Forest Investment Account

Abstract of FIA Project 4036007

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Operational sampling program to determine the effect of site variables on the natural regeneration density of lodgepole pine after harvest

Author(s): Kaffanke, Torsten
Imprint: Fraser Lake, B.C. : Fraser Lake Sawmills, 2003
Subject: Forest Investment Account (FIA), Forest Reproduction, British Columbia, Pine Cones
Series: Forest Investment Account (FIA) - Land Base Investment Program - Innovative

Abstract

Fraser Lake Sawmills has been conducting ground cone surveys for a number of years in its operating areas near Fraser Lake, B.C. in order to identify harvested sites that are likely to be regenerated naturally with lodgepole pine, or for which ingress of natural pine regeneration will be sufficient to warrant reduced planting densities. It is thought that such operational surveys could be modified so that data are collected that would allow a better prediction of natural regeneration to be made. A review of literature and various practitioners' opinions was done to identify potentially important variables affecting regeneration success. A review of historic ground cone survey data collected for Fraser Lake Sawmills was conducted to assess the likelihood of operationally collecting suitable data for the development of a predictive regression. A survey procedure was conceived in which data will be collected by sampling various combinations of the following independent variables: biogeoclimatic subzone and variant seedbed conditions seed loading solar radiation levels. The data will be collected in 2 m2 plots each year over a period of five years in order to include the effects of yearly weather variation. Regeneration response will be measured after 5 years. A group of regressions will be sought in which regeneration density expressed as a function of the independent variables listed above. It may be necessary to use variable transformations. Based on the examination of historic cone survey data, specific recommendations are made for the interim to ensure better collection and analysis of operational survey data.
Torsten Kaffanke.


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Updated August 02, 2006 

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