Understanding Multi-Environment Trials

Multi-Environment Trials Plant Adaptation Regional Environments

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”

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

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

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

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

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

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

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

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

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)

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

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

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

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

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

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

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