Spatial component for the decision support systems of Colorado's forest products industry - industry cluster analysis on sawmills in northern Colorado
Date
2016
Authors
Richardson, Emily Anne, author
Mackes, Kurt, advisor
Wei, Yu, advisor
Coleman, Robert, committee member
Evangelista, Paul, committee member
Journal Title
Journal ISSN
Volume Title
Abstract
The Colorado State Forest Service (CSFS) has received numerous requests for a resource that provides a consolidated, up-to-date spatial representation of facilities and contractors associated with Colorado's forest products industry (CFPI). The overall purpose of this project was to provide methods for creating the spatial component to be used in the decision support systems (DSS) of CFPI. The spatial component provides visual aids and decision-making assistance in order to locate potential biomass sources, plan future forest management, estimate transportation costs, understand the accessibility of a potential treatment site, understand which processing facilities are located in closest proximity to treatment sites to maximize efficiency, find prime facility candidates for woody biomass conversion and more. The first part of this study provides methods for obtaining the necessary data and creating a series of maps to be used in the tool. State-wide trends and relationships discovered by combining various map layers are discussed. The second part of the study demonstrates the potential and utility of the decision-making tool by performing an industry cluster analysis that investigated spatial interactions of sawmills and their feedstock in northern Colorado. Variables included in the industry cluster analysis were: sawmill capacity (annual production volume in board feet), species being processed, feedstock ownership origin, distance to recent forest management activities and competition (number of sawmills occurring within a 50-mile radius of a sawmill). The analysis attempted to discover any significant relationships between these independent variables and working distance (distance sawmills are willing to travel for log procurement), the dependent variable. Information was collected through spatial analysis using the mapping tool in addition to telephone or in-person interviews with sawmills in the industry cluster area. All sawmills in the industry cluster analysis region were contacted and 12 responded to the interviews, representing over 90% of the sawmill capacity in the cluster analysis region. Two significant relationships were found, though R-squared values were around 50%, indicating weak correlations. A statistically significant relationship was found between maximum working distance and annual production volume. Another significant relationship was found between annual production volume and the number of sawmills occurring within a 50-mile radius (competition). No significant relationships were found between working distance and proximity to treatments, feedstock origin, species being processed or competition. The tool was valuable for collecting spatial information such as proximity to recent forest management sites and the number of sawmills that occur within a 50-mile radius. The industry cluster analysis provided insight for general trends of sawmills in northern Colorado and helped to recognize where further research is needed. Additionally, the data collected in the study contributes in the effort to collect sawmill data state-wide. Mapping Colorado's forest products facilities and contractors using ArcGIS software proved to be a very useful way to visualize the data and discover meaningful relationships that can be used in the decision support systems of Colorado’s forest products industry. This tool provides aid as forest management becomes more complex with insistent factors like wildfire, insects and disease, product availability in the market and wood biomass utilization. It provides a resource for a diverse set of stakeholders with many different DSS objectives including sustainability, future forest management, identifying industry hotspots and biomass availability.