Research In Brief

Conflict-Laden Issues: A Learning Opportunity

Mary K. Corp
Assistant Professor, Department of Crop and Soil Science
Oregon State University Extension Service
Pendleton, Oregon
Internet Addresses: mary.corp@orst.edu

Tom Darnell
Professor, Department of Horticulture
Oregon State University Extension Service
Milton Freewater, Oregon
Internet Address: thomas.darnell@orst.edu

Introduction

Increasingly complex and controversial issues are challenging Extension faculty, agriculturists, and support industries/agencies. One issue is the development of a more diverse agricultural base in areas that have a long history of limited cropping diversity. We present preliminary results of conflict resolution efforts related to the development of vineyards in wheat country. The following sections provide a background to the issue, the process used to address it, and some observations about what is working.

Conflict-laden issues are likely to increase as agriculture focuses less on commodity-type crops and moves towards higher value specialty crops and products. We believe additional training in facilitation and group process will position Extension faculty to create learning environments in which conflicts can be successfully resolved.

Background

Growers introducing newer, higher value crops may have conflicts with growers of traditional crops. In the Walla Walla River Valley, located in southeastern Washington and northeastern Oregon, conflicts between wine grape growers and dry-land wheat farmers are increasing. Historically, the valley's irrigated crops have been treefruits, alfalfa, alfalfa seed, row crops, pasture, green peas, wheat, and miscellaneous crops surrounded by the vast dryland wheat-producing region of the Columbia Plateau. Crop diversity in the valley has increased with the introduction of over 1,100 acres of wine grape plantings. Conflict stems from the sensitivity of grapes to synthetic auxin-type herbicides used on cereal crops, pastures, right-of-ways, and rangeland for weed control throughout the valley and surrounding area.

The use of agricultural herbicides and various application methods, such as ground and aerial applications, has been practiced for decades in ways that are usually acceptable for the traditional mix of crops. However, the neighboring farmer with the new "valuable" and sensitive crop can view the same practices as controversial and irresponsible. Auxin-type herbicides can move off-target by physical drift and/or by volatizing and moving off target with air currents. Research has shown measurable damage to vineyards due to reduced foliage development and fruit set from auxin-type herbicides.

Conflict is compounded by the fact that adjacent producers —the wheat farmer and the wine grape grower—may not know each other and see the other as impeding their right to earn a living. Furthermore, one party has a long history in the community and is simply following traditional practices; the other party may be a newcomer expecting others to change their farming practices. The underlying cultures—the values, beliefs and norms—are sufficiently dissimilar to create distrust. The support industries of traditional producers and wineries are sufficiently different that they do not build connections.

In this type of conflict-laden situation, all individuals involved have self-interests and are struggling to apply what they know to influence a favorable outcome. It is a situation rich with opportunities for learning and behavioral change(s) that could result in improvements for the individual and the community (Cooley, 1994). While conflict is a difficult and controversial topic in our culture, it is the lack of understanding of the conflict process, as well as a lack of training in ways of handling interpersonal confrontations effectively, that needs to be addressed. (Whetten, 1991.)

Collaborative Process Utilized

Whetten (1991) proposes that people's responses to interpersonal confrontations tend to fall into five categories:

• Forcing,
• Accommodating,
• Avoiding,
• Compromising, and
• Collaborating (Figure 1).

Collaboration is the only response that attempts to fully address the concerns of all parties involved and is an appropriate strategy for Extension to support. We developed a process using collaboration with the goal to achieve resolution of the important concerns and to achieve quality outcomes.

Figure 1.
Two-Dimensional Model of Conflict Behavior

The process was designed to engage participants so that they felt they had an active role and shared control. County-based Extension faculty facilitated the process, acting in the dual role of mediator/coach. Extension faculty facilitators were impartial although they had worked with some participants far more than they had worked with others. Major steps in the process were:

• Set rules of conduct,
• Identify issues,
• Develop a coordinated action plan to address issues,
• Implement the plan, and
• Evaluate the outcomes.

While these steps appear linear, there was opportunity for feedback between the steps.

Our first step in the collaborative process was to organize a meeting of the essential players. The meeting included vineyard representatives, traditional producers from the area, commercial pesticide applicators, and crop consultants. The 19 participants were selected so that diverse and often outspoken voices could be heard. We wanted all critical voices in the meeting, not on the outside looking in.

The first meeting was a half-day session at a local community center. The setting chosen for the meeting was an informal circle where all participants could easily see and hear each other. Participants were given nametags so they could easily call each other by name.

At the start of the meeting, participants introduced themselves and explained their connection to the issue. Participants then established rules-of-conduct for the meeting and overall process. Participants' rules were:

• No side conversations,
• Each person would listen without interruptions and
• Participate in good faith to resolve differences between the groups.

These rules were adopted and followed.

The next step was the identification of the issues. The facilitators were successful in keeping the discussion focused on the issues instead of personalities, a critical strategy recommended by Fisher (1991). To ensure that one side did not dominate the discussion, the Extension facilitators directed questions to different participants, asked follow-up questions for clarification, maintained eye contact with the speakers, and gently kept the discussion on track.

As part of this step, the facilitators guided the group in exploring the "why's" behind the conflicts. As participants increased their understanding of the "why's," they were able to see commonalties between their goals and values. The participants experienced a moderation of attitudes as expected due to exposure of contrasting viewpoints. (Goodwin, 1993.)

It was during this stage that traditional producers came to appreciate the substantial financial commitments required by the wine grape grower to produce grapes and the need for the highest quality standards. They also recognized that herbicide drift from traditional crops onto vineyards created a serious threat to the economic success of the grape grower and winery owner(s). Similarly, vineyard managers became more familiar with the challenges faced by the traditional producer, who has narrow "windows" for herbicide applications, a limited array of herbicide alternatives, and narrow profit margins.

Major issues identified were:

• The window of greatest susceptibility to herbicide damage in grapes is during the canopy development to fruit set period (May-June).
  
  
• The period of greatest concern is the month of May because auxin-type herbicides are applied on spring grains, pastures, range, and roadsides.
  
  
• Most of the wheat acreage is planted in the winter, and herbicides are applied in late winter and early spring. This is before the period when grapes are the most susceptible.
  
  
• The Walla Walla River Valley has natural physical features that create inversion conditions that could bring suspended, vaporized herbicides in from upper canyons and a related web of side canyons.
  
  
• Vineyards are likely experiencing drift attributable to a combination of direct particle drift and vapor drift.
  
  
• Various types of applications could be contributing to the problem, including aerial, pull-type ground sprayers, and roadside applicators.

The facilitation team then guided the participants through the process of developing a coordinated action plan to manage the conflict for the 2000 growing season. This plan was developed at two early spring meetings. The plan included:

• Increase awareness through educational presentations and targeted mailings.
• Improve awareness of vineyard locations in Walla Walla Valley.
• Investigate the feasibility of weather forecasting for the valley and surrounding areas.
• Increase communication among growers, applicators, and consultants.
• Post informational publications on the Umatilla County Dryland Cropping Web page for easy access.

