United States 

Department of 

Agriculture 

Forest Service 

Intermountain 

Forest and Range 

Experiment Station 

Ogden, UT 84401 

Research Paper 

INT-337 

March 1985 

Predicting Duff and 

Woody Fuel 

Consumed by 

Prescribed Fire in 

the Northern Rocky 

Mountains 

This file was created by scanning the printed publication.

Errors identified by the software have been corrected;

however, some errors may remain.
THE AU'THORS RESEARCH SUMMARY 

JAMES K. BROWN received his bachelor's degree from 

the University of Minnesota in 1960, his master's from 

Yale University in 1961, and his Ph.D. from the University of Michigan in 1968, all in forestry. From 1961 to 

1965, he did research on field measurement of fuel 

properties and fire-danger rating systems while with 

the Lake States Forest Experiment Station. In 1965, he 

transferred to the Northern Forest Fire Laboratory, 

Missoula, MT, where he conducted research on the 

physical properties, inventory, and prediction of fuels. 

Since 1979, he has been leader of a fire effects and 

use project in Missoula. 

MICHAEL A. MARSDEN began his career as a forestry 

aid at the Forest Insect Research Laboratory, Beltsville, MD, in 1960. He received his B.S. from Utah State 

University in 1963 (applied statistics), and his M.S. 

from Colorado State University in 1973 (forest biometry). He was employed as a statistician for the Intermountain Forest and Range Experiment Station from 

1963 through 1982. During this time he was stationed 

first at the Forestry Sciences Laboratory at Moscow, 

ID, and later at the Northern Forest Fire Laboratory. 

His present position is a biometrician with the 

Methods Application Group of Forest Pest Management Staff (Washington Office) stationed at Fort Collins, CO. 

KEVIN C. RYAN received his bachelor's degree in forest biology from Colorado State University in 1973 and 

his master's degree in forest ecology, also from 

Colorado State University, in 1976. From 1975 to 1976, 

he did prescribed fire research at the Rocky Mountain 

Forest and Range Experiment Station, Fort Collins, 

CO. In 1976, he transferred to the Pacific Northwest 

Forest and Range Experiment Station, Seattle, WA, 

where he conducted research on prescribed burning in 

mixed conifer shelterwoods. Since 1979 he has been 

at the Northern Forest Fire Laboratory, where he is involved in research on predicting the effects of fire on 

fuels, soils, and vegetation in the coniferous forests of 

the Northern Rocky Mountains. 

Relationships for predicting consumption of forest 

floor duff and downed, dead, woody fuel were determined to assist managers in planning prescribed fires. 

Data were analyzed from three previous prescribed fire 

studies in slash and nonslash fuels in cover types 

comprising a mixture of western larch, Douglas-fir, 

ponderosa pine, lodgepole pine, Engelmann spruce, 

subalpine fir, and grand fir. Duff depth reduction, percentage duff depth reduction, and percentage mineral 

soil are shown as regression functions of lower duff 

moisture content, entire duff moisture content, National Fire-Danger Rating System (NFDR) 1,000-hour 

moisture content, Canadian Adjusted Duff Moisture 

Code, preburn downed woody fuel loading, and 

preburn duff depth. Tests of the duff consumption 

relationships against other published data support 

their wide application. 

Lower duff moisture content was the best predictor. 

Preburn downed woody fuel loading was of minor importance in the relationships. The NFDR 1,000-hour 

moisture predicted duff consumption with adequate 

precision for general guidance in developing fire 

prescriptions. 

The NFDR 1,000-hour moisture was a better predictor of duff consumption and lower duff moisture than 

were two Canadian Duff Moisture Codes. The relationship between percentage mineral soil exposure and 

percentage duff reduction indicates that combustion 

in duff progresses both downward and laterally. 

Consumption of downed woody fuel correlated 

strongly with preburn loadings. Percentage consumption, however, related weakly to all independent variables. Consumption differed substantially between 

slash (81 percent) and nonslash (46 percent). An evaluation of Sandberg and Ottmar's (1983) diameter reduction model based on large pieces of fresh slash underestimated by 35 percent the consumption of mostly 

rotten nonslash fuels, indicating the extent that consumption differs between sound and rotten material. 

ELIZABETH D. REINHARDT received a bachelor's degree in English from Harvard University in 1978 and a 

master's degree in forestry from the University of Montana in 1982. She is currently employed as an operations research analyst at the Northern Forest Fire 

Laboratory. 
Predicting Duff and Woody 

Fuel Consumed by Prescribed 

Fire in the Northern Rocky 

Mountains 

James K. Brown 

Michael A. Marsden 

Kevin C. Ryan 

Elizabeth D. Reinhardt 

INTRODUCTION 

Predicting the consumption of forest floor duff is essential to skillful planning of prescribed fires. Duff includes both the fermentation and humus layers of the 

forest floor. Soil scientists call duff the 02 soil horizon. 

Duff lies below the litter and above mineral soil. Duff influences many facets of the forest ecosystem; thus broad 

ecological knowledge is needed to evaluate the effects of 

its combustion. Duff and associated downed woody fuel 

must often be removed to reduce fire hazard, prepare 

seedbeds, kill selected vegetation, and stimulate desired 

plants. In contrast, retention of duff and woody material 

may be needed to protect sites from sun and erosion, enhance microbial activity, and provide small animal habitat. Smoke from the burning of duff may adversely affect air quality. To successfully accomplish the 

sometimes complex objectives of prescribed fires, consumption of duff and woody fuel must be competently 

planned and executed. 

This paper presents numerical relationships of known 

precision for predicting duff consumption. They were established by assembling and analyzing data from three 

previous prescribed fire investigations and then testing 

them against other published data. Fuel loadings by diameter classes that differed among the studies were converted to a common set of diameter classes. The purpose 

of assimilating data from several studies was to develop 

predictive models that could be easily used by practitioners and that would apply over a wide range of 

conditions. 

Duff consumption is often expressed in three ways: 

depth reduction, percentage depth reduction, and percentage mineral soil exposed. Each expression is appropriate to evaluating certain prescribed fire objectives. 

Mineral soil exposure, for example, is commonly used to 

define site preparation needs. Depth of duff reduction relates to actual amount consumed and smoke production. 

Percentage duff reduction is useful for describing and 

setting objectives of prescribed fire to leave specified 

amounts of duff on site. 

Consumption of duff is strongly influenced by moisture content. Van Wagner (1972) developed a model of 

duff consumption based on theoretical considerations of 

variation of flame emissivity with water content. Empirical coefficients for the model were derived from jack 

pine (Pinus banksiana), red pine (Pinus resinosa), and 

eastern white pine (Pinus strobus) stands in eastern 

Canada. Sandberg (1980) found that Van Wagner's 

models required modifications to provide good predictions for underburning in partially cut Douglas-fir 

stands in Washington and Oregon. 

Sandberg's (1980) findings, and those by Artley and 

others (1978), Norum (1977), and Shearer (1975) in western Montana agreed well and suggested that duff burns 

independently of surface fuels below a duff moisture content of about 30 percent. In contrast, burning is meager 

above a moisture content of about 120 percent. Between 

these moisture limits, combustion of duff appears related 

to its moisture content and heat from surface fire. Other 

factors such as preburn duff depth and physical disturbances of duff may also influence the degree of consumption. Because prescribed burning is frequently done at 

duff moisture contents between 30 and 120 percent, the 

relationships for predicting consumption need to be better understood and quantified. 

Some fire weather indices correlate reasonably well to 

duff consumption. For example, Van Wagner (1972) and 

Chrosciewicz (197 8a,b) related duff consumption to the 

Duff Moisture Code of the Canadian Fire Weather System. Beaufait and others (1977) regressed duff consumption against upper duff moisture content and the Buildup Index from the old National Fire-Danger Rating System (USDA Forest Service 1964). Sandberg (1980) developed equations for predicting duff depth reduction and 

mineral soil exposure from the National Fire-Danger 

Rating System (NFDR) 1,000-hour moisture model 

(Deeming and others 1977). Although fire-danger rating 

indices relate only indirectly to duff moisture content, 

they are easily determined. Thus, relationships between 

fire-danger rating indices and duff consumption can be 

valuable for planning if they are adequately precise. Additional investigation is needed to confirm or modify 

Sandberg's findings with the widely used NFDR 

1.000-hour moisture model. 
Mineral soil exposure following fire can vary considerably. Efforts to predict exposure have sometimes been unsuccessful (Van Wagner 1972; Chrosciewicz 1978a,b). 

Norum's (1977) procedure for predicting mineral soil exposure, which assumes that duff burns off in uniform 

layers, was successful in a study of undisturbed fuels, 

but was unsuccessful when applied following partial cuttings (Sandberg 1980). Physical disturbance of the duff 

and a tendency for duff to burn laterally rather than 

downward may cause inconsistent results when using 

this method. 

Martin and others (1979) concluded that fuels less 

than M-inch diameter are almost completely consumed 

by fire over a wide range of environmental conditions. 

Branchwood from M- to 3-inch diameter is largely consumed. Norum (1976) found that consumption of 0- to 

M-inch, M- to 1-inch, and 1- to 3-inch diameter woody 

fuels was strongly correlated to preburn loading of these 

fuels and moisture content. Other than this, little has 

been reported on predictive equations for consumption of 

small, downed, woody fuels (3-inch diameter and 

smaller). 

Consumption of large woody fuels (larger than 3-inch 

diameter) depends primarily on their moisture content, 

degree of rot, and arrangement. Albini 11976) developed 

a theoretical model that predicts consumption of large 

fuels and time history of intensity. The model, which is 

largely untested, assumes a random distribution of 

pieces and depends on moisture content and planform 

overlap. In underburning of natural fuels, Norum (1976) 

found that moisture content and loading of smaller fuels 

were significant predictors of large fuel consumption. 

Sandberg and Ottmar (1983), in cable-yarded logging debris, observed that unit average consumption was not influenced by piece arrangement, species composition, or 

age of slash. They furnished equations for estimating 

large fuel consumption as a function of fuel moisture 

content. 

By examining data from several sources, this study 

furnishes predictive equations over a wide range of 

preburn fuel conditions. Experimental fires in slash and 

nonslash fuels from several cover types and ignited in 

varying patterns are included in the analysis. A new 

technique is used to evaluate exposure of mineral soil. 

