United States Department of Agriculture

DEPAR TMENT OF AGRICULT URE

Forest 

Service

Pacific Northwest 

Research Station

General Technical Report

PNW-GTR-960

October

2017

OpCost: an Open-Source System 

for Estimating Costs of StandLevel Forest Operations 

Conor K. Bell, Robert F. Keefe, and Jeremy S. Fried
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Authors

Conor K. Bell is a graduate research assistant, Forest Operations Research Lab, 

University of Idaho, 875 Perimeter Dr., Moscow, ID 83844-1133; Robert F. Keefe

is assistant professor of forest operations and director, University of Idaho Experimental Forest, 875 Perimeter Dr., Moscow, ID 83844-1133; Jeremy S. Fried is a 

research forester, U.S. Department of Agriculture, Forest Service, Pacific Northwest 

Research Station, 620 SW Main St., Suite 400, Portland, OR 97205. 

Published in cooperation with the University of Idaho.

Cover photo: Log deck resulting from a mechanical forest thinning operation in 

Washington Douglas-fir forest. Photo by Robert Keefe. 
Abstract

Bell, Conor K.; Keefe, Robert F.; Fried, Jeremy S. 2017. OpCost: an open-source 

system for estimating costs of stand-level forest operations. Gen. Tech. Rep. 

PNW-GTR-960. Portland, OR: U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Research Station 23 p.

This report describes and documents the OpCost forest operations cost model, a key 

component of the BioSum analysis framework. OpCost is available in two editions: 

as a callable module for use with BioSum, and in a stand-alone edition that can be 

run directly from R. OpCost model logic and assumptions for this open-source tool 

are explained, references to the literature used in all of the submodels included in 

OpCost are provided, and guidance is offered on how to change the default hourly 

machine rates associated with overall logging cost calculations. OpCost enhancements such as cost component breakout, and identifying the least-cost harvest 

system, are also described and explained.

Keywords: Harvest systems modeling, mechanical fuel treatment logging cost 

estimation calculator.
Contents

1 Introduction

2 OpCost Framework Overview 

3 Estimating Machine Time Required

3 Estimating Machine Cost

4 Estimating Harvest System Cost

4 Enhancements

5 How It Works, in Detail

10 Inputs

10 Transformations

12 Filtering

12 Size Class Specific Versus Total/Average

12 OpCost Workflow

13 Putting It All together

14 OpCost Stand-Alone Edition (SE)

18 Future Directions 

19 Acknowledgments 

19 English Equivalents

20 Literature Citations
1

OpCost: an Open-Source System for Estimating Costs of Stand-Level Forest Operations

Introduction

Estimating costs of forest operations is essential for evaluating the economic 

feasibility of forest management alternatives, especially those involving partial 

harvest conducted as a component of forest restoration, where costs may be a large 

fraction of, or exceed, revenues from sales of harvested wood. The Fuels Reduction 

Cost Simulator (FRCS) (Fight et al. 2006), the development of which began over 

two decades ago, sought to address this need with a multisheet Excel®1 spreadsheet 

tool into which users could enter parameters associated with stand conditions and 

the material to be harvested as part of fuel treatment operations. In FRCS, and in 

OpCost, the user chooses a harvest system appropriate for a given site and stand 

conditions, specifies the “cut list” that results from applying a silvicultural prescription designed to reduce fuel loadings, and obtains estimated costs associated with 

implementation of that prescription. As interest grew in obtaining treatment cost 

estimates for large numbers of stands treated via a range of alternative prescriptions 

simulated on the landscape simultaneously, as implemented in the BioSum model 

(Fried et al. 2016) and other approaches to modeling landscape-scale operational 

logistics (Jacobson et al. 2016), the inherent limitations of the spreadsheet-based 

paradigm for simulating operational costs became evident. Operations foresters at 

the University of Idaho, with support from the Joint Fire Sciences Program, undertook a revision and restructuring of the FRCS model. The products of their efforts 

include the OpCost forest operations cost model, programmed in R, and a validation study that compared OpCost estimates with expert judgments collected via 

a formally designed survey of logging professionals (Bell et al. 2017). In addition 

to updating currently available production functions and cost estimates based on 

recent literature, OpCost includes some additional harvest system capabilities, such 

as simulation of shovel logging, and provides a more reliable interface to BioSum. 

