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Screening Germplasm Collections

  • Gitte Petersen
  • Ole Seberg
Chapter

Abstract

The numerous accessions stored in gene banks are in many respects a haphazard sample of both the man-made variation found in breeding lines, landraces and genetic stocks, and of the natural variation found in the related wild species. The main reason for the discrepancy between the stored and the actual variation is the fact that the material is only rarely sampled with any clear objective in mind other than simple storage of actual or potential breeding material. Rarely, if ever, has sampling been done according to a predefined strategy and with a specific scientific purpose in mind. This makes the strategies for screening germplasm widely different from the strategies used in more traditional projects (1). Hence, finding only a few accessions carrying resistance genes may be worth the effort almost irrespective of how many accessions have to be screened (to find 23 accessions of cultivated rice (Oryza sativa) resistant to the striped stem borer (Chilo suppressalis) it was necessary to screen 22 920 accessions (2)). The purposes for screening germplasm may be varied, however, just as may be the size, structure, and content of the collections to be screened. For example, a gene bank manager may, for purely economic reasons, screen a collection in order to eliminate duplicates, or in order to assess the level of diversity, whereas a breeder may wish to screen for useful agronomic traits. The choice of the most appropriate methodology is the subject of several considerations, most importantly, the actual problem at hand and the available time and money. A helpful and indispensable invention for accessing very large collections is the core collection (see below), which is being developed for many major crops.

Keywords

Core Collection Plant Genetic Resource Genetic Stock Land Race Related Wild Species 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Chapman & Hall 1998

Authors and Affiliations

  • Gitte Petersen
  • Ole Seberg

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