The predictive equations include one or two independent 

variables readily obtained by practitioners. Practical 

results are presented in a section on management applications and the details of analysis discussed in another 

section. 

METHODS 

Data from the following prescribed burn studies in 

western Montana and northern Idaho were assimilated 

and analyzed: 

1. Beaufait and others (1977)-This study was conducted in clearcut logging slash created in old-growth 

forests when utilization standards were less stringent 

than today (fig. 1). This was perhaps the first study in 

Figure 7.-A broadcast burn in clearcut logging slash at Miller Creek. 
the Western United States to relate burn accomplishment to fuel quantity and moisture content quantitatively. Slashing and directional felling were used to 

maintain fuel continuity. No mechanical preparation was 

done within the study units. The study is referred to as 

Miller Creek-Newman Ridge (MN) in this paper. 

2. Norum (1976)-This study occurred in an uncut 

mature stand dominated by Douglas-fir (fig. 2). It was 

prompted by the need for guides to assist in the planning of underburns to reduce fuels. This study is referred to as Lubrecht (L). Data were analyzed as spring 

(Ls) and fall (Lf) sets. 

3. Baaken and Neuenschwander (1981)-This study 

was conducted in seral ponderosa pine stands that had 

been selectivelv harvested followed bv mechanical ~iling 

in 1978. ~og&ng disturbance to the surface fuel aid soil 

varied substantially among units. The purpose of the 

study was to relate preburn conditions and fire behavior 

to accomplishment of silvicultural objectives. This study 

site is referred to as northern Idaho (NI). 

Study sites are described further in table 1. 

Analysis of duff was emphasized in this report because 

the data were sampled with respectable reliability in all 

studies and promised to offer relationships useful to 

planning of prescribed fires. Small woody fuel, which inTable 1.-Description of study sites 

Figure 2.-Fuel and stand conditions prior to 

underburning a unit at Lubrecht. 

cludes material less than 3 inches in diameter, was also 

adequately sampled in all studies and was used as an independent variable to predict duff consumption. Sampling of large fuels, however, was adequate only for 

Lubrecht and northern Idaho. Our analysis was directed 

at determining relationships not explored in the other 

studies and examining relationships from combined data. 

Site Miller - Newman 

Location Flathead and Lolo 

National Forests, 

MT 

Cover type Western larch1 

Douglas-fir 

primarily; other 

species were grand fir, 

ponderosa pine, 

lodgepole pine, 

subalpine fir, 

Engelmann spruce, and 

western white pine 

Plot size, 

acres 2- % 

Slope, 

percent 9 to 76 

Elevation, 

feet 4,200 to 5,400 

Burn dates May, June, July, 

Aug., Sept., Oct., 

1967 - 69 

Dead 1 -hour 

timelag fuel 

moisture, 

percent 6 to 21 

Ignition 

pattern Single headf ire 

Lubrecht Northern Idaho 

University of Montana Coeur d'Alene 

Lubrecht Experimental Indian Rese~ation, 

Forest, MT ID 

Western IarchlDouglas - Ponderosa pine; 

fir sparse Douglas -fir 

understory 

May, June, July, 

Sept., Oct., 

1973 

2,590 to 2,890 

Sept., Oct., 1978 

Strip headfire Strip headfire, 

single headfire, 

backfire 
Small fuel loadings ranged from 0.1 to 36 tonslacre (0.2 

to 81 tlha). Duff depths ranged from 0.1 to 4.3 inches 

(0.3 to 11 cm). Ranges of all variables and their abbreviations are shown in table 2. 

To facilitate comparison of data for small, woody fuels, 

loadings by diameter class for Miller Creek and Newman 

Ridge were converted to the conventional 0- to M-inch 

(0- to 0.6-cm), M- to 1-inch (0.6- to 2.5-cm), and 1- to 

3-inch (2.5- to 7.6-cm) classes, appendix I. These correspond to the NFDR I-, lo-, and 100-hour average 

moisture timelag size classes (Fosberg 1970) adhered to 

in the other studies. 

No attempt was made to model fuel reduction 

separately for 0- to M-inch, M- to 1-inch, and 1- to 3-inch 

classes. Instead, diameter classes were grouped. Woody 

fuels 0 to 1 inch and 0 to 3 inches were analyzed as separate variables. Fuel consumption for 0- to M-inch, M- to 

1-inch, and 1- to 3-inch classes was not evaluated because incomplete combustion may cause woody pieces to 

change diameter classes. This introduces error in determining consumption for any single class. The 0- to 1-inch 

and 0- to 3-inch classes were considered large enough to 

furnish reasonably accurate estimates of consumption by 

minimizing these errors. 

Table 2.-Ranges of variables studied 

Study Location1 

Variable Abbreviation MN L~ L s NI 

Duff depth reduction, 

percent DR% 

M% 

DR 

DPRE 

LDM 

EDM 

Mineral soil exposure, 

percent 

Duff depth reduction, 

inches 

Preburn duff depth, 

inches 

Lower duff moisture 

content, percent 

Entire duff moisture 

content, percent 

NFDR 1,000-hour 

timelag moisture 

content, percent TH 

ADMC 

Canadian Adjusted 

Duff Moisture Code 

Preburn 0-3-inch 

woody fuel load, 

tonslacre 

0-3-inch woody fuel 

consumed, tonslacre 

Preburn 0- 1-inch 

woody fuel load, 

tonslacre 

Preburn 3+-inch 

woody fuel load, 

tonslacre WTLG 

CWTLG 

RI 

3 + woody fuel 

consumed, tonslacre 

Reaction intensity, 

Btulminlft2 

'MN = Miller-Newman; Lf = Lubrecht, fall; L, = Lubrecht, spring; NI = northern Idaho 

4 
A strategy for analysis involved these steps: Preliminary Duff Analysis 

1. Examine scattergrams to identify outliers and possible relationships. A few implausible outliers were 

discarded. 

2. Perform regression analysis on candidate relationships for separate and pooled data sets. Criteria followed 

in determining functional relationships were to keep 

functional forms as simple as possible, keep the number 

of variables to a minimum, and involve only variables 

that have a physical reason for existence and can be obtained by managers. 

3. Select good-fitting relationships: regressions that 

have low standard errors of estimate and predict well for 

other data sets. Goodness-of-fit of given functions to 

their own and to other data sets was evaluated using the 

average difference between observed and predicted 

values to indicate bias (table 3). The root mean square of 

differences between observed and predicted values 

provided estimates of precision. Standard errors of the 

estimate from regression analysis were numerically close 

to the root mean square of differences. They differed 

only in that the denominator for the standard error was 

degrees of freedom rather than number of observations. 

Standard errors and r2 values for several segmented 

regressions were based on data for both segments. 

4. Pick the best fitting relationships and test them 

against other fuel consumption data and relationships in 

the literature. "Best" equations were those having the 

smallest standard errors and most consistent predictions. 

Determination of mineral soil exposure.-Mineral soil 

exposure was not observed directly on the study sites. 

Instead, it was calculated from preburn and postburn 

duff depths measured at duff spikes (fig. 3). Mineral soil 

was considered exposed when 0.4 inch (1.0 cm) or less of 

unburned organic material was left after a fire. This 

criterion of mineral soil was chosen because postfire duff 

less than 0.5 inch (1.3 cm) in depth can be considered 

mineral soil for germinating seedlings (DeByle 1981; 

Shearer 1983). Germinating seedlings can penetrate 

through 0.5 inch of duff to become established in 

mineral soil. Also, additional duff reduction due to physical deterioration and decomposition seems to occur during the year or so following fire. This reduces residual 

duff even further beyond its immediate postfire quantity. A third reason for the 0.4-inch (1.0-cm) residual duff 

criterion is that residual duff depths cannot be measured 

very accurately until more than about 0.2 inch (0.5 cm) 

of duff is present. Scattergrams of percentage mineral 

soil versus median residual duff depth, using 0.2 inch 

and 0.4 inch as criteria for mineral soil showed less scatter for the 0.4-inch criterion. Thus, a 0.4-inch residual 

depth appeared large enough to measure accurately and 

small enough to qualify as a mineral soil seedbed. 

Minimum duff depth.-Examination of scattergrams 

indicated that at preburn duff depths of less than about 

0.8 inch (2 cm), the relationships between duff consumption and the independent variables became erratic. For 

Table 3.-Statistics on precision and bias for duff reduction and mineral soil exposure equations shown in the text 

Data 

source1 

Average (0 - P)2 

se MN LI Ls NI 

Root mean square (0 - P) 

Equation MN L t LS NI 

Duff Depth Reduction, Inch 

9 0.03 -0.67 - 1.26 

0 .34 - .32 - 1.43 

- .02 .14 - .81 - .89 

0 .47 -.41 - 1.10 

- .04 .25 -.40 - .45 

.06 .08 -.62 - .06 

- .05 .27 -.I5 - .59 

Duff Depth Reduction, Percent 

.6 -3.6 -25.1 - 

.6 -2.6 -21.6 - 

- .3 1.6 -30.4 -49.4 

.7 6.6 - 14.1 - 

- 54.7 -.l -18.1 - 

.6 -3.2 -25.5 - 15.1 

1.9 4.9 - 16.0 - 3.6 

.5 -2.6 - 15.3 -20.5 

- 2.8 13.8 2.4 - 

Mineral Soil Exposure, Percent 

2.6 -14.3 -23.5 - 

21.6 0 - 10.0 - 

1.7 -8.2 -30.3 - 

1.8 -1.4 -12.2 - 

0 -18.3 -31.1 - 

22.6 0 - 10.6 - 

2.7 - 13.4 -5.9 - 

MN, Lf 

MN 

MN, Lf 

MN 

MN, L, 

MN, L,, NI 

MN, L' 

MN, L, 

MN, Lf 

MN, Lf 

MN, L 

L f 

MN, Lf 

MN, Lf, NI 

MN, Lf 

MN, L, 

MN, Lf '- f 

MN, Lf 

MN, L 

MN 

f 

MN, Lf 

'L = all Lubrecht data; Lf = Lubrecht fall; L, = Lubrecht spring. 

