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Research Note RMRS-RN-50WWW. 2012

Rocky Mountain Research Station

Research Note

United States Department of Agriculture

Forest Service

Rocky Mountain Research Station

Research Note RMRS-RN-50WWW

October 2012

Abstract: The U.S. Department of Agriculture, Forest Service (USFS) has, in recent years, 

increasingly emphasized the importance of safety to its employees, but wildfire management 

remains a risky endeavor. While wildfire management decisions affecting safety and exposure 

of firefighters to the wildland fire environment may be aided by decision support tools such 

the Wildfire Decision Support System , use of such tools may be influenced by the way information is presented and by manager responses to risk. A recent survey of wildfire managers 

conducted by the USFS Rocky Mountain Research Station and the National Fire Decision 

Support Center used a fire management lottery experiment to elicit manager attitudes toward 

aviation personnel exposure and several dimensions of risk. Preliminary results suggest that 

wildfire managers have assimilated the USFS’ recent emphasis on personnel safety, though 

their degree of sensitivity to potential personnel risk depends on how relevant information 

is presented.

Keywords: wildfire management; safety; risk preferences; decision support

Introduction

Over the past several years, the U.S. Department of Agriculture, Forest Service 

(USFS) has reaffirmed its commitment to safety. The Chief of the USFS recently 

initiated an agency-wide “Safety Journey,” which aims to make the USFS a zerofatality organization (Tidwell 2011). Within the hazardous arena of wildfire management, the agency has defined success as “safely achieving objectives with the 

least firefighter exposure necessary, while enhancing stakeholder support for our 

management” (Tidwell 2012). 

Preliminary Results From a Survey of U.S. 

Forest Service Wildfire Managers’ Attitudes 

Toward Aviation Personnel Exposure and Risk

Matthew Wibbenmeyer, Michael Hand, and David Calkin

Wibbenmeyer, Matthew; Hand, Michael; Calkin, David. 2012. Preliminary results from a survey 

of U.S. Forest Service wildfire managers’ attitudes toward aviation personnel exposure and 

risk. Res. Note RMRS-RN-50WWW. Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 38 p.
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Research Note RMRS-RN-50WWW. 2012

In making fire management decisions that affect personnel safety and other management objectives, managers rely, in part, on decision support tools such as the 

Wildfire Decision Support System (WFDSS). However, such tools cannot predict 

outcomes with certainty (wildfire management is inherently risky), available information may be imperfect, and managers may face external pressure from a variety of 

sources. Therefore, a variety of human factors, including incentives, socio-political 

constraints, and cognitive decision biases, may govern managers’ responses to the 

information available to them within decision support tools (Canton-Thompson 

and others 2008; Maguire and Albright 2005; Wilson 2010). Managerial attitudes 

toward risk may influence outcomes with respect to fire fighter safety, for example. 

Further, risk attitudes may influence outcomes to suppression cost and values at 

risk, such as private property, and risk attitudes may vary over these several attributes—fire managers may be risk-averse with respect to safety and risk-seeking 

with respect to suppression cost.

Evidence suggests that the way relevant information is presented can also affect 

decision-making. The degree of risk fire managers are willing to expose firefighters 

to may differ depending on the way decision support systems present information 

and the associations fire managers make between exposure and risk of injury or 

fatality. Understanding how USFS managers perceive and respond to risk and how 

the information they use shapes this response can help the USFS improve decision 

support tools available to fire managers. Ultimately, such improvements may result 

in increased safety, better risk management, and better fire outcomes.

We recently administered a survey to USFS wildfire managers in which managers 

were asked to choose from a series of fire management strategy pairs the strategy 

they would prefer to use in managing a hypothetical wildfire, where every strategy 

involved risk over exposure to aviation personnel, private property, and suppression 

costs. Results from this survey will facilitate investigation of the questions: how do 

fire managers understand and respond to risk, and how does the information they 

rely on shape these responses? We briefly describe the process used to design and 

implement the survey and present preliminary results from it. Finally, we review 

preliminary conclusions that can be drawn from these results.

Survey Development and Administration

In addition to the fire management strategy choices, the survey included a series of 

questions regarding managers’ attitudes toward relevant aspects of wildfire management and risk, and a monetary lottery experiment in which respondents were 

asked to choose their preferred gamble in a series of paired hypothetical monetary 

lotteries (see Appendix A). 

We pre-tested the complete survey with three groups of USFS employees—first, with 

students at the Washington Institute Technical Fire Management class in Financial 
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Research Note RMRS-RN-50WWW. 2012

Forest Management; second, with employees at the National Fire Decision Support Center; and third, with employees at the Rocky Mountain Research Station. 

Pre-tests were used to refine the survey instrument and ensure that it effectively 

communicated the intended information to respondents.

Because of our specific interest in employees with decision-making authority on 

wildfire incidents, we identified potential survey respondents primarily using USFS 

public distribution lists of agency administrators and wildfire managers, including 

Fire Management Officers (FMOs), Assistant Fire Management Officers (AFMOs), 

and command and general staff of incident management teams. Command and 

general staff of incident management teams sometimes include managers employed by Department of Interior (DOI) agencies in addition to USFS employees; 

however, nearly 95% of our final sample was USFS employees. April 3, 2012, we 

sent e-mail invitations to participate in the survey to a total of 1934 USFS and DOI 

employees, accompanied by a letter of support from Tom Harbour, USFS Director 

of Fire and Aviation Management, emphasizing the importance of completing the 

survey. Invitations included a link to a web site where managers could complete the 

web-based questionnaire. In the three weeks following the initial contact, we sent 

three reminders to respondents who had not yet started the survey and who had only 

partially completed the survey. We received at least partial responses from 1197 

managers, and 1073 managers completed the fire management lottery experiment 

portion of the survey. Based on this latter number, we achieved a response rate of 

55.5%. Twelve days after the final reminder, we sent very brief questionnaires to 

those who had not responded and who had partially completed the survey to investigate their reasons for not completing the survey and potential non-response bias.

Characteristics of the sample are given in Table 1. These characteristics were 

elicited near the end of the survey; therefore, percentages given in the table reflect 

Table 1—Sample characteristics.

