April 2007
Volume 45 Number 2

Research in Brief

Discovering the Legal Concerns of the Tennessee Agricultural Community

Ernest Bazen
Assistant Professor
University of Tennessee
Knoxville, Tennessee
ebazen@utk.edu

Julie Pedigo Bowling
Law student
Emory University
Atlanta, Georgia
jpedigo@law.emory.edu

Introduction

As we begin the twenty-first century, the challenges to successful farm management are numerous. Farmers face changes in farm, environmental, and tax policy, plus significant advances in emerging technologies. Changes in regulatory and environmental policy have also increased farmers' legal risks. Legal issues alone can take their toll on someone financially and emotionally (Williams, 1996).

In the mid-70s, Kershen (1976) reported farmers' need for legal information, which required involvement of attorneys in the planning and formulation of institutional arrangements, legislative strategy, and agricultural interests. Production by contract or organizing the farm into a partnership or corporation can significantly affect a farmer's bottom line by increasing the farm's profitability or efficiency. However, there may be an accompanying increase in legal and/or financial risks.

For many farmers, as legal concerns and issues increase, farm financial resources remain limited. Morehart and Ryan (2002) reported that farming as the primary source of household income (i.e., families earning 80% or more from farming) has continued to decline to just 12% of farm households, and off-farm wages and salaries have continued to increase (i.e., 45% of farm households earning majority of income off-farm). In 2001 the USDA classified 28% of U.S. farmers as "limited-resource" and "low-sales" small family farmers, most of whom simply cannot afford legal representation (Flink, 2002).

Likewise, the agricultural education sector faces many challenges, including whether to assist independent producers with their greatly expanding and changing legal issues. The challenge of addressing these legal concerns poses complex issues for most state Extension programs. Determining which legal area(s) to address is a tremendous task for Extension program administrators. Given limited financial resources, many Extension programs steer clear of addressing legal issues or concerns altogether, while a few states have chosen to provide educational information on specific topics.

Three universities currently have agricultural law research centers: Drake University in Iowa, University of Arkansas, and Pennsylvania State University. Two of these research centers are funded by the USDA, and none of them provide any legal representation. They are staffed by attorneys and law students, and work in conjunction with their law school. They provide valuable research for attorneys and people working in the agricultural sector across the country.

At Arkansas' National Center for Agricultural Law Research and Information, a Web-based library of resources is available as well as an updated list of USDA decisions (NCALRI, 2005). Pennsylvania's Agricultural Law Research and Education Center is funded by the Pennsylvania Department of Agriculture and began as a joint venture between the PSU College of Agriculture and the Dickinson Law School (McConnaughay, 2003). It provides continuing education programs for lawyers, information and outreach services to farmers, and training for extension agents on legal issues affecting their farmers.

At least two states, Iowa and Ohio, have some legal education available through state Extension Services. Iowa Concern started in 1985 to help the agricultural community during the S&L crisis but now services both urban and rural communities (ISU, 2005). People can call toll-free for access to an attorney for legal education or information and referral services. Iowa Concern can also be accessed through the Web by email. Likewise, the Ohio program is designed for educational purposes and has information on a variety of agricultural legal topics that producers can access. These programs are staffed through the Extension Service and generally administered by a staff attorney. Purdue University has a staff attorney in agricultural law in the Department of Agricultural Economics, but does not have a specific educational legal center (Purdue, 2006).

Additionally, North Carolina and Illinois provide limited legal education through university centers. North Carolina State University has a program called "Ask the Specialist" through the Department of Family and Consumer Sciences (NCSU, 2005). The service answers questions about legal issues, particularly estate planning for farm families.

The University of Illinois at Urbana-Champagne has an educational Web site available to farmers called "farmdoc." The goal of the Farmdoc (farm decision outreach central) Project is to improve farm decision-making under risk through education and research. One of the subject headings available is Law and Taxation. The Web site includes a disclaimer telling visitors that, "the information on this Web site does not constitute legal advice. The law is constantly changing, and we make no warranty of the accuracy of information on this site or any site to which we link. If you need legal advice, you should contact an attorney" (UIUC, 2005).

Several non-profit agricultural law centers exist that specifically provide legal representation for farmers. These are typically funded through donations or grants, but not with state dollars. The Minnesota-based Farmers Legal Action Group uses four methods to help its clients: education, backup support, impact litigation, and administrative and technical legal assistance (FLAG, 2005). FLAG conducts seminars around the country for farmers and lawyers and produces user-friendly publications. The organization's support services include explaining laws, providing research, reviewing and analyzing cases, and maintaining a toll-free line that Minnesota farmers, lawyers, and advocates can call for brief advice and referrals.

Although there are many types of legal service centers throughout the United States, farmers are often reluctant to seek assistance due to financial and/or social reasons (Weigel, 2003). One source of information most often relied upon by farmers is their State Extension Services. This is true of the University of Tennessee Extension (UT Extension), which caters to the fourth largest number of farms in the nation, 87,595 (TASS, 2002).

The UT Extension made 3.4 million contacts from January 1 - December 31, 2004 with farmers/producers and farm organizations (Donaldson, 2005). While many of the farms are small, a significant portion (about 30%) earns over 50% of their income from farming (TASS, 2002). Due to several labor-intensive crops (e.g., vegetables, melons, nursery, fruits, etc.), Tennessee has the sixth highest number of migrant workers in the country (Effland & Runyan, 1998). However, there are no agricultural law emphases, programs, clinics, or committees at any of the law schools or universities in the state or within the Tennessee Bar Association (TBA, 2005).

While these facts make a case for directing educational resources toward agricultural legal issues in Tennessee, surveying farm operators about their legal concerns could help determine whether educators should consider developing a legal service/educational program.

Objective

The goal of the project reported here was to assess the legal concerns that are most important to Tennessee farm operators. The UT Extension, Farm Service Agency (FSA), Farm Bureau Staff, Natural Resources Conservation Service (NRCS), publications (journals, magazines, newspapers, etc.), Web sites, and other sources were included in this study as alternative sources of information. However, only farm operators' attitudes about UT Extension legal information are presented here.

Methods

In December 2002, data on farm and farm operator characteristics and financial characteristics were collected at the Tennessee Farm Bureau Federation Convention held in Nashville, TN. A booth was set up in the foyer of the conference center where convention attendees were invited to complete an on-site survey. Of the approximately 600 Tennessee farmers attending the convention, 145 completed a survey. The survey was developed by the authors with help from members of the UT Extension faculty. The survey was reviewed and approved by the UT Office for Human Research as well as the Tennessee Farm Bureau administration. The Tennessee Farm Bureau Convention was chosen for the study as a least-cost method due to funding limitations.

The survey included questions regarding farm and farm operator characteristics and 10 additional questions regarding legal concerns/issues. The first question asked respondents if they had used any legal services within the last year and within the last 5 years. Respondents were then asked to mark all categories related to their need for legal services. These categories included: nuisance complaints, animal liability, environmental regulations, negligence, zoning/planning, contracts, estate/wills/trusts, bankruptcy, labor, loan mediation, liability, and other issues.

Additionally, respondents were asked to rate the "importance" of legal issues as valid agricultural concerns for profitability and/or sustainability of an enterprise. A rating scale from 1 to 10 was used, with 1 denoting "not important" and 10 denoting "very important." In the study, responses were judged "important," if the issue received a rating of 8, 9, or 10. Additional questions involved: 1) resisting legal assistance due to cost; 2) how well current available services met the legal needs of Tennessee producers; and 3) how well did people assisting with legal services understand their agricultural legal issue(s).

Findings and Interpretation

Fifty-eight of Tennessee's 95 counties are represented in the survey. The average farm operator in this study was 52.3 years old with 2001 personal adjusted gross income of $56,000. About 24% owned fewer than 100 acres, with 68% owning more than 300 acres. Table 1 shows the comparison between survey respondents and the average Tennessee farmer demographics. Thirty-nine percent had used a legal service within 2002 and 60% within the last 5 years.

Table 2 reports farmers' responses for the categories that best represent the reason for their need for legal services within the last 5 years. By far the category with the greatest frequency of response was Estates/Wills/Trusts, with 66. The next two categories were Contracts and Loan Mediation, with 26 and 22 responses, respectively. Zoning had 10 responses, and the other categories (nuisance complaints, environmental regulations, labor, liability, negligence, and bankruptcy) each had fewer than 10 responses. These are the categories that had concerned farm operators enough to use an attorney within the last 5 years.

When asked which of the following sources are you likely to contact about new or changing rules, regulations, or laws for the commodities they produce (Table 3), 75% said they had contacted the UT Extension agents, with 66% and 65% marking Farm Bureau Staff and Farm Service Agency, respectively. Natural Resource Conservation Service staff was chosen by 52% of respondents. The farmers overwhelmingly chose these organizations over print resources such as magazines, journals, and Web sites. When asked to identify reasons for contacting their County Extension Agent, 76% marked disease control/pest management showing the importance farmers place on Extension expertise and educational materials. Additionally, 45% marked farm management, government policy and regulations, and new technology, and 15% marked legal concerns.

