December 2007
Volume 45 Number 6

Research in Brief

Perceptions and Preferences of Extension Programming and Sources Among Extension Users and Non-Users: 10 Kansas Counties

Kristina M. Boone
Department Head
Department of Communications
Manhattan, Kansas
kboone@ksu.edu

Linda Sleichter
Marketing Specialist
Department of Communications
Manhattan, Kansas
lsleicht@oznet.ksu.edu

Rick Miller
County Director
Johnson County Extension Office
Olathe, Kansas
rmilller@oznet.ksu.edu

Sharon Breiner
Graduate Assistant
Manhattan, Kansas
glaenzer@ksu.edu

Kansas State University

Introduction

County Extension personnel are challenged to produce timely and useful services while considering two key issues: do people understand the function and value of Extension, and does Extension fulfill the needs of those that they serve? Finding the answers to these questions is particularly difficult in areas that are facing an increase in population density and diversity.

To better understand these issues among Extension clients and non-clients, a university researcher conducted an evaluation to determine the views of those groups, using the 10 most populous counties in Kansas. What the researcher found is refocusing the approach to addressing these groups and guiding programming and marketing efforts for the future.

Kansas counties have had strong Extension programming and maintained strong support in general from county boards. However, Kansas has seen increasing urbanization. In 2002, K-State Research and Extension realigned its areas, forming one of its five areas based not on geography but on population. This area comprises the most populous counties in the state. Prior to the formation of this new area, 10 of the most populous counties in the state decided to devise a marketing plan together and to collect data upon which to base that plan.

The counties were different in many ways, but facing a common issue: a dramatically changing county population. Kansas population grew 8.5% from 1990 to 2000, but these counties saw growth of 10% on average, indicating that much of the increase in population came from these more populous counties. These counties tended to have higher percentages of ethnicity and Hispanics. The percent of people under 18 years old also is higher in these counties than in the state in general. Income and percent of people living in poverty is variable in these counties, with some of the highest and lowest incomes and percentages in the state, further indicating the diversity in these counties. Despite growth in population, agriculture is still the highest land use in these areas (USDA Kansas Agricultural Statistics Service, 2001).

The Cooperative Extension Service, like other public institutions, is facing greater pressure for accountability and demonstration of results (Boone & Furbee, 1998; Chapman-Novakofski, Boeckner, Canton, Clark, Keim, Britten, & McClelland, 1997; Rennekamp, Warner, Nall, Jacobs, & Maurer, 2001). Extension is challenged to provide timely, useful service, which has become the organization's hallmark (Greene, 1995).

When a service is not recognized as having significant public value, citizens believe it should be purchased on the private market for a price (Kalambokidis, 2004). Information that illustrates the value of an organization is vital to the decision makers of an organization. When considering value, these decision makers analyze both customer service and measurement of performance through outcomes.

Meeting clientele needs has become increasingly difficult for Extension, as the audiences have grown and diversified. At the same time, resources have diminished (Smith & Swisher, 1986). Some systems have sought differing solutions to address these issues, including building relationships with other service organizations (Martin-Milius, 1994). In Kansas, demographic shifts have prompted a realignment of Extension areas, leading to a creation of an area office serving not a geographic region but population centers. These counties are working together to address urbanization issues and marketing efforts. For their marketing plan, they developed a study to analyze client and non-client attitudes and needs. The purpose of the study reported here was to guide marketing and planning processes.

A mixed-modal survey was used in which clients received a mail survey, while non-clients were contacted using a telephone survey (Dillman, 2000). Data were collected in fall 2002. The questionnaires were very brief and were evaluated by a panel of experts with K-State Research and Extension for face and content validity.

Findings indicate variability from county to county, but in general non-clients and clients prioritize programming differently and prefer different delivery mechanisms. The findings are being used as a basis for a collective marketing plan and as a means to achieve agreement among the counties.

Methods

Surveys were administered to Extension users and non-users in the summer and fall of 2002. These individuals were located in the 10 most populous counties in Kansas. Questionnaires were developed for both groups based on prior work in Johnson County. The survey relied upon the Johnson County instrument for reliability. The survey was mixed modal.

County offices submitted mailing lists for their users. A random sample of 150 was drawn from each county list. These surveys were then administered utilizing the Tailored Design Method (Dillman, 2000). For non-users, a sampling company drew random telephone numbers totaling 450 numbers per county. Trained data collectors telephoned non-users, for their responses.

Data was analyzed in the Department of Communications and Department of Statistics using SPSS/PC+.

Results

Data were collected from 481 known Extension users and 449 people who were randomly sampled from the same counties (referred to as "non-users" for this article). The summary data are presented here. For both samples, more women responded than men, although the percentage of men responding was not particularly low. In comparing users to non-users, users were generally older and had higher household income levels. More than 40% of non-users were younger than 45, while only 22% of users were under 45. Almost ¼ of non-users had incomes of less than $20,000 per year, while only 6% of users fell into the same category. Thirty-five percent of users had household incomes of $40,000 or less, while 53% of non-users earned $40,000 or less per year (Table 1).

Among non-users, there was significant recognition of the organization, much more so than in previous statewide surveys. Seventy percent had heard of the organization, and 56% correctly identified its affiliation with Kansas State University. Almost 40% indicated they had used the service at one time (Table 2).

Both user and non-user groups indicated satisfaction with the services/materials they had received from K-State Research and Extension (Table 3). This question was asked only of the non-users who had indicated they had received information/services from the organization. Of the users, 95% indicated that they were very satisfied or satisfied, while 93% of non-users indicated the same.

Data on preferred methods of delivery for educational information are presented in Table 4. For this question, respondents were asked to rate each method on a scale of 1 to 5, with 1 being not very likely to use and 5 being very likely to use. The mean is the average of the ratings, while the standard deviation (s.d.) provides a measure of the dispersion of the data. The mode is the most frequently occurring category, and, like the mean, is a measure of central tendency. The ranking based on means is presented as another way to compare the methods.

Among users, newsletters were the most highly rated method, followed by newspaper and classes/meetings. Television, which was not rated highly overall, received ratings of 5 from more than 20% of users, indicating that it is used highly by a portion of the group but not overall. Eighty-five percent of users indicated that they read the county Extension newsletter.

The non-user group rated the methods differently. Newspaper, television, and radio were rated the highest. Classes/meetings were rated lowest. The Internet was rated by 35% of non-users as not very likely to use, but 27% rated it as very likely to use, indicating that they either rely on it heavily or not at all.

The remaining questions asked both groups about the importance of subject matter areas on which K-State Research and Extension provides information/expertise. The groups were asked to rate the subject areas based on their importance to the respondents as individuals (Table 5) and their importance to the community (Table 6).

Among users, most subject areas were rated as important, with six subjects with modes of great importance (5). The mode for community development was 3, while the mode for family skills was 4. Family skills might have been rated somewhat lower because the user group was older. While the farming/ranching mode was 5, the next most frequently occurring category was 1, indicating a split distribution. Responses for environmental preservation and family skills clustered around ratings of 3, 4, and 5.

