February 2007
Volume 45 Number 1

Research in Brief

Extension Education and Volunteer Service: Assessing Motivation and Action

Lynetta V. Cleveland
Conservation Intern
Natural Resource Conservation Service
Willmar, Minnesota
lynettavc@yahoo.com

Jan R. Thompson
Associate Professor
Department of Natural Resource Ecology and Management
Iowa State University
Ames, Iowa
jrrt@iastate.edu

Introduction

Extension educators increasingly rely on the efforts of volunteers to provide service to enhance the reach and impact of Extension programming (Boyd, 2004). At the same time, Extension program developers have been faced with the need to account for program effectiveness and to demonstrate impact (Aguilar & Thornsbury, 2005).

To address both imperatives, program facilitators have engaged in evaluation of program content and delivery by measuring learning that would enable participants to provide such service (e.g., Jayaratne, Hanula, & Crawley, 2005). Many Extension educators also track levels of volunteer activity after program completion (e.g., Meyer & Hanchek, 1997). In both cases, information provided by carefully structured evaluations is useful for formative and summative purposes (Mark, Henry, & Julnes, 2000). The link between specific program content and level of volunteer engagement, however, has not been frequently examined by Extension educators, in spite of the fact that such information would be helpful in modifying program content and delivery to enhance service.

Volunteer motivations for service have been studied in a variety of contexts to determine the underlying reasons for helping behaviors, to identify factors related to frequency and duration of volunteer service, and to examine changes in participant attitudes following volunteer activities (Omoto & Snyder, 1995; Donald, 1997; Clary et al., 1998; Ryan, Kaplan, & Grese, 2001). These efforts have identified several motivation factors that are related to voluntary action in general, including individual value sets, the desire to learn, personal development, community involvement, and enhancement of self-esteem (Omoto & Snyder, 1995). In one effort to identify motivations for volunteer action by participants in an Extension education program (focused on youth literacy), Schmiesing, Soder, & Russell (2005) found that participants' values (altruism) were most important in the decision to engage in volunteer activity. However, these workers did not attempt to assess degree of involvement as a function of motivation.

We were interested in examining motivations for service and the relationship between motivation and the level of voluntary action by graduates of an Extension education effort, the Iowa Community Tree Steward (ICTS) program. The goal of this program is to educate citizen volunteers to care for and help manage community tree resources throughout the state of Iowa. The program provides information on tree identification, proper tree planting and maintenance, tree diseases, insect pests, and how to implement community tree programs within their communities. Participants in the ICTS program complete 24 hours of training and are expected to contribute a total of 24 hours of community service.

As of 2004, 519 people had completed the ICTS program and according to program staff records had contributed a total of 14,971 hours of service. This amounts to an average of 29 hours per person (121% of what is requested), indicating success at engaging participants in community tree care. However, volunteer hours have not been evenly distributed, as some volunteers have contributed hundreds of hours, while others have not contributed any.

Understanding the link between program characteristics and volunteer motivation/action is important for both accountability and for increasing the capacity for resource management. Thus, the objectives of the study reported here were twofold: (1) to assess factors affecting motivation to contribute volunteer hours; and (2) to examine the relationships between motivation factors and the activity level of participants.

Materials and Methods

Survey Administration and Content

We used a mail questionnaire designed to survey graduates of the Iowa Community Tree Steward program (ICTS) about their experiences after completing the program. The questionnaire was mailed in the fall of 2004 to 374 ICTS graduates who had completed the program between 1994 and 2004 (those with verified address records, including only one member of two-member households where both participated). We followed the Total Design Method of Dillman (2000). Each survey was accompanied by a cover letter explaining the study and a pre-paid response envelope. Follow-up postcards and second surveys were mailed to non-respondents at two-week intervals after the first mailing.

The survey contained 31 questions about motivation, advocacy for trees and natural resources, types of activities that volunteers engaged in, and impact on community tree management. Questions were presented in three formats: multiple choice, a five-factor Likert scale (strongly agree to strongly disagree), or as "yes" or "no" queries. Between three and five questions were posed to respondents about motivation in each of five areas that are known to influence volunteers in other contexts: values, understanding, personal development, community concern, and esteem enhancement (Omoto & Snyder, 1995).

Data Analysis

Summary data were tabulated to describe percentages of survey participants that selected one of five Likert-scale responses to each questionnaire statement. Statistical analyses included calculating Cronbach's alpha (Cronbach, 1951) for the motivation subscales (values, understanding, personal development, community concern, and esteem enhancement) using the ALPHA function in SAS.

A regression analysis procedure was used to examine relationships between (1) number of years since ICTS participation, (2) employment status, and (3) aggregate responses to motivation subscale items with reported total volunteer hours using the PROC GLM function in SAS. Cronbach's alpha values > 0.70 were accepted for internal consistency for the subscales tested (Nunaly, 1978). Statistical significance for the regression analyses was determined for p < 0.05.

Results

Respondent Characteristics

We received 219 completed questionnaires and 14 incomplete surveys (out of 374 mailed), for a response rate of 62%. The majority of respondents were males (60%), and the average age of respondents was 52 years. Most respondents were full-time employed (56%), followed by retired persons (26%) and part-time employed individuals (11%).

Responses to Motivation Statements

A majority of respondents agreed with statements related to values and strongly agreed with statements related to understanding (examples are provided in Table 1). Survey respondents also agreed with statements related to personal development and community concern. Respondents generally disagreed with items related to esteem enhancement. Values of Cronbach's alpha indicated good internal consistency for two of the motivation subscales (values and personal development) as well as for motivation overall (Table 2).

Reported Volunteer Service

Respondents reported a wide range of volunteer service, from zero to over 100 total hours (Figure 1). The average length of participation was 5.6 years, and average volunteer service was close to 15 hours per year. About one-fourth of respondents indicated cumulative service levels below that requested by ICTS program facilitators. However, a significant proportion (37%) reported serving totals of over 100 hours.

Figure 1.
Total Number of Hours Served and Average Annual Volunteer Hours

Regression Analyses

The regression model did not indicate a significant relationship between employment status and hours of service (Table 2). The model did indicate significant relationships between length of time since program enrollment and total hours of service and between aggregate responses to personal development items and total hours of service (Table 2). Relationships with total hours of service were detected for values, understanding, and community concern, although low internal consistency was determined for items on the understanding and community concern subscales. Responses to esteem items also were not internally consistent and in aggregate not related to total hours of service (Table 2).