The producers were allowed to add any final comments they had about current legal service(s) available or how best to meet their needs. Fourteen percent added comments, and the comments reflected a wide range of views. One producer wrote that he had never needed to use legal services, and another said he did not know what was available. Another stated that he thought lawyers caused 98% of the problems in government. Several producers stated that farmers need better legal services and more information on estates, wills, taxes, contract issues, leases, and environmental regulations.

Four of the respondents suggested that either Farm Bureau and/or the UT Extension should provide a staff attorney for consultation about legal matters relating to agriculture. One of these four respondents suggested that having workshops on legal issues conducted by persons with legal background on agricultural matters, coordinated and advertised by the county Extension office, would be helpful.

A logistic regression model was used to estimate the influence of farm and farm operator characteristics on farm operators' legal concerns. The logistic regression model was considered appropriate because the results could be used to assess the influence of characteristics on the likelihood that a farm operator judged a particular legal concern as important. The Statistical Package for Social Sciences (SPSS) version 13.0 was used for all statistical analyses (SPSS, 2004).

The logistic model results presented in Table 4 show the significant variables and corresponding coefficients for the top three legal concerns from Table 2; Estates/Wills/Trusts, Loan Mediation, and Contracts. Three models were developed utilizing these three legal concerns as separate dependent variables. The independent variables for each model included operator age, livestock enterprises, crop enterprises, owned land, rented land, income, and a dummy variable on whether the county was rural or urban.

Results for Estates/Wills/Trusts show that producers that raised dairy cattle or grew tobacco were more likely to be concerned with Estates/Wills/Trusts. Additionally, as the size of owned land increased, producers were more likely to be concerned with Estates/Wills/Trusts. It was initially hypothesized that Age would have a positive relationship with Estates/Wills/Trusts, but it was not a significant predictor. One explanation of this occurrence could be a result of conducting the survey at the Tennessee Farm Bureau convention. The age of those participating in the survey ranged from19 to 92 years old, with an average of 52.3 years and a standard deviation of 14.4 years. With the majority (53.8%) of respondents failing in the 45 - 60 range, the independent variable Age was not a good predictor of the top three legal concerns.

Results for Contracts revealed that dairy cattle producers were less likely to be concerned with Contracts. Furthermore, as producer incomes increased, producers were more likely to be concerned with Contracts and Loan Mediation. Additionally, producers who raised poultry or rented land were more likely to be concerned with Contracts. In the third model, Loan Mediation, a positive relationship was shown between producers that grew cotton or other livestock.

Summary and Implications

An internal review of the study's results reinforces the notion that factors affecting farm operators seeking legal services are not easily discovered. When faced with limited or non-existing agricultural legal services and/or educational sources (TBA, 2005), farmers are left to navigate the increasing farm level legal issues on their own. Findings of Williams (1996) indicate that pride and lack of awareness of available Extension information prevented farmers from seeking information regarding legal concerns. The results of the survey/model suggest that farmers would react positively to Extension educational programs in several legal areas.

Perhaps the most important implication of the study is that farmers understand the role of the UT Extension as a source of technical and production information and recognize that the UT Extension does not provide legal counsel. A comprehensive study of legal service centers in other states (e.g., Iowa, Ohio, Pennsylvania, North Carolina, and Arkansas) could provide possible designs to improve information on legal topics in Tennessee.

Weaknesses that may have affected the outcome of the research were identified. Lack of survey funding constrained the administration of the survey to Tennessee Farm Bureau convention participants. Limiting survey participation slightly skewed the data toward larger, more affluent farm operators. We suggest that the survey be expanded in scope across Tennessee and additional questions added to improve the data set for analysis.

References

Donaldson, J. (2005). The University of Tennessee Extension Service MIS 2004 Database Statistics.

Effland, A., & Runyan, J. (1998). Hired farm labor in U.S. agriculture. Agricultural Outlook [On-line], Available at: http://www.ers.usda.gov/Publications/Agoutlook/oct1998/ao255f.pdf

FLAG. (2005). Farmers' Legal Action Group, Inc. Available at: http://flaginc.org/

Flink, C. (2002). Finding a place for low-income family farmers in the legal services equation. Clearinghouse Review, 35(11-12), 677-694.

ISU. (2005). Iowa State University Extension: Iowa Concern. Available at: http://www.extension.iastate.edu/iowaconcern

Kershen, D.L. (1976). Introduction. South Dakota Law Review, 21, 481-485.

McConnaughay, P. J. (2003). Contending with a merger. University of Toledo Law Review 35(1).

Morehart, M., & Ryan, J. (2002). Farm Income, Finance, & Credit Outlook for 2002. Agricultural Outlook [On-line], Available at: http://www.ers.usda.gov/publications/agoutlook/Mar2002/ao289e.pdf

NCALRI. (2005). National Center for Agricultural Law Research and Information Available at: http://www.nationalaglawcenter.org/

NCSU. (2005). North Carolina State University: Ask the Specialist. Available at: http://www.ces.ncsu.edu/depts/fcs/

Purdue. (2006). Purdue University, Agricultural Economics Department. Available at: http://www.agecon.purdue.edu/directory/details.asp?username=harrisog

SPSS 13.0. (2004). SPSS 13.0 for Windows [Computer Program] Chicago: SPSS, Inc.

TASS. (2002). Tennessee Agricultural Statistics Service. Bulletin No. 37.

TASS. (2001). Tennessee Agricultural Statistics Service. Bulletin No. 36.

TBA (2005). Tennessee Bar Association. Available at: http://www.tba.org

UIUC. (2005). University of Illinois at Urbana-Champaign: farmdoc Project. Available at: http://www.farmdoc.uiuc.edu/index.html

Weigel, R. (2003). Why ranchers and farmers are reluctant to seek counseling and how family practitioners can help. The Forum for Family and Consumer Issues 8(2).

Williams, R. T. (1996). The on-going farm crisis: Extension leadership in rural communities. Journal of Extension [On-line], 34(1). Available at: http://www.joe.org/joe/1996february/a3.html

Sustainable Farm Tourism: Understanding and Managing Environmental Impacts of Visitor Activities

Carol Kline
Tourism Extension Specialist
Parks, Recreation and Tourism Management
North Carolina State University
Raleigh, North Carolina
carolkline@bellsouth.net

David Cardenas
Assistant Professor
Department of Recreation, Tourism, and Hospitality Management
University of North Carolina Greensboro
Greensboro, North Carolina
dacarden@uncg.edu

Yu-Fai Leung
Associate Professor
Parks, Recreation and Tourism Management
North Carolina State University
Raleigh, North Carolina
Leung@ncsu.edu

Stacy Sanders
Graduate Research Assistant
Parks, Recreation and Tourism Management
North Carolina State University
Raleigh, North Carolina
slsander@ncsu.edu

Introduction

The family farm--it's not just for growing food anymore. Today's farms are looking at a new cash crop to supplement their revenue from agricultural commodities--tourism, the world's largest export industry and the third largest employer (TIA, 2002). Farm tourism, which is also known as "agritourism," has been defined as the opportunities for tourists to "reside and sometimes participate in the working activities of farms and ranches" (Smith & Long, 2000:222).

The continual growth of farm tourism in America is a recent phenomenon when compared to farm stay programs and working farms that have existed for decades in Europe (Anthopoulou, 2000; Roberts, 2002). Tourism in rural areas is growing partly because economic developers are gradually embracing tourism and cottage industries as viable means for diversifying their investment and increasing wealth of farmers. Rural landowners are also searching for a means to supplement their incomes, keep children working on the family farm, and act as a farming community ambassador to the urbanized visitors who are disconnected from their food source (Garcia-Ramon, Cannoves, & Valdonvinos, 1995; McIntosh & Campbell, 2001).

Rural farms are becoming attractive tourist destinations also because more visitors are nostalgic for a "simpler" time. They want to escape the hustle of city life and connect with natural and cultural heritage and enjoy a richer and authentic leisure experience. They want to learn, connect with meaning, and meet genuine people engaged in a rural/agricultural lifestyle.

Many traditional farmers are accustomed to growing and selling their tangible commodity at wholesale to a distributor. With the introduction of tourism, farm operators have begun to think beyond crop development and create an intangible experience to sell at a retail price directly to the end consumer (Lynch, 1998; Fogarty & Renkow, 2002). This can be a paradigm leap for some.

Once the mental shift is made, farmers must cultivate their tourism product. They need to determine appropriate price structures for their product, find out how it fits into the regional tourism product, nurture partnering opportunities, employ a number of measures to market it, and then manage and cater to visitors as they arrive (Telfer, 2001). Extension faculty can be of assistance in each of these steps as well as in determining how farm tourists impact the local economy, community, and land.

In an ideal situation, visitors leave behind part of their salaries, but common sense would tell us outsiders might leave litter, congestion problems, and exotic species in their wake as well. While it may be a stretch to imagine the local farm as another over-trampled tourist trap, it is critical to understand how these impacts affect the sustainability of this new venture. Indeed, bucolic beauty is a key element in drawing visitors to our rural communities. If physical/environmental impacts caused by visitors are neglected, the very element that attracts tourism activity may be threatened.

