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Research in the area of spatial decision support (SDS) and resource allocation has recently generated increased attention for integrating optimization techniques with GIS. In this paper we address the use of spatial optimization techniques for solving multi‐site land‐use allocation (MLUA) problems, where MLUA refers to the optimal allocation of multiple sites of different land uses to an area. We solve an MLUA problem using four different integer programs (IP), of which three are linear integer programs. The IPs are formulated for a raster‐based GIS environment and are designed to minimize development costs and to maximize compactness of the allocated land use. The preference for either minimizing costs or maximizing compactness has been made operational by including a weighting factor. The IPs are evaluated on their speed and their efficacy for handling large databases. All four IPs yielded the optimal solution within a reasonable amount of time, for an area of 8 × 8 cells. The fastest model was successfully applied to a case study involving an area of 30 × 30 cells. The case study demonstrates the practical use of linear IPs for spatial decision support issues.  相似文献   
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Thomas Wieland's book is the first survey on the history of scientific plant breeding in Germany from 1889 to 1945. There are two mainlines of analysis: (1) The transformation of an agricultural practise of peasants into an academic discipline of scientists and (2) the importance of political arguments for this process of scientification. Most of the time Wieland's methods to present his thesis are exemplary: either as biographies or as breeding project histories. So he can write about a great diversity of aspects; but from his point of view – the discipline history as applied science – he cannot show the great importance of economic forces controlling plant breeding. This short article will not diminish the high value of Wieland's book. My aim is only to outline some desiderata for a history of plant breeding which is not yet written.  相似文献   
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