United States Department of Agriculture

Forest 

Service

Pacific Southwest 

Research Station

Research Paper

PSW-RP-268 July 

2016

Evaluation of Standard Methods 

for Collecting and Processing Fuel 

Moisture Samples 

Sally M. Haase, José J. Sánchez, and David R. Weise 
In accordance with Federal civil rights law and U.S. Department of Agriculture 

(USDA) civil rights regulations and policies, the USDA, its Agencies, offices, and 

employees, and institutions participating in or administering USDA programs are 

prohibited from discriminating based on race, color, national origin, religion, sex, 

gender identity (including gender expression), sexual orientation, disability, age, 

marital status, family/parental status, income derived from a public assistance program, political beliefs, or reprisal or retaliation for prior civil rights activity, in any program or activity conducted or funded by USDA (not all bases apply to all programs). 

Remedies and complaint filing deadlines vary by program or incident. 

Persons with disabilities who require alternative means of communication for 

program information (e.g., Braille, large print, audiotape, American Sign Language, 

etc.) should contact the responsible Agency or USDA’s TARGET Center at (202) 

720-2600 (voice and TTY) or contact USDA through the Federal Relay Service at 

(800) 877-8339. Additionally, program information may be made available in languages other than English. 

To file a program discrimination complaint, complete the USDA Program Discrimination Complaint Form, AD-3027, found online at http://www.ascr.usda.gov/complaint_

filing_cust.html and at any USDA office or write a letter addressed to USDA and 

provide in the letter all of the information requested in the form. To request a copy 

of the complaint form, call (866) 632-9992. Submit your completed form or letter to 

USDA by: (1) mail: U.S. Department of Agriculture, Office of the Assistant Secretary 

for Civil Rights, 1400 Independence Avenue, SW, Washington, D.C. 20250-9410; 

(2) fax: (202) 690-7442; or (3) email: program.intake@usda.gov. 

USDA is an equal opportunity provider, employer, and lender.

Authors 

Sally M. Haase is a research forester (retired), José J. Sánchez is a research 

statistician, and David R. Weise is a research forester, Pacific Southwest Research 

Station, 4955 Canyon Crest Drive, Riverside, CA 92507. 
Abstract
 

Haase, Sally M.; Sánchez, José J.; Weise, David R. 2016. Evaluation of 

standard methods for collecting and processing fuel moisture samples. 

Res. Pap. PSW-RP-268. Albany, CA: U.S. Department of Agriculture, 

Forest Service, Pacific Southwest Research Station. 28 p. 

A variety of techniques for collecting and processing samples to determine 

moisture content of wildland fuels in support of fire management activities were 

evaluated. The effects of using a chainsaw or handsaw to collect samples of largediameter wood, containers for storing and transporting collected samples, and 

quick-response ovens for estimating moisture content were evaluated against the 

ASTM standard method D4442 to determine the moisture content of wood. Saw 

type and most sample containers did not influence the moisture content; however, 

polyethylene bags lost moisture owing to poor seal or perforation, making them 

unsuitable for storing fuel for processing. Generally, most of the quick-response 

oven tests indicated that drying fuels at higher temperatures than the ASTM 

standard method provided comparable results. 

Keywords: Oven-dry, rapid-response, coarse woody debris, 1-hr, 10-hr, 

100-hr, 1000-hr. 
Summary 

Fuel moisture is key to the rate and completeness of fuel consumption by wildland 

fire and is therefore fundamental to the knowledge of fire behavior and effects. 

In 1979, the Pacific Southwest Research Station (PSW) published a fuel moisture 

collection guide, which came to be used in California and other parts of the United 

States where live fuels are an important fuel type. This publication served as the 

standard for sampling live fuel moisture for many years. Since then, additional 

guides based on the original methods have been developed but with various modifications to address additional fuel types. Many of these modifications have not been 

tested to determine how they might affect fuel moisture estimation. The Forest Service’s San Dimas Technology and Development Center accepted a project to revise 

and standardize fuel moisture sampling procedures and produced two guides to 

assist fire and fuel managers. As part of the effort to revise and standardize methods to collect and process live and dead fuels to estimate fuel moisture content, we 

performed a series of experiments to evaluate several of the methods and equipment 

that have come into use since the original guide was produced. 

A variety of techniques to collect and process samples to determine moisture 

content of wildland fuels in support of fire management activities were evaluated. 

The effects of using a chainsaw or handsaw to collect samples of large-diameter 

wood, containers to store and transport collected samples, and quick-response 

ovens to estimate moisture content were evaluated to determine moisture content 

of wood. Saw type and most sample containers did not influence the moisture 

content; however, polyethylene bags lost moisture owing to poor seal or perforation making them unsuitable for storing fuel for processing. Generally, most of the 

quick-response oven tests indicated that drying fuels at higher temperatures than 

the ASTM standard method provided comparable results. 
 

 

 

 

 

 

 

 

 

Contents
 

1 Introduction 

2 Evaluation of Two Methods of Collecting 100-Hr Time Lag Fuels: 

a Handsaw Versus a Chainsaw 

2 Study Design and Analysis 

3 Results and Discussion 

5 Evaluate Three Containers for Fuel Moisture Sampling 

5 Design 

7 Results and Conclusions 

16 Compare Quick-Response Ovens With Convection Drying Ovens 

to Determine Fuel Moisture Content 

17 Methods 

22 Statistical Analysis 

23 Results and Discussion 

25 Metric Equivalents 

25 Literature Cited 
Evaluation of Standard Methods for Collecting and Processing Fuel Moisture Samples

Introduction
 

Fuel moisture is key to the rate and completeness of fuel consumption by wildland 

fire and is therefore fundamental to the knowledge of fire behavior and effects. 

In 1979, the Pacific Southwest Research Station (PSW) published a fuel moisture 

collection guide, which came to be used in California and other parts of the United 

States where live fuels are an important fuel type (Countryman and Dean 1979). 

This publication served as the standard for sampling live fuel moisture for many 

years. Since then, additional guides based on the original methods have been developed but with various modifications to address additional fuel types (Brenner 2002, 

Norum and Miller 1984, Pollet and Brown 2007). Many of these modifications 

have not been tested to determine how they might affect fuel moisture estimation. 

The Forest Service’s San Dimas Technology and Development Center accepted a 

project to revise and standardize fuel moisture sampling procedures and produced 

two guides to assist fire and fuel managers (Zahn and Henson 2011a, 2011b). As 

part of the effort to revise and standardize methods to collect and process live and 

dead fuels to estimate fuel moisture content, we performed a series of experiments 

to evaluate several of the methods and equipment that have come into use since 

the original guide was produced. The management alternatives from some of these 

experiments have been previously published in Haase and Zahn (2007), and Zahn 

and Haase (2006). This paper presents detailed information on these two studies 

and additional experiments that form the basis for the management alternatives. 