As farm tourism expands in the United States, it is important that its development and potential challenges are investigated extensively to ensure its sustainability. One issue facing any land-based tourism is the impact of tourism operations on natural resources. The exploratory study reported here aimed to address the on-farm environmental impacts relevant to this growing niche of tourism.

Impacts of Visitors

The impacts that visitors have on communities can be classified as economic, socio-cultural, and physical/environmental. The study focused on the physical/environmental impacts of farm visitors. There is only a small body of research literature on agritourism/farm tourism. In the rare instance that agritourism research is conducted, it is often focused on the economic sector (Bushy & Rendle, 2000; Kuehn & Hilchey, 2002), and, while that is a critical piece for justification of effort, it does not tell the entire story of tourism's impact to a farm. The literature especially reveals little evidence that the physical/environmental impacts of visitors are being considered by farmers, planners, and tourism professionals.

Within the past three decades, research attention has been given to the environmental impacts of visitors in a variety of settings, including coral reefs, rock-climbing sites, and park lands in general (Hammitt & Cole, 1998). Past research has shown that the amount of impacts is dependent on use-related, environmental, and managerial factors. In the recreational pursuits of rock climbing and camping, a number of impact zones have been identified in order to classify the patterns and types of impacts made by visitors (Pyke, 2001). Each zone needs separate management strategies to correct or minimize the impacts. Similar "zones" on a farm would enable farm management to implement appropriate steps to control these outcomes.

Drawing from the literature on visitor impacts to outdoor and wilderness settings, this study attempts to fill a void by identifying the physical/environmental impacts of farm visitors, reporting rapid visual assessments on selected North Carolina farms and discussing management implications. Three specific questions were addressed:

1. Are farm tourism operators aware of visitor impacts occurring in their establishments?


2. What types of visitor impact problems exist and can be identified by rapid visual assessment on farm tourism establishments?


3. Can visitor impacts on farm tourism establishments be characterized by zones?

The answer to the above questions may assist farm owners or operators in developing effective management strategies based on impact zone.

Study Methods

The data collection occurred during the months of October and November of 2001. The survey team consisted of an Extension associate and doctoral student based out of a College of Natural Resources in the southeastern United States. The five farm sites in central and eastern North Carolina were selected because they offered a variety of agritourism experiences. Soil types in this region of the state range from sandy to clay.

1. A Day at the Farm: children's activities, tour of the dairy farm, nature trail and flower gardens, corn maze


2. The Inn at Celebrity Dairy: country inn and breakfast, farm animals to see, homemade goat cheese for sale


3. Mike's Farm and Country Store: Christmas tree farm, Christmas gift shop, restaurant and bakery, hay rides, outdoor shelter, petting zoo


4. Vollmer Farm: pick your own strawberries, homemade ice cream, corn maze, Autumn Harvest Festival, live music barn stage, Windmill Mountain underground slide, petting zoo


5. Noah's Landing: petting zoo of over 150 animals

A two-part method of data collection was used. The first part consisted of a problem awareness survey that was completed by the farm owner. The survey contained 14 questions that ranged from the number of years the farms had been operating (both as a farm and as a tourist attraction), hours of operations (for visitors), and their average number of visitors. In addition, respondents were asked to rate the severity of the physical/environmental impacts. Data were analyzed by both compiling the descriptive statistics and identifying commonalities between the open-ended responses.

The second part consisted of a rapid visual assessment of the farm site. The site assessment instrument provided space to list the weather, number of visitors during visit, and farm attributes (parking lot, trails etc.). In addition, each farm attribute was assessed for the type and severity of environmental impacts. Impact severity was rated on a 4-point condition class scale, ranging from no visible impacts(1=), slight or low level of impact (2=), moderate level of impact (3=), to high level of impact (4=). Finally, using Pyke's (2001) reference as a guide, potential impact "zones" at each of the five farm destinations were identified and classified according to patterns of impacts occurring on all of the farms.

Results

Problem Awareness

The survey instrument, which was initially designed to be completed by a farm manager, became an outline for face-to-face interviews. While all of the farm managers happily obliged, most preferred to be asked questions during the farm tour rather than stopping to complete a survey. Based on their responses, it appears that farm management had a general awareness of physical impacts brought on by their visitors, and the types of impacts perceived were generally consistent with the actual type and severity of impacts observed during the site assessment. However, none of the study farms had developed any kind of systematic assessment of visitor impacts or the condition of tourist facilities.

Impact Types and Zones

Vegetation loss and soil compaction were the most often noted type of impact on the five farms studied, especially on parking lots (Table 1). One or two instances of other types of impacts were observed, but it is unclear if these impacts are common to the farm attribute or specific to a particular site. In the one case of litter that was found around a snack bar, it may have been merely a matter of timing in that the assessment took place immediately following an event. By and large, if litter is a problem caused by visitors to these five farms, it was well controlled by well-placed containers and frequent clean-up "sweeps" by staff. The severity of impacts observed ranged from non-existent to a high level of severity, and, while most instances were slight or low, several cases of moderate severity were seen (Table 1).

Table 1.
The Existence and Level of Impacts Found for Selected Farm Attributes

Attraction\Impact	Vegetation Loss	Soil Compaction	Soil Erosion	Informal Social Trails	Litter	Introduction of New Plant Species
Animal Pens
Parking Lot
Picnic Area (Unsheltered)
Pond
Restrooms
Shelter
Walking Trail

Note: The number of symbols indicates the number of farms that have the farm attribute. The type of symbols indicates the level of impact on the farm attribute ( = No visible impacts, = Slight or low level of impact, = Moderate level of impact, = High level of impact).

After considering the various attribute areas of each farm and the common activities and movement patterns at each, general categories or zones were identified.

1. All five farms had a parking area.


2. All five farms had a walking trail and/or trails between attractions and/or from the parking lot.


3. The attraction staging area includes any "station" or stand-alone attraction, such as a restaurant, gift shop, tobacco barn exhibit, and hayride "hitching" area.


4. Another common zone found is a meeting area. Meeting areas would include sheltered or unsheltered picnic facilities and any area designated for the purpose of a group gathering. These are different from an attraction staging area because of the traffic patterns and usage of the area.


5. Because of the nature of the farm experience, it is expected to find some part of the experience based in or around farm crops. Therefore, the fifth zone identified is a field zone. Included in these areas of the farm are corn mazes, demonstration plots, Christmas tree stands, and pick-your-own fields.


6. Another common element between the farms is a play area, which would include swing sets and other stationary playground pieces.


7. Finally, animal viewing zones might include fenced petting areas, barns, hen houses, or beehives.

Implications and Conclusion

Extension faculty are often the best link for farmers wishing to engage in sustainable tourism by providing resources that can help them to succeed. As any new ag-related product appears on the horizon, it is their responsibility to explore, evaluate, and educate about the product. In this regard, tourism is no different from a new variety of seed corn.

Management strategies to minimize impacts or facilitate recovery in each of these zones should be considered as visitation to the farm increases in volume and duration. Strategies to control for and counteract negative impacts should also be tested. Based on the seven impact zones identified from the study farms and the common types of impacts that occur in these zones, a summary of potential management strategies and practices for the most common impact is provided in Table 2.

Further research is needed to understand more about visitor-related environmental effects and how such effects might influence visitor experience. Also, research is needed to refine the assessment procedures and confirm or improve upon the notion of impacts zones on farms. Additional zones may be considered as more research is conducted in this area. For example, a water-edge zone might include the impacts that are specific to ponds, streams, and boggy areas. Variation in soil type, vegetation, seasons, climates, visitor activity, and current management strategies could have a significant affect on the type and severity of impacts observed. The instruments used for site assessment and manager interviews may be refined to address these factors.

While increasing the objectivity and sophistication of assessment procedures would yield more accurate data, a balance between efficiency and accuracy should be considered if the application of assessment is to be sustained for the long term. Alternative rapid assessment approaches, such as fixed-point photography or photopoint monitoring (e.g., Hall, 2001), should be explored to evaluate their usefulness and efficiency in documenting site conditions. Besides impact monitoring purposes, photos can also help farm owner/manager and visitors appreciate the change of site and landscape conditions over time.

Despite the limitations, the assessment approach taken in this study provides the first assessment example for Extension faculty and farm tourism operators and will stimulate ideas on how the rapid visual assessment tool applied can be refined and customized to maximize its benefits to farm tourism establishments. As tourism becomes an important source of income of many farm owners and a key element in rural development, sustaining the quality of resource conditions and reducing visitor impacts on farm tourism destinations deserves greater attention because it not only reflects the farm's character and the level of care, but it also influences the visitors' experience and the likelihood they will return or recommend the farm to their relatives and friends (Fridgen, 1991). In that sense, it may be one of the factors that determine the long-term success of farm tourism businesses.

References

Anthopoulou, T. (2000). Agrotourism and the rural environment: constraints and opportunities in the mediterranean less-favoured areas. In Briassoulis, H. & van der Straaten, J., (eds.) Tourism and the environment: Regional, economic, cultural and policy issues (pp. 357-372). Boston, MA: Kluwer Academic Publishers.