Discussion

Respondent Characteristics

We were surprised to learn that many respondents were employed full-time, since retirees would be more likely to have time to devote to participation in the program itself as well as subsequent volunteer activity. We were also surprised that there was no significant relationship between employment status and hours of volunteer service. The majority of ICTS graduates have pursued both training and volunteer work in addition to full-time jobs, which does not appear to influence the level of volunteer time committed to the program.

Responses to Motivation Statements

Respondents most strongly agreed with statements related to understanding, followed by values and community concern. This suggests that respondents' desire to learn new skills and their interest in helping the environment and promoting community betterment are important motivators for initial participation in the ICTS program. These findings support those of previous research with volunteers in natural resource-related programs (Still & Gerhold, 1997; Donald, 1997; Ryan, Kaplan, & Grese, 2001). These responses also reflect the materials used to recruit participants for the ICTS program, which outline the program's focus on education and resource management for community betterment.

However, respondents' level of agreement with items related to the five subscales was not strongly related to their level of volunteer service. This corroborates earlier work examining the relationship between respondent rankings of motivation factors and their actual service in both human services (e.g., Omoto & Snyder, 1995) and natural resource stewardship contexts (Ryan, Kaplan, & Grese, 2001).

Regression Analyses

Our overall model accounted for almost 30% of the variation in number of reported hours. We found a significant relationship between length of time since program enrollment and number of total hours of service, which we expected but included in the overall model to allow us to subsequently detect the effects of other variables.

In spite of respondents' relatively high rankings of understanding, community concern, and values items, the strongest relationship we detected between the motivational factors and total hours of service was for the personal development subscale. This also corroborates previous reports (Omoto & Snyder, 1995; Ryan, Kaplan, & Grese, 2001) that volunteers are most strongly encouraged by interaction with others, providing impetus to engage in volunteer activities more frequently and for longer periods of time.

Extension educators need to be cognizant of the apparently consistent contradiction between participant rankings of motivational elements (typically high for learning/understanding, values, and community concern) versus those that appear to be more closely linked to volunteer service (personal development, social interaction). In addition, for many programs it may be appropriate to shift the focus of recruiting materials to attract and retain volunteers based on factors that lead to greater commitment (as suggested by Clary et al., 1998; Schmiesing, Soder, & Russell, 2005).

It is also important to note that a number of other factors we did not investigate could be related to volunteer commitment--quality of the program itself, competence of staff conducting the program, variations in recruitment, and demographic factors that we did not assess.

Conclusion

From the perspective of Extension educators, a return on the investment made in program development and delivery via voluntary action by participants after program completion is increasingly important. Participants' enrollment in some Extension programs appears to be strongly linked to the importance they assign to learning and skill development. However, regression analyses have consistently indicated that respondents' rankings of importance for different motivational factors are not necessarily related to the frequency or duration of their volunteer activities. Thus, evaluation aimed at understanding the relationship between program characteristics and subsequent voluntary activity should go beyond analysis of participants' self-reported motivations.

Coordinators for the ICTS program are interested in increasing the duration and frequency of volunteer participation in active resource management. Our results indicate that volunteer efforts are in fact ongoing--that is, length of time since enrollment is positively related to hours of service.

However, our results also indicate that program recruitment should address the importance of personal development, perhaps by inviting participation of small groups (rather than individuals) from the same community to increase voluntary action after program completion. In addition, program coordinators may wish to create or support opportunities for volunteers to conduct activities in groups (during the program itself or by facilitating their knowledge of potential future project partners) to gain more consistent and possibly greater levels of volunteer service.

References

Aguilar, C., & Thornsbury, S. (2005). Limited resources--growing needs: Lessons learned in a process to facilitate program evaluation. Journal of Extension [On-line], 43(6). Available at: http://www.joe.org/joe/2005december/a3shtml

Boyd, B. (2004). Extension agents as administrators of volunteers: Competencies needed for the future. Journal of Extension [On-line], 42 (2). Available at: http://www.joe.org/joe/2004april/a4shtml

Clary, E., Snyder, M., Ridge, R., Copeland, J., Stukas, A., Haugen, J., et al. (1998). Understanding and assessing the motivations of volunteers--A functional approach. Journal of Personality and Social Psychology, 74 (6), 1516-1530.

Cronbach, L. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297-334.

Dillman, D. (2000). Mail and Internet surveys: The tailored design method. New York: Wiley.

Donald, B. (1997). Fostering volunteerism in an environmental stewardship group: A report on the task force to bring back the Don, Toronto, Canada. Journal of Environmental Planning and Management, 40(4), 483-505.

Jayaratne, K., Hanula, G., & Crawley, C. (2005). A simple method to evaluate series-type Extension programs. Journal of Extension [On-line], 43(2). Available at: http://www.joe.org/joe/2005april/tt3shtml

Mark, M., Henry, G., & Julnes, G. (2000). Evaluation: An integrated framework for understanding, guiding, and improving policies and programs. San Francisco: Jossey-Bass.

Meyer, M., & Hanchek, A. (1997). Master gardener training costs and payback in volunteer hours. HortTechnology 7(4), 368-370.

Nunaly, J. (1978). Psychometric theory. New York: McGraw-Hill.

Omoto, A., & Snyder, M. (1995). Sustained helping without obligation: Motivation, longevity of service, and perceived attitude change among AIDS volunteers. Journal of Personality and Social Psychology, 68(4), 671-686.

Ryan, R., Kaplan, R., & Grese, R. (2001). Predicting volunteer commitment in environmental stewardship programs. Journal of Environmental Planning and Management, 44(5), 629-648.

Schmiesing, R., Soder, J., & Russell, S. (2005). What motivates volunteers to serve in a youth literacy program? Journal of Extension [On-line], 43(6). Available at: http://www.joe.org/joe/2005december/rb4shtml

Still, D., & Gerhold, H. (1997). Motivations and task preferences of urban forestry volunteers. Journal of Arboriculture, 23(3), 116-129.

Strengthening Our Partnering Efforts to Aid Rural, Low-Income Families by Listening to Employer Experiences

Margaret Manoogian
Assistant Professor
Family Studies
Ohio University
Athens, Ohio
manoogia@ohio.edu

Sally R. Bowman
Extension Family Development Specialist
Oregon State University
Corvallis, Oregon
bowmans@oregonstate.edu

Debra Minar Driscoll
County Family and Community Development Faculty
Oregon State University
Dallas, Oregon
debra.driscoll@oregonstate.edu

Extension has a rich history of working with displaced agricultural workers and limited resource families to help them achieve and maintain financial self-sufficiency. Extension also has assisted small-, micro-, and home-business owners and worked with rural communities on economic and community development (Lasley & Korsching, 1984; Roper & Davis, 2000; Taylor, 1989). Working with the business community, Extension has contributed to family-friendly policies and targeted employees through brownbag seminars on topics such as child and elder care, stress management, and money management issues (Corbin, 1998).