OpCost incorporates production rates from over 85 studies, many with a vintage 

more recent than the last version of FRCS, released in 2009, of both manual and 

mechanized operational systems. One edition of the model, OpCost BioSum Edition 

(BE), is distributed with the BioSum software. OpCost BE is a callable code package, running under R (R Development Core Team 2016), that tightly integrates with 

BioSum to estimate the costs for modeled forest harvest operations simulated under 

that analysis framework. The other edition, OpCost Stand-alone Edition (SE), can be 

run directly from the R development environment. OpCost is driven by essentially 

the same variables used as inputs to its predecessor, FRCS. For trees simulated as 

1 The use of trade or firm names in this publication are for reader information and do not 

imply endorsement of the U.S. Department of Agriculture of any product or service.
2

GENERAL TECHNICAL REPORT PNW-GTR-960

harvested, 35 variables account the for the average volume per tree, number of trees 

harvested per acre, hardwood fraction, merchantable volume as a percentage of total 

volume and wood density for each of three user-defined tree size classes, along with 

trees per acre and average tree volume of small (“brush cut” size, in FRCS parlance) 

trees that are cut but not used. Other inputs include slope and average distance to the 

nearest landing and, for small and large log trees, the percentage of wood volume 

transported to the landing that will be chipped owing to its derivation from trees 

of submerchantable size or noncommercial species. Average travel time, in hours, 

required to move-in a harvest system equipment component (e.g., a skidder) to the 

harvest site and operation size, in acres, are required to estimate move-in costs on a 

per-acre basis.

OpCost greatly streamlines workflow of a batch operation relative to FRCS by 

eliminating the need to export data to Microsoft (MS) Excel as an intermediate 

step prior to cost estimation. OpCost is also designed to operate in batch mode, 

but reads inputs from and writes outputs to MS Access database tables, making it 

equally easy for users to estimate costs for one stand and treatment or thousands of 

stands and dozens of treatment alternatives. The integration of OpCost with BioSum produces cost estimates for each analyst-specified combination of silvicultural 

prescription and harvest system on each inventory plot modeled in BioSum. This 

facilitates estimates of cost effectiveness for the wide range of fuel treatments on 

managed forests in the Western United States (e.g., Jain et al. 2012). The system can 

also estimate harvest costs for implementing any silvicultural prescription, not just 

those intended to reduce hazardous fuels.

OpCost Framework Overview 

Production functions in OpCost use, as inputs, the estimated amount of removed 

or altered material within forest stands based on summary metrics from simulated 

silvicultural prescriptions that generate a “cut list” in the Forest Vegetation Simulator 

(FVS) (Dixon 2002) format. These predict, on a per-acre basis, the time and expense 

required to harvest and process wood volume. In essence, OpCost is an equation filtering and aggregation engine that, considering the specifics of a stand, silvicultural 

treatment to be modeled, and harvest system to be implemented, applies several 

possible equations extracted from peer-reviewed forest operations literature. These 

are typically based on past elemental time analysis and work sampling studies that 

are identified as applicable to the stand and site under consideration; OpCost computes the average of the predictions of all applicable equations. By using the regression equations from descriptive studies, OpCost can predict the production rates of 

the equipment within certain conditions. Applicability is determined by whether an 
3

OpCost: an Open-Source System for Estimating Costs of Stand-Level Forest Operations

equation is eliminated from consideration by “rejection” criteria that differ among 

equations for a given harvesting, extraction, or processing machine. Examples of 

rejection criteria, which are evaluated for each operation based on whether the 

OpCost inputs fall within the acceptable range for an equation, include slope, average volume per tree, average distance to landing, whether hardwoods are part of the 

harvest, average tree diameter, average tree weight, and harvested trees per acre. 

Nonrejected equations for each machine used in a harvest system advance to the 

next computation phase. The rejection criteria are based on the range of data over 

which original studies were evaluated, in order to avoid extrapolation of production 

or cost functions beyond the range of their source data and intended application.

Estimating Machine Time Required

A harvest system can be thought of as a collection of the equipment, or machines, 

and labor required to implement operations under that system. For example, a cable 

manual whole-tree system typically operates on steep slopes and, after manual 

tree felling by a sawyer, brings entire trees, including bole, branches and top (as 

opposed to previously bucked logs) to the landing using a yarder. At the landing, 

whole trees are processed into merchantable logs and nonmerchantable residues 

with a processor, which may be either a stroke-boom delimber or danglehead processor. Logs are then loaded onto log trucks with a loader and residues are chipped 

and blown into a chip van. Thus, this system involves five machines: manual felling (sawyer with a chainsaw), yarder, processor, loader, and chipper, each of which 

may have up to a dozen or more published equations that could be used to predict 

the time required per unit of trees or volume handled. Given the diversity of study 

purposes and locales behind the published equations, and differences in the factors 

affecting costs for different machines, tree units differ among machines and among 

studies. Wood characteristics tracked by the model may be expressed on a volume 

or mass basis, depending on the relevant production function and study. Volumes 

may be expressed in cubic feet, board feet, cords or cubic meters, and mass values 

may be in pounds or kilograms. Times are summed across size classes, and these 

sums are then combined into the mean time in hours per acre. 