'0 = observed average values from burned units; P = predicted values 

5 
to improve precision or show consistent positive correlaFigure 3.-Measurement of duff reduction 

along a spike. The head of the spike was 

flush with the top of the duff before the fire. 

example, sometimes plots with very dry duff shoked almost total consumption and sometimes very limited consumption. Variability in consumption was more plausible 

for duff greater than 0.6 to 0.8 inch (1.5 to 2 cm) in 

depth. 

In a current study of duff combustion, Frandsen 

(1983) similarly observed that sustained burnout of duff 

ceases at an average depth of 0.6 inch (1.5 cm). 

Frandsen (1983) also found that such shallow duff 

tended to have more incorporated mineral matter, which 

may retard combustion. Because of the erratic consumption of shallow duff layers, preburn duff depth observations less than 0.6 inch (1.5 cm) were eliminated from 

our analyses. 

Possible bias in MN data.-Beaufait and others (1977) 

reported that a bias due to dependence between fuel 

loadings and fuel moisture content developed at Miller 

Creek. Plots having heavy downed woody loadings were 

burned at higher fuel moistures because of a tendency to 

prescribe-burn the most flammable plots under relatiGely 

safe fire-weather conditions. 

Correlation analysis and scattergrams suggest that the 

bias has more influence on downed woody fuel consumption than on duff consumption. Plots of duff depth over 

NFDR 1,000-hour moisture content show no correlation 

(r = 0.026). Duff depth and lower duff moisture were 

weakly correlated (r = 0.17). Duff depth and upper duff 

moisture content, which we did not include as a variable. 

were correlated (r = 0.37). Regressions using NFDR 

1,000-hour moisture content and 0- to 3-inch woody fuel 

loading as dummy variables were attempted to overcome 

possible bias difficulties. This attempt, however, failed 

tions between duff consumption and 0- to 3-inch woody 

fuel loading. 

In analysis of small woody fuels, 0- to %-inch and %- 

to 1-inch diameter loadings were positively correlated 

with 1- and 10-hour tirnelag moisture contents. However, 

NFDR 1,000-hour moisture content and 1- to 3-inch 

woody fuel loadings were not correlated. The extent of 

possible bias in duff and small woody fuel consumption 

remains vague, but was probably inconsequential in duff. 

Spring fires.-Fires occurring during May and June 

were considered spring burns. Eight Miller-Newman 

spring fires were recorded. Scattergrams indicated that 

Miller-Newman spring and fall fires were from a common 

population. Nine Lubrecht spring fires were recorded. 

Our scattergrams agreed with Norum's (1976) previous 

analysis and indicated that spring and fall fires were 

from different populations. The Lubrecht spring fires 

happened during a particularly dry spring following a 

winter of low snowfall. Because these fires burned during unusual spring weather and indicated a different pattern of fuel consumption than the fall fires, we (like 

Norum) analyzed the data as separate sets. Analyses including Lubrecht spring fires usually showed reduced 

precision compared to analyses not including spring 

fires. In a few cases, however, inclusion of these fires 

with other data sets improved precision. 

Transformation of data.-Nonlinear relationships existed among some variables. To improve linearity, 

reciprocal and logarithmic transformations of most variables were tried in correlation and regression analysis. 

Transformations improved some correlations, but the improvements were generally small and not consistent 

among the data sets. Correlation coefficients between 

transformed and untransformed variables are displayed 

in appendix 11. Both NFDR 100-hour and 1,000-hour 

timelag moisture contents were analyzed as independent 

variables because the moisture timelag of duff may be 

between the two. They were positively correlated as expected. In regression analysis, the 1,000-hour timelag 

moisture related more precisely with duff consumption; 

thus, equations having the 100-hour were not reported. 

Lower and upper duff moisture.-Moisture contents 

were measured for the lower half and upper half of duff 

at Miller-Newman and Lubrecht, and for the entire duff 

profile in northern Idaho. An average moisture content 

(EDM) for the entire duff profile at Miller-Newman and 

Lubrecht was calculated as the mean moisture content 

for the lower half and upper half of the duff. Moisture 

contents for the lower and upper duff layers were positively correlated. Moisture for the lower layer was more 

highly correlated with dependent variables than was true 

for the upper layer. In regression analysis, upper duff 

moisture failed to explain significant variation beyond 

that explained by lower duff moisture. Thus, the results 

reported are based on moisture for the lower duff layer 

and the entire duff profile. 

MANAGEMENT APPLICATIONS 

This section contains the best relationships for predicting fuel consumption to help in planning prescribed fires. 
They should apply to a wide range of fuels and cover 

types. The duff consumption relationships are the most 

useful because they are reasonably precise and relate to 

many important fire effects. 

The first step in planning is to clearly define the objectives of the fire apart from the purpose for prescribing 

it. The objectives should specify what the fire itself is to 

accomplish immediately. These fire objectives basically 

involve specifying how much organic material should be 

consumed and what vegetation should be killed or left 

alive. Constraints on allowing a fire to burn must also be 

clearly defined. Constraints obviously include control of 

the fire and may also involve needs to retain some duff 

and large downed woody material on the burned sites. 

Once the desired removal of duff and woody material is 

specified, fuel moisture contents expected to achieve it 

can be determined from the relationships in this report. 

Determining a fire prescription to result in specified 

amounts of fuel consumption can be viewed as a threestep process: 

1. Decide how much fuel should be left on the ground 

after the fire. 

2. Determine the amount of fuel that should be 

removed. 

3. Determine a range in fuel moisture contents for 

achieving the specified fuel removal. 

Steps 1 and 2 require consideration of many factors, 

including needs for seedbed preparation, fuel hazard 

reduction, protection of sites to resist erosion, and provision for maintaining desirable nitrogen levels. Needs for 

duff removal and duff retention may conflict and may 

require either a compromise on objectives and constraints or the selection of another alternative. The 

ranges in conditions tabulated below are suitable for application of the predictive relationships reported here. 

Conditions Range 

Average duff 0.5 to 4.5 

depth, inches 

Average lower 30% and greater 

duff moisture, pct 

Average entire 

duff moisture, pct 

2090 and greater 

Forest overstory species Best suited to 

midelevation forests 

dominated by western 

larch, Douglas-fir, 

grand fir, ponderosa pine, 

lodgepole pine, 

Engelmann spruce, 

subalpine fir, and western 

white pine 

Woody surface fuels 

Ignition method 

Slash and nonslash 

Strip head fires, 

possibly back fires; 

area ignition untested 

Application beyond these conditions could result in 

large errors. Most of the duff consumption relationships 

were derived from variable data. Thus, wide limits on 

reliability of predictions should be kept in mind when 

predicting duff consumption. Deviations from predicted 

values are probably influenced primarily by degree of 

soil disturbance, variation in duff depth and moisture 

content, and method of ignition. Generally, less consumption than predicted can be expected where logging 

disturbance is considerable. In contrast, more consumption can be expected where the firing method results in 

ignition of all surface fuels. The most accurate predictions can be expected where the terrain is uniform and 

the stands have developed under closed or nearly closed 

canopies. In prescribed burn units where stand history 

and microclimate are varied, the area can be stratified 

and more than one prediction made. 

Duff Depth Reduction 

The depth of duff consumed may be used to estimate 

the actual change in organic matter and nutrients on a 

site. It also relates to the amount of smoke produced by 

burning (Ottmar 1984). If seed stored in the forest floor 

and the rooting depth of plants are known, the depth of 

duff consumed can be used to evaluate a fire's impact on 

postburn succession. 

Duff depth reduction can be predicted from lower duff 

moisture content in figure 4. Knowledge of preburn duff 

depth (DPRE), from either on-site measurements or observations of similar sites, should be utilized in figure 4 

for best accuracy. If preburn depth is not known, assume it is 2 inches (5 cm). This value is close to the 

average preburn duff depth of this study. It is slightly 

greater than the average duff depth found over a broad 

range of cover types in the Northern Rocky Mountains 

(Brown and See 1981). 

LOWER DUFF MOISTURE (PCT) 

Figure 4.-Duff depth reduction versus lower 

duff moisture content by preburn duff depths 

(equation 1). One standard error about 

predictions is 0.3 inch (0.8 cm). Numbers 

along curves identify preburn duff depths. 
Moisture content for the entire duff profile can be substituted for lower duff moisture in figure 4. This will result in duff reduction being overpredicted by an average 

0.15 inches (0.4 cm), an inconsequential amount for practical purposes. Lower duff moisture is recommended for 

planning duff consumption in prescribed fires because it 

relates more closely to duff consumption over a wider 

range of conditions than entire duff moisture. Sometimes, however, it is not practical to separate duff moisture samples into upper and lower strata, particularly 

when duff depths are 1 inch or less. In such cases or for 

personal preference, the entire duff moisture can be applied satisfactorily to predicting duff consumption. 

The NFDR 1,000-hour moisture content can be used to 

predict duff depth reduction in figure 5. If duff depth is 

unknown, assume a depth of 2 inches (5 cm). The recommended use of figure 5 is for long-range planning and for 

judging when duff moisture contents are approaching 

the prescribed range. Users should be aware of two 

potential sources of error. First, the NFDR 1,000-hour 

moisture content is intended to apply to 3- to 6-inch (7.6- 

to 15-cm) logs (Deeming and others 1977), not duff. Log 

moisture content is dependent primarily on the duration 

of rain, whereas the duff moisture is more strongly determined by the amount. The second potential source of 

error is in extrapolating from a weather station to a 

burn site. If precipitation differs between these locations, predictions of duff consumption will be in error in 

proportion to the difference in precipitation. 

NFDR 1000 - HR MOISTLIRE (PCT) 

Figure 5.-Duff depth reduction versus 

NFDR 1,000-hour moisture by preburn duff 

depths (equation 6). One standard error 

about predictions is 0.3 inch (0.8 cm). Num. 

bers along curves identify preburn duff 

depths. 