The plan continues to be added to and modified as needs are identified. A statement of intent was developed by the group that states: "It is our intention to work together to resolve our local issues without outsider intervention (e.g., government regulators, lawyers, lawsuits, or the press)."

The participants and Extension faculty compiled an email list to keep participants informed about the plan, issues, and concerns. A fact sheet was developed and distributed to help educate traditional producers about grapes and their level of sensitivity at different periods of growth. A vineyard location map was compiled and made available. Educational presentations were made at grower meetings. Articles about the safe use of herbicides were included in Extension newsletters. Extension facilitated a summer vineyard tour to bring participants together to assess mid-season impacts of the plan.

An evaluation program to assess the impact of the plan has begun and will continue. A mid-summer survey was sent to all task force participants asking for their opinions on the conflict resolution. The response rate was 26%. Forty percent of the respondents were vineyard owners, and 60% represented traditional growers and applicators.

Figure 2.
Focus Group Rates Initial Impacts of Efforts

All respondents believed that communication had improved between the two groups. Vineyard owners (+1.38) rated the improvement slightly better than the traditional respondents (+1.17) did.

The survey asked if growers and chemical applicators changed their practices because of the increased awareness. The overall response was that changes had been made. The traditional respondents (+1.83) believed the changes to be greater than the vineyard owners (+1.50) did.

Findings

Our observation of the process supports five "findings."

Participants with substantially different views and concerns were able to come together. Each group was able to develop their ability to deal with differences. The process helped participants find a balance between reason and emotion in sorting out differences. Increased understanding and open communication helped to reduce suspicion between the two groups. Both groups used persuasion instead of forcing while working toward resolution of the conflict.

Participants bought into the process. Starting with this step, participants were able to set their own ground rules for the discussion. With participants "in control," Extension faculty were permitted to step back and guide the process, making suggestions only when needed and/or requested. Participants identified the issues and then fashioned the plan and its implementation. Once the plan was completed, participants were motivated to implement the plan and to work together to successfully resolve the issue. Participants are involved in evaluating the program.

Participants learned about solving conflicts. The two groups now have a new on-going process for addressing issues and resolving conflicts. Future conflicts will likely benefit from the skills learned.

Extension faculty is in a unique situation to help address these conflict-laden situations. Not only do faculty typically know people from "both sides," but they have a great deal of technical knowledge that can be inserted into the process. For example, although the participants identified the issues, they often drew on Extension faculty and Extension Service information or its networks of contacts to explore solutions.

Extension faculty who are trained in a technical specialty, such as crop science or horticulture, may not be prepared to manage the process. Training in facilitation is very valuable in making the process work smoothly. In our case, one faculty member's background in business and non-profit management served well in designing the overall process. Many others, with additional training, would better understand the science behind the process and recognize the learning potential that conflict-laden situations provide. Fiske suggested this 10 years ago, and the need still remains.

In summary, conflict-laden problems are becoming more common in agriculture and the world in which Extension faculty work. The process to solve these problems need not be complex, but rather can be a set of simple steps that engages a key group of participants in setting their own ground rules, defining the issues and creating, and implementing a plan of action. As communities gain the capacity to address issues, they are more capable of helping themselves to achieve their own goals. Extension faculty can be a key player in developing this capacity.

The process in the Walla Walla River Valley is not complete, but taskforce members have developed skills that will allow them to work toward resolving issues as they arise.

References

Whetten, D. A., & Cameron, K. S. (1991). Developing management skills. New York: HarperCollins Publishers Inc.

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

Fisher, R., & Brown, S. (1988). Getting together, building a relationship that gets to yes. Boston: Houghton Mifflin Company.

Fisher, R., Ury, W., & Patton, B. (1991). Getting to yes, negotiation agreement without giving in. (2^nd ed.). New York: Houghton Mifflin Company.

Goodwin, J. (1993). Contrasting viewpoints about controversial issues. Journal of Extension [On-line] 31(3). Available at: http://www.joe.org/joe/1993fall/a7.html

Fiske, Emmett (1991). Controversial issues as opportunities. Journal of Extension [On-line] 29(3). Available at: http://www.joe.org/joe/1991fall/a8.html

Measuring and Benchmarking Customer Satisfaction: Implications for Organizational and Stakeholder Accountability

Rama Radhakrishna
Associate Professor, Agricultural and Extension Education
The Pennsylvania State University
University Park, Pennsylvania
Internet Address: brr100@psu.edu

Background

Cooperative Extension, like many public agencies, has seen an increased emphasis on measuring quality of programs through customer satisfaction surveys (CSS). Customer satisfaction is becoming an important part of the culture of many organizations (Ladewig, 1997). Customer satisfaction provides for better understanding of services provided by Extension from the customers' perspective. In addition, it provides for better understanding of expectations of customers and the extent to which an organization is satisfying the needs and wants of its customers (Biggs, Gordan, & Zimmerman, 1995).

Customer satisfaction also serves as a link between the customers and a performance reward system, thus providing rewards for desired outcomes and identifying consequences for undesired outcomes (Cummings & Ladewig, 1998). According to Merritt (1998), customer satisfaction is an indicator of the quality of Extension programs and is critical for evaluation of the effectiveness of the organization.

Israel (1998) indicated that customer satisfaction surveys provide a number of benefits for Extension. First, they tell us what differences our programs are making in the communities. Second, they serve as a mechanism to show our supporters and critics that our customers have a high level of satisfaction and they are using Extension information. Third, they help identify strengths and weaknesses of Extension programs so that continuous improvements can be made. Finally, they help to showcase successful programs in the annual report of accomplishment.

In recent years, several states have conducted Customer Satisfaction Surveys (CSS) to assess quality of Extension programs. Prominent among the states were Florida, Kentucky, and Texas. Consensus from these studies indicated that Extension clientele are very satisfied with the delivery and content of the information provided.

Provisions of the 1993 Government Performance Results Act (GPRA) included the use of customer satisfaction as a key component of performance measurement. The South Carolina Budget Control Board (SCBC), in 1998, recommended that all public agencies in South Carolina periodically measure customers' satisfaction through obtaining information from the customers they serve. Berrio and Henderson (1998) suggested that Extension organizations should conduct customer satisfaction surveys as an effort to assess the performance of the services from the customer perspective.

Purpose and Objectives

In the summer and fall of 1999, Clemson Cooperative Extension Service conducted a Customer Satisfaction Survey to determine what recipients of Extension programs felt about the quality of services provided. Specifically, the study was designed to assess the:

1. Relevance and quality of programs delivered by Clemson Extension Service,
2. Extent to which clients have used and shared the information, and
3. Satisfaction with the services provided by Clemson Extension Service.

In addition, an attempt was made to benchmark the Clemson CSS study with other states (Florida and Texas) that have conducted similar customer satisfaction studies.

CSS Process and Procedures

In the following paragraphs, an overview of Clemson Extension's programs and client contact information for year 1999-2000 is described. In addition, the process and procedures involved in conducting the study are also described.