The Oregon State University Extension Service initiated a collaborative project, the Oregon Workforce Development Project, which sought to understand employer needs and create effective partnerships in a rural county. One goal was to assess the expectations and experiences of rural, small business employers regarding employment needs of adults with limited resources. Because rural employers are accustomed to personal contact within a small-town context, a qualitative methodology was employed. Our intent was to gain a rich understanding of employer experiences and to apply their expertise in developing and strengthening programs and partnerships aiding low-income families. Using the design and analysis methods suggested by Berg (2006) and Strauss and Corbin (1990), we interviewed employers over two points in time.

In cooperation with the county Chamber of Commerce, we recruited employers representing different sectors of the economy. All employers contacted initially by phone agreed to participate. Conducted at the employer's site, interviews included questions on employer hiring and retention needs, the marketability of employees with limited resources, county partnership efforts, and assessment of current programs serving low-income employees and their families. During second interviews, the same questions were posed and employers were asked to assess any changes since initial interviews.

Our sample size (N = 20) was determined when no new information was replicated in the earlier interviews (Glaser & Strauss, 1967). Participants represented 53% of major employers in a rural county with a population of 61,720 when interviews were first conducted in 1999 and a population of 63,450 when second interviews occurred in 2002. Unemployment rates were 5.3% in 1999 and increased to 6.3% by 2002. The county contains a significant Hispanic and migrant population. Major employers include small businesses, manufacturing, and education. Smaller communities in the county still struggle with a declining timber economy.

Interviews first occurred in 1999, 2 years after Congress passed The Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA). Because changes occurred in how federal programs assisted families in need, including increased expectations that welfare recipients seek and obtain employment, we felt that employers would have insights regarding hiring and retaining employees entering the workforce. Three years later, we interviewed employers (N = 18) to see how business perspectives and experiences may have remained the same or changed during a period of economic decline.

Employers predominantly were located in two larger communities within the county and represented the following businesses: restaurants (n = 4), assisted living facilities (n = 2), manufacturing (n = 4), groceries/markets (n = 4), education (n = 1) and other (n = 5). One manufacturing employer and one retail sales employer were unavailable for second interviews.

Indicative of small businesses in rural areas, there was some turnover in the personnel who made hiring decisions (n = 7). Regardless of personnel changes, we were able to gain critical information regarding employment trends and experiences specific to the company and found consistency in attitudes and perceptions across interviews. Interviews were analyzed and coded across both waves of data and themes emerged that focused on candidate pools, employer experiences with employees, and community partnerships. Initial and second interviews were also analyzed by company to assess change over time.

Results

Rural employers shared their perspectives on job applicants and their working relationships, if any, with county organizations serving families with limited resources. Because we interviewed employers during strong economic times and again when economic conditions were less favorable, we were able to gain a broad perspective as to employer hiring practices, attitudes about low-income job applicants, and activities with county agencies and organizations.

Size and Quality of Candidate Pool

When employers were initially interviewed, they appeared satisfied with the size of candidate pools and felt that there was little need to advertise for positions. Some employers indicated that the large candidate pool did not imply quality. One restaurant owner stated that "only 10% of 300 applicants had the potential to be hired." Many employers generally indicated that most of the applicants lacked qualifications and were ill prepared when interviewing for positions. Employer comments particularly focused on the lack of candidates with specific job skills e.g., nurses for assisted living facilities) and their unprofessional attire and personal presentation when meeting employers and picking up applications. One employer explained, "Many of them when they come in for applications the way they are dressed, I wouldn't hire them anyway."

During second interviews, employers were less positive about their hiring needs. One employer reported a hiring freeze, and many others explained that they were fully staffed and/or not hiring. When positions did become available, employers reported large numbers of applicants. One employer exclaimed that she had candidates "applying left and right."

Frequent employer comments regarding the quality and depth of candidate pools indicated that there were little differences across time. For most, the size of candidate pools was strong, yet it was the suitability of applicants that was of most concern. Employers were also aware that they were unable to attract many candidates who would have been more suitable for positions because of wages. One employer explained, "The payscale is minimum wage and most people can't support a family on that so there is always an issue getting those people in here and keeping them."

Perceptions of Potential Employees with Limited Resources

When asked specifically about their experiences with job candidates, all employers indicated that they had hired individuals who were transitioning into positions after welfare reform and subsequent time limitations for aid. Their experiences varied and changed little over time. Some found employees "eager to work" because "they tend to be more hungry and want a stable job." One employer remarked that "some of the employees from families with limited resources are more diligent." She also indicated that single women, often the heads of low-income households, were "actually stronger candidates because they are more driven due to the changes in their lives."

Other employers were less positive about experiences with low-income employees. Employers frequently noted the lack of work ethic, poor attendance, childcare and/or transportation problems, weak interpersonal skills, and inappropriate dress or hygiene among employees. The most often expressed observation concerned employee work ethic, with similar responses during both interviews.

Employers described how employees were often late or absent, failed to notify employers when missing work, or were unable to maintain a strong work orientation over time. The employer at a manufacturing company reported that "90 percent of the terminations that we do are attendance violations." Another employer from a restaurant mentioned that some of his employees "don't know how to work because their families are failing in preparing them to be good workers." Employers particularly elaborated on the lack of work ethic among younger employees, with some noting that these problems were not specific to low-income workers. As one restaurant owner explained, "They [employees] don't do extra and try to excel . . . they do what they are told, nothing more."

Effective Partnering with County Agencies and Organizations

Most employers in initial interviews indicated that they were not aware of agencies or organizations that could assist them in finding viable candidates for their organizations or in supporting employees and their families. In some cases, employers were aware of other county organizations or agencies but refrained from using their services because they had not found the services useful or were unsure as to how they could be specifically helped. Several employers mentioned using high school career centers and seemed impressed, for the most part, by the community partnerships they developed. They also commented positively on the "Certificate of Employability," a regional program that certifies that high school students have completed an educational series that emphasizes interpersonal and work skills.

