Schärer, Hans-Jakob; Peter, Nadine; Thürig, Barbara; Oberhänsli, Thomas; Vonzun, Seraina; Scheiner, Christine; Dolder Laaraichi, Barbara; Kussmann, Sebastian and Messmer, Monika M. (2023) Development of an Ascochyta blight screening system in vivo and in vitro for the selection of resistant pea (Pisum sativum L.) accessions. In: The International Society for Plant Pathology, The French Phytopathological Society (Eds.) 12th International Congress of Plant Pathology. Book of Abstract ICPP 2023, 20-25 August 2023, Lyon, France, p. 978.
PDF
- English
(Poster)
9MB |
Summary
The cultivation of pea (Pisum sativum L.) among other leguminous crops has become more and more important in respect to biological nitrogen fixation for sustainable cropping systems and as important plant-based protein source for human nutrition. However, pea production is challenged by many biotic stresses, such as fungal and viral pathogens and insect pests.
Among fungal pathogens, Didymella pisi, D. pinodes and D. pinodella contributing to the Ascochyta blight complex are causing severe yield losses in pea production. The disease is stubble-, air-, soil- and seed-borne, hence disease control includes certified seed production and fungicide applications. However, particularly in organic agriculture the latter is not available and disease resistant varieties are needed. In collaboration with an organic pea breeder, we have established a reproducible screening system for selection of resistant pea lines using artificial inoculation. Main achievements are the isolation and identification of Didymella strains which contribute most to Ascochyta blight under local conditions, and differential scoring scales of pea leaf or tendril symptoms caused by the different Didymella species used for inoculation. This screening system is fundamental for phenotypic selection of resistant breeding lines independent of the disease pressure in the field. Moreover, it can be employed for identification of resistance genes using genome-wide association studies or genomic prediction approaches.
Repository Staff Only: item control page