Count Percent Count Percent

Position Geographic area

Agency administrator 354 29.57 Alaska 11 0.92

Fire manager (fuels/fire use) 103 8.60 Eastern 72 6.02

Fire manager 298 24.90 Eastern Great Basin 81 6.77

(suppression/operations) Northern California 59 4.93

Other 292 24.39 Northern Rockies 118 9.86

No response 150 12.53 Pacific Northwest 219 18.30

 Rocky Mountain 95 7.94

Current Federal grade (GS) level Southern 114 9.52

5-6 41 3.42 Southern California 81 6.77

7-8 132 11.03 Southwest 121 10.11

9-10 132 11.03 Western Great Basin 49 4.09

11-12 352 29.41 Other 30 2.51

13-15 381 31.83 No response 147 12.28

SES 3 0.25 

Other 10 0.84 

No response 146 12.20
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Research Note RMRS-RN-50WWW. 2012

the fact that a significant number of respondents did not complete the survey and 

therefore gave no response. Because we targeted respondents in decision-making 

roles, the majority of respondents had Federal grade (GS) levels of 11 to 15. We 

received responses from a variety of geographic areas, particularly the Pacific 

Northwest. Respondents were primarily fire managers with suppression and/or 

operations foci and agency administrators; however, many respondents specified 

their position as “Other,” which can include a variety of position descriptions. 

Preliminary Results

Results from Attitudinal Questions

Figure 1 provides respondents’ attitudes toward a series of statements regarding 

wildfire management. The figure indicates that a majority of respondents disagreed that risk to aviation and ground-based personnel can be eliminated through 

operational risk mitigation, and that respondents generally agreed that increased 

exposure tends to lead to greater probability of an injury or fatality. Nearly 59% 

of respondents agreed that agency administrators appropriately weight firefighter 

exposure relative to values protected.

Managers generally supported conventional notions of risk management. About 

63% of respondents agreed that the cost of an incident is an outcome of risk management decisions, and 65.4% of respondents said they believed that probability 

of success should be considered when determining whether to protect homes and 

private property from wildfire. Indeed, slightly more respondents agreed (43%) than 

disagreed (35%) that “Agency leadership supports appropriate risk-based decisions 

in the field, even when those decisions sometimes lead to bad fire management 

outcomes.” Almost half of respondents disagreed that “aggressive suppression 

strategies are typically the most effective way to minimize damage while limiting 

fire fighter exposure.” Despite these views, about 48% of respondents disagreed 

that “Incident managers only staff fires to the degree those resources can be efficiently utilized to meet established objectives.”

Respondents appeared to believe that community expectations with respect to 

wildfire management are somewhat unreasonable. About 79% of respondents 

agreed that “meeting community and partner expectations frequently requires 

higher levels of suppression effort than would otherwise be necessary.” However, 

a substantially greater number of respondents agreed that Federal agencies do a 

good job managing community wildfire management expectations during wildfire 

incidents (62%) than agreed Federal agencies do a good job managing community 

expectations before wildfire incidents (33%).
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Research Note RMRS-RN-50WWW. 2012

Figure 1—Degree of agreement and disagreement with fire management statements.
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Although 43% of respondents agreed that “current fire management objectives 

overemphasize protection of private property,” responses to several other statements 

indicated that respondents generally felt that private property owners should take 

greater responsibility for protecting their property. More than 81% of respondents 

strongly agreed that it is the responsibility of individual private landowners to 

take actions that reduce the risk of fire on their property, and 62% of respondents 

disagreed that it is the responsibility of Federal agencies to invest in large-scale 

suppression efforts to protect private property within fire-prone areas. Interestingly, 

despite these strong results, respondents were relatively evenly split on whether 

“the potential effects of fires on ecological values should be given equal weight 

with private property values when deciding how to manage a wildland fire.”

Figure 2 provides results from a question asking fire managers to rank a series of 

factors in terms of importance to fire management decision-making. More than 94% 

of respondents ranked safety of firefighting personnel as the most important factor. 

Protection of private property was most often listed as the second most important 

factor. Opinions varied somewhat regarding the importance of the remaining factors, but total cost of suppression efforts was most often listed as the least important 

factor in fire management decision-making.

Figure 2—Relative rankings of fire management priorities.
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Research Note RMRS-RN-50WWW. 2012

Figure 3 shows respondents’ attitudes toward a series of statements regarding risk 

and uncertainty in the household financial context and in the fire management 

context. We asked fire managers to respond to statements about risk in both these 

contexts because (1) we were interested in how well managers’ understanding of 

their attitudes toward risk mapped to their fire management strategy choices later 

in the survey; and (2) we were interested in how personal risk preferences, as indicated in responses to these statements and in the Holt-Laury lottery task (described 

below), related to attitudes toward risk in the professional context. We have yet to 

explore these questions fully, but preliminary results indicate that most fire managers 

are at least somewhat risk-averse. This is not altogether surprising since previous 

studies have indicated that risk aversion is very common. Interestingly though, 

attitudes toward risk differed somewhat across the household financial and fire 

management contexts. More managers agreed that they are risk-averse and prefer 

certainty to uncertainty in the household financial context than in the fire management context, though slightly more managers indicated that they would be willing 

to take higher risks to achieve better outcomes in the household financial context 

than in the fire management context. Also of interest is that in both the household 

and professional contexts, more respondents indicated strong preferences for 

certainty than those who identified strongly as risk-averse.

Figure 3—Degree of agreement and disagreement with risk attitude statements.
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Holt-Laury Experiment Results

In the lottery experiment of Holt and Laury (2002), respondents are asked to select 

their preferred gamble from a series of pairs, each of which is described in Table 2. 

Pay-offs for both options remained the same throughout the experiment, but the 

probability that the respondent would win either gamble increased over the series 

of choices. As the probability of winning each gamble increased, the expected value 

of the risky option, which has a better up-side but a worse down-side than the safe 

option, increased relative to the expected value of Option A. Economic theory 

defines a risk-neutral person to be someone who will always prefer the gamble 

with a higher expected value. Therefore, looking at the third column in Table 2, a 

risk-neutral person will choose Option A in rows 1 through 4 and choose Option B 

in rows 5 through 10. The extent that respondents vary from this response pattern 

reveals whether they are more risk-averse or risk-seeking.

