Yield prediction based on QTLs for yield components using crop ecophysiological models.

Project: PhD

Project Details


The improvement of yield is of general importance in most crop species. Traditional breeding approaches try to improve yield by selecting on yield as a single trait itself. However, yield is a complex quantitative trait, which results from multiple genes that in general have small effects and interact with the environment. It can therefore be expected that selection on yield itself leads to only limited improvement. This proposal is aimed at improving yield by using an approach based on selecting on yield components rather than on yield itself. We will develop prediction and selection strategies for yield by a synthesis of crop ecophysiological modelling on the one hand and QTL mapping and genomic prediction of yield components on the other hand. The approach consists of five steps: (I) dissecting yield into yield components, where several dissections (e.g. static versus dynamic) are considered; (II) identify the genetic basis for these yield components from marker profiles by QTL mapping and genomic prediction; (III) build a prediction model for these yield components; (IV) insert the predicted yield component values in a crop ecophysiological model to predict yield as single output; and (V) use the crop ecophysiological model for ranking and selecting genotypes for high yield. To conduct these 5 steps we will use a unique genetic resource in tomato, a multi-parent recombinant inbred line (RIL) population consisting of as many as 700 RILs. The four parent lines of this population are two elite lines and two wild lines. The 700 RILs will be grown for phenotyping during a full production season, producing a unique phenotype dataset. To improve our crop growth and prediction models sensitivity analysis methodologies like Fisher information and Bayesian approaches will be used. A validation experiment with partly different genotypes and performed in a different environment will be conducted. The project will deliver efficient selection and breeding strategies based on idealised plant types (ideotypes) for yield and yield components.
Effective start/end date1/02/167/02/23


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