Estimating Machine Cost

Once total machine working time has been adjusted into hours per acre, 

machine cost per acre is computed as the product of hours and machine cost 

rates, which are based on conventional cost control processes (Matthews 

1942). Calculations occur in productive machine-hours (PMH) and following convention in forest operations studies, a utilization rate is assigned to 

distinguish between production and delay time. Because OpCost generates 
4

GENERAL TECHNICAL REPORT PNW-GTR-960

estimates at a stand level, without regard to stand location, it cannot assign 

location-specific move-in costs associated with each machine’s use. However, 

when using OpCost within a BioSum analysis, per-acre move-in costs can be 

accounted for based on travel time to wood processing facilities (a parameter 

calculated as part of the BioSum workflow and a viable proxy for move-in 

time) and assumed stand area undergoing treatment. Move-in costs could 

also be affected by the locations of scheduled work in a particular year; 

however, this level of detail is beyond the scope of OpCost and BioSum and 

is not addressed. Wherever available, machine rates include fixed and variable costs associated with owning and operating the equipment. These have 

been updated with current estimates (Dodson et al. 2015). Default machine 

cost rates, developed for the Pacific Northwest region, are supplied in the 

OpCost code, but can be changed by the user, if desired and necessary, via 

a straightforward text edit to the open source model code (for OpCost BE) or to 

the appropriate input table (for OpCost SE).

Estimating Harvest System Cost

After machine cost estimates are complete, harvest cost per acre can be calculated as 

the sum of each of the predicted machine-specific treatment costs associated with the 

harvest system working in the BioSum-supplied site and stand conditions. For the 

cable manual whole-tree system example, this would be the sum of treatment costs 

per acre for sawyer, yarder and carriage combination, processor, loader, and chipper. 

Treatment cost accounts for both the machine rates and the production rates for each 

piece of equipment. Production rates are typically expressed in tons per PMH or 

thousand board feet (MBF) per PMH and provide predictions of the total treatment 

time required by each piece of equipment to treat volume removed per acre. See, 

for example, Keefe et al. (2014) for further description of the relationship between 

machine rates, production rates, and treatment costs. Move-in cost, derived from the 

number of machines transported to the harvest site and assumptions that account for 

delays in setting up equipment, when applicable, is added to obtain the final harvest 

cost reported in the table OpCost_Output. That table also contains calculated movein cost in a separate column to better inform the analyst wishing to consider, for 

example, savings that might accrue from consolidating forest operations.

Enhancements

A significant enhancement compared to FRCS is the simultaneous estimation of 

costs for multiple, potentially more cost-efficient harvest systems. These estimates 

can be compared to the costs of the analyst-specified harvest system, replacing 
5

OpCost: an Open-Source System for Estimating Costs of Stand-Level Forest Operations

them if desired. By default, cost estimates for the lowest cost harvest system are 

output to a table called “OpCost_Ideal_Output.” Users wishing to prevent the 

creation of this table, thus slightly shortening execution time, can change the “1” to 

a “0” in the following OpCost statement: idealTable1 (currently line 20 in version 

8.7.9) of the executable code.

Another new feature is the reporting of chipping costs in a separate column in 

both the OpCost_Output and OpCost_ Ideal_Output tables. This could be useful 

when considering alternative approaches to disposal of harvest residues, as when 

a treatment leaves residues at the landing for collection as firewood or later burning—chipping cost might then be deducted from the harvest cost. 

How It Works, in Detail

This section describes OpCost processing in greater detail, including the specific inputs and intermediate processing steps. This information is current as of 

version 8.7.9.

OpCost predicts operations costs for 11 harvest systems. Each system can be 

thought of as an integrated sequence of activities using several pieces of equipment, operating in a coordinated fashion, to move wood from trees to trucks, while 

accomplishing management objectives. For example, a harvest system may be used 

to transform a forest stand in terms of its density, tree species and size distribution, 

surface vegetation and fuels, and emergent properties such as forest health, resistance to fire, and overall resilience that derive from these. In addition to differences 

in how harvested material is felled and transported, harvest systems differ in the 

materials that are collected for utilization. OpCost provides for five harvest system 

categories to reflect these differences when estimating operations costs (table 1). 

The 11 systems included in OpCost (table 2) cover nearly all forest management 

activities that involve mechanical fuel treatment and other harvests. To estimate 

the cost of applying these systems to implement particular prescriptions on specific 

stands, OpCost relies on 124 equations extracted from 82 published articles (table 3) 

covering all 11 machine types used in these 11 harvest systems.