We asked respondents to complete the Holt-Laury experiment, but with hypothetical payoffs, in order to test managers’ personal risk preferences and the degree to 

which they correspond or diverge from the managerial risk preferences revealed in 

the fire management lottery experiment. Half of respondents were assigned a “5x” 

version of Holt-Laury experiment in which hypothetical winnings for both lottery 

options were multiplied by five. For example, in this version, Option A provided a 

10% chance of winning $10 and a 90% chance of winning $8 in the first row. The 

remaining respondents received a “60x” version of the Holt-Laury experiment. 

The sizes of the gambles were varied to test whether respondents demonstrated 

more or less risk aversion when the magnitude of the gambles offered increased.

Table 2—Lottery choices in the Holt-Laury task, adapted from Holt and Laury (2002).

Difference between

expected payoffs of

Option A Option B options A and B

10% of $2.00, 90% of $1.60 10% of $3.85, 90% of $0.10 $1.17

20% of $2.00, 80% of $1.60 20% of $3.85, 80% of $0.10 $0.83

30% of $2.00, 70% of $1.60 30% of $3.85, 70% of $0.10 $0.50

40% of $2.00, 60% of $1.60 40% of $3.85, 60% of $0.10 $0.16

50% of $2.00, 50% of $1.60 50% of $3.85, 50% of $0.10 -$0.18

60% of $2.00, 40% of $1.60 60% of $3.85, 40% of $0.10 -$0.51

70% of $2.00, 30% of $1.60 70% of $3.85, 30% of $0.10 -$0.85

80% of $2.00, 20% of $1.60 80% of $3.85, 20% of $0.10 -$1.18

90% of $2.00, 10% of $1.60 90% of $3.85, 10% of $0.10 -$1.52

100% of $2.00, 0% of $1.60 100% of $3.85, 0% of $0.10 -$1.85
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Figure 4 summarizes results from the Holt-Laury experiment. As the probability 

that respondents would win each gamble increased, their propensity to select Option 

B also increased. The shapes of the curves indicate that many respondents began 

selecting Option B after row 5, signifying some level of risk aversion. On the other 

hand, a significant number of respondents selected Option B prior to row 5, which 

is consistent with risk-seeking behavior. Patterns of response were similar to those 

found by Holt and Laury in the first iteration of this experiment, which suggests 

that fire managers may not have personal risk preferences that differ substantially 

from those of the general population. Further, the red line in Figure 4 lies above 

the blue line for most of its length. This suggests that managers were more risk 

averse over large-magnitude gambles than over small-magnitude gambles, which 

is consistent with results found by Holt and Laury.

Figure 4—Responses to Holt-Laury lottery experiment.
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Fire Management Lottery Experiment Results

We extended the Holt-Laury design to choices with multiple attributes in the fire 

management context using a fire management lottery choice experiment in which 

we presented managers with a hypothetical wildfire management scenario (see Appendix A) and asked them to indicate their preferred management strategy from a 

series of paired fire management lotteries. Each pair, which we refer to as a “choice 

set,” offered a relatively “safe” strategy and a relatively “risky” strategy. Under the 

safe strategy, the hypothetical wildfire could be contained with reasonable assurance of success through moderate use of suppression resources and only moderate 

damage to private property, though the specific outcomes of this strategy would 

vary probabilistically. Under the risky strategy, the wildfire would be monitored 

and would probably result in a fire that would do only slightly greater damage to 

private property and require far fewer suppression resources, including less aviation personnel exposure to potential hazards. However, the risky strategy involved 

some possibility that extreme fire weather would result in a large and damaging 

fire, which would require heavy use of suppression resources. 

Potential outcomes to each of the attributes under both the safe and risky strategies 

are given in Table 3. Across the choice sets seen by each respondent, good and 

bad outcomes under the safe strategy and good outcomes under the risky strategy 

were held constant. However, the risky strategy’s bad outcomes were varied systematically using an experimental design to enable examination of the trade-offs 

managers were willing to make over potential outcomes to private property, aviation 

personnel exposure, and suppression cost, and to enable examination of managers’ 

risk preferences over these various attributes. We also varied the probabilities of 

each strategy achieving its good and bad outcomes. Using the same design as the 

Holt-Laury experiment, the probability of achieving the good outcome was the 

same for each choice set’s safe and risky strategy. Across our choice sets, strategies 

achieved their good outcomes with probabilities of .7, .85, .9, .95, .98, and .995, 

while bad outcomes were assigned complementary probabilities.

Table 3—Potential outcomes of safe and risky strategies used in the experimental design.

Safe Risky

Attribute Good Bad Good Bada

Aviation exposure 50 hours 75 hours 10 hours 300 hours

 1200 hours

Private property damage $600,000 $1.25 million $700,000 $3 million

 $14 million

Suppression cost $300,000 $500,000 $25,000 $2 million

 $12.5 million

a

Each attribute has two potential bad outcomes under the risky strategy. Bad outcomes to the attributes, and the probability the bad outcome would result, were varied systematically among the choice 

sets using an experimental design.
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Research Note RMRS-RN-50WWW. 2012

Half of the surveyed sample (called the “control group”) received a version of the 

survey where aviation exposure was given in aviation person-hours. USFS statistics 

indicated that over the past 10 years, the agency has experienced 4.801 fatalities in 

every 100,000 flight hours (U.S. Forest Service 2010). We used this average historical fatality rate to calculate expected frequencies of fatalities on fires requiring 

the given levels of aviation exposure. The remaining sample (called the “treatment 

group”) received a version of the survey with aviation exposure described in terms 

of these expected frequencies (see Appendix B). Aviation exposure attribute levels 

provided to the control group and the corresponding treatment group levels are 

given in Table 4. Both the control group and treatment group were presented with 

the 10-year average USFS aviation fatality rate in the fire management lottery 

experiment instructions.

An example treatment group choice set is provided in Figure 5. Respondents were 

asked to select the strategy they would most likely choose if actually faced with the 

choices offered to them in the choice set. We asked every respondent to complete 

eight strategy choice sets.

Table 4—Treatment group aviation exposure attribute levels, by corresponding control group 

attribute level.

Control group Treatment group

10 hours 0.5 deaths in 1000 fires

50 hours 2.4 deaths in 1000 fires

75 hours 3.6 deaths in 1000 fires

300 hours 14 deaths in 1000 fires

1200 hours 58 deaths in 1000 fires

Figure 5—Example fire management lottery experiment choice set from treatment group.
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Research Note RMRS-RN-50WWW. 2012

Limited summaries of results from this experiment are presented in Tables 5 and 6. 