Clemson University Cooperative Extension Service offers educational programs in five different areas: agrisystems productivity and profitability, community and economic development, environmental conservation, food safety and nutrition, and youth development. These five areas mirror the Government Performance Results Act (1993) goals developed by USDA-CSREES.

Extension agents in the counties conduct a variety of activities and educational programs to create awareness, knowledge, skills, and behaviors to bring about desired changes in the clientele they serve. Extension agents provide research-based information to the people to help them make informed decisions about problems that they face.

Extension staff contacted 732,126 people in 1999-2000. Figure 1 shows that 40.1% of these contacts were White males, followed by 31.4% White females, 12.7% African-American males, 14.9% African-American females, and 1.0% others. Extension agents also conducted over 19,800 educational programs and activities in 1999-2000. Activities and programs included meetings, workshops, demonstrations, field visits, hands-on experiences, and other activities.

Figure 1.
Total Contacts by Race and Gender, 1999-2000

Each of the 46 counties in the state was asked to develop a list of customers who participated in county Extension programs. Counties were advised to use lists such as program registration, sigh-up sheets, office visits, attendance at field days, and demonstrations, etc. Once the list of customers was developed, a team of Extension personnel (agents, county Extension directors, and administrative assistants) randomly selected 30A total of 1,380 customers were identified as subjects for the study. However, only 42 of the 46 counties agreed to participate in the study, resulting in 1,260 subjects.

A customer satisfaction questionnaire similar to the one developed by the University of Florida was modified and used for the study. The questionnaire contained 14 questions that were grouped into:

1. Type of contact,
2. Extension information use,
3. Satisfaction with Extension Service, and
4. Demographic information (age, gender, race, education level, occupation, and number of years using Extension Service for information).

In addition, the questionnaire also recorded information on client address and telephone number, and date of data collection. The questionnaire was evaluated for content validity by a panel of three experts consisting of Extension specialists and agents.

Prior to distributing the questionnaires to the counties, the entire process of data collection was shared with the 14 cluster directors. A detailed explanation on selecting respondents, data collection process, and other logistics was presented to the cluster directors. After addressing several questions and other issues concerning the study and review of the questions, cluster directors agreed to collect data. Counties collected data in summer and fall of 1999. A total of 1,068 completed questionnaires were returned (84.7%).

Counties were asked to send the completed questionnaires to the Extension Staff Development office for analysis. Completed questionnaire sent by counties were checked, coded, and entered into a database. SPSS program for Windows was used to analyze the data. Frequencies, means, and percentages were used to summarize the data.

Because the sampling was not scientific, two demographic variables (gender and race) were compared with Clemson Extension Service contacts data (through CUMIS) and South Carolina population estimates (1998). Both gender and race variables were representative of the state's population (Table 1).

Table 1.
Comparison of CSS Sample with CUMIS Contact Data and South Carolina Population Estimates

Findings

Demographic Information

As indicated earlier, a total of 1,068 completed surveys were included in the analysis. The demographic information revealed that 32% of the respondents lived on a farm, 30% in a rural area, 33% in towns and cities under 50,000 population, while 5% lived in cities with a population of over 50,000. Fifty-four percent of those responding were female and 75% white Caucasian, 24% black, and 1% other (Hispanic, Asian). Regarding age, 14% were under the age of 35 years, 43% between ages 36-45, 22% between 56-65, and the remaining 21% were over 66 years. Examination of education level of respondents revealed that 23% had at least a high school education or GED, 48% had completed college, 20% had completed post-graduation degrees. Only 9% reported less than a high school education. On an average, respondents have been using Extension Service for information for over 16 years.

Relevance and Quality of Extension Information:

As shown in Figure 2, customers who participated in Clemson Extension programs or visited the Extension office for information were very impressed by the quality of information they received relative to food safety, water quality, crops, financial management, community development, youth development, and others. Ninety-seven percent said that the information presented was up-to-date and accurate, useful, and easy to understand. Ninety-six percent found it to be relevant to their situation.

Figure 2
Relevance and Quality of Extension Programs/Information

Extension Information Use

Eighty-two percent of the respondents indicated that they have used the information (Figure 3). Seventy-six percent of those who had used the information said that it had solved their problems or answered the questions. However, 21% said "don't know" because they are still in the process of using the information they had received from participating in Extension programs.

Figure 3.
Extension Information Use

This is what those who used the information said:

• "Followed directions, continue to do so and fire ant mounds are non-existent in two acres."
  
  
• "I have used the information to determine sodium contents of foods since I need to watch sodium in my diet."
  
  
• "Recently had a Will made because of the program, and the information helped out a great deal."
  
  
• "Information helped to decide what variety of corn is suitable for our farm from Extension's 'Corn Letter'—same for vegetables. Also, soil sample results are a great help each year."
  
  
• "Information referring to horticultural practices and pesticide management very helpful."
  
  
• "I started being more careful about shopping and reading labels, and putting food away when I have finished cooking."
  
  
• "I was interested in public speaking and the 4-H office gave me a course. I later won the 4-H State Public Speaking Contest."

Seventy-nine percent of the participants said that they have shared the information with their friends and neighbors.

Figure 4.
Shared Information with Someone

In response to the question, "How do you feel about the services provided by Clemson Extension?" 78% (N=834) said that they were "very satisfied" with the service, 20% said they were "satisfied," less than 1% said they were "dissatisfied," and 1% of the respondents had "no opinion." (Figure 5)

Figure 5.
Satisfaction with Clemson Extension Service

According to David Kearns, CEO of Xerox Company, benchmark is the continuous process of measuring products, services, and practices against the toughest competitors or those recognized as industry leaders. Ladewig (1998) suggested that Cooperative Extension Service develop benchmarks on three major aspects of Extension: programsÑrelevance, quality, and accomplishments.

In this study, we have made an attempt to benchmark "quality" of Extension programs by measuring and comparing customer satisfaction. Two Extension organizations in the Southern Region (University of Florida Extension Service and Texas A & M Extension Service) were used as benchmarks to compare with Clemson CSS.

As shown in Table 2, data from the three organizations reveal somewhat similar findings. Overall, customers, regardless of the state or the type of Extension program they attended, are very satisfied with the service provided by Extension. Satisfaction was very high in all the three states, with Texas A & M having the highest (82%), followed by University of Florida (80%), and Clemson (78%). Extension information in terms of accuracy, usefulness, ease of understanding, and being up-to-date was high for Clemson Extension, followed by University of Florida and Texas A & M. A higher percentage of Clemson customers had the opportunity to use and share the information when compared to University of Florida customers.

Table 2.
Benchmarking of Clemson CSS Study with University of Florida and Texas A & M

Conclusions and Discussion

Extension customers are very satisfied with the information they received from Clemson Extension offices in the counties. They indicated that the information was accurate, up-to-date, and relevant to their situations. In addition, customers indicated that they made use of the information to solve the problem or answer a question regarding an issue or a concern. This positive use of information can be linked to not only knowledge and skills acquisition, but also inclination toward adopting a recommended practice.