When a manager is making decisions about a wildland fire or prescribed burn, 

one of the major goals is to accurately predict fire behavior under current conditions. Fuel moisture is one of the main variables that influence fire behavior. In 

addition to being an important calculation in fire danger rating, moisture content is 

one of the variables used to define acceptable conditions under which a prescribed 

burn or wildland fire can be used to accomplish resource management objectives. 

While methods to predict dead fuel moisture content have improved (Nelson 2000) 

and remote sensing is used to estimate live fuel moisture content (Weise and Wright 

2014), these estimates may not represent actual conditions on the project site. This 

is because local weather patterns that determine fuel moisture are influenced by 

highly variable topography, and remotely sensed data do not always detect important fuels. The moisture content of litter can vary appreciably within a forest stand 

depending on the ability of solar radiation to penetrate the canopy (Countryman 

1977). The most accurate way to determine fuel moisture content for a project is 

to measure or collect fuels on site and then process the collected data following 

1 
2 

PACIFIC SOUTHWEST RESEARCH STATION PSW-RP-268

established procedures that assure quality control. As part of the handbook revision, 

we conducted three experiments: 

1. Evaluate two methods of collecting 100-hour time lag fuels 

2. Evaluate three containers used to store fuel moisture samples 

3. Compare quick-response ovens (QROs) to a convection drying oven to 

determine moisture content 

The detailed results of these experiments will be presented in this report. 

Evaluate Two Methods of Collecting 100-Hr Time Lag 

Fuels: A Handsaw Versus a Chainsaw 

Dead large woody fuels (100-hr and 1000-hr time lag; Fosberg 1970, Fosberg et al. 

1981), also known as coarse woody debris, are an important component of ecosystems and can contribute significantly to fire behavior on a site (Albini and Reinhardt 1995). Because of the size of these fuels (100-hr and 1000-hr fuel sizes are 1 

to 3 and 3 to 8 inches in diameter, respectively), a sample is usually collected using 

a saw. Field personnel have been instructed not to use a chainsaw when collecting 

large woody fuels for moisture content information; it was thought that chainsaw 

bar oil would affect the sample by adding additional weight to it. A traditional 

pruning handsaw has always been the recommended cutting tool. The objective of 

this study was to determine if the sawing method affected the estimated moisture 

content of 100-hr time lag woody fuel. 

Study Design and Analysis 

Two fuel moisture conditions (dry, wet) were examined in separate test phases. The 

dry phase tests occurred in three batches on November 19, 2004, November 22, 

2004, and December 2, 2004. The wet phase tests occurred similarly on June 10, 

2005, June 13, 2005, and June 17, 2005. Three-inch-diameter posts of 10-inch-long 

lodgepole pine (Pinus contorta Douglas ex Loudon) were marked to make 1-inch 

discs, allowing for individual saw kerf. The dry test used low moisture content 

material obtained by submerging ten 10-inch-long sample pieces of 3-inch-diameter 

kiln-dried lodgepole pine poles in tubs of water for 6 hours, producing moisture 

contents that ranged from 14 to 18 percent. After the soaking period, the pieces 

were shaken to remove surface water and then placed in an environmental chamber for 24 hours, humidity set at 90 percent. The cutting treatment, chainsaw or 

handsaw, was randomly assigned to a piece as it was removed from the chamber. 
3 

Evaluation of Standard Methods for Collecting and Processing Fuel Moisture Samples

The sample piece was placed on a “jig” to safely hold the piece while “discs” were 

cut with either a handsaw (a collapsible pruning saw) or a chainsaw (model 520SP 

Jonsered with a 16-inch bar1). Six discs or cookies were quickly cut from each piece 

using the same tool, a wood chisel and hammer, and all pieces from that cookie 

were sealed in an autoclavable polypropylene bottle (fig. 1). All 10 pieces were 

treated this way (n = 60). Each bottle was weighed then uncapped and placed in a 

drying oven previously set at 95 oC (203 °F) and dried until there was no weight 

loss from drying (ASTM International 2007). The ASTM standard specifies an 

oven temperature of 103 ± 2 oC (215.6 + 35.6 °F) unless the material contains volatile products. If the material does contain volatile products, a distillation method 

to determine moisture content is recommended. After 24 hours of drying, test 

bottles (3 and 4) were randomly selected and capped, cooled, and weighed at 6- and 

12-hour intervals. All sample bottles were capped, cooled to room temperature, and 

weighed after the test bottles showed no further weight loss. 

In the wet phase, immersion of the sample pieces for 6 days achieved higher 

fuel moisture than the dry phase. The resulting moisture content of the sample 

pieces ranged from 16 to 51 percent, with the interior discs exhibiting lower moisture content. The only difference in procedure for the wet phase occurred when we 

used a different chainsaw (model 141 Husqvarna with a 16-inch bar). Analysis of 

the wet and dry phases occurred separately using paired t-tests to detect differences 

between the two cutting methods. 

Results and Discussion 

A total of 90 fuel moisture samples was collected for each cutting method/moisture 

content combination. One observation was omitted from the analysis owing to a 

recording error. The complete data set is available in the Forest Service Research 

Data Archive (Haase et al. 2014). Mean moisture content of the dry test phase was 

15 percent, and moisture content of the wet test phase was 39 percent (table 1). The 

fiber saturation point of dead wood is nominally 30 percent, and the moisture content of green lodgepole pine heartwood has been reported to be 41 percent (Glass 

and Zelinka 2010). The standard errors were less than 1 percent of the mean values, 

1

 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. 
4 

PACIFIC SOUTHWEST RESEARCH STATION PSW-RP-268

USDA Forest Service 

Figure 1—Three types of containers used in fuel storage container study. The containers are 

(from left to right) 1-quart steel paint can, 32-ounce autoclavable polypropylene bottle, and 

1-quart self-sealing freezer-grade polyethylene plastic bag. 

Table 1—Moisture content (percentage) of wet and dry samples 

of lodgepole pine slices extracted with a chainsaw or a handsaw 

Chainsaw Handsaw 

Mean Mean 

Experiment (std. error) Number (std. error) Number 

Dry test 15.41 (0.11) 90 15.41 (0.12) 89
 

Wet test 39.28 (0.42) 90 39.24 (0.42) 90
 

indicating very good experimental control. There was no significant difference 

between the two cutting methods at either level of moisture content: 

The results of these tests demonstrate it is appropriate to use a well-operating 

chainsaw to collect 100-hr time lag or larger woody fuels for moisture content 

provided that there is not excessive bar oil use. 
5 

Evaluation of Standard Methods for Collecting and Processing Fuel Moisture Samples

Evaluate Three Containers for Fuel Moisture Sampling 

The primary objective of this study was to determine which of three commonly 

used fuel sampling containers maintained the most accurate moisture content of 

three representative fuel-size-class materials over time under two ambient conditions. Containers currently used by field personnel include (fig. 1): 

•	 One-quart steel paint cans (Countryman and Dean 1979) 

•	 Thirty-two ounce polypropylene plastic bottles 

•	 One-quart (7- by 8-inch, 2.7 mil thickness) self-sealing polyethylene 

plastic bags 

ASTM Standard D4442 dictates that an airtight container be used for fuel 

moisture samples. The variety of airtight containers has increased over time from 

the originally recommended paint cans and soil cans as plastics have become more 

readily available. Fuel sampling recommendations include storing collected fuels in a 

cool location prior to initial weighing. Personal-use ice chests and firehose packs store 

samples during transport from the field to the office for further processing, which 

typically occurs within a day of collection. Fuel sampling occurs from spring through 

fall (and sometimes in winter in areas where live fuels grow year round) and ambient 

conditions may affect storage effectiveness. The second objective of the study was to 

determine if sample processing time in the lab affected moisture content. 