Busby, G., & Rendle, S. (2000). The transition from tourism on farms to farm tourism. Tourism Management, 21, 635-642.

Fogarty, D., & Renkow, M. (2002). Agritourism opportunities for North Carolina. Retrieved August 21, 2006 at: http://www5.bae.ncsu.edu/programs/extension/publicat/arep/arep2.html

Fridgen, J. (1991). Dimensions of tourism. East Lansing, MI: American Hotel and Motel Association Educational Institute.

Garcia-Ramon, M., Cannoves, G., & Valdonvinos, N. (1995). Farm tourism gender and the environment in Spain. Annals of Tourism Research, 22, 267-282.

Gunn, C. (1994). Tourism planning: Basics concepts and cases (3rd Ed.). Washington, DC: Taylor & Francis.

Hall, F. C. (2001). Photo point monitoring handbook: Part A--field procedures. General Technical Report PNW-GTR-526. Portland, OR: USDA Forest Service, Pacific Northwest Research Station.

Hammitt, W., & Cole, D. (1998). Wildland recreation: Ecology and management (2nd Ed.). New York, NY: John Wiley & Sons.

Kuehn, D., & Hilchey, D. (2002). Agritourism in New York: Management and operations (New York Sea Grant Fact Sheet). Retrieved August 21, 2006 at: http://www.nysgextension.org/tourism/tourism/agmgtfs.pdf

Lynch, L. (1998). Using private lands for natural resource based tourism: what are the obstacles? Session Proceedings from 1998 National Extension Tourism Conference.

McIntosh, A., & Campbell, T. (2001). Willing workers on organic farms (wwoof): a neglected aspect of farm tourism in New Zealand. Journal of Sustainable Tourism, 9, 111-127.

Pyke, K. (2001). Climbing management: A guide to climbing issues and the production of a climbing management plan. Boulder, CO: The Access Fund.

Roberts, L. (2002). Farm tourism--its contribution to the economic sustainability of Europe's countryside. In: Harris, R., Griffin, T., & Williams, P., (eds.) Sustainable tourism: A global perspective (pp. 195-208). Oxford, UK: Butterworth-Heinemann.

Smith, V., & Long, V. (2000). Farm tourism. In: Jafari, J., (ed.). Encyclopedia of tourism (pp. 222-223). New York: Routledge.

Telfer, D. (2001). Strategic alliances along the Niagara wine route. Tourism Management, 22, 21-30.

TIA (Travel Industry Association of America) (2002). Tourism works for America, annual report, 11th edition. Washington, DC: Travel Industry Association of America.

Comparison of Best Management Practice Adoption Between Virginia's Chesapeake Bay Basin and Southern Rivers Watersheds

B. L. Benham
Extension Specialist and Assistant Professor
Virginia Tech
Blacksburg, Virginia
benham@vt.edu

A. Braccia
Post Doctoral Associate
Virginia Tech
Blacksburg, Virginia
abraccia@vt.edu

S. Mostaghimi
Professor
Virginia Tech
Blacksburg, Virginia
smostagh@vt.edu

J. B. Lowery
Project Manager
Sebago Technics, Inc.
Westbrook, Maine
jlowery@sebagotechnics.com

P. W. McClellan
President
MapTech, Inc.
Blacksburg, Virginia
pmcclellan@maptech-inc.com

Introduction

Agricultural nonpoint source (NPS) pollution is a major water quality concern throughout the United States. The 2000 National Water Quality Inventory (USEPA, 2002) reported that runoff from agricultural lands was the leading source of pollution in the impaired rivers and streams assessed. Best management practices (BMPs) are thought to be effective means to reduce NPS pollution (USEPA, 2003a).

Traditionally, agricultural NPS pollution has been addressed by implementing BMPs through an incentive-based, voluntary adoption approach. This voluntary approach has many benefits over a mandatory program, but distinctly contrasts with the regulatory structure of point source controls (Gomez, 1995). The traditional incentive-based approach to resource management appeals to the typical, highly independent farm operator who does not want to be told what he or she should do with privately owned land (Logan, 1990). This approach also allows for flexibility in site-specific characteristics, relies upon the producer's ability to adopt and maintain the practice, and includes no associated enforcement costs. However, with an incentive-based system there can be uncertainty in the consistency and quality of BMPs, conservation plans, monitoring, and adherence to conservation compliance policies (Gomez, 1995).

Agricultural researchers and rural sociologists have repeatedly attempted to understand the factors that influence producers to implement BMPs. Bultena and Hoiberg (1986) state that:

Most researchers believe that producers' lack of awareness of environmental problems in their county or community is not a major factor in their decision not to implement a BMP. Producers do, however, underestimate the severity of their own contribution to environmental problems.

In a survey of approximately 3,200 producers in erosion-prone areas of 13 states, Bultena and Hoiberg (1986) found that 92% of the producers perceived soil erosion as a problem in their home counties, 78% in their local communities, but only 66% on their own farms. Hoban and Wimberley (1992) found similar results in a study of producers that participated in the Rural Clean Water Program and producers that were eligible, but did not participate. Napier, Thraen, and Camboni (1988) concluded that ultimately, if producers adopt a positive attitude towards conservation practices, they will act from self-interest, adopting BMPs they believe will solve perceived problems. This finding suggests that with enough education and resulting voluntary BMP adoption, NPS pollution may be reduced without the need for regulations.

In Virginia, the Department of Conservation and Recreation (VDCR) is charged with administering state agricultural NPS pollution control programs through local Soil and Water Conservation Districts. The VDCR has no regulatory authority but conducts educational programs to increase producer awareness about the potential negative impact of agriculture activities on water quality and on the potential benefits BMPs offer in terms of improved water quality and increased crop production. Funds are systematically allocated to the areas of the state determined to have the most significant agricultural NPS pollution problems. While some cost-share assistance is generally available to producers, the goal of the Virginia's BMP implementation program is to encourage producers to implement additional BMPs on their farms without cost-share assistance (i.e., non-cost-share BMPs).

For several years, concern about the pollution of the Chesapeake Bay has been a high profile issue because it is the nation's largest estuary and a valuable natural resource (CBP, 2004). It supplies millions of kilograms of seafood, functions as a major hub for shipping and commerce, provides habitat for an extensive array of wildlife, and offers a variety of recreational opportunities for residents and visitors. According to the Environmental Protection Agency, the largest anthropogenic contributor of NPS pollution to the Chesapeake Bay is agriculture (EPA, 2003b). Due to the Chesapeake Bay's economic and ecological importance, funds have been allocated to pollution abatement and remediation in the Chesapeake Bay drainage basin. By comparison, substantially less focus and fewer resources have been directed to watersheds in southern Virginia that do not drain into the Bay.

Although information on the extent of BMPs implemented through cost-share programs in Virginia is well documented (VDCR, 2004), very little data are available regarding the extent of non-cost-share BMPs implemented in the state. Developing information on the extent of non-cost-share BMP adoption within the state would enable the VDCR to evaluate the success of its BMP program and modify the program to increase its effectiveness.

The objectives of the investigation reported here were to assess farming operations to determine the extent of implementation of cost-share and non-cost-share BMPs and to gain insight into the impact of selected factors on the adoption of BMPs in Virginia.

Methodology

The study was designed to survey producers in two regions of Virginia: (1) 67 counties where >90% of the area in the county drains to the Chesapeake Bay (hereafter referred to as the Bay basin) and (2) producers in 30 counties where ≥ 10% of the area in the county drain away from the Chesapeake Bay (hereafter referred to as Southern Rivers region). The Bay basin contains five river basins (Chesapeake Bay and Small Costal Rivers, James River, Potomac-Shenandoah Rivers, Rappahannock River, and York River basins) (Figure 1a, VDACS, 1992). The Southern Rivers region contains five river basins (Big Sandy River, Chowan River, Holston River, New River, and Roanoke River basins) (Figure 1b).

Figure 1.
Illustration of River Basins in Virginia's (a) Bay Basin and (b) Southern Rivers Region

Figure 1a

Figure 1b

A survey instrument was developed with input from representatives from the VDCR, the Virginia Farm Bureau, the U.S. Department of Agriculture-Farm Service Agency (USDA-FSA), Virginia Cooperative Extension, and the Center for Survey Research at Virginia Tech. Additionally, experts in hydrology, nonpoint source pollution, sociology, and natural resource economics were consulted. The instrument included 35 questions grouped into four categories: demographic information, farming operation information, producer attitudes related to causes of water pollution, and BMP implementation. The survey was pilot-tested with Virginia Farm Bureau Federation staff from across the state.

Geospatially referenced data and a Geographic Information System (GIS) were used to randomly select producers to receive the survey. Due to the lack of land use data for some counties, the probability that selected locations would not be in an agricultural area increased. Thus, the number of randomly selected sites was increased by a factor of 1.75 to insure the required number of agricultural parcels was surveyed. Maps developed using the GIS data were taken to county USDA Service Centers, where sites were identified from aerial photos and the names of the farm operators corresponding to the randomly selected locations were determined. County tax maps were used to identify property owners when other records were unavailable. Property owner addresses were obtained from courthouse records, were entered into a database, and sorted so that a producer with several farms in one county would not receive multiple copies of the survey.

