MCF = Thousand Cubic Feet
r = Mean Procurement Radius
INTRO
This analysis uses response-level data from the USDA Forest Service’s Timber Products Output Survey (2019-2021), namely self-reported mean procurement radius (miles) and annual procurement volume (MCF), to predict aggregate demand for non-pulp* roundwood at primary processing mills within the USDA Forest Service, FIA** Northern Region.
Outputs will aid policy-makers concerned with preserving heavily-harvested forested areas, as well as those within the industry who may seek to source logs closer to home, ideally minimizing haul distance.
METHODS
Given qualified responses account for just ~1/6 of the sample, procurement radius imputations for non-respondents is necessary.
Thus, regression analyses of varying complexity are utilized, with the leader chosen for sample-wide imputation.
All entities now house a measure of procurement radius, whether a direct response or model-based prediction.
Next, mill locations are geocoded, drawing a buffer around each mill corresponding to the imputed/response procurement radius. These buffers are further limited to only include areas from which a truck could arrive at the mill in one hour or less and are clipped to the outline of the US.
All areas of radius-coverage are weighted by the SqRt of the mill’s annual procurement volume, SqRt(MCF). All weighted areas of radius-coverage are then summed to produce a final demand ‘heat map’.
RESULTS
Demand for non-pulp* roundwood varies substantially at a geographic scale. Notably, key ‘wood baskets’ exist in the region of coverage.
DISCUSSION
Given demand for non-pulp* roundwood is concentrated in prominent ‘wood baskets’, relocation of select primary processing mills and/or log suppliers may be warranted.
Quantifying Aggregate Demand for Non-Pulp* Roundwood at Primary Processing Mills in the Northern US – A Machine Learning Approach
Models Assessed
4. Random Forest Reg. (5x CV):
sqrt(r) ~ x
sqrt(r) ~ x
sqrt(r) ~ x
Preliminary Results
Ian Kennedy, MS Geography
Research Assistant, FFRC
iankennedy@umass.edu
i = Region (NE, Mid Atl, Central, Lake, Plains)
x = log10(MCF), State, Mill Type (Sawmill/Other), Lat/Lon, # of employees, Equipment, Portable?, Logs exported?
Scan for project overview^
Sample Statistics (Procurement Radius)
Sample Size - 2,586
Qual. Respondents - 427
Nonrespondents - 2,159
Normal QQ-Plots:
All regression analyses were conducted in RStudio. Mapping was completed in ArcGIS Pro, through usage of a Python-based script.
*Non-Pulp = saw, house/cabin, post, pole, & res. firewood logs*
**FIA = Forest Inventory & Analysis**