Optimizing Inventory and Yield Data Collection for Forest Management Planning
Abstract:This work uses a Cost+Loss approach to estimate the optimal investment in inventory information for forest planning. A bootstrapping approach is used to simulate the impact of different inventory intensities on the quality of decisions in a linear programming harvest scheduling model. Multiple formulations of the harvest model based on varying inventory intensities are used to calculate the value of a variable labeled Loss that measures the monetary losses resulting from the use of imperfect yield information in the harvest model. The variable Loss and the cost of obtaining the inventory information are used to estimate empirical relationships between their expected value and the amount of inventory information (number of inventory plots and number of experimental plots) used in the harvest models. These two relationships are combined to give an explicit estimate of the expected Cost+Loss as a function of the inventory intensity variables. This Cost+Loss relationship is minimized to estimate optimal number of inventory plots and the optimal number of experimental plots. An example is developed with radiata pine information from southern Chile. Results for this example suggest that current practice uses too many experimental plots and too few inventory plots.
Document Type: Research Article
Publication date: December 1, 2010
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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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