Forest Rocky Mountain Research Paper

Service Research Station RMRS-RP-107 June 2018

Characterizing Fire Behavior from Laboratory 

Burns of Multi-Aged, Mixed-Conifer Masticated 

Fuels in the Western United States

Heinsch, Faith Ann; Sikkink, Pamela G.; Smith, Helen Y.; and Retzlaff, Molly L.

United States Department of Agriculture
All Rocky Mountain Research Station publications are published by U.S. Forest Service employees and are in the 

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are formatted according to U.S. Department of Agriculture standards and research findings and formatting cannot 

be altered in reprints. Altering content or formatting, including the cover and title page, is strictly prohibited.

Heinsch, Faith Ann; Sikkink, Pamela G.; Smith, Helen Y.; and Retzlaff, Molly L. 2018. Characterizing fire behavior 

from laboratory burns of multi-aged, mixed-conifer masticated fuels in the western United States. RMRSRP-107. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 23 p.

Abstract

Mastication is the process of chipping or shredding components of the tree canopy or above-ground vegetation to 

reduce the canopy, alter fire spread rates, and reduce crown fire potential. Mastication as a fuel treatment, either 

alone or in combination with prescribed fire, has been the subject of much research. This research has shown that 

modeling expected fire behavior in these fuels is challenging. Masticated materials from different ecosystems are 

unique and may react differently to fire. Therefore, there are no standard guidelines to help managers understand 

the potential fire behavior in treated areas. In this study, we evaluated burn characteristics for several mixed-conifer 

masticated fuels that range from 0 to 10 years since treatment. Overall, there was great variety in observed fire 

behavior, and time since treatment did not affect fire behavior characteristics. The method used to masticate fuel 

has some impact on burning, with larger pieces of fuel tending to act as a barrier to fire spread. From our limited 

experimental burns, fire behavior in the laboratory was best represented by the SB1 (low load activity fuels) fuel 

model. These results may not reflect how variations in fuel bed moisture and in situ environment would alter fire 

behavior characteristics in masticated fuels in management units.

Keywords: mastication, fire behavior modeling, BehavePlus

Cover photo: Masticated fuel from the Santa Fe National Forest, New Mexico, is burned under controlled conditions 

to characterize fire behavior.
Authors

Faith Ann Heinsch is a Physical Scientist with the Fire, Fuel, and Smoke Science Program at the Fire Sciences 

Laboratory in Missoula, Montana, where she focuses on improving fire behavior and fire danger modeling systems, 

BehavePlus training, outreach, and climate change science. She received a B.S. degree from the University of 

Alabama, Master’s degree from Oregon State University, and Ph.D. from Texas A&M University.

Pamela G. Sikkink is a postdoctoral Research Biologist with the Forest and Woodland Ecosystems Program in 

Moscow, Idaho, and stationed at the Missoula Fire Sciences Laboratory in Missoula, Montana. Her most recent 

research includes (1) sampling and describing masticated fuels, (2) evaluating methods of describing fire and burn 

severity, (3) sampling and describing grassland fuels, and (4) testing sampling methods for long-term vegetation 

monitoring in Yellowstone National Park. She received a B.S. degree in biology and geology from Bemidji State 

University in Bemidji, Minnesota, Master’s degrees in geology and forestry from the University of Montana, and a 

Ph.D. in forestry from the University of Montana. 

Helen Y. Smith is an Ecologist with the Fire, Fuel, and Smoke Science Program located at the Rocky Mountain 

Research Station’s Fire Sciences Laboratory in Missoula, Montana. She completed an A.A.S. degree at Flathead 

Valley Community College in Kalispell, Montana, and a B.S. degree in Wildlife Biology and M.S. degree in Resource 

Conservation, both from the University of Montana.

Molly L. Retzlaff is a north Idaho native who has worked at the Fire Sciences Laboratory in Missoula since 2013 as a 

forestry technician. She has a B.S. degree in Biology and is currently finishing her Master’s degree in Forestry at the 

University of Montana. Her primary interests are restoration and fire ecology. 

Acknowledgements

We would like to thank the Joint Fire Science Program for funding a portion of this research through grant JFSP13-

1-05-8. We would like to thank Cyle Wold and Bret Butler of the Fire, Fuel, and Smoke Science Program, for their 

technical expertise and advice during this project.

Disclaimer

The use of trade or firm names in this publication is for reader information and does not imply endorsement by the 

U.S. Department of Agriculture of any product or service. 

Photographs and images in this report are publicly owned, having 

been produced by Forest Service employees on official time.
iv USDA Forest Service RMRS-RP-107. 2018.
USDA Forest Service RMRS-RP-107. 2018. 1

Introduction

Mastication is the process of chipping or shredding components of the tree canopy 

or above-ground vegetation to reduce and protect against fire incursion into areas 

such as the wildland-urban interface (WUI). In recent years, mastication has gained 

popularity as a fuel treatment because it (1) effectively redistributes fuel from the 

tree canopy to the ground to reduce or eliminate the danger of fire in tree and shrub 

canopies (Battaglia et al. 2010); (2) may reduce detrimental effects on human health 

from smoke that occurs when prescribed burning is used to manipulate the fuel load 

(Naeher et al. 2006; Weinhold 2011); and (3) affects the probability of fire occurrence across landscapes by changing spread rates and reducing crowning behavior 

(Cochrane et al. 2012).

Mastication has been used either singly as a fuel treatment (Battaglia et al. 2010; 

Perchemlides et al. 2008; Wolk and Rocca 2009) or in conjunction with prescribed 

fire to reduce fuel (Bradley et al. 2006; Brewer et al. 2013; Keeley et al. 2013; Knapp 

et al. 2011; Kreye and Kobziar 2015; McIver et al. 2013; Reiner et al. 2009; Schwilk 

et al. 2009; Shakespear 2014; Southworth et al. 2011; Stottlemeyer et al. 2015). Most 

land management agencies leave masticated materials on the ground because it can 

provide other benefits such as improved nutrient cycling or wildlife habitat (Bradley 

et al. 2006). In addition, it can be difficult to burn a unit because of restrictions (e.g., 

smoke limits), limited windows for burning, and cost. However, prescribed fire, 

either alone or in conjunction with mastication, reduces surface fuel load in treatment 

areas making it a desirable treatment option (Brennan and Keeley 2015; McDaniel 

2013).