Some 6,800 surveys were mailed out in the Bay basin and 6,000 in the Southern Rivers region. Three weeks after the survey was mailed, a reminder card urging recipients to complete the survey was mailed. A second survey was mailed two-months after the initial mailing, followed by another reminder card some 4 weeks later.

Results

The information presented here represents a summary of responses from 1,377 producers who farmed some 474,772 acres or 13% of the farmland in the Bay basin (23.5% response rate) and from 1,114 producers who farmed some 140,676 acres or 7% of the farmland in the Southern Rivers region (16.4% response rate). Given the response rates, the survey error was 3% for the Bay basin and 3.3% for the Southern Rivers region (95% confidence level).

Demographic/Commodity Production Information

The average age and years of farming experience of producers varied little between the two regions (Table 1). A larger proportion of producers in the Southern Rivers region had a high school diploma and associates or bachelor degree. Farming operations made up a larger proportion of family income for producers in the Bay basin than the Southern Rivers region.

Hay, corn, and beef cattle were important commodities in both regions (Table 2). More producers in the Bay basin grew small grains, alfalfa, and soybeans, while tobacco was raised by more producers in the Southern Rivers region. Livestock production, especially beef cattle, was an important commodity in both regions.

Producer Attitudes Towards Pollution and Water Quality

Producer attitudes towards pollution and water quality were similar in both regions. The majority of producers in both regions (85% in the Bay basin and 76% in the Southern Rivers region) indicated that they were either "very concerned" or "somewhat concerned" about pollution (possible responses were Very Concerned, Somewhat Concerned, Not Concerned, and Do Not Know). Similarly, most producers in each region (73% in the Bay basin and 74% in the Southern Rivers region) believed that they did not have the right to farm in ways that were detrimental to water quality.

When asked what contribution their farm makes to water quality degradation, the majority of producers in both regions believed their farm did not contribute to reduced or impaired water quality (Table 3). Respondents from both regions indicated that industrial wastes and litter or garbage were the significant sources of pollution; more respondents in the Bay basin believed urban sources contributed to water pollution (Figure 2). The vast majority of respondents from both regions (90% from the Bay basin and 85% from the Southern Rivers region) agreed with the statement "water pollution can best be controlled through educational programs that encourage farm operators to adopt BMPs" (Table 4).

Figure 2.
Perceived Causes of Pollution in Virginia's Bay Basin and Southern Rivers Region

Best Management Practice Implementation

Approximately 81% of producers in the Bay basin, versus 63% of producers in the Southern Rivers region, have implemented some type of agricultural BMP (Figure 3). The majority of the BMPs in both regions were implemented without cost-share assistance, and the percent of respondents who implemented BMPs with cost-share funds was similar between the two regions. However, there were more producers in the Bay basin who had implemented BMPs without cost-share assistance. Survey results indicated that in the Bay basin, 4.4 non-cost-share BMPs were implemented for every cost-share BMP, while 3.6 non-cost-share BMPs were implemented for every cost-share BMP in the Southern Rivers region.

Figure 3.
Percent of Producers Who Implemented BMPs With Cost-Share Funds, Without Cost-Share Funds, and Both With and Without Cost-Share Funds (Combined) in Virginia's Bay Basin and Southern Rivers Region

To further examine the differences in BMP implementation levels between the two regions, the ratios of the number of specific BMPs implemented without the use of cost-share funds to those implemented with cost-share funds were compared for various BMPs (Figure 4). The ratio was higher in the Bay basin than in Southern Rivers region for cover crops, field scouting, irrigation improvement, and plant tissue analysis. Each of these BMPs has traditionally been associated with production of high-yield field crops that are primarily grown in the Bay basin (corn, small grains, and soybeans). In the steeper Southern Rivers region the ratio for sediment detention basins was much greater than in the Bay basin. This difference is likely due to the steeper topography in most of the Southern Rivers region, as compared to the topography in the Bay basin.

Figure 4.
Ratio of Non-Cost-Share to Cost-Share Funded BMPs Implemented in the Bay Basin and Southern Rivers Regions of Virginia

Conclusions

Although there were many similarities in farm characteristics and producers' attitudes toward water quality issues, BMP implementation is greater in the Bay basin. Across the state, a majority of producers have implemented BMPs, and most have implemented BMPs without the use of cost-share assistance. The fraction of survey respondents who implemented BMPs without cost-share assistance and who implemented BMPs regardless of the funding source were both higher in the Bay basin than in Southern Rivers region of Virginia. This difference may be in part due to a more focused, longer-term coordinated BMP programmatic effort in the Bay basin. However, it is also likely that the non-cost-share/cost-share ratio in a particular basin is a function of the dominant type of agriculture being practiced in that basin, the level of BMP and water quality educational efforts, as well as the prevalent socioeconomic circumstances.

In both regions of Virginia, producers who had implemented more non-cost-share practices tended to agree with the statement, "Water pollution can best be controlled through educational programs that encourage producers to use BMPs." To maintain this positive influence and to increase the current ratio of non-cost-share BMP for every cost-share BMP implemented (4.4), the information presented here suggests that future educational programs should focus on the water quality benefits associated with implementing BMPs.

Acknowledgments

Funding for this study was provided, in part, by the Virginia Department of Conservation and Recreation, Division of Soil and Water Conservation, Richmond, Virginia.

References

Bultena, G. L., & Hoiberg, E. O. (1986). Sources of information and technical assistance to farmers in controlling soil erosion. In: Conserving Soil: Insights from Socioeconomic Research, S. B. Lovejoy and T. L. Napier (Editors). Soil Conservation Society of America, Ankeny, IA. pp. 71-82.

CBP (Chesapeake Bay Program). (2004). Available at: http://www.chesapeakebay.net

Environmental Protection Agency (EPA). (2003a). National management measures to control agricultural sources of NPS pollution. Office of Water, U.S. EPA, 314 pp.

Gomez, B. (1995). Assessing the impact of the 1985 farm bill on sediment-related NPS pollution. Journal of Soil and Water Conservation 50(4):374-377.

Hoban, T. J., & Wimberley, R. C. (1992). Farm operators' attitudes about water quality and the RCWP. Proceedings of the National RCWP Symposium. pp. 247-253.

Logan, T. J. (1990). Agricultural best management practices and groundwater protection. Journal of Soil and Water Conservation 45(3):201-206.

Napier, T. L., Thraen, C. S., & Camboni, S. M. (1988). Willingness of land-owners to participate in government-sponsored soil erosion control programs. Journal of Rural Studies 4(4):339-347.

Novotny, V., & H. Olem. (1994). Water quality: Prevention, identification, and management of diffuse pollution. New York: Van Norstrand Reinhold.

USEPA (United States Environmental Protection Agency). (2003b). Technical support document for identification of Chesapeake Bay designated uses and attainability. Office of Water, U.S. EPA, 277 pp.

USEPA (United States Environmental Protection Agency): (2002). 2000 national water quality inventory, Office of Water, USEPA. Available at: http://www.epa.gov/305b/2000report

Virginia Department of Agriculture and Consumer Services (VDACS). (1992). Virginia agricultural statistics summary. Commonwealth of Virginia, Department of Agriculture and Consumer Services.

Virginia Department of Conservation and Recreation, Virginia Division of Soil and Water Conservation (VDCR). (2004). Virginia agricultural BMP cost-share program database. Commonwealth of Virginia, Department of Conservation and Recreation. Available at: http://192.206.31.52/cfprog/dswc/bmpprm.cfm

Response Patterns: Effect of Day of Receipt of an E-Mailed Survey Instrument on Response Rate, Response Time, and Response Quality

Glen Shinn
Professor
Department of Agricultural Leadership, Education, and Communications
Texas A&M University
College Station, Texas
g-shinn@tamu.edu

Matt Baker
Professor & Chair
Department of Agricultural Education and Communications
Texas Tech University
Lubbock, Texas
matt.baker@ttu.edu

Gary Briers
Department of Agricultural Leadership, Education, and Communications
Texas A&M University
College Station, Texas
g-briers@tamu.edu

Professional and technical landscapes can change quickly, and it is important to understand and describe changes that affect Extension programming as they occur. Descriptive research tools provide promise for insights. Survey questionnaires are one of the most popular methods of collecting information from a target population. However, in a time when appraisals are more frequently needed, the rate of response in survey research is declining (Sheehan, 2001).

Phillips (1941) criticized mail surveys because of low response rates. Throughout the following six decades, researchers examined a myriad of techniques and their effects on response rate. Wright (2005) concluded that ". . . online survey researchers should conduct a careful assessment of their research goals, research timeline, and financial situation before choosing a specific product or service" (p. 1). Valuable best practices have been developed and proposed (Brashears, Akers, & Bullock, 2003; Bruzzone, 1999; Dillman, 2000; Dillman & Carley-Baxter, 2000; Fraze, Hardin, Brashears, Haygood, & Smith, 2003; Lindner, Murphy, & Briers, 2001; Mehta & Sivadas, 1995; Miller & Smith, 1983; Nie, Hillygus, & Erbring, 2002; Schaefer & Dillman, 1998; Sheehan & Hoy, 1999; Tse, 1998; Tse, Tse, Yin, Ting, Yi, Yee, & Hong, 1995; Walonick, (n.d.), Witmer, Colman, & Katzman, 1999; and Yun & Trumbo, 2000).

