### Introduction to my Master’s Thesis Research

#### by ustroetz

During my internship at Ecotrust I created a cost model that estimates timber harvest costs. It tells you, how much does it cost to harvest a timber stand.

The cost model is part of Ecotrust’s Forest Planner, an online tool for forest management and scenario planning.

In my research for my master thesis I want to analyze the driving factors that influence harvest costs based on the create model. The model takes in four input variables in order to calculate the harvest cost for a stand:

*Slope in % (S)**Skidding Distance in feet (SD)**Trees per Acres (TPA)**Volume per Tree in cubic feet (VPT)*

It returns a *Harvest Cost* per ton, which can be extrapolated to the stand level.

**Research Question**

I want to investigate if it is possible to predict relative *Harvest Costs *(per ton) with only explicit spatial variables, which are in the case of the model S*lope* and S*kidding Distance*.

**Data Production**

In order to investigate the influence of each variable on the *Harvest Costs*, I need test input data. For that I will take timber sales data of the last ten years of the Colorado State Forest (CSF) and run the data with my cost model.

**Statistical Analysis**

With the create test datasets I will conduct a regression analysis and want to come up with a regression model like this:

Harvest Cost ≈

βTPA +_{0}+β_{1}βVPA +_{2}βSD +_{3}βS_{4}

In the next step I will remove the non-spatial variables *Trees per Acre* and *Volume per Acre*.

Harvest Cost ≈

βSD +_{0}+β_{1}βS_{2}

My hypothesis is that *Slope* and* Skidding Distance* are sufficient to still come up with a reasonable *Harvest Cost*.

**What is it good for?**

Based on this formula I would be able to predict cost for any given location in the CSF, solely based on *Skidding Distance *and *Slope*. I could pre-generate a *Harvest Cost* raster covering the entire CSF showing for every location the relative H*arvest Cost*.