The use of mastication alone as a fuel treatment for local fire-adapted vegetation 

communities has been examined by a number of researchers within the past few years 

(Kobziar et al. 2013; McIver et al. 2013; Schwilk et al. 2009). The arguments against 

a “mastication only” treatment range from vegetation losing its adaptation to fire in 

the absence of burning to prescribed fire treatments being necessary to produce the 

chemical or natural ecological effects required by a plant community (McIver et al. 

2013). In some areas of the U.S., such as the southeast, mastication treatments must 

be revisited often because the shrub layer grows so rapidly after the treatment that 

it is rendered ineffective after as little as 2 years. Regrowth does not typically occur 

as quickly when mastication is followed by prescribed burning (Kobziar et al. 2013; 

Kreye et al. 2013; Kreye and Kobziar 2015).

Combining mastication treatments with prescribed fires can be problematic for 

managers for several reasons. Scheduling prescribed fires can be difficult once fuels 

have been masticated because of the narrow burn window available for burning such 

a fuel bed. Burning masticated sites increases in complexity when the treatments 

are within the WUI as there is potential for property damage should the fire escape 

(Bass et al. 2012; McDaniel 2013). It may be difficult to get approval to burn a 

management unit because of air quality regulations, leaving mastication alone as the 

only viable treatment option. Depending on weather and fuel conditions, prescribed 

fires may burn longer, and the depth of the flame zone may be greater. The resulting 

increase in consumption may lead to a higher total energy release, adversely affecting 
2 USDA Forest Service RMRS-RP-107. 2018.

roots and underground vegetative structures, and thereby affecting regeneration 

(Agee and Skinner 2005; Perchemlides et al. 2008; Stottlemeyer et al. 2015).

Because masticated materials from different ecosystems may burn differently, 

there are no standard guidelines to help managers make decisions on whether or not 

to burn these materials. Fire behavior modeling is used to estimate the range of fire 

behavior that may occur in a treatment area. Anderson (1982) and Scott and Burgan 

(2005) developed standard fuel models to help managers estimate fire behavior in 

typical forest conditions, including activity fuels (e.g., slash). Masticated fuels form 

novel fuel beds, and standard fuel models may not represent the fire behavior seen 

in these fuel beds (Dickinson et al. 2013; Glitzenstein et al. 2006; Knapp et al. 2008, 

2011; Kreye et al. 2012; Schiks and Wooten 2015) because of the changes to the 1-hr 

and 10-hr time lag fuels (e.g., fuel load and surface area-to-volume ratio) that are 

needed for modeling fire behavior (Rothermel 1972).

Several studies have attempted to use the fire behavior models described by 

Anderson (1982) and Scott and Burgan (2005) to predict fire behavior in masticated 

fuels. In California and southwest Oregon, Knapp et al. (2008) and Busse et al. 

(2005) tested burns in chaparral (Arctostaphylos and Ceanothus spp.). They concluded that, of the standard fuel models, Scott and Burgan’s (2005) fuel models SB1 (low 

load activity fuels) and SB2 (moderate load activity fuels or low load blowdown) best 

predicted fire behavior for masticated materials in this vegetation type. In addition, 

masticated fuel beds from ponderosa pine (Pinus ponderosa), Kellogg oak (Quercus 

kelloggii), and chaparral (Ceanothus and Arctostaphylos spp.) were burned and the 

resulting fire behavior was compared to modeled results using a number of standard 

and custom fuel models, producing varying results (Knapp et al. 2011).

The purpose of this study was to characterize fire behavior for several types of 

mixed-conifer masticated fuels from four States across the western U.S. using materials that ranged in age from 0 to 10 years since treatment. Material from the sites was 

burned in a series of laboratory experiments to answer the following primary research 

questions.

1. What are the burn characteristics of the fuel beds produced by four different types 

of masticators?

2. How well do the standard or published custom fuel models describe the 

characteristics of these masticated burns?

Methods

Site Description

Masticated materials were collected from 15 mixed-conifer forests in Idaho, 

Colorado, New Mexico, and South Dakota (fig. 1; table 1). The masticated materials 

varied in age from 0 to 10 years since initial treatment. The treated areas were composed predominately of ponderosa pine (Pinus ponderosa), Douglas-fir (Pseudotsuga 

menziesii), western hemlock (Tsuga heterophylla), western white pine (Pinus monticola), and western red cedar (Thuja plicata). At each site, 20 samples of masticated 
USDA Forest Service RMRS-RP-107. 2018. 3

materials were collected for analysis using the destructive plot method of Hood and 

Wu (2006). Ten of these samples were used in the experimental burns. These samples 

were first separated in the laboratory into the standard 1-hr, 10-hr, and 100-hr size 

classes for particle characterization studies (Keane et al. 2018) and then recombined 

as described below for use in the experimental burns. More information on the experimental protocol can be found in Keane et al. (2018).

Fuel Bed Creation

Each fuel bed (fig. 2; table 2) was created on a burn platform consisting of an 

aluminum frame with wire mesh and removable heat-resistant 0.5 inch (1.27 cm) 

Thermal Ceramics Kaowool M Board (fig. 3). Experimental fuel beds were created 

using the relative proportions of 1-hr, 10-hr, and 100-hr woody fuels; wood chips 

(wood < 3mm thick); wood ribbons; litter; 1-hr and 10-hr bark; 100-hr bark; and bark 

ribbons from each masticated site (Keane et al. 2018). Because duff load does not 

contribute substantially to fire behavior at the flaming front, no duff from the sample 

locations was used in the experimental burn beds. The masticated material from the 

10 sample plots was combined, and three fuel beds were created as representations of 

each study site. The amount of material selected in each fuel category was typically 

based on the mean fuel load for each fuel category from the field site. Occasionally, 

this mean seemed unreasonably high because of the variability in fuel deposition 

resulting from the mastication process itself, which resulted in uneven distribution 

of fuel across the site. In these rare cases, either the 50th or 90th percentile fuel load 

was used to more accurately represent the fuel load across the entire treatment site. 

Additionally, the fuel moisture content decreased during long-term storage at the lab. 

When weighing out fuels to create the burn beds, we adjusted fuel loading by size 

class accordingly.

