fall 2013: volume 12, number 2 western economics ... documents/communit… · western economics...

WesternEconomicsForumA Journal of the

Western Agricultural EconomicsAssociation

Farm & Ranch Management

Marketing & Agribusiness

Natural Resources & the Environment

Policy & Institutions

Regional & Community Development

Farm & Ranch Management

Marketing & Agribusiness

Natural Resources & the Environment

Policy & Institutions

Regional & Community Development

Fall 2013: Volume 12, Number 2

Western Economics Forum

Volume XII, Number 2

FALL 2013

Table of Contents Amy M. Nagler, Christopher T. Bastian, David T. Taylor, and Thomas K. Foulke

Community Economic Contributions from Recreational Trails Usage on Public Lands: Implications from a Comprehensive Wyoming Case Study .............................................. 1

James W. Richardson, Brian Herbst, Tom Harris, and Mike Helmar

Economic Analysis of Management Options Following a Range Fire in Elko County, Nevada ...................................................................................................... 12 Travis Warziniack and Matthew Thompson

Wildfire risk and optimal investments in watershed protection ....................................... 19 David B. Bilby and Paul N. Wilson

Regulatory Capture? Arizona’s BMP Water Conservation Program ............................... 29

The Western Economics Forum A peer-reviewed publication from the Western Agricultural Economics Association Purpose One of the consequences of regional associations nationalizing their journals is that professional agricultural economists in each region have lost one of their best forums for exchanging ideas unique to their area of the country. The purpose of this publication is to provide a forum for western issues. Audience The target audience is professional agricultural economists with a Masters degree, Ph.D. or equivalent understanding of the field that are working on agricultural and resource economic, business or policy issues in the West. Subject This publication is specifically targeted at informing professionals in the West about issues, methods, data, or other content addressing the following objectives: • Summarize knowledge about issues of interest to western professionals • To convey ideas and analysis techniques to non-academic, professional economists

working on agricultural or resource issues • To demonstrate methods and applications that can be adapted across fields in economics • To facilitate open debate on western issues Structure and Distribution The Western Economics Forum is a peer reviewed publication. It usually contains three to five articles per issue, with approximately 2,500 words each (maximum 3,000), and as much diversity as possible across the following areas: • Farm/ranch management and production • Marketing and agribusiness • Natural resources and the environment • Institutions and policy • Regional and community development There are two issues of the Western Economics Forum per year (Spring and Fall). Editor – Send submissions to: Dr. Don McLeod Editor, Western Economics Forum Dept. of Ag & Applied Economics University of Wyoming Dept. 3354 1000 E. University Avenue Laramie, WY 82071 Phone: 307-766-3116 Fax: 307-766-5544 email: [email protected]

Special Issue: Public Lands and Resources Public lands and resources play a significant role in the welfare of western communities, producers and citizens. Four articles are featured in this special issue as follows: Nagler, Bastian, Taylor and Foulke provide evidence of the contribution to the Wyoming State Economy of motorized recreation on public lands trails. They juxtapose these with values for non-motorized recreation. Richardson, Herbst, Harris and Helmar demonstrate the value of private irrigation investments in concert with different public lands post-fire grazing regimes. They convey this exercise with the use of a representative public lands grazing ranch in Nevada and present the alternatives based on simulation techniques. Warziniack and Thompson approach fire prevention and watershed protection investments on public lands with a financial tool aimed at reducing wildfire risk in northern Colorado. The tool is based on measures of excess benefits and the (risk) standard deviation of the portfolio of prevention investments. Bilby and Wilson revisit state water management in Arizona and examine the extent to which the policy met its intended objectives. A Best Management Practices program was evaluated by an expert panel to determine the impact of the program on water conservation practices by irrigators. USFS fire prevention investment, post fire production decisions by grazers, public lands recreation and state water policy all reflect key issues in public lands and resources. These articles provide insights into some of the facets of these issues. Donald M McLeod, editor Western Economics Forum, Fall 2013 Edition

Western Economics Forum, Fall 2013

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Community Economic Contributions from Recreational Trails Usage on Public Lands: Implications from a

Comprehensive Wyoming Case Study1

Amy M. Nagler, Christopher T. Bastian, David T. Taylor, and Thomas K. Foulke2

Introduction

Many rural economies in the West are dependent on tourism dollars generated by outdoor recreation on public lands. Controversy and conflict has arisen between groups concerned with preservation versus recreational use on public lands (Wilson 2008). The largest controversy surrounds increased motorized recreation and environmental damages (Barton and Holmes 2007; Deisenroth, Loomis, and Bond 2009; Groom et al. 2007; Havlick 2002). Others have expressed concerns with activities such as hiking and climbing and the potential for negative ecological effects (Lohman 2010). Public lands agencies are faced with decisions related to managing recreational use, trail provision or maintenance, and the potential for environmental damage. Such decisions may affect local economies. While studies regarding the economic impacts, contributions, and non-market values for recreation are found in the literature, most of these studies are use or area specific (Bergstrom et al. 1990; Bowker, Bergstrom, and Gill 2007; Cordell et al. 1990; Diesenroth, Loomis, and Bond 2009; Jakus et al. 2010; Keske and Loomis 2008; Pollock et al. 2012). There is a paucity of published research that provides a broad, regional analysis of the economic contributions of recreational trails usage on public lands.

We provide a more comprehensive assessment of the potential economic contributions of recreational trail usage on public lands than has been previously reported, using Wyoming as a case analysis. The research objective is achieved by providing a broad look at seasonal motorized and nonmotorized recreational trails usage and associated spending across Wyoming. This dataset is unique in that it allows for a statewide investigation of multiple trail types, uses, and use seasons. Relative economic importance of the activities can be compared.

This paper presents a brief description of methods used to collect information and data, an overview highlighting relevant results, and a summary of economic contribution estimates. Discussion focuses on potential implications for policy makers, local decision makers, and economic researchers in light of increasing controversies regarding recreational usage versus the provision of ecosystem services from public lands (Jakus et al. 2010; Lohman 2010; Torell et al. 2013).

Methods

A variety of methods were employed to inventory Wyoming’s recreational trails, gather information regarding usage and expenditure associated with trail use, as well as to estimate

1 Research was supported by the State of Wyoming Department of State Parks and Cultural Resources Trails Program in cooperation with the University of Wyoming Department of Agricultural Economics and Wyoming Survey and Analysis Center. 2 The authors are, respectively, Research Associate, Department of Agricultural and Applied Economics, University of Wyoming; Associate Professor, Department of Agricultural and Applied Economics, University of Wyoming; Professor, Department of Agricultural and Applied Economics, University of Wyoming; and Senior Research Scientist, Department of Agricultural and Applied Economics, University of Wyoming. Amy Nagler is the corresponding author, [email protected].


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the economic contributions associated with trail use. Inventory, survey, and economic analysis methods are summarized below.

Wyoming Trail Inventory Updates In order to quantify and describe recreational trails available for public use in Wyoming, a 2013 inventory of Wyoming trails was conducted as an update and extension of a 1998 Wyoming Statewide Trails Inventory (Pindell et al. 1999). The inventory includes routes, trails, walkways, and pathways with public access, intended, managed, or designated for motorized and/or nonmotorized recreational use. Additional and updated trail descriptions were acquired via email, telephone, internet resources, and personal interviews with officials across all agencies and organizations potentially administering trails in Wyoming between March 2012 and March 2013. Information gathered included a range of trail characteristics, location, administration, and usage to accompany each listing.

Motorized Recreational Trail User Survey Process A survey process for snowmobile and ORV Wyoming trail users was designed to collect information on trail usage, expenditure information, and user satisfaction. Surveys were generally comparable to earlier studies (Foulke et al. 2006; McManus, Coupal, and Taylor 2001) to facilitate comparisons over time. A combination of mail and electronic survey instruments followed a Dillman survey design (Dillman 2007).

The Wyoming State Trails Program (WSTP) manages and maintains a snowmobile trail system on public lands in the state as well as partnering with public land agencies, enrolling ORV trails, and providing trail information and maintenance, with trail users required to pay registration fees (WSTP 2013). Sample pools of residents and nonresidents were chosen randomly from the total WSTP registration databases. Snowmobile permit holders selected in the sample were sent a series of mailings in the spring of 2012. ORV permit holder surveys were mailed in two waves, in the fall of 2012 and winter of 2013, in order to capture seasonal differences. Respondents could fill out either a web-based or paper copy of the survey. A total of 361 resident and 414 nonresident snowmobile surveys were returned, representing 34 and 38 percent response rates, respectively. A total of 498 resident and 546 nonresident surveys were returned from the ORV survey, representing 40 and 44 percent response rates, respectively. A related intercept survey of snowmobile outfitter clients, conducted at vendor locations, collected 113 surveys which contribute to the economic analysis presented below.

Economic Contribution Estimation An economic contributions analysis was conducted incorporating descriptive information from both snowmobile and ORV surveys as well as estimates of economic contributions to the state obtained from a 2011 IMPLAN model. IMPLAN is a regional modeling system capable of providing economic resolution down to the county level that is commonly used for economic contribution analysis (MIG 2012). No modifications were made to the IMPLAN model for the analysis presented here.

An economic contribution analysis of nonmotorized trail usage was conducted using US Forest Service data on visitor trail use and expenditure. Unlike motorized trail use, there are generally no permits associated with nonmotorized trail use. As a result no centralized database exists with contact information that can be used to survey nonmotorized trail users to determine trail use and associated expenditures. Due to the disbursed nature of trails and the time-consuming and expensive process of intercept sampling at trail sites, information on the economic contributions of nonmotorized trail use was estimated using available National Visitor use


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Monitoring (NVUM) data collected on four National Forests primarily located in Wyoming (USFS 2013).

Results A series of reports provide detail on methods used to collect information as well as complete descriptions of the trail inventory (Nagler et al. 2013b), snowmobile survey (Nagler et al. 2012), ORV survey (Nagler et al. 2013a), and nonmotorized economic analysis (Taylor et al. 2013) outcomes. The selection of results presented below is intended to give an overview highlighting this comprehensive set of information, data, and analysis.

2013 Wyoming Trail Inventory The 2013 Wyoming Trail Inventory represents a comprehensive description of trails in Wyoming. A total of 2,160 trails in Wyoming identified by federal, tribal, state, and local agencies and private organizations are described in the inventory totaling 10,472 miles (table 1). The majority of trails in Wyoming are administered by the US Forest Service. Combined, federal agencies manage 92 percent of total trail miles in Wyoming.

Table 1. Number and miles of trails in Wyoming by administering agency

Administering agency Number of trails

Percent of total Miles of

trails Percent of

total Federal 1,755 81% 9,605 miles 92% US Forest Service 1,399 65% 7,610 miles 73% National Park Service 284 13% 1,474 miles 14% Bureau of Land Management 71 3% 520 miles 5%

US Fish and Wildlife service 1 <1% 1 mile <1%

Wind River Indian Reservation 39 2% 131 miles 1%

State 244 11% 127 miles 1% Wyoming State Parks, Historic Sites and Trails 236 11% 115 miles 1%

Wyoming Game and Fish 5 <1% 6 miles <1% Wyoming Department of Transportation 3 <1% 6 miles <1%

Local 111 5% 495 miles 5% Municipal agencies 86 4% 393 miles 4% County agencies 25 1% 102 miles 1% Private 11 <1% 112 miles 1%

All Wyoming agencies 2,160 trails 100% 10,472 miles 100%


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Although specific-use trails are listed (groomed skate ski trails, for example) in most cases trails have more than one allowed or designated use. The majority of trails listed are for nonmotorized pedestrian use, about half include for cross-country ski, snowshoe, backpacking, and equestrian use, and just over a third include bicycle riding as a designated use. A quarter of trails listed by Wyoming agencies include motorized use designations with 11 percent designated for snowmobile riding and 18 percent for ORV uses.