When asked about any changes they had seen in terms of the preparation, marketability, and retention of low-income job candidates and how county organizations may have stimulated positive outcomes, most employers responded similarly. For example, one employer indicated that he was active in participating in certain job activities initiated through the local JOBS program but observed that he had "not noticed a lot of change." Two employers did mention during second interviews that candidates with limited resources appeared to be more eager to work and had received help with their resumes, applications, and interviewing skills. One employer mentioned specifically that her candidate pool in 2002 seemed to have higher aspirations as they were working harder at getting jobs during hard economic times.

Regardless of county economic shifts, the overall perspective among the majority of employers appeared to remain stable over time. Employers emphasized how their applicant pools and employees with limited resources appeared unprepared for work. As one employer commented, "They don't have the work ethic and they are not trying to get out there to better themselves." One employer offered a rare and different perspective on the changes she had observed over the course of interviews. She observed that the shift in time had little to do with improved applicant pools but rather a change in her hiring expectations. She stated that,

My standards have gone down since the interview three years ago, I could care less if you interview in shorts or whatever. If you are going to do a good job for me, I don't care what you wear or what you look like. I guess I'm not as judgmental as I used to be. Now I am really looking inside the person and if they are wanting a job and if they care.

Conclusions

Our experience with an employer breakfast summit in one rural county convinced us that rural employers and the social service agencies that assist people to move into the workforce were not in touch with each other (Bowman, Manoogian, & Driscoll, 2002). The outcomes from the summit and both sets of employer interviews were shared with the workforce development team members and resulted in more relevant programs. For example, an experiential training program was created in which potential employees who have had difficulties securing employment learn to run a copy/print shop and develop skills to prepare them for office jobs.

Rural employers want employability skills rather than particular qualifications. Intangible characteristics, such as attendance, work ethic, attitude, and appearance, matter in entry-level positions (Wilson & Stewart, 2000). Employers liked the Certificate of Employability program. In this program, wallet-sized certificates signed by the school principal are issued to students whose teachers evaluate them in the categories of personal management, teamwork, problem solving, and communication <http://www.mwec.org/instructors/certificate-employability.php>. Currently, a new employability "soft skills" assessment program has been developed in this county for the local Enterprise Youth Council. This program evaluates a person's strengths and weaknesses and provides suggestions for improvement.

Assisting the working poor and their families in rural areas requires a strategy that includes employers. Because they are likely to know when an employee is having problems that affect job performance, employers in small businesses can benefit from existing Extension programs that could aid employee retention in such areas as transportation and childcare assistance, the Earned Income Tax Credit, or Medicaid (Rupured, Koonce, & Bales, 2002). Some small-business employers will hire a new employee rather than support an existing employee. Others, however, may invest in an employee to reduce turnover and improve productivity, particularly when unemployment rates are low and rehiring costs are high.

Extension has a key role in the development of educational opportunities and materials for both limited resource families and employers in rural areas (Rupured, Koonce, & Bales, 2002). Current and future niches include: teaching basic living skills to the unemployed; assisting the newly or sporadically employed who lack intangible skills and resources to remain employed; working with employers; and bridging the gap between employer and employee (workforce education for both limited resource adults and young adults).

The rural poor are faced with structural challenges that inhibit employability and earnings (Whitener, Weber, & Duncan, 2001). Extension programs can play a role in educating rural employers in such areas as cultural diversity and family-friendly policies as well as bring the needs of rural employers to the agencies and educational organizations that provide workforce education. When we work with individuals, families, and agencies, but overlook small rural employers, we miss the opportunity to bridge the gap between employer and employee needs in rural communities.

References

Berg, B. L. (2006). Qualitative research methods for the social sciences (6th ed.). Boston: Allyn and Bacon.

Bowman, S. R., Manoogian, M., & Driscoll, D. M. (2002). Working with rural employees: An interagency partnership. Journal of Extension [On-line], 40(4). Available at: http://www.joe.org/joe/2002august/rb1.shtml

Corbin, Marilyn. (1998). Trends and emerging issues related to welfare reform. The Forum for Family and Consumer Issues [On-line], 3(2). Available at: http://www.ces.ncsu.edu/depts/fcs/pub/1998/welfare.html

Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Chicago: Aldine.

Lasley, P., & Korsching, P. F. (1984). Examining rural unemployment. Journal of Extension [On-line], 22(5). Available at: http://www.joe.org/joe/1984september/index.html

Roper, R. G., & Davis, K. T. (2000). Share yourself--Work-First mentor education program. The Forum for Family & Consumer Issues [On-line], 5(1). Available at: http://www.ces.ncsu.edu/depts/fcs/pub/2000/showcase_nc_100.html

Rupured, M., Koonce, J., & Bales, D. (2002). Moving the working poor to financial self-sufficiency. Journal of Extension [On-line], 40(2). Available at: http://www.joe.org/joe/2002april/a6.html

Strauss, A., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques. Newbury Park: Sage.

Taylor, W. N. (1989). Targeting transitional clients. Journal of Extension [On-line], 27(2). Available at: http://www.joe.org/joe/1989summer/a2.html

Whitener, L. A., Weber, B. A., & Duncan, G. (2002). As the dust settles: Welfare reform and rural America. In B. A. Weber, G. Duncan, & L. A. Whitener (Eds.), Rural dimensions of welfare reform (pp. 1-24). Kalamazoo, MI: W. E. Upjohn Institute for Employment Research.

Wilson, B. B., & Stewart, D. L. (2000). Employer's perceptions of welfare reform: Implications for Cooperative Extension personnel. Journal of Extension [On-line], 38(5). Available at: http://www.joe.org/joe/2000october/a1.html

Evaluation of a Group Administered 24-Hour Recall Method for Dietary Assessment

Amanda R. Scott
Extension Program Specialist
Expanded Food and Nutrition Education Program
Texas Cooperative Extension
Department of Nutrition and Food Science
College Station, Texas
arscott@ag.tamu.edu

Debra B. Reed
Professor
Texas Tech University
Department of Nutrition, Hospitality, and Retailing
Lubbock, Texas
debra.reed@ttu.edu

Karen S. Kubena
Professor
Texas A&M University
Department of Nutrition and Food Science
Associate Dean for Academic Affairs
College of Agriculture and Life Sciences
College Station, Texas
k-kubena@tamu.edu

William A. McIntosh
Professor
Texas A&M University
Department of Department of Recreation, Park & Tourism Sciences
College Station, Texas
w-mcintosh@tamu.edu

Introduction

The Expanded Food and Nutrition Education Program (EFNEP) provides food and nutrition education to limited resource families. Typically, EFNEP uses individually administered 24-hour recalls (IARs), pre and post intervention, to evaluate the effectiveness of nutrition education lessons. However, this time-intensive procedure makes it difficult for program staff to meet the educational needs of clients and conduct accurate program evaluation. To maximize personnel time, EFNEP of Texas developed a group administered 24-hour recall (GAR) to expedite dietary assessment of adult clients (Suter, 1993).