Figure 1—Masticated fuels were collected at 15 different 

sites in Idaho (6 sites), Colorado (3 sites), New Mexico 

(4 sites), and South Dakota (2 sites). See table 1 for 

site locations. 
4 USDA Forest Service RMRS-RP-107. 2018.

Table 1—Location and treatment information for each of the masticated sites. Sites are sorted by age since time of treatment 

oldest to youngest.

Land management 

agency Site code

Moisture 

regime Dominant species

Mastication 

method

Treatment 

year

Time since 

mastication 

(yrs)

Boise Experimental 

Forest, Idaho Amber Dry Pinus ponderosa Rotating head 2004 10

Manitou Experimental 

Forest, Colorado MEFChip Dry

Pinus ponderosa, 

Pseudotsuga menziesii, 

Symphoricarpos sp., 

Juniperus sp.

Chipper 2004 10

Deception Creek 

Experimental Forest, 

northern Idaho

DC1 Moist

Tsuga heterophylla, 

Pinus monticola, Larix 

occidentalis, Clintonia 

uniflora, Linnaea borealis

Rotating head 2004 9

Manitou Experimental 

Forest, Colorado MEFWS Dry

Pinus ponderosa, 

Pseudotsuga menziesii, 

Arctostaphylos uva-ursi

Rotating head 2005 9

Santa Fe National 

Forest, New Mexico LG Dry Pinus ponderosa, 

bunchgrass, Fragaria sp.

Horizontal 

drum head 2006 8

Priest River 

Experimental Forest, 

northern Idaho

PRCC1 Moist

Pinus monticola, Tsuga 

heterophylla, Larix 

occidentalis, Clintonia 

uniflora

Rotating head 2007 6

Valles Caldera National 

Preserve, New Mexico VC1 Dry Pinus ponderosa, Carex 

sp., bunchgrass

Horizontal 

drum head 2007-2008 6

Boise Experimental 

Forest, Idaho AmberNew Dry

Pinus ponderosa, 

Pseudotsuga menziesii, 

Purshia tridentata, 

Symphoricarpos sp.

Rotating head 2010 4

San Juan National 

Forest, Colorado Skelton Dry

Pinus ponderosa, 

Pseudotsuga menziesii, 

Artemisia tridentata

Rotating head 2010-2011 3

Black Hills Experimental 

Forest, South Dakota BHMix Dry Pinus ponderosa, 

Arctostaphylos uva-ursi

Mower and 

whole tree 

yarding

2012 2

Black Hills Experimental 

Forest, South Dakota BHMow Dry

Pinus ponderosa, 

Arctostaphylos uva-ursi, 

Symphoricarpos sp.

Mower 2012 2

Santa Fe National 

Forest, New Mexico PAL Dry Pinus ponderosa, Carex 

sp.

Horizontal 

drum head 2011-2012 2

Priest River 

Experimental Forest, 

northern Idaho

PR3 Moist

Thuga plicata, Tsuga 

heterophylla, Pinus 

monticola, Larix 

occidentalis

Horizontal 

drum head 2011 2

Valles Caldera National 

Preserve, New Mexico VC2 Dry Pinus ponderosa, 

bunchgrass, Ribes sp.

Horizontal 

drum head 2012 2

University of Idaho 

Experimental Forest, 

northern Idaho

UI Moist Pinus ponderosa, 

Physocarpus malvaceus

Horizontal 

head, boom 

mounted

2014 0
USDA Forest Service RMRS-RP-107. 2018. 5

Fuel beds were conditioned in an environmental chamber at 95 oF (35 oC) and 3 

percent humidity for at least 36 hours prior to burning to reduce moisture content as 

much as possible in all fuel categories. At the time of ignition, samples of 1-hr, 10-hr, 

and 100-hr fuels were collected and placed in a drying oven set to 212 oF (100 oC) 

for 72 hours. These fuel moisture measurements were used to determine the moisture 

Figure 2—This sample fuel bed 

construction shows the masticated 

fuel prior to adding the excelsior 

and pine needles at the bottom of 

the bed for ignition.

Figure 3—The burn platform consisted of a long bed 

with three graduated range poles on the left and 

a video camera on the right. Three halogen lights 

(yellow circles) simulated solar heating. The actual 

fuel bed was constructed within the dotted section 

of the platform. Two scales (dark grey rectangles) 

were used to collect data on the total mass lost as 

the bed burned. The slope of the bed was adjusted 

by raising one end of the fuel bed to the correct 

slope.
6 USDA Forest Service RMRS-RP-107. 2018. Table 2—The table below shows preburn conditions for moisture levels, slope of bed, fuel load, and bulk density for each site. Bulk density is computed for the total beds at each site then averaged for the number of microplots at each site. Fuel bed depth is for the masticated material only. SE = standard error. The sites are arranged from oldest to youngest in years since time of treatment. Mastication age Burn name Slope Fuel load Total fuel load Total oven-dry fuel load Bulk density per site (Mean ± SE) Fuel bed depth per site (mean ± SE) 1-hra 10-hrb 100-hrc yrs Percent g m-2 g m-2 g m-2 g m-2 g m-2 kg m-3 cm 10 Amber 12 1356.78 851.28 367.77 2575.82 2506.72 52.96 ± 6.98 1.7 ± 0.35 10 Amber 21 1456.41 742.86 430.77 2630.04 2524.72 10 Amber 21 1463.00 755.31 375.09 2593.41 2480.74 10 MEFChipd 21 1162.64 800.73 82.05 2045.42 1949.22 77.30 ± 6.00 1.8 ± 0.38 10 MEFChip 21 1165.57 800.73 83.52 2049.82 1951.98 10 MEFChip 21 1164.10 800.73 82.05 2046.89 1983.04 10 MEFChip 21 885.71 610.26 63.00 1558.97 1489.23 9 DC1 12 1200.73 1584.62 2463.00 5249.82 5029.62 64.86 ± 12.80 4.4 ± 0.80 9 DC1 21 1188.28 1591.21 2470.33 5245.42 4925.02 9 DC1 21 1195.60 1583.88 2465.93 5248.35 4930.74 9 MEFWS 12 980.95 229.30 297.44 1507.69 1447.15 90.95 ± 12.70 1.4 ± 0.46 9 MEFWS 21 975.09 233.70 300.37 1509.16 1451.32 9 MEFWS 21 978.02 230.77 290.11 1498.90 1449.22 8 LG 12 753.48 1741.76 435.16 2926.01 2718.83 102.60 ± 13.16 2.0 ± 0.40 8 LG 21 749.08 1740.29 436.63 2927.47 2729.64 8 LG 21 747.62 1741.76 438.10 2930.40 2722.95 6 PRCC1 12 724.54 1424.91 823.44 2965.57 2761.43 72.49 ± 24.12 7.4 ± 2.08 6 PRCC1 21 724.54 1423.44 817.58 2964.10 2751.89 6 PRCC1 21 724.54 1423.44 816.12 2972.89 2755.41 6 VC1 12 1973.63 2338.46 1522.34 5834.43 5368.44 93.85 ± 6.93 3.3 ± 0.57 6 VC1 21 1973.63 2338.46 1522.34 5834.43 5475.41 6 VC1 21 1973.63 2338.46 1525.27 5837.36 5485.51
USDA Forest Service RMRS-RP-107. 2018.