Motorized Recreational Trail User Survey Highlights Results are presented from Wyoming snowmobile and ORV trail user surveys highlighting motorized trail user characteristics, expenditure information, and management opinions. Table 2 includes a summary of responses to selected questions from Wyoming residents and nonresident respondents.

Table 2. Motorized trail user survey response highlights

Snowmobile ORV Survey request Resident Nonresident Resident Nonresident

Gender 89% male 96% male 90% male 90% male

Average age 50 years 44 years 55 years 52 years Employed full time 72% 80% 61% 70% Some college or technical school or more

education 76% 80% 71% 75%

Household income $50,000 or more 79% 84% 74% 83% Average expenditure per person/per day in WY $98 $160 $41 $61 Average annual expend. per person in

Wyoming $3,367 $625 $1,789 $231

Overall satisfaction or dissatisfaction with

recreational riding in Wyoming* 4.2 4.4 3.7 4.3

Support trailhead parking fee for improvements 27% 43% 28% 34% Strongly support or support wheeled ATVs

sharing snowmobile trails if fee helps pay for grooming

31% 25% 43% 41%

* Very Satisfied = 5; Satisfied = 4; Neutral = 3; Dissatisfied = 2; Very Dissatisfied = 1.

Information on demographic characteristics of respondents is important to understanding demand for motorized trail recreation and any important differences in trail users relative to the general population. While registered users surveyed may not be representative of all users, respondents to both motorized trail surveys reported distinct demographic characteristics. The average respondent was male, 45 to 55 years old, employed full time, with some college or technical education, and a reported household income of $50,000 or more (table 2).

Trip expenditures reported per person, per day spent in Wyoming varied from $160 for nonresident snowmobilers to $41 for resident ORV riders. Overall, snowmobile riders spent more per day on their most recent trip and nonresident riders spent more than residents (table 2). In addition to trip expenditures associated with recreational riding on Wyoming trails, information was collected on annual expenditures spent in Wyoming to purchase, maintain, and outfit recreational vehicles. Not surprisingly, resident riders spent more in-state than nonresidents, totaling $3,367 per snowmobiler and $1,789 per ORV rider. Nonresident annual expenses paid in Wyoming totaled $625 per snowmobiler and $231 per ORV rider (table 2).


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Annual and trip expenditures reported were incorporated into economic contributions of Wyoming’s motorized trail programs summarized below.

Overall, riders reported a high level of satisfaction regarding their experience in Wyoming with average responses of “very satisfied” to “satisfied” for all groups surveyed. Riders, however, generally were not supportive of increased fees to pay for trailhead improvements or wheeled ATVs sharing snowmobile trails with fees paying for grooming services (table 2).

Sixty percent of nonresident visitors coming to snowmobile on Wyoming trails were from Minnesota (21 percent), Colorado (16 percent), Iowa (13 percent), and South Dakota (12 percent). A similar proportion of nonresident ORV users traveled from Colorado (18 percent), Montana (18 percent), Utah (15 percent), and Nebraska (7 percent).

Comparison of Economic Contributions A summary economic analysis first compares expenditure estimates from resident and nonresident motorized and nonmotorized trail users. Total state expenditures are estimated for motorized trail users based on survey results. Nonmotorized expenditures are estimated using US Forest Service NVUM data. Nonresident expenditures represent new money to the Wyoming economy; resident expenditures may be regionally important to local economies. This analysis considers the gross economic activity in the state’s economy that can be attributed to the state’s motorized trail program. The analysis does not consider the net economic activity associated with the program. Expenditures associated with motorized and nonmotorized trail use in Wyoming is presented in table 3.

For the 2011-2012 season a total of 33,851 snowmobiles and 56,137 ORVs were registered with the Wyoming State Trails Program. Roughly half of snowmobiles were registered by Wyoming residents, half by nonresidents, with an additional 2 percent registered by commercial outfitters. Resident ORV registrations accounted for 80 percent of the total with only 20 percent registered by nonresident riders (table 3).

Combining survey estimates of the average annual days riding in Wyoming per registered recreational vehicle with the number of registrations results in total annual visitor days reported for motorized users. Visitor days for nonmotorized users reported from US Forest Service NVUM data are individual visits with no specified length of time. Expenditures per day of recreational trail use, reported from motorized survey and nonmotorized NVUM data, vary from about $12/day for non-primary nonmotorized users to $257/day for snowmobile outfitter clients, impacting total trip expenditures. Total trip expenditures (combining visitor days with trip expenditures) are highest overall for ORV trail users ($123 million), the majority of which ($105 million) is contributed by Wyoming residents. Estimated total trip expenditures for all snowmobile trail users is $83 million with resident and nonresident users each contributing about $30 million and outfitter clients about $20 million. Nonmotorized trail user estimated trip expenditures are $52 million, with $37 million from nonresident users(table 3). 3

3 Direct spending associated with downhill resort skiing and snowboarding in Wyoming, estimated from US Forest Service NVUM data was $84.2 million in 2011-2012. Of the four national forests primarily located in Wyoming, only three had downhill ski areas at the time of the NVUM surveys. Resorts on the Bridger-Teton National Forest accounted for 97 percent of this total. This spending was not on designated trails and is not included in our contributions estimates in this paper.


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Annual amounts spent in Wyoming to purchase, outfit, and maintain recreational trail vehicles are reported for motorized trail users. Multiplying this by the number of registered machines results in estimated total annual expenditures for Wyoming of $64 million for snowmobile and $83 million for ORV trail users. Total estimated expenditures in Wyoming related to recreational trail use for motorized users, which combines trip and annual expenses, is $145 million for snowmobile, and $206 million for ORV trail users (table 3).

Registrations, trail use, and value contributed by Wyoming residents versus nonresidents was different for snowmobile and ORV users. Resident snowmobilers reported nearly twice as many days of trail riding per season. This, coupled with annual expenditures in Wyoming, roughly five times that for nonresidents, results in residents contributing 57 percent of all snowmobile expenditures in the state. Higher trip expenditures accounted for more of the total nonresident and commercial contribution, which combined accounted for nearly half (43 percent) of totals spent by snowmobile trail users in Wyoming.


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Table 3. Motorized and nonmotorized trail use expenditures in Wyoming

Measure Snowmobile ORV Nonmotorized*

Resident Non-

resident Commercial Total

Snwmbl. Resident Non-

resident Total ORV Local

Non- local

Non- primary†

Total Nonmtr.

Registered machines 15,781 17,370 700 33,851 44,910 11,227 56,137 N.A. N.A. N.A.

Percent of total 47% 51% 2% 100% 80% 20% 100% Ave. days in WY per machine‡ 20.1 11.0 118.0 17.4 57.9 26.3 51.6 Total visitor days in WY§ 316,725 190,202 82,620 589,546 2,599,592 295,730 2,895,322 718,211 245,739 129,378 1,093,328

Percent of total 54% 32% 14% 100% 90% 10% 100% 66% 22% 12% 100%

Expenditures per day in WY $98.29 $159.80 $257.32 $140.42 $40.54 $60.61 $42.59 $18.91 $149.47 $11.93 $47.43 Total trip expend. in WY ($ million) $31.1m $30.4m $21.3m $82.8m $105.4m $17.9m $123.3m $13.6m $36.7m $1.5m $51.9m

Percent of total 38% 37% 26% 100% 85% 15% 100% 26% 71% 3% 100%

Annual expend. per machine in WY $3,367 $625 N.A. $1,930 $1,789 $231 $1,477

Total annual expend. in WY ($ million) $53.1m $10.6m N.A. $64.0m $80.3m $2.6m $82.9m

Total expend. in WY ($ million) $84.3m $41.2m $21.3m $146.8m $185.7m $20.5m $206.2m

Percent of total 57% 28% 14% 100% 90% 10% 100% * Nonmotorized recreation represents only use on Shoshone, Bridger-Teton, Bighorn, and Medicine Bow-Routt National Forest Service trails in Wyoming. † Defined as individuals who were recreating on a national forest but indicated that recreating on the national forest was not the primary purpose of the trip. ‡ In some cases there may be more than one rider per machine. § Nonmotorized recreation use is measured in terms of visits with no specific length of time rather than visitor days.


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Table 4 summarizes the estimated economic contributions to the Wyoming economy of motorized and nonmotorized recreational trail users’ expenditures reported above. Estimates are obtained from a 2011 IMPLAN model of the Wyoming state economy.

Table 4. Economic contribution of motorized and nonmotorized trail use in Wyoming ($ million)

Indicator Snowmobile ORV USFS

Nonmotorized Combined Output Direct $146.8m $206.2m $51.9m $404.9m Secondary $29.0m $38.0m $16.0m $83.0m Total $175.8m $244.2m $67.9m $487.9m Employment* Direct 1,005 1,170 477 2,652 Secondary 264 298 123 686 Total 1,269 1,469 600 3,338 Labor income† Direct $26.2m $37.0m $12.4m $75.7m Secondary $9.1m $12.5m $5.3m $27.0m Total $35.3m $49.5m $17.8m $102.6m State and local government‡ Direct $6.2m $8.1m $2.6m $16.8m Secondary $1.2m $1.5m $0.8m $3.5m Total $7.4m $9.6m $3.4m $20.4m * Annual equivalent income generated for employees. † All forms of employment wages and benefits. ‡ Taxes and fees.

Estimates for total economic output combine direct contributions, reported as total expenditures in table 3, with secondary economic activity estimated through the IMPLAN model. It is important to note differences in source data as well as the absence of annual expenditures for nonmotorized users when comparing expenditure estimates.

Total estimated economic output was greatest for ORV users ($244 million). Total economic output for snowmobile users is estimated at $176 million. USFS nonmotorized trail users generated a total estimated economic output of $68 million in the Wyoming economy. The estimated economic contribution for all motorized and nonmotorized US Forest Service recreational trail users to the state economy is $488 million. The IMPLAN model estimates that this economic activity supports the equivalent of nearly 1,269 annual jobs related to snowmobile trail use, 1,469 jobs related to ORV, and 600 jobs related to nonmotorized recreational trail use in the Wyoming economy. Combined, these jobs contribute $103 million in labor income and state and local government revenue of $20 million in Wyoming (table 4).

Summary and Discussion The comprehensive set of information, data, and analysis on recreational trail use in the state of Wyoming summarized above indicate that more than 10,000 miles of trails available for both motorized and nonmotorized use not only provide access to recreate and enjoy public lands but contribute to the state’s economy. Total expenditures for snowmobile, ORV, and nonmotorized trail users are estimated to be $405 million, generating $488 million in direct and indirect economic output, $103 million in labor income, and $20 million in state and local government revenue to the Wyoming economy. This suggests that recreational trails usage is a significant


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contributor to state and local economies. Comparing these values to direct travel spending by all resident and nonresident visitors, estimated to be $3.1 billion in 2012 (Dean Runyan Associates 2013), indicates that trail use accounts for roughly 13 percent of all travel spending within the state.

