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Pathogenicity of different forms of the Beet necrotic yellow vein virus (BNYVV) on sugar beet – is there evidence for the development of pathotypes?

  • Autor/in: Büttner, G., A. Büchse, B. Holtschulte, B. Märländer
  • Jahr: 2004
  • Zeitschrift: Proc. 67. IIRB-Congress
  • Seite/n: 263-270


The pathogenicity of different forms of the Beet necrotic vein virus and the development of pathotypes has again become the focus of interest since observations in France showed significant differences in the ranking of yield performance of rhizomania restistent varieties at different trial sites. Greenhouse experiments and a field trial series with rhizomania resistant sugar beet genotyps were performed at IfZ and KWS to evaluate factors which interact with the disease process of rhizomania on sugar beet. Sugar beet genotypes differing in the origin of rhizomania resistance were grown in the greenhouse and in the field in soils infested with different forms of the rhizomania virus ( A-, B- or P-type, respectively). Plant development, yield and virus content of the beet roots were assessed. Statistical analysis was performed to examine the interactions between sources of resistance and BNYVV types. The results indicate that, in general, the P-type of the rhizomania virus seems to be more aggressive than the A-type or B-type. Significant interactions between soils or trial sites containing different virus types and beet genotypes with different sources of rhizomania resistance were also observed. However, at the moment we cannot exclude the possibility that the level or homogeneity of infestation in the field, other beet related soil-borne pathogens or specific environmental conditions at a certain trial site, as well as the different genetic background of beet varieties, may also have influenced host-pathogen-relations. Therefore, investigations should continue with a more specific experimental design, including standardized bioassays with BNYVV inoculum free of other beet pathogens, and with more sophisticanted statistical models to detect and quantify interactions between different factors (e.g. AMMI-analysis).
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