7

Table 2—Continued. 

Mastication 

age Burn name Slope

Fuel load

Total fuel load

Total oven-dry 

fuel load

Bulk density per 

site (Mean ± SE)

Fuel bed depth 

per site 

(mean ± SE) 1-hra 10-hrb 100-hrc

yrs Percent g m-2 g m-2 g m-2 g m-2 g m-2 kg m-3 cm

4 AmberNew 12 2099.63 1421.25 1164.84 4783.88 4514.13 52.30 ± 5.49 3.1 ± 0.664 AmberNew 21 2184.62 1444.69 1154.58 5194.14 4934.04

4 AmberNew 21 2348.72 1454.95 1390.48 4685.71 4626.993 Skelton 12 1650.55 913.55 216.85 2773.26 2690.64 79.39 ± 13.95 2.6 ± 0.84

3 Skelton 21 1641.39 912.09 219.78 2766.96 2679.25

3 Skelton 21 1643.88 904.76 218.32 2780.95 2664.422 PR3 12 1050.55 962.64 512.82 2527.47 2372.57 70.53 ± 12.81 3.6 ± 1.50

2 PR3 21 1053.48 968.50 505.49 2534.80 2368.60

2 PR3 21 1057.88 964.10 512.82 2526.01 2350.852 BHMixd 21 2027.84 369.23 104.03 2501.10 2349.12 75.34 ± 7.32 0.7 ± 0.17

2 BHMix 21 2027.84 367.77 104.03 2499.63 2279.79

2 BHMix 21 2030.04 369.96 104.03 2504.03 2446.132 BHMow 12 1144.32 246.15 70.33 1460.81 1374.84 65.70 ± 8.49 1.0 ± 0.31

2 BHMow 21 1148.72 246.15 76.19 1471.06 1365.59

2 BHMow 21 1147.25 246.15 74.73 1468.13 1424.842 BHMow 21 1147.25 246.15 67.40 1460.81 1390.392 PAL 12 1570.70 1986.81 316.48 3871.06 3610.44 101.55 ± 10.72 2.7 ± 0.93

2 PAL 21 1570.70 1986.81 313.55 3872.53 3610.12

2 PAL 21 1570.70 1986.81 315.02 3873.99 3582.632 VC2 12 1518.68 780.22 1049.08 3347.99 3164.76 90.40 ± 10.26 2.9 ± 0.90

2 VC2 21 1521.61 781.68 1030.04 3333.33 3184.59

2 VC2 21 1518.68 783.15 1030.04 3331.87 3145.140 UIe 21 1410.30 973.63 610.99 2994.87 2820.99 N/A N/A

a -hr fuels include 1h woody particles, wood chip litter, wood ribbons, 25% of the 1-10-hr bark category, and the fresh litter (dried pine needles, grass, dried leaves and roots, pine cones, and more). b 10-hr fuels include 10h woody particles and 75% of the 1-10-hr bark category.c 100-hr fuels include 100h woody particles and the 100-hr bark.d All of the beds at this site were burned at the higher slope in an attempt to get the fire to spread.e Only one fuel bed was built from materials received from a University of Idaho mastication study site. More information on this site can be found in the paper by Lyon et al. (submitted).
8 USDA Forest Service RMRS-RP-107. 2018.

content of each fuel category, as well as the oven-dry weight for the fuel load needed 

in fire behavior modeling (table 2).

Experimental Burns

Experiments were conducted at the U.S. Forest Service’s combustion facility at 

the Missoula, Montana, Fire Sciences Laboratory. The combustion facility is a large, 

environmentally controlled chamber. Additional information on the combustion facility may be found in Christian et al. (2004). During the experiments, air temperature 

in the burn chamber was approximately 69.8 °F (21 °C). Relative humidity was not 

controlled and approximated that of the outside ambient air. The burn chamber does 

not include the ability to adjust wind speed, and the burns had the potential to be too 

intense to burn in the wind tunnel. Therefore, wind speed was not explicitly factored 

into the experiments.

The fuel bed was inclined at either 11.75 (low) or 21.25 (moderate) percent slope. 

Graduated range poles were placed at 0.5, 1.0, and 2.0 ft (0.15, 0.30, 0.61 m) along 

the fuel bed. Each burn was filmed using a GoPro Hero 3+ Silver Edition HD video 

camera. Cloth screens were set up on each side of the burn platform to block air flow 

during the experimental burn. The screens also aided in blocking any light from interfering with the video.

Two halogen work lights on either side of the video camera tripod (fig. 4) were 

focused on the material in an effort to preheat the air above the fuel and facilitate 

burning to simulate solar heating. A third light was placed on the opposite side of 

Figure 4—The actual burn bed configuration 

consisted of the burn platform, graduated 

range bars, halogen lights, and a video 

camera. Notice the excelsior and pine 

needles at the bottom of the photo that were 

used to ignite the fuel bed.
USDA Forest Service RMRS-RP-107. 2018. 9

the fuel bed over a layer of shredded aspen wood (excelsior) used for igniting the 

experimental burn beds. This third light was turned off at the time of ignition since it 

interfered with the video recording.

All of the fires were ignited from a line of excelsior and pine needles (5.9 inches; 

15 cm wide) at the beginning of the fuel bed (fig. 3). The material was ignited with 

a single pass at the bottom edge of the excelsior mix using a handheld butane torch. 