Table 5 shows that as the probability that each strategy would achieve its preferred 

outcome increased, respondents were more willing to select the risky alternative. 

Interestingly, respondents in the treatment group were consistently less willing 

to select the risky option; that is, respondents were less willing to risk a larger 

wildfire when presented with information on the expected odds of a fatality, based 

on historical USFS statistics, on such a fire. Differences between the control and 

treatment groups are substantial enough to suggest that the way aviation exposure 

information is presented to fire managers can influence preferences and decisionmaking on wildfire events. However, it should be noted that Table 5 over-simplifies 

results from this experiment. Table 5 presents percentages of respondents selecting 

Option B, the risky strategy, but potential outcomes under Option B were not consistent across choice sets. Rather, we varied the risky strategy’s bad outcomes to 

the three attributes using an experimental design so we would be able to measure 

relative preferences over outcome attributes and risk. These variations are obscured 

in Table 5 and will require more thorough analysis.

Table 5—Percentage of respondents selecting Option B by 

probability of good outcome and exposure frame.

Probability of

good outcome Control group Treatment group

.7 30.09 24.55

.85 50.63 41.81

.9 58.95 51.01

.95 66.25 58.05

.98 70.82 67.37

.995 75.42 70.14

Table 6—Percentage of respondents selecting Option B by attribute outcomes 

under the risky strategy’s bad outcome and by exposure frame.

Risky strategy bad outcome Control group Treatment group

Aviation exposure

300 hours/14 deaths in 1000 fires 62.58 56.77

1200 hours/58 deaths in 1000 fires 58.06 48.62

Private property damage

$3 million 63.20 57.47

$14 million 53.32 52.08

Suppression cost

$2 million 62.01 51.94

$12.5 million 58.64 53.47
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Research Note RMRS-RN-50WWW. 2012

Table 6 indicates that, in general, as the risky strategy’s bad outcomes for each of 

the three attributes became increasingly worse, the percentage of managers willing to select the risky alternative decreased. However, the percentage of managers 

selecting the risky strategy under each potential attribute outcome varied across the 

control and treatment groups. Tellingly, respondents in the treatment group were 

uniformly less likely than control group respondents to select the risky alternative. Further, respondents in the control frame were more sensitive to the potential 

outcomes for private property damage and suppression cost than respondents 

in the treatment frame. However, the disclaimer applied to Table 5 also applies 

here—Table 6 obscures how probabilities of each strategy’s bad outcome and how 

other attributes potential outcomes were experimentally varied across choice sets.

Discussion

A stated preference choice experiment survey of Federal wildfire managers conducted in 2009 indicated that personnel exposure was not a primary determinant 

of managers’ preferences over suppression strategies (Calkin and others). In the 

time since that study, the USFS has increased the emphasis it places on personnel 

safety. Though our present results cannot be directly compared with those of the 

earlier study, they suggest that USFS managers have assimilated this message. 

Fire managers consistently ranked the safety of firefighting personnel as their top 

concern when deciding upon fire management strategies. Respondents, especially 

in the fire management lottery experiment treatment group, were sensitive to potential consequences for aviation exposure and were typically sensitive to potential 

consequences for private property damage and suppression cost. Treatment group 

respondents were less likely to select the risky option in general, but they were less 

sensitive to the magnitude of potential consequences for private property damage 

and suppression cost. More detailed analysis is necessary to indicate whether this 

reflects a greater degree of risk-aversion with respect to aviation exposure within 

the treatment frame, or a lesser degree of risk-seeking, and to reveal the roles of 

managers’ relative risk preferences and preferences over the three attributes in determining decision-making in this experiment. Future work will explore the potential 

for integration of knowledge from this and other studies into the decision support 

available to Federal wildfire managers in order to improve risk management and 

enhance personnel safety.

References

Calkin, D.E.; Venn, T.J.; Wibbenmeyer, M.J.; Thompson, M.P. In press. Estimating U.S. Federal 

wildland fire managers preferences toward competing strategic suppression objectives. International Journal of Wildland Fire. 

Canton-Thompson, J.; Gebert, K.M.; Thompson, B.; Jones, G.; Calkin, D.E.; Donovan, G.H. 2008. 

Estimating U.S. Federal wildfire managers’ preferences toward competing strategic suppression 

objectives. Journal of Forestry. 106(8): 416-424.
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Research Note RMRS-RN-50WWW. 2012

Holt, C.; Laury, S. 2002. Risk aversion and incentive effects. The American Economic Review. 

92(5): 1644-1655.

Maguire, L.A.; Albright, E.A. 2005. Can behavioral decision theory explain risk-averse fire management decisions? Forest Ecology and Management. 211: 47-58.

Tidwell, T.L. 2011. Next steps of the safety journey. E-mail to all U.S. Forest Service employees, 

U.S. Forest Service Correspondence Archive Database, February 2011.

Tidwell, T.L. 2012. Chief’s letter of intent–2012 fire season. E-mail to U.S. Forest Service Regional 

Foresters, Station Directors, Area Directors, IITF Directors, and Deputy Chiefs, U.S. Forest 

Service Correspondence Archive Database, February 2012.

U.S. Forest Service. 2010. FY2010 Aviation Safety Summary. Available at: http://www.fs.fed.us/

foresthealth/aviation/safety/safety-statistics.shtml.
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Research Note RMRS-RN-50WWW. 2012

Version 1.1.1

Federal Wildfire Manager Decision-Making

We would like to know about the factors that are important in determining managers’ choices

among potential fire management strategies. This information will be highly valuable in

improving the efficiency of wildfire management. Your responses to this questionnaire will be

kept anonymous and will be used only for statistical purposes.

This questionnaire will consist of five sections and will take approximately 45 minutes to

complete. The first section asks about your attitudes related to fire management. The second

section provides a short warm-up task in which you will be asked to choose between a series of

lotteries. The third section describes a hypothetical wildfire management scenario followed by a

series of “choice sets.” In each choice set, you will be asked to choose the wildfire management

strategy you would be most likely to select if faced with the given wildfire scenario. The fourth

section asks about your perceptions and understanding of risk, and the fifth section asks about

your demographic characteristics and level of experience in wildfire management.