Design 

The study used three fuel sizes (aspen [Populus tremuloides Michx.] excelsior, 

1⁄4-inch pine dowels, 3-inch lodgepole discs), three container types (described 

above), two storage containers (ice chest with no ice, firehose pack), and two 

ambient conditions (cool, hot). Two locations, a greenhouse with temperatures 

100 to 131 °F and a laboratory with temperatures 62 to 75 °F, provided a range of 

temperature extremes. The fuels nominally represented 1-, 10-, and 100-hr time 

lag fuels (fig. 2) based on particle diameter, although 3 inches is the breakpoint 

between the 100- and 1000-hr fuel classes. The diameter classes associated with 

1-, 10-, 100-, and 1000-hr time lag classes are 0 to 1⁄4 inch, 1⁄4 to 1 inch, 1 to 3 

inches, and 3 to 8 inches, respectively (Deeming et al. 1974, Lancaster 1970). Note 

that Anderson (1990) reported that many response times for fine fuels classified 

as 1-hr fuels were in fact longer than anticipated. The range of response time 

represented by the timelag classes are 0 to 2, 2 to 20, 20 to 200, and 200 to 2000 hr, 

which correspondingly cover a broad range of fuel particle diameters. For instance, 

Carlson et al. (2007) conducted a long-term moisture study in Oklahoma using 

wooden cylinders with 1⁄6-, 1⁄2-, 11⁄2-, and 5-inch diameters to represent 1-, 10-, 100-, 
6 

PACIFIC SOUTHWEST RESEARCH STATION PSW-RP-268

 USDA Forest Service 

Figure 2—Fuel types used in container evaluation study. The fuels are (clockwise from right) 

aspen excelsior, 1⁄4-inch diameter pine dowel, 3-inch diameter lodgepole pine post.

and 1000-hr fuels. While the fuel diameters in our study were different, the 

objectives of the study were not affected. 

Within each size class, we cut fuels into similar-length pieces prior to soaking. 

Excelsior samples were standardized into hand-size clumps. Dowels were cut to 

3-inch lengths, and 10 pieces made an individual sample. Each 3-inch-diameter 

cookie was 1-inch thick and split into quarter sections when placed in a container. 

All fuel samples were initially oven-dry and then submerged fully in water for 67 

hours. After soaking, samples were placed in an environmental chamber for 24 

hours to stabilize the surface moisture. Each fuel type was placed in a container 

and then placed in a storage container. The study design contained three replications of the 18 treatments within the two locations. Two control samples of each fuel 

processed as above in a convection oven estimated the moisture content at the start 

of the test. Sample mass was weighed using a calibrated electronic balance to the 

nearest 0.01 gram (0.0004 ounces) in 30-minute increments for the first 6 hours followed by a break of 14 hours. The next measurements were taken every 30 minutes 

for the next 4 hours, and the last six measurements were taken every hour for a total 

of 27 measurements. The test terminated when the sample was placed in a convection oven to determine final moisture content. The plastic bags we used melted at 
7 

Evaluation of Standard Methods for Collecting and Processing Fuel Moisture Samples

the drying temperature in the oven so we placed these samples in bottles prior to 

drying. The standard basis for determining moisture content in the wood industry 

(and fire management) is oven-dry weight. 

Two analyses were performed to accomplish the two objectives. An assessment of the change over time as determined by the slopes of the relatively smooth 

moisture content history (drying curve) accomplished the primary objective. An 

assessment of deviation from the initial moisture content from the control samples 

examined if processing time affected moisture content (secondary objective). 

Both analyses used an analysis of variance process that accounted for correlations 

induced by the experimental design using PROC MIXED in SAS. Type III sums of 

squares were used to test the significance of the fixed effects on the initial moisture 

content. Because of the relatively smooth linear transition of moisture content over 

time for all samples and combinations of conditions, linear fits to the data were constructed and the estimated slopes were used as the response variable in an analysis 

of variance. 

Results and Conclusions 

Plots of the moisture content over time for each sample showed a relatively smooth 

linear trend throughout the 29-hr experimental time period for all fuel class sizes, 

ambient temperatures, transport methods, and container types. The smooth linear 

trend enabled the identification of the few data entry errors and removal of extreme 

data points that had no obvious explanation from the data set. A more detailed look at 

the changes in moisture content over time can be seen in figure 3. The initial moisture 

content of a sample was subtracted from all subsequent values so figure 3 displays the 

change in moisture content. Examination of these plots immediately shows polyethylene bags consistently lost more moisture than the other two container types. 

The initial moisture content for the various combinations of fuel size class, 

ambient temperature, transport method, and container type compared to control 

samples showed few differences (table 2). The analysis of variance of the initial 

moisture content found significant differences between the fuel size classes. 

The significant interaction among combinations of fuel size classes and ambient 

temperature was most likely due to the Excelsior/High Ambient Temp/Bag/Can 

combination having a lower mean moisture content (104.5 percent) than any other 

excelsior fuel combination. The only way to explain this is that it was just the luck 

of the draw when the sample containers were filled with material. Within the other 

two fuel size classes, all other combinations of the remaining factors showed homogeneity with respect to initial moisture content. The fuel size classes differed by the 

initial moisture content in a predictable way: the larger the fuel size class, the lower 
8 

PACIFIC SOUTHWEST RESEARCH STATION PSW-RP-268

Figure 3—Change in moisture content (percentage) over time from initial moisture content value. 
9 

Evaluation of Standard Methods for Collecting and Processing Fuel Moisture Samples

Figure 3—continued 
10 

PACIFIC SOUTHWEST RESEARCH STATION PSW-RP-268

Figure 3—continued 
11 

Evaluation of Standard Methods for Collecting and Processing Fuel Moisture Samples

Table 2—Mean initial moisture content for various combinations 

of fuel size classes, ambient temperature, transport method, and 

container typesa

Ambient temperature 

Fuel size class Transport method Container type High Low 

Percent 

Excelsior Bag Bottle 154.1 155.3 

Can 104.5 154.9 

Zip 140.5 164.4 

Cooler Bottle 151.3 157.6 

Can 130.7 160.5 

Zip 153.1 170.3 

Control Control 162.0 162.7 

1⁄4-inch dowel Bag Bottle 98.9 94.2 

Can 92.5 89.0 

Zip 94.1 100.0 

Cooler Bottle 93.2 95.8 

Can 96.9 81.4 

Zip 96.2 98.0 

Control Control 92.5 98.7 

3-inch disc Bag Bottle 80.5 76.5 

Can 83.7 77.2 

Zip 77.6 73.5 

Cooler Bottle 83.1 77.2 

Can 79.3 77.5 

Zip 73.8 71.6 

Control Control 79.5 76.6 a The control moisture content was determined using an oven bottle and immediate drying 

following collection of the sample. Estimated standard error for the control = 6.9 and for the 

treated samples = 9.6. 

the mean initial moisture content. In other words, the larger the fuel size, the longer 

it would take to soak up the same amount of water as a much finer fuel size class. 