Dillman (2000), when examining mail and Internet survey methodologies, argued that "no other method of collecting survey data . . . offers so much potential for so little cost" (p. 400). Sheehan (2001), in a review of e-mail survey response rates, noted,

. . . while the number of studies that use e-mail to collect data has been increasing over the past fifteen years, the average response rate to the surveys appears to be decreasing (Table 1). On average, the 31 studies reported a mean response rate of 36.83%. The 1995/6 period showed seven studies using e-mail surveys with an average response rate of about 46%. The 1998/9 period, in contrast, showed thirteen studies using e-mail surveys with an average response rate of about 31%. (p. 7)

There is evidence that response rates continue to decrease.

While Rea and Parker (1997) found length and format have a significant effect on return, Dillman, Tortora, and Conradt (1998) reported that fancy vs. plain designs may not. Other "features" of survey instruments are not known. For example, what is the influence of the day of receipt of the instrument and does that day influence response patterns? This investigation examined the effects of the day of receipt of an e-mailed survey instrument on: 1) the response rate, 2) the length of time lapsed in responding by scholars, and 3) the quality of the response as measured by the number of nominations to a panel of experts. With the increased expectation of Extension program accountability, one of the most frequently used evaluation methodologies, survey research, is becoming less useful due to declining response rates.

Methods

The target population consisted of authors from the United States who published in one or more of the following journals: Journal of Agricultural Education, Journal of International Agricultural and Extension Education, or Journal of Extension. The sample frame was developed by the researchers through a listing of all authors who had published in one or more of these journals between January 2004 and August 2005. The accessible population included 192 authors. Five authors from the target population were deleted, two because of direct involvement in the study and three who had undeliverable e-mail addresses.

Authors were randomly assigned in this experimental study to receive the e-mailed survey questionnaire at the beginning of each workday, Monday through Friday, with one-fifth of the authors receiving the questionnaire each of the 5 days. An individual and personalized e-mail was sent to each author after the close of business on the previous day prior to arrival. The original e-mail message was sent to the Monday group on November 7, 2005, and each of the following four workdays. Because of the Thanksgiving holidays, the Thursday and Friday groups had two workdays less to respond in the 28-day period.

The questionnaire asked recipients to nominate themselves or colleagues to participate in a research project of common professional interest. The attempted census of an accessible population was treated as a time and place sample (Oliver & Hinkle, 1982), and inferential statistics were used in the analyses.

The independent variable, day delivered, was recorded nominally for each potential participant in the study as the day on which the e-mail questionnaire was delivered to that person (i.e., as Monday, Tuesday, Wednesday, Thursday, or Friday). Then, the value for each of the three dependent variables, response (operationalized nominally as yes or no), days to respond (if returned, operationalized as number of days to respond), and quality of response (operationalized as number of nominees provided), was recorded as responses were received. The variable "days to respond" was recorded as the number of workdays, Monday through Friday, that transpired from the day the e-mail was sent to the day response was received.

Consequently, receipt of a response on a weekend day was assigned the same value as the subsequent Monday. Data were collected for 35 days following the 5 days of delivery of the e-mailed questionnaires. Because "response" was the variable under examination, no follow-up of non-respondents was conducted.

Data were recorded in an Excel database and analyzed using SPSS/v.13. Descriptive statistics including frequencies, means, and standard deviations were used to describe response rate. Due to the categorical nature of the measures, Chi-square analysis was used to examine the day of receipt/rate of response relationship. One-way analyses of variance (ANOVA) were used to compare days to respond and quality of response (dependent variables) as influenced by day of receipt (the independent variable). ANOVA was selected as an inferential statistic due to the categorical measure of the independent variable (day of the week) and the interval measure of the dependent variables.

Findings

E-mailed questionnaires were sent on five consecutive days to 192 authors of three professional journals, with approximately one-fifth of the possible participants receiving their e-mails each of the 5 days. Data collection was terminated 35 days after the e-mail was received. Thus, each potential respondent--regardless of day e-mail was sent--was given 35 days to respond. At the conclusion of data collection, data that had been received yielded the following results.

Of the 192 potential participants contacted, 60 responded, a response percentage of 31.25% (Table 1). Response rate by day of week contacted ranged from a low of 20.51% from Monday contacts to a high of 43.59% from those contacted on Wednesday. On average, participants responded in 4.57 days (SD=5.00). Those contacted on Monday tended to respond most quickly (3.25 days), while Friday contacts responded most slowly (5.90 days). The number of nominations provided by participants ("quality of response") averaged 4.76 nominees (SD=4.74), with a range of 3.21 (for Tuesday contacts) to a high of 7.25 (for Monday contacts). Also calculated were the total nominations per day. Total nominations per day ranged from a low of 48 nominations from those respondents e-mailed on Tuesday to a high of 67 nominations from Thursday participants.

To examine the results of the study inferentially, "day of week contacted" and "response/no response" were cross-tabulated, and a chi-square analysis was conducted. Data in Table 2 show the results of that analysis. Based on the chi-square value of 5.27 (p=.26), there is little evidence to suggest that day of the week contacted and rate of response are associated.

Next, the dependent variable "days to respond" was examined based on the independent variable "day of week contacted." An ANOVA was used to compare the average days to respond among the 5 days of the week on which participants were contacted. Data in Table 3 provide the results of the analysis.

On average, each of the 60 participants responded in 4.57 days. For the various days of the week, mean days to respond ranged from 3.25 days (for those contacted on Monday) to 5.90 days (for Friday contacts). An inferential comparison of the five means resulted in a statistically insignificant F(4, 55) = .624, p = .64.

Finally, "quality of response" (operationalized as number of persons nominated) was examined based on day of week contacted (Table 4). An ANOVA was performed to compare quality of response by day of the week on which participants were contacted.

The mean number of nominees ranged from 3.00 (for those participants contacted on Tuesday) to 7.25 (for participants e-mailed on Monday), with an overall mean number of nominees of 4.68 by each of the 60 respondents. The ANOVA revealed a statistically insignificant F(4, 55) = 1.99, p = .11.

Conclusions

Nonresponse error continues to concern survey researchers and Extension professionals. Our goal was to identify practices that increase the response rate for electronic survey research instruments. Researchers, including Bruzzone (1999), Dillman (2000), Dillman and Carley-Baxter (2000), Hewson, Yule, Laurent, and Vogel (2003), and Yun and Trumbo (2000) recognized that rapid change affects knowledge management systems. Consequently, there is an ongoing need to better understand the changing behaviors of "customers and organizations." The adoption of new electronic technologies, particularly e-mail, short message service (SMS), and radio frequency identification technology (RFID), changes the way we communicate with Extension audiences. Gingrich (2001) recognized two patterns of change stemming from computers and the combination of the nanotechnology-biology-information revolution. Gingrich called this the "age of transitions."

The literature is abundant with recognized best practices to improve effectiveness and efficiency of survey design and delivery. Recognized practices include salience, anonymity or confidentiality, general layout and format considerations, length of instrument, and order of questions. Several traditionally used practices, such as "fancy" layouts, handwritten postscripts, incentives, original signatures, and personalized cover letters, do not explain a significant difference in survey response.

When examining the effect of day of receipt of an electronic survey instrument on the response rate, we found no significant difference in the rate of response by day the instrument was e-mailed/received. Our target audience members, agricultural education and Extension journal authors, were just as likely to respond if they received the instrument on Monday as on any other workday.

Yun and Trumbo (2000) noted ". . . an interesting effect was observed in the timing of the e-mail and Web responses. Over 80% of the electronic responses were collected within three days after the initial e-mail was sent out" (p. 12). In the research reported here, the average response time 4.5 days. Further, the length of time taken by the target audience to respond was not associated with the day of receipt of the electronic survey instrument. This research found no significant difference in the length of response time based on the workday on which potential participants received the instrument.

The quality of the response, as judged by the number of nominations, was not associated with the day of receipt of the electronic survey instrument. This research found no significant difference in the quality of response as influenced by the workday on which subjects received the instrument.

Limitations

This target audience was a well defined, individually connected, and accurately identified cohort of authors. Their behavior may not be similar to that of other types of target audiences. This research sought simple response data asking for the identification and nomination of experts within a specified discipline. The behavior may not be similar when more complex issues or time-consuming requests are made. This research offered clear benefits to the target audience and generalized value for the larger professional organization. The behavior may not be similar when benefits are less obvious.

Recommendations

Because response rate was 37%, unacceptably low (Miller & Smith, 1983; Lindner, Murphy, & Briers, 2001), strategies must be employed to reduce the threat of nonresponse error. Consequently, the use of valuable best practices, including advance organizers such as postcards and repeated follow-up contacts, should be considered to increase participation of potential nonrespondents (Dillman, 2000). Efforts should be made to connect the value of survey research as a priority concern to the field of study.

