Harvest scheduling with spatial wildlife constraints: an empirical examination of tradeoffs.

A study was conducted to examine the effect of imposing spatial wildlife constraints on long-range timber management schedules. Data were gathered on a public forest in northern Virginia under varying levels of a wildlife habitat constraint. Linear programming-based timber management scheduling mo...

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Publicado en:Journal of Environmental Management Vol. 43; pp. 333 - 349
Autores principales: Cox, Eric S., Sullivan, Jay
Formato: Artículo
Publicado: Academic Press Inc. April 1995
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        03014797
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      dt: April 1995
      vid: 43
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      pub: Academic Press Inc.
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        512600915
        10.1016/S0301-4797(95)90252-X
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        atl: Harvest scheduling with spatial wildlife constraints: an empirical examination of tradeoffs.
      aug:
        au:
          Cox, Eric S.
          Sullivan, Jay
      su:
        Zoogeography
        Mathematical models
        Forest management
        Mathematical programming
        Lumber industry & the environment
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        subj:
          Zoogeography
          Mathematical models
          Forest management
          Mathematical programming
          Lumber industry & the environment
      ab: A study was conducted to examine the effect of imposing spatial wildlife constraints on long-range timber management schedules. Data were gathered on a public forest in northern Virginia under varying levels of a wildlife habitat constraint. Linear programming-based timber management scheduling models are solved with standard linear programming, mixed-integer programming with computer-determined stand allocations, and mixed-integer programming with predetermined stand allocations to determine the degree to which the failure to consider explicitly the spatial aspects of a forest management problem with wildlife concerns may result in an overestimation of timber production capacity. The results reveal that present net value and annual sawtimber harvest volume are overestimated when the standard linear programming approach is employed.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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