Non-users also rated subject areas highly, with all but farming receiving a mode of 5. Farming/ranching had the lowest mean and mode.

When asked to describe the importance subject areas to their communities, both user and non-user groups showed greater agreement. Standard deviations for every subject area decreased when compared to the data related to importance on an individual basis. Thus, there was less variability and greater agreement exhibited in the data.

Users rated every subject area high for the importance in the community, with each having a mode of 5. Modes for non-users were 5 in each area, except lawn and gardening, where they were equally split between 3 and 4. Interestingly, farming and ranching, which had a mode of 1 for individual importance to non-users, had a mode of 5 when the group viewed its importance to the community. This probably relates to the recognition of the economic value of agriculture to the community.

Discussion

Among non-users there was strong awareness of K-State Research and Extension and recognition of the tie to Kansas State University. This indicates success of these identity awareness programs.

Among those who had used K-State Research and Extension, there were high levels of satisfaction, both among users and non-users. Users differ from non-users in several important areas, and some of these are demonstrated by demographics. Users tended to be older and had higher incomes. They also preferred traditional methods of information delivery (newsletters and classes/meetings). Non-users were more oriented to mass media, which might be used to create more awareness and bring them to reliance on newsletters, etc. Among non-users, those who use the Internet rely on it for information but those who do not use the Internet did not value it as an information delivery method, a finding that demonstrates the digital divide.

Respondents rated Extension's subject areas as important for almost every category. Among users, the overall rating of farming/ranching was high, but there was a split in those data, with many users indicating it was unimportant to them. Users also exhibited less agreement on environmental preservation and community development, perhaps because these are considered more societal goods than individual goods.

There was greater agreement about the importance of subject areas to the community, with high ratings to all subjects. These data can be interpreted as community values/benefits. As one writes key messages they may consider positioning messages as individual or community benefits.

From a marketing perspective, these data could be used to build strategies to reach key audiences and reach beyond traditional clientele groups. Mass media may be an important tool for reaching these non-users. Once they have greater awareness of the organization, they may become more reliant on more traditional informational tools, especially newsletters. Given the pace of lifestyles today, it is doubtful that classes/meetings will grow much in popularity, but may be more important for particular hands-on/interactive learning activities or for particular targeted groups. The Internet also holds potential here. It is important as well to remember to provide existing users with the information and informational tools that they value and to continue to serve their needs.

References

Boone, K. M., & Furbee, R. (1998). Are you being served? Gauging customer service. Journal of Applied Communications, 82 (3), 7-19.

Chapman-Novakofski, K., Boeckner, L. S., Canton, R., Clark, C. D., Keim, K., Britten, P., & McClelland, J. (1997). Evaluating evaluation--What we've learned. Journal of Extension [On-line], 35(1). Available at: http://www.joe.org/joe/1997february/rb2.html

Dillman, D. A. (2000). Mail and Internet surveys: The tailored design method. Second Edition. John Wiley & Sons, Inc., New York. (p.217-244).

Greene, S. S. (1995). Cooperative Extension: The service challenge. Journal of Extension [On-line], 33(6). Available at: http://www.joe.org/joe/1995december/comm1.html

Kalambokidis, L. (2004). Identifying the Public Value in Extension Programs. Journal of Extension [On-line], 42(2). Available at: http://www.joe.org/joe/2004april/a1.shtml

Martin-Milius, T. (1994). University to community and back: Creating a customer focused process. Paper presented at the Fifth Annual Conference on TQM in Colleges and Universities: Reengineering the University. Chicago, IL.

Radhakrishna, R. (2002). Measuring and benchmarking customer satisfaction: Implications for organizational and stakeholder accountability. Journal of Extension [On-line], 40(1). Available at: http://www.joe.org/joe/2002february/rb2.html

Rennekamp, R. A., Warner, P. D., Nall, M. A., Jacobs, C., & Maurer, R. C. (2001). An examination of customer satisfaction in the Kentucky Cooperative Extension Service. Journal of Extension [On-line], 39(2). Available at: http://www.joe.org/joe/2001april/rb5.html

Smith, M. F., & Swisher, M. E. (1986). The best little programming tool in Extension: Audience identification helps determine needs and justify programs. Journal of Extension [On-line], 24(3). Available at: http://www.joe.org/joe/1986fall/a3.html

U.S. Census (2001). State and county quick facts for Johnson County, Kansas. Available at: http://quickfacts.census.gov/qfd/states/20/20091.html

U.S. Department of Agriculture, Kansas Agricultural Statistics Service. (2001). 1997 Census of Agriculture County Profile, Johnson, Kansas. Available at: http://www.nass.usda.gov/census/census97/profiles/ks/ks.htm

Communication Channel Preferences of Corn and Soybean Producers

Melea A. R. Licht
Communications Specialist
Department of Agronomy
mreicks@iastate.edu

Robert A. Martin
Professor and Chair
Department of Agricultural Education and Studies
drmartin@iastate.edu

Iowa State University
Ames, Iowa

Introduction

Extension educators use a variety of communication channels to deliver their educational programs. Numerous studies show producers prefer a combination of communication channels when getting their agricultural information and specifically prefer interpersonal communication methods(Bruening, Radhakrishna, & Rollins, 1992; Dollisso & Martin, 1999; Israel, 1991; Kotile & Martin, 2000; Lasley, Padgitt, & Hanson, 2001; Richardson & Mustian, 1994; Rollins, Bruening, & Radhakrishna, 1991; Suvedi, Campo, & Lapinski, 1999; Trede & Whitaker, 1998; Vergott III, Israel, & Mayo, 2005).

However, limited financial resources may force Extension educators to choose among communication channels. In such cases, understanding the target audience, including the methods by which they prefer to receive information, allows educators to select communication channels accordingly and to transfer information efficiently (Bouare & Bowen, 1990; Radhakrishna, Nelson, Franklin, & Kessler, 2003; Richardson & Mustian, 1994; Riesenberg & Gor, 1989; Rollins, 1993).

Purpose and Objective

The overall research goal and purpose of the study reported here was to determine the agricultural information preferences of crop producers in Iowa and to assess the implications for agricultural Extension education. The objective of the study was to identify the types of communication channels that producers prefer to use to obtain agricultural information.

Methods and Procedures

The study consisted of five focus groups held in December 2004 in five communities throughout Iowa. Focus groups are guided interactive group discussions designed to gather perceptions, opinions, and ideas from participants about a defined area of interest in a friendly, non-threatening environment (Litosseliti, 2003; Morgan, 1998a; Morgan & Krueger, 1993). Focus group size ranged from three to nine participants; in total, 29 producers participated in the study. Eight to 12 producers in each group were confirmed for participation. Expert recommendations vary, but generally a focus group consists of six to 12 people per discussion session, and includes three to five sessions (Gamon, 1992; Grudens-Schuck, Allen, & Larson, 2004; Litosseliti, 2003; Morgan, 1998b).