Given that all sample material was soaked in water for the same amount of time, 

the finer fuel size classes soaked up more water. The mean initial water content for 

the excelsior, 1⁄4-inch dowels, and 3-inch discs was 151.6, 94.4, and 77.7 percent, 

respectively. We accounted for the blocking of sets of replicates in the experimental 

design and found homogeneity: the blocking by replicates showed no additional 

variation over and above the residual variability. Tables 3, 4, and 5 present the 

mean slope (and the associated standard error) for various combinations of fuel size 
12 

PACIFIC SOUTHWEST RESEARCH STATION PSW-RP-268

Table 3—Fitted hourly rate of change (slope), standard errors, and 

P-values for moisture content over a 30-hr time period of aspen excelsior 

for various combinations of ambient temperature, transport method, and 

container type 

Transport Container 

Ambient temperature method type Slope Std. error P-value 

High Bag -0.0392 0.0037 0.0000 

Cooler -0.0354 0.0037 0.0000 

Low Bag -0.0027 0.0011 0.0160 

Cooler -0.0025 0.0011 0.0255 

High Bottle -0.0082 0.0019 0.0000 

Can -0.0109 0.0015 0.0000 

Zip -0.0929 0.0074 0.0000 

Low Bottle 0.0008 0.0003 0.0211 

Can 0.0019 0.0004 0.0000 

Zip -0.0104 0.0022 0.0000 

Bag Bottle -0.0041 0.0013 0.0027 

Can -0.0037 0.0011 0.0012 

Zip -0.0550 0.0055 0.0000 

Cooler Bottle -0.0032 0.0013 0.0174 

Can -0.0052 0.0011 0.0000 

Zip -0.0483 0.0055 0.0000 

High Bag Bottle -0.0088 0.0026 0.0012 

Can -0.0088 0.0022 0.0001 

Zip -0.1001 0.0105 0.0000 

Cooler Bottle -0.0075 0.0026 0.0051 

Can -0.0130 0.0022 0.0000 

Zip -0.0858 0.0105 0.0000 

Low Bag Bottle 0.0005 0.0005 0.2285 

Can 0.0013 0.0005 0.0177 

Zip -0.0099 0.0031 0.0023 

Cooler Bottle 0.0011 0.0005 0.0226 

Can 0.0025 0.0005 0.0000 

Zip -0.0109 0.0031 0.0008 
13 

Evaluation of Standard Methods for Collecting and Processing Fuel Moisture Samples

Table 4—Fitted hourly rate of change (slope), standard errors, and P-values 

for moisture content over a 30-hr time period of 1⁄4-inch pine dowels for 

various combinations of ambient temperature, transport method, and 

container type 

Transport Container 

Ambient temperature method type Slope Std. error P-value 

High Bag -0.0379 0.0034 0.0000 

Cooler -0.0379 0.0034 0.0000 

Low Bag -0.0045 0.0004 0.0000 

Cooler -0.0032 0.0004 0.0000 

High Bottle -0.0108 0.0021 0.0000 

Can -0.0097 0.0020 0.0000 

Zip -0.0932 0.0066 0.0000 

Low Bottle 0.0005 0.0007 0.4602 

Can 0.0010 0.0003 0.0023 

Zip -0.0131 0.0005 0.0000 

Bag Bottle -0.0048 0.0016 0.0028 

Can -0.0016 0.0014 0.2685 

Zip -0.0573 0.0047 0.0000 

Cooler Bottle -0.0055 0.0016 0.0006 

Can -0.0071 0.0014 0.0000 

Zip -0.0490 0.0047 0.0000 

High Bag Bottle -0.0099 0.0030 0.0011 

Can -0.0032 0.0028 0.2592 

Zip -0.1005 0.0094 0.0000 

Cooler Bottle -0.0117 0.0030 0.0002 

Can -0.0162 0.0028 0.0000 

Zip -0.0858 0.0094 0.0000 

Low Bag Bottle 0.0004 0.0009 0.6595 

Can 0.0000 0.0005 0.9709 

Zip -0.0140 0.0007 0.0000 

Cooler Bottle 0.0006 0.0009 0.5455 

Can 0.0021 0.0005 0.0000 

Zip -0.0122 0.0007 0.0000 
14 

PACIFIC SOUTHWEST RESEARCH STATION PSW-RP-268

Table 5—Fitted hourly rate of change (slope), standard errors, and 

P-values for moisture content over a 30-hr time period of 3-inch 

lodgepole pine discs for various combinations of ambient temperature, 

transport method, and container type 

Transport Container 

Ambient temperature method type Slope Std. error P-value 

High Bag -0.0112 0.0004 0.0000 

Cooler -0.0096 0.0004 0.0000 

Low Bag -0.0010 0.0001 0.0000 

Cooler -0.0008 0.0001 0.0000 

High Bottle -0.0012 0.0002 0.0000 

Can -0.0030 0.0005 0.0000 

Zip -0.0271 0.0007 0.0000 

Low Bottle 0.0000 0.0002 0.8915 

Can 0.0005 0.0001 0.0014 

Zip -0.0031 0.0002 0.0000 

Bag Bottle -0.0005 0.0002 0.0031 

Can -0.0009 0.0004 0.0243 

Zip -0.0169 0.0005 0.0000 

Cooler Bottle -0.0007 0.0002 0.0000 

Can -0.0016 0.0004 0.0002 

Zip -0.0133 0.0005 0.0000 

High Bag Bottle -0.0010 0.0002 0.0000 

Can -0.0020 0.0008 0.0112 

Zip -0.0308 0.0010 0.0000 

Cooler Bottle -0.0013 0.0002 0.0000 

Can -0.0040 0.0008 0.0000 

Zip -0.0234 0.0010 0.0000 

Low Bag Bottle 0.0000 0.0002 0.8521 

Can 0.0001 0.0002 0.3904 

Zip -0.0031 0.0002 0.0000 

Cooler Bottle 0.0000 0.0002 0.9622 

Can 0.0008 0.0002 0.0000 

Zip -0.0031 0.0002 0.0000 
15 

Evaluation of Standard Methods for Collecting and Processing Fuel Moisture Samples

class, ambient temperature, transportation method, and container type. Most of the 

estimated slopes were significantly different from zero. 