While IARs are commonly used (Willett, 1998), the use of a GAR has only been reported in children (Farris, Frank, Webber, & Berenson, 1985). Thus, the study reported here evaluated both GARs and IARs compared to observed meals consumed by female food service workers, who served as surrogates for EFNEP clients.

Methods

The study involved meal observation and subsequent individual or group dietary assessment of subjects. Data were collected at nine university dining centers where subjects (female food service workers) were employed and ate meals. Subjects were targeted for this study because they were similar to Texas EFNEP clients in gender and income, and their meals could be observed. Approval to conduct this study was granted by the Institutional Review Board Human Subjects in Research, Texas A&M University. Enrollment was voluntary.

Research teams comprised of a Registered Dietitian and an undergraduate nutrition student spent two consecutive days at each dining facility. All members of the research team received training on interview, observation, and plate waste assessment protocols prior to data collection. Researchers conducting meal observation did not participate in individual or group assessment of subjects.

Day 1 of the study involved recruitment of subjects, collection of demographic and socioeconomic data, and meal observation. Researchers observed subjects consuming lunch at the dining center and then determined plate waste. Subjects knew they were being observed but did not know if they would complete an individual or group recall.

On Day 2, researchers returned to the dining facility to conduct individual or group recalls. Subjects were randomly assigned to a group or individual recalls based on their subject number. Following collection of all data, researchers provided free nutrition education information to subjects as an incentive for their participation.

Meal Observation and Plate Waste Assessment Methods

Meal observation is an objective standard against which other dietary assessment methods are compared (Mertz, 1992). Thus, meal observation served as the reference standard for evaluation of group and individual recalls. In the university dining centers, researchers observed a maximum of four subjects each and recorded food items and portion sizes selected by subjects. The size of the utensil used on the serving line helped researchers estimate initial food portion selected. Following the meal, researchers assessed plate waste to determine final food portions consumed (Baranowski et al., 1986). Subjects did not know that plate waste was evaluated.

Individual Recall Methods

A three-step multiple-pass recall was used for individual dietary assessment of subjects. Passes used in this study included a quick list, detailed description, and review (Guenther, DeMaio, Ingwersen, & Berlin, 1995; Guenther, DeMaio, Ingwersen, & Berlin, 1996). During the first pass, or quick list, subjects listed foods and beverages consumed in any order they chose for a specified 24-hour period. Next, during the detailed description, interviewers used probing questions to gather specific information about foods such as portion size, brand name, and preparation method. Food models and graduated measuring utensils were available to help subjects estimate food portion size. The final pass, or review, involved the interviewer reviewing the recorded information with the subject to check for accuracy. After each pass, the interviewer probed for additional foods or beverages consumed but not initially reported.

Group Recall Methods

The GAR involved the same three passes as described for IARs but modified slightly for a group setting. Subjects completed the first pass of the GAR by writing down all foods consumed (Guenther et al., 1995; Guenther et al., 1996). During the second pass, or detailed description, they recorded detailed information such as portion size, brand name, etc., for each food item. Subjects were encouraged to use the food models and measuring utensils to help them estimate portion size. To simulate probing questions used in individually administered recalls, a poster with seven questions was displayed (Figure 1). These questions were read aloud to subjects. During the final pass, or review, subjects evaluated their recalls for completeness.

GARs were administered by two members of the research team. Subjects assigned to GARs were sub-divided into groups of five and given oral instructions. They completed each pass together with each subject completing their own form. Both research team members were available to assist subjects with reading or writing and moved from subject to subject to answer questions, spot check recalls, and ensure that subjects were completing recalls according to the instructions.

Figure 1.
Questions Read Aloud and Displayed to Subjects During Group Administered Recalls

Nutrient and Statistical Analyses

Food Processor version 7.14 was used for nutrient analysis (The Food Processor, 2000) and SAS, version 6.12 (The SAS System for Windows, 1996) for statistical analyses. A p-value of <0.05 determined statistical significance.

Observational data served as the reference standard for evaluation of group and individual recalls (Figure 2). Statistical analyses compared observational and corresponding recall data (either individual or group administered) for the observed meal (Conway, Ingwersen, & Moshfegh, 2004; Lytle, Murray, Perry, & Eldridge, 1998). However, recall data for the entire 24-hour period were collected.

Figure 2.
Graphical Representation of the Study's Statistical Design

Paired t-tests were performed using both crude and log(e) transformed data sets. All data presented in this document were crude, as results were the same with either data set. Pearson's correlation coefficients were also computed (Lytle et al., 1993).

Differences in demographic and socioeconomic characteristics between groups of subjects were evaluated using t-tests or Chi-Square statistics where appropriate.

Results

Subject Characteristics

Forty-two of 47 eligible women completed the study. Some individuals did not participate, while others were present on Day 1 of the study, but not at the dining facility on Day 2. Only subjects for whom both recall and observational data were collected were used. Forty percent of subjects were Hispanic, 31% African-American, 26% White, and 3% other ethnicities. Using a weighted average, mean monthly household income was $1,173. Mean age of subjects was 41 ± 11.6 years. T-test and Chi-square statistics indicated no significant differences in demographic and socioeconomic characteristics (including income) between sets of subjects completing group or individual recalls.

Evaluation of the Group and Individual Recall Methods

Twenty-three women completed GARs and were observed consuming lunch. Paired t-tests showed no significant differences for energy or selected nutrients (Table 1). Pearson's correlation coefficients calculated between GARs and observational data ranged between 0.15-0.82 (Table 1), and nine were significant. Paired t-tests yielded no significant differences for energy or any nutrient (Table 2) among 19 subjects completing IARs. Correlation coefficients ranged from 0.01-0.92 (Table 2) for IARs, and 10 were statistically significant.