Customers also indicated that they shared the information with their friends and neighbors, indicating the confidence and trust they have for Extension programs and the information received. The findings of this study are more or less similar to the studies conducted by University of Florida and Texas A & M University.

The feedback from this survey has helped agents to continue to improve delivery of Extension programs. Data from the customer satisfaction study has provided a wealth of information for agents on how to improve programs and meet the needs of the clientele they serve. Cluster directors have used data from customer satisfaction surveys for justification of county budgets and to show stakeholders that Extension programs do make a difference.

Furthermore, the successful completion of the customer satisfaction survey demonstrated to the legislators that Cooperative Extension is willing to ask customers how it is doing, and then act on their suggestions for improvement. And Clemson Extension Service has demonstrated that it can fulfill the requirements and/or mandates of the South Carolina Budget Control Board. Finally, the results of benchmarking have provided a basis for comparing Clemson Extension with its counterparts relative to program performance and accountability.

The process used in conducting CSS could have immense value to other states and Extension professionals interested in assessing customer satisfaction. States could modify the questions and/or the process to suit their needs. In addition, the states can also decide on the best way of identifying customers for accurate assessment.

Extension administrators and program evaluation specialists in each state or Extension region should come together and develop a "Generic Customer Satisfaction Survey" and a process suitable to all states and regions. However, the proposed survey should also consider the needs of individual states and Extension regions. Such an effort will allow for comparison of efforts and sharing of resources.

References

Berrio, A. A., & Henderson, J. L. (1998). Assessing customer orientation in public non-profit organizations: A Profile of Ohio State University Extension. Journal of Agricultural Education, 29(4), 11-17.

Biggs, E., Gordan, J., & Zimmerman, S. (1995). How to plan and conduct a customer satisfaction survey. MERIT Seminar Series: Praia Group.

Cummings, S. R., (1998). Measuring customer satisfaction: How to get the most of extension programs. Unpublished manuscript, Department of Agricultural Education, Texas A & M University, College Station, TX.

Israel, G.D. (1998). Conducting a customer satisfaction survey. Florida Cooperative Extension Service, Fact Sheet PDE-98-05, University of Florida.

Israel, G. D., & Fugate, A. M. (1998). Extension helps residents; customer satisfaction high. Florida Cooperative Extension Service, Fact Sheet PDE-98-06, University of Florida.

Ladewig, H. (1998). Organizational accountability and program evaluation in the Cooperative Extension System. Unpublished manuscript, Department of Agricultural Education, Texas A & M University, College Station, TX.

Ladewig, H. (1997). Demonstrating accountability through collaboration and partnerships. Paper presented to the Joint Southern Region Program Committee Meeting, Tallahassee, Florida.

Merritt, H. (1998). Getting started with performance measurement. Paper presented at the South Carolina State Government Quality Network Association Conference, Columbia, SC.

South Carolina State Government Quality Network Association (1998). Changing the face of accountability: Performance measurement in government. South Carolina Budget Control Board, Columbia, SC.

South Carolina State Government Quality Network Association (1998). Measurement Development Guide. South Carolina Budget Control Board, Columbia, SC.

Factors Affecting the Location of Precision Farming Technology Adoption in Tennessee

Roland K. Roberts
Professor
Internet Address: rrobert3@utk.edu

Burton C. English
Professor
Internet Address: benglish@utk.edu

James A. Larson
Associate Professor
Internet Address: jlarson2@utk.edu

The University of Tennessee
Knoxville, Tennessee

Introduction

Precision farming uses information about the differences in soil and other characteristics within a farm field to make management choices. It often uses computers and other digital technologies to aid in decision making and applying crop inputs such as seed, fertilizer, lime, and chemicals more accurately (Khanna, Epouhe, & Hornbaker, 1999; Swinton & Lowenberg-DeBoer, 1998). More precise placement of inputs with precision farming may increase farm profits and reduce adverse environmental consequences of crop production (Watkins, Lu, & Huang, 1998). However, the key to farmer adoption of site-specific farming is the profitability of the technology (Roberts, English, & Mahajanashetti, 2000).

Available information on where precision farming practices have been adopted is primarily for a few higher value crops and for several crops grown in the Midwestern United States (Khanna, Epouhe, & Hornbaker, 1999). Little information currently exists on where precision farming practices have been adopted in the southern United States.

A March 1999 survey of Tennessee Agricultural Extension Agents helped overcome this lack of information for Tennessee. That survey identified 284 producers using at least one precision farming technology in 38 of Tennessee's 95 counties (English, Roberts, & Sleigh, 2000). An April-May 1999 companion survey found that firms providing precision farming services to Tennessee farmers expected the demand for their services to grow rapidly over the next 5 years (Roberts, English, & Sleigh, 2000).

A focus group of 18 Tennessee farmers who had adopted at least one precision farming technology was conducted in July 1999. One purpose of the meeting was to improve understanding of how Tennessee farmers might benefit from using these technologies and how the University of Tennessee can help them make better decisions concerning these technologies. The discussion indicated that farmers would benefit from:

1. An upgraded, well trained, and informed agricultural workforce capable of calibrating, operating, and maintaining their equipment,
2. Well-trained and -informed agribusiness personnel capable of calibrating, operating, and maintaining their firms' equipment and giving accurate technical advice,
3. Standardized equipment and software,
4. Regular training for agricultural laborers, agribusiness personnel, and owner-operators in the use of these technologies, and
5. Digitized soils maps available on the Internet for farmers to download and overlay on other field maps.

With the anticipated growth in demand for precision farming technologies, the question of interest is, "Where in Tennessee should we focus our scarce resources?" For example, which counties should receive priority in creating downloadable soil maps and in receiving precision farming training programs? The objective of the research reported here was to identify factors that influence the geographic location of precision farming technology adoption in Tennessee and, given those factors, estimate the probabilities of precision farming technology adoption for Tennessee's 95 counties. Estimation of these probabilities could help establish county priorities for precision farming programs.

Methods

Other studies have identified farmer characteristics associated with adoption (Daberkow & McBride, 1998; Khanna, 2001). In this study, however, the focus was on where precision farming technologies have been used and how location characteristics influence the probabilities of adoption among counties. Data from the simple March 1999 survey of Agricultural Extension Agents and the Census of Agriculture were used to analyze the effects of location characteristics on the likelihood of precision farming technologies being adopted by farmers in Tennessee's counties.

A farmer's decision to invest in precision farming technology is related to its potential to earn the farmer a profit. Assume the probability of precision farming technology adoption by farmers depends on location characteristics that affect crop-farm profitability according to a cumulative logistics probability function (Pindyck & Rubinfeld, 1998, pp. 307-308). The impacts of the location characteristics on the probability of adoption can be estimated from a Logit regression model (Pindyck & Rubinfeld, 1998, pp. 309-317). The probability of adoption for a county can then be estimated using those estimated impacts along with the location characteristics of the particular county (Pindyck & Rubinfeld, 1998, pp. 309-317).