Not surprisingly, the trails inventory indicates that the majority (92 percent) of trail opportunities are located on federal public lands for Wyoming. This is likely not uncommon for Western states. As federal budgets decline and more emphasis is placed on the provision of ecosystem services or mitigation of environmental damages from recreation, our analyses suggest there is potential for significant declines in economic contributions from motorized trail use. This potential decline in motorized trail emphasis is perhaps evidenced in changes in trail miles in the state since the last inventory. Comparing the 2013 Wyoming Statewide Trails Inventory to 1998 listings reveals an overall increase in trail miles in Wyoming, primarily mountain bike trail systems and municipal nonmotorized trails managed by state and local agencies. Development of nonmotorized trails systems often in or near cities and towns are an indication that outdoor trail recreation is important to Wyoming residents and visitors, but they likely tend to generate less economic contributions per mile developed. Nonmotorized trail use is important but represents a smaller proportion on US Forest Service lands. Nonmotorized use varies by forest, accounting for between 29, 27, and 23 percent of visits to Bridger-Teton, Medicine Bow-Routt, and Shoshone Forests, respectively, but only 9 percent to the Bighorn National Forest.

While Wyoming residents benefit from proximity to trails and spend more in-state to purchase and maintain snowmobiles and ORVs, Wyoming’s low population and popularity as a tourist destination balance out resident contributions for snowmobilers. Likewise, non-local visitor use of nonmotorized National Forest trails contributed 71 percent of total reported expenditures. Resident ORV riders on the other hand contribute 90 percent of total ORV trip and annual expenditures reported. A high proportion of ORV registrations, higher number of days on the trail, and higher in-state annual expenditures all contribute to resident ORV riders’ economic contribution.

Overall, the expenditures data and economic contributions analysis indicate that motorized recreation generates the biggest contributions to the Wyoming economy, and the vast majority of trails reside on federal lands. Yet, motorized recreation seems to garner the most criticism from both government officials and environmental groups. This suggests that economic contributions from these activities are likely at the greatest risk as decision makers search for ways to address environmental concerns. There may be a larger role for state and local agencies regarding trail development or marketing of alternative tourism opportunities to mitigate potential declines in economic contributions. As these controversies grow, economists will likely need to provide more studies associated with the benefits, costs, and economic impacts associated with changes in recreation regulations, motorized versus nonmotorized recreation, or trail provision.


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References Barton, D. C., and A. L. Holmes. 2007. “Off-Highway Vehicle Trail Impacts on Breeding

Songbirds in Northeastern California.” Journal of Wildlife Management 71 (5): 1617-1620.

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Bowker, J. M., J. C. Bergstrom, and J. Gill. 2007. “Estimating the Economic Value and Impacts of Recreational Trails: A Case Study of the Virginia Creeper Rail Trail.” Tourism Economics 13 (2): 241-260.

Cordell, H. K., J. C. Bergstrom, G. A. Ashley, and J. Karish. 1990. “Economic Effects of River Recreation on Local Economies.” Journal of the American Water Resources Association 26 (1): 53–60.

Dean Runyan Associates. 2013. The Economic Impact of Travel on Wyoming: 1998-2012p Detailed State and County Estimates. Report prepared for the Wyoming Office of Tourism, Cheyenne, Wyoming. http://www.wyomingofficeoftourism.gov/industry/pdf/homepage/2012 EconomicImpactStudyp.pdf.

Deisenroth, D., J. Loomis, and C. Bond. 2009. “Non-market Valuation of Off-Highway Vehicle Recreation in Larimer County, Colorado: Implications of Trail Closures.” Journal of Environmental Management 90 (11): 3490-3497.

Dillman, D. A. 2007. Mail and Telephone Surveys: The Total Design Method, Second Edition (2007 Update). Hoboken, NJ: John W. Wiley and Sons, Inc.

Foulke, T., D. Olson, D. T. Taylor, C. T. Bastian, and R. H. Coupal. 2006. “A Survey and Economic Assessment of Off-Road Vehicle Use in Wyoming.” Report prepared for the Wyoming Department of State Parks and Cultural Resources, Division of State Parks and Historic Sites, State Trails Program, Department of Agricultural and Applied Economics, University of Wyoming. http://wyocre.uwagec.org/Publications/ORVRptFinal10Aug06.pdf.

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Havlick, D. G. 2002. No Place Distant: Roads and Motorized Recreation on America’s Public Lands. Washington, DC: Island Press.

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Keske, C. M., and J. B. Loomis. 2008. “Regional Economic Contribution and Net Economic Values of Opening Access to Three Colorado Fourteeners.” Tourism Economics 14 (2): 249-262.

Lohman, G. 2010. “Economic and Ecological Impacts Associated with Recreation on Colorado Fourteeners.” Master’s thesis, Colorado State University. http://digitool.library.colostate.edu///exlibris/dtl/d3_1/apache_media/L2V4bGlicmlzL2R0bC9kM18xL2FwYWNoZV9tZWRpYS8xMTE2NjU=.pdf.

McManus, C., R. Coupal, and D. Taylor. 2001. “Results from 2000-2001 Wyoming Snowmobile Survey: Nonresident Report.” Report prepared for the Wyoming Department of State


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Parks and Historic Sites, Wyoming State Trails Program, Department of Agricultural and Applied Economics, University of Wyoming. http://wyocre.uwagec.org/Publications/WYOMING%20SNOWMOBILE%20EXECUTIVE.pdf.

Minnesota IMPLAN Group (MIG). 2012. http://implan.com/V4/Index.php.

Nagler, A. M., C. T. Bastian, D. T. Taylor, and T. K. Foulke. 2012. “2011-2012 Wyoming Comprehensive Snowmobile Recreation Report.” Report prepared for the State of Wyoming, Department of State Parks and Cultural Resources, Department of Agricultural and Applied Economics, University of Wyoming. http://wyocre.uwagec.org/Archive.html.

Nagler, A. M., C. T. Bastian, D. T. Taylor, and T. K. Foulke. 2013a. “2012 Wyoming Comprehensive Off Road Vehicle Recreation Report.” Report prepared for the State of Wyoming, Department of State Parks and Cultural Resources, Department of Agricultural and Applied Economics, University of Wyoming. http://wyocre.uwagec.org/Archive.html.

Nagler, A. M., C.T. Bastian, D.T. Taylor, and T. K. Foulke. 2013b. “2013 Wyoming Statewide Trails Inventory Report.” Report prepared for the State of Wyoming, Department of State Parks and Cultural Resources, Department of Agricultural and Applied Economics, University of Wyoming. http://wyocre.uwagec.org/Archive.html.

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Economic Analysis of Management Options Following a Range Fire in Elko County, Nevada

1James W. Richardson, Brian Herbst, Tom Harris, and Mike Helmar

Introduction Ranchers throughout the West who lease federal rangeland for part or all of their grazing land are dependent upon the US Forest Service (USFS) and US Bureau of Land Management (USBLM) to determine the number of animal unit months (AUMs) they can run each year. The number of AUMs permitted on federal rangeland is determined by USFS and USBLM based on range conditions and other factors such as fire and wildlife presence. In Nevada where a number of large range fires have occurred, ranchers have experienced grazing re-entry delays ranging from one to five years.

The uncertainty of how long rangeland must be idled after a range fire has forced ranchers to consider alternative range management options. The most frequently discussed management options include:

• reduce the herd size, • lease more rangeland and maintain herd size, • buy alfalfa hay and maintain herd size, • plant alfalfa hay and maintain herd size, and • retire.

Certainly the length of the delayed re-entry after a fire affects the ranchers’ decision. A short delay may lead ranchers to purchasing alfalfa hay and redoubling their efforts to grow meadow hay. Longer delays caused by range fires which cover most of the federal grazing land could encourage ranchers to invest in their own alfalfa production (if water is available) or even reduce herd size or retire.

The objective of this paper is to analyze the economics of alternative range management options for ranchers in Nevada following a range fire.

Elko County, Nevada was selected for the analysis because the region has suffered several large range fires over the past ten years. A panel of ranchers representative of full-time, moderate to large cow/calf operations in the region was interviewed to obtain specific information for the analysis. The panel has been interviewed semi-annually for the past ten years by the Texas A&M Agricultural and Food Policy Center to obtain the production data and costs for a representative ranch in Northeast Nevada. The representative ranch has been used for numerous policy analyses, e.g., Richardson, et. al (2013). The methodology for the analysis involves simulating the representative ranch for alternative range management options using

1 The authors are, respectively, Regents Professor & Texas AgriLife Research Senior Faculty Fellow, Department of Agricultural Economics, Texas A&M University; Research Associate, Department of Agricultural Economics, Texas A&M University; Professor, State Extension Specialist, and Director, Department of Economics, University of Nevada, Reno; Research Analyst, Department of Economics, University of Nevada, Reno. James W. Richardson is the corresponding author, [email protected]


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the Farm Level Income and Policy Simulation (FLIPSIM) model developed by Richardson and Nixon (1986).

Range Management Options Interviews with ranchers in Elko County, Nevada on the subject of range/herd management after a range fire indicated that the least preferred option ranchers would consider was reducing herd size. The reasons for not reducing the herd size revolved largely around the issue of restocking the herd when re-entry was permitted. Due to the extensive size of the pastures, limited number of water holes, and seasonal movement of herds, the ranchers have found it best to maintain a closed herd. They raise replacement heifers, buy bulls, and only cull cows due to age and sickness. Purchasing replacements from other areas has not been successful in the past and the ranchers did not consider it as a viable option. The ranch panel indicated that their experience with purchasing replacements were shared by ranchers across Nevada and most likely shared by the majority of ranchers in the West.

Leasing additional grazing land is an option but one which is not feasible as rangeland in Elko County is fully occupied and the same is true for most of the rangeland in the Western states. Additionally, leasing rangeland incurs added costs of transportation, management, and loss in performance from grazing rangeland unfamiliar for the cattle.

The ranch panel indicated that planting alfalfa would be the only option short of retiring if the delayed entry was expected to last more than a year. Meadow hay presently grown on the ranch currently covers their needs when there is no fire but would not be sufficient when fire delayed re-entry. The ranch panel indicated that Elko County has adequate ground water for irrigating alfalfa, if the representative ranch used a pivot irrigation system.

Representative Ranch The representative ranch in Elko County, Nevada has 650 mother cows, 100 replacement heifers, and 38 bulls. The ranch raises its replacement heifers, has a 96 percent calf crop on bred cattle, and culls 10 percent of the cows each year. The ranch has 1,300 acres of meadow hay which has an average yield of 1.75 tons per acre. The owned pastureland is 8,725 acres and the ranch leases 5,450 AUMs of grazing; 4,500 AUMs leased from the government and 1,000 AUMs leased from private landowners. Costs of production for the ranch are provided by the panel and are summarized as: $226 of variable costs per cow and $364 of fixed costs per cow per year.

The representative ranch is simulated for a ten year planning horizon assuming initial long-term debt is one percent and intermediate term debt is five percent. Debt to purchase an irrigation pivot and additional haying equipment is financed for five years and the debt is added to the ranchers initial assumed intermediate term debt.

