Knowledge Based Bio-Economy

SPICY

Peppers point the way to higher crop yields

Project acronym: SPICY

Title of project: Smart tools for prediction and improvement of crop yield

Research area: Agriculture & Forestry (Development of new tools and processes to support R&D in crop plants: molecular breeding)

Contract No:KBBE-1-1 -211347

EU contribution: €2 872 000 000 EURO

Start date: April 2008

Duration: 48 months

Objectives

This project is developing a method that will reduce the time taken to identify new crop varieties that can contribute to sustainable agriculture on a competitive basis. Typically, the plant growth response (phenotypic) to various environmental conditions depends on the interaction of a large number of genes (genotype) that may be combined in stretches of DNA which are closely linked to the genes that underlie the trait in question. These sequences of DNA, known as quantitative trait loci (QTLs), have to be identified at the molecular level in order to map regions of the genome that contain the genes involved in specifying a particular quantitative trait.

The aim is to develop a method to identify such clusters of genes using a single crop (pepper) rather than a range of crops. The project uses an existing crop growth model to predict the phenotypic response of a genotype under different environmental conditions. At the same time, it is necessary to develop methods of measuring large numbers of phenotypic traits automatically as an early step in identifying and sequencing these genes as well as specific QTL-analysis methods to find the corresponding QTL for the crop-growth parameters.

Although QTL can be used directly in marker-assisted breeding, the aim is to find the genes in the QTL region which account for the genotypic differences in the model parameters. These genes will help to unravel the genetic basis of complex traits such as yield. SPICY will use two approaches to find these genes: ‘candidate gene finding’, using known genes from other species, and ‘differential gene expression’. At the same time, automated and fast high-throughput tools are required to reduce the amount of manual labour necessary in phenotyping experiments. Finally, an experiment will be conducted to show the potential use of the total concept developed in this study and the potential impact of each tool or technique.

Expected impacts

The knowledge gained by this project is expected to give the EU plant breeding industry a competitive edge in the breeding of crop plants for sustainable and competitive agriculture. Mechanisms of technology transfer include an industrial advisory board that has been set up to enable exchange of information between academic and industrial researchers plus a series of workshops and specialised courses. It will also make an impact by increasing the underlying scientific knowledge of this subject through presentations and posters at conferences and other publications, including articles aimed at a more general audience.

Expected results

The project is expected to characterise the genetic components attributed to yields in order to enhance the efficiency of selection for complex traits. It should produce a wide range of a segregating population of recombinant inbred lines and establish a genetic map for this crop using crosses between different varieties of pepper. Studies should reveal changes in the expression between lines of segregating progeny, enabling differential expression to be mapped as a primary quantitative trait. Genes involved in the growth mechanisms of the plant or fruit will be identified.

The project should develop fast, automated tools for large-scale phenotyping in a range of environmental conditions, such as the creation of a mobile recording device called SPY-SEE that enables the collection of sequences of images to assist with the automated quantification of plant characteristics. A device will also result from this work that can be used to assess fluorescence kinetics.

Modified deterministic crop models will be produced that are applicable for use in QTL analysis. They will include a simple model that simulates growth of vegetative and generative biomass based on light-use efficiency. A second model will resemble the simplest model, but includes a boxcar train method to simulate fruit development. The most complex model will be INTKAM containing many sub-models for parameters such as light interception, photosynthesis, respiration, dry-matter partitioning and fruit growth.

These should produce information covering the change in phenotypic traits over time, and more specifically to the changes (increase/decrease and acceleration/deceleration) that these traits show. Information should accumulate indicating aspects like total dry-matter production and its partitioning derived from weekly fruit harvests and the final destructive harvest of the plants. Information on fruit load, fruit growth duration and potential fruit weight will also be obtained.

Website of project:www.spicyweb.eu

Contacts:

Coordinator: Fred Van Eeuwijk, fred.vaneeuwijk@wur.nl

Organisation: Wageningen University, www.wageningenuniversiteit.nl

Partners

French National Institute for Agricultural Research, France, www.international.inra.fr

Plant Systems Biology, University of Gent, Belgium, www.psb.ugent.be/

Biomathematics and Statistics Scotland, Scottish Crop Research Institute, UK, www.bioss.ac.uk

Agricultural Research Organization, The Netherlands, (no general website, partners contacts all indicate U of Wageningen)

Budapest University of Technology and Economics, Hungary, portal.bme.hu

Experimental Station of the Foundation Cajamar, Spain, www.fundacioncajamar.es/