For each equipment type, the available equations differ in the inputs that are 

required, so the “independent variables” column in table 3 is an exhaustive list of 

attributes used by any of the equations in the equipment category. The meaning 

of most of these attributes is straightforward. Piece volume is average volume per 

tree; total volume is volume per acre. Depending on the equation, species group 

is ultimately either a binary descriptor indicating whether hardwoods comprise 

more than 1 percent of the harvested material or the percentage of volume that is 

in hardwoods.
6

GENERAL TECHNICAL REPORT PNW-GTR-960

Table 1—Utilization of wood in tops and limbs varied by harvest system category 

and slope

Harvest system category Slope Outcome for tops and limbs

1 Low Left in the woods

2 Low Forwarded to landing for chipping

3 High Left in the woods

4 Any Except for large log trees, forwarded to landing 

for chipping

5 Any Forwarded to landing for chipping only when size 

is submerchantable or species is noncommercial

Table 2—Harvest systems options available in the OpCost model and the equipment types used in each system

Harvest system name Equipment types used

Harvest system 

category

Ground-based manual whole tree Sawyer, skidder, processor, loader, chipper 2

Ground-based mechanical whole tree Feller-buncher, skidder, processor, loader, chipper 2

Ground-based cut to length (CTL) CTL harvester, forwarder, loader, chipper 1

Ground-based manual log Sawyer, forwarder, loader, chipper 1

Cable manual whole tree Sawyer, yarder, processor, loader, chipper 4

Cable manual whole tree/log Sawyer, feller-buncher, processor, loader, chipper

Note: all large trees are assumed to be manually bucked into 

logs prior to yarding

5

Cable manual log Sawyer, yarder, loader, chipper 3

Cable CTL CTL harvester, yarder, loader, chipper 1

Helicopter manual whole tree Helicopter, sawyer, processor, loader, chipper 3

Helicopter CTL Helicopter, CTL harvester, loader, chipper 1

Shovel manual whole tree Shovel, sawyer, processor, loader, chipper 4
7

OpCost: an Open-Source System for Estimating Costs of Stand-Level Forest Operations

Table 3—OpCost source of equations to estimate machine time requirements for 

forest harvest operations by machine type, and input variables equations draw 

from as independent variables for predicting machine time per unit tree volume 

or mass

References for production equations 

by machine type Required independent variables

Sawyer:

Behjou and Majnounian 2009 Diameter at breast height (d.b.h.)

Ghaffariyan et al. 2012 Total volume

Hartsough et al. 2001 Piece volume

Klepac et al. 2011 Distance between trees

Kluender and Stokes 1996 Slope

Lortz et al. 1997

Visser and Spinelli 2012

Wang et al. 2004

Cut-to-length (CTL) harvester:

Acuna and Kellogg 2013 D.b.h.

Adebayo et al. 2007 Slope

Berhongaray et al. 2013 Trees/acre

Bolding et al. 2002 Total volume

Bolding and Lanford 2001 Piece volume

Drews et al. 2001 Species group

Eliasson 1999 Distance between trees

Hiesl 2013

Hiesl and Benjamin 2012

Jiroušek et al. 2007

Kärhäet et al. 2004

Jiroušek et al. 2007

Keegan et al. 2002

Klepac et al. 2006

Klepac et al. 2011

Numinen et al. 2006

Visser and Spinelli 2012

Skidder:

Adebayoet al. 2007

Bolding et al. 2002 D.b.h.

Boswell 1998 Piece volume 

Ghaffariyan et al. 2012 Species group

Hiesl and Benjamin 2012 One-way yarding distance

Keegan et al. 2002 Total volume

Kluender and Stokes 1996 Trees/acre
8

GENERAL TECHNICAL REPORT PNW-GTR-960

Table 3—OpCost source of equations to estimate machine time requirements for 

forest harvest operations by machine type, and input variables equations draw 

from as independent variables for predicting machine time per unit tree volume 

or mass (continued)

References for production equations 

by machine type Required independent variables

Wang et al. 2004 Slope

Feller-buncher: D.b.h.

Adebayo et al. 2007 Distance between trees

Berhongaray et al. 2013 Piece volume

Bolding and Lanford 2001 Trees/acre

Boswell 2001 Species group

Dykstra 1976 Trees/acre

Hartsough et al. 1997 Slope

Hartsough et al. 1997

Hartsough et al. 2001

Hiesl 2013

Hiesl and Benjamin 2012

Kärhä et al. 2004

Kluender and Stokes 1996

Spinelli et al. 2007

Wang et al. 2004

Helicopter:

Christian and Brackley 2007 Piece weight

Dykstra 1976 One-way yarding distance

Flatten 1991 Elevation

Flatten 1991 Piece volume

Trees/acre

Forwarder:

Acuna and Kellogg 2013 One-way yarding distance

Bolding et al. 2002 Piece volume

Bolding and Lanford 2001 Slope

Drews et al. 2001 Trees/acre

Dykstra 1976 Total volume

Hiesl 2013 D.b.h.