Rate of spread was calculated as the amount of time it took the fire to travel the 1-ft 

distance between the 2nd and 3rd graduated poles in the measurement zone (fig. 3). 

Flame height was recorded using the height measures on the graduated poles. Both 

minimum and maximum flame heights were recorded for each burn. Consistency of 

the flaming front was first measured visually and later verified using the video from 

each burn.

We tested for statistical relationships between time since mastication and fire 

behavior using the Kendall tau (Kτ) statistical method, which is a nonparametric 

rank-correlation measure that estimates and assesses the strength of the relationship 

between fire behavior and time since mastication. With this metric we do not assume 

a cause-and-effect; we only test for a relationship between time since mastication and 

fire behavior. All experimental beds were used in the analyses.

Fire Behavior Modeling

Surface fire behavior was estimated using BehavePlus version 5.0.5. Predicted 

surface fire rate of spread was obtained using Rothermel’s fire spread model (Albini 

1976; Rothermel 1972), while flame length was estimated using Byram’s (1959) 

equation. Moisture values were calculated from samples collected and oven-dried at 

the start of each burn; values are summarized in table 3. Surface fire behavior rate of 

spread and flame length from our experimental burns were compared to three standard and five custom fuel models (table 4) (Anderson 1982; Glitzenstein et al. 2006; 

Knapp et al. 2011; Scott and Burgan 2005). The three standard fuel models were 11 

(light logging slash), SB1 (low load activity fuel), and SB2 (moderate load activity or 

low load blowdown) (Anderson 1982; Scott and Burgan 2005). These three fuel models were the most representative of the 53 standard fuel models given the fuel loads 

measured for the experimental sites. Custom fuel models for masticated fuel have 

been developed by Knapp et al. (2011) for California chaparral and by Glitzenstein 

et al. (2006) for pine forests in the southern U.S. These custom fuel models were also 

compared to the experimental burns since they were designed specifically for masticated fuel.

Table 3—Fuel moisture values for 1-hr, 10-hr, and 100-hr fuel 

classes were measured during the experimental burns.

Fuel moisture Minimum Average Maximum

---------------------percent---------------------

1-hr fuel 3 5 8

10-hr fuel 2 5 7

100-hr fuel 2 5 8

Live woody fuel 150 150 150
10 USDA Forest Service RMRS-RP-107. 2018.

We compared the results of the modeling effort to observed values from the experimental burns. While we recorded measured values of flame height and modeled 

estimates of flame length, the two are quite similar in this instance. Since the experiment did not include wind and the slopes were relatively low, the resulting flames 

were nearly vertical, so that flame height could be used as a proxy of flame length in 

these experiments.

Results

Experimental Burns

Fires That Did Not Burn Completely

In 16 of the 45 experimental fires, the fuel bed did not burn completely (fig. 5), 

and we were unable to calculate a rate of spread in the measurement zone (fig. 3). 

These beds exhibited smoldering fire behavior, which cannot be calculated using 

Rothermel’s (1972) fire spread model. All 10 of the fuel beds containing fuels masticated using a mower or chipper exhibited smoldering fire behavior. Most failed to 

burn the entire fuel bed as there was very little fuel (fuel bed depth) to facilitate fire 

spread.

Table 4—Standard and published custom fuel model characteristics were used in fire behavior simulation. A dash represents 

a value that was not needed in a given fuel model.

Standard fuel models Knapp et al. (2011)

Glitzenstein et al. 

(2006)

11 SB1 SB2

Low 

loada

Moderate 

loada

High 

loada

Transect, 

shallowb

Transect, 

deepb

1-hr fuel load Mg ha-1 0 3.36 10.09 7.8 12.7 17.6 8.31 8.31

10-hr fuel load Mg ha-1 0 6.73 9.53 5.5 13.3 29.4 24.1 24.1 

100-hr fuel load Mg ha-1 0 24.66 8.97 0.7 2.8 13.1 35.15 35.15

Live herbaceous fuel 

load

Mg ha-1 0 0 0 0 0 0 0 0

Live woody fuel load Mg ha-1 0 0 0 0 0 0 0.95 0.95

1-hr surface area-tovolume ratiom2 m-3 4921 6562 6562 2461 2461 2461 6562 6562

Live woody surface 

area-to-volume ratio

m2 m-3 — — — — — — 5249 5249

Fuel bed depth m 0.30 0.30 0.30 0.11 0.16 0.27 0.05 0.30

Dead fuel moisture of 

extinction

percent 15 25 25 25 25 25 25 25

Dead fuel heat 

content

kJ kg-1 18622 18622 18622 18622 18622 18622 18622 18622

Live fuel heat content kJ kg-1 — — — — — — 18622 18622

a Knapp et al. (2011) developed custom fuel models for sites in California that were initialized with standard model SB2 for three levels of 

loading.

b Glitzenstein et al. (2006) developed a custom fuel model for sites in South Carolina that were initialized with standard fuel model SB3 and 

used two different fuel bed depths.
USDA Forest Service RMRS-RP-107. 2018. 11

Figure 5—This fuel bed from the Black Hills Experimental Forest, South Dakota (BHMix) showing fire behavior during the 

flaming (a) and smoldering (b) phases, is typical of burns that did not completely burn the fuel bed, with the fire stopping 

about halfway. The gray ash at the beginning of the burn is the excelsior used to ignite the fuel bed. Arrows indicate 

upslope.

Figure 6—This fuel bed from the Priest River Experimental Forest, in northern Idaho (PRCC) during (a) and after (b) the burn, 

is typical of fires that exhibited edge effects. In this example, the flaming front at the center of the bed is 1 ft (0.3 m) 

ahead of that at the edges. The fire then burned to the end of the fuel bed through a combination of flanking and head 

fires. Arrows indicate upslope.

Fires That Did Not Burn Homogeneously

An additional 11 burns either failed to burn the entire fuel bed or exhibited strong 

effects from interactions with the sides of the fuel bed. Two different behaviors were 

observed. In some cases, the center of the bed burned more quickly than the edges 

(fig. 6). In other cases, one side burned to the end of the bed before the other (fig. 7). 

The remainder of the fuel bed then burned primarily through flanking fire (fig. 7). 

In most of these experiments, we were unable to obtain reliable estimates of either 

flanking fire rate of spread or head fire rate of spread in the measurement zone.