Discussion

Subject Characteristics

Subjects were racially diverse and similar to EFNEP clients in Texas. Typically, more than 90% of Texas EFNEP clients are female. Individuals of Hispanic origin comprised the greatest percentage of both subjects and typical EFNEP clients in Texas. In 2004, 78% of EFNEP clients were Hispanic, 11% were African American, and 8% were White (Expanded Nutrition Program, 2004).

The weighted, mean monthly income reported by subjects in this study was $1,173, a figure below the poverty line for a family of three (The 2006 HHS Poverty Guidelines, 2006). Typically, 70% of Texas EFNEP client families fall below the poverty line for the number of individuals in their home (Expanded Nutrition Program, 2004). Although not a direct comparison, these data suggest that both groups were low-income.

Evaluation of the Group Administered 24-Hour Recall

One objective of the study was to evaluate the accuracy of the GAR compared to an observed meal. Only one study previously reported the use of a group approach to 24-hour recalls. Farris, Frank, Webber, and Berenson compared nutrient estimates from individual recalls and group recall workbooks in children (1985). To our knowledge, evaluation of GAR in an adult population has not been previously reported.

Results of statistical analyses suggest that the GAR may be effective in assessing dietary intakes of the macronutrients, fiber, niacin, thiamin, vitamins D and E, and energy in these subjects.

Evaluation of the Individual 24-Hour Recall

Recent literature that addressed the relationship between observational and recall data on dietary intake of adults could not be found by our research team. In 1985, Karvetti and Knuts reported correlation coefficients between observational and recall data greater than 0.70 for cholesterol and vitamin C as noted in this study (1985). Among children, Lytle, Murray, Perry, and Eldridge reported a similar correlation coefficient (0.48) for fiber as found in this study (0.40) (1998). Emmons and Hayes reported correlation coefficients between 0.60 and 0.92 for energy and several key nutrients among 4th grade children (1973). These were greater than correlation coefficients observed in this study for most nutrients including energy.

Results of t-tests and correlation coefficients suggest the accuracy of the IAR in estimating intakes of certain nutrients, including carbohydrate, fat, vitamins B6, B12, C, D, riboflavin, and folate plus iron. However, these data suggest that the IAR may not be accurate to assess energy or protein as the correlation coefficients were not significant.

Comparison of Group and Individual Recalls

Conventional wisdom may suggest that an IAR would provide more accurate results than the GAR because the interviewer is able to fully utilize probing questions and provide one-to-one assistance to the interviewee.

Although both recall methods yielded results that were comparable to the data from observed meals, results of Pearson's correlation coefficients for the GAR suggest this method may be more accurate. For example, significant correlation coefficients for energy, carbohydrate, and fat greater than 0.60 were noted for the GAR. Data from IARs yielded only significant correlation coefficients for carbohydrate and fat.

One explanation for better reporting with the GAR is the effect of social desirability on subjects. Social desirability is defined as the propensity for an individual to portray an image that follows perceived social norms to avoid criticism when being tested (Hebert, Clemow, Pbert, Ockene, & Ockene, 1995). It is possible that in a group setting, social desirability is reduced as subjects may feel less scrutinized than during an individual interview and are therefore more truthful. However, the potential also exists for group assessment to impair truthful reporting. Further investigation is needed to evaluate the effects of group assessment on levels of social desirability among subjects.

Limitations of Results

One limitation of the research study is the small number of subjects. T-tests used to determine statistical differences between groups did not detect any significant differences. However, with a larger sample size, or repeat administrations of the recall, differences in means may have been detected. Furthermore, a larger sample size would have provided results that are more reliable for all statistical methods used.

Logistics of the study made obtaining a large number of subjects problematic. Food service supervisors found it difficult to allow time during the workday for employees to participate in the study. Some subjects were present on Day 1 for observation but not scheduled to work on the following day. However, the research team considered limitations associated with using these employees instead of actual EFNEP clients to be outweighed by the ability to obtain observational data.

Data analyses in the study used a single meal or a partial recall, which has been reported in other studies (Lytle et al., 1998). One concern with this type of analysis is that the full extent of under- or over-reporting present cannot be evaluated. For example, consumption of between meal snacks is commonly under-reported (Krebs-Smith et al., 2000) and would not be captured with observational data for one meal only. Additionally, all subjects ate the observed meal in a single setting with a finite number of food choices, as opposed to EFNEP clients who would be eating in multiple settings in which a larger number of food choices would be likely.

Valid quantitative and qualitative data can be obtained if meal observation is completed in an unobtrusive manner and subjects do not know they are being observed (Mertz, 1992). In the study reported here, subjects knew they were being observed. The possibility exists that subjects paid more attention to the foods they consumed or altered their dietary habits for the observed meal. However, the purpose of the study was not to capture usual dietary habits of these individuals, but to evaluate the accuracy of the group and individual recall methods. Therefore, modified dietary habits of subjects were not a great concern in this study. Additionally, because subjects completing both group and individual recalls were observed, whatever bias existed was present for both groups of subjects.

Although subjects were demographically similar to Texas EFNEP clients, they were food service workers and may have been able to estimate portion sizes more accurately than other individuals through their use of structured serving sizes on serving lines and in food preparation. The study did not explore the possibility of this bias, and no studies were found that specifically assessed how accurately food service workers estimate food portions. One study reported that Women, Infants, and Children (WIC) clients (similar to subjects) could not accurately estimate portion sizes (Webb & Yuhas, 1988).

A five-step multiple-pass dietary assessment method has been validated and used in national surveys (Conway, Ingwersen, Vinyard, & Moshfegh, 2003). Conway, Ingwersen, Vinyard, and Moshfegh describe the use of the five-step multiple pass method with which Food Model Booklets were used to improve portion size estimates of consumed foods (2003). Testing of the five-step multiple-pass method as part of a GAR with EFNEP clients may show improvements in the correlations between observed and reported recalls compared to the three-step multiple pass method used in this study.

Conclusions

In conclusion, results comparing IARs and GARs to observed intake for one meal suggest that the GAR may be at least as effective in assessing intakes of energy, the macronutrients, and certain micronutrients as the IAR. Although these results were encouraging, further studies of the GAR are needed.

The GAR could be used to expedite dietary assessment of clients participating in nutrition education programs.

Acknowledgements

This study was made possible by the cooperation of the Texas A&M University Department of Food Services.

References

Baranowski, T., Dworkin, R., Henske, J. C., Clearman, D. R., Dunn, J. K., Nader, P. R., et al. (1986). The accuracy of children's self-reports of diet: Family Health Project. Journal of the American Dietetic Association, 86, 1381-1385.