Composing a group of people with similar characteristics enhances the quality of focus group data because people tend to disclose more to others they perceive as similar to themselves (Grudens-Schuck et al., 2004; Litosseliti, 2003). To attain this, participants were selected based on recommendations from Iowa State University Extension Field Crop Specialists. The Field Crop Specialists were asked to provide a convenience sample of producers they thought were users of agricultural information, would actively participate in the study, and conducted similar farming operations. A total of 115 producers were recommended for the study and were contacted by the researcher to determine their interest in participating. Participants in the focus groups were Caucasian males who farmed corn and soybeans, with ages ranging from late twenties to early sixties.

Only the participants and the researcher were present during each focus group. The researcher served as both moderator and recorder, which Morgan and Krueger (1993) indicate as acceptable, and in some cases preferable. In addition to following published focus group procedures, the researcher participated in two workshops prior to conducting the research to gain a greater understanding of conducting focus groups and analyzing the resulting data (Boone & Doerfert, 2003; Miller, 2004). Focus group sessions were limited to 90 minutes, as experts recommend (Grudens-Schuck et al., 2004; Morgan, 1998b). Participants were provided with a meal following or preceding the focus group session and were also given a small incentive gift, a coffee mug, for participating.

A discussion plan was created prior to the focus groups. As suggested by focus group experts, questions were written to be open-ended and nonbiased, and the question sequence progressed from general and unstructured to specific, and from greater to lesser importance (Gamon, 1992; Grudens-Schuck et al., 2004; Krueger, 1993, 1998a, 1998b). Questions were reviewed by an experienced focus group moderator and research analyst and altered according to her recommendations (N. Grudens-Schuck, personal communication, Nov. 18, 2004).

Focus group discussions began with introductions followed by an explanation of discussion rules and expectations, including information about voluntary participation and confidentiality. Participants were able to self-define communication channel terms according to their popular usage, so discussion would not be limited to terms introduced by the moderator. The researcher coded similar communication channels together from across all focus groups to make conclusions. (The complete question route is available on request from the lead author.)

Focus group data consisted of transcriptions of audio tapes and moderator notes, as Krueger recommends (1998a). Following published focus group procedures, data analysis was performed through theme coding and qualitative data charts, rather than quantitative methods ( Grudens-Schuck et al., 2004; Krueger, 1998a; Litosseliti, 2003). A theme was considered valid when mentioned by two or more focus groups (Nordstrom, Wilson, Kelsey, Maretzki, & Pitts, 2000). One participant from each group reviewed discussion summaries to check for accuracy, as Krueger suggests (1998a). No discrepancies were noted.

Results

The results of the study indicate producers preferred to obtain agricultural information through personal consultations to all other communication methods. Producers liked consultations because they provide reliable, timely, and local information specific to their operation and problems. In general, producers preferred communication channels that were quick to access and easy to use and provided information specific to their operations. Participants were not asked to rank communication channels numerically, but rather to compare and contrast their use of individual channels. Collective preference of participants was determined by the researcher based on interpretation of all participant comments. Illustrative comments organized according to participant preferences are listed in Tables 1-3.

Producers did not indicate a preference between the general categories of interpersonal and mass media communication channels. However, they said they receive the bulk of their information from mass media, but rely on interpersonal communication for detailed, local, and farm-specific information. Producers said they believed interpersonal communication was more reliable than information from mass media. Overall, producers perceived interpersonal communication as a way to evaluate the quality of information and determine how or if it applies to their operations.

Within mass media communication channels, producers preferred radio the most because it is "more timely." They also said radio was easy to use, provided local information, and was accessible while they were doing other things. They especially preferred it during busy farming seasons.

Producers ranked television as their least preferred mass media communication channel. Many felt there were few opportunities to view agricultural programs and when agriculture was on television the industry was often portrayed negatively.

Producers discussed numerous other mass media channels they preferred, including magazines, the Internet, and newspapers, but a clear ranking did not surface among them. Magazines tended to be preferred for in-depth information, especially for photographs and charts. Producers also said they liked the advertisements, but they did not see magazines as a source of timely information.

The producers who preferred the Internet cited the timeliness of the information as a major factor. However, many producers did not prefer the Internet because of slow dial-up connections, the time necessary to access the information, and the need to devote their attention solely to getting the information. Almost all producers in the study were using Data Transmission Networks (DTN) to some degree, though many were accessing the DTN information through Web sites. Some said they preferred a DTN machine to other methods because it had a familiar interface that does not change, was accessible when the family computer was in use, and did not require a phone line. Those who preferred DTN believe it is quicker than the Internet.

Consultations were the most preferred communication channel and the method producers preferred among interpersonal communication channels. Demonstrations were preferred next, followed by meetings, and then by workshops. Demonstrations were especially preferred for situations where visually comparing products or practices was important to the message. Meetings were less preferred because of the perceived broad nature of information presented and the amount of time required to attend.

For the purpose of coding participant responses and making conclusions in the study, a workshop was considered longer than 2 hours with a participatory function; a meeting was 2 hours or less in length and conducted in lecture or presentation format; and a demonstration was a demonstration of new practice or technology, often outdoors, such as a field day or farm show.

Conclusions and Implications

The results of the study illustrate the following conclusions:

1. Rather than acquiring information from Extension, producers look to Extension personnel for assistance in evaluating information gathered from other sources;


2. Producers identified they use a variety of communication channels;


3. Among communication channels, producers indicated a preference for consultations;


4. Producers indicated a preference for mass media channels for general information and interpersonal communication channels for specific and applicable information;


5. Among mass media channels, producers indicated a preference for radio; and


6. Among interpersonal channels, producers indicated a preference for consultations.

The findings from the study provided insight into Iowa corn and soybean producers' preferences regarding interpersonal versus mass media communication channels. Participants indicated mass media and interpersonal communication channels were preferred for different types of information, while previous studies concluded producers preferred interpersonal communication methods to mass media methods overall (Bruening et al., 1992; Israel, 1991; Lasley et al., 2001; Riesenberg & Gor, 1989; Rollins et al., 1991; Suvedi, Lapinski, & Campo, 2000; Vergott III et al., 2005). The results reaffirm the findings of previous studies that established producers preferred a variety of communication methods (Bruening et al., 1992; Dollisso & Martin, 1999; Kotile & Martin, 2000; Lasley et al., 2001; Richardson & Mustian, 1994; Rollins et al., 1991; Suvedi et al., 1999; Trede & Whitaker, 1998).

Producers' preferences for consultations indicated in the study were consistent with that of previous literature that found consultations were highly rated (Bruening et al., 1992; Israel, 1991; Rollins et al., 1991; Suvedi et al., 2000; Vergott III et al., 2005). Other specific findings, such as the preference for radio within mass media methods and the use of the Internet as supplemental communication, were in accordance with previous literature (Lasley et al., 2001; Nelson & Trede, 2004; Richardson & Mustian, 1994; Suvedi et al., 1999; Trede & Whitaker, 1998).

These results of the study are especially significant in that they reveal a burgeoning role played by agricultural Extension educators: that of an information filter for producers. Since producers consider interpersonal communication methods more reliable, even though they used mass media methods more often, Extension educators have the opportunity to influence producers more significantly than mass media. This role is especially important as producers receive an increasing amount of information through an increasing variety of methods. Extension educators could grow in their "information-filtering" role to assist producers in reaching greater understanding of agriculture information presented in mass media in order to better their farm operation and way of life.

Although the results of the study cannot be widely generalized, Extension educators may find them transferable to other similar situations and groups through Krueger's concept of transferability, which is, "parallel to the positivistic concept of generalizability, except that it is the receiver who decides if the results can be applied to the next situation, rather than the sender or researcher" (1998a, p.70). Extension educators may find the results of the study are especially transferable in selecting communication methods to deliver educational programs to Iowa corn and soybean producers. Those with limited resources who must choose among communication channels rather than use a combination of methods may also find the data especially useful. The research provides an introduction to communication channel use that would be helpful for those new to Extension or for use in agricultural education classrooms.

Future research is needed on a broad scale to assess the communication channel preferences of Iowa producers. In order to allow for generalization, the data could be gathered from a random sample of Iowa producers using large-scale survey research methods. The data from the study presented here could serve as a resource for selecting objectives and designing questions for such a survey.

Acknowledgment

This article is a product of the Iowa Agriculture and Home Economics Experiment Station, Ames Iowa. Project #3613 and sponsored by Hatch Act and State of Iowa Funds.

References

Bouare, D., & Bowen, B. E. (1990). Formal and nonformal instruction delivered to producers by adult instructors, secondary agriculture teachers, and extension agents. Journal of Agricultural Education, 31, 68-73.

Bruening, T., Radhakrishna, R., & Rollins, T. (1992). Environmental issues: producers' perceptions about usefulness of information and organizational sources. Journal of Agricultural Education, 31, 34-42.

Dollisso, A. D., & Martin, R. A. (1999). Perceptions regarding adult learners'motivation to participate in educational programs. Journal of Agricultural Education, 40(4), 38-46.

Grudens-Schuck, N., Allen, B. L., & Larson, K. (2004). Focus group fundamentals (Extension Publication PM 1969b): Iowa State University Extension.

Israel, G. (1991). Reaching Extension's clientele: Exploring patterns of preferred information channels among small farm operators. Southern Rural Sociology, 8.

Kotile, D. G., & Martin, R. A. (2000). Sustainable agricultural practices for weed management: implications to agricultural extension education. Journal of Sustainable Agriculture, 16(2), 31-51.

Krueger, R. A. (1993). Quality control in focus group research. In D. L. Morgan (Ed.), Successful focus groups: advancing the state of the art. Newbury, London, New Delhi: Sage Publications, Inc.

Krueger, R. A. (1998a). Analyzing & reporting focus group results (Vol. VI). Thousand Oaks, London, New Delhi: Sage Publications, Inc.

Krueger, R. A. (1998b). Developing questions for focus groups (Vol. III). Thousand Oaks, London, New Delhi: Sage Publications, Inc.

Krueger, R. A. (1998c). Moderating focus groups (Vol. IV). Thousand Oaks, London, New Delhi: Sage Publications, Inc.

Langer, J. (2001). The mirrored window: focus groups from a moderator's point of view. New  York: Roper Starch Worldwide.

Lasley, P., Padgitt, S., & Hanson, M. (2001). Telecommunication technology and its implications for producers and extension services. Technology in Society, 23, 109-120.

Litosseliti, L. (2003). Using focus groups in research. London, New York: Continuum.

Morgan, D. L. (1998a). The focus group guidebook (Vol. I). Thousand Oaks, London, New Delhi: Sage Publications, Inc.

Morgan, D. L. (1998b). Planning focus groups (Vol. II). Thousand Oaks, London, New Delhi: Sage Publications, Inc.

Nelson, D. R., & Trede, L. D. (2004). Educational needs of beginning producers as perceived by Iowa extension professional staff. Journal of Extension [On-line], 42(1), Available at: http://joe.org/joe/2004february/rb2.shtml

Radhakrishna, R., Nelson, L., Franklin, R., & Kessler, G. (2003). Information sources and extension delivery methods used by private longleaf pine landowners. Journal of Extension [On-line], 41(4), Available at: http://www.joe.org/joe/2003august/rb3.shtml

Richardson, J. G., & Mustian, R. D. (1994). Delivery methods preferred by targeted extension clientele for receiving specific information. Journal of Applied Communications, 78(1), 22-32.

Riesenberg, L. E., & Gor, C. O. (1989). Producers'preferences for methods of receiving information on new or innovative farming practices. Journal of Agricultural Education, 30, 7-13.

Rollins, T. (1993). Using the innovation adoption diffusion model to target educational programming. Journal of Agricultural Education, 34, 46-54.

Rollins, T., Bruening, T., & Radhakrishna, R. (1991). Identifying extension information delivery methods for environmental issues. Journal of Applied Communications, 75(2), 1-9.

Suvedi, M., Campo, S., & Lapinski, M. K. (1999). Trends in Michigan producers'information seeking behaviors and perspectives on the delivery of information. Journal of Applied Communications, 83(3), 33-50.

Suvedi, M., Lapinski, M. K., & Campo, S. (2000). Producers'perspectives of Michigan State University Extension: trends and lessons from 1996 and 1999. Journal of Extension [On-line], 38(1), Available at: http://www.joe.org/joe/2000february/a4.html

Trede, L. D., & Whitaker, S. (1998). Perceptions of Iowa beginning producers toward the delivery of education. Journal of Applied Communications, 82(4), 22-33.

Vergott III, P., Israel, G., & Mayo, D. E. (2005). Sources and channels of information used by beef cattle producers in 12 counties of the Northwest Florida Extension District. Journal of Extension [On-line], 43(2), Available at: http://www.joe.org/joe/2005april/rb6.shtml

Marketing Practices of Indiana Soybean Producers

Corinne Alexander
Extension Economist
Purdue University
West Lafayette, Indiana
cealexan@purdue.edu

Shawn P. Conley
Soybean Extension Specialist
University of Wisconsin-Madison
Madison, Wisconsin
spconley@wisc.edu

Craig Dobbins
Extension Economist
Purdue University
West Lafayette, Indiana
cdobbins@purdue.edu

Chris Hurt
Extension Economist
Purdue University
West Lafayette, Indiana
hurtc@purdue.edu

George Patrick
Extension Economist
Purdue University
West Lafayette, Indiana
gpatrick@purdue.edu

Introduction

Over the last decade, agricultural policy has become more market oriented, exposing soybean producers to greater commodity price and income variability. Concurrently, South America has dramatically increased soybean production, greatly intensifying international competition. Between the competition from South American and rising input prices, soybean profit margins have narrowed, generating producer interest in value-added markets. Today, more than ever, producers' access to markets and use of forward pricing tools may have a major impact on their income.

A Guide for Extension Educators

There are several objectives for the survey of the marketing practices used by soybean producers reported here. While the survey was conducted in Indiana, these results will extend to all soybean producers. The objective of the project is to aid Extension and research faculty in developing Extension programs and educational materials that meet current and future clientele needs.

One existing need is to provide information that producers can use to benchmark their marketing practices; the most common questions in educational programs on marketing are "How common is forward pricing? Who is doing it?" Another existing need is to understand the types of markets available to Indiana soybean producers as well as how far producers are willing to haul their soybeans. Furthermore, this survey will provide a benchmark with which to measure the market impact of soy diesel; since this survey was conducted there have been several announcements of biodiesel facilities to be constructed in Indiana.

Methodology

A seven-page direct mail survey was sent to a sample of 5,000 Indiana soybean growers in August of 2005. Purdue University consulted with the Indiana Agricultural Statistics Service (IASS) to develop and distribute the survey to soybean growers representing various farm sizes and geographic regions within Indiana. IASS generated the mailing list, distributed the surveys, conducted follow-up phone calls to non-respondents, and entered all of the data into a database.

Once the information was entered into the database, all of the personal information about respondents was deleted. The database was then transferred to Purdue University for statistical analysis. The statistical inferences given in this report were developed using chi-square tests (p ≤ 0.05). Statistical analysis was conducted using SAS (SAS Institute Inc. Cary, NC.).

A total of 1330 growers completed the survey. The response rate of 27% was similar to the response rates reported by others (Bourgeois, Morrison, & Kelner, 1997; Czapar, Currey, & Wax, 1997). For presentation purposes the survey was broken into three sections: crop management, pest management, and crop marketing. Responses to each question were characterized by farm size and crop reporting district.

In this article, we focus on grain marketing differences found across farm size and region. The farm size responses are grouped into the following total cropland categories: 0-99 acres, 100-249 acres, 250-499 acres, 500-999 acres, and 1,000+ acres, with 206, 320, 263, 262, and 259 respondents, respectively (1,310 total). The crop reporting districts were grouped into three regions: north, central, and south, with 507, 470, and 353 respondents, respectively (1,330 total). We chose to examine these three regions because of their differences in climate and types of soybean buyers.

Results

Available Markets

Specialty soybean market access is an important issue for producers. Specialty soybean buyers and seed companies offer producers an opportunity to deliver value-added soybeans that receive a premium. Producers can also receive higher prices when there is competition between multiple buyers. We examine the types of markets normally available to producers with respect to farm size and to region (Tables 1 and 2).

The local elevator is the dominant market for soybeans in Indiana. Regardless of farm size, the majority of producers (87.1%) in Indiana consider a local elevator as a market. Northern Indiana producers are more likely to say they deliver to a local elevator than producers in central and southern Indiana.

Overall, larger producers are significantly more likely to say that they have access to crushing plants, an elevator more than 30 miles away, seed companies, and specialty soybean markets. As will be discussed in the next section, this partly reflects a willingness on the part of larger growers to haul their soybeans further. Larger farmers may also be better informed about their markets and may be more willing to provide the additional effort to produce under contract for seed and specialty markets.

Clearly, there are regional differences in Indiana soybean markets. Producers in central Indiana are significantly more likely to say they have access to a crushing plant, which reflects the presence of several large soybean crushing plants in central Indiana. Producers in southern Indiana are significantly more likely to deliver to an elevator more than 30 miles away, which could reflect either the pull of the terminal markets along the Ohio River or the larger distances between buyers in the region. Producers in central and northern Indiana are significantly more likely to say they deliver to a seed company, probably a reflection of the several major seed companies located in the region. One implication of these findings is that the marketing decisions reported by producers in different regions are based on the types of available markets, which vary between the regions. Soybean producers in other states will make marketing decisions based on the available markets.

Producers with an available specialty market were asked to detail their alternatives. The dominant specialty soybean market is for non-GMO soybeans (60%), followed by STS soybeans (12%), tofu or food-grade soybeans (12%), and finally low linolenic soybeans (5%). Given that the low linolenic soybean market is relatively new, 5% of the specialty soybean production is significant. While there are regional differences in specialty soybean markets, with central and southern Indiana having more farmers delivering to specialty markets than northern Indiana, these differences were not statistically significant.

Very few producers (2.7%) deliver to "other" markets. When asked to identify the "other" market, most producers say that they delivered to a river terminal (52%) or directly to a livestock operation (18%). While farm size is not a factor in delivering to "other" markets, producers in southern Indiana are significantly more likely to deliver to an "other" market, which is logical given that the majority of these "other" markets are on the Ohio River in southern Indiana.

Distance to Market and Method of Hauling

Producers' distance to market is an indicator of the number of buyers in an area (i.e., shorter distances indicate more buyers). Producers who are willing to haul their soybeans further will have access to more markets. Finally, distance to market also has implications for the cost of delivering soybeans.

Overall, larger farmers are significantly more likely to haul their soybeans a longer distance which increases market access (Table 3). Over 60% of the smallest farms (1-99 acres) haul their soybeans less than 10 miles, compared to around 50% for medium farms (100-999 acres) and only 37% for the largest farms (1,000+ acres).

There are significant regional differences in the distance producers haul their soybeans to market, which is reflected in the average hauling charge (Table 4). Producers in northern Indiana tend to haul their soybeans the shortest distance and have the lowest average hauling charges for hired trucks, followed by central Indiana, while producers in southern Indiana haul their soybeans the longest distances and have the highest average hauling charges. Only 38% of producers in northern Indiana haul more than 10 miles, compared to 52% in central Indiana, and 74% in southern Indiana.

Larger farmers are significantly more likely to transport soybeans with their own truck than smaller farmers (Table 5). Smaller farmers are significantly more likely to not own a truck, but instead hire someone else to haul their soybeans. For instance, 40% of farms that are 1-99 acres hire someone else to do their hauling, compared to 9% of farms over 1,000 acres.

Farmers who have longer distances to market appear to be more likely to hire someone else to haul their soybeans (Table 6). Of those who haul more than 10 miles, only 45% do it all themselves compared, to 69% of those who hire someone else to do the hauling and 64% of those who both do their own hauling and hire someone else to do it.

Forward Pricing

Some previous research suggests that producers can enhance their returns by pricing a portion of their crop production prior to harvest (Wisner, Blue, & Baldwin; Hagedorn, Irwin, Good, & Colino). As a consequence, many of the Extension efforts in the marketing area have focused on educating producers about a) the benefits of forward pricing, and b) how to use a variety of contracts to forward price. Producers were asked which pricing tools they used to price soybeans prior to July 15, 2004 and prior to July 15, 2005 (Tables 7-9).

In both 2004 and 2005, larger farmers were significantly more likely to forward price and to use more than one forward pricing tool compared to smaller farmers. For instance, 10% of producers with less than 100 acres used forward pricing tools in 2004 compared with 57% of producers with more than 1000 acres. There are several explanations for why larger producers are more likely to forward price. First, larger producers have more bushels to price and most pricing tools require a minimum of 1,000 bushels or more. Futures contracts require that the producer price in units of 5,000 bushels. For example, a 100-acre producer with a 50:50 corn soybean rotation and an average yield of 50 bushels of soybeans would only expect to produce 2,500 bushels of soybeans, an amount too small for using futures hedges. Second, many pricing tools are very demanding of a producer's time and effort. The larger the operation, the more producers can spread this cost in time and effort over many units. Third, and more importantly, larger producers tend to earn a larger share of their household income from farming than smaller producers. With such stakes, managing price risk by forward pricing is much more important to larger producers.