Name ______________________________________________

Date_______________________

Appendix A
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Research Note RMRS-RN-50WWW. 2012

Section 1: Attitudes Related to Wildfire Management and Risk

In this section we will ask you about your attitudes regarding risk and the management of

wildland fires. There are no right or wrong answers; the best response is the one that most

closely matches what you think.

Attitudes about risk

Indicate your level of agreement (from “Strongly Disagree” (1) to “Strongly Agree” (5)) with

each of the following statements by checking the appropriate box for each statement. Disagre Strongly

e

Disagre

Somewhat

e

Neutral

Somewhat

Agre

e

Strongly

Agre

e

Don’t kno

w

I like “playing it safe.”

With respect to household financial decisions, I am

risk averse.

With respect to household financial decisions, I prefer

certainty to uncertainty.

When making household financial decisions, I am

willing to take higher risks in order to realize higher

average returns.

With respect to managing fires, I am risk averse.

With respect to managing fires, I prefer certainty to

uncertainty.

When managing fires, I am willing to pursue riskier

strategies that I believe will result in better outcomes on

average.

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9
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Attitudes about wildfire management

Indicate your level of agreement (from “Strongly Disagree” (1) to “Strongly Agree” (5)) with

each of the following statements by checking the appropriate box for each statement. Disagre Strongly

e

Disagre

Somewhat

e

Neutral

Somewhat

Agre

e

Strongly

Agre

e

Don’t kno

w

Risks of an escaped fire associated with prescribed

burning are negligible if fire managers follow

established guidelines.

The benefits of prescribed burning outweigh the

potential harm of an escaped prescribed fire.

A large commitment by the Forest Service to reduce

heavy fuels would help reduce the need for aggressive

fire suppression in the future.

The potential effects of fires on ecological values

should be given equal weight with private property

values when deciding how to manage a wildland fire.

The risk of an accident involving aircraft increases

when the use of helicopters and air tankers on a fire

increases.

The risks to pilots of helicopters and air tankers are

minimal when established safety guidelines are

followed.

Factors outside of a manager’s control, such as the

weather and fuel loads, mostly determine whether a fire

can be quickly contained.

When a fire threatens homes and other private property,

managers should only try to protect those structures

when the probability of success is high.

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9
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Disagre

Strongly

e

Disagre

Somewhat

e

Neutral

Somewhat

Agre

e

Strongly

Agre

e

Don’t kno

w

Managers should not pursue an aggressive suppression

strategy for a fire when the probability of damage to

private property is very low.

Whether or not a fire management strategy achieves its

objectives is largely determined by the actions of

managers and fire crews.

Letting a fire burn, rather than aggressively suppressing

a fire, is a good way to reduce the costs of managing

wildland fires.

Limiting the amount of resources used to suppress a

fire is likely to increase a fire’s potential damage.

Limiting the amount of resources used to suppress a

fire is likely to reduce the risks of injury or fatalities for

firefighters.

It is the responsibility of individual private landowners

to take actions that reduce the risk of fire on their

property, like creating “defensible space.”

It is the responsibility of the Forest Service to invest in

large-scale suppression efforts to protect private

property within fire-prone areas.

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9
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How important are the following factors when deciding how to manage a fire? Rank from 1

(most important) to 6 (least important) the importance of each of the following factors that you

consider when choosing a management strategy:

_____ Reducing heavy fuel loads

_____ Encouraging beneficial effects of fire for wildlife habitat, plant life, and ecological

values

_____ Minimizing damage to ecological values

_____ Protecting private property

_____ Ensuring the safety of firefighting personnel

_____ Total cost of suppression efforts
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Research Note RMRS-RN-50WWW. 2012

Section 2: Warm-up Task

To prepare you to complete the fire management choices in section 3 of this questionnaire, this

section asks you to complete a series of choices between two lotteries. You will choose “Option

A” and “Option B” by selecting the lottery you would prefer if your chosen lottery were being

played for real money. If you choose Option A in the row shown below, you will have a 1 in 10

chance of earning $2.00 and a 9 in 10 chance of earning $1.60. Similarly, Option B offers a 1 in

10 chance of earning $3.85 and a 9 in 10 chance of earning $0.10.

Choice

10% $2.00 10% $3.85

90% $1.60 90% $0.10

Option A Option B I select option...

Example A B

You will complete a decision table in which each row contains a pair of choices similar

to Option A and Option B. You make your choice by checking the box beside the letter denoting

your preferred gamble. Only one option in each row can be selected, and you may change your

decision as you wish.
21

Research Note RMRS-RN-50WWW. 2012

Please complete the decision table below by selecting in each row the lottery you would

prefer if your chosen lottery were being played for real money.

Choice

10% $10.00 10% $19.25

90% $8.00 90% $0.50

20% $10.00 20% $19.25

80% $8.00 80% $0.50

30% $10.00 30% $19.25

70% $8.00 70% $0.50

40% $10.00 40% $19.25

60% $8.00 60% $0.50

50% $10.00 50% $19.25

50% $8.00 50% $0.50

60% $10.00 60% $19.25

40% $8.00 40% $0.50

70% $10.00 70% $19.25

30% $8.00 30% $0.50

80% $10.00 80% $19.25

20% $8.00 20% $0.50

90% $10.00 90% $19.25

10% $8.00 10% $0.50

100% $10.00 100% $19.25

0% $8.00 0% $0.50

5)

7)

8)

9)

1)

2)

3)

4)

10)

6)

Option A Option B I select option...

A B

A B

A B

A B

A B

A B

A B

A B

A B

A B
22

Research Note RMRS-RN-50WWW. 2012

Section 3: Wildfire Management Strategy Choices

In this section you will be asked to consider a hypothetical wildfire scenario followed by a series

of “choice sets.” Choice sets will consist of two alternative fire management strategies, and you

will be asked to select the more appropriate management strategy. Fire management is inherently

risky. Fire and fuels management professionals face risk when choosing to undertake fuels

management, when choosing to manage a fire for resource benefit, and when exposing fire

management staff to hazards. Therefore, in this section, each wildfire strategy you encounter will

have risk associated with its potential outcomes to private property, suppression cost, and hazard

to aviation personnel.

Please read the following wildfire scenario and explanations of the attributes used to describe

potential outcomes of wildfire management strategies, and then complete the subsequent choice

sets.