Scenarios Analyzed The number of years after a fire a rancher must wait to re-enter is dependent on how extensive the fire was and regrowth of the grass. Because the number of years to re-enter the rangeland is an exogenous variable, a number of scenarios are analyzed. Another exogenous variable is the percent of the USBLM land that is closed out due to fire. The following scenarios were simulated to accommodate the range of possible re-entry conditions.

• Base scenario has no fire on federal land. • 25 percent of federal land is damaged by fire and re-entry is delayed for 1, 2, 3, 4, or 5

years.


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• 50 percent of federal land is damaged by fire and re-entry is delayed 1, 2, 3, 4, or 5 years.



The ranch panel indicated that purchasing one or two 120-acre pivots for alfalfa should be considered given the uncertainty of the re-entry period. So for each of the scenarios indicated above, the representative ranch was analyzed assuming:

• Buy alfalfa hay to supplement meadow hay produced on the ranch. • Install one 120-acre pivot to grow alfalfa hay. • Install two 120-acre pivots to grow alfalfa hay.

Economic Model FLIPSIM is a firm level simulation model that simulates the annual production, marketing, financial, tax, and cash flow for a ranch (or farm) over a 10 year planning horizon. The model simulates annual beef production using empirical distributions for calving rates and weaning weights provided by the ranch panel. Annual cattle and hay prices for the representative ranch are simulated by localizing stochastic cattle prices developed by the Food and Agricultural Policy Research Institute (FAPRI) in their August 2013 Baseline.

The number of federal AUMs available to the ranch has ranged from 1,723 to 6,200 with an average of 4,500 over the past ten years in the absence of delayed re-entry due to fire. To incorporate the risk for federal AUMs an empirical probability distribution for AUMs was added to FLIPSIM. Data to fit the distribution was provided by the representative ranch panel. The representative ranch panel also provided 10 years of meadow hay yields. Meadow hay is used to supplement the random loss of federal AUMs and to provide supplemental feed. FLIPSIM simulates annual meadow hay and alfalfa hay yields using a multivariate empirical distribution fitted to ten years of actual yields for ranches in Elko County, Nevada.2 Alfalfa hay is purchased in the model if the meadow hay plus alfalfa hay production is less than the feed required by the herd.

The base costs of production provided by the ranch panel were inflated over the planning horizon using annual inflation rates in the August 2013 FAPRI Baseline. Interest rates for the analysis are the annual FAPRI Baseline interest rates plus a basis adjustment for the local market. Similar adjustments were made to adjust FAPRIs stochastic annual national cattle and hay prices to the Nevada market assuming the average basis for the past three years remains constant in the future.

The FLIPSIM model simulated the ranch for ten years and repeated the planning horizon for 500 iterations. Each iteration used a different draw of stochastic prices, yields, and AUMs. The key output variable reported for this paper is the average annual net cash farm income (NCFI). The NCFI equals total receipts minus total cash production expenses. Cash outlays not included in NCFI are: family living withdrawals, federal income taxes, social security and Medicare contributions, and principal payments.

2 The multivariate empirical distribution simulation methodology is described by Richardson, Klose, and Gray (2000).


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Results The average annual NCFI and standard deviation of NCFI for the base scenario without delayed re-entry due to a fire are $67,450 and $35,530, respectively (Table 1). In Table 1 two variations of the base scenario are presented; adding one 120-acre pivot of alfalfa and adding two 120-acre alfalfa pivots, both without AUM reduction due to fire. Adding one pivot of alfalfa increases average annual NCFI 47 percent to $99,330 and reduces relative risk more than 50 percent3. Adding one pivot of alfalfa produces sufficient forage to cover supplemental feed requirements caused by AUM risk and meadow hay yield risk 90 percent of the time. However, adding two pivots of alfalfa increases NCFI by only 11 percent as the added debt servicing requirements for the second pivot and larger haying equipment does not pay for itself in the base case. The results indicate that even in the absence of a fire the ranch would be financially better off by adding one 120-acre pivot for alfalfa, if adequate water is available.

The results for the 60 scenarios assuming different combinations of fire coverage (25, 50, 75, and 100 percent) on the public land and number of years with no grazing (1, 2, 3, 4, or 5) are summarized in Table 2. Not adding a 120-acre pivot for alfalfa and depending only on buying alfalfa hay is the worst option. If the years of no federal grazing is only one and the fire is only on 25 percent of the federal rangeland the ranch’s NCFI is reduced 11 percent from the base.

But if the fire prevents grazing for five years on 50 percent of the federal land, average annual NCFI falls from $67,450 to -$29,630 (Tables 1 and 2). The results for the buy alfalfa hay scenario indicate that the more extensive the fire and the longer the period of no grazing on federal lands, the lower the average annual NCFI. These results indicate that if a ranch is forced to buy alfalfa hay for an extended number of years, it may be better off financially to reduce the herd and/or retire.

Installing one 120-acre pivot of alfalfa greatly improves the ranch’s NCFI relative to the buy alfalfa option (Table 2). The 50 percent fire damage and five year no grazing of federal lands has a projected average annual NCFI of $47,560, but this value is much greater than its buy alfalfa hay counterpart of (-$29,630). The addition of one 120-acre alfalfa field yields a higher average annual NCFI than buying alfalfa hay across all combinations of fire damage and years of no grazing. Additionally the relative risk on NCFI is reduced by growing alfalfa.

3 Relative risk is another term for coefficient of variation which is calculated as the ratio of the standard deviation and the mean expressed as a percentage.

Table 1. Average Annual Net Cash Farm Income for a Representative NevadaRanch, Assuming No Fire and Alternative Hay Production Scenarios.

($1,000s)Buy Alfalfa HayNCFI 67.45 Std Dev 35.53

1 PivotNCFI 99.33 Std Dev 24.24

2 PivotsNCFI 75.31 Std Dev 23.52


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Table 2. Average Annual Net Cash Farm Income for a Representative NevadaRanch, Assuming Alternative Years of Reduced Grazing on Federal Land Due to Fire.

1 Year 2 Year 3 Year 4 Year 5 Year($1,000s) ($1,000s) ($1,000s) ($1,000s) ($1,000s)

25% of Federal Land Closed Due to FireBuy Alfalfa HayNCFI 60.08 52.82 45.80 38.92 32.07 Std Dev 35.63 35.63 35.68 35.72 35.70

1 PivotNCFI 98.39 97.34 96.14 94.69 92.65 Std Dev 25.03 26.06 27.14 28.51 29.69

2 PivotsNCFI 75.26 75.18 75.03 74.85 74.42 Std Dev 23.62 23.74 23.94 24.14 24.35

50% of Federal Land Closed Due to FireBuy Alfalfa HayNCFI 47.04 27.07 7.80 (10.87) (29.63) Std Dev 34.61 33.10 31.69 30.60 29.44

1 PivotNCFI 94.64 84.33 71.49 58.86 47.56 Std Dev 26.59 28.82 29.55 29.97 30.43


75% of Federal Land Closed Due to FireBuy Alfalfa HayNCFI 34.53 2.50 (28.85) (59.65) (90.68) Std Dev 34.32 32.00 29.78 27.95 26.03

1 PivotNCFI 85.44 60.09 35.27 11.17 (13.21) Std Dev 27.05 26.26 25.52 25.00 24.60


100% of Federal Land Closed Due to FireBuy Alfalfa HayNCFI 21.11 (24.00) (68.07) (111.29) (154.70) Std Dev 34.51 32.01 29.60 27.67 25.87

1 PivotNCFI 72.62 34.81 (2.75) (40.56) (79.63) Std Dev 26.46 25.00 24.02 23.35 22.63

2 PivotsNCFI 71.03 42.79 13.34 (16.27) (45.52) Std Dev 24.11 22.84 21.43 20.94 19.53 *Bold values indicate the maximum NCFI for each rangeland damage assumption.

Years with No Grazing on Federal Land


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As the extent of the fire damage and the number of years with no federal grazing increase, the benefit of having two 120-acre pivots with alfalfa increases (Table 2). For half of the scenarios, average annual NCFI is greater for the two-pivot option than the one pivot option.

For example, a five-year close out due to a 75 percent fire damage has a -$13,210 NCFI with one pivot and a $23,290 NCFI with two pivots.

Summary Rangeland fires in the West are a significant problem for the economic viability of ranches due to the slow recovery of grasses and browse and the extensive nature of fires that burn thousands of acres. The land managers of USFS and USBLM restrict grazing on federal lands until the range has recovered from fire damage. Ranchers who depend on federal land grazing for part or all of their pasture are limited in their management options when fires prevent their use of federal land. Reducing the herd is often the least preferred option, which leaves buying, raising alfalfa hay, or retiring as their remaining options.

The purpose of this study was to analyze the economic consequences of buying or raising alfalfa hay on a representative ranch in Northeast Nevada if the ranch was faced with one to five years of no grazing on federal rangeland. An economic model of a representative ranch with 650 mother cows in Elko County, Nevada was used to analyze alternative scenarios for buying vs. raising alfalfa hay assuming four different fire damage scenarios and five different number of years without federal grazing.

The results showed that the ranch should invest in one 120-acre pivot of alfalfa as insurance against normal AUM risk and meadow hay yield risk. Doing so would increase average annual NCFI from $67,450 to $99,330 (Table 1) and reduce the relative risk on NCFI by 50 percent.

In the event of a range fire that affects 25 or 50 percent of the federal AUMs, the ranch would see a much higher average NCFI if it had one pivot of alfalfa rather than buying alfalfa hay; regardless of the number of years without federal grazing. However, as the extent of the fire damage increases to 75 or 100 percent the ranch would need two 120-acre pivots of alfalfa in an effort to maintain NCFI if the number of years of no federal land grazing exceeds one year.

The results of the analysis indicate that if ranchers in Elko County, Nevada have adequate ground water they should invest in one 120-acre pivot to raise alfalfa hay. The investment would increase NCFI over buying alfalfa hay even when there is no loss in federal AUMs due to range fire. If there is a range fire which damages up to 50 percent of the federal AUMs that delay grazing for 1, 2, or 3 years the ranch is better off financially with one pivot rather than two. But if a range fire damages more than 50 percent of the federal rangeland and the delay in grazing is longer than one year the ranch should invest in a second 120-acre pivot of alfalfa. From a risk management angle the results suggest buying one pivot for alfalfa immediately and buying a second pivot if fire prevents grazing more than 50 percent of the federal land for more than one year. If grazing is delayed more than three years, negative average NCFI will likely occur even with 240 acres of alfalfa so herd reductions must be considered.

Other factors that must be considered if the re-entry period exceeds three years are: ground water availability for multiple alfalfa pivots, impacts of increased herd pressure on feeding areas and private rangeland, and labor availability for raising, harvesting and feeding the increased hay production for 240 acres of alfalfa. These areas are open for future research for range management in regions affected by wild fires.


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References Food and Agricultural Policy Research Institute. August 2013. August 2013 Baseline Update

for U.S. Agricultural Markets. University of Missouri, Columbia, MO. FAPRI – MU Report #4-13.

Richardson, James W., Joe L. Outlaw, George M. Knapek, J. Marc Raulston, Brian K. Herbst, David P. Anderson, Henry L. Bryant, Steven L. Klose, and Peter Zimmel. “Representative Farms Economic Outlook for the January 2013 FAPRI/AFPC Baseline.” Texas AgriLife Research, Texas AgriLife Extension Service, Texas A&M University, Department of Agricultural Economics, Agricultural and Food Policy Center Working Paper 13-1, January 2013.