Obtaining a variety of moisture contents for each fuel size class was desirable, 

but the larger fuel size classes ended up with significantly smaller mean initial 

moisture contents. Reasonable variation in moisture content did exist for like 

combinations of fuel size class, ambient temperature, transport method, and container type, so that inferences to the variety of moisture content found in the field 

are appropriate. There was very little difference in transportation methods (coolers 

and fire packs). Not surprisingly, higher ambient temperatures were associated 

with larger losses of moisture content. Only the use of polypropylene bags showed 

a relatively large loss of moisture content after 30 hr from initial collection in the 

field. For high ambient temperatures and fuel size class 0 to 1⁄4 inch (excelsior) there 

was an average change in moisture content of -0.0392 per hour, which is about 9 

to 10 times higher than that of cans and bottles (table 6). This corresponds to a 2.2 

percent decrease in moisture content in 24 hours. We estimated a measurement 

error owing to the scale and other factors of about 1 percent. 

A contributing factor that led to this result was the necessity of transferring 

sample material to a container that could withstand drying temperatures. Two 

issues contributed to the errors: the loss of sample material and the unaccounted for 

weight of condensation on the bag wall. If material remained in the sample bag after 

Table 6—Rate of change in moisture content (percentage per 

hour) of three woody fuels transported in two container types 

in two ambient environments 

Fuel type 

Excelsior 

Ambient 

temperature 

High 

Transport 

method 

Bag 

Cooler 

Moisture 

content 

-0.0392 

-0.0354 

P-value 

0.0000 

0.0000 

Low Bag 

Cooler 

-0.0027 

-0.0025 

0.0160 

0.0255 

1⁄4-inch dowel High Bag 

Cooler 

-0.0379 

-0.0379 

0.0000 

0.0000 

Low Bag 

Cooler 

-0.0045 

-0.0032 

0.0000 

0.0000 

3-inch disc High Bag 

Cooler 

-0.0112 

-0.0096 

0.0000 

0.0000 

Low Bag 

Cooler 

-0.0010 

-0.0008 

0.0000 

0.0000 
16 

PACIFIC SOUTHWEST RESEARCH STATION PSW-RP-268

weighing and transferring, the weight of that material was erroneously included as 

moisture loss. This would produce higher moisture content than the actual value 

for that sample. We made a concerted effort to avoid this material loss. The loss of 

sample material could be expected more with the smaller size class material that 

had some small or loose pieces, but this relationship was still seen with the larger 

fuel size class (dowels or post) material where sample material remained in solid 

intact pieces. Condensation was not an issue using the other two ovenproof containers because it was included in the sample wet weight. 

When using natural fuels, the problem of moisture condensation and loss of 

sample could be a factor for all size class material since natural, dead fuels are usually in various stages of decomposition. When ambient conditions were extremely 

warm, the plastic bags expanded and seals were easily broken when pressure 

was applied to the sides of the bag. They had to be checked regularly when being 

handled for proper seals and punctures. 

Polypropylene bottles maintained fuel moisture content of the sample best for 

the duration of the 29 hours measured. They consistently had small weight loss 

under both ambient temperatures (high and low) and were easiest to use in terms of 

weighing, uncapping, and putting directly into the drying oven. Paint cans also did 

well in maintaining sample moisture, as they are able to go directly into the drying 

oven without transferring the sample. Negative aspects of using paint cans include 

the requirement of using a special paint can opener or key to reduce damage to the 

lip of the lid, and the needed care of not collapsing the can when using a hammer to 

seal the can. The seal also seems to deteriorate with repetitive opening and sealing, 

which reduces the container’s longevity. Cans also require regular inspection for 

rust and need frequent updating of tare weights. 

Comparison of Quick-Response Ovens With 

Convection Drying Ovens to Determine Fuel 

Moisture Content 

Forest Service Research worked with the Motorola Company to define appropriate 

parameters of the initial drying system to determine the moisture content of fine 

dead fuels (Sackett 1980). At that time, the system used the 95 °C (203 °F) temperature for drying, and moisture content was determined based on rate of weight loss. 

The current device (Computrac®

) is now capable of drying with a large range of 

temperatures (25 to 225 °C [77 to 437 °F]) and multiple sample sizes. With so many 

additional options and combinations of settings, concerns about drying at higher 

temperatures and the small sample size have arisen. The impact of these higher 

drying temperatures on volatilization of more than just water on moisture content 
17 

Evaluation of Standard Methods for Collecting and Processing Fuel Moisture Samples

is unknown. Other companies have developed similar systems. Many agencies are 

acquiring “quick-response” systems (i.e., Computrac and Neosystem®

) for use in 

their local areas to estimate fuel moisture content in live and dead fuels. The main 

objective in using these devices is to reduce the time involved in determining the 

moisture content of fuels, which takes a minimum of 24 hours if a conventional 

forced-air convection oven is used following ASTM standard D4442 (ASTM International 2007). The distillation method described in D4442 also takes an appreciable amount of time to determine moisture content. Methods to provide a quick 

estimate of fuel moisture content of forest fuels and woody biomass have been 

examined (e.g., Harris 1982, Hartmann and Böhm 2001, Norum and Fischer 1980, 

Nyström and Dahlquist 2004, Palmer and Pace 1974). Some of the indirect methods 

were designed to estimate the moisture content of a moving feedstock of wood 

chips. A desirable characteristic for use with forest fuels is that the devices be selfcontained and portable, removing the need to have a balance and convection drying 

oven to obtain moisture content of organic material or soils. Stirring of the drying 

sample to ensure uniform drying, as is recommended with microwave methods, is 

an undesirable characteristic. There has been much debate on the appropriate use of 

the self-contained fast-response systems within the research/forest community as 

a result of the limited size of sample (less than 60 grams [2.12 ounces]) and higher 

than recommended drying temperatures; the ASTM standard is 103 °C (214.4 °F), 

and the approach recommended by Countryman and Dean (1979) is 103 to 105 °C 

[214.4 to 221.1 °F]. 

A recent study used foliage from lodgepole pine, ponderosa pine (Pinus ponderosa Lawson & C. Lawson), western larch (Larix occidentalis Nutt.), and subalpine 

fir (Abies lasiocarpa (Hook.) Nutt.) to compare a Computrac system (145 °C [293 

°F]) with a convection oven (95 °C [203 °F]) and found that the estimated moisture 

contents were very similar (Jolly and Hadlow 2012). The objective of the present 

study was to compare fuel moisture estimates from these QROs using higher drying 

temperatures and shorter drying times with estimates from a convection oven with 

longer drying time and lower drying temperature. 