Wildfire Scenario

A fire has started on National Forest land resulting from a lightning strike. Initial and extended

attack efforts have failed to contain the fire’s spread, and you have been assigned to manage the

response for this fire, including selecting the overall suppression strategy. The fire is currently

burning at a moderate intensity in mixed timber with moderate-to-heavy fuel loads.

Over the next three to four days, the weather is forecasted to be calm. Beyond this time, the

forecast is less certain, and there is the potential that extreme weather will result in a large and

damaging fire. If calm weather conditions continue, the fire will likely spread slowly and help

reduce heavy fuel loads in the area.

The area where the fire is burning has limited road access. Terrain is moderate to high difficulty,

with varied slope and elevation. The area immediately threatened by the fire does not currently

include any specially designated areas, such as critical endangered species habitat. However, a

large and damaging fire would likely degrade some valuable specially designated areas and

critical habitat.

The area is used for recreation (such as hiking, camping, and fishing), and some timber of

commercial value is nearby. There are no large towns nearby, but the fire threatens to spread

beyond the forest boundaries on to private land. Private grazing land, several residences, and

outbuildings on a nearby ranch would be in the path of the fire if it expands quickly. However,

residents in the area have already prepared for evacuation and there is little threat to the safety of

nearby residents. Other impacts of the fire, such as potential dangers to human health from

smoke, damage to critical public infrastructure, or wildlife habitat degradation, appear to be of

low to moderate concern, even if the fire expands rapidly.
23

Research Note RMRS-RN-50WWW. 2012

Attributes

Aviation exposure – Aircraft such as air tankers and helicopters are frequently used to help

control wildland fires. “Aviation exposure” refers to the total number of personnel hours

necessary for operating air tankers and helicopters over the course of the fire for each potential

outcome.

For example:

• If a single helicopter manned by a pilot and an “overhead” personnel is used on a

wildland fire incident for a total of 20 flight-hours, this incident uses a total of 40

aviation-hours.

• On average, a death resulting from an aviation accident occurs once every 21,000

aviation personnel hours. So 520 fires requiring 40 aviation-hours would result in one

fatality, on average.

Consider:

• Both ground operations and aviation activities are dangerous; however, current accident

rates indicate that aviation poses greater risk to firefighters than ground-based activities.

• Increasing the use of aircraft can accelerate containment and prevent further damage from

the fire, but also carries the risk of injuries and fatalities for flight crews.

• Median personnel hours used for combined air tankers and helicopters was approximately

40 hours per fire in recent years, meaning that half of fires used less personnel time for

aviation, and half of fires used more.

Suppression costs – Managing wildland fires costs money. The suppression cost of a strategy’s

outcome is the total cost of suppression and post-fire emergency response to taxpayers (including

federal, state, and local costs). These costs include payments for aviation activity, ground crews,

engines, and overhead.

Consider:

• Costs increase in proportion to the use of ground resources. If more crews and other

resources are used on a wildland fire incident, the final suppression costs of that fire will

be higher.

• The median total suppression cost of USFS fires over 300 acres is about $250,000,

meaning that half of the fires cost more than $250,000, and half cost less than $250,000.

• Since 2009, the total USFS budget for fire suppression (excluding other fire-related

activities, such as rehabilitation and hazardous fuels treatments) has been between $1.0

billion and $1.4 billion per year.
24

Research Note RMRS-RN-50WWW. 2012

Private property damage – Wildland fires can damage private property, including private

homes, businesses, farm and ranch land, and other infrastructure (such as telecommunications

equipment). “Private property damage” refers to the total dollar value of damage to private

property caused by the fire for each potential fire outcome.

Consider:

• Extreme fires that burn uncontrolled will cause more private property damage than fires

that are controlled with suppression or that are less severe due to favorable conditions.

• Since 2000, wildland fires have caused an average of $484 million of private property

damage per year.

• Most wildland fires cause little or no damage to private property, but a small number of

fires can cause large losses to private property. For example, the 2010 Fourmile Canyon

fire in Colorado caused an estimated $217 million in damage. In 2007, several fires in

Southern California destroyed over 3,300 homes and caused $1.8 billion in damage.

Outcome probability –“Outcome probability” represents the chance that a fire management

strategy will achieve a particular outcome. The outcomes of a given wildfire management

strategy are seldom known with certainty. How large and damaging a wildfire turns out to be is

determined in part by fire management and in part by chance or by factors outside the control of

fire managers (like the weather).

For example:

• The probability that a fire crosses a ridge and damages homes may be predicted to be

0.70. This means that 7 times out of 10 a similar fire would damage the homes, and 3

times out of 10 it would stop spreading and not damage the homes.

• A probability of 0.50 indicates that each outcome is equally likely to occur, like flipping

a coin.

Consider:

• For the purposes of the choices you make below, assume that outcome probabilities are

generated with state-of-the-art wildfire risk assessment models.
25

Research Note RMRS-RN-50WWW. 2012

Example

For the fire scenario described above, there are a number of potential wildfire management

strategies that might be used. We will ask you to make a series of choices between two potential

wildfire management strategies. As in the warm-up task, each option will have two potential

outcomes, and the chance that each outcome will occur will be given as a probability. The figure

below provides an example.

Option A Option B

I select...

75 hours

Personnel

Exposure

Suppression

cost $300,000

Private property

damage

700 of 1000 

wildfires Suppression

cost

$2 million

Option A Option B

300 of 1000 

wildfires

300 of 1000 

wildfires Suppression

cost $500,000 Suppressioncost $1.5 million

30.0% 30.0%

150 hours

Private property

damage

$1.25 million

Private property

damage

Personnel

Exposure

Private property

damage

$600,000

70.0%

10 hours

Personnel

Exposure 70.0%

$700,000

700 of 1000 

wildfires $25,000

Personnel

Exposure

50 hours

If you check the box marked Option A in the final row, there will be a 70% probability that fire

suppression will require 50 hours of aviation exposure and cost $600,000, and that it will cause

$300,000 in private property damage. However, there is a 30% probability fire suppression will

require 75 hours of aviation exposure and cost $1.25 million, and that it will cause $500,000 in

private property damage. If you select Option B, there will be a 70% probability that the fire will

require fewer aviation-hours, cost less, but do only slightly more damage to private property than

in either of the potential outcomes if Option A is chosen. However, there is a 30% probability

that substantially worse outcomes for the three attributes will result.
26

Research Note RMRS-RN-50WWW. 2012

Choice Sets

For the following 8 choice sets, please select the fire management strategy you expect you

would be more likely to choose if actually faced with the wildfire scenario described above.