In terms of future research, our findings offer two recommendations. First, although e-mail is a valuable tool for data collection by Extension professionals, the day of receipt does not affect response patterns. Future research is needed to validate the efficacy of approved best practices (Dillman, 2000). Second, this research has validated that response rate issues in survey research are complex and multi-faceted. Response rate is likely a complex interaction of audience, time, innovation, modality, meaning, and value.

References

Brashears, M.T., Akers, C., & Bullock, S. (2003). A test of a bimodal survey model on the cooperative communicators association: A case study. Proceedings of the National Agricultural Education Research Conference, December 9-11, Orlando, FL. Retrieved September 6, 2006, from http://aaae.okstate.edu/proceedings/2003/Proceedings.pdf#search=%222003%20NAERC%22

Bruzzone, D. (1999). The top 10 insights about the validity of conducting research online. Advertising Research Foundation. Retrieved November 19, 2005, from http://www.swiftinteractive.com/white2.asp

Dillman, D. (2000). Mail and Internet surveys: The tailored design method (2nd ed.). New York: John Wiley and Sons.

Dillman, D., & Carley-Baxter, L. (2000). Structural determinates of mail survey response rates over a 12 year period, 1988-1999. Retrieved November 19, 2005, from http://www.sesrc.wsu.edu/dillman/papers/2000%20ASA%20Proceedings--Dillman.pdf

Dillman, D., Tortora, R., & Conradt, J. (1998). Influence of plain vs. fancy designs on response rates for web surveys. Retrieved November 19, 2005, from http://www.sesrc.wsu.edu/dillman/papers/asa98ppr.pdf

Fraze, S. D., Hardin, K. K., Brashears, M. T., Haygood, J. L., & Smith, J. H. (2003). The effects of delivery mode upon survey response rate and perceived attitudes of Texas agri-science teachers. Journal of Agricultural Education, 44(2), 27-37.

Gingrich, N. (2001). Vision for the converging technologies. In M. Roco & W. Bainbridge. (2005). Converging technologies for improving human performance: Nanotechnology, biotechnology, information technology and cognitive science (pp. 37-55). The Netherlands: Kluwer Academic Publishers.

Hewson, C., Yule, P., Laurent, D., & Vogel, C. (2003). Internet research methods: A practical guide for the social and behavioural sciences. London: Sage Publications, Inc.

Lindner, J. R., Murphy, T. H., & Briers, G. E. (2001). Handling nonresponse in social science research. Journal of Agricultural Education, 42(4), 43-53.

Mehta, R., & Sivadas, E. (1995). Comparing response rates and response content in mail versus electronic surveys. Journal of the Market Research Society, 4(37), 429-440.

Miller, L. E., & Smith, K. L. (1983). Handling nonresponse issues. Journal of Extension, 21(5), 45-50.

Nie, N., Hillygus, S., & Erbring, L. (2002). Internet use, interpersonal relations and sociability: Findings from a detailed time diary study. In B. Wellman (Ed.), The Internet in everyday life (pp. 215-243). London: Blackwell Publishers.

Oliver, J., & Hinkle, D. (1982). Occupational educational research: Selecting statistical procedures. Journal of Studies in Technical Careers, 4(3), 199-207.

Phillips, M. (1941). Problems of questionnaire investigation. Research Quarterly, 12, 528-537.

Rea, L., & Parker, R. (1997). Designing and conducting survey research (2nd Edition). San Francisco: Jossey-Bass.

Schaefer, D., & Dillman, D. (1998). Development of a standard e-mail methodology: Results of an experiment. Public Opinion Quarterly, 3(62), 378-390. Retrieved November 5, 2005, from http://survey.sesrc.wsu.edu/dillman/papers/E-Mailppr.pdf

Sheehan, K. (2001, January). E-mail survey response rates: A review. Journal of Computer-Mediated Communication, 6(2). Retrieved November 5, 2005, from http://jcmc.indiana.edu/vol6/issue2/sheehan.html

Sheehan, K., & Hoy, M. (1999). Using e-mail to survey Internet users in the United States: Methodology and assessment. Journal of Computer Mediated Communication, 4(3). Retrieved November 19, 2005, from http://jcmc.indiana.edu/vol4/issue3/sheehan.html

Tse, A. (1998). Comparing the response rate, response speed and response quality of two methods of sending questionnaires: E-mail vs. mail. Journal of the Market Research Society, 40(4), 353-361.

Tse, A., Tse, K., Yin, C., Ting, C., Yi, K., Yee, K., & Hong, W. (1995). Comparing two methods of sending out questionnaires: E-mail versus mail. Journal of the Market Research Society, 37(4), 441-46.

Walonick, D. (n.d.) Everything you wanted to know about questionnaires but were afraid to ask. Retrieved November 6, 2005, from http://www.statpac.com/research-papers/questionnaires.htm

Witmer, D., Colman, R., & Katzman, S. (1999). From paper-and-pencil to screen-and-keyboard: Toward a methodology for survey research on the Internet. In S. Jones (Ed.), Doing Internet research: Critical issues and methods for examining the Net (pp. 145-161). Thousand Oaks, CA: Sage.

Wright, K. (2005). Researching Internet-based populations: Advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey services. Journal of Computer-Mediated Communication, 10(3), article 11. Retrieved November 19, 2005, from http://jcmc.indiana.edu/vol10/issue3/wright.html

Yun, G., & Trumbo, C. (2000). Comparative response to a survey executed by post, e-mail and web form. Journal of Computer Mediated Communication, 6(1). Retrieved November 21, 2005, from http://jcmc.indiana.edu/vol6/issue1/yun.html#conclusion

The Food Stamp Nutrition Education Program Focuses on the Learner

Lucia Kaiser
Nutrition Specialist
llkaiser@ucdavis.edu

Tammy McMurdo
Program Representative
tjmcmurdo@ucdavis.edu

Amy Block Joy
Nutrition Specialist
abjoy@ucdavis.edu

Department of Nutrition
University of California at Davis
Davis, California

Introduction

Learner-centered education is an approach that focuses on the learner--his/her experiences, perspectives, interests, talents, and needs--and on the best practices to motivate people and promote learning (Henson, 2003). At the core of a learner-centered approach are the following principles: 1) learning is based on experiences; 2) the characteristics of each individual learner are considered when planning experiences; 3) the learner's perceptions shape what is taught; 4) the learner's curiosity is nurtured; 5) learning is best when it involves the emotions; and 5) the learning environment is free from fear.

Learner-centered education is not a new idea. In fact, its basic principles can be traced back to early philosophers and educators in the 4th and 5th centuries BC (Hensen, 2003). Both Confucius and Socrates stressed the need to develop the individual in their teaching. In the 17th century, Englishman John Locke introduced the idea of experiential learning or learning by doing. In the 1900's, John Dewey, an American educator, embraced the idea that learning should involve problem-solving and be fun.

A large evaluation study of learner-centered education in the 1930's found the approach to contribute to greater student achievement, compared to traditional teaching methods which relied heavily on drills, memorization, and lecture. There is evidence that clientele participating in Cooperative Extension programs prefer a learner-centered over a teacher-centered approach (Simeral & Hogan, 2001). Moreover, a learner-centered approach is more likely to result in behavior change, compared to teacher-centered Extension delivery methods (Cooley, 1994).

Today, in the Food Stamp Nutrition Education Program (FSNE), nutrition educators are also discovering that their most effective teaching techniques are "learner-centered." Because individual instruction, compared to group instruction, appears to be more effective in changing nutrition behavior (Dickin, Dollahite, & Habicht, 2005), using a learner-centered approach may be a way to improve outcomes in group educational settings. In 2004, FSNE provided regional and statewide training events in California to encourage Cooperative Extension paraprofessional in that state to use more learner-centered techniques (Figure 1).

Figure 1.
Learner-Centered Teaching Techniques (Norris, 2003)

In an assessment conducted prior to these training events, paraprofessionals expressed some uncertainty and even concern that learner-centered education would divert their classes away from appropriate nutrition topics. After the nutrition educators shared ideas and honed their teaching skills during the regional and statewide training in spring and summer of 2004, many of these concerns may have been alleviated. We conducted the study reported here to find out how FSNE educators are implementing learner-centered education in their classrooms and the barriers they encounter.

Methods

The UC Davis Institutional Review Board exempted the protocol for this project from full Human Subjects review. We sent an 18-item survey via e-mail to all of the FSNE educators (n=67) in California in October 2004. A reminder and second e-mail were sent in December. A total of 46 people (69% of all surveyed) responded, but two were eliminated because they were not front-line educators in the program.

Of the 44 eligible respondents, 48% work with youth, 44% work with adults, and the remainder work with both groups. At the time of the survey, all had attended at least one FSNE training on learner-centered education (35%-statewide only; 18%-regional only; and 47% both statewide and regional training). The mean years of employment in FSNE was 5.5 + 5.6 years.

