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Free Content Quantifying Uncertainty in Forest Nutrient Budgets

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Abstract:

Nutrient budgets for forested ecosystems have rarely included error analysis, in spite of the importance of uncertainty to interpretation and extrapolation of the results. Uncertainty derives from natural spatial and temporal variation and also from knowledge uncertainty in measurement and models. For example, when estimating forest biomass, researchers commonly report sampling uncertainty but rarely propagate the uncertainty in the allometric equations used to estimate tree biomass, much less the uncertainty in the selection of which allometric equations to use. Change over time may have less uncertainty than a single measurement, if the measures are consistently biased, as by the use of inaccurate allometric equations or soil sampling techniques. Quantifying uncertainty is not as difficult as is sometimes believed. Here, we describe recent progress in quantifying uncertainty in biomass, soils, and hydrologic inputs and outputs, using examples from the Hubbard Brook Experimental Forest, New Hampshire, USA.

Keywords: Hubbard Brook Experimental Forest; allometry; input-output; soil

Document Type: Review Article

DOI: http://dx.doi.org/10.5849/jof.11-087

Publication date: December 1, 2012

More about this publication?
  • The Journal of Forestry is the most widely circulated scholarly forestry journal in the world. In print since 1902, the Journal has received several national awards for excellence. The mission of the Journal of Forestry is to advance the profession of forestry by keeping forest management professionals informed about significant developments and ideas in the many facets of forestry: economics, education and communication, entomology and pathology, fire, forest ecology, geospatial technologies, history, international forestry, measurements, policy, recreation, silviculture, social sciences, soils and hydrology, urban and community forestry, utilization and engineering, and wildlife management. The Journal is published bimonthly: January, March, May, July, September, and November.
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