Jiroušek et al. 2007 Weight

Jiroušek et al. 2007

Kluender and Stokes 1996

Numinen et al. 2006

Sirén and Aaltio 2003
9

OpCost: an Open-Source System for Estimating Costs of Stand-Level Forest Operations

Table 3—OpCost source of equations to estimate machine time requirements for 

forest harvest operations by machine type, and input variables equations draw 

from as independent variables for predicting machine time per unit tree volume 

or mass (continued)

References for production equations 

by machine type Required independent variables

Cable:

Drews et al. 2001 Trees/acre

Aubuchon 1982 Slope

Dykstra 1976 Piece volume

Boswell 2001 Piece weight

LeDoux 1987 One-way yarding distance

Hartsough et al. 2001

Huyler and LeDoux 1997

Chipper:

Bolding et al. 2002 Piece volume

Bolding and Lanford 2001 Species group

Cuchet 2004 D.b.h.

Spinelli and Magagnotti 2014

Shovel:

Fisher 1986 One-way yarding distance

Sessions and Boston 2013 Elevation

Wang and Haarlaa 2002 Piece volume

Piece volume

Trees/acre

Weight

Stroke-boom delimber:

Ghaffariyan et al. 2012 Piece volume

Spinelli and Hartsough 2006 D.b.h.

Hiesl 2013 Species group

Spinelli and Magagnotti 2010
10

GENERAL TECHNICAL REPORT PNW-GTR-960

Inputs

BioSum initiates an instance of R, passing to it the OpCost BE filename and the name 

of an Access file that contains all input data in a table named opcost_input. This table 

consists of 36 attributes per stand (table 4). In general, but with a few exceptions for 

size classes, where some are not needed, there is a quintet of harvested tree descriptors 

for each of four size classes defined by the user in BioSum, plus a few site descriptors. 

Note that the size class definitions themselves (in terms of minimum and maximum 

diameter at breast height [d.b.h.]) are not passed to, or needed by, OpCost. The four 

size classes are brush cut (BC), chip trees (CT), small log trees (SLT) and large log 

trees (LLT). Each of these size classes is determined by the analyst. The BC trees are 

harvested but not used, so OpCost accounts only for the costs of their felling. The CT 

boles are assumed to be used as “dirty” chips (with bark not removed before chipping) 

and have no merchantable value that would exceed their value as dirty chips; limbs are 

also used as chips when whole-tree harvest systems are used. To account for the costs 

of chipping trees in the SLT and LLT size classes that are noncommercial species not 

used to produce merchantable wood products, inputs to OpCost include the percent 

of log volume that would be chipped. These “chip percent” parameters are provided 

for each of three groups of harvest system categories, because the chip percentages 

vary depending on exactly what material (e.g., boles-only or tops and limbs also) gets 

transported to the landing, for example, depending on system, size and species. Tops 

and limbs that harvest systems do not bring to the landing are assumed to be left in the 

woods and incur no chipping costs. Note that average tree volumes provided as inputs 

to OpCost are total volumes, including tops and limbs. 

Transformations

A comparison of the variables listed in table 3 (required input variables) and table 4 

(OpCost inputs) shows that while most are aligned, there are some required variables not provided by BioSum. As each stand is processed, OpCost effects transformations to ensure that the input requirements for all 124 equations can be met. 

For example, OpCost estimates average d.b.h., which is required by at least some 

production functions for most equipment types, by inverting formulas associated 

with common log scale rules such as the international 1⁄4-inch rule (Grosenbaugh 

1952) and Scribner’s Decimal C (Bruce and Schumacher 1950), to predict d.b.h. 

from BioSum-supplied average volume per tree. In several cases, it is necessary 

to estimate average intertree distance (which is required by at least some of the 

sawyer, CTL harvester, and feller-buncher equations) from trees per acre (TPA) 

supplied by BioSum. Mean inter-tree distance is estimated from TPA as follows:

Intertree distance = 

Because some equations estimate time required per unit mass, OpCost calculates 

mass per acre, by tree size class and in total, by multiplying BioSum-supplied average 
11

Table 4—OpCost inputs and what they mean OpCost: an Open-Source System for Estimating Costs of Stand-Level Forest Operations

