One of the major goals for plant breeders is to identify candidate varieties that are well adapted to the set of environmental conditions that are relevant for the agricultural system of interest. To achieve this goal, breeders characterize their genotypes in multi-environment trials or in phenotyping platforms. The ranking of genotypes might change across trials, indicating that different genotypes will be well adapted to different environmental conditions. This phenomenon is called genotype-by-environment interaction (G×E) and provides an opportunity to increase the sustainability of agricultural systems by using varieties that will perform well in specific growing conditions. For example, breeders might want to develop varieties that are well-adapted to drought or to specific agronomic management conditions (e.g. organic agriculture).
Thanks to the new genotyping and phenotyping technologies, breeders are facing the exciting challenge of how to integrate multiple data sources, generating predictions that will inform selection decisions. Such predictions can be made for yield performance, in combination with other parameters like adaptability, plasticity sensitivity and stability. The main goal of this course is to provide students with data science and statistical tools to analyse single – and multi-environment trials, generate predictions for the genotypic response to the environment and visualize the genotypic response across environments.
For the analysis of single-environment trials, this course will discuss about the principles of experimental design and illustrate them by means of commonly used designs as RCBD, lattice designs, alpha designs and row-column designs. Issues related to the estimation of heritability will be discussed. BLUPs and adjusted means will also be addressed. Phenotypes across multiple environments will be modelled focusing on G×E, using models with different levels of integration of biological knowledge. The course will start with a simple variance components models, and continuing with linear-bilinear models and factorial regression models. Later, G×E will be discussed from the perspective of QTL and genomic prediction models. Finally, the use of models with a larger level of biological integration (e.g. crop growth models) will also be discussed. The utility of these models to classify genotypes and environments will also be discussed.