Fires That Burned Completely and Homogeneously

The remaining 18 burns demonstrated fairly steady progression with sustained 

burning rates throughout the experiment. We were, therefore, able to calculate a rate 

of spread and flame length across the measurement zone for further analysis. Fire 

behavior was minimal in all of these burns, and there was no measurable difference 

a) b)

a) b)
12 USDA Forest Service RMRS-RP-107. 2018.

in fire behavior among sites (table 5, fig. 8). Rate of spread at all sites was less than 

1.0 ft/min (0.3 m/min), with an average of 0.3 ± 0.2 ft/min (0.1 ± 0.06 m/min). Flame 

heights ranged from 0.25 ft to 3.0 ft (0.08 m to 0.91 m). Average minimum flame 

height was 0.5 ± 0.2 ft (0.2 ± 0.06 m), while average maximum flame height was 1.4 

± 0.8 ft (0.4 ± 0.2 m). Behind the flaming front, flames typically died out in less than 

30 minutes (table 5), while smoldering may have continued for significantly longer 

(data not shown). The two fuel beds with the longest flame duration across the fuel 

bed (VC1 and PAL) contained a mix of fuels from all fuel load categories.

Statistical Relationships

We tested all of the experimental burns to determine if there was a relationship 

between the age of the fuel and fire behavior (table 5; see also Sikkink et al. 2017). 

Statistical analysis reveals that there was no relationship between time since mastication and any of the following parameters: fuel load (Kτ = 0.009; P = 0.94), maximum 

flame length (Kτ = 0.014; P = 0.90), or surface rate of spread (Kτ = 0.020; P = 0.93). 

Figure 7—This fuel bed from the Santa Fe National Forest, New Mexico (PAL), showing fire behavior during the flaming (a) 

and smoldering (b) phases, is typical of burns that exhibited flanking fire. This flanking fire was responsible for burning the 

entire fuel bed. Arrows indicate upslope.

Figure 8—These observed rates of spread and flame lengths for the 18 burns demonstrate that overall rates of spread (a) were 

low, and flame lengths (b) were fairly small.

a) b)

a) b)
USDA Forest Service RMRS-RP-107. 2018. 13 Table 5—Observed fire behavior is shown for the 18 burns that demonstrated steady and sustained burning. Site codes are listed in table 1. Time since mastication Burn name Slope Rate of spread Min flame height Max flame height Flaming duration years percent ft/min km/hr ft m ft m min:sec 10 Amber 12 0.30 0.005 0.50 0.15 0.75 0.23 21:33 21 0.31 0.006 0.75 0.23 1.50 0.46 23:00 21 0.24 0.004 0.33 0.10 1.25 0.38 24:10 9 DC1 12 0.90 0.016 0.33 0.10 2.00 0.61 26:16 21 0.18 0.003 0.67 0.20 3.00 0.91 26:53 21 0.17 0.003 0.33 0.10 3.00 0.91 26:09 9 MEFWS 12 0.21 0.004 0.25 0.08 0.33 0.10 16:22 21 0.41 0.008 0.33 0.10 0.67 0.20 26:10 21 0.41 0.008 0.50 0.15 0.67 0.20 19:01 8 LG 21 0.50 0.009 0.67 0.20 0.75 0.23 19:20 6 PRCC1 21 0.13 0.002 0.50 0.15 1.50 0.46 27:37 6 VC1 21 0.30 0.005 0.25 0.08 0.75 0.23 39:01 3 Skelton 21 0.43 0.008 0.50 0.15 1.25 0.38 18:25 2 PR3 12 0.23 0.004 0.50 0.15 1.00 0.31 18:06 21 0.28 0.005 0.50 0.15 2.00 0.61 26:53 21 0.19 0.003 0.50 0.15 2.00 0.61 19:32 2 PAL 21 0.17 0.003 0.50 0.15 0.67 0.20 40:53 0 UI 21 0.56 0.010 1.00 0.31 1.50 0.46 27:10
14 USDA Forest Service RMRS-RP-107. 2018.

There was a slight relationship between time since mastication and minimum flame 

length (Kτ = –0.250; P = 0.03).

Fire Behavior Modeling

Rothermel’s (1972) fire spread model requires a steady-state fire burning through 

relatively homogeneous fuels. Therefore, 18 of the 45 experimental burns were used 

in our fire behavior modeling effort described below. The remaining 27 experimental 

burns were excluded from the fire behavior modeling because they failed to meet 

these criteria.

All of the fuel models described previously were compared with results from the 

18 fuel beds that could be modeled for fire behavior in this study (fig. 9). Modeled 

fire behavior was minimal, with rates of spread less than 2 ft/min (0.6 m/min) and 

flame lengths less than 4 ft (1.2 m). Most estimates of flame length were less than 

2.5 ft. (0.8 m). In general, all of the fuel models overestimated both observed rate of 

spread and minimum flame length, with SB2 generating the highest estimates. The 

fuel models more accurately modeled observed maximum flame length values.

Within the masticated fuels that could be modeled for fire behavior, several important aspects of burning were observed, none of which can be predicted using current 

fire behavior models.

1. Once ignited, fuel beds from sites with larger (100-hr and larger) fuel typically 

burned longer (fig. 10).

2. The smaller (1-hr and 10-hr) fuel facilitated fire spread, while the large fuel 

continued burning after the flaming front had passed. Once the flames died out, 

many beds continued to smolder for at least an hour.

3. Rate of spread and flame height tended to increase as the bed burned, which is an 

indication that the fires did not reach steady-state conditions.

4. Mastication method appeared to have some impact on burning. As stated earlier, 

Figure 9—Rate of spread (a) and flame length (b) were modeled using three standard and five custom fuel models. The five 

custom fuel models were originally developed for fuel beds in CA and SC (see table 4). In all cases, the modeled fire 

behavior overestimated actual fire behavior. ROS = surface rate of spread

a) b)
USDA Forest Service RMRS-RP-107. 2018. 15

none of the fuel beds from sites treated with a chipper or mower burned the 

complete length of the fuel bed, and we were unable to estimate the fire behavior.

5. Often, larger pieces of fuel acted as a barrier to fire spread. Flames burned around 

or under the larger fuel. These larger fuels tended to light after the flaming front 

had passed, a result of residual burning and heat generation during the smoldering 

phase.