Methods 

We chose the NeoSystem Moisture Meter Model FMM-1 manufactured by Sigma 

Technologies (Australia) and the Computrac MAX 2000XL system as the two 

rapid-response systems to compare with a convection oven/manual weighing 

approach (fig. 4). We used three dead fuel sizes (fig. 5) and two live fuel types: 

aspen excelsior, 3⁄4-inch aspen dowels, 3-inch lodgepole pine discs, mountain 

laurel (Kalmia latifolia L.) leaves, and scrub oak (Quercus berberidifolia Liebm.) 
18 

PACIFIC SOUTHWEST RESEARCH STATION PSW-RP-268

Figure 4—Quick-response ovens compared with conventional forced convection oven to determine 

effects of departures from standard drying method on moisture content determination. 
19 

Evaluation of Standard Methods for Collecting and Processing Fuel Moisture Samples

Figure 5—Dead fuels (excelsior, 3⁄4-inch dowel, 3-inch 

slice) used in the evaluation of quick-response ovens in 

test 1 and test 2. (Photo courtesy of USDA Forest Service) 
20 

PACIFIC SOUTHWEST RESEARCH STATION PSW-RP-268

leaves. Dead sample material was cut into similar size pieces for each size class. 

Hand-size clumps (about 40-grams [1.4 oz] dry weight) of excelsior were bound 

with rubber bands prior to soaking. The 3⁄4-inch dowels were cut into 1-inch lengths 

and the 3-inch pine posts were cut into 1-inch discs. The dry, dead fuels were fully 

submerged in water for 12, 48, or 72 hours for the excelsior, dowels, and discs, 

respectively, to ensure measurable moisture content changes. After soaking, the 

dowels and discs were air dried on a table for 12-hours to remove surface moisture, 

and excelsior samples were spun in a salad spinner to remove surface water. 

Several different drying test configurations were performed (table 7). In the 

first test (test 1), excelsior and the dowels were dried in the Computrac at 170 oC 

(338 °F) and the discs were dried at 180 oC (356 °F). The “prediction ending criterion” was used to conclude each run. With this criteria, the instrument predicted 

the final moisture content as a function of the moisture loss rate and concluded the 

test when the prediction stabilized (Arizona Instrument LLC 2002). All fuel sizes 

were dried in the NeoSystem at 140 oC (284 °F). Two sets of dead fuels were dried 

in the convection oven at 95 oC (203 °F). One set (denoted “soil can”) used a sample 

mass equal to that used in the QROs, and one set used a much larger sample mass 

(denoted “bottle”). A second test (test 2) was performed with the Computrac using 

the “rate ending criterion.” This criterion ended the test when the rate of weight loss 

dropped below a preset rate. The third set of tests (test 3 and test 4) dried 4- and 

8-gram (0.14 to 0.28 oz) samples of the live fuels at 140 oC (284 °F) in the Computrac and NeoSystem and at 95 oC (203 °F) in the convection oven. 

A paper cutter cut the bound excelsior into ≤ 1⁄2-inch pieces. Two randomly 

selected discs were each broken into at least 16 smaller pieces. No additional handling of the dowels was necessary. All material was sealed immediately in moisture 

proof, autoclavable polypropylene bottles until dried in their assigned ovens. Each 

8-gram (0.28-ounces) sample was placed in each QRO, the “soil can” and “bottle” 

samples were sealed for bulk sample drying in the convection ovens. Ten samples 

of each fuel type were dried in each of the ovens and the entire set of runs was 

replicated three times over the course of the experiment (table 8). The wet mass of 

the bottle samples ranged 47 to 76 (1.66 to 2.68), 40 to 49 (1.41 to 1.73), and 122 to 

144 grams (4.30 to 5.08 ounces) for the excelsior, 3⁄4-inch dowel, and 3-inch discs, 

respectively. 

Live fuel tests used both 4- and 8-gram (0.28-oz) samples separately. Ten 

polypropylene bottles of mountain laurel foliage harvested from a single site in 

the Pisgah National Forest in North Carolina and shipped overnight to Riverside, 

California, provided the first evaluation using live fuels. The harvested material 

was cut into 1⁄2-inch pieces in the field before shipping. Samples were processed the 
21 

Evaluation of Standard Methods for Collecting and Processing Fuel Moisture Samples

 

 

 

Table 7—Configuration of fuel type, drying temperature, and algorithm criteria for experiments used to 

test effect of oven type on fuel moisture 

Test 

Variable 1 2 3 4 

Fuel type Excelsior Excelsior Mountain laurel Scrub oak leaves 

3⁄4-inch dowel 3⁄4-inch dowel leaves 

3-inch disc 3-inch disc 

Computrac QRO Excelsior–170 °C 95 °C 140 °C 140 °C 

Dowel–170 °C 95 °C 

Disc–180 °C 95 °C 

NeoSystem QRO 140 °C 140 °C 140 °C 140 °C 

Convection oven 95 °C 95 °C 95 °C 95 °C 

Total number of samples 360 360 210 210 

Note: Different criteria were used to end each run in the Computrac quick-response ovens (QROs) between the different tests. See the text for a 

detailed explanation. 170 °C = 338 °F, 180 °C = 356 °F, 140 °C = 284 °F, 95 °C = 208 °F. 

Table 8—Structure of analysis of variance tables used to test effects 

of drying oven type on fuel moisture estimates for live and dead fuels 

Fuel type Source 

Degrees of 

freedom Effect type 

Excelsior Replication 2 Random 

3⁄4-inch dowel Oven type 3 Fixed 

3-inch disc Samples/oven type 9 

(test 1, 2) Error 105 

Total 

Mountain laurel Replication 2 Random 

Scrub oak Oven type 3 Fixed 

(test 3, 4) Sample mass 1 Fixed 

Samples/oven type 9 

Error 194 

Total 209 

119 
22 

PACIFIC SOUTHWEST RESEARCH STATION PSW-RP-268

next day or stored in a cool location until the next workday. A full set of samples 

consisted of 70 runs (10 samples by 2 fixed masses by 3 ovens + 10 bottles). Sample 

material collected on 3 separate days comprised three replications. A bottle was 

randomly selected and shaken before a sample was extracted with forceps and 

placed in the QRO or in the soil can. Soil cans and bottles were bulk dried in the 

convection ovens. Soil cans and bottles were weighed using an electronic balance. 

Scrub oak leaves harvested near Riverside, California, using the same procedures 

produced the second live fuel evaluation. The QRO systems came to room temperature between runs. The final moisture and time involved in each run were recorded. 

Wet mass of the bottle samples ranged from 25 to 70 (0.88 to 2.47) and 36 to 122 

grams (1.27 to 4.30 ounces) for the scrub oak and mountain laurel, respectively. 

Statistical Analysis 

Four specific hypotheses correspond to the four tests performed. In test 1, we 

tested to see if there was any difference between moisture content estimated from 

a convection oven method and from the QROs at the various drying temperatures. 

Each dead fuel type was analyzed separately. Test 2 repeated test 1 with the exception that the temperature used by the Computrac QRO matched the convection 

oven temperature. Tests 3 and 4 matched the two QRO drying temperatures and 

examined differences caused by the oven type and fuel sample mass for the two live 

fuel types. 

A mixed-effects linear model (Pinheiro and Bates 2000) described the experimental design for all four tests (table 8). For tests 1 and 2, the random effect was 

the experimental run, and oven type was the fixed effect. The variance-covariance 

structure was based on the random effect (sample id) and on the replication of oven 

type (bottle, can, Computrac 2000 MAX 2000XL, and NeoSystem). The sample id 

and replication of oven type and material type were selected as the random-effect 

variables owing to the sampling design; the same sample material was placed 

randomly into the different oven types and was different for each replication. For 

the live fuel tests (tests 3 and 4) oven type and size of sample were fixed effects. 