Choice 1

Option A Option B

I select...

50 hours 10 hours

Aviation

Exposure

Aviation

Exposure 0.5%

99.5% 99.5%

0.5%

75 hours 300 hours

Aviation

Exposure

Aviation

Exposure

995 of 1000 

wildfires Suppression

cost

Option A Option B

Private property

damage

$700,000

$1.25 million Private property

damage

Private property

damage

$600,000

995 of 1000 

wildfires

5 of 1000 

wildfires

$25,000

5 of 1000 

wildfires Suppression

cost $500,000 Suppressioncost $2 million

Suppression

cost $300,000

Private property

damage

$14 million

Choice 2

Option A Option B

I select...

$3 million

990 of 1000 

wildfires

990 of 1000 

wildfires Suppression

cost $300,000 Suppressioncost $25,000

1.0% 1.0%

99.0%

Aviation

Exposure

50 hours

99.0%

Aviation

Exposure

10 hours

Private property

damage

$600,000 Private property

damage

$700,000

Option A Option B

10 of 1000 

wildfires

10 of 1000 

wildfires Suppression

cost $500,000 Suppressioncost $2 million

Private property

damage

$1.25 million Private property

damage

Aviation

Exposure

75 hours

Aviation

Exposure

1200 hours
27

Research Note RMRS-RN-50WWW. 2012

Choice 3

Option A Option B

I select...

97.5%

Aviation

Exposure

50 hours

97.5%

Aviation

Exposure

10 hours

Private property

damage

$600,000 Private property

damage

$700,000

Suppression

cost $500,000 Suppressioncost $2 million

Option A Option B

975 of 1000 

wildfires

975 of 1000 

wildfires Suppression

cost $300,000 Suppressioncost $25,000

Aviation

Exposure

75 hours

2.5% 2.5%

Private property

damage

$1.25 million Private property

damage

$3 million

25 of 1000 

wildfires

25 of 1000 

wildfires

Aviation

Exposure

300 hours

Choice 4

Option A Option B

I select...

$12.5 million

125 of 1000 

wildfires

125 of 1000 

wildfires Suppression

cost $500,000 Suppressioncost

Option A Option B

$25,000

875 of 1000 

wildfires

875 of 1000 

wildfires Suppression

cost $300,000 Suppressioncost

12.5%

Aviation

Exposure

75 hours

12.5%

Aviation

Exposure

1200 hours

Private property

damage

$1.25 million Private property

damage

$14 million

Private property

damage

$600,000 Private property

damage

$700,000

87.5%

Aviation

Exposure

50 hours

87.5%

Aviation

Exposure

10 hours
28

Research Note RMRS-RN-50WWW. 2012

Choice 5

Option A Option B

I select...

Aviation

Exposure

1200 hours

$12.5 million

200 of 1000 

wildfires

200 of 1000 

wildfires Suppression

cost $500,000 Suppressioncost

Option A Option B

Private property

damage

$1.25 million Private property

damage

$3 million

20.0%

Aviation

Exposure

75 hours

20.0%

Aviation

Exposure

50 hours

Aviation

Exposure

10 hours

Private property

damage

$600,000 Private property

damage

$700,000

80.0% 80.0%

800 of 1000 

wildfires

800 of 1000 

wildfires Suppression

cost $300,000 Suppressioncost $25,000

Choice 6

Option A Option B

I select...

0.1% 0.1%

Private property

damage

$700,000

999 of 1000 

wildfires

999 of 1000 

wildfires Suppression

cost $300,000 Suppressioncost $25,000

1 of 1000 

wildfires

1 of 1000 

wildfires Suppression

cost $500,000 Suppressioncost $12.5 million

Option A Option B

Aviation

Exposure

75 hours

Aviation

Exposure

300 hours

Private property

damage

$1.25 million Private property

damage

$14 million

Aviation

Exposure

50 hours

Aviation

Exposure

10 hours

99.9% 99.9%

Private property

damage

$600,000
29

Research Note RMRS-RN-50WWW. 2012

Choice 7

Option A Option B

I select...

Suppression

cost $500,000 Suppressioncost $2 million

92.5%

Aviation

Exposure

50 hours

92.5%

Aviation

Exposure

10 hours

Option A Option B

925 of 1000 

wildfires

925 of 1000 

wildfires Suppression

cost $300,000 Suppressioncost $25,000

Aviation

Exposure

75 hours

Aviation

Exposure

300 hours

Private property

damage

$600,000 Private property

damage

$700,000

7.5% 7.5%

Private property

damage

$1.25 million Private property

damage

$3 million

75 of 1000 

wildfires

75 of 1000 

wildfires

Choice 8

Option A Option B

I select... Option A Option B

5.0%

Aviation

Exposure

75 hours

5.0%

Aviation

Exposure

1200 hours

Private property

damage

$1.25 million Private property

damage

$14 million

50 of 1000 

wildfires

50 of 1000 

wildfires Suppression

cost $500,000 Suppressioncost $12.5 million

95.0%

Aviation

Exposure

50 hours

95.0%

Aviation

Exposure

10 hours

Private property

damage

$600,000 Private property

damage

$700,000

950 of 1000 

wildfires

950 of 1000 

wildfires Suppression

cost $300,000 Suppressioncost $25,000
30

Research Note RMRS-RN-50WWW. 2012

Section 4: Questions regarding perceptions of risk

Wildland fires involve risk and uncertainty, and they require managers to make judgments about

how to respond in risky situations. This section asks questions about how you understand and

respond to probabilities and risk. Remember, your answers are confidential and will not be

associated with any personally identifiable information.

Write the correct answer in the space provided, or circle the letter beside the correct

answer if the question is multiple choice. Do not use a calculator.

1. In the BIG BUCKS LOTTERY, the chance of winning a $10.00 prize is 1%. What is your

best guess about how many people would win a $10.00 prize if 5,000 people each buy a

single ticket to BIG BUCKS?

__________________

2. In the ACME PUBLISHING SWEEPSTAKES, the chance of winning a car is 1 in 1,000.

What percent of tickets to ACME PUBLISHING SWEEPSTAKES will win a car?

__________________

3. Imagine that we rolled a fair, ten-sided die 1,000 times. Out of 1,000 rolls, how many times

do you think the die would come up even (2, 4, 6, 8, or 10)?