Discussion

We did not find a relationship between time since mastication and fire behavior. 

All of the sites were treated no more than 10 years prior to sampling, and most of the 

wood was quite sound. Decomposition was not readily apparent and would likely not 

have been great enough to affect fire behavior. Sites with masticated fuel older than 

10 years or in which decomposition was readily apparent (e.g., “punky” or soft, rotted wood) would be expected to show different fire behavior than the ones included 

in this study.

Mastication method appeared to have some impact on burning. As stated earlier, 

none of the fuel beds from sites treated with a chipper or mower burned the complete 

length of the fuel bed, and we were unable to estimate the fire behavior. We hypothesize that the fuel beds that exhibited smoldering fire behavior were too shallow and 

dense to provide the necessary air flow to ignite the fuel without the assistance of 

wind. These results are similar to those of Glitzenstein et al. (2006), who measured 

slow rates of spread and identified large patches of unburned fuel in their prescribed 

burn experiments in field conditions that were composed of shallow fuel beds. There 

did not appear to be a clear relationship between mastication type and fire behavior 

for the sites where a rotating head or horizontal drum head was used. Some of the 

fuel beds with these mastication methods were able to be used in analysis; others 

were not.

Figure 10—The fuel bed on the left (a) is taken from the Manitou Experimental Forest, Colorado (MEFWS). The fuel bed on 

the right (b) is taken from the Deception Creek Experimental Forest, northern Idaho (DC1). Mastication was done using a 

rotating head in both areas. Arrows indicate upslope. These two different fuel beds burned very differently, even though 

the same masticator head was used.

a) b)
16 USDA Forest Service RMRS-RP-107. 2018.

There are two possible reasons why fuel beds did not burn homogeneously. First, 

our fuel beds were relatively narrow and the edges of the fuel bed had an effect on 

the fuel as mentioned previously. Second, the halogen lights affected fire behavior 

in unpredictable ways. In many cases, the side of the fuel bed with the halogen light 

burned faster than the side on which the halogen light was removed. There were also 

experimental burns in which the side without the halogen light burned faster. These 

edge effects were not consistent among sites, or even within a site, making it difficult 

to determine exactly what caused the fire behavior observed in these 11 burns.

Given that all of our burns occurred at low moisture levels, we were unable to 

distinguish between fire behavior at moist and dry sites. However, we were unable to 

get any of the fuel beds to burn at higher moisture contents, such as those typically 

found in areas like the Priest River Experimental Forest in northern Idaho (data not 

shown). Our sites had very little litter to carry the fire or provide the energy necessary 

to ignite the larger fuels. This limits the utility of our experiments in determining the 

importance of such factors on observed fire behavior.

With the exception of fuel model SB2, the predicted values of rate of spread 

and flame length from the fuel models included in this report are reasonably close 

to observed values of fire behavior in this study (fig. 9). Observed rates of spread 

in the fairly dry conditions were minimal—at less than 1.0 ft/min (0.3 m/min) and 

flame lengths less than 3.0 ft (0.9 m). These rates are consistent with those found in 

other studies of fire behavior in masticated fuel (Glitzenstein et al. 2006; Knapp et 

al. 2011). The fuel model with the shallow fuel bed as described in Glitzenstein et 

al. (2006; fig. 9, transect, shallow) modeled a zero rate of spread and flame length 

for our fuel beds. Glitzenstein et al. (2006) found similar results when developing 

the fuel model. They hypothesized that the depth of the fuel bed included in the 

fuel model was too shallow for the Rothermel fire spread model to calculate a rate 

of spread or flame length. We have included it in figure 9 for completeness, but 

it has been removed from further discussion. These results, however, support our 

hypothesis for patchy burning in the shallow fuel beds observed in our laboratory 

experiments.

Results are also consistent with field measurements from Glitzenstein et al. 

(2006) and Knapp et al. (2011) who documented low rates of spread and minimal 

flame lengths. In standard firefighting nomenclature, this indicates that the fire can 

generally be attacked at the head by firefighters using hand tools (NWCG 2018; 

Roussopoulos 1974).

Limitations were also evident from these burns that made further analyses difficult. 

These include the following conditions:

1. All of the fuel beds had similar moisture values because of conditioning (table 3). 

While this did not allow us to test the full range of fire behavior expected in the 

field, we were able to test a near worst-case scenario for extremely dry fuel.

2. No live fuels were included in the experimental burns. However, live woody fuel 

moisture is a required input for two of the custom fuel models. It was assumed 

to be 150 percent to represent mature foliage in which new growth is nearly 

complete. Both “low” (12 percent) and “moderate” (21 percent) slopes were used 

in the calculation of fire behavior.
USDA Forest Service RMRS-RP-107. 2018. 17

3. Wind speed was assumed to be zero.

4. Differences in rate of spread during the burn, while visually apparent, could not be 

measured in the experimental design.

5. Although fire behavior can be measured and modeled in any direction, many of 

the burns exhibited only flanking fire, which is much slower than heading fire. We 

were unable to measure flanking fire rate of spread because the fuel beds were too 

narrow.

6. Intensity was not measured for the burns in definitive units.

7. We were unable to burn these fuels in a field setting. Therefore, we cannot apply 

the results from our modeling experiment directly to fire behavior in masticated 

fuel treatment areas, but rather indirectly with experimental results from more 

targeted research on field burning of masticated fuel.

8. Residual burning, likely important in these scenarios, cannot be modeled using 

current fire behavior modeling systems (fig. 11).

9. Several of our experimental burns in heavy fuel, although limited in size, 

smoldered for more than an hour.

10. We were unable to determine the role of spotting of embers into untreated fuel, 

particularly under windy conditions.

Facilitated learning analyses (FLAs) specifically written for incidents occurring in 

masticated fuels document that wind plays an important role in reigniting fires from 

smoldering embers (residual burning). Given the minimal fire behavior observed 

in this and other studies, resistance to control as a result of glowing combustion is 

likely to be of greater concern. Anecdotal evidence and FLAs (e.g., Bass et al. 2012; 

McDaniel 2013) indicate that masticated fuel burns much longer than other fuel 

types. Masticated fuel is rarely spread evenly across an area, and pockets of high fuel 

concentrations have been known to smolder for long periods of time (Kreye et al. 