Column Input Item Description

1 Stand Stand identifier (condition id + rxPackage + rx + rxCycle)

2 Percent slope Slope 

3 One-way yarding distance Distance in feet between volume centroid of the stand and nearest road

4 YearCostCalc Year of harvest (used only in OpCost stand-alone edition)

5 Project elevation Stand elevation in feet above mean sea level

6 Harvesting system Name of harvest system

7 Chip trees per acre Trees per acre (TPA) of chip trees

8 Chip trees Merch As Pct Of Total Percentage of chip tree volume in merchantable-sized wood (used for 

harvest systems that do not recover tops and limbs)

9 Chip trees average volume(ft3) TPA weighted average of total volume

10 Chip trees average density (lb/ft3) TPA and volume weighted average wood density of all chip trees

11 Chip tree hardwood percent TPA and volume weighted percentage of chip tree volume that is in 

hardwoods

12 Small log trees per acre TPA of small log trees

13 Small log trees Merch As Pct Of Total Percentage of small log volume in merchantable-sized wood

14 Small log trees ChipPct_Cat1_3 Percentage of harvested small log volume that will be chipped under harvest 

systems in category 1 or 3

15 Small log trees ChipPct_Cat2_4 Percentage of harvested small log volume that will be chipped under harvest 

systems in category 2 or 4

16 Small log trees ChipPct_Cat5 Percentage of harvested small log volume that will be chipped under harvest 

systems in category 5

17 Small log trees total average volume(ft3) TPA weighted average total volume per small log tree

18 Small log trees average density(lb/ft3) TPA and volume weighted average wood density of small log tree volume

19 Small log trees hardwood percent TPA and volume weighted percentage of small log tree volume that is in 

hardwoods

20 Large log trees per acre TPA of large log trees

21 Large log trees Merch As Pct Of Total Percentage of large log tree volume in merchantable-sized wood

22 Large log trees ChipPct_Cat1_3_4 Percentage of harvested large log tree volume that will be chipped under 

harvest systems in category 1 or 3 or 4

23 Large log trees ChipPct_Cat2 Percentage of harvested large log tree volume that will be chipped under 

harvest systems in category 2

24 Large log trees ChipPct_Cat5 Percentage of harvested large log tree volume that will be chipped under 

harvest systems in category 5

25 Large log trees total average vol(ft3) TPA weighted average total volume per large log tree

26 Large log trees average density(lb/ft3) TPA and volume weighted average wood density of all the large log tree 

volume

27 Large log trees hardwood percent TPA and volume weighted percentage of large log tree volume that is in 

hardwoods

28 BrushCutTPA TPA of brush cut trees

29 BrushCutAvgVol TPA weighted average bole+branch volume of brush cut trees

30 RxPackage_Rx_RxCycle Code indicating silvicultural sequence, prescription and Forest Vegetation 

Simuator cycle under which trees were harvested

31 BioSum_cond_id Needed for table joins

32 Rxpackage Needed for table joins

33 rx Needed for table joins

34 rxcycle Needed for table joins

35 Move-in hours Time required to move in and setup logging equipment

36 Harvest area assumed acres Area assumed for the size of the harvest operation
12

GENERAL TECHNICAL REPORT PNW-GTR-960

wood density by average tree volume and TPA. Depending on the referenced equations used, some volumes are converted to cubic feet, board feet, or cords per acre. All 

volumes and masses are also converted to metric units (cubic meters per hectare or 

kilograms per hectare) to fulfill the requirements of equations defined in metric units. 

Filtering

When processing each stand, OpCost applies the rejection criteria associated with 

each of the 124 equipment equations to eliminate equations deemed unsuitable for 

estimating costs for that stand. Typically, these rejection criteria are derived from 

the range of an attribute reflected in the empirical data on which an equation was 

fit. Any of the attributes may be the basis for rejection criteria. For CTL harvesters 

and feller-bunchers, d.b.h. is an important filter; for yarders, distance to landing and 

slope might be attributes with rejection criteria, but the criteria will differ among 

equipment types and among equations for a given equipment type, thus the sets of 

equations used for any two stands that are in some respects similar, may still differ.

Size Class Specific Versus Total/Average

Equations differ as to whether or not they take stand-level- or size-class-specific 

inputs, even for a given equipment type. For those that operate with individual size 

classes (e.g., CT, SLT, and LLT), harvested tree descriptors such as average volume 

and TPA are processed for a specific size class to obtain equipment time associated with that size class. Ultimately, the equipment times for all size classes are 

summed. With mixed systems, for example, when only large log trees are manually 

felled, sawyer equations would use only the LLT descriptors. Because the requirements differ among equations, OpCost calculates overall stand averages and totals 

for all harvested trees, as noted under “Transformations” above, so that these are 

available for the equations that rely on them.