The variance-covariance structure was based on the random effect (sample id) 

and on the replication of oven type and size of sample. Evaluation of the residuals determined if there was a trend in the ranking of each of the treatments when 

analyzed within each of the samples. Each test was analyzed independently. Equality of treatment means was tested using Tukey’s test (Mason 1989), a conservative 

multiple comparison test, with a significance level of 5 percent. 
23 

Evaluation of Standard Methods for Collecting and Processing Fuel Moisture Samples

Results and Discussion
 

Test 1 was designed to determine if the ovens operated under the manufacturer’s 

recommended settings would produce equal moisture content estimates. Mean 

moisture content was about 167, 75, and 58 percent for the excelsior, dowel, and disc 

fuel types, respectively. Mean moisture content of the 3⁄4-inch dowels and 3-inch 

discs did not differ between the QRO and convection oven (table 9). For excelsior, 

the Computrac mean was significantly higher than the NeoSystem, and the soil 

cans dried in the convection oven. Drying the fuels in cans or bottles in the convection oven did not significantly affect moisture content. The second test (test 2) 

compared the effect of using a 95 oC (203 °F) drying temperature in the Computrac 

to determine moisture content of dead fuels as was done originally (Sackett 1980). 

At this drying temperature, the mean moisture content of the Computrac treatment 

was significantly lower for all fuel size classes by 16 to 30 percent. There was no 

significant difference between the other ovens. 

Test 3 and 4 evaluated the effects of the different ovens and sample mass on 

moisture content of mountain laurel and scrub oak leaves, respectively. The Computrac QRO ran at the same temperature as the NeoSystem QRO, which was lower 

than the manufacturer’s recommendation. For both live fuels, the means for the 

fuel containers used in the convection oven did not differ significantly (table 10) 

and the values were within 1 to 2 percent. For scrub oak (test 4), the NeoSystem 

did not differ from the convection oven nor did the Computrac with a small sample 

Table 9—Summary of multiple comparison tests of fuel moisture 

means for three dead fuel types using two quick-response ovens 

and a convection ovena

Fuel type 

Test Oven type Excelsior 3⁄4-inch dowel 3-inch disc 

disc 

Test 1 Computrac 172.03a 76.95a 57.71a 

NeoSystem 165.06b 74.95a 57.08a 

Convection–can 165.22b 75.41a 57.95a 

Convection–bottle 170.28ab 74.78a 58.49a 

Test 2 Computrac 127.92a 58.27a 49.19a 

NeoSystem 155.06b 79.81b 65.76b 

Convection–can 155.58b 79.87b 65.50b 

Convection–bottle 160.98b 79.67b 65.71b 

a

 Test description contained in table 7. Comparison of means performed using Tukey test. 

Moisture content means (percentage) followed by same letter did not differ at 0.05 level. 
24 

PACIFIC SOUTHWEST RESEARCH STATION PSW-RP-268

Table 10—Summary of multiple comparison tests of 

fuel moisture means for two live fuel types using two 

quick-response ovens and a convection ovena

Fuel type 

Mountain 

Oven type Sample mass Scrub oak laurel 

Convection–bottle (variable) 64.14 a 146.89a 

Convection–can 4 61.45 ab 147.42a 

Convection–can 8 62.50 a 145.75a 

Computrac 4 60.28 ab 139.7 b 

Computrac 8 57.30 b 132.14c 

NeoSystem 4 60.01 ab 138.93b 

NeoSystem 8 60.78 ab 141.68b 

a Test description contained in table 7. Comparison of means performed 

using Tukey test. Moisture content means (percent) followed by same 

letter did not differ at 0.05 level. Sample mass in grams; variable ranged 

from 25 to 122 g. 

mass. The Computrac with the large sample mass was significantly lower than the 

convection oven; however, the difference ranged from 4 to 7 percent. For mountain 

laurel (test 3), which had a mean moisture content of 147 percent, estimates from 

both QROs were lower than the convection oven, and the Computrac QRO with the 

large sample mass was l5 percent lower than the convection oven. 

When discussing the results of all of these tests, the treatment means are 

ultimately, compared to the bottle results, considered most accurate. Using the 

manufacturer’s recommended drying temperatures of 170 or 180 oC (338 or 356 °F), 

the Computrac performed well when compared to the bottle for the 1-hr dead fuelsize class (higher but not significantly higher) and consistently well when compared 

with the other treatments for large fuel size material. It was unclear whether the 

difference in treatment mean moisture contents was due to the size of material or 

the level of moisture in the sample material. Additional testing of the three fuel-size 

classes with more similar moisture content levels would easily determine the relationship between these two issues. Because the test design incorporated the original 

drying temperature, we expected the results of the Computrac to be similar to the 

bottle results, especially for the 1-hr fuel-size class. Currently, we recommend that 

field personnel use the quick-response systems for general information about project work and not for drying trend information. They appear to be accurate when run 

at higher drying temperatures recommended by the manufacturer. The Computrac 

was not accurate when set at the lower temperature of 95 oC (203 °F). The present 
25 

Evaluation of Standard Methods for Collecting and Processing Fuel Moisture Samples

 

 

 

study supports the conclusion reached by Jolly and Hadlow (2012) that drying live 

fuels in a Computrac with temperature set at 145 °C (298 °F) will provide results 

comparable to a convection oven in a shorter period of time. 

Metric Equivalents 

When you know: Multiply by: To find: 

Inches 2.54 Centimeters 

Feet .305 Meters 

Mils 2540 Centimeters 

Pounds 454 Grams 

Ounces (fluid) 0.0296 Liters 

Quarts 0.947 Liters 

Literature Cited 

Albini, F.A.; Reinhardt, E.D. 1995. Modeling ignition and burning rate of large 

woody natural fuels. International Journal of Wildland Fire. 5(2): 81–91. 

Anderson, H.E. 1990. Moisture diffusivity and response time in fine forest fuels. 

Canadian Journal of Forest Research. 20(3): 315–325. 

Arizona Instrument LLC. 2002. COMPUTRAC® MAX® 2000XL moisture 

analyzer operation manual. Chandler, AZ: Arizona Instrument LLC. 69 p. 

http://www.azic.com/wp-content/uploads/2014/12/700-0028-ComputracMAX%C2%AE-2000XL-Users-Manual.pdf (May 16, 2015). 

ASTM International. 2007. D 4442–07 Standard test methods for direct moisture 

content measurement of wood and wood-base materials. West Conshohocken, 

PA: ASTM International. 3 p. 

Brenner, J. 2002. Measuring live fuel moistures in Florida: standard methods 

and procedures. Tallahassee, FL: Florida Division of Forestry. http://www. 

freshfromflorida.com/content/download/4761/30337/procedures.pdf. 14 p 

(May 14, 2015). 

