Analysis of realistic genotype by environment interactions simulated using a crop growth model with a large number of physiological parameters

Description

A different response of genotypes across environments is frequent in multi-location trials and is known as genotype by environment interaction (GxE). The study and understanding of these interactions is a major challenge for breeders and agronomic researchers.

To better understand the GxE a simulation study may be conducted using a crop growth model with a large number of physiological parameters, to obtain yields for different genotypes in different environmental conditions. The obtained two-way data table (with yields of different genotypes across environments) should be analysed with some of the standard techniques to study GxE (e.g. AMMI models, mixed linear models, etc.) in order to answer questions such as:

(i) are the GxE interactions significantly important? (e.g. are crossovers present in the data?);

(ii) which kind of parameters make the GxE stronger?

(iii) which characteristics (regarding parameter specification) should have the genotypes to have higher yield in a particular environment?

(iv) which parameters are the most important to explain the final yield?

The parameter specification will be based on previous experimental observations and the model will be adapted to sweet pepper, an important greenhouse vegetable crop in the Netherlands.

Aim of the MSc project

A literature study on crop growth models to answer the following questions is desirable:

(i) which types of crop growth models are commonly found in the literature?

(ii) which are the most used crop growth models?

(iii) what are the advantages and disadvantages of simple and complex crop growth models?

Type of work: This topic only allows for a desk study thesis on modelling/simulation/analysis. The modelling/simulation will be done through eco-physiological crop growth models and the analysis with statistical methods.

Requirements: Interest in quantitative methods. Some knowledge of crop growth models and good statistical knowledge, preferably including multivariate methods.

The thesis can start at any moment in the academic year 2014/2015.

Credits: 24-36