Percentage Duff Depth Reduction 

Percentage duff depth reduction is useful because it 

describes duff consumption independent of duff depth. It 

is easily understood by others as a prescribed fire objective and is especially suitable for describing the amount 

of duff to be left unburned for needs such as site protection. Percentage duff reduction can be predicted from 

lower duff moisture, using figure 6. It can also be 

predicted by substituting entire duff moisture for lower 

duff moisture in figure 6 and adjusting percentage duff 

reduction as follows: 

Entire duff moisture Duff depth reduction 

Percent Percent 

Less than 80 Subtract 10 

80 to 120 Subtract 5 

Greater than 120 No change 

When duff moisture content exceeds 150 percent for 

either the entire duff profile or the lower layer, duff consumption will remain about 10 to 15 percent. This assumes that a spreading fire is possible. At low duff 

moistures, 80 to 100 percent consumption can be 

The relationship between percentage duff reduction 

and NFDR 1,000-hour moisture is imprecise and best 

used as an indicator of when duff moisture may be 

within prescription. Generally, when NFDR 1,000-hour 

moisture falls below 25 percent, percentage duff consumption begins increasing. Duff moisture contents are 

apt to be within prescription when NFDR 1,000-hour 

moisture is between 10 and 25 percent. 

LOWER DUFF MOISTURE (PCT) 

Figure 6.-Percentage duff reduction versus 

lower duff moisture content (equation 9). The 

dashed lines are one standard error prediction bands. 
The relationship between percentage duff reduction 

and percentage mineral soil exposure in figure 7 shows 

how much duff must be removed to expose varying 

amounts of mineral soil. It can be used, for example, to 

evaluate whether objectives to create mineral seedbeds 

are compatible with objectives to retain duff for site protection and as a source of nitrogen. 

MINERAL SOIL EXPOSURE (PCT) 

Figure 7.-Percentage duff reduction versus 

percentage mineral soil exposure (equation 

16). The dashed lines are one standard error 

prediction bands. As a rule of thumb, percentage duff reduction equals percentage 

mineral soil exposure plus 15 percent. 

Percentage Mineral Soil Exposure 

The predicting of mineral soil exposure is most commonly used for planning use of fire to prepare seedbeds. 

Percentage mineral soil can be predicted using figure 8, 

which offers separate curves for light and moderate to 

heavy downed woody surface fuels. Light fuels are considered as less than 10 tons per acre (22.4 tlha) of 0- to 

3-inch woody fuel. This includes slash from light cuttings and most nonslash fuels. Moderate to heavy fuels 

are considered as 10 tons per acre and greater of 0- to 

3-inch downed woody fuel typically from partial cutting 

and clearcutting activities. Moisture content for the entire duff profile can be substituted for moisture of the 

lower duff layer when estimating percentage mineral soil 

for the heavier fuel loading curve in figure 8, using the 

following adjustments: 

Entire duff moisture Mineral soil exposure 

Percent Percent 

Less than 40 Subtract 5 

40 to 90 Subtract 10 

90 to 120 Subtract 5 

Greater than 120 No change 

The relationship between percentage mineral soil exposure and NFDR 1,000-hour moisture was imprecise as 

it was for percentage duff reduction. The following 

guidelines indicate when duff moistures are within 

prescription: 

NFDR 1,Whour moisture Mineral soil exposure 

Percent Percent 

Greater than 25 Less than 10 

10 to 25 10 to 50 

Less than 10 Greater than 50 

Small Woody Fuels 

Generally, percentage consumption of small woody 

fuels is consistently high when loadings of this material 

exceed 10 tonslacre (22.4 tlha). Consumption is variable 

but considerably less at lighter loadings. The following 

tabulation offers as much precision for predicting percentage small woody fuel consumption as is reliable and 

practical: 

Small woody fuel loading Consumption 

Tons/acre Percent 

Less than 10 Less than 50 

10 and greater 70 to 90 

The influence of small woody fuel moisture content on 

percentage consumption appears minor once fire spread 

is sustained. When quantities of small woody fuels are 

light, however, percentage consumed depends partly on 

fuel continuity. Spread of fire is disrupted in sparsely 

distributed fuels, thus creating unburned fuel patches. 

In prescribed burning, however, the method of ignition 

LOWER DUFF MOISTURE (PCT) 

Figure 8. -Percentage mineral soil exposure 

versus lower duff moisture for 0- to 3-inch 

downed woody surface fuel loadings of less 

than 10 tons/acre (equation 18) and 10 

tons/acre and greater (equation 17). Standard 

errors for predictions are 10 and 16 percent, 

respectively. 
can partially overcome the breakdown in fire spread to 

increase fuel consumption. Because of this, prediction of 

percentage consumption in light quantities of small 

woody fuels may remain imprecise. 

Large Woody Fuels 

Although fuel moisture is clearly the primary influence 

on large woody fuel consumption, quantified guidance 

for predicting consumption is meager, especially in the 

Northern Rocky Mountains. Until new information is 

published, we suggest consulting the following sources 

of information for guidance: 

Recent harvesting slash (Sandberg and Ottmar 

1983).-The approximate relationship between large fuel 

consumption and NFDR 1,000-hour moisture (based on 

midpoint diameters of size classes) is: 

NFDR 1,000-hour Consumption 

moisture 3 to 6 inches 6 to 9 inches 

Percent Percent Percent 

10 100 80 

15 9 5 7 0 

20 80 5 5 

25 6 5 40 

3 0 40 25 

Nonslash fuel underburns (Norum 1976).-The following generality is based on the finding that large woody 

fuel consumption and duff reduction are correlated: 

Lower duff 

moisture Consumption 

Percent Percent 

0 to 40 50 to 100 

40 to 100 10 to 50 

100 + Less than 20 

ANALYSIS AND DISCUSSION 

Results of analyses are discussed separately under 

duff depth reduction, percentage duff depth reduction, 

and percentage mineral soil exposure. Statistics on precision and bias of the equations in this section are shown 

in table 3. In addition, numbers of burn units (N), coefficients of determination (r2), and standard errors of 

regression (se) are shown below each equation. Metric 

units for all equations are shown in appendix 111. When 

addition of a second independent variable resulted in a 

significant and meaningful improvement in precision, 

equations having one and two independent variables are 

presented. 

Duff Depth Reduction (DR) 

The best equations having lower duff moisture (LDM) 

as an independent variable were: 

DR = 1.028 - 0.0089 LDM + 0.417 DPRE (1) 

(N = 71, r2 = 0.76, se = 0.31) 

DR = 1.801 - 0.0079 LDM (2) 

(N = 60, r2 = 0.66, se = 0.34) 

The best equations having entire duff moisture (EDM) 

as an independent variable were: 

DR = 0.8811 - 0.0096 EDM + 0.439 DPRE (3) 

(N = 71, r2 = 0.72, se = 0.33) 

DR = 1.682 - 0.0085 EDM (4) 

(N = 60, r2 = 0.63, se = 0.36) 

Where duff layers are thin, such as in northern Idaho, 

it is impractical to separate upper and lower duff for 

moisture sampling. In these situations entire duff 

moistures are the most appropriate for characterizing 

duff. Where duff depth exceeds about 1 inch (2.5 cm), 

however, there is a tradeoff between simplicity and precision. Collecting lower duff samples by separating the 

lower one-half of the duff from the duff and litter above 

should produce the most precise prediction. In contrast, 

collecting samples from the entire duff layer avoids the 

task of separation but may result in a less precise estimate of duff consumption. 

Using both duff moisture and preburn duff depth 

(DPRE) as independent variables produces regression 

equations that are more precise than those with duff 

moisture as a single independent variable (compare equations l and 2 and equations 3 and 4). For preburn duff 

depths less than 2 inches and lower duff moistures less 

than about 50 percent, depth reduced approaches the 

preburn depth (fig. 4). Examination of plotted data 

showed that when duff is dry, preburn duff depth and 

duff depth reduced are highly correlated because dry 

duff generally burns out well. When damp, such as 

above about 100 percent moisture content, the correlation is poor; hence, preburn duff depth alone is not a 

reliable predictor. 

Predicted by fire weather indices.-The best equations 

having NFDR 1,000-hour moisture (TH) as an independent variable were: 

DR = 2.698 - 0.1035 TH (5) 

(N = 71, r2 = 0.58, se = 0.41) 

DR = 1.773 - 0.1051 TH + 0.399 DPRE (6) 

(N = 119, r2 = 0.75, se = 0.31) 

Although duff probably varies in its time response for 

drying, the NFDR 1,000-hour moisture correlated 

reasonably well with duff consumption over the range of 

depths studied. For regression with NFDR 1,000-hour 

moisture alone, equation 5 provided the best fit to all 

data of any combination of data sets. Addition of 

preburn duff depth to regression equation 6, improved 

precision for all data set combinations. Equation 6 is the 

most robust for application because it is based on the 

largest range in data. Bias averaged less than 0.08 

inches (2 mm) except for Lubrecht spring (table 3). The 

range in NFDR 1,000-hour moisture (7 to 25 percent) for 

equations 5 and 6 was reasonably large but somewhat 

limited on the moist end of the scale for extra~olation. 

Lower duff moisture content predicted duff consumption with only slightly more precision and less bias than 

NFDR 1,000-hour moisture, which was surprising because NFDR 1,000-hour moisture is only an indicator of 

moisture content. The relationship between lower duff 

moisture and NFDR 1,000-hour moisture (fig. 9) illustrates the possible limitations of relating duff consumption to fire-danger moisture models due to the imprecise 
association between moisture models and duff moisture 

content. Regression of duff consumption on the Canadian Adjusted Duff Moisture Code (Van Wagner 1974), 

equation 7, was slightly less precise than on NFDR 

1,000-hour moisture: 

DR = 0.4094 + 0.0070 ADMC (7) 

(N = 71, r2 = 0.48, se = 0.46) 

- 

MILLER-NEWMAN 

LUBRECHT. FALL 

A LUBRECHT, SPRING . . 

NFDR - 1000 HR MOISTURE (PCT) 

Figure 9.-Lower duff moisture content 

versus the NFDR 1,000-hour moisture 

content. 

Percentage Duff Depth Reduction (DR%) 

Predicted by duff moisture.-Prior work by Shearer 

(1975) and Norum (1977) at Miller Creek and Lubrecht 

showed similar relationships between percentage duff 

reduction and lower duff moisture content. Statistics on 

precision, however, were not furnished. We reexamined 

this relationship primarily to estimate sampling precision. Scattergrams indicated a curvilinear relationship 

between percentage duff reduction and lower duff moistilre and that the Miller-Newman and Lubrecht fall data 

could be pooled (fig. 10). The best linear regression fit 

was: 

DR% = 87.8 - 0.390 LDM (8) 

(N = 71, r2 = 0.71, se = 14.1) 

High variability in duff consumption at the lower duff 

moistures made the data difficult to fit. Reciprocal and 

logarithmic transformations failed to improve fit. Likewise, analysis using MATCHACURVE (Jensen and 

Homeyer 1970) failed to improve fit over equation 8. Finally, the following segmented regression improved precision slightly and fit Lubrecht fall data with slightly 

less bias than equation 8 (table 3, fig. 3): 

DR% = 97.1 - 0.519 LDM, LDM 5 160 percent (9) 

13.6, LDM > 160 percent 

(N = 71, r2 = 0.74, se = 13.5) 

MILLER-NEWMAN 

LUBRECHT. FALL 

LOWER DUFF MOISTURE (PCT) 

Figure 10.-Percentage duff depth reduction 

versus lower duff moisture content. Equations 8 and 9 and Norum's (1977) curves are 

graphed. 

The segmented regression is appealing because the 

data form a knee where consumption levels off, and the 

regression avoids unreasonable solutions when applied at 

high moisture contents. 

The best equation having entire duff moisture as an 

independent variable was: 

DR% = 83.7 - 0.426 EDM (10) 

(N = 71, r2 = 0.69, se = 14.7) 

Influence of surface fuel.-Duration of surface fire 

should be better than surface fuel consumption as a 

predictor of duff consumption, because preheating and 

drying of duff should relate more closely to duration of 

free-burning fire than to intensity or other fire characteristics. In a practical sense this may be a minor point 

because woody fuel consumption and duration are probably positively correlated. We were unable to estimate 

surface fire duration from our data but did examine the 

significance of 0- to 1-inch and 0- to 3-inch woody fuel 

preburn loadings and consumption in regression. 

Preburn loadings of the 0- to 3-inch woody fuel (WT3) 

and the consumed loadings were highly correlated. In 

the first screening using regression analysis, the preburn 

loading performed as well as consumed loading in explaining variation. Thus, consumed loading was omitted from 

later regression analyses because in practice it is more 

difficult to determine than preburn loading. Generally, 

0- to 3-inch woody fuel was a better predictor than 0- to 

1-inch woody fuel. 

The following equation for the Miller-Newman and 

Lubrecht ~ooled data shows an influence of surface fuels 

that is intermediate to equations for Miller-Newman and 

Lubrecht separately: 

DR% = 75.8 - 0.397 LDM + 0.656 WT3 (11) 

(N = 79, r2 = 0.66, se = 15.2) 
In multiple regression analysis, 0- to 3-inch woody fuel 

was nonsignificant when entered after lower duff moisture for the Miller-Newman data, but was highly significant in the following equation for the Lubrecht data: 

DR% = 70.2 - 0.384 LDM + 3.72 WT3 (12) 

(N = 11, r2 = 0.58, se = 10.5) 

Miller-Newman data confirmed Sandberg's (1980) findings that once lower duff moisture was known, woody 

fuel loading in slash had an inconsequential influence on 

duff consumption. Perhaps downed woody slash fuel is 

less influential on duff consumption because much of it 

is suspended above the duff. In naturally occurring 

fuels, such as at Lubrecht, much of the woody fuel lies 

on the litter and duff. Heat transfer between duff and 

woody fuel would be more efficient and highly correlated. In this situation, burnout of woody fuel would 

probably support burnout of the duff. 

We believe, due to variability in the data, findings of 

Sandberg (19801, and possible bias in the Miller-Newman 

data, that the effect of surface fuel consum~tion on duff 

consumption remains poorly quantified. Windspeed and 

shrub load were each regressed on percentage duff reduction after lower duff moisture for combinations of data. 

These variables were either nonsignificant or inconsequential. 

The preliminary duff consumption guideline by Norum 

(1977), based largely on Lubrecht data, shows a strong 

influence of surface fuel loading on duff consumption 

that has not been verified by others. It may be appropriate for naturally occurring fuels but not in slash. Also, 

for many prescribed burning situations, especially where 

cutting activities have disturbed the ground, it seems 

unlikely that duff consumption would approach 100 percent (fig. 10). 

Predicted by fire weather indices.-The best equations 

having NFDR 1,000-hour moisture as an independent 

variable were: 

Prediction from fire weather indices is imprecise, as illustrated by the wide scatter of data in figure 11. Surface fuel loading contributed little to explaining variation in percentage duff reduction and was secondary to 

NFDR 1,000-hour moisture, as shown by equation 14 

and table 3. A change of 20 tonslacre (45 tlha) changes 

percentage duff reduction by only 10 percent. 

In an effort to improve the prediction of percentage 

duff reduction using NFDR 1,000-hour moisture, three 

sets of data were created by eliminating plots having at 

least 0.05, 0.10, and 0.25 inches of rain occurring over 

the past 5 days. This was expected to reduce variability 

between the NFDR 1,000-hour moisture and duff moisture 

and hence between NFDR 1,000-hour moisture and duff 

consumption. The coefficient of determination and standard error terms were improved slightly but not enough 

to be of practical importance: 

MILLER-NEWMAN 

LUBRECH1,FALL 

A NORTH IDAHO 

60 - 

40 - 

20 - 

0 5 10 15 20 25 

NFDR - 1000 HR MOISTURE (PCT) 

Figure 11.- Percentage duff depth reduction 

versus NFDR 1,000-hour timelag moisture 

content. Equation 13 and Sandberg's (1980) 

curves are graphed. 

Data set r2 se N 

PC t 

Eliminate 0.05 inch 0.64 16.1 87 

Eliminate 0.10 inch .59 16.7 104 

Eliminate 0.25 inch .60 16.4 11 1 

All .55 16.9 133 

Similar minor improvements in precision were found 

for the relationships between duff depth reduction 

(inches) and NFDR 1,000-hour moisture. 

Regression of percentage duff reduction on the Canadian Adjusted Duff Moisture Code, equation 15, was 

slightly less precise than on NFDR 1,000-hour moisture, 

equation 13: 

DR% = 21.2 + 0.293 ADMC (15) 

(N = 71, r2 = 0.49, se = 18.9) 

A linear relationship provided the best fit between percentage duff reduction and percentage mineral soil 

exposure: 

DR% = 15.2 + 0.948 M% (16) 

(N = 66, r2 = 0.85, se = 9.9) 

Percentage Mineral Soil Exposure (M%) 

The northern Idaho data were omitted from analyses 

of mineral soil because little of it was exposed by fire 

and the effect of logging disturbance on mineral soil 

could not be quantified. Duff spike observations at 

Miller-Newman and Lubrecht indicated no mineral soil 

exposure before burning. Logging disturbance did not I 

complicate interpretation of data at these sites. 

Predicted by duff moisture.-The best fit for the rela- i 

tionship between percentage mineral soil and lower duff 

moisture, using pooled data for Miller-Newman and 
Lubrecht (fall), was provided by the following broken 

line regression (fig. 12): 

80.0 - 0.507 LDM, LDM 5 135 percent 

M' = 23.5 - 0.0914 LDM, LDM ,135 percent (17) 

(N = 72, r2 = 0.58, se = 16.1) 

The break between regressions fell at 135 percent, 

which agrees reasonably well with observations by 

MILLER-NEWMAN 

LUBRECHT,FALL 

0 50 100 150 200 250 

LOWER DUFF MOISTURE (PCT) 

Figure 12.-Percentage mineral soil exposure versus lower duff moisture content. 

Equation 17 is for moderate to heavy slash 

and equation 18 primarily for nonslash. 

Equation 20 is shown for 5 and 20 tons/acre 

loadings of 0- to 3-inch woody fuel. 

Sandberg (1980). Mineral soil exposure varied considerably when moisture content of lower duff was below 135 

percent, resulting in imprecise predictions. For nonslash 

fuels, some improvement in prediction may be possible 

using equation 18: 

M% = 60.4 - 0.440 LDM 

(N = 11, r2 = 0.53, se = 10.0) 

Lower duff moisture content in equation 17 and entire 

duff moisture content in the following equation were 

nearly equally effective as predictors of mineral soil 

exposure: 

M% = 167.4 - 31.6 In EDM 

(N = 72, r2 = 0.56, se = 16.4) 

When 0- to 3-inch woody fuel was added to regression, 

the best fitting equation included spring and fall fires: 

M% = 51.7 - 0.357 LDM + 0.983 WT3 (20) 

(N = 80, r2 = 0.55, se = 16.7) 

Equation 20 suggests that woody fuel loading has a 

greater influence on prediction of percentage mineral soil 

than on percentage duff reduction. A change of 1 tonlacre 

causes percentage mineral soil to change 1 percent. 

Although equation 20 performs reasonably well, the separate functions in figure 8 were suggested for application over equation 20 because they appear to fit the data 

better when lower duff moistures are greater than 100 

percent and less than 30 percent (fig. 12). 

Predicted by fire weather indices.-The relationship between percentage mineral soil and NFDR 1,000-hour 

moisture displayed considerable variation (fig. 13). Observations having low NFDR 1,000-hour moistures were 

scrutinized to explain why apparently dry duff was 

poorly consumed; however, explanations were not found. 

NFDR 1000 - HR MOISTURE (PCTI 

Figure 13.-Percentage mineral soil exposure versus NFDR 

1,000-hour moisture content. Confidence bands (broken 

lines) are for one standard error of the mean. 
- MILLER-NEWYAN 

LUBRECHT, FALL 

DUFF DEPTH REDUCTION (PCT) 

Figure 14.-Percentage mineral soii exposure versus percentage duff depth reduction. Equation 23 is graphed. 

A function fitted to pooled data appeared unreasonable, 

thus equations for Miller-Newman and Lubrecht (fall) 

were derived separately: 

M% = 93.0 - 3.55 TH, Miller-Newman (21) 

(N = 61, r2 = 0.40, se = 20.3) 

M% = 94.3 - 4.96 TH, Lubrecht fall (22) 

(N = 11, r2 = 0.36, se = 11.7) 

Addition of 0- to 3-inch woody fuel loading to regression analysis resulted in coefficients that were either 

nonsignificant or of very small consequence. Regression 

with a stratification of NFDR 1,000-hour moisture serving as dummy variables also failed to improve prediction 

over regression without dummy variables. 

Predicted by percentage duff consumption.-The relationship between percentage mineral soil and percentage 

duff reduction, which is the reverse of figure 7, is 

described by: 

M% = -8.98 + 0.899 DR% (23) 

(N = 66, r2 = 0.85, se = 9.6) 

This relationship (fig. 14) suggests that burnout of 

duff proceeds both downward and laterally. If duff 

burned only downward, little mineral soil would have 

been exposed until duff was reduced 40 percent or more, 

and this was not found. The pattern of burnout probably 

depends on moisture content of duff at microsites and 

heating from surface fire. Duff moisture content varies 

considerably over short distances (Hillhouse and Potts 

1982), which complicates the burnout processes. 

Other Independent Variables 

Reaction intensity (Btulft21s), computed by Rothermel's 

(1972) fire spread model, was used as an independent 

variable in regression analysis for seven data set combinations. Reaction intensity was computed from loadings 

of 0- to 3-inch downed woody material, shrubs, and 

herbaceous vegetation; thus, it is a measure of surface 

fire intensity. Reaction intensity was either nonsignificant or weakly correlated with duff depth reduction, percentage duff depth reduction, and percentage mineral 

soil exposure. Overall, reaction intensity was not a useful predictor, which was not surprising because it 

represents energy release only from the propagating 

flame front. 

Regression with Duff Moisture Code, Drought Code, 

and Adjusted Duff Moisture Code of the Canadian Forest Fire Weather Index (Van Wagner 1974) as independent variables showed the ADMC to be the best predictor of duff consumption. In all data sets analyzed, 

however, the NFDR 1,000-hour moisture provided a 

slightly more precise relationship with duff consumption 

than did the ADMC (appendix IV). The NFDR 

1,000-hour moisture also was more highly correlated 

with lower duff moisture than were the Canadian duff 

moisture codes (appendix IV). This probably explains 

why it was a more precise predictor of duff consumption. 

Testing Equations 

Sandberg's (1980) research in partially cut Douglas-fir 

slash offered the only opportunity we found to compare 

findings involving the NFDR 1,000-hour moisture as a 

predictor. A comparison of equations derived in our 

study and by Sandberg for predicting percentage duff 

depth reduction are shown in figure 11. The differences 

between our findings and Sandberg's are large enough to 

be puzzling. Perhaps most of the difference would be 

eliminated if our data set contained higher observations 

of NFDR 1,000-hour moisture. A comparison of equations for predicting percentage mineral soil exposure 

shows reasonable agreement between Miller-Newman 

slash and Sandberg's slash over a portion of the independent variable (fig. 13). Again, however, the agreement between equations would probably be better if the 

range in data included larger values of NFDR 1,000-hour 

moisture. Figure 13 suggests that cutting activity may 

influence the relationship between mineral soil exposure 

and the NFDR 1,000-hour moisture. 

The relationship between percentage mineral soil and 

percentage duff reduction reported by Sandberg (1980) 

agrees closely with ours. He fitted percentage duff 

reduction as a squared term. Our equation with percentage duff reduction as a squared term resulted in almost 

identical precision to the untransformed variable in equation 23. 

Besides Sandberg's (1980) work in partially cut 

Douglas-fir slash, already discussed, several other 

studies on duff consumption were adequately 

documented for testing accuracy of equations. Predicted 

values from our equations were compared with values 

reported from the studies in table 4. 

The performance of the equations is summarized in 

table 5. The number of tests per equation differed because information required for computing predictions 

was not equally available for all equations. Equation 2 

for predicting duff depth reduction performed well (fig. 

15). One observation by Ryan (1982) was underestimated 
Table 4.-Description of studies used to test equations 

Overstory Number 

Study Location type Fuel observations 

Harrington (1981) Santa Catalina 

Mtns.. AZ 

Artley and others (1978) Flathead N.F., 

M T 

Little and others (1982) Willamette N.F., 

0 R 

Ryan (1982) Flathead N.F., 

MT 

Ponderosa pine Nonslash 6 

Larc hl Slash 

Douglas - fir 

Douglas-fir Slash 

Engelmann Slash 

spruce, 

Douglas -fir, 

larch 

Table 5.-Comparison of predicted values from selected equations with observed values 

from other studies. The variables are duff depth reduction (DR), percentage duff 

depth reduction (DRY0), percentage mineral soil exposure (M%), lower duff 

moisture content (LDM), and entire duff moisture content (EDM) 

Average2 

Dependent Independent Number Average Average1 percent 

Equation variable variable tests observed difference difference 

LDM 

EDM 

LDM 

LDM 

EDM 

LDM 

EDM 

DR7o 

10 0.76 inch 

4 .73 inch 

16 38.370 

16 38.3% 

4 21 % 

8 14.5% 

4 14.5% 

8 14.570 

0.12 inch 

.35 inch 

- 2.8% 

- 1.2% 

3.070 

6.670 

6.470 

4.370 

'Average of observed minus predicted values. 

2~verage of (observed minus predicted)lobserved values expressed as percentage. One Ryan observation was omitted because it computed as an infinite difference. 

Figure 15.-Comparison of duff depth reduction values observed by Artley and others 

(1978). Little and others (1982), and Ryan 

(1982), table 4, with predictions from equation 2. 

ARTLEV 

. LITTLE 

A RYAN 

0. 00 0.25 0.50 0.75 1. 00 1. 25 1.50 

PREDICTED DUFF DEPTH REDUCTION (IN) 
considerably, but not surprisingly, considering that approximately 25 tonslacre (56 tlha) of large woody material was also consumed. It seems reasonable to expect increased duff consumption where burnout of large 

quantities of downed woody fuels provides prolonged 

heating of duff. Equation 9 for predicting percentage 

duff depth reduction performed well over a wide range 

(fig. 16). Particularly interesting was the close agreement 

between predictions and Harrington's (1981) observations in ponderosa pine. In contrast, the northern Idaho 

ponderosa pine was difficult to predict using any function. The reason for the disparity in fit is probably due 

in part to logging disturbance. Harrington's (1981) ponderosa pine stands were undisturbed, whereas the northern Idaho stands had been selectively logged. Possibly 

another difference is due to a greater preburn duff depth 

in Harrington's stands compared to northern Idaho 

where preburn duff depth was marginal for consistent 

combustion under any conditions. 

Overall, the equations performed well. Most differences 

between observed and predicted values were within one 

standard error of the mean of the tested equations. 

These tests lend confidence to use of the equations over 

the broad range of conditions suggested in the section 

on Management Applications. 

PREDICTED DUFF DEPTH REDUCTION (PCT) 

Figure 16.-Comparison of duff depth reduction values observed by Artley and others 

(1978), Harrington (1981), Little and others 

(1982), and Ryan (1982), table 4, with predictions from equation 9. 

Small Woody Fuels 

Absolute consumption.-As also reported by Norum 

(1976) and Beaufait and others (1977), amount of fuel 

consumed was strongly dependent on fuel loading before 

burning, table 6. Other independent variables analyzed 

were preburn loadings of shrubs, herbaceous vegetation, 

litter, and combinations of these; moisture content variables, windspeed, foliage remaining on slash, reaction intensity, and ignition method. All independent variables 

were either nonsignificant or contributed little to the 

consumption model beyond the contribution of preburn 

loading. 

Percentage consumption.-Percentage consumption 

was weakly correlated with all independent variables 

studied. Even fuel moisture content, which strongly affects combustion, was a weak, often illogically correlated 

predictor, possibly because of bias in the Miller-Newman 

data. The limited moisture content measurements taken 

there prior to burning were highly variable and may 

have masked any true relationship between fuel consumption and moisture content. 

A trend in percentage consumption, however, did appear (table 7). For moderate to heavy slash (MN), percentage consumption was uniformly high at 86 percent 

for 0- to 1-inch and 81 percent for 0- to 3-inch woody 

fuels. For light woody fuels (NI and L), percentage consumption varied substantially and averaged 48 percent 

for 0- to 1-inch and 46 percent for 0- to 3-inch. Based on 

the range in data (table 2), a high percentage consumption can be expected for 0- to 3-inch preburn loadings 

greater than about 10 tonslacre and variable but less 

consumption for loadings under about 10 tonslacre. This 

is consistent with a state-of-knowledge review by Martin 

and others (1979) who reported that slash consumption 

in clearcuts averaged 70 to 90 percent for small woody 

fuels. Observations in nonslash fuels from the Western 

United States varied considerably. 

Large Woody Fuels 

Consumption of large woody fuels, like small woody 

fuels, related closely to preburn fuel loadings. Lower duff 

moisture content and the NFDR 1,000-hour moisture entered as second independent variables were either nonsignificant or illogically correlated. The best equation 

based on Lubrecht was: 

CWTLG = -2.7 + 0.79 WTLG (32) 

(N = 20, r2 = 0.72, se = 6.64) 

Both absolute and percentage consumption for the 

Lubrecht data were weakly and inconclusively correlated 

with NFDR 1,000-hour moisture and lower duff moisture. The relationship between percentage consumption 

and large fuel load was also vague. Plotted northern 

Idaho data revealed a lack of relationships among percentage consumption and the independent variables (including TH, WTLG, and WT3). Due to this and the narrow range in large fuel loadings, further analyses were 

not attempted. 
Table 6.-Equations for consumption of 0- to 1 -inch (CWTl) and 0- to 3inch (CWT3) downed woody fuels as 

functions of preburn loading of 0- to 1 -inch (WT1) and 0- to 3- inch (WT3) downed woody fuels 

Range in 

Equation 100 (se) preburn 

number Data source r2 se Y n loading Equations 

Miller- Newman 

Miller- Newman 

Lubrecht 

Lu brecht 

Northern ldaho 

Northern ldaho 

All studies 

All studies 

Tons /acre Tons /acre 

CWTl = 0.195 + 0.831 WT1 

CWT3 = - 1.24 + 0.873 WT3 

CWTl = -0.496 + 0.920 WT1 

CWT3 = - 1.751 + 0.925 WT3 

CWTl = 0.278 + 0.926 WT1 

CWT3 = - 0.396 + 0.918 WT3 

CWTl = -0.269 + 0.890 WT1 

CWT3 = - 0.670 + 0.845 WT3 

Table 7.-Preburn and consumed loadings for 0- to 1 -inch and 0- to 3-inch downed 

woody fuels 

Fuel 

0- to 1 -inch load 

Preburn, tonslacre 

Consumed, tonslacre 

Consumed, percent 

0- to 3-inch load 

Preburn, tonslacre 

Consumed, tonslacre 

Consumed, percent 

Coefficient of 

Mean Standard deviation variation 

MN L, NI MN L, NI MN L, NI 

---- Percent ---- 

7.13 1.03 2.29 1.30 32 126 

6.12 .61 1.96 1.25 32 205 

86.2 48.1 7.4 35.4 8.6 74 

Percentage consumption from diameter reduction.- 

Sandberg and Ottmar (1983) developed a method of estimating percentage volume reduction based on the relationship between diameter reduction and NFDR 

1,000-hour moisture. The relationship was derived from 

experimental burns in cable-yarded logging slash. 

