for a Genotype-by-Strain DatasetApplication of GGE Biplot to Other Types of Two-way DataGGE Biplot Continues to EvolveCultivar Evaluation Based on Multiple TraitsWhy Multiple Traits?Cultivar Evaluation Based on Multiple TraitsIdentifying Traits for Indirect Selection for Loaf VolumeIdentification of Redundant TraitsComparing Cultivars as Packages of TraitsInvestigation of Different Selection StrategiesSystems Understanding of Crop ImprovementThree-Mode Principal Component Analysis and VisualizationQTL Identification Using GGE BiplotWhy Biplot?Data Source and ModelGrouping of Linked MarkersGene Mapping Using BiplotQTL Identification via GGE BiplotInterconnectedness among Traits and Pleiotropic Effects of a Given LocusUnderstanding DH Lines through the Biplot PatternQTL and GE InteractionBiplot Analysis of Diallel DataModel for Biplot Analysis of Diallel DataGeneral Combining Ability of ParentsSpecific Combining Ability of ParentsHeterotic GroupsThe Best Testers for Assessing General Combining Ability of ParentsThe Best CrossesHypothesis on the Genetic Constitution of ParentsTargeting a Large DatasetAdvantages and Disadvantages of the Biplot ApproachBiplot Analysis of Host Genotype-by-Pathogen Strain InteractionsVertical vs. Horizontal Re