__________________

4. Imagine that we rolled two fair, ten-sided dice 1,000 times. Out of 1,000 rolls, how many

times do you think both would come up even (2, 4, 6, 8, or 10)?

__________________

5. Imagine that we rolled two fair, ten-sided dice 1,000 times. Out of 1,000 rolls, how many

times do you think either: (1) both will come up ones or (2) both will come up tens?

__________________
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Research Note RMRS-RN-50WWW. 2012

6. Which of the following numbers represents the biggest risk of getting a disease?

a) 1 in 100

b) 40 in 5000

c) 5 in 400

7. If Person A’s risk of getting a disease is 1% in ten years, and Person B’s risk is double that of

A’s, what is B’s risk?

__________________

8. If the chance of getting a disease is 10%, how many people would be expected to get the

disease:

A: Out of 50 people?

__________________

B: Out of 850 people?

__________________

9. If the chance of getting a disease is 20 out of 500, this would be the same as having a ___%

chance of getting the disease.

__________________

10. The chance of getting a viral infection is .0005. Out of 10,000 people, about how many

people are expected to get infected?

__________________
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Research Note RMRS-RN-50WWW. 2012

Section 5: Tell us about yourself

Please complete the following questions:

1. Are you:

a. Male

b. Female

2. What year were you born?

1 9 ____ ____

3. Are you Hispanic or Latino(a)?

a. No

b. Yes

4. With which racial group(s) do you most closely identify? Select one or more:

a. American Indian/Alaskan Native

b. Asian

c. Black/African American

d. Native Hawaiian or other Pacific Islander

e. White
33

Research Note RMRS-RN-50WWW. 2012

5. What is the highest level of schooling you have completed?

a. Fourth grade or less

b. 5th through 8th grades

c. 9th through 11th grades

d. 12th grade, no diploma

e. High-school graduate (including

GED)

f. Some college

g. Associate’s degree

h. Bachelor’s degree

i. Graduate or professional degree

6. How long have you worked with federal land management agencies?

a. 0-4 years

b. 5-9 years

c. 10-14 years

d. 15-19 years

e. 20-29 years

f. 30 or more years

7. What is your current Grade level?

a. 5-6 

b. 7-8

c. 9-10

d. 11-12

e. 13-15

f. SES

g. Other

8. For which federal agency do you currently work?

a. FS

b. BIA

c. BLM

d. NPS

e. FWS

f. Interagency
34

Research Note RMRS-RN-50WWW. 2012

9. Which of the following most closely describes your current position?

a. Agency administrator

b. Fire manager (fuels/fire use focus)

c. Fire manager (suppression/operations focus)

d. Other (please specify) _____________________________

10. On how many wildfire incidents have you been involved as a firefighter?

a. 0

b. 1-5

c. 6-10

d. 11-15

e. 16-25

f. More than 25

11. On how many large fires (> 300 acres) have you had management responsibilities?

a. 0

b. 1-5

c. 6-10

d. 11-15

e. 16-25

f. More than 25

12. Have you ever been involved in a wildfire incident on which there was a fatality?

a. Yes

b. No

If yes, was the fatality (or at least one of the fatalities, if you have been involved in

multiple incidents involving fatalities) related to the use of aviation resources?

i. Yes

ii. No
35

Research Note RMRS-RN-50WWW. 2012

Section 5: Debriefing Questions [Pre-test Only]

For the following statements, please indicate your degree of agreement by checking one box for each

statement:

Disagre

Strongly

e

Disagre

Somewhat

e

Neutral

Somewhat

Agre

e

Strongly

Agre

e

Don’t kno

w

I do not think the wildland fire scenario presented in

section 3 was realistic.

I do not think the choices among fire management

strategies in section 3 were realistic.

I do not think the dollar values for private property

damage in section 3 were realistic.

I think the probabilities of each of the management

strategy outcomes in section 3 were within a reasonable

range.

I do not believe that outcome probabilities can be

known with as much accuracy as was presented in the

survey questions. 

The fire outcomes described in the choice sets were not

believable.

I primarily considered the worst-case scenario

outcomes while completing choice sets in section 3.

I primarily considered the best-case scenario outcomes

while completing choice sets in section 3.

I considered possible outcomes to each of the

attributes and the probabilities of those outcomes

while completing choice sets in section 3.

I found the survey confusing.

The survey was biased.

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9

1 2 3 4 5 9
36

Research Note RMRS-RN-50WWW. 2012

Please rank from 1 (most important) to 3 (least important) the importance you placed on each of

the following attributes in determining your choices of fire management strategies in section 3.

_____ Aviation exposure

_____ Private property damage

_____ Suppression costs

For the following questions, please write your answers in the space provided.

1) Did you find the instructions to sections 2 and 3 easy to understand? If not, what did you find

confusing?

2) About how long did it take you to complete the choice sets in section 2? Did you find the

choices presented to be difficult?
37

Research Note RMRS-RN-50WWW. 2012

3) Please describe the decision process you used to complete the choice sets in section 2. How

did you decide what was an acceptable probability at which to select option B?

Thank you for completing the questionnaire!
38

Research Note RMRS-RN-50WWW. 2012

Appendix B

Example of a survey question (from the web-based survey) to illustrate how the 

experimental treatments were presented.
The Rocky Mountain Research Station develops scientific information and 

technology to improve management, protection, and use of the forests and 

rangelands. Research is designed to meet the needs of the National Forest 

managers, Federal and State agencies, public and private organizations, 

academic institutions, industry, and individuals. Studies accelerate solutions to 

problems involving ecosystems, range, forests, water, recreation, fire, resource 

inventory, land reclamation, community sustainability, forest engineering 

technology, multiple use economics, wildlife and fish habitat, and forest insects 

and diseases. Studies are conducted cooperatively, and applications may be 

found worldwide.

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Rocky Mountain Research Station

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require alternative means for communication of program information (Braille, large print, 

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To file a complaint of discrimination, write to: USDA, Assistant Secretary for Civil Rights, 

Office of the Assistant Secretary for Civil Rights, 1400 Independence Avenue, S.W., Stop 

9410, Washington, DC 20250-9410.

Or call toll-free at (866) 632-9992 (English) or (800) 877-8339 (TDD) or (866) 377-8642 

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