Conway, J. M., Ingwersen, L. A., Vinyard, B. T., Moshfegh, A. J. (2003). Effectiveness of the US Department of Agriculture 5-step multiple-pass method in assessing food intake in obese and nonobese women. American Journal of Clinical Nutrition, 77, 1171-1178.

Conway, J. M., Ingwersen, L. A., Moshfegh, A. J. (2004). Accuracy of dietary recall using the USDA five-step multiple-pass method in men: An observational validation study. Journal of the American Dietetic Association, 104, 595-603.

Emmons, L., & Hayes, M. (1973). Accuracy of 24-hr. recalls of young children. Journal of the American Dietetic Association, 62, 409-414.

Expanded Nutrition Program. (2004). EFNEP report for Texas (FY2003-2004). College Station, TX: Texas Cooperative Extension.

Farris, R. P., Frank, G. C., Webber, L. S., & Berenson, G. S. (1985). A group method for obtaining dietary recalls of children. Journal of the American Dietetic Association, 85, 1315-1320.

The Food Processor (Version 7.14) [Computer software and manual]. (2000). Salem, OR: ESHA Research.

Guenther, P. M., DeMaio, T. J., Ingwersen, L. A, & Berlin, M. (1995, January). The Multiple-pass approach for the 24-hour recall in the Continuing Survey of Food Intakes by Individuals (CSFII) 1994-1996. Presented at the 2nd International Conference of Dietary Assessment Methods, Boston, MA.

Guenther, P. M., DeMaio, T. J., Ingwersen, L. A., Berlin, M. (1996). The multiple-pass approach for the 24-h recall in the Continuing Survey of Food Intakes by Individuals [Abstract]. FASEB Journal, 10, A198.

The 2006 HHS Poverty Guidelines, Fed. Reg. 71, 3843–3849 (Jan. 24, 2006).

Hebert, J. R., Clemow, L., Pbert, L., Ockene, I. S., & Ockene, J. K. (1995). Social desirability bias in dietary self-report may compromise the validity of dietary intake measures. International Journal of Epidemiology, 24, 389-398.

Karvetti, R. L., & Knuts, L. R. (1985). Validity of the 24-hour dietary recall. Journal of the American. Dietetic Association, 85, 1437-1442.

Krebs-Smith, S. M., Graubard, B. I., Kahle, L. L., Subar, A. F., Cleveland, L. E., & Ballard-Barbash, R. (2000). Low energy reporters vs. others: a comparison of reported food intakes. European Journal of Clinical Nutrition, 54, 281-287.

Lytle, L. A., Murray, D. M., Perry, C. L., & Eldridge, A. L. (1998). Validating fourth-grade students' self-report of dietary intake: results from the 5 A Day Power Plus program. Journal of the American Dietetic Association, 98, 570-572.

Lytle, L. A., Nichaman, M. Z., Obarzanek, E., Glovsky, E., Montgomery, D., Nicklas, T., et al. (1993). Validation of 24-hour recalls assisted by food records in third-grade children. The CATCH Collaborative Group. Journal of the American Dietetic Association, 93, 1431-1436.

Mertz, W. (1992). Food Intake Measurements: Is there a "gold standard"? Journal of the American Dietetic Association, 92, 1463-1465.

The SAS System for Windows (Version 6.12) [Computer software and manual]. (1996). Cary, NC: SAS Institute, Inc.

Suter, C. B. (1993). The 24-hour food recall in the EFNEP Evaluation/Reporting System: A programmed learning guide for paraprofessionals in the Expanded Nutrition Program. College Station, Texas A&M University, Texas Cooperative Extension.

Webb, C. A., & Yuhas, J. A. (1988). Ability of WIC clientele to estimate food quantities. Journal of the American Dietetic Association, 88, 601-602.

Willett, W. C. (1998). Nutritional epidemiology (2nd ed.). New York: Oxford University Press.

Small Businesses and the Community: Their Role and Importance Within a State's Economy

Glenn Muske
Associate Professor
glenn.muske@okstate.edu

Michael Woods
Professor
mdwoods@okstate.edu

Jane Swinney
Assistant Professor
jane.swinney@okstate.edu

Chia-Ling Khoo
Graduate Assistant
Oklahoma State University

Oklahoma State University
Stillwater, Oklahoma

Introduction

As communities focus on local economic development efforts, one common response is to key on business and industry attraction or "smokestack chasing." Every community wants the next big large employer. Left out or given minimal attention in many of these economic development plans is support for existing micro businesses, those that employ fewer than 10 employees or are sole proprietorships. These businesses call the community "home" and may be on Main Street, on the outskirts, or a farm/ranch operation. One reason for the lack of attention to micro businesses often stems from a perception that they generate little in terms of jobs and dollars for the community's economic engine.

The purpose of the research reported here was to evaluate micro business contributions to a community's economic engine. The evaluation includes an analysis of the impact of micro businesses overall and as divided among urban, micropolitan, and other areas. Suggestions for the further development of the micro business segment are offered. The goal is to increase awareness and support for this business segment among Extension educators, community/economic development specialists, local leaders, and community members.

Review of Literature

Small businesses, most often defined using the Small Business Administration's definition of "fewer than 500 employees" (Small Business Administration, 2001), are well recognized worldwide as vital and significant contributors to economic development, job creation, and the general health and welfare of economies (Korsching & Allen, 2004; Morrison, Breen, & Ali, 2003). Yet the citizens of smaller towns and rural communities often define "small" far differently. They see a business with 50 to 100 employees as "big" business. A solution is to divide the "small" category into various subcategories. One subcategory is the micro business, a firm that employs fewer than 10 people (Devins, 1999).

Micro businesses form a dynamic, integral part of the market economy, providing goods and services and a gateway by which millions enter the economic and social mainstream of American society (Sexton, 1999). U. S. micro businesses accounted for 94% of all firms and 84% of employer firms. They employ up to 25% of all individuals or over 11% of all employees within employer-only firms (Family Economics and Nutrition Review, 2001; U. S. Census, 2001).

By 2002, 50.1% of the USA's 112.4 million private-sector workers were employed by micro business firms. In one substantially rural state, 77% of firms are micro in size, and they employ 13% of all workers (Small Business Administration, 2001). In the boom 80's, micro businesses generated the majority of new job growth. They continued that role during the slow down of the 1990's. Not only did they absorb all of the jobs that big businesses cut but, in fact, added 225,000 workers (Hopkins, 2005; Small Business Administration, 2004).