Five Logit regression models were estimated. Each model had a dependent variable with a value of 1 for Tennessee counties with at least one farmer using a yield monitor with GPS (Global Positioning Systems), a yield monitor without GPS, grid soil sampling, variable rate fertilizer or lime application, or any precision farming technology, and a value of 0 for counties with no farmers using the respective technologies (Table 1). Data required to form the dependent variables were obtained from the aforementioned survey of Tennessee Agricultural Extension Agents. Two weeks prior to the telephone survey, Extension Agents were informed by mail about the questions that would be asked and about Extension Administration support for the survey. The response rate was 100%.

Data for the explanatory variables were obtained from the 1997 Census of Agriculture (U.S. Department of Agriculture, 1999). These location characteristics are reported in Table 1. Gibson and Knox Counties were chosen as examples to illustrate the extremes of precision farming technology adoption in Tennessee. Gibson County had several farmers using most of the technologies analyzed, while Knox County had no farmers using these technologies.

Table 1.
Variable Definitions and Other Information for Tennessee and Gibson and Knox Counties

Six location variables were included in the Logit models to capture resource differences among counties and, hence, the relative potential for farmers to earn higher profits from adopting precision farming technology (Table 1). Percentage of County Land in Farms attempted to capture the general importance of agriculture within a county and was hypothesized to positively influence the likelihood of adoption. Total Cropland was hypothesized to positively influence the odds of adoption within a county, while Livestock and Poultry Sales were hypothesized to negatively impact adoption. Percentage of Farmland in Crops was hypothesized to positively influence adoption. Percentage of Farmland in Large Farms was hypothesized to positively impact adoption because larger farmers are more likely to have the resources to cost effectively use these technologies and are more likely to be in a position to bear the risk. Finally, Value of Crop Sales/Acre was hypothesized to positively influence adoption.

Four tenure variables were specified. Adoption was considered more likely on owned cropland than on rented cropland. Therefore, Full-Owner Farmers was hypothesized to positively influence adoption in a county, while Part-Owner Farmers and Tenant Farmers were hypothesized to negatively influence adoption. Finally, part-owner farmers renting smaller amounts of land compared to the amounts of land owned were considered more likely to adopt precision farming technologies. Therefore, Land Owned Minus Land Rented was hypothesized to positively influence the likelihood of adoption.

Results

All regressions were highly significant, and percentages of concordant predictions were all greater than 91% (Table 2). The Logit models had from two to six significant location variables. Only Value of Crop Sales/Acre had significant coefficients with unexpected signs. Higher valued crops such as tobacco, nursery crops, fruits, and vegetables are typically produced on smaller fields relative to row crops and in Tennessee counties where row crops are relatively unimportant. The technologies evaluated were not typically used on small fields in counties where these higher valued crops are important.

Table 2.
Logit Regressions for the Location of Precision Farming Technology Adoption in Tennessee

Estimated adoption probabilities, evaluated at the means of the Tennessee county data and for Gibson and Knox Counties, are presented in Table 3. When evaluated at the means of the county data, probabilities of farmers in the average Tennessee county adopting these technologies ranged from 0.093 for Yield Monitor/Out GPS to 0.431 for Any Precision Farming Technology. The estimated probabilities for Gibson County ranged from 0.703 for Variable Rate Application to 0.999 for Grid Soil Sampling. Knox County had very low estimated adoption probabilities, ranging from almost zero for Yield Monitor/Out GPS to 0.015 for Yield Monitor/GPS. Estimated adoption probabilities for Gibson and Knox Counties followed expected patterns, given differences in county location characteristic (Table 1).

Table 3.
Estimated Adoption Probabilities at the 95-County Means for Tennessee and for Gibson and Knox Counties

The map in Figure 1 shows where precision farming technology adoption was predicted to be more or less likely in Tennessee. Counties with high estimated probabilities of adoption (greater than 0.5) were located mostly in the western and central portions of the state. Adoption was estimated to be less likely in the more mountainous eastern portion of the state, although a few counties in that area had high estimated adoption probabilities.

Figure 1.
Estimated Precision Farming Adoption Probabilities for Tennessee Counties

The University of Tennessee may want to target its programs toward counties with estimated adoption probabilities greater than 0.5, where they would most likely benefit farmers, agribusiness personnel, and the agricultural workforce in general. Counties with estimated The University of Tennessee may want to target its programs toward counties with estimated adoption probabilities greater than 0.5, where they would most likely benefit farmers, agribusiness personnel, and the agricultural workforce in general. Counties with estimated adoption probabilities less than 0.25 would not likely be fruitful areas to target in the near future. Those counties with estimated probabilities between 0.25 and 0.5 may be fruitful for precision farming technology adoption in the future and should be reevaluated over time and as resources permit.

Between 11 and 18 counties with estimated adoption probabilities greater than 0.5 did not have farmers using precision farming technologies (Table 4 and Figure 1). The high estimated adoption probabilities for these counties, coupled with the apparent unavailability of firms providing precision farming services or farmers using their own equipment, suggest that these counties may be profitable areas for expansion of precision farming. Rather than emphasizing training programs or downloadable digitized soil maps in these counties, perhaps information-oriented programs to help farmers and agribusiness firms make decisions about adoption would be more important at the outset. For example, education programs that emphasize the costs and benefits of yield monitoring technology, grid or management zone soil sampling, and variable rate application of inputs would be useful in providing information to farmers who are considering the adoption of these technologies.

Table 4.
Estimated Adoption Probabilities Compared with Extension Agent Survey Results

Conclusions

To meet the anticipated growth in demand for precision farming technologies revealed by an April-May 1999 survey of agribusiness firms, the University of Tennessee is interested in knowing where to concentrate its information and training resources. Data from a March 1999 survey of Agricultural Extension Agents and the 1997 Census of Agriculture were used to develop five Logit regression models to estimate the probabilities of Tennessee counties having farmers adopting various precision farming technologies.

Probabilities estimated from these models can help identify regions of the state where favorable factors exist for the adoption of precision farming technologies. The estimated adoption probabilities could be used in deciding where to target precision farming information and training programs. According to model predictions, counties targeted would be those with sufficient crop acreage, land in large farms, and farmers who own the land they farm.

The simple survey methods and Logit analysis presented in this article could be used by other states to help prioritize the geographic allocation of their precision farming program resources.

References

Daberkow, S. G., & McBride, W. D. (1998). Socioeconomic profiles of early adopters of precision agriculture technologies. Journal of Agribusiness, 16:151-68.

English, B. C., Roberts, R. K., & Sleigh, D. E. (2000). Spatial distribution of precision farming technologies in Tennessee. Tennessee Agr. Exp. Sta. Res. Rep. 00-05.

Khanna, M. (2001). Sequential adoption of site-specific technologies and its implications for nitrogen productivity: A double selectivity model. American Journal of Agricultural Economics, 83:35-51.

Khanna, M., Epouhe, O. F., & Hornbaker, R. (1999). Site-specific crop management: Adoption patterns and incentives. Review of Agricultural Economics, 21:455-472.

Pindyck, R .S., & Rubinfeld, D. L. (1998). Econometric models and economic forecasts, 4^th Ed. New York: McGraw-Hill.

Roberts, R. K., English, B. C., & Mahajanashetti, S. B. (2000). Evaluating the returns to variable rate nitrogen application. Journal of Agricultural and Applied Economics, 32:133-143.

Roberts, R. K., English, B. C., & Sleigh, D. E. (2000). Precision farming services in tennessee: results of a 1999 survey of precision farming service providers. Tennessee Agr. Exp. Sta. Res. Rep. 00-06.

Swinton, S. M., & Lowenberg-DeBoer, J. (1998). Evaluating the profitability of site-specific farming. Journal of Production Agriculture, 11:439-446.

U.S. Department of Agriculture. (1999). 1997 census of agriculture: Tennessee state and county data. National Agricultural Statistics Service. AC97-A-42. Washington D.C.: U.S. Government Printing Office.

Watkins, K. B., Lu, Y. C., & Huang, W. Y. (1998). Economic and environmental feasibility of variable rate nitrogen fertilizer application with carry-over effects. Journal of Agricultural and Resource Economics, 23:401-426.

Assessment of Wildlife Depredation to Agricultural Crops in New Jersey

David Drake
Extension Wildlife Specialist
Rutgers Cooperative Extension
New Brunswick, New Jersey
Internet Address: drake@aesop.rutgers.edu

John Grande
Director, Snyder Research and Extension Farm
Rutgers University
Pittstown, New Jersey
Internet Address: grande@aesop.rutgers.edu

Introduction

Wildlife damage to agriculture has increased over the last 30 years (Conover & Decker, 1991; Jonker et al., 1998). A nationwide survey of agricultural producers in the United States reported that 80% of respondents had experienced wildlife damage to their crops, and 53% indicated that depredation exceeded their tolerance (Conover, 1998). Conservative estimates of agricultural losses nationwide from wildlife ranged as high as $2 billion (Conover, 1998).

A wide range of wildlife has been documented to depredate agricultural crops. A national survey of agricultural and wildlife professionals discovered at least 27 wildlife species that damaged agricultural crops (Conover & Decker, 1991). Other researchers have documented agricultural depredation by wildlife ranging from large mammals (Conover, 1994; Jonker et al., 1998; Van Tassell et al., 1999) to rodents (Messmer & Schroeder, 1996; Conover, 1998) and birds (Flegler et al., 1987; Wager-Page & Mason, 1996). White-tailed deer (Odocoileus virginianus), however, have been estimated to cause more damage to agricultural crops nationwide than any other vertebrate species (Conover, 1998; Conover & Decker, 1991).

While numerous wildlife species depredate agricultural crops in New Jersey, most of the attention has focused on the effect white-tailed deer have on agriculture. The New Jersey Agricultural Experiment Station conducted a survey of 4,403 New Jersey farmers who grossed more than $10,000 annually. Fifty-one percent of survey recipients responded and estimated that deer were responsible for 70% of their wildlife-caused crop losses (unpublished data, 1998). Furthermore, 39% responded that crop losses due to deer were intolerable (unpublished data, 1998). Overall, survey respondents estimated between $5 and $10 million in crop losses from deer during the 1997 growing season (unpublished data, 1998).

Farmers' perceptions regarding wildlife damage provide valuable information. However, documented validation of crop depredation to support or refute farmers' perceptions is required so wildlife damage management policies and strategies can be enacted, if necessary, to reduce/eliminate conflicts between agriculture and wildlife. Furthermore, county Extension agents are often the first source farmers turn to for assistance with wildlife depredation to agricultural crops. In order to implement successful wildlife damage management strategies, it is necessary to identify the wildlife species responsible for the damage.

Therefore, we conducted a survey during the 2000 growing season to document wildlife damage to vegetable, fruit, grain, and nursery crops. Our objectives were to:

1. Better understand the economic impact wildlife has on agriculture for use in developing and implementing policies to alleviate wildlife damage and
2. Identify the more common wildlife species causing depredation so county Extension agents can tailor strategies to reduce wildlife conflicts.

Methods

We collected names, addresses, and phone numbers from New Jersey agricultural directories for growers of vegetable, fruit, grain, and nursery crops. We called farmers to inquire if they had experienced past crop damage from wildlife or were currently experiencing damage. If the farmer answered "YES," we asked to visit his farm and document the damage. We documented wildlife damage to agricultural crops during May - October of 2000. For comparative purposes, we divided the state into northern and southern regions, with the dividing line being roughly drawn east-west from Trenton, New Jersey.

Our selection of farmers to call and visit was not completely randomized. There are an estimated 9,600 farms in New Jersey (New Jersey Agricultural Statistics Service, 2001). However, due to the rapid pace of farmland conversion to urban development, there is no updated, comprehensive list of every farm in the state. Therefore, we chose to locate farmers via agricultural directories. As a result, an obvious bias exists because only farmers listed in the agricultural directories could be selected for our study.

We documented wildlife depredation, by species, using three methods. If more than one species depredated the surveyed area, we appropriated responsibility for damage by percentage for each species. We used Hyngstrom et al. (1994) to verify species identification of wildlife damage.

The first method we used involved exclosures that measured 10-feet by 10-feet and were constructed of 5-foot tall plastic mesh fencing with 1-inch mesh openings. Exclosures were randomly placed in grain fields prior to sprouting. We hand harvested the grain within each exclosure prior to harvest by the farmer and recorded the green weight. We hand harvested a randomly selected and equal-sized area 30 feet outside each exclosure and recorded the green weight. Prior to harvesting the area outside the exclosure, we documented any wildlife damage. Yield differences were calculated for each pair of harvested areas. An average yield difference was calculated for all exclosure pairs in a given field, and then extrapolated across the entire field. We erected 59 exclosures in 172 acres planted to grain.

The second method we employed was based on Wisconsin's Wildlife Damage Abatement and Claims Program (WDACP, 2000). We employed this method in fruit, vegetable, grain, and nursery crops. The WDACP provides detailed instructions regarding how large an area to sample for a given crop and how to determine the extent of wildlife depredation for a particular field based on sampled areas. We extrapolated results from sampled areas across the entire field.

The third method we used involved total crop depredation in part or all of a field. This method was employed most often in grain or vegetable crops. If no yield was evident in part or all of a field due to wildlife depredation, we measured the perimeter of the affected area and calculated total yield loss within the area.

For every crop surveyed, we converted yield loss into dollar loss based on the retail, wholesale, or commodity price the farmer was currently receiving.

Results

We visited 111 farms in 18 of New Jersey's 21 counties. We surveyed a total of 1,410 acres and documented $1,767,404.77 worth of economic damage to agricultural crops caused by at least 10 wildlife species. Based on the 1,410 acres surveyed, the average economic loss per acre equaled $1,253.48 (Table 1).

Table 1.
Number of Agricultural Acres Surveyed for Wildlife Damage, the Economic Loss from Wildlife, and the Average Dollar Loss per Acre in Northern and Southern New Jersey, 2000

Vegetable Crops

From 1997-1999, an average of 50,317 acres were in vegetable production statewide, with cash receipts totaling $135,098,000.00, or an average $2,685.00 per acre (New Jersey Agricultural Statistics Service, 2001). We surveyed 583 acres in vegetable production (337 acres in northern New Jersey, 246 acres in southern New Jersey) (Table 2). Economic loss to vegetable growers from wildlife totaled $1,424,287.00, or $2,443.00 per acre. Compared to the 1997-1999 statewide per acre average, vegetable growers that we surveyed lost 91% of their crop's value due to wildlife depredation.

Table 2.
Amount of Wildlife-Caused Yield Loss by Vegetable Type in Northern and Southern New Jersey, 2000

Fruit Crops

From 1997-1999, an average of 23,283 acres were in fruit production statewide, with cash receipts totaling $88,811,000.00, or an average $3,814.00 per acre (New Jersey Agricultural Statistics Service, 2001). We surveyed 406 acres in fruit production (129 acres in northern New Jersey, 277 acres in southern New Jersey) (Table 3). Economic loss to fruit growers from wildlife totaled $154,636.89, or $379.94 per acre. Compared to the 1997-1999 statewide per acre average, fruit growers that we surveyed lost 10% of their crop's value due to wildlife depredation.

Table 3.
Amount of Wildlife-Caused Yield Loss by Fruit Type in Northern and Southern New Jersey, 2000

Grain Crops

From 1997-1999, an average of 220,332 acres were in corn (grain and silage) and soybean production statewide, with cash receipts totaling $35,797,000.00, or an average $162.00 per acre (New Jersey Agricultural Statistics Service, 2001). We surveyed 402 acres in corn (grain and silage) and soybean production (366 acres in northern New Jersey, 36 acres in southern New Jersey) (Table 4). Economic loss to corn and soybean farmers from wildlife totaled $46,979.38, or $116.86 per acre. Compared to the 1997-1999 statewide per acre average, corn and soybean growers that we surveyed lost 72% of their crop's value due to wildlife depredation.

Table 4.
Amount of Wildlife-Caused Yield Loss to Corn and Soybeans in Northern and Southern New Jersey, 2000

Nursery Crops

From 1997-1999, an average of 14,508 acres were in nursery production statewide, with cash receipts totaling $272,579,000.00, or an average $18,788.00 per acre (New Jersey Agricultural Statistics Service, 2001). We surveyed 19 acres in nursery production (17 acres in northern New Jersey, 2 acres in southern New Jersey) and documented damage to a variety of flowers, shrubs, and trees. Economic loss to nursery growers from wildlife totaled $141,501.50, or $7,447.45 per acre. Compared to the 1997-1999 statewide per acre average, nursery growers that we surveyed lost 40% of their crop's value due to wildlife depredation.

We documented agricultural damage caused by at least 10 wildlife species (Table 5). In some instances, we documented bird damage but were unable to identify an individual species. The majority of documented agricultural damage was caused by deer (79%). Groundhogs (Marmota monax) caused the second-largest amount of economic loss (14%).

Table 5.
Wildlife Species Responsible for Economic Loss to Agriculture in Northern and Southern New Jersey, 2000

Discussion

A number of issues limit the extrapolation of our results across New Jersey. One limitation is that we were not able to fully randomize the selection of farms to visit. Second, it is impossible to separate from total statewide agricultural statistics the acreage and cash receipts from livestock, dairy, and poultry—crops we did not survey—because New Jersey is a mixed agriculture state and farmers are rarely dedicated to raising only livestock, dairy, or poultry.

For example, from 1997-1999, an average 830,000 acres were in agricultural production in New Jersey, with cash receipts totaling $757,134,000.00, or an average $912.00 per acre statewide (New Jersey Agricultural Statistics Service, 2001). We found the average economic loss per acre from wildlife depredation equaled $1,253.48, which clearly exceeds total per acre cash receipts. However, an unknown percentage of the 830,000 acres and total cash receipts were for the production of livestock, dairy, and poultry. If we were able to subtract from total statewide agricultural statistics the acreage and cash receipts from livestock, dairy, and poultry, it would raise the statewide average per acre cash receipt. Finally, we have no idea what percentage of farmers throughout New Jersey employ wildlife damage management practices or the success of practices that are employed.

While a straight extrapolation of our results across New Jersey is not a fair estimate, we also suggest that the amount of wildlife damage that we documented to surveyed crops is conservative for a number of reasons. First, we visited 111 farms, which is only 1% of the estimated 9,600 farms in the state. Second, we surveyed 1,410 acres, or less than 1% of the estimated 830,000 acres in agricultural production. Third, we visited most farms only once during the growing season, so we only documented damage that had occurred prior to our visit to a particular farm. Any wildlife depredation that occurred throughout the remainder of the growing season and after our visit was not documented. Fourth, we may not have surveyed an entire farm for wildlife depredation. For example, if we visited a 500-acre farm, we may have only surveyed 100 acres due to time constraints.

Frequently, county Extension personnel are contacted as a first resource to assist farmers in New Jersey with reducing wildlife depredation to agricultural crops. Our results will aid county Extension agents in making cost-effective recommendations to farmers. Prior to recommending any wildlife damage management practice, it is necessary to correctly identify the species responsible for the damage. It is also useful to conduct a cost-benefit analysis to determine the annual economic impact from wildlife depredation. Determining annual economic loss can help a farmer decide how much money to invest in reducing/eliminating agricultural yield loss and at what point in time the investment will pay for itself.

References

Conover, M. R. (1994). Perceptions of grass-roots leaders of the agricultural community about wildlife damage on their farms and ranches. Wildlife Society Bulletin, 22(1), 94-100.

Conover, M. R. (1998). Perceptions of American agricultural producers about wildlife on their farms and ranches. Wildlife Society Bulletin, 26(3), 597-604.

Conover, M. R. & Decker, D. J. (1991). Wildlife damage to crops: Perceptions of agricultural and wildlife professionals in 1957 and 1987. Wildlife Society Bulletin, 19(1), 46-52.

Flegler, Jr., E. J., Prince, H. H., & Johnson, W. C. (1987). Effects of grazing by Canada geese on winter wheat yield. Wildlife Society Bulletin, 15(3), 402-405.

Hygnstrom, S. E., Timm, R. M., & Larson, G. E. (Eds.). (1994). Prevention and control of wildlife damage (Vols. 1-2). Lincoln, NE: University of Nebraska.

Jonker, S. A., Parkhurst, J. A., Field, R., & Fuller, T. K. (1998). Black bear depredation on agricultural commodities in Massachusetts. Wildlife Society Bulletin, 26(2), 318-324.

Messmer, T. A., & Schroeder, S. (1996). Perceptions of Utah alfalfa growers about wildlife damage to their hay crops: Implications for managing wildlife on private land. Great Basin Naturalist, 56(3), 254-260.

New Jersey Agricultural Experiment Station. (1998). How are white-tailed deer affecting agriculture in New Jersey. Unpublished manuscript.