2014). This was also reflected in our experimental burns. When residual heat remains, 

Figure 11—Residual heating and glowing combustion can be an inportant factor when burning these fuels. The fuel bed on 

the left (a) is taken from the Manitou Experimental Forest, Colorado (MEFWS). The fuel bed on the right (b) is taken from 

the Deception Creek Experimental Forest, northern Idaho (DC1). Both photos were taken approximately 26 minutes after 

the experimental fuel beds were ignited and when the last flame had disappeared. Arrows indicate upslope. Many of these 

burns continued for more than an hour.

a) b)
18 USDA Forest Service RMRS-RP-107. 2018.

a change in the weather could cause transition to flaming in surface fuels, leading 

to issues related to resistance to control (Bass et al. 2012). Thus, there is a need to 

ensure that a fire is completely out (admittedly a difficult task in heavily masticated 

fuel) or to establish extended patrols to verify that fires do not reignite and spread.

While spotting models have been developed for torching trees, wind-driven surface fires, burning piles, and active crown fire (Albini 1979, 1981, 1983; Albini et al. 

2012), they cannot be used for masticated fuel because of the unique nature of these 

fuel beds. Currently, no model can adequately estimate the spotting distance of an 

ember from masticated fuel. Therefore, managers should consider if there is a need 

for a buffer between masticated fuels and the treatment unit boundary. This buffer 

would be free of masticated fuels and treated if necessary to minimize potential rate 

of spread and flame length (Bass et al. 2012).

Several authors have described a need for custom fuel models in masticated 

fuels (e.g., Kreye et al. 2014), but there are many uncertainties when modeling fire 

behavior in these novel fuel beds. When Rothermel’s fire spread model is used, the 

assumptions and limitations are well-documented (e.g., Andrews 2014; Rothermel 

1972, 1983; Rothermel and Rinehart 1983): the fuel bed is assumed to be homogeneous, fine fuel is considered the primary carrier of fire, and fire behavior is 

calculated for a heading fire (in direction of maximum spread). We were unable to 

create custom fuel models for our burns because (1) there were no observational data 

from the field for comparison to results from any fuel models we would create, (2) 

wind was not considered during the experiments, and (3) we were unable to measure 

the observed flanking fire behavior or associate it with forward spread predictions 

from the model. In addition, other factors must also be considered in the modeling 

effort. Masticated fuel is unique in that there can be a large component of woody fuel 

in all dead fuel classes and the fuel load may be dominated by larger fuel. Fuel bed 

depth can also be heavily influenced by the machine used for mastication as can the 

particle sizes and shapes (Keane et al. 2017). These factors make it difficult to calculate the components of a custom fuel model using the data from our experimental 

burns.

Conclusion

This study was designed to characterize fire behavior in masticated fuels at sites 

in these four States through experimental burns and fire behavior modeling. Overall, 

there was great variety in the observed fire behavior, and age of the fuel did not appear to play a role in fire behavior. The type of mastication and resulting fuel bed 

depth did play a role. Some fuel beds, such as those that were chipped or mowed, 

failed to burn completely. These beds tended to have more densely packed fuel or 

shallow fuel beds. Other beds exhibited flanking fire behavior caused by edge effects 

from the fuel bed or from large pieces of fuel restricting fire spread. Still other fuel 

beds burned fairly evenly and consistently across the entire bed.

Given the experimental design, we were unable to create custom fuel models to 

potentially improve fire behavior estimates. With the minimal fire behavior observed 

in this study, uncertainties in the fire behavior models themselves (Byram 1959; 
USDA Forest Service RMRS-RP-107. 2018. 19

Rothermel 1972) could account for differences between the observed and predicted 

fire behavior (Rothermel and Rinehart 1983).

Even though the results from this study appear to contradict claims that fire in 

masticated fuels is difficult to manage, these results are limited to fire behavior of a 

flaming fire front in a controlled setting. The fuel beds in this study also came from 

sites with little litter or grass to carry a fire. Posttreatment regeneration of grasses 

and shrubs may also change the surface fuels, potentially increasing fire behavior in 

masticated fuel beds.

Concerns regarding burning masticated fuel may or may not exist during initial 

fire spread. Residual burning makes containment and mop up difficult, particularly in 

areas with deep pockets of masticated fuel, such as those created by rotating or horizontal head drums. Residual burning and spotting across the containment line have 

been documented in many wild and prescribed fires in these fuel types. These issues, 

particularly under windy conditions, lead to very different fire behavior both within 

and outside the treated area. Wind contributes to spotting, causing embers to land 

in the untreated fuel outside the treatment zone. Unfortunately, no model currently 

exists to estimate spotting potential for masticated fuel. It is important, therefore, to 

model not only the fire behavior within the treatment area, but also the fire behavior 

in surrounding vegetation.

Glitzenstein et al. (2006) wrote of their masticated materials, “Practically speaking, all the fire behavior predictions ... were close enough to measured values to 

satisfy a prescribed burner or wildland firefighter,” (p. 25). Similarly, none of the observed or modeled fire behavior in this study would seem to indicate changes to fire 

management operations. With this in mind, the SB1 fuel model effectively approximated fire behavior in our experimental fuel beds. However, rate of spread and flame 

length at the flaming front are arguably not the most important considerations for 

land managers during a wildland fire in masticated fuels. Canopies at masticated sites 

are usually opened to reduce the potential for crown fire. This characteristic alone 

reduces the potential for wildfire severity, transition to crown fire, and firefighter 

injury (Battaglia et al. 2010; Kreye and Kobziar 2015; Reiner et al. 2009; Schwilk et 

al. 2009).

There is still abundant research needed for these novel fuel beds. Future research 

should focus on the length of time pockets of fuel are capable of burning to assist 

managers in prioritizing patrol and mop up. Spotting models should be developed, 

and these models could well depend on the type of masticator used to treat the fuels. 

Laboratory models need to be verified in the field to be of more use to managers. 

Further, models need to be developed for rate of spread for a flanking fire in addition 

to rate of spread for a heading fire because both behaviors are important in this type 

of fuel. Research is also needed regarding the increased loading of surface fuels that 

burn for long periods of time and that could cause long-term heating of soil or plant 

parts (such as roots and stems), affecting a masticated site’s ability to regenerate after 

a fire.
20 USDA Forest Service RMRS-RP-107. 2018.

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