Richardson, J.W., S.L. Klose, and A.W. Gray. Aug 2000. “An Applied Procedure for Estimating and Simulating Multivariate Empirical (MVE) Probability Distributions in Farm-Level Risk Assessment and Policy Analysis.” Journal of Agricultural and Applied Economics, 32(2): 299-315.

Richardson, James W. and Clair J. Nixon. July 1986. “Description of FLIPSIM V: A General Firm Level Policy Simulation Model.” Texas Agricultural Experiment Station, Bulletin B-1528.


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Wildfire risk and optimal investments in watershed protection

Travis Warziniack1 and Matthew Thompson2

Introduction Following what was then one of the most destructive fire years on record, President Bush signed into law the Healthy Forests Restoration Act of 2003. The law requires no less than fifty percent of all funds allocated for hazardous fuels reductions to occur in the wildland-urban-interface (WUI), with the aim of enhancing the protection of homes and reducing the costs of fighting wildfire. Available resources, however, have not been able to keep up with the accumulation of fuels and a rapidly expanding WUI. In 2012, wildfires burned 9 million acres in 67,000 separate fires throughout the United States. Total acreage burned was roughly equivalent in size to the states of Massachusetts and Connecticut combined and was the third largest annual acreage burned since 1975. That same year, Colorado saw its two most destructive fires on record, and Oregon saw its largest fire in more than a century. Costs for fighting these fires totaled about $1.6 billion, in line with a twenty year trend of increasing costs of wildfire suppression (National Interagency Fire Center 2013).

In the West, these fires burn in the same forests that supply our drinking water. Language in the Healthy Forests Restoration Act relating to the importance of healthy forests for drinking water focuses on forests’ role in filtering pollutants but says nothing about the risk unmanaged forests may cause to drinking water supplies. This omission is remarkable considering 80 percent of freshwater originates in forested areas in the US, 50 percent of Western freshwater originates in National Forests, and most of those watersheds are highly threatened by wildfire (Weidner and Todd, 2011).

High severity wildfires can destroy the forest canopy and vegetation that usually intercepts falling precipitation, and burned soils can acquire a water-repellent layer. These changes lead to increased overland flow and runoff composed of ash, soil, rocks, and vegetative matter during precipitation events (Parise and Cannon 2012). Streams, in turn, see changes in flow regimes, flood frequency, erosion, debris flows, and ultimately degraded water quality (Moody and Martin 2009; Shakesby et al. 2006; Neary et al. 2005; Ice et al. 2004). Such impacts to watersheds increase drinking water treatment costs, increase sedimentation of reservoirs, and damage critical infrastructure (Cannon and Gartner 2005; Meixner and Wohlgemuth 2004).

Reducing wildfire risk throughout the West has proven to be a Herculean task, requiring both public and private land owners to take action. In an effort to engage multiple stakeholders, and reduce the cost to the federal government, agencies have begun partnering with some of the country’s largest water suppliers. The Western Watershed Enhancement Partnership, for example, is a collaborative effort between the US Department of Agriculture and the US Department of Interior to work with local water users to mitigate risks of wildfire to the nation’s water supply and critical infrastructure, often located on federal lands. The pilot project for the initiative focuses on the watersheds of the Upper Colorado Headwaters and the Big Thompson, which are managed by a diverse group of state, federal, and private owners and supply drinking water to much of Colorado’s Front Range.

1 Research Economist, USFS Rocky Mountain Research Station, Fort Collins, CO, [email protected] 2 Research Forester, USFS Rocky Mountain Research Station, Missoula, MT, [email protected]


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Buy-in for these collaborations has been mixed. While water suppliers are well aware of the costs associated with protecting their watershed, measurements of the benefits are usually anecdotal. Arguments for protection often cite, for example, the effects of heavy rains in 2002 that followed the Hayman Fire and filled Denver Water’s reservoirs with sediment, leading to $28 million in removal costs (Meyer 2006). Similarly, heavy rains following the High Park Fire in 2012 forced Fort Collins Utilities to shut down its Cache la Poudre River intakes and prompted a 4 percent rate hike to fund capital improvements necessary for maintaining the river as a viable drinking water source (Fort Collins Utilities 2012). Proponents also often cite cross-sectional studies that look broadly at the benefits of investments in watersheds and costs associated with watershed degradation (Ernst 2004, Moore and McCarl 1987, Forster et al. 1987, Dearmont et al. 1998, Holmes 1998). Holmes (1988), on the other hand, found that water utilities in the US with raw water turbidity levels over 10 Nephelometric Turbidity Units (NTUs) already use sedimentation or flotation during treatment and are not likely to see sizeable benefits from investments in green infrastructure. Anecdotal evidence is useful in understanding post-fire costs, but does not address the probability of a fire actually occurring. The cross-sectional studies are useful in valuing small changes in turbidity but are not useful in assessing the costs of extremely large and sudden increases in sediment loads associated with post-fire floods.

In this paper, we argue that a more thorough assessment of the benefits from green infrastructure should treat investments in watershed protection like a risky portfolio of assets. We use a familiar tool in finance, the Sharpe ratio, to model efficient wildfire loss mitigation, taking into account constrained budgets and risk of wildfire across multiple watersheds. The application of a finance tool to set priorities for wildfire protection is novel, though they have been used to prioritize investments in other areas of environmental protection (e.g., Qui et al. 1998). Decision support tools for wildfire mitigation are becoming increasingly sophisticated in terms of their ability to evaluate wildfire risk, but efforts at prioritizing mitigation activities have generally not considered investment theory or financial risk. Here, in an illustrative example for Colorado and Fort Collins, we present spatial mapping tools and data useful for assessing watershed risk, and combine these tools with our investment model to show how land managers can optimize investments in wildfire risk reduction.

Wildfire threats to water supplies and mitigation opportunities During a wildfire, incident managers must consider fire behavior and spread with respect to the location and susceptibility of water supply systems and other highly valued resources and assets (HVRAs), for which the decision support functionality is embedded within the Wildland Fire Decision Support System (Calkin et al. 2011). In a post-fire environment, efforts are typically aimed at assessing severity and associated water quality impacts in order to prioritize watershed stabilization and rehabilitation efforts (Robichaud and Ashmun 2013, Cannon et al. 2010, and Calkin et al. 2008). In a pre-fire environment, however, wildfire activity is uncertain, necessitating projections of potential wildfire-watershed interactions and their consequences. Increasingly, spatial wildfire risk assessments are used to support such pre-fire assessment and mitigation planning (Miller and Ager 2012). A general framework for wildfire risk assessment considers three main factors: fire likelihood, fire intensity, and resource or asset susceptibility (Scott et al. 2013). The use of stochastic wildfire simulation modeling can capture spatial variability in ignition patterns, weather patterns, topography, and fuel conditions, providing a key probabilistic foundation for risk modeling (Thompson and Calkin 2011). Overlaying fire modeling outputs with maps of HVRAs can quantify HVRA exposure to wildfire in terms of burn probability, fire intensity, and HVRA area burned (Thompson et al. 2013a; Scott et al. 2012). Further, the characterization of potential wildfire impacts to HVRAs, based in part off of linkages between fire intensity and fire severity (Heward et al. 2013; Keeley 2009), can allow for the


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quantification of wildfire risk in terms of expected loss (Thompson et al. 2013b; Thompson et al. 2013c).

As an illustration of exposure analysis, we overlaid fire modeling outputs with maps of high value watersheds (Figure 1, left panel) and populated areas (Figure 2, right panel) for the state of Colorado. Maps of high value watersheds come from the US Forest Service’s Forests to Faucets project, and maps of residentially developed populated areas come from a new risk-based WUI layer (Haas et al. 2013). Both panels rely on spatially-resolved fire modeling outputs from the FSim fire modeling system (Finney et al. 2011). In the left panel, we use a derived product called Wildland Fire Potential (WFP), which is produced by the US Forest Service’s Fire Modeling Institute and which integrates burn probability and fire intensity outputs. Areas with higher WFP values have a higher probability of having fires with torching, crowning and other forms of extreme fire behavior, which could lead to increased difficulty of control and to increased damages. In the right panel, by contrast, raw burn probabilities are used and intersected with population density to categorically depict risk to populated places. Areas with the highest likelihood of burning and the highest population densities pose the greatest risk.

Figure 1: Wildfire exposure analysis for high value watersheds (left panel) and populated places (right panel), for the state of Colorado.


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As the figure shows, areas of northwestern Colorado exhibit the greatest wildfire potential (left panel) and commensurately some of the highest risk to populated places (right panel). However, the absence of highly valued watersheds in these areas means there is little risk to water supplies in this region of the state. This result highlights a key point for risk mitigation planning – the need to account for spatial variation in HVRA location with respect to spatial variation in wildfire potential (Ager et al. 2013).

Optimal investments in fuel treatments Whereas in some cases it may be possible to design treatments to simultaneously protect multiple HVRAs (e.g., Ager et al. 2010), in other cases there are likely to be tradeoffs associated with treatment location and opportunities for HVRA protection. While a formalized or widely used framework for assessing these tradeoffs has yet to emerge for wildfire, it is a rather old finance problem, one of maximizing the Sharpe ratio, given by

𝑀𝑎𝑥 𝑆 = 𝑤!𝑅!!

𝜎 where, as modified to capture watershed benefits, 𝑅! is the expected return of fuel treatments in watershed n (excess returns above the status quo management scenario), 𝑤! is the percent of the budget spent on treatments in watershed n, and σ is the standard deviation of benefits in the portfolio. The Sharpe ratio measures the expected return per unit of risk. In this case, returns can be quantified in terms of avoided water treatment costs or via proxy by reduced post-fire sediment volumes. Volatility in returns stems from stochasticity in ignition processes and fire weather, and their spatial patterns with respect to treatment and watershed locations.

Wise investors know that diversifying a portfolio can reduce risk by investing in assets that are negatively correlated with each other. Positively correlated investments, on the other hand, increase risk. Negative correlations for wildfire risk between watersheds are probably not possible, but near zero correlations are possible by investing in geographically disjoint watersheds. The risk-return tradeoffs associated with these types of decisions are clear. Investing heavily to protect a single high value watershed could yield significant returns, but could leave multiple other watersheds unprotected. By contrast, spatially distributing fuel treatments across multiple watersheds could diversify risk since not all watersheds are likely to burn in the same fire event or the same fire season, but could lead to dampened treatment effectiveness in any given watershed.

Expected returns will be a function of both the probability of a watershed experiencing high severity fire as well as the magnitude of watershed response given the fire does occur. The benefits from fuel treatments will tend to be highest in watersheds where probability and/or consequences are high. The degree of correlation between investing in different watersheds will depend largely on the degree of spatial connectivity, from a fire growth perspective, between watersheds.


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Figure 2: Fort Collins, Colorado water supplies.

