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This article in CS

  1. Vol. 51 No. 5, p. 2036-2055
    Received: Dec 21, 2010

    * Corresponding author(s): dag.endresen@nordgen.org
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Predictive Association between Biotic Stress Traits and Eco-Geographic Data for Wheat and Barley Landraces

  1. Dag Terje Filip Endresen *a,
  2. Kenneth Streetb,
  3. Michael Mackayc,
  4. Abdallah Barib and
  5. Eddy De Pauwb
  1. a Nordic Genetic Resources Center (NordGen), Smedjevägen 3, 230 53 Alnarp, Sweden
    b International Center for Agricultural Research in the Dry Areas (ICARDA), P.O. Box 5466, Aleppo, Syria
    c Bioversity International, Via dei Tre Denari 472/a, 00057 Maccarese (Fiumicino) Rome, Italy


Collections of crop genetic resources are a valuable source of new genetic variation for economically important traits, including resistance to crop diseases. New sources of useful crop traits are often identified through evaluation in field trials. The number of relevant accessions in genebank collections available to be evaluated is often substantially larger than the capacity of the evaluation project. The focused identification of germplasm strategy (FIGS) is an approach used to select subsets of germplasm from genetic resource collections in such a way as to maximize the likelihood of capturing a specific trait. This strategy uses a range of methods to link the expression of a specific trait (of a target crop) with the eco-geographic parameters of the original collection site. This study contributes to the development of the approach by which a FIGS subset could be assembled for biotic traits. We have evaluated trait-specific subset selection methods for two fungal crop diseases, namely stem rust (Puccinia graminis Pers.) in wheat (Triticum aestivum L. and Triticum turgidum L.) and net blotch (Pyrenophora teres Drechs.) in barley (Hordeum vulgare L.). The results indicate that the climate layers from freely available eco-geographic databases are well suited to model and predict the reaction in these crops to biotic stress traits. This result has the potential to improve the efficiency of field screening trials to find novel sources of economically valuable crop traits.

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