OpCost Workflow

For a given equipment type, OpCost calls a machine-specific function. For a yarder 

time estimate, for example, it would call the yarder function, passing two arguments: 

the input variables generated by BioSum via the opcost_input table and the list of 

candidate production functions for that equipment. Parsing each opcost_input record, 

that function would reject inappropriate yarder equations, create the necessary transformed attributes, estimate yarder times per unit of material for all the appropriate 

equations, and adjust time per tree unit to time per acre for each tree size class separately, if necessary. It would then sum across size classes, compute the average over 

estimates from all yarder equations, multiply the estimated time per acre by yarder 

unit cost in dollars per hour, and return that cost component to be used when adding 

up the cost of all machines used on that stand, at that time, with that harvest system. 
13

OpCost: an Open-Source System for Estimating Costs of Stand-Level Forest Operations

When using OpCost SE, harvest year can be included as an input and optionally, if a discount (interest) rate is provided, harvest cost estimates can be expressed 

in present or future value terms. When using OpCost BE with BioSum, discounting 

and inflation considerations are handled within the BioSum modeling framework.

Putting It All together

Figure 1 summarizes the seven major processing steps that occur within OpCost 

to compute the multiple components of harvest cost and combine them into an 

estimate of complete harvest cost per acre. Model output is deterministic in that 

a given set of stand inputs will always produce the same results as long as the 

same forest vegitation simulator (FVS) stand projection (output) data are included. 

Note that, apart from BioSum, users sometimes choose to run FVS in a stochastic 

mode. Thus, OpCost users that repeat growth and yield predictions for the same 

stand multiple times in FVS prior to postprocessing with OpCost should expect 

that projected stand conditions may differ owing to inclusion of random numbers 

in underlying FVS code. This, in turn, may result in variability among associated 

OpCost treatment cost estimates.

Figure 1—Example of workflow within OpCost to estimate complete harvest cost for a stand harvested via ground-based mechanical 

whole-tree harvest system.

2. Select equipment: 

e.g., feller-buncher, skidder, 

processor, loader, chipper.

1. Stand input:

Read in 37 summary 

attributes, including harvest 

system (e.g., ground-based 

mechanical whole tree).

3. Apply rejection criteria to each 

candidate equation: 

For each equipment type, e.g., for a 

feller-buncher, for each equation, for 

which of the associated production 

equations do stand parameters not 

fall within the required range for the 

equation? Reject these.

4. Estimate machine time: 

For an equation and tree size class, compute feller-buncher time per tree, e.g.: 

24.796 + 0.31419*DBH2. Compute feller-buncher time per acre: Time per tree 

× TPASum time per acre for all size classes. Average delay-free time per acre 

across all applicable feller-buncher equation results.

5. Estimate machine cost: 

Multiply time per acre by a 

calculated machine rate that 

accounts for fixed and variable 

costs to obtain a cost per acre 

for that machine.

6. Repeat 4 and 5:

For all other machines 

used in a system (e.g., 

skidder, processor, 

loader, chipper).

7. Estimate complete harvest cost:

Sum per acre machine costs for all 

machines used in the system, e.g., 

for feller-buncher, skidder, processor, 

loader, and chipper. Return this total 

harvest cost to BioSum.
14

GENERAL TECHNICAL REPORT PNW-GTR-960

OpCost Stand-Alone Edition (SE)

OpCost SE is designed to operate without BioSum, and relies on an installed version of R 3.0 or greater (R Development Core Team 2016), with the R (language) 

Open Database Connectivity (RODBC) (Ripley and Lapsley 2016), Shiny (Chang 

et al. 2016), and ggplot2 (Wickham 2009) packages installed and loaded. There 

are three required script files that are available for download, and all three files 

should be saved to a single reference folder in the analyst’s computer. The files are 

called ui.r, server.r, and OpCostShiny.r. These files are available at the following 

URL: http://www.uidahoforestoperations.com/forest-operations-modeling.html. 

Each R script needs to be stored in the same directory on the user’s computer so 

the server code can locate the other files. After the files have been loaded, they are 

then available to open in the R environment using either the R console or a development environment such as RStudio. The user then runs the Shiny-based OpCost 

Graphical User Interface in R and the app will appear in the computer’s default 

web browser (tested with Internet Explorer) allowing for a simple, interactive and 

intuitive user interface. The inputs are the same as for OpCost BE and can be stored 

in either comma or space delimited text files. 