For each of 12 learner-centered techniques (shown in Figure 2), the respondents self-reported how well that technique was working in their classrooms (3= very well; 2=okay; 1=not very well; 0=have not used). An overall implementation score was calculated by summing the responses across the 12 items, yielding a possible range of 0-36. Two open-ended questions were also included to determine how learners' needs were assessed and what specific challenges were encountered in implementing learner-centered techniques.

Figure 2.
Twelve Survey Items Asking, "How Well Are the Following Learner-Centered Techniques Working for You in Your FSNE Classes?"

To delve more into both the successes and challenges of learner-centered education, we conducted two focus groups in 2005 with FSNE educators--one in Riverside and the other in Contra Costa County in California. Of the 15 nutrition educators who attended, six worked with the youth program; seven worked with the adult program, and two worked with both programs. Average length of time employed in FSNE was 5.8 years. The sessions were audio- and videotaped and transcribed verbatim for analysis of emerging themes.

Results

Results of the e-mail survey indicated that most FSNE educators felt that they were doing "okay" or "very well" in implementing different learner-centered techniques. Because we were unable to determine the validity of the survey instrument, we cannot tell to what extent respondents either over- or underestimated their implementation success. However, the techniques used less successfully included establishing ground rules; using icebreakers; using partner activities; and setting goals.

Total average implementation score for this sample was 30.3 + 4.1 (median: 30). Using analysis of variance, we did not find that degree of success in implementation varied by program (i.e., youth, adult, both--data not shown). There was a nonsignificant tendency for employees who have worked for FSNE longer to have lower implementation scores (r= -0.16, NS). A more detailed, validated instrument might be needed to determine whether longer-term employees are having more difficulty adopting new teaching techniques, compared to recently hired staff. In response to the open-ended questions, about one-third reported challenges in dealing with inadequate space and/or time constraints. Some of the following quotes capture the problems in these two areas:

• "I struggle with group activities because it is impossible in a soup kitchen setting."


• "Requires more prep time and transport of props."


• "Time is always a factor. Often participants are late or agencies do not prepare students for the teaching event (sometimes students are not aware I am coming)."


• "Classes have gone overtime. This method really opens up trust and communication between participants and me. They all seem to want to participate on a more personal level."

Some of the challenges also involve group management skills:

• "Partner and small group activities are hard to control."


• "Many open-ended questions take us off topic or skip ahead and I find it difficult to get back on track. I find it difficult to use a variety of teaching methods."


• "I frequently work with groups where many people speak different languages in the same workshop. Working with mixed languages (English-Spanish) and having them interact with each other [is a challenge]. Translating and using time wisely in that activity is a challenge."

Participants in both focus groups expressed the viewpoint that nutrition educators have a lot of advanced preparation to do and may need to be more assertive with schools and agencies to achieve results from learner-centered education. Many comments reflect that educators have tried some new techniques introduced in the FSNE regional and statewide workshops and found them to be successful (Table 1). However, not all innovations have worked in every situation. For example, some participants, particularly those who are in homeless shelters or domestic violence situations, may not want to make eye contact or engage in group discussions. Educators proposed a few ways to reach out to these participants in their classes:

• "Bring colored paper and pens to give them something else creative to do."


• "A food demonstration is a good way to get people together, assign them each a different task."


• "Try to talk with them at break time."

One of the key points that educators emphasized for success in working with teachers and other adults is to provide information that is immediately useful. With teachers, that might be as simple as providing a single lesson plan at an outreach meeting. A quote from one of the FSNE educators in the focus groups illustrates the importance of learner-centered education:

There was this gal in one of my programs. It was a one time thing and I thought I'd never see her again. She wanted a list of food pantries in the area. I told her, 'Make sure I have your address on the list and I'll send it to you.' I went back to my office…something told me she needed that list now. I made a copy and put it in the mail. A year later, I saw her in a residential treatment program. She asked if I remembered her…She said, 'that day you came and fed me… I hadn't eaten in 4 days. You sent me that list and it kept me from starving.' Now, if that isn't a success and makes this job worthwhile, nothing will.

Conclusions

Learner-centered education requires advanced preparation to implement successfully. In working with low-income, ethnically diverse communities, extension educators often face challenges in using learner-centered approaches and may need to be more assertive with agencies in arranging space and assessing participant needs to achieve results. Staff training, at the statewide and regional level, should focus on ways to overcome these and other challenges and extend the learner-centered approach to all programs. At statewide events, we have used poster sessions and small round table discussions to facilitate sharing and problem-solving on specific challenges. In smaller regional trainings, the educators have presented segments of their lessons and receive group feedback from the group on their techniques. For new staff and those teaching classes in isolated areas, we are developing a training kit, including video segments of best practices in actual classrooms and tools (such as example open questions and ice breakers) that were requested by the educators in our focus groups.

We have found that our FSNE paraprofessional staff are creative, enthusiastic, and sensitive to the needs of others. Thus, our best trainers are our educators themselves. Extension specialists and educators across the nation should be encouraged to continue sharing their experiences, evaluation instruments, and training ideas related to learner-centered education so that together we can improve our nutrition programs targeting low-income audiences.

References

Cooley, F. E. (1994). Facilitating conflict-laden issues: an important extension faculty role. Journal of Extension [On-line]. 32 (1). Available at: http://www.joe.org/joe/1994june/a10.html

Dickin, K. L., Dollahite, J. S., & Habicht, J. P. (2005). Nutrition behavior change among EFNEP participants is higher at sites that are well managed and whose front-line educators value the program. Journal of Nutrition, 135, 2199-2205.

Henson, K. T. (2003). Foundations for learner-centered education: a knowledge base. Education, 124, 5-16.

Norris, J. A. (2003). From telling to teaching: a dialogue approach to adult learning. North Myrtle Beach, SC: Learning by Dialogue.

Simeral, K. D, & Hogan, M. P. (2001). Everyone a teacher, everyone a learner: a learner-centered pesticide private applicators recertification training. Journal of Extension [On-line]. 39 (3). Available at: http://www.joe.org/joe/2001june/iw1.html

Stakeholders' Input on 4-H Science and Technology Program Areas: An Exploratory Study

Bradley S. Barker
Extension 4-H Youth Development Specialist/Assistant Professor
4-H Youth Development
University of Nebraska-Lincoln
Lincoln, Nebraska
bbarker@unl.edu

Introduction

The Internet, digitalization, and high-speed data networks have spawned the 3rd wave of globalization, the so-called globally integrated knowledge economy (Engardio, Bernstein, & Kripalani, 2003). The integrated knowledge economy permits high technology jobs like computer programming, engineering, and even corporate accounting to be outsourced to professionals in other countries for a fraction of the costs.

The new global integrated knowledge economy is a boom for developing nations as high technology jobs are being shifted from the United States and Europe to countries like China and India. The outsourcing of these jobs has been a wake-up call to the educational system in the United States.

Our youth need more than domain knowledge in subject areas; they need job skills that will allow them to compete in the integrated knowledge economy. To compete, youth need skills such as information and communication skills, thinking and problem-solving skills, and interpersonal and self-directed skills, such as the ability to retrain and lifelong learning. Combined, these skills are referred to as the "21st century job skills" (Partnership for 21st Century Skills, 2003). Nationally, 4-H has emphasized curriculum development in the areas of science and technology as a way to prepare youth for the 21st century workplace (The National 4-H Strategic Directions Team, 2001).

While 4-H develops projects to help youth develop 21st century workplace skills, it is important to receive input from stakeholders. The determination of priorities within the science and technology program areas by stakeholders, in this case, 4-H families, is important because it ties the program priorities back to what is needed by the community (Kelsey & Mariger, 2003).

Purpose

The purpose of the exploratory study reported here was to collect input from 4-H families pertaining to science and technology programs areas. As a follow-up to self-reported questions concerning the importance of science and technology development areas, a narrative type open-ended question was used at the end of a mail survey to elicit ideas and reactions from respondents.

Procedure

Population

A random sample of 1,414 families out of a total population of 13,516 Nebraska 4-H families with club members was selected from the 2004 4-H Plus database. Randomly selected families were sent the paper-based survey via US mail with a pre-paid return envelope. To inform selected families of the survey, a postcard was mailed approximately 2 weeks before the survey was mailed. Follow-up postcards were sent after 2, 4, and six weeks to participants who had not returned the survey. There were 498 surveys returned for a response rate of 35.2%. Of the 498 surveys, 56 respondents provided qualitative data.

Instrument

A survey was developed based on the U.S. Census Bureau's Computer and Internet Use in the United States: 2003 survey instrument. The survey consisted of 19 questions concerning technology 4-H families have in their home and how they utilize the technology. Questions 1 through 14 of the survey examined technology 4-H families currently have in their homes. Results for questions 1 through 14 will be reported in a subsequent article.

Questions 15 through 18 examined possible development areas in science and technology, while question 19 was an open-ended question asking respondents for specific program ideas. Using a Likert-type scale where 1 = not a priority and a 4 = high priority, questions 15 and 17 asked respondents to rank the priority of possible science and technology program areas. The possible science and technology program areas listed were meant to represent the broadest array of science and technology disciplines. For example, question 15 asked respondents about possible technology areas such as basic computing, Web site development and robotics (Table 1).