Carlson, J.D.; Bradshaw, L.S.; Nelson, R.M.; Bensch, R.R.; Jabrzemski, 

R. 2007. Application of the Nelson model to four timelag fuel classes using 

Oklahoma field observations: model evaluation and comparison with National 

Fire Danger Rating System algorithms. International Journal of Wildland Fire. 

16(2): 204–216. 
26 

PACIFIC SOUTHWEST RESEARCH STATION PSW-RP-268

Countryman, C.M. 1977. Radiation effects on moisture variation in ponderosa 

pine litter. Res. Pap. PSW-RP-126. Berkeley, CA: U.S. Department of 

Agriculture, Forest Service, Pacific Southwest Forest and Range Experiment 

Station. 23 p. 

Countryman, C.M.; Dean, W.A. 1979. Measuring moisture content in living 

chaparral: a field user’s manual. Gen. Tech. Rep. PSW-GTR-36. Berkeley, CA: 

U.S. Department of Agriculture, Forest Service, Pacific Southwest Forest and 

Range Experiment Station. 27 p. 

Deeming, J.E.; Lancaster, J.W.; Fosberg, M.A.; Furman, R.W.; Schroeder, 

M.J. 1974. National Fire-Danger Rating System. Res. Pap. RM-RP-84 (Rev.). 

Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Forest and Range Experiment Station. 53 p. 

Fosberg, M.A. 1970. Drying rates of heartwood below fiber saturation. Forest 

Science. 16(1): 57–63. 

Fosberg, M.A.; Rothermel, R.C.; Andrews, P.L. 1981. Moisture content 

calculations for 1000-hour timelag fuels. Forest Science. 27(1): 19–26. 

Glass, S.V.; Zelinka, S.L. 2010. Moisture relations and physical properties 

of wood. In: Wood handbook: wood as an engineering material: chapter 4. 

Centennial ed. Gen. Tech. Rep. FPL-GTR-190. Madison, WI: U.S. Department 

of Agriculture, Forest Service, Forest Products Laboratory: 4.1–4.19. 

Haase, S.M.; Zahn, S.M. 2007. Evaluation of fuel-sample containers. Fire 

Management Tech Tips 0751 1306P. San Dimas, CA: U.S. Department of 

Agriculture, Forest Service, San Dimas Technology and Development 

Center. 6 p. http://www.fs.fed.us/t-d/php/library_card.php?p_num=0751%20 

1306P. (May 14, 2015). 

Haase, S.M.; Zahn, S.M.; Burke, G.M.; Corcoran, B.G. 2014. Fuel moisture 

extraction data measured to compare chainsaw and handsaw methods. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, Forest Service 

Research Data Archive. http://dx.doi.org/10.2737/RDS-2014-0011. 

(May 14, 2015). 

Harris, R.A. 1982. Rapid determination of wood fuel moisture content using a 

microwave oven for drying. Forest Products Journal. 32(10): 56–58. 
27 

Evaluation of Standard Methods for Collecting and Processing Fuel Moisture Samples

Hartmann, H.; Böhm, T. 2001. Rapid moisture content determination of wood 

chips-results from comparative trials. In: Kyritsis, S., ed. Proceedings, 1st world 

conference on biomass for energy and industry. Sevilla, Spain: James & James 

Ltd.: 571-574. http://www.cem.de/documents/pdf/publikation/feuchte/ 

Sevilla00.pdf. (May 14, 2015). 

Jolly, W.M.; Hadlow, A.M. 2012. A comparison of two methods for estimating 

conifer live foliar moisture content. International Journal of Wildland Fire. 21(2): 

180–185. 

Lancaster, J.W. 1970. Timelag useful in fire danger rating. Fire Control Notes. 

31(3): 6–8, 10. 

Mason, R.L. 1989. Statistical design and analysis of experiments: with applications 

to engineering and science. Wiley Series in Probability and Mathematical 

Statistics. New York: Wiley. 

Nelson, R.M., Jr. 2000. Prediction of diurnal change in 10-h fuel stick moisture 

content. Canadian Journal of Forest Research. 30(7): 1071–1087. 

Norum, R.A.; Fischer, W.C. 1980. Determining the moisture content of some 

dead forest fuels using a microwave oven. Res. Note INT-RN-277. Ogden, UT: 

U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range 

Experiment Station. 7 p. 

Norum, R.A.; Miller, M. 1984. Measuring fuel moisture content in Alaska: 

standard methods and procedures. Gen. Tech. Rep. PNW-GTR-171. Portland, 

OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Forest 

and Range Experiment Station. 34 p. 

Nyström, J.; Dahlquist, E. 2004. Methods for determination of moisture content 

in woodchips for power plants—a review. Fuel. 83(7-8): 773–779. 

Palmer, T.Y.; Pace, G.D. 1974. Microwave ovens for drying wildland fuels. 

Journal of Microwave Power. 9(4): 289–293. 

Pinheiro, J.C.; Bates, D.M. 2000. Mixed-effects models in S and S-PLUS. New 

York, NY: Springer. 528 p. 

Pollet, J.; Brown, A. 2007. Fuel moisture sampling guide. Salt Lake City, UT: U.S. 

Department of Interior, Bureau of Land Management, Utah State Office. 30 p. 

http://www.wfas.net/nfmd/references/fmg.pdf (May 14, 2015). 
28 

PACIFIC SOUTHWEST RESEARCH STATION PSW-RP-268

Sackett, S.S. 1980. An instrument for rapid, accurate determination of fuel 

moisture content. Fire Management Notes. 41(2): 17–18. 

Weise, D.R.; Wright, C.S. 2014. Wildland fire emissions, carbon and climate: 

characterizing wildland fuels. Forest Ecology and Management. 317: 26-40. 

Zahn, S.; Henson, C. 2011a. A synthesis of fuel moisture collection methods and 

equipment—a desk guide. 1151 1806P. San Dimas, CA: U.S. Department of 

Agriculture, Forest Service, San Dimas Technology and Development Center. 

31 p. http://www.fs.fed.us/t-d/php/library_card.php?p_num=1151%201806P. 

(May 14, 2015). 

Zahn, S.; Henson, C. 2011b. Fuel moisture collection methods: a field guide. 1151 

1803P. San Dimas, CA: U.S. Department of Agriculture, Forest Service, San 

Dimas Technology and Development Center. 11 p. http://www.fs.fed.us/t-d/php/ 

library_card.php?p_num=1151%201803P. (May 14, 2005). 

Zahn, S.M.; Haase, S.M. 2006. Chain-saw use in sampling fuel moistures. 

Fire Management Tech Tips 0651 1307P. San Dimas, CA: U.S. Department 

of Agriculture, Forest Service, San Dimas Technology and Development 

Center. 4 p. http://www.fs.fed.us/t-d/php/library_card.php?p_num=0651%20 

1307P. (May 14, 2015). 
 

This publication is available online at www.fs.fed.us/psw/. 

Pacific Southwest Research Station 

800 Buchanan Street 

Albany, CA 94710