Variety trials are an essential step in crop breeding and production. These trials are a significant investment in time and resources and inform numerous decisions from cultivar development to end-use.

Preface vi

Chapter 1 Theoretical Framework for Crop Variety Trials 1

Chapter 2 An Overview of Variety Trial Data and Analyses 23

Chapter 3 Introduction to Biplot Analysis 31

Chapter 4 Data Centering for Biplot Analysis 51

Chapter 5 Data Scaling and Weighting for GGE Biplot Analysis 75

Chapter 6 Frequently Asked Questions About Biplot Analysis 91

Chapter 7 Single-Trial Data Analysis 107

Chapter 8 Genotype-by-Location Two-Way Data Analysis 133

Chapter 9 Genotype-by-Trait Data Analysis and Decision-Making 163

Chapter 10 Trait Association-by-Environment Two-Way Table Analysis 187

Chapter 11 Location-by-Trait Two-Way Data Analysis 199

Chapter 12 Mega-environment Analysis Based on Multiyear Data 207

Chapter 13 Test Location Evaluation Based on Multiyear Data 231

Chapter 14 Genotype Evaluation Based on Multiyear Data 255

Chapter 15 Building and Utilizing a Relational Database for Crop Variety Trial Data 279

Chapter 16 Experimental Design for Variety Trials and Breeding Nurseries 295

Chapter 17 Modules and Functions in GGEbiplot 315

Chapter 18 Conclusions 341

References 345

Index 349

Variety trials are an essential step in crop breeding programs. These trials are a significant investment in time and resources and inform numerous decisions from cultivar development to end-use. Crop Variety Trials: Data Management and Analysis is a practical volume that provides valuable theoretical foundations as well as a guide to step-by-step implementation of effective trial data management and analysis in determining the best varieties and cultivars for targeted regions.

Crop Variety Trials: Data Management and Analysis provides a holistic view of crop variety trials. The book opens with chapters that provide a sound theoretical framework of variety evaluation and trial analysis. Subsequent chapters provide insights into the theories of quantitative genetics, principles of data analysis, and tools for managing and analyzing this data. The book then provides readers with a practical step-by-step guide to accurately analyzing crop variety trial data. Combined these sections provide the reader with fuller understanding of the nature of variety trials, their objectives, and user-friendly database and statistical tools that will enable them to produce efficient and accurate analysis of data.

Accessibly written for breeders, statisticians, and advanced students, Crop Variety Trials: Data Management and Analysis will be a vital practical reference for anyone working in agricultural sciences.

• Provides valuable theoretical framework that underpins crop variety evaluation
• Practical, step-by-step guide to implementing variety trial data analysis
• Bridges the gap between crop breeding and statistical analysis
• A valuable resource on an essential aspect of crop breeding, production, and end-use

Preface Part I 1. The theoretical framework of variety evaluation 1.1.   Multiyear multilocation tests: each location-year combination is an environment 1.2.   Heritability (H): the ability of the trials in detecting the genetic differences 1.2.1.      Heritability and Experimental design 1.2.1.1.            How many years and locations are needed? 1.2.1.2.            Individual test: replications and field layout 1.3.   Heritability and mega-environment analysis 1.4.   Heritability and test location evaluation 2. Four levels of variety trial data and data analyses 2.1.   Single traits 2.1.1.      Single location in a single year: importance in terms of H 2.1.2.      Multiple locations in a single year 2.1.3.      Multiple locations in multiple years 2.2.   Multiple traits 2.2.1.      Independent culling 2.2.2.      Independent selection 2.2.3.      Index selection 3. Principles of Biplot Analysis 3.1.   Matrix multiplication and biplot 3.2.   Data decomposition and biplot 3.3.   Singular value partition 4. GGE biplot analysis 4.1.   Data centering and biplot properties 4.1.1.      correlation and cosine 4.1.2.      Euclidean distance and biplot distance between genotypes 4.2.   The concept of ?G+GE? 4.2.1.      Heritability is a GGE model 4.2.2.      Variety evaluation: Not G, not GE, but G+GE 4.2.3.      GE may be omitted when varieties are fully tested 4.2.4.      GE alone is meaningless and misleading 4.3.   Data scaling and biplot properties 4.3.1.      The quantitative genetics theory of indirect selection: rgh 5. Frequently asked questions in biplot analysis 5.1.   Is the 2-D biplot sufficient? 5.2.   What if it is not sufficient? 5.3.   Is an observed difference statistically significant? 5.4.   Is an observed crossover GE statistically significant? 5.5.   How to conduct biplot analysis with incomplete data? 5.6.   GGE biplots versus AMMI graphs 5.7.   GGE biplot versus FA biplot Part II 6. Mega-environment analysis 6.1.   Identification and utilization of repeatable genotype by region interaction 6.1.1.      Improve H within mega-environments 6.1.2.      Improve overall productivity 6.1.3.      Reduce evaluation cost 6.1.4.      Inappropriate sub-region division comes with a cost 6.2.   Single year approach vs. multiyear approach 6.3.   Which-won-where 6.4.   Which-lost-where 6.5.   Test location grouping 6.6.   Mega-environments can change as new varieties are introduced 7. Test location evaluation 7.1.   Single year approach vs. multiyear approach 7.2.   Discriminating power 7.3.   Representativeness 7.4.   Repeatability 8. Variety evaluation 8.1.   Means and Stability 8.2.   single location in a single year 8.3.   Multilocation in a single year 8.4.   Multiyear multilocation 8.5.   Misconceptions on the use of stability 9. Multi-trait analysis and decision making 9.1.   Undesirable associations among breeding objectives 9.2.   Trait profiles of genotypes 9.3.   Strategies on selection based on multiple traits 9.3.1.      Independent culling ?use only a few, critical, traits 9.3.2.      Index selection 10.  Variety trial database construction and utilization 10.1.                    Data extraction at will 10.2.                    Relational database 10.3.                    Data version from any data format 10.4.                    Data unification 11.  Additional functions in the GGEbiplot software 11.1.                    Tools for visualizing a biplot 11.2.                    Tools for data management and subset generation 11.3.                    Tools for modifying the biplot appearance 11.4.                    Tools for numerical output 11.5.                    Tools generation advancement in plant breeding 11.6.                    Analysis of variance 11.7.                    Field trend adjustment 11.8.                    Experimental design Concluding