The most commonly used pricing tool is the cash forward contract, with almost 90% of the farmers who forward price using these contracts. The next most popular contracts, used by about 8% of the producers who forward price, are futures hedges and average price contracts. Average price contracts, also referred to as New Generation Contracts, were introduced about 6 years ago and are now used at about the rate of futures contracts indicating growing acceptance. About 5% of producers who forward price use options contracts, 4 to 5% use minimum price contracts, and only about 2 to 3 % use more complex pricing tools which involve more than one position on the same grain.

Conclusions

The results of the survey reported here suggest that farm size and location determine producers' access to markets both in terms of type of market and distance to market. Producers' forward marketing practices vary depending on farm size. Large producers are more willing to haul soybeans longer distances, which increases their access to markets, and they are more likely to use forward pricing tools. Overall, producers and especially small producers show some preference for using the local elevator to market soybeans.

Purdue Extension programming currently offers marketing programs on price risk management for both commodity and specialty soybeans. In order to better meet the marketing needs of Indiana soybean producers, Extension programming should take into account producers' current market environment.

The survey showed that despite major efforts to teach farmers about futures and options, they are used by only a small fraction of farmers. One explanation is that farmers may be limited in using these pricing tools because of the minimum size of the contract rather than their understanding of the use of these contracts. As a consequence, educational programs should place more emphasis on how to make the best use of the cash pricing tools offered by local elevators. Local elevators often have multiple cash pricing tools including spot, forward contracts, hedge to arrive, basis contracts and minimum and maximum price contracts and these contracts link producers to the futures and options markets.

Extension programming could also encourage smaller producers to look more broadly for markets. For example, if a small producer does not have enough grain to fill a semi, he could pool his grain with other small producers and go greater distances to market.

Acknowledgements

The authors would like to thank the Indiana Soybean Board for funding this research and the Indiana Agricultural Statistics Service for their cooperation in developing, distributing, and tabulating the results of this survey.

References

Bourgeois, L., Morrison, I. N., & Kelner, D. (1997). Field and producer survey of ACCase resistant wild oat in Manitoba. Can. J. Plant Sci. 77:709-715.

Czapar, G. G., Currey, M. P., & Wax, L. M. (1997). Grower acceptance of economic thresholds for weed management in Illinois. Weed Technol. 11:828-831.

Hagedorn, L. A., Irwin, S. H., Good, D. L., & Colino E. V., (2005). Does the performance of Illinois corn and soybean farmers lag the market? American Journal of Agricultural Economics, 87(5): 1271-1279.

Wisner, R. N., Blue, E. N. & Baldwin, E. D., (1998). Preharvest marketing strategies increase net returns for corn and soybean growers. Review of Agricultural Economics, 20(2): 288-307.

County Extension Agents' Perceptions of Positive Developmental Assets for Vulnerable Youth

Kenneth R. Jones
Assistant Professor & Youth Development Specialist
kenrjones@uky.edu

Kerri L. Ashurst
Senior Extension Associate
kgoodman@uky.edu

Janet Kurzynske
Associate Professor
jkurzyns@uky.edu

University of Kentucky
Lexington, Kentucky

Introduction

In today's society, there are occurring issues that predispose youth to a multitude of risk factors. Youth development has evolved over the years to address broader concerns, thus going beyond the scope of prevention to emphasizing skill and competency building (Roth, Brooks-Gunn, Murray, & Foster, 1998). With social ills being a culprit, one positive approach to elevating youth development has been to assure that programs are attaining desired results.

Effective youth programming entails an integration of family, school, and community efforts to promote positive development (Flanagan & Faison, 2001; Lerner, 2004). The constant shift from prevention foci to more proactive appeals has instituted an infusion of the "building blocks of positive youth development" (Perkins, Borden, Keith, Hoppe-Rooney, & Villaruel, 2003, p. 10). These building blocks, most commonly known as "developmental assets" (Search Institute, 2005; also, see Benson, 1997; Benson, Leffert, Scales, & Blyth, 1998), are the key factors that help to ensure matriculation into productive, responsible adulthood.

As reported by researchers, the relationship between specific protective factors, such as developmental assets, can lead to healthy outcomes among youth (Benson et. al, 1998; Lerner, 2002; Werner & Smith, 1992). This may include intrinsic characteristics, such as personal values and social/interpersonal skills. On the other hand, external factors, such as adult support, community engagement, and youth leadership, may also play a role in advancing the abilities of youth. Scholars have also concluded that young people who are afforded such opportunities experience less risk and higher rates of positive development (Eccles & Gootman, 2002; Larson, 2000; Vandell & Posner, 1999).

Gathering information from those who live and work within high-risk communities can be an effective way to address priority needs. Identifying potential concerns can aid in formulating concrete goals and objectives, such as providing youth with critical developmental assets. Moreover, youth-serving organizations, such as 4-H, can fill a desired niche in the communities in which they serve. As a result, the likelihood for sustainability is greatly increased through engaged community members. Furthermore, Extension and its community partners are viewed as valuable resources to assist in improving the lives of children, youth, and families.

This article presents findings from a needs assessment conducted to determine priority areas for youth. County 4-H Youth Development (4-HYD) and Family Consumer Sciences (FCS) agents were asked to complete a survey to determine their perceptions of what developmental assets are of most importance to the perpetuation of positive youth development. In addition, data were collected to help assess what target audience (i.e., age group of youth) is in most need of risk-reducing program efforts from Extension.

Background

In November 2000, Kentucky Child Now (a Kentucky 4-H partner) conducted a survey with over 12,000 young people from various communities throughout the state. The survey evaluated the status of 40 developmental assets--key characteristics as presented by Search Institute--that help young people make wise decisions, choose positive paths, and grow up competent, caring, and responsible. On average, Kentucky's youth had access to 19 of the assets, falling short of 21 others that were considered ideal. Equally shocking was that only 27% of the youth indicated they were given meaningful roles (i.e., recognized as leaders) in their community. Kentucky Child Now also proposed recommendations for more adequate programming and resources after conducting a statewide youth policy assessment that revealed gaps in youth services.

In 2001, Kentucky 4-H coordinated Community Conversations on the Future of Youth Development in 108 of the 120 counties in the state. This initiative involved 1,065 youth and 1,702 adults to identify the top issues that were most prevalent in the state. These conversations included dialogue on what actions are necessary to create the brightest future for youth and the entire community. An overwhelming majority of the responses centered upon young people having opportunities to develop essential leadership skills and be able to put them into practice as engaged citizens. In order to gather more data on the needs of youth within communities, the study reported here aimed to address the following questions:

1. Based on agents' perceptions, which key developmental assets are most important for meeting the needs of youth within communities?


2. In regard to program efforts, what age group (of youth) should receive the highest priority from Extension?


3. Is there a difference between 4-HYD and FCS agents' perceptions of the most important key developmental assets?


4. Is there a relationship between agents' perceived importance of key developmental assets?

Methods

The Kentucky Assessment of Needs for Youth at Risk Scale was developed by the state CYFAR (Children, Youth and Families at Risk) team to determine county agents' perceptions toward the importance of key developmental assets in the lives of vulnerable youth. The scale was also used to examine agents' perceptions of which age group (i.e., PreK-3; grades 4-6; grades 7-8; grades 9-12) should receive highest priority from Extension.

The following constructs (i.e., assets) were measured: adult support, youth leadership, personal values, and social competencies. Kentucky 4-HYD agents (n = 122) and FCS agents (n = 80) rated the four constructs on a 5-point Likert-type scale from 1(not important at all) to 5 (very important) based on the level of importance to youth programming in their communities.

As a measure of reliability for the Assessment of Needs Scale, a pilot test was conducted with 4-HYD and FCS agents in Florida, Oregon, and Pennsylvania. Participants in the pilot test were not included in the actual study. The results of the pilot test revealed an overall Cronbach's alpha reliability coefficient of .92. The reliability coefficients for each of the constructs were as follows: Adult Support (.80), Youth Leadership (.84), Personal Values (.77), and Social Competencies (.82).

The study assessed the perceptions of a total of 202 county Extension agents in Kentucky. The scale was administered in the fall of 2005 to 4-HYD agents during a statewide retreat and to the FCS agents during the statewide Annual FCS Update in-service. The scale consisted of 17 items classified into four (4) developmental assets serving as attitudinal constructs for the analysis: Adult Support (five items); Youth Leadership (five items); Personal Values (four items); Social Competencies (three items). Agents ranked each construct based on their perception of the need to focus on specific areas as pertinent developmental assets for youth (1=lowest priority, 5 = highest priority).

Agents also ranked which age group of youth should receive highest priority in regard to program efforts from Extension. Independent t-tests were used to determine any significant differences between perceptions of the 4-HYD and FCS agents. Pearson correlations analyses were also conducted to determine any relationships between the perceived importance of key assets.

Results

Ninety-two percent (92%) of the total number of agents strongly agreed that there is a need to focus on both youth leadership and personal values (Table 1). Moreover, 94% of the county agents indicated that Extension, as a whole, should place emphasis on middle school youth as a high priority for youth programming (Table 2).

Mean scores were computed for each of the developmental asset constructs, thus creating separate index variables (i.e., adult support, youth leadership, personal values, social competencies). A t-test was used to determine significant differences in perceptions of the developmental assets between 4-HYD and FCS agents. As shown in Table 3, both 4-HYD and FCS agents perceived all assets to be important or very important to youth in the state. Hence, there was no significant difference found between the perceptions of the 4-HYD and FCS agents on any of the four constructs.

Given that the t-test model results revealed no significant differences between the perceptions of the Kentucky 4-HYD and FCS agents, Pearson's correlations analyses were used to determine relationships between the importance of specified developmental assets as perceived by agents. There were significant, positive relationships found between perceptions toward adult support, youth leadership, personal values and social competencies.

Moderate, positive correlations were found between: adult support and youth leadership(r= .53, p< .01); youth leadership and personal values (r = .56, p<.01); youth leadership and social competencies (r = .54, p<.01); personal values and social competencies(r = .52, p<.01). Although statistically significant, adult support and personal values(r = .38, p<.01) and adult support and social competencies (r = .30,p <.01) had lower correlations.

Conclusions & Implications

Although middle school-aged youth were viewed as a primary target audience, no significant differences were found between the perceptions of the 4-H YD and FCS agents. However, there were positive relationships between agents' perceptions toward the importance of key assets for youth development. Agents who felt as though adult support was very important to nurturing youth development also felt that youth leadership, personal values, and social competencies were important developmental assets. Hence, this finding suggests that if agents are to collaborate on youth programs, there should be some consistency in determining what assets are essential.

These findings are consistent with the literature that indicates the need for youth to have access to caring adults and opportunities to develop life skills (Jarret, Sullivan, & Watkins, 2005; Ferrari, 2003; Scheer, 1997). Youth leadership and social competencies were also ranked high as being important to the youth development process. Scholars have reported that youth having opportunities to take on meaningful roles foster decision-making abilities and leadership and social skills (Checkoway, 1996; Flanagan & Faison, 2001). These findings indicate that the agents are aware of the importance of key developmental assets that can strengthen the lives of young people.

Based on these findings, Extension should conduct periodic assessments to prioritize youth program efforts. Emphasis should be placed on age groups in most need of age-appropriate programming. In the case of the study reported here, youth in middle school were deemed the audience that could benefit the most from Extension programs. Due to this critical time of transitions for youth, Extension and other youth-serving organizations must take a proactive stance to ensure their well-being. Moreover, organizations must remain conscientious not to neglect middle school youth in a quest to address the issues of younger children and adolescents.

Collaborations among all Extension programs (i.e., 4-HYD, FCS, and Agriculture/Natural Resources) would also be useful in designing and evaluating programs that promote positive development among youth within communities. Extension staff may also want to solicit the opinions of those youth and adults directly affected by programs. While creating attractive opportunities for specific target audiences, this information is especially relevant for developing strategies that ensure sustainability among youth programs in the future.

References

Benson, P. L. (1997). All kids are our kids: What communities must do to raise caring and responsible children and adolescents. San Francisco, CA: Jossey-Bass.

Benson, P.L., Leffert, N., Scales, P. C., & Blyth, D. A. (1998). Beyond the "village " rhethoric: Creating healthy communities for children and adolescents. Applied Developmental Science, 2(3), 138-159.

Checkoway, B. (1996). Adults as allies. W.K. Kellog Foundation. Battle Creek, MI: Available at: http://www.wkkf.org/pubs/YouthED/Pub564.pdf

Eccles, J. S., & Gootman, J. A. (Eds.). (2002). Community programs to promote youth development. Committee on Community-Level Programs for Youth. Washington, DC: National Academy Press.

Ferrari, T. (2003). Working hand in hand. In F. A. Villaruel, D. F. Perkins, L. M. Borden & J. G. Keith (Eds.), Community youth development: Programs, policies, and practices (pp. 201-223). Thousand Oaks, CA: Sage.

Flanagan, C. A., & Faison, N. (2001). Youth civic development: Implications of research for social policy and programs. Social Policy Report, 15(1). A report of the Society for Research in Child Development. Available at: http://www.srcd.org/documents/publications/SPR/spr15-1.pdf

Jarret, R. L., Sullivan, P. J. & Watkins, N. D. (2005). Developing social capital through participation in organized youth programs: Qualitative insights from three programs. Journal of Community Psychology. 33(1), pp. 41-55.

Kentucky Child Now (2000). Healthy kids, better students. Available at: http://www.kychildnow.org/publications/index.html

Kids Count (2004). State-level data. Available at: http://www.aecf.org/kidscount/sld/

Larson, R. (2000). Toward a psychology of positive youth development. American Psychologist. 55, pp. 170-183.