Consider the source watersheds for Fort Collins, Colorado, shown in Figure 2. Fort Collins draws water from the Cache la Poudre River and Horsetooth Reservoir, which gets its water from the Western Slope as part of the Colorado-Big Thompson Project. That the two watersheds are physically separated by the Continental Divide is more an artifact of growing water needs along the Front Range than a strategic decision related to wildfire risk. Nonetheless, enough topography and geography separate the watersheds to make it unlikely that the same fire will affect both. Separate fires did occur, however, in both watersheds in 2012. The High Park Fire burned in the Cache la Poudre watershed in June of 2012, and the Fern Lake Fire burned in the Big Thompson watershed in October of 2012 (InciWeb 2012a, InciWeb 2012b). Further damages were caused by the Galena Fire near the banks of Horsetooth Reservoir in March 2013 (Gabbert 2013), and massive flooding in September 2013 that destroyed some of the Colorado-Big Thompson Project’s delivery infrastructure (Scrongin 2013). Of these, only the High Park Fire caused serious problems for water treatment, leading to the closing of the Cache la Poudre River intakes and sole reliance on Horsetooth Reservoir (Oropeza and Heath, 2013). During the 2013 flood, sediment clogged the canals that deliver water to Horsetooth reservoir, but those are expected to be cleared relatively quickly (Scrongin 2013).

We can apply the Sharpe ratio framework to Fort Collins’ watershed management decisions – between investments in sediment reduction in the High Park burn area, fuels treatments to


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reduce burn probability and potential severity in the Colorado-Big Thompson watershed, and maintenance of Horsetooth Reservoir.3 Expected returns in the numerator are given by

𝐸𝑅 = 𝑤!"#$%& ×𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠 𝑜𝑓 𝑠𝑒𝑑𝑖𝑚𝑒𝑛𝑡 𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑝𝑜𝑠𝑡 𝐻𝑖𝑔ℎ 𝑃𝑎𝑟𝑘 +𝑤!"#$%&""&! ×𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠 𝑜𝑓 𝑚𝑎𝑖𝑛𝑡𝑎𝑖𝑛𝑖𝑛𝑔 𝐻𝑜𝑟𝑠𝑒𝑡𝑜𝑜𝑡ℎ 𝑟𝑒𝑠𝑒𝑟𝑣𝑜𝑖𝑟 +𝑤!!!" ×𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠 𝑜𝑓 𝑓𝑢𝑒𝑙𝑠 𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛𝑠 𝑖𝑛 𝐶𝑂

− 𝐵𝑖𝑔 𝑇ℎ𝑜𝑚𝑝𝑠𝑜𝑛 The standard deviation of the portfolio is

𝜎 = 𝑤!"#$%&!×𝜎!"#$%!! + 𝑤!"#$%&""&!!×𝜎!"#$%&""&!! + 𝑤!!!"!×𝜎!!!"!+2𝑤!"#$%&""&!𝑤!!!"𝐶𝑜𝑣(𝐻𝑜𝑟𝑠𝑒𝑡𝑜𝑜𝑡ℎ,𝐶 − 𝐵𝑇)

For simplicity we assume the covariance between returns on investments in the Poudre and elsewhere are zero, though we do consider the covariance between returns on investments in Horsetooth Reservoir and the Colorado-Big Thompson.

The cost of sediment in the Poudre has already shown itself to be high, thus benefits for sediment reduction are also high. In comparison, maintaining Horsetooth Reservoir beyond the status quo is not likely to yield significant benefits. The reservoir is not threatened by erosion, and the surrounding vegetation cannot sustain a high intensity fire. Benefits from reducing fuels in the Colorado-Big Thompson watershed come from reducing the probability of a high severity fire. The project delivers water to a large population, which is why it was chosen as the pilot project for the Western Watershed Enhancement Partnership. Treatments that can successfully reduce wildfire risk in the Colorado-Big Thompson watershed will have large benefits.

Measuring the variance of the portfolio is a difficult task. A best-guess ranking of variances would place investments in the Colorado-Big Thompson as the most risky; they depend on the probability of a wildfire ignition, the probability of an ignition leading to a large severe fire, and the probability of a severe fire leading to excess sediment. The Wildland Fire Potential map can speak to the probabilities for the first two components, but additional information on topography, soil type, precipitation patterns, etc., would need to be considered to assess the likelihood of excess sedimentation. Due to the nested probabilities that vary across space, even with well-located fuel treatments the variance of benefits to Fort Collins is likely to be high. Direct investments in Horsetooth Reservoir are less risky; the science behind reservoir management is well-developed. Most of the risk associated with investments in Horsetooth comes through its covariance with benefits in the Colorado-Big Thompson watershed, as seen during the September 2013 flooding. Post-fire investments in sediment reduction in the Cache la Poudre watershed are of medium risk compared to investments in Horsetooth Reservoir and the Colorado-Big Thompson. We have already seen periods of high rainfall and high sediment runoff. These areas are being targeted for stabilization; it is mainly the effectiveness of that stabilization that is in question.

3 The City of Fort Collins does not operate the Colorado-Big Thompson Project, and does not routinely make investments in its watershed. This is not to say, however, that such agreements could not be put into place.


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It is a straightforward exercise to expand this investment framework to consider the perspective of the Forest Service or other public land management agencies. The basic investment framework remains the same, but the composition of the investment portfolio changes. The portfolio of options may now include fuel treatments for WUI protection in addition to watershed protection. As with comparison across treatments to protect watersheds, how returns co-vary will depend on spatial relationships and connectivity between municipal watersheds and the WUI.

Discussion The investment framework introduced here provides a pathway forward for analysis and design of economically efficient fuel treatment strategies. We illustrated how the model can be used to identify optimal combinations of fuel treatments to protect municipal water supplies, as well as potential extensions to consider other treatment objectives such as WUI protection. The model can also highlight tradeoffs across objectives, and could identify conditions under which watershed-oriented treatments may be more or less efficient relative to WUI protection. In future applications it could also be possible to expand the portfolio of options to consider not just fuel treatments but also investments in capacity building for suppression response and post-fire rehabilitation.

As an example we focused on the state of Colorado, where recent fire seasons have resulted in highly devastating consequences to human life, homes, and water supply infrastructure, and where multiple stakeholders are currently partnering to fund fuel treatments to protect municipal water supplies. The issue of protecting water supply in this region will likely remain if not grow in the future, due in part to population growth and climate change forecasts signaling increases in temperatures, warmer springs, earlier runoff, and longer fire seasons (Litschert et al. 2012). We illustrated alternative approaches for characterizing HVRA exposure and identified potential geographic disparities in where fuel treatments would be implemented for watershed protection. Although we focused on Colorado, the general framework and the tension between alternative fuel treatment investment portfolios can be applied across the western United States.

The investment framework also shows us the type of information that we need to gather. What do we readily have? Where do we need to focus our data efforts so we can make better investment choices? At root, the basic pieces we need are expected excess benefits and standard deviation of the total investment portfolio. Although we have a limited empirical basis of wildfire-treatment interactions, recent and ongoing work may enable us to mine historical data and quantify these relationships directly. Alternatively, from a modeling perspective it would be possible to generate this information, and in fact the use of wildfire simulation is increasingly being applied to quantify how treatments affect the distribution of possible fire outcomes (e.g., Thompson et al. 2013d, e).

An important question moving forward is how to identify the “excess returns” from the perspective of the Forest Service. Any proposed action, such as a fuel treatment, requires an assessment of potential environmental impacts, but the alternative of doing nothing is not a risk free option given the inevitable occurrence of wildfire (Fairbrother and Turnley 2005). One possibility is to consider the expected HVRA impacts, be it degradation of water quality or home destruction in the WUI, under a no-treatment scenario and assuming some default suppression response. This in effect becomes a cost-benefit analysis of treatments, while accounting for the volatility of treatment returns. Ongoing and future research describing the return on investment for fuel treatments and other pre-fire risk mitigation activities will be critical for successful implementation of this investment analysis framework.


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Calkin, D. E., Thompson, M. P., Finney, M. A., & Hyde, K. D. (2011). A real-time risk assessment tool supporting wildland fire decisionmaking. Journal of Forestry, 109(5), 274-280.

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Cannon, S. H., Gartner, J. E., Rupert, M. G., Michael, J. A., Rea, A. H., & Parrett, C. (2010). Predicting the probability and volume of postwildfire debris flows in the intermountain western United States. Geological Society of America Bulletin, 122(1-2), 127-144.

Dearmont, D., McCarl, B. A., & Tolman, D. A. (1998). Costs of water treatment due to diminished water quality: a case study in Texas. Water Resources Research, 34(4), 849-853.

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Finney, M. A., McHugh, C. W., Grenfell, I. C., Riley, K. L., & Short, K. C. (2011). A simulation of probabilistic wildfire risk components for the continental United States. Stochastic Environmental Research and Risk Assessment, 25(7), 973-1000.

Forster, D. L., Bardos, C. P., & Southgate, D. D. (1987). Soil erosion and water treatment costs. Journal of Soil and Water Conservation, 42(5), 349-352.

Fort Collins Utilities. (2013). City of Fort Collins Economic E-Newsletter, Fort Collins, Colorado, December, http://www.fcgov.com/business/archive/201212-newsletter.php?cmd=6, accessed December 30, 2013.

Gabbert, Bill. (2013). Update on the Galena Fire near Fort Collins, March 18, 2013, Wildfire Today, http://wildfiretoday.com/tag/galena-fire/ accessed December 30, 2013.

Haas, J. R., Calkin, D. E., & Thompson, M. P. (2013). A national approach for integrating wildfire simulation modeling into Wildland Urban Interface risk assessments within the United States. Landscape and Urban Planning, 119, 44-53.

Heward, H., Smith, A. M., Roy, D. P., Tinkham, W. T., Hoffman, C. M., Morgan, P., & Lannom, K. O. (2013). Is burn severity related to fire intensity? Observations from landscape scale remote sensing. International Journal of Wildland Fire, 22(7), 910-918.

Holmes, T. P. (1988). The offsite impact of soil erosion on the water treatment industry. Land Economics, 64.


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Ice, G. G., Neary, D. G., & Adams, P. W. (2004). Effects of wildfire on soils and watershed processes. Journal of Forestry, 102(6), 16-20.

InciWeb. (2012a). High Park Fire, InciWeb incident 2904, http://inciweb.nwcg.gov/incident/2904/, accessed December 30, 2013.

InciWeb. (2012b). Fern Lake Fire, InciWeb incident 3294, http://inciweb.nwcg.gov/incident/3294/, accessed December 30, 2013.

Keeley, J. E. (2009). Fire intensity, fire severity and burn severity: A brief review and suggested usage. International Journal of Wildland Fire, 18(1), 116-126.

Litschert, S. E., Brown, T. C., & Theobald, D. M. (2012). Historic and future extent of wildfires in the Southern Rockies Ecoregion, USA. Forest Ecology and Management, 269, 124-133.

Meixner, T., & Wohlgemuth, P. (2004). Wildfire impacts on water quality. Journal of Wildland Fire, 13(1), 27-35.

Meyer, Jeremy P. (2006). Hayman Fire still mucking up water. The Denver Post, November 24, 2006.

Miller, C., & Ager, A. A. (2013). A review of recent advances in risk analysis for wildfire management. International Journal of Wildland Fire, 22(1), 1-14

Moody, J. A., & Martin, D. A. (2009). Synthesis of sediment yields after wildland fire in different rainfall regimes in the western United States. International Journal of Wildland Fire, 18(1), 96-115.

Moore, W. B., & McCarl, B. A. (1987). Off-site costs of soil erosion: a case study in the Willamette Valley. Western Journal of Agricultural Economics, 12(1), 42-49.

National Interagency Fire Center. 2013. Statistics website, http://www.nifc.gov/fireInfo/fireInfo_statistics.html, accessed December 27, 2013.

Neary, D. G., Ryan, K. C., & DeBano, L. F. (2005). Wildland fire in ecosystems: effects of fire on soils and water. US Forest Service Gen. Tech. Rep. RMRS-GTR-42-vol, 4, 250.

Oropeza, Jill and Jared Health. 2013. Effects of the 2012 Hewlett and High Park Wildfires on Water Quality of the Poudre River and Seaman Reservoir, City of Fort Collins Utilities, Fort Collins, Colorado.

Parise, M., & Cannon, S. H. (2012). Wildfire impacts on the processes that generate debris flows in burned watersheds. Natural Hazards, 61(1), 217-227.

Qiu, Zeyuan, Tony Pareto, and Michael Kaylen. (1998). Water Shed Economic and Environmental Trade-Offs Incorporating Risks: A Target MOTAD Approach, Agricultural and Resource Economics Review, 27, 231-240.

Robichaud, P. R., & Ashmun, L. E. (2013). Tools to aid post-wildfire assessment and erosion-mitigation treatment decisions. International Journal of Wildland Fire, 22(1), 95-105.

Scott, J. H., Thompson, M. P., & Calkin, D. E. (2013). A wildfire risk assessment framework for land and resource management. Gen. Tech. Rep. RMRS-GTR-315. US Department of Agriculture, Forest Service, Rocky Mountain Research Station.

Scott, J., Helmbrecht, D., Thompson, M. P., Calkin, D. E., & Marcille, K. (2012). Probabilistic assessment of wildfire hazard and municipal watershed exposure. Natural Hazards, 64(1), 707-728.


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Scrongin, Dana (editor). 2013. Northern Water WaterNews, Northern Colorado Water Conservancy District, Berthoud, Colorado.

Shakesby, R. A., & Doerr, S. H. (2006). Wildfire as a hydrological and geomorphological agent. Earth-Science Reviews, 74(3), 269-307.

Thompson, M. P., and Calkin, D. E. (2011). Uncertainty and risk in wildland fire management: A review. Journal of Environmental Management, 92(8), 1895-1909

Thompson, M. P., Scott, J., Kaiden, J. D., & Gilbertson-Day, J. W. (2013a). A polygon-based modeling approach to assess exposure of resources and assets to wildfire. Natural Hazards, 67(2), 627-644.

Thompson, M. P., Scott, J., Langowski, P. G., Gilbertson-Day, J. W., Haas, J. R., & Bowne, E. M. (2013b). Assessing watershed-wildfire risks on National Forest System lands in the Rocky Mountain Region of the United States. Water, 5(3), 945-971.

Thompson, M. P., Vaillant, N. M., Haas, J. R., Gebert, K. M., & Stockmann, K. D. (2013c). Quantifying the potential impacts of fuel treatments on wildfire suppression costs. Journal of Forestry, 111(1), 49-58.

Thompson, M. P., Vaillant, N. M., Haas, J. R., Gebert, K. M., & Stockmann, K. D. (2013d). Quantifying the potential impacts of fuel treatments on wildfire suppression costs. Journal of Forestry, 111(1), 49-58.

Thompson, M. P., Hand, M. S., Gilbertson-Day, J. W., Vaillant, N. M., & Nalle, D. J. 2013e. Hazardous fuel treatments, suppression cost impacts, and risk mitigation. In: González-Cabán, Armando, tech. coord. Proceedings of the fourth international symposium on fire economics, planning, and policy: climate change and wildfires. Gen. Tech. Rep. PSW-GTR-245. Albany, CA: US Department of Agriculture, Forest Service, Pacific Southwest Research Station, 66-80.

Weidner, Emily and Al Todd. 2011. From the Forests to the Faucet: Drinking Water and Forests in the US, USDA Forest Service http://www.fs.fed.us/ecosystemservices/FS_Efforts/forests2faucets.shtml.


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Regulatory Capture? Arizona’s BMP Water Conservation Program

David B. Bilby and Paul N. Wilson1

Introduction The 1980 Groundwater Management Act (GMA) elevated water conservation issues in Arizona to a predominant position in policy discussions concerning state- and regional-level economic growth (Colby and Jacobs 2007). The GMA established an agency (Arizona Department of Water Resources (ADWR)), four initial Active Management Areas (AMAs) for ground and surface water control, a new water measurement culture throughout the state, and five specified planning periods (1980-1990,1990-2000, 2000-2010, 2010-2020, and 2020-2025). Water conservation in the agricultural sector, necessary to reach the safe yield goal of the GMA in 2025, would be achieved by requiring increased irrigation efficiencies, thereby reducing water allotments for farming operations in the AMAs over time (Anderson, Wilson and Thompson 1999).

As early as the late 1980s, municipalities and farmers were calling for an alternative to the GMA’s Base Program that established the initial water use guidelines (i.e. efficiencies, water allotments) for these users. First, the majority of agricultural producers in the AMAs, for a variety of economic, agronomic and program design reasons, were using water at levels far below their allocations under the Base Program and banking the unused portion of their assigned allotment in their flexibility accounts2. As a result, by the end of the Second Management Plan growers already had banked over 15 million acre feet in their flex credit accounts (Needham and Wilson 2005). Municipal interests feared land developers would lobby the state legislature to make these banked credits legally transferable to housing developments to meet the state’s Assured Water Supply rules that required municipalities and developers to prove they had legal access to a 100-year renewable water supply.

Additional concerns persisted. First, water regulators concluded that the high number of flex credits indicated that the Base Program’s water conservation requirements were largely ineffective. Secondly, irrigation district personnel and farm operators were required to provide annual reports for each irrigation grandfathered right (IGFR) under the Base Program3. Some farms contained multiple IGFRs, so the agricultural sector was interested in an alternative plan with less intensive reporting. Thirdly, some producers were upset with the historical period of 1975-1980 for the Base Program. These growers felt that to be globally competitive they needed new crop mixes that did not reflect the crops, and their estimated water use, grown during the historical period. In addition, the water allotment defined under the Base Program excluded fallowed land and any growers fallowing land during the historical period believed that they were unfairly penalized by the GMA.

1 The authors are, respectively, Agribusiness Analyst, CF Industries; Professor, Department of Agricultural and Resource Economics, University of Arizona. Paul Wilson is the corresponding author, [email protected] 2 Flexibility accounts afforded farmers the opportunity to balance water supplies over changing market and climatic conditions. Accounts could be credited when the full allotment was not utilized and debited when more than the allotment was used. Because of the predominance of positive water balances, these accounts generally are referred to as flex credit accounts. 3 The IGFR is a legal right to pump/use a specific amount of water each year for a specific farm based on the historic water use of cropped acreage between 1975 and 1980.


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After intensive and extensive negotiations, both private and public, the Arizona State Legislature amended the GMA in 2002 to include a best management practices (BMP) program as a voluntary alternative to Base Program requirements. The legislation specified that a BMP program must be determined by the Director of ADWR to be at least as water conserving as the Base Program. BMP farmers would be free from the allotment requirements of the Base Program but have to surrender their flex credit balances.

This research explores the hypothesis that agricultural interests negotiated a BMP program that required few, if any, water management changes for growers signing up for the program. We briefly review the pros (e.g., less future litigation) and cons (e.g., regulatory capture) of utilizing regulatory negotiation (reg-neg) in policy formulation. Utilizing a case study research method, we reconstruct the policy-making process that extended over a period of nearly eight years, ultimately producing the BMP program. Finally, an expert panel evaluated the BMP requirements for enrolling in the program in light of water conservation practices being implemented by central Arizona growers prior to the passage of the BMP legislation.

Regulatory Negotiation and Capture A reg-neg process encourages the design of bargained solutions acceptable to the regulator and the regulatee. Federal agencies utilizing reg-neg procedures are required to follow the guidelines of the Negotiated Rulemaking Act of 1990 while most states follow an ad-hoc reg-neg process based on their adaptation of the federal guidelines to their unique circumstances (Hadden 1995; Harter 2000; Pritzker and Dalton 1995; Ryan 2001)4. Advantages of reg-neg procedures include direct representation, reduced commentary on the final rules, higher compliance rates, improved relationships between regulators and regulated parties, better information flows, and greater public support for the rules. Exclusion of some affected parties, high short-term transaction costs, and outcome (i.e., rule) risk represent documented disadvantages (Fiorino 1998; Kazmierczak and Hughes 1997; Langbein 2002; Polkinghorn 2000).

Outcome risk may emerge when state agencies encourage special interests to collaborate in rulemaking and these interests capture the reg-neg process by designing rules that benefit them and not the general public (Stigler 1971). In their synthesis of the regulatory capture literature, Levine and Forrence (1990) argue that slack--discretion or freedom in rulemaking--represents a necessary condition for regulatory capture. Slack generally exists when monitoring the rulemaking process represents significant transaction costs for other stakeholders. Capture, according to these authors, only occurs when the regulator expects to benefit personally by adopting rules that favor the regulatee and would be opposed by the public.

Zinn (2002), rather than defining regulatory capture as binary in nature (e.g., personal reward or not for the regulator) as do Levine and Forrence (1990), argues that the degree of capture falls on a continuum, where personal benefit on the part of the regulator represents an extreme position. Regulators are exposed to a wide range of pressures and incentives that may move them towards the regulatee’s position without requiring some form of personal reward. Political pressure for a timely agreement, budgetary concerns associated with negotiation and implementation, shared regulatory norms and interests, agency discretion (i.e., slack), a desired reputation for collaboration, and the lack of competing interest group involvement (i.e., asymmetric participation) are all factors that determine where the rulemaking process will 4 Negotiated Rulemaking Act of 1990, Pub. L. No. 101-648, 104 Stat. 4969 (codified at 5 U.S.C. §§ 561-570). The Act was permanently reauthorized by the Administrative Dispute Resolution Act of 1996, Pub. L. No. 104-32, § 11.


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emerge on the capture continuum. State agencies may benefit, at least organizationally, from capture-like outcomes because they simply want to minimize complaints from special interests and reduce rulemaking costs.

Case Study Design Methods Case study analysis provides an opportunity to analyze events of a cause and effect nature that are outside the investigator’s control, thereby making this research approach valuable for understanding the origin, operations, and impact of reg-neg BMP programs and the possibility of regulatory capture (Eisenhardt 1989; Helper 2000; Kennedy and Luzar 1999; Yin 1994). Our triangulated, mixed method case analysis first relies on background information from regulatory documents, published literature, and interviews with state officials familiar with, but not directly associated with, the rulemaking process (Patton 2002). The second component, BMP working group and Advisory Committee interviews conducted in 2007, provides us with a participant’s understanding of the reg-neg BMP design process. Finally, a scoring survey of agricultural experts evaluates the use of BMPs at the farm level prior to program implementation.

Interview questions for BMP working group and Advisory Committee members were developed utilizing a two-stage process. First, a list was constructed of all relevant questions about the negotiation process and BMP design decisions. Then these questions were reviewed and revised following the chronological sequence expected for reg-neg design (i.e., decision to use reg-neg, negotiation, rule implementation). This two-stage process produced an interview protocol of six open-ended questions and a series of in-depth, follow-up questions. The open-ended questions were designed so respondents could provide their observations concerning the negotiation process and decision-making. The follow-up questions were reserved for obtaining further details and pacing the interview. Questions required recall concerning negotiation processes that occurred up to six years prior to the interview. Each respondent received a copy of the questions several days before the interview to allow them time to reflect on past events. Interviewees were informed that their individual views would be kept confidential. Responses for all interviewees were aggregated under each open-ended question and regrouped, when appropriate, into subtopics. Finally a review of emergent themes within the interviews was constructed to capture, into a single narrative, the key components of the reg-neg BMP process.

