Crop Variety Trials

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