Lerner, R. M. (2004). Liberty: Thriving and civic engagement among America's youth. Thousand Oaks, CA: Sage Publications.

Lerner, R.M. (2002). Adolescence: Development, diversity, context, and application. Upper Saddle River, NJ: Prentice Hall.

Perkins, D. F., Borden, L. M., Keith, J. G., Hoppe-Rooney, T. L., & Villarruel, F. A. (2003). Community youth development. In F. A. Villaruel, D. F. Perkins, L. M. Borden & J. G. Keith (Eds.), Community youth development: Programs, policies, and practices (pp. 1-23). Thousand Oaks, CA: Sage.

Roth, J., Brooks-Gunn, J., Murray, L., & Foster, W. (1998). Promoting healthy adolescents: Synthesis of youth development program evaluations. Journal of Research on Adolescence, 8(4), 423-459.

Scheer, S. D. (1997). Programming parameters for 5-to-8-year-olds in 4-H. Journal of Extension [Online], 35(4). Available at: http://www.joe.org/joe/1997august/a2.html

Search Institute (2005). 40 Developmental assets. Available at: http://www.search-institute.org/assets/

Vandell, D. L., & Posner, J. K. (1999). Conceptualization and measurement of children's after-school environments. In S. L. Friedman & T.D. Wachs, (Eds.), Assessment of the environment across the life span (pp.167-198). Washington, DC: American Psychological Association Press.

Villaruel, F. A., Perkins, D. F., Borden, L. M., & Keith, J. G. (2003). Community youth development: Programs, policies, and practices, Thousand Oaks, CA: Sage.

Werner, E. E., & Smith, R. S. (1992). Overcoming the odds: High risk children from birth to adulthood. Ithaca, NY: Cornell University.

The Motivation for and Developmental Benefits of Youth Participation in County 4-H Fairs: A Pilot Study

Mary E. Arnold
4-H Youth Development Specialist
Oregon State University
Corvallis, Oregon
mary.arnold@oregonstate.edu

Jana L. Meinhold
Child and Family Studies
Portland State University
meinhold@pdx.edu

Tammy Skubinna
4-H Youth Development Agent
Oregon State University
Corvallis, Oregon
tammy.skubinna@oregonstate.edu

Carolyn Ashton
4-H Youth Development Agent
Oregon State University
Corvallis, Oregon
carolyn.ashton@oregonstate.edu

Introduction and Review of Literature

For most 4-H agents, summer means one thing--FAIR! This long-standing tradition in many 4-H programs consumes a great deal of time and energy, and sometimes leaves agents wondering if their time would be better spent doing other youth development programming. Traditionally, the county 4-H fair is viewed as a way for 4-H youth to showcase their project work, receive recognition for their efforts, and develop leadership and teamwork skills (Diem & Rothenburger, 2001), but the fair can also provide important opportunities for positive youth development.

Two of the main goals of the 4-H program are to help build life skills and increase developmental outcomes in youth. Hendricks (1996) developed a comprehensive framework of the different life skills that 4-H programs help youth to develop. This framework is one of the main foundations for describing the effect of 4-H programming to date.

Recently, however, there has been a more in-depth analysis of the developmental benefits of positive youth development programs that allows us to describe the effect of 4-H programs beyond life skill development. Roth (2004) outlines the benefits of youth development programs as an increase in levels of confidence, caring, connection, character, and competence (often referred to as the five "Cs" of positive youth development). Lerner, Dowling, and Anderson (2003) call the 5 "C's" "functionally valued behaviors" and propose that attaining these outcomes increases a young person's thriving, which in turn leads to positive development through to adulthood. One of the identifiers of positive adulthood is the degree to which a person is a contributing member to self, family, community, and society.

Thus, an additional "C" developmental outcome has been conceptualized as "contribution" (Pittman, Irby, & Ferber, 2001).

In 4-H youth development programs, life skill and developmental outcomes are accomplished through non-formal educational opportunities (Russell, 2001) that take place in settings that provide opportunities for belonging, mastery, generosity, and belonging (Kress, 2004). Although unique in structure, the county 4-H fair fulfills these programmatic requirements and provides an important venue for youth development. Despite this recognition, very little research evidence has been gathered to support the effectiveness of fairs.

The purpose of the study reported here was to determine the impact of fair on youth development outcomes. In addition, the study looked at the motivation of youth for participating in county fair. If fair is an effective venue for youth development, then a clearer understanding of why youth choose to participate in fair can help with future programming efforts.

Methodology

The study took place in two adjacent counties in the summer of 2004. These sites were chosen because the 4-H agents in the counties were interested in assessing the impact of county fair participation and agreed to serve as pilot counties for a potential future statewide evaluation of county fair participation.

Participants

Intermediate and senior 4-H members in both counties (N = 718; 332 from one county and 386 from the other) who signed up to participate in their 2004 county fair were selected for participation in the study. Responses were obtained from 199 participants, for a 28% overall return rate (31% from one county and 25% from the other). Twenty-nine percent of the respondents were boys, and 71% were girls, which is approximately the gender distribution of 4-H members in the two counties and statewide. Age of the respondents ranged from 12 to 18.

Instruments

A questionnaire was developed specifically for the study. In addition to basic demographic information, including county fair participation, the instrument contained a set of questions about motivations for participation in fair. For these questions, respondents were asked to rate how important each item was to their participation in fair. The ratings were made on a five-point Guttman scale, with a rating of "1" indicating "not important" and a rating of "5" indicating "extremely important." Internal reliability (Cronbach's alpha) for this set of items was .80.

The survey also included six scales designed to measure specific developmental outcomes. The Rosenberg Self-Esteem Scale (Rosenberg, 1989) contains 10 items. Respondents were prompted to respond to each of the 10 statements using a 4-point Likert scale indicating their level of agreement or disagreement with each of the statements (Strongly disagree [1], disagree [2], agree [3], and strongly agree [4]).

The Proactive Coping Scale (Greenglass, Schwarzer, & Taubert, 1999) contains 14 items. Respondents are prompted to respond to each of the 14 statements using a 4-point Guttman scale indicating their level of agreement or disagreement with each of the statements (not true at all [1], barely true [2], somewhat true [3], and completely true [4]).

In addition to the established scales, four scales were created to measure four of the "C" developmental outcomes identified by Roth (2004). Items for the scales were developed by the first author in consultation with seven youth development practitioners who provided refinement and content validation (Carmines & Zeller, 1991). The scales were pilot tested in a previous study and the results from the pilot suggest that each scale possesses good psychometric properties, including high internal reliability, face and content validity, and factor structure (Arnold & Meinhold, 2004).

The character scale is composed of nine items that assess the positive values and integrity of youth. Two of the items are reverse-scored. The connection scale is composed of nine items that assess the feelings of connection to peers, family, teachers, and their community. Two of the items are reverse-scored. The caring scale is composed of eight items that address the feelings and emotions youth have towards others, including friends, family, and "others." Two of the items are reverse-scored.

Finally, the contribution scale is composed of seven items and assesses the level of value an individual places on personal, familial, and civic co