The expert survey, also conducted in 2007, was utilized as a “second best” approach to a farm-level survey due to reported participation limitations (i.e., “privacy concerns”) associated with asking “before and after” type questions. Each agricultural expert worked in the counties impacted by the BMP program and was asked to provide their best estimate of how farm operations would have scored in qualifying for the BMP program prior to the implementation of the new law.

Results and Discussion Informal Working Group and BMP Committee Interview Results An informal group (i.e., Pinal AMA working group) of ADWR staff, irrigation district managers and farmers began meeting in the mid-1990s to design a BMP program as an alternative to the Base Program adhering, on an informal basis, to the spirit of reg-neg objectives. The working group utilized USDA documents, conservation programs in other states, and personal expertise to specify the (1) definitions of BMPs and categories, (2) scoring requirements of the program, (3) scores attributed to specific BMPs, (4) scoring worksheets, and (5) land-use permission forms. In December of 1999, under threatened legal pressure from the agricultural community, ADWR postponed adopting the Base Program for the Third Management Plan (2000-2010). The primary concern of the agricultural sector was the stringency of the irrigation efficiency requirements for the Third Management Plan (i.e., 80 percent) compared to the lower


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requirements (65-70 percent range) in the preceding plans (ADWR 2001). Avoiding a protracted lawsuit, three letter agreements took place between ADWR and agricultural interests dated December 10, 1999, February 28, 2000, and April 13, 2000. These letter agreements established advisory committees to review the Base Program requirements of the Third Management Plan and to propose alternatives to the plan. A fourth letter of agreement was dated September 18, 2001, consisting of signatures from agricultural representatives, irrigation district representatives, agricultural water associations, and the acting director of ADWR that called for changes in GMA legislation to enact a BMP program, and specified the wording for this new legislation.

The Pinal AMA working group, prior to the formation of the BMP Advisory Committee, developed the majority of the BMP program. Interviews revealed that individuals not involved in the working group viewed the Governor’s BMP Advisory Committee as a means to put a seal of approval on the working group’s program design. According to interviews, the main goals of BMP committee members were to (1) finalize the program and (2) balance ADWR goals with a program that was flexible and represented reduced reporting requirements for growers and irrigation districts. The regulatory goal, at least on paper, was to identify and implement practices that would conserve water. Several of the working group members were included on the BMP Advisory Committee while other working group participants were regular spectators at committee meetings. Interviews revealed that working group members publicly defended the BMP design process, guided committee discussions, and urged the committee to finalize the program. The BMP Advisory Committee made only minor changes to the program designed by the Pinal AMA working group.

Respondents reported several program shortcomings. First, there was a lack of data for both the Pinal AMA workgroup and the BMP Advisory Committee on the effectiveness of water management practices. Although the point-scoring system suggests that some BMPs are more effective than others, there was limited scientific evidence to support the awarded weight (i.e., points received) for each practice other than “common sense”. Secondly, several interviewees mentioned that some of the BMPs could be interpreted differently by regulators and agriculture, or even by different farmers. Even though there were some perceived deficiencies, both the ADWR and farmers reported satisfaction with the results of the design process and the final program.

Expert Panel Results To enroll in the BMP program, a farming operation must achieve ten points. Farms can receive a maximum of three points toward their total from each of the four categories: Agronomic Management, Water Conveyance Systems, Farm Irrigation Systems, and Irrigation Water Management (Table 1). The BMPs in the Irrigation Water Management and Agronomic Management categories are each given a point value of one, and each of these categories requires a minimum of one point to qualify for enrollment. A different scoring technique is used for the Water Conveyance System and Farm Irrigation System categories. The Water Conveyance Systems category is based on the percent of acreage using a particular BMP. The point score ranges from one point for 50 percent of on-farm acreage using the BMP to three


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Table 1: BMPs by Category for Water Conservation Agronomic Management Crop rotation (1 point) Crop residue management (1 point) Soil and water quality testing (1 point) Pre-irrigation surface conditioning (1 point) Transplants (1 point) Mulching (1 point) Shaping furrow or bed (1 point) Planting on bottom of furrow (1 point) Water Conveyance Systems (points range from 1=50 percent of acreage to 3=100 percent of acreage) Concrete-Lined Ditch Pipelines Drainback System Farm Irrigation Systems Slope systems without uniform grades with tailwater reuse (1 point) Uniform slope systems without tailwater reuse (1 point) Uniform slope systems with tailwater reuse (2 points) Uniform slope within an irrigation district that captures/redistributes return flows (2 points) Modified slope systems (2 points) High pressure sprinkler systems (2 points) Near level systems (2.5 points) Level systems (3 points) Low pressure sprinkler systems (3 points) Trickle irrigation systems (3 points) Irrigation Water Management Laser touch-up (1 point) Alternate row irrigation (1 point) Furrow checks (1 point) Angled rows/contour farming (1 point) Surge irrigation (1 point) Temporary sprinklers (1 point) Participation in an educational irrigation water management program (1 point) Participation in a consultant or irrigation district sponsored irrigation scheduling service (1 point) Participation in an irrigation district program to increase flexibility of water deliveries (1 point) Measure flow rates to determine the amount of water applied (1 point) Soil moisture monitoring (1 point) Computer based model using meteorological data (1 point)


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Table 2: Expert scoring summary for water conservation BMPs utilized prior to 2002 Expert BMP Category 1 2 3 4 5 6 7 8 9 10

80 Percent of Farms

Agronomic Management 5 2 4 4 2 4 2 5 2 4 Water Conveyance Systems 3 3 3 3 3 3 3 3 3 3 Farm Irrigation Systems 3 - 3 3 3 2 3 2 3 3 Irrigation Water Management 6 - 0 6 1 0 0 3 1 1

Sum/Sector Score 17 5 10 16 9 9 8 13 9 11

90 Percent of Farms

Agronomic Management 3 2 4 4 2 0 0 4 1 4 Water Conveyance Systems 3 3 3 3 3 3 0 3 3 3 Farm Irrigation Systems 3 - 3 3 3 2 3 2 3 3 Irrigation Water Management 3 - 0 2 1 0 0 2 1 1

Sum/Sector Score 12 5 10 12 9 5 3 11 9 11 points when 100 percent of on-farm acreage uses the BMP. The Farm Irrigation Systems category has a range of point values from one to three, depending on the expected efficiency of the BMP. A minimum of two points must be achieved in this category. The experts were asked to score each category for the agricultural sector prior to program implementation; then a composite sector score was calculated by adding the points earned in each category (Table 2).

Threshold estimates of 80 percent and 90 percent for farms meeting the individual BMP requirements were used in the expert scoring system to test the sensitivity of the experts’ perceptions.

Nine of the ten members of the expert panel provided complete answers for the water conservation program. Expert 2 only answered for two categories, and scored three points for Water Conveyance Systems (the ‘Concrete Ditches’ BMP) and two points to Agronomic Management at both levels of confidence, citing a lack of information about specific BMP implementation at the farm level. At the 80 and 90 percent threshold levels, six of the nine remaining experts estimated that the agricultural sector, prior to the passage of the regulation, met the minimum rule requirements for each of the four categories. The remaining three expert scores show that minimum requirements for three out of the four categories were met at the 80 percent level. The exception in all three cases was the Irrigation Water Management category.

The composite (sum/sector) score indicates that five of the nine experts believe the agricultural sector met the requirements (i.e. 10 total points) of the BMP program without any changes in water management. At the 80 percent level, three additional experts’ composite scores are within one point and another expert is within two points of the required ten points for program implementation. At the 90 percent level, experts 3, 5, and 9 have farming operations within two points of meeting program requirements.

Several BMPs received high scores (not shown in Table 2). In the Agronomic Management category, ‘Crop Rotation’, ‘Residue Management’, ‘Surface Conditioning’, and ‘Shaping of Bed or Furrow’ received consistent points from the panel. For Irrigation Water Management, the ‘Laser Touch-Up’ and ‘Alternate Row Irrigation’ also received repeated recognition. However,


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some experts identified several common practices that failed to meet the BMP definitions. These include ‘Soil Moisture Monitoring’, for which farmers generally use a “feel method” (if the soil is moist, no need to irrigate), and ‘Flow Rate Measurement’, which irrigators generally are skilled at determining through practice, using an “eyeball method”. The BMP definitions specify using a measuring device in both cases. ADWR personnel reportedly were willing to accept the “feel method” as acceptable, if the farm was consistent in irrigation practices over time. Thus, the experts may have expected a lower qualification score than the actual score a farm might receive on a program application to ADWR (depending on the farm operator’s level of communication with ADWR personnel).

In the Water Conveyance System category the “Concrete Ditch” BMP was identified at 90 percent of farms or higher by all but one expert. In the Farm Irrigation Systems category, most respondents reported a combination of “Near Level Systems” and “Level Systems” at 60 percent or greater (these receive 2.5 and 3 points, respectively). There was much confusion among experts concerning this particular category. Several panel members believed that most fields are engineered so the land can fit into “Level Systems” or “Near Level Systems” and the “Uniform Slope” BMPs in the category. ADWR, however, will not allow any field to apply to more than one BMP in this category. As a result, in several cases ADWR’s BMP definitions did not adequately differentiate the BMPs, thereby creating confusion among the experts.

These expert results imply that many, widely recognized water conservation technologies and practices were commonly utilized in the agricultural sector prior to the initiation of the BMP program. Most farming operations qualified outright for the BMP program or would qualify with the adoption of low-cost changes in their agronomic and/or irrigation management practices. The data implies that any incremental water conservation attributable to the BMP program would be insignificant, at best.

Conclusion The agricultural sector-led BMP program design process insured that many, if not most, farmers qualified for the new program. But does this finding imply that agriculture captured the reg-neg process? The BMP program benefited a small group of influential growers who were unable to respond to changing market forces due to their historical water allotments while the majority of farmers remained largely unconstrained in their future water use due to their accumulation of credits over the first two management plans (1980-2000) and into the third (2000-2010). So on the capture continuum, ADWR utilized its slack to reduce its future regulatory costs (i.e. lawsuits) in exchange for the reg-neg output risk of increased water use on a relatively small number of farms. As a result, Arizona’s BMP Water Conservation Program meets Zinn’s (2002) criteria for a capture-like outcome but falls far short of Levine and Forrence’s (1990) definition requiring personal gain on the part of the regulator.

A Postscript In 2010, ADWR sponsored an evaluation of the design and implementation of the BMP Water Conservation Program (Bautista and Waller 2010). The authors found that approximately six percent of the eligible irrigated lands were enrolled in the BMP program, with most of those enrollments occurring during the first year of program implementation. Participants reported that lower transaction costs and future water use flexibility were the principle drivers for enrolling in the program. Seventy percent of the participants noted that no adjustments in their water management practices or irrigation systems were necessary to qualify for the BMP program. Any change in water use on their farms had been driven by relative market prices and weather, with the average BMP farm’s water use consistently exceeding its base program allotment over the evaluation period.


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