New Jersey Agricultural Statistics Service. 2001. 2000 New Jersey agriculture annual report: Agricultural statistics (Circular No. 556). Trenton, NJ: New Jersey Department of Agriculture.

Van Tassell, L. W., Phillips, C., & Yang, B. (1999). Depredation claim settlements in Wyoming. Wildlife Society Bulletin, 27(2), 479-487.

Wager-Page, S. A., & Mason, J. R. (1996). Exposure to volatile D-Pulegone alters feeding behavior in European starlings. Journal of Wildlife Management, 60(4), 917-922.

Wisconsin Wildlife Damage Abatement and Claims Program. (2000). Technical manual field handbook. Madison, WI: Department of Natural Resources.

Student Wellness Needs in Rural Appalachia

Sally Summers
Extension Agent, Associate Professor
4-H Youth, Family, and Adult Development
West Virginia University
Winfield, West Virginia
Internet Address: ssummers@wvu.edu

Paul Leary
Professor, Educational Leadership
Marshall University
South Charleston, West Virginia
Internet Address: pleary@marshall.edu

Background

National youth wellness and wellness related behaviors are an increasing concern for the Centers for Disease Control (Wisconsin State Department of Public Instruction, 1994; Werner, 1991; Sobal & Marquart, 1994). Some states have inquired about adolescent health risk behaviors. These states include Wisconsin (1993), Ohio (1993), and Missouri (1993).

Various aspects of youth risk behaviors have been studied by states. Most of these studies used adaptations of the Youth Risk Behavior Survey developed by the Centers for Disease Control and Prevention (1991). For example, smoking behavior was the concern of the Ohio and Maine studies (1993). Vitamin/mineral supplement use by high school athletes was the concern of the Sobal and Marquart study (1994).

Adolescent substance abuse was the focus of the study of the National Center for Education in Maternal and Child Health (1991). Likewise, adolescent seat belt usage was also a part of the Ohio study (1993). Alcohol consumption, smokeless tobacco use, and safety related behaviors were the concerns of the North Dakota study.

Given the foregoing, it became apparent that similar data could be gathered using a West Virginia (WV) sample, thereby allowing comparisons with national and other states data. Accordingly, the construction of a questionnaire based upon the National Youth Risk Behavior Survey was developed by the researchers. The survey included items relating to respondents' personal safety, tobacco/steroid use, body image, dietary behaviors, and physical activity.

Program Objectives

The primary goal of the program was to increase the physical, mental, and social wellness of Putnam County, WV high school students. The objectives of this research project were:

1. To assess the wellness needs of students;
2. To develop Extension programming that will provide useful knowledge to students to improve their level of physical, mental, and social wellness; and
3. To educate the community on the risk factors facing our youth today.

Methodology

Data were collected over a 3-year time frame. In order to assess the wellness needs of the students, a health habits survey was administered to students at Winfield High School, Winfield, WV. During the pilot testing of the first year, students in the ninth and eleventh grade physical education classes filled out the assessment instrument (N = 189). These findings resulted in rewriting the instrument and determining the appropriate timetable to administer the survey to assure objective responses. The results of the pilot study prompted us to survey the entire student body during the next two iterations.

In the second year of the data collection, a health habits survey was administered to students at Winfield High School. All of the students completed the assessment instrument (N = 648). The research consultant selected a sample and approved 100 random numbers from among the 648 respondents. Based on the random sample numbering, the responses were available from 99 of the students (Campbell and Stanley, 1963). The random sample of all of the instruments was analyzed.

This article focuses on the second and third years of the project, when the entire student body was surveyed. Thus, the respondents from the first year are not included in the analysis of this report.

Instrumentation

The instrument consisted of 44 questions focusing on behaviors that fall into four categories:

1. Behaviors that result in unintentional and intentional injuries (personal safety);
2. Tobacco/steroid use;
3. Dietary behaviors; and
4. Physical activity.

These categories were adapted from the "Youth Risk Behavior Survey" administered every 2 to 3 years to randomly selected ninth through twelfth grade students in public schools in WV by the WV Department of Education. The youth risk behavior survey was developed by the Division of Adolescent and School Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC) in collaboration with representatives from 71 state and local departments of education and 19 federal agencies.

Data Analysis

The data from the 2 years when the entire student body were surveyed are arrayed as preliminary analysis of the total percentage of the respondents by item. The independent variables of age, gender, and grade in school were used as predictors of responses to each item. Analysis of Variance (ANOVA) was calculated on every item. If significant results were indicated, a Duncan's Multiple Range Test was run to determine differences between groups or categories.

Findings

As an example of the foregoing issue, the following data are arrayed as an illustration (Table 1).

Question: How often do you wear a seat belt when riding in a car driven by someone else?

Table 1.
Seatbelt Use

Student seat belt usage for both years was extremely high. In both years, age was a significant predictor of seat belt usage. There was a tendency for younger students to have lower seat belt usage than older students did. In addition, in 1996, students in the eleventh and twelfth grades wore seat belts significantly more often than ninth and tenth grade students did.

As an example of the foregoing issue, the following data are arrayed as an illustration (Table 2).

Question: Have you ever smoked cigarettes regularly, that is, at least one cigarette every day for 30 days?

Table 2.
Cigarette Smoking

The percentage of students who smoked at least one cigarette a day was identical between the years. Approximately three fourths of the students answered "no" to this questions in both years. The characteristics of students did not predict any difference if response to this item.

As an example of the foregoing issue, the following data are arrayed as an illustration (Table 3).

Question: How do you think of yourself?

Table 3.
Weight Perception

In both years approximately half of the students felt that they were about the right weight. In 1996, older students had significantly poorer perceptions of body image than the younger students did. Conversely, in 1998, the older students had significantly more positive perceptions of their body image than the younger students did (Table 4).

Question: Which of the following are you trying to do?

Table 4.
Weight Goals

Approximately half of the students in both years indicated that they were trying to lose weight. The characteristics of the students did not predict any difference in response to this item.

As an example of the foregoing issue, the following data are arrayed as an illustration (Table 5).

Question: Yesterday, did you eat french fries or potato chips?

Table 5.
French Fry and Potato Chip Consumption

In both years, almost three fourths of the students had eaten french fries or potato chips the prior day. The characteristics of the students did not predict any difference in response to this item.

As an example of the foregoing issue, the following data are arrayed as an illustration (Table 6).

Question: During the past 12 months, did you ever seriously consider attempting suicide?

Table 6.
Consideration of Suicide

For both years, there was a substantial number of students who indicated that they had seriously considered suicide. The number of students who indicated "yes" to this question increased from 1996 to 1998. There were significant differences in responses to this item based upon age. Younger students significantly more often answered "yes" to this question than the older students did.

As an example of the foregoing issue, the following data are arrayed as an illustration (Table 7).

Question: On how many of the last 7 days did you exercise or participate in sports activities for at least 20 minutes that make you sweat or breath hard?

Table 7.
Exercise or Sports Participation