1. Understanding Multi-Environment Trials

2. Multi-Environment Trials
Plant Adaptation
Regional Environments

3. Definition
Genotype: The crop in the field (a community of plant is genetically fairly uniform), represent the “genotype”
Environment: A particular combination of soil, climate, pests, and other species (weeds) forming a unique “environment”
Attribute: Measurements to characterise the crop, such as “yield”, “grain moisture”, “plot height”

4. Definition
Multi-Environment Trial (MET)
Plots in the fields

5. Definition Multi-Environment Trial (MET) Data:
i. I genotypes grown in J environments for a single year for a single attribute (G: Genotypes; E: Environments; A: Attributes)
I genotypes grown in J environments across years for a single attribute … E1 G1 … Gi GI Ej EJ E1 G1 … Gi GI Ej EJ E1 G1 … Gi GI Ej EJ
Year 1
Year N

6. Definition Multi-Environment Trial (MET) Data:
I genotypes grown in J environments for a single year for K attributes
I genotypes grown in J environments across years for K attributes
Note: for some cases, environment is a combination of locations (geographical places) and years, here, we focus on environment only involving the location E1 G1 … Gi GI Ej EJ E1 G1 … Gi GI Ej EJ A1 … AK

7. Definition Multi-Environment Trial (MET)
Multi-environment trial helps breeders combine multiple attributes into a single genotype

8. Purposes Widely use MET data by plant breeders
To evaluate the relative performance of genotypes
To help understanding the plant breeding objectives
To consider the development of the analytical methodology
To recommend and provide suggestions

9. Evaluation of genotypic performance
Crop performance is a function of the genotype of the crop and the nature of the production environment
Performance of genotypes may vary in different environments, in different years, in different attributes
Reflection the interaction of genotype by environment, or genotype by attribute, or genotype by year, or genotype by environment by year, or genotype by environment by attribute, or genotype by environment by attribute by year
Plant adaptation associated with the differences in performance of genotypes and in particular the interactions

10. Understand the Plant breeding
Understanding and prediction of crop response to environments are central in the field of crop physiology and modelling
The use of information on plant adaptation in plant breeding
Selection for plant adaptation
Interaction effects in plant breeding
Cases studies on how to accommodate the interaction effects
Developing an understanding of the physiological and genetic basis of plant adaptation

11. Interaction Effects
Analysis of data from MET shows that interactions are ubiquitous and large compared with genotype main effects
Interactions observed as changes in the relative performance of genotypes over environments, over years, over attributes
Help to understand quantitative characters in plant breeding
Improvement of quantitative characters by selecting among genotypes based on their phenotypic performance

12. Interaction Effects
Variation for quantitative characters is under the control of many genes and their contribution can differ among environments
Characterization of patterns (Partitions of Interactions)
Identify specific differences among patterns
Contribution of environments (years, attributes) to specific differences among patterns
Identify similarities or dissimilarities of environments (years, attributes)

13. Methods
The phenotypic performance of genotypes in MET is commonly investigated in different models which can express phenotypic observations in terms representing the genotypes entered in the trials, the environments, and the interaction
Analysis of Variance
Stability analysis (e.g. linear regression)
Ordination analysis
Cluster analysis

14. Methods The analysis of variance
Enables the effects, variances of effects, and variances of differences among effects to be estimated
The Stability analysis
It is commonly associated with the use of joint linear regression methods, to measure similarity among genotypes
Pattern analysis
The joint use of cluster analysis of ordination methods

15. Methods Pattern Analysis
Compare multiple genotypes in multiple environments for several attributes
Provide essential information for selecting and recommending crop cultivars
Exam of the nature of interaction effects
Repeatable interactions are target to be determined

16. Research questions of interest
Analysis of multi-environment data
The best representative environment
The superior genotypes with high and stable performance
Note: The high performance is defined for different attributes for different plant

17. The best representative environments
Ideal environments for selecting generally adapted genotypes
Ranking environments based on the performance of a genotype
Relationships among environments
Similarity and dissimilarity between environments

18. The superior genotypes
Performance of the genotypes in specific environments
Ranking genotypes based on the performance in one environment
Mean performance and stability of the genotypes
Ranking genotypes relative to the ideal genotype
Comparison among genotypes

19. Next Steps
Some demonstrations are provided to show particular analysis applied to multi-environment trials
One aspect that is complex is the hierarchical and either crossed or nested aspects of the design
Also there is frequently unbalanced elements
The use of agronomic treatment to look at the management as G by E by M ( ie Management) is another complex and developing area in research