Multiobjective Regional Forest Planning Using the Noninferior Set Estimation (NISE) Method in Tanzania and the United States
Abstract:Multiple objective management of public forests based on centralized regional planning is mandated by law in the United States and by development policy in developing countries. Multiple objective programming (MOP) methods have been applied to this problem, but public forests have multiple users whose relative preferences for forest products often cannot be determined, so preference-based goal programming and multiobjective linear programming solutions may be inaccurate or inflexible. The use of noninferior set generating algorithms to solve MOP's encourages more flexible planning by providing a range of possible solutions (the noninferior set), but requires or generates too much information. The NISE (noninferior set estimation) method is an MOP solution technique that uses repeated linear program (LP) solutions to generate an estimate of the true noninferior set, with a known error. The method provides explicit tradeoffs between objectives and requires little interaction with forest users. The NISE method is illustrated for a problem of forest management in Tanzania, and its applicability to centralized forest planning in the United States and developing countries is discussed in the light of legal, political, and institutional constraints. Forest Sci. 32:517-533.
Document Type: Journal Article
Affiliations: Assistant Professor, Department of Geography, University of California, Santa Barbara, CA 93106
Publication date: 1986-06-01
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- Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
Forest Science is published bimonthly in February, April, June, August, October, and December.
2015 Impact Factor: 1.702
Ranking: 16 of 66 in forestry
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