Although their method was based on slash, we tested 

it using Norum's nonslash Lubrecht data. Predicted unit 

consumption was computed using root-mean-squared diameter and NFDR 1,000-hour moisture in their algorithm (Sandberg and Ottmar 1983). Observed values 

were percentage consumption unit averages. The model 

substantially underpredicted percentage consumption as 

shown in the following tabulation of averages: 

Observed Predicted Difference 

Mean 64 28 36 

Range 0 to 99 16 to 50 -25 to 80 

The underprediction is probably due to a high degree 

of rot in the fuels at Lubrecht and perhaps more contact 

between fuel pieces and the forest floor. At Lubrecht, 85 

percent of the large fuels were classed as rotten (can be 

kicked apart with the foot). Rotten fuels were excluded 

from Sandberg and Ottmar's analysis. This may explain 

the greater than predicted consumption at Lubrecht, because burnout of rotten fuel should be more complete 

than sound fuel. Contact with a smoldering forest floor 

would also enhance burnout of large woody pieces. Fuels 

in place for long periods such as the naturally accumulated ones at Lubrecht would normally have more contact with duff than logging slash. 

Sandberg and Ottmar's model was further tested using 

two slash fires (Ryan 1982). Results were a small overprediction (8 percent) for sound fuel and a large underprediction (42 percent) for rotten fuel. These tests certainly indicate that adjustments to Sandberg and 

Ottmar's model are needed if it is applied to rotten large 

woody fuels. 
CONCLUSIONS 

This paper provides land managers a quantitative 

means of predicting duff and woody fuel consumption 

that can be especially useful in planning prescribed fires. 

Tests of the duff consumption relationships against 

other data suggest wide application is possible. The 

predictions should be reasonably accurate where duff is 

continuous and averages more than 0.5 inch (1.3 cm) 

deep. Application of results is not recommended in open 

stands where duff is discontinuous. 

Duff moisture content was by far the most influential 

variable on duff consumption. Loadings of small woody 

fuels also influenced duff consumption but to a lesser extent. Quantification of this influence remains poorly 

understood. The NFDR 1,000-hour moisture related more 

closely to duff consumption than did the Canadian duff 

moisture codes. The NFDR 1,000-hour moisture should 

be helpful for developing fire prescriptions. The relationship between percentage mineral soil exposure and percentage duff reduction indicates that duff consumption 

involves both downward and lateral movement of the 

combustion interface. Consumption of small woody fuels 

can be explained simply as most of these fuels are consumed (80 to 90 percent) wherever fire spreads. 

To improve knowledge for predicting and understanding fuel consumption, the primary factors influencing 

large fuel burnout on a practical area basis need to be 

identified and their relationships to consumption quantified. The relationships between duff consumption, logging disturbance, and consumption of small and large 

woody fuels need better definition. The need for more 

precise knowledge to predict duff consumption will grow 

as future utilization leaves less woody surface fuel to 

support fire and as prescribed fire objectives become 

more closely tied to integrated land management 

objectives. 

REFERENCES 

Albini, Frank A. Estimating wildfire behavior and effects. General Technical Report INT-30. Ogden, UT: 

U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station; 1976. 

92 p. 

Artley, Donald K.; Shearer, Raymond C.; Steele, Robert 

V. Effects of burning moist fuels on seedbed preparations in cutover western larch forests. Research Paper 

INT-211. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Forest and Range Experiment Station; 1978. 14 p. 

Bakken, Stephen; Neuenschwander, Leon F. Prescribed 

burning guidelines for ponderosa pine in northern 

Idaho. Final Report Cooperative Agreement with U.S. 

Department of Agriculture, Forest Service, and the 

University of Idaho, Supplement 91. On file at the 

Northern Forest Fire Laboratory, Drawer G, Missoula, 

MT 59806; 1981. 120 p. 

Beaufait, William R.; Hardy, Charles E.; Fischer, 

William C. Broadcast burning in larch-fir clearcuts: the 

Miller Creek-Newman Ridge study. Research Paper 

INT-175. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Forest and Range Experiment Station; 1977. 53 p. 

Brown, James K. Handbook for inventorying downed 

woody material. General Technical Report INT-16. 

Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Intermountain Forest and Range Experiment 

Station; 1974. 24 p. 

Brown, James K.; Roussopoulos, Peter J. Eliminating 

biases in the planar intersect method for estimating 

volumes of small fuels. Forest Science. 20(4): 350-356; 

1974. 

Brown, James K.; See, Thomas E. Downed dead woody 

fuel and biomass in the Northern Rocky Mountains. 

General Technical Report INT-117. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station; 1981. 

48 p. 

Chrosciewicz, Z. Slash and duff reduction by burning on 

clearcut jack pine sites in southwestern Manitoba. Information Report NOR-X-199. Edmonton, AB: Canadian Forestry Service; 1978a. 11 p. 

Chrosciewicz, Z. Slash and duff reduction by burning on 

clearcut jack pine sites in central Saskatchewan. Information Report NOR-X-200. Edmonton, AB: Canadian 

Forestry Service; 1978b. 12 p. 

DeByle, Norbert V. Clearcutting and fire in the 

larchlDouglas-fir forests of western Montana-a multifaceted research summary. General Technical Report 

INT-99. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Forest and Range Experiment Station; 1981. 73 p. 

Deeming, John E.; Burgan, Robert E.; Cohen, Jack D. 

The National Fire-Danger Rating System - 1978. 

General Technical Report INT-39. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station; 1977. 

63 p. 

Fosberg, Michael A. Drying rates of heartwood below 

fiber saturation. Forest Science. 16(1): 57-63; 1970. 

Frandsen, William H. 1983. Unpublished research results 

on file at: U.S. Department of Agriculture, Forest 

Service, Intermountain Forest and Range Experiment 

Station, Northern Forest Fire Laboratory, RWU 2103 

files, Missoula, MT. 

Harrington, Michael G. Preliminary burning prescriptions for ponderosa pine fuel reductions in southwestern Arizona. Research Note RM-402. Fort Collins, 

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

Rocky Mountain Forest and Range Experiment Station; 1981. 7 p. 

Hillhouse, Margaret I.; Potts, Donald F. Duff reduction 

in site preparation. In: Baumgartner, David M., ed. 

Site preparation and fuels management on steep terrain: proceedings of a symposium; 1982 February 

15-17; Spokane, WA. Pullman, WA: Washington State 

University, Cooperative Extension; 1982: 67-73. 

Jensen, Chester E.; Homeyer, Jack W. Matchacurve-1 

for algebraic transforms to describe sigmoid- or bellshaped curves. Ogden, UT: U.S. Department of 

Agriculture, Forest Service, Intermountain Forest and 

Range Experiment Station; 1970. 22 p. 
Little, Susan N.; Ward, Franklin R.; Sandberg, D. V. 

Duff reduction caused by prescribed fire on areas 

logged to different management intensities. Research 

Note PNW-397. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Forest 

and Range Experiment Station; 1982. 8 p. 

Martin, R. E. [and others]. Effects of fire on fuels. 

General Technical Report WO-13. Washington, DC: 

U.S. Department of Agriculture, Forest Service; 1979. 

67 p. 

Norum, R. A. Preliminary guidelines for prescribed burning under standing timber in western larch/Dougks-fir 

forests. Research Note INT-229. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station; 1977. 

15 p. 

Norum, Rodney A. Fire intensity-fuel reduction relationships associated with understory burning in 

larch/Douglas-fir stands. In: Proceedings Tall Timbers 

fire ecology conference No. 14 and Intermountain Fire 

Research Council fire and land management symposium; October 1974; Missoula, MT. Talahassee, FL: 

Tall Timbers Research Station; 1976: 559-572. 

Ottmar, Roger D. Predicting fuel consumption by fire 

stages to reduce smoke from slash fires. In: Proceedings 1983 annual meeting, Northwest Forest Fire 

Council; 1983 November 21-22; Olympia, WA. Olympia, WA: Northwest Forest Fire Council; 1984: 87-106. 

Rothermel, Richard C. A mathematical model for 

predicting fire spread in wildland fuels. Research Paper 

INT-115. Ogden, UT: U. S. Department of Agriculture, 

Forest Service, Intermountain Forest and Range Experiment Station; 1972. 40 p. 

Ryan, Kevin C. Burning for site preparation on steepslope helicopter- logged clearcuts in northwestern 

Monana. In: Baumgartner, David M., ed. Site preparation and fuels management on steep terrain; proceedings of a symposium; 1982 February 15-17, Spokane, 

WA. Pullman, WA: Washington State University, 

Cooperative Extension; 1982: 25-33. 

Sandberg, D. V.; Ottmar, R. D. Slash burning and fuel 

consumption in the Douglas-fir subregion. In: Seventh 

conference on fire and forest meteorology symposium 

proceedings; April 1983; Fort Collins, CO. Fort Collins, 

CO: American Meteorological Society; 1983: 90-93. 

Sandberg, David V. Duff reduction by prescribed underburning in Douglas-fir. Research Paper PNW-272. 

Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Forest and Range Experiment Station; 1980. 18 p. 

Shearer, Raymond C. Seedbed characteristics in western 

larch forests after prescribed burning. Research Paper 

INT-167. Ogden, UT: U.S. Department of Agriculture, 

Forest Service, Intermountain Forest and Range Experiment Station; 1975. 26 p. 

Shearer, Raymond C. Personal communication. 1983. 

Missoula, MT: U.S. Department of Agriculture, Forest 

Service, Intermountain Forest and Range Experiment 

Station. 

U.S. Department of Agriculture, Forest Service. National fire-danger rating handbook. Forest Service 

Handbook 5109.11. Washington, DC: U.S. Department 

of Agriculture, Forest Service; 1964. 

Van Wagner, C. E. Duff consumption by fire in eastern 

pine stands. Canadian Journal of Forest Research. 2: 

34-39; 1972. 

Van Wagner, C. E. Structure of the Canadian Forest 

Fire Weather Index. Publication No. 1333. Ottawa, 

Canada: Department of the Environment, Canadian 

Forestry Service; 1974. 44 p. 
APPENDIX I. ADJUSTING FUEL 

DIAMETER CLASSES 

In all studies, loadings of downed woody material were 

determined using the planar intersect method where 

counts of particles by diameter class are converted to 

loadings (Brown 1974). To convert particle count data 

from 0- to 0.4-inch (0- to 1-cm) and 0.4- to 4-inch (1- to 

10-cm) classes used in the Miller-Newman study to the 

conventional classes, it was assumed that the frequency 

of fuel pieces by diameter could be represented by a single probability distribution (fig.17). The relative frequency of intercepts in the diameter classes in (A) was 

partitioned into the diameter classes in (B) (fig. 17) using 

data on diameters from randomly intersected particles. 

Allocating intercepts to the 0- to %-inch class.-Using 

data from a study by Brown and Roussopoulos (1974), it 

was determined that 95 percent of the intercepts in the 

0- to 0.4-inch class were less than one-fourth inch. The 

adjusted frequency for the 0- to %-inch class is then 0.95 

times the frequency of intercepts in the 0- to 0.4-inch 

class. It follows that the remaining intercepts in the 0- 

to 0.4-inch class would be part of the %- to 1-inch class. 

Allocating intercepts to the %- to 1-inch class.-This 

class includes intercepts from the upper end of the 0- to 

0.4-inch class and a portion of the 0.4- to 4-inch class. 

The proportion of fuel pieces between 0.4 and 1 inch was 

calculated by species using intercept data from the 

Miller Creek and Newman Ridge Study (table 8). The allocation of intercepts at a given sample point can be expressed by: 

8 

Y, = 0.05 X, + X, ( C P R1 ,) J=1 J 

where 

Y,= frequency of intercepts allocated to the ?h- to 

1-inch class 

XI= sample frequency for the 0- to 0.4-inch class 

X,= sample frequency for the 0.4- to 4-inch class 

PJ= fraction of slash estimated to be the jth species 

RJ,,= fraction of jth species 0.4- to 4-inch class that is 

0.4 to 1 inch. 

Allocating intercepts to the 1- to 3-inch class.-This 

class is the mid-portion of the 0.4- to 4-inch class. It can 

be expressed as: 

8 

Y, = X, (,glPIR,,) 

where 

Y,= frequency of intercepts allocated to the 1- to 

3-inch class 

R1, = fraction of the jth species 0.4- to 4-inch class 1 

4 1 2 3 4 that is 1 to 3 inches. 

DIAMETER (IN) 

Figure 17.-Fuel particle diameter distributions showing the size classes used at 

Miller-Newman (A) and the conventional diameter classes used in the other studies (8). 

Table 8.-Fractions of particle intercepts by species and diameter classes 

at Miller Creek and Newman Ridge 

Species 

Western larch 

Douglas-fir 

Subalpine fir 

Grand fir 

Lodgepole pine 

Engelmann 

spruce 

Ponderosa pine 

Western redcedar 

Diameter classes 

0.4 to 1 inch 1 to 3 inches 3 to 4 inches 

(1 to 2.5 cm) (2.5 to 7.6 cm) (7.6 to 10 cm) 
APPENDIX 11. CORRELATIONS AMONG VARIABLES 

Table 9.-Simple correlation coefficients1 between transformed and untransformed duff consumption variables 

Dependent Independent variables 

variable EDM 1IEDM LDM 1ILDM TH WT3 In(WT3) RI ADMC DPRE DR% In(DR%) 

Miller - Newrnan 

DR 

In DR 

JDR 

DR% 

M% 

In M% 

JM% 

Lubrecht fall 

DR 

In DR 

JDR 

DR% 

M% 

In M% 

Jr 

Lubrecht spring 

and fall 

D R 

In DR 

JDR 

DR% 

In DR% 

,DR% 

M% 

In M% 

JM% 

Northern Idaho 

D R 

In - DR 

.'DR 

DR% 

In DR% 

JDR[ro 

'significance levels for r at 90 and 95 percent levels listed respectively are 0.211 and 0.250, MN; 0.521 and 0.602, Lf; 0.389 and 0.456, L,; and 0.243 

and 0.288, NI. 
APPENDIX 111. EQUATIONS IN METRIC UNITS 

Table 10.-Metric units for equations and related statistics on precision and bias 

Equation Data 

number source 

Std. Root mean square (0 - P) 

error MN Lf L, NI 

MN, L, 

MN 

MN, L, 

MN 

MN, L, 

MN, L,, NI 

MN, Lf 

MN, Lf 

MN, L,, L, 

L f 

MN, L, 

MN, L,, NI 

MN, Lf 

MN, Lf 

MN, L, 

MN 

MN 

L 

L 

N I 

N I 

MN, L, NI 

MN, L, NI 

L 

DR = 2.612 - 0.0225 LDM + 0.417 DPRE 

DR = 4.574 - 0.0201 LDM 

DR = 2.238 - 0.0244 EDM + 0.439 DPRE 

DR = 4.272 - 0.0215 EDM 

DR = 6.854 - 0.263 TH 

DR = 4.503 - 0.267 TH + 0.399 DPRE 

DR = 1.04 + 0.0179 ADMC 

DR% = 87.8 - 0.390 LDM 

DR% = 97.1 - 0.519 LDM, LDM 5160% 

= 13.6, LDM > 160% 

DR% = 83.7 - 0.426 EDM 

DR% = 75.8 - 0.397 LDM + 0.2927 WT3 

DR% = 70.2 - 0.384 LDM + 1.660 WT3 

DR% = 114.7 - 4.20 TH 

DR% = 11 1.4 - 4.69 TH + 0.2347 WT3 

DR% = 21.2 + 0.293 ADMC 

DR% = 15.2 + 0.948 M% 

M% = 80.0 - 0.507 LDM, LDM I 135% 

= 23.5 - 0.0914 LDM, LDM > 135% 

M% = 60.4 - 0.440 LDM 

M% = 167.4 - 31.6 In (EDM) 

M% = 51.7 - 0.357 LDM + 0.4386 WT3 

M% = 93.0 - 3.55 TH 

M% = 94.3 - 4.96 TH 

M% = -8.98 + 0.899 DR% 

CWTl = 0.437 + 0.831 WT1 

CWT3 = -2.779 + 0.873 WT3 

CWTl = - 1.112 + 0.920 WT1 

CWT3 = -3.924 + 0.925 WT3 

CWTl = -0.623 + 0.926 WT1 

CWT3 = -0.887 + 0.918 WT3 

CWTl = -0.602 + 0.890 WT1 

CWT3 = - 1.501 + 0.845 WT3 

CWTLG = -6.05 + 0.79 WTLG 

'DR, DPRE: cm 

WT1, WT3, WTLG. CWTI, CWT3, CWTLG: tlha. 
APPENDIX IV. COMPARING FIRE WEATHER INDICES 

Table 11.-Statistics for comparing regression relationships between fire weather indices and 

duff consumption 

Data DC DMC ADMC TH 

set r2 se r2 se r2 se r2 se 

f 

MN 

MN, Lf 

N I 

Lf, NI 

All 

L f 

MN 

MN, Lf 

N I 

Lf, NI 

All 

f 

MN 

MN, Lf 

MN, L 

Duff Depth Reduction 

Inch Inch 

0.71 0.14 0.67 

.49 .44 .44 

.55 .34 .51 

.26 .28 .26 

.39 .33 .51 

.57 .33 .49 

Duff Depth Reduction 

PC t PC t 

15.3 0.28 13.0 

23.1 .28 22.3 

24.4 .44 19.9 

11.3 .44 11.3 

12.6 .51 13.0 

24.2 .43 18.9 

Mineral Soil Exposure 

14.6 0.46 10.8 

23.1 .28 22.2 

24.5 .29 21 .O 

Lower Duff Moisture Content - 0.30 45.5 

lnch 

0.68 

.40 

.47 

.26 

.47 

.48 

PC t 

12.7 

19.9 

18.2 

11.2 

12.6 

18.7 

11 .o 

21.1 

20.3 

42.3 

lnch 

0.54 

.36 

.43 

.26 

.35 

.42 

PC t 

12.3 

18.5 

16.6 

11.4 

11.5 

16.1 

11.7 

20.4 

19.8 

37.1 

2 3 

*U.S. GOVERNMENT PRINTING OFFICE: 1985-0-576-040/10527 
Brown, James K.; Marsden, Michael A.; Ryan, Kevin C.; Reinhardt, Elizabeth D. 

Predicting duff and woody fuel consumed by prescribed fire in the Northern 

Rocky Mountains. Research Paper INT-337. Ogden, UT: U.S. Department of 

Agriculture, Forest Service, Intermountain Forest and Range Experiment Station; 1985. 23 p. 

Relationships for predicting duff reduction, mineral soil exposure, and consumption of downed woody fuel were determined to assist in planning 

prescribed fires. Independent variables included lower and entire duff moisture 

contents, loadings of downed woody fuels, duff depth, National Fire-Danger Rating System 1,000-hour moisture content, and Canadian Duff Moisture Codes. 

Results apply to a number of mesic forest cover types. 

KEYWORDS: fuel consumption, duff, downed woody fuel, forest fuels, 

prescribed fire