Yet for all of the positives surrounding micro businesses and their contributions, there are unanswered questions about them. Three research questions are addressed in the study reported here. First, what is the prevalence rate of micro businesses within a state, and what is the most common type of businesses found?

A related question is if the prevalence rate differs among metropolitan areas, micropolitan areas, and other areas, often labeled incorrectly as rural (Barta, 2003; U. S. Census Bureau, 2005). By the U. S. Census Bureau (2005), metropolitan areas, in the 2000 census, have an urbanized area of 50,000 or more inhabitants; micropolitan areas have an urban cluster of 10,000 to 49,999 inhabitants; and "other" areas are those that do not fit into either of the other two categories.

The third research question looked at two broad indicators of business contribution to the local community in terms of the number of people employed and gross income generated, thus expanding on the work of Muske and Woods (2004). This last question may offer information to local community leaders as to contributions to the community.

Methodology

Even though the numbers and contributions of micro businesses sound significant, it is suspected that many such businesses are missed in research studies. Typically business research draws from a business sampling frame only. Winter, Fitzgerald, Heck, Haynes, and Danes (1998) confirmed this in a study of family businesses of which many micro businesses are a part. To overcome this problem, Winter, et al. (1998) suggested using a household sampling frame and asking if the household owns a business.

For this study, the survey sample consisted of all households within Oklahoma that were included in Oklahoma telephone exchanges. Business phone numbers were excluded from the sampling frame. A computer-based questionnaire for the study was developed and pre-tested with micro business owners for accuracy and completeness. Using random digit dialing, households were contacted by telephone through the OSU Bureau of Social Research. In the fall of 2002, 1,224 household surveys were obtained, a 32% response rate.

Three screening questions were asked. The first question asked if the household owned a business. If yes, a second question asked if a member of the household was also involved in managing the business. Again if a positive answer was received, the last question asked was the number of people employed by the business. If the business employed 10 or fewer employees, a set of questions about the business was asked, and various demographic information was also gathered. Businesses were classified as being located in one of three location categories, metropolitan, micropolitan, and other. Frequencies and percentages were calculated and chi-square analysis, ANOVA and T-tests, based on data type, were used to determine variable significance.

Results

Overall, 221 of the 1,224 respondent households indicated that a business was owned and operated by the household, an 18% frequency rate (Table 1). The rate of business ownership was slightly less in metropolitan areas, 16.9%, and slightly higher in other areas (22.2%), but the differences were not statistically significant. Among the business owners, 68% were considered to be family businesses, with 69% of them based at home and 49% involving both spouses.

The most common businesses operated were service businesses (25.1%). Businesses related to agriculture, forestry, fishing, and hunting were second (20.9%), with retail stores third at 15.2% (Table 2).

The differences in type of business found in each location were significantly different (X² = 31.868, df = 14, p = .004). In metropolitan areas, service businesses were the most common (31.7%), while agriculture businesses were the most common in micropolitan areas (24.4%) and other areas (45.5%).

Table 3 looks at what micro businesses offer to the community. Two measures were used, the number of jobs the business provides and gross business income. While neither is a perfect measure of contributions, they provide insight into the business and are questions that owners to which owners will respond. If the study were focused on the well-being of the family, net income might be a better measure. However, considering the economic concept of the "multiplier effect," all income generated by a business will in some form be returned to the economy.

Overall micro business owners reported a mean gross business income of nearly $136,000 and employed 2.34 workers. Thirty-six percent of the businesses indicated they had only one employee, the owner-manager. Gross income ranged from $0 for 13% of the businesses to $3,000,000 for one business. This high income level for one business means that income figures must be viewed cautiously. With mean income for all businesses over $135,000 and median income only $38,000, nearly $100,000 less, most of the business owners reported income levels much lower than the mean (Table 3).

Although income averages were widely dispersed with other finance, real estate, and insurance businesses reporting income of $2.4 million dollars (n=5) to $20,000 for wholesale businesses located in the other location, these differences were not significant. On average, other reported the highest gross business income at just over $163,000, with micropolitan reporting the lowest at slightly over $108,000. This finding is in contrast to Gorodesky and McCarron (2003) and Levy and Weitz's (2001) findings that small-to-medium sized, non-urban other businesses would have lower income.

The average number of employees was found to be 2.34 employees and was significantly different between locations (X² = 20.04, df = 10, p = .029). Finance, real estate, and insurance businesses in micropolitan areas had the largest average (4), with other (rural) transportation businesses having the smallest (1).

Discussion and Conclusion

The research suggests that micro business play a crucial role in a community's economic system. The importance begins with the fact, found across all locations, that at a minimum one in every six households owns and operates such a business. Micro businesses employ local citizens and are an economic engine that causes cash to move through the community's economy. Successful local businesses allow owners to remain in place and generate opportunities for in-migration and more opportunities for other entrepreneurs.

So what should local communities do? Based on the data presented, economic development efforts need to include an effort to build the local economy through development of these resources, i.e., local businesses, already in place (ICMA, 2005). As mentioned earlier, this idea is often forgotten, and instead most economic development activities focus on business attraction and retention.

Probably the first element in this effort is finding one or more key local leaders, termed "sparkplugs" by some authors, willing to develop and move local economic development forward. One such person can be the Extension educator. The educator is uniquely placed and often recognized as providing applied research-based solutions to local problems. He or she is aware of existing local leadership, has a sense for other community members with a desire for leadership roles, is trained in the team approach, and is local. More and more development experts are recognizing that the answer to local problems comes from local people.

Once the decision is made to help develop existing businesses, three key steps must be taken. The first step, and perhaps the most effective, would be the identification of all of the businesses that exist within a community. Most communities would find it difficult to identify the potentially 18% of households that operate a business and what that business is. Second, the needs and issues of local business owners must be identified (Muske & Woods, 2004). Third, communities need to identify available resources, local, regional, state, and national, that can work with the business owner. This means getting on the phone or Internet and bringing help to the community and not taking "no" for an answer.

Often the help comes from many different agencies, each of which has a part, often overlapping in type and area of coverage. Again, the local community can help by developing local ombudsmen who understand the programs available and then make the necessary connections between owner and available services (Lowe & Talbot, 2000). Other ideas can be found in the works of Muske and Woods (2004); Nolan (2003); Lyons (2003); Koven and Lyons (2003); Bradshaw and Blakely, (1999); Lichtenstein and Lyons, (2001); and Devin (1999).

Today, effective community economