A Bayesian procedure is presented for evaluating potential sampling schemes to update previously calibrated models. Consideration can be given to biological restrictions on the range measurement interval of variables. The criterion used to evaluate the schemes is the mean variance of prediction in the response region where the model will be used. Two examples of sampling schemes for improving existing forest growth models are assessed. For. Sci. 33(3):632-643.
Associate Professor, Department of Forestry, University of Illinois, Urbana, IL 61801
Publication date: September 1, 1987
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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.