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Abstract
This paper develops a theoretical model for analyzing gene bank management decisions
regarding the search for traits of economic value in ex situ collections of wheat. The model
is applied to data on the probability of finding useful sources of resistance to Russian wheat
aphid (Diuraphis noxia) and septoria tritici leaf blotch, using Monte Carlo simulations for
sampling distributions, simulations of varietal diffusion paths, and actual cost data from
searches. Three specific questions are posed and answered: (1) what is the optimal size of
search among genetic resources of a given type for a trait of economic value? (2) what is the
value of specialized knowledge about which genetic resources are most likely to display
resistance? and (3) how should search resources be allocated across types of genetic
resources? Results demonstrate the sensitivity of the optimal size of search to the economic
importance of the problem, the probability distributions of the trait in different types of
genetic resources, and the costs and time lags associated with transferring the trait into
usable breeding material. The costs and time lags involved with conventional pre-breeding
techniques imply that in some searches, certain categories of genetic resources (such as
landraces) will be systematically ignored. The fact that they may be rarely utilized does not
imply that collections of landraces have no value, however, as shown in the case of Russian
wheat aphid. Though applied here to data on insect and disease resistance, the model can
be adapted to search decisions for other types of traits.