Once the application is open in the browser window, the user begins by navigating to and selecting the input file. A progress bar provides feedback as this data is 

loaded and processed, typically within seconds. When the progress bar achieves 

100 percent, the estimated costs per acre using the harvest system chosen by the 

user will appear in a table, with one row per stand ID and prescription year. Each 

table generated by OpCost may be selected and copied or downloaded as a text 

file using the table buttons. After the table appears, the user also has the option of 

selecting the “ideal” table, present cost of future treatments, and ideal present costs 

of future treatments. The present cost and ideal present cost tables contain the discounted costs of harvest prescribed for a future year. The analyst also has the ability 

to select a different state, with different assumed machine rates, or can designate 

custom machine rates using the third tab of the interface by selecting the designated 

check box, which activates the custom machine rate table (figs. 2, 3, and 4).
15

OpCost: an Open-Source System for Estimating Costs of Stand-Level Forest Operations

Figure 2—Initial “Tables” page in OpCost stand-alone.
16

GENERAL TECHNICAL REPORT PNW-GTR-960

Each loaded table actively updates as an analyst adjusts prescription year or 

machine rates, streamlining the process for experimenting with different scenarios. 

After creating these tables, the analyst can proceed to the figures page where each 

of the tables previously created can be displayed graphically. The figures page also 

provides a graphical cost comparison of the analyst-defined harvest system and the 

system estimated by OpCost as the lowest cost alternative. Note that figures will 

only be viewable after tables have been created. Figures can be copied and pasted 

to a separate document, or saved to a disk, using the menu options that appear when 

right-clicking on the image. 

To run another set of stand operations without starting a new session, save 

the output data, if desired, before loading a new dataset. OpCost SE does not 

automatically save outputs to files or maintain previous estimates in memory, so 

any information from the previous analysis will be overwritten when new data are 

loaded. All output tables and figures will also be lost if exiting the program without 

first saving these to disk.

Figure 3—The “Custom Cost” page in OpCost stand-alone edition.
17

OpCost: an Open-Source System for Estimating Costs of Stand-Level Forest Operations

Figure 4—The “Plots” page of the OpCost stand-alone edition.
18

GENERAL TECHNICAL REPORT PNW-GTR-960

Future Directions 

OpCost’s adaptable design enables it to handle a wide variety of systems and to 

accommodate new systems yet to be developed. Development is underway to enable 

production rates for new kinds of equipment to be implemented by entering the 

parameter and predictor variables associated with production and machine rates for 

new equipment and systems into a preformatted Excel table. The production function supplied should include output in total treatment time (delay-free productive 

machine hours), expressed on a per-acre basis. Users should also supply the key components of equipment machine rates: purchase price, utilization rate, etc (See, e.g., 

Brinker et al. 2002 for a description of machine rate calculations). The user-supplied 

production function parameters and machine rates will be incorporated into systemlevel logging cost predictions as described in, for example, Keefe et al. (2014). A 

sample Excel table and details on how this table will be accessed by OpCost will be 

documented in the release notes accompanying a future OpCost version.

One of the great benefits of providing OpCost in the open-source R framework is that it facilitates coordinated growth, development, and testing by a more 

expansive set of users than did earlier Excel, and Visual Basic for Applications or 

VBA-based versions of FRCS and related models. We anticipate making OpCost 

available as part of a package of R functions in the future in order to foster widespread distribution and use, especially for automated landscape-scale analyses. In 

its current incarnation, OpCost relies on stand-level FVS summaries of harvested 

trees. We envision refinements to the model that would base cost estimates on the 

full FVS tree list data, instead of summary metrics, potentially leading to greater 

accuracy and higher resolution simulation of operational logistics. 
19

OpCost: an Open-Source System for Estimating Costs of Stand-Level Forest Operations

Acknowledgments 

Terrie Jain and Nicholas Crookston from the U.S. Forest Service Rocky Mountain 

Research Station provided valuable assistance in the development of OpCost. We 

appreciate the useful suggestions contributed by reviewers Beth Dodson, Sandor 

Toth, and Kevin Boston that improved this report, and the extensive quality 

assurance testing by Portland State University Dynamic Ecosystems Lab’s Tina 

Mozelewski which ensured the validity of OpCost output. We also acknowledge 

and appreciate funding provided by the Joint Fire Sciences Program (Project # 13-1-

03-8) and the Pacific Northwest Resource Monitoring and Assessment Program. 

Additional funding support was provided by the University of Idaho College of 

Natural Resources McIntire-Stennis project IDAZ-MS-0103 and the Idaho Forest 

Utilization Research (FUR) program.

English Equivalents

When you know: Multiply by: To get:

Acres 0.4046 Hectares

Cubic feet 0.0283 Cubic meters
20

GENERAL TECHNICAL REPORT PNW-GTR-960

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