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ETH researchers are involved in the development and implementation of a method to efficiently breed for disease-resistant beans in different regions of the world. Their work will help to improve the livelihood and food security of smallholders in developing countries.
For many people in Africa and Latin America, beans are an important staple. In many regions, however, plant diseases severely reduce bean yields. For example, the dreaded angular leaf spot disease can cause yield losses of up to 80 percent – especially in Africa, where smallholders rarely have the opportunity to protect their crops with fungicides.
Working with Bodo Raatz and his team at the International Center for Tropical Agriculture (CIAT), ETH researchers from the group led by Bruno Studer, Professor of Molecular Plant Breeding, investigated the resistance of beans to angular leaf spot disease. Their findings are now enabling disease-resistant bean varieties to be bred more rapidly and selectively for the world’s various bean-producing regions.
Their method is built upon genome analyses of those beans that are potentially suitable for breeding new, resistant varieties. The resulting genetic profiles provide information as to whether the progeny from crossbreeding two varieties will be resistant to the pathogenic fungus’s different, locally occurring strains.
The group’s work to provide disease-resistant beans will also help to cut down on global pesticide use. CIAT distributes the seeds from this project to various sub-organisations who then supply them to breeders. The analytical method for determining genetic markers is relatively simple and inexpensive to apply, making it viable for use in agricultural laboratories in the countries concerned.
A follow-up project in collaboration with CIAT and supported by the Worlk Food System Center at ETH will be conductes to refine the beeding method. While the researchers previously focused on markers for one specific disease, the new project will take a more holistic approach as they attempt to use such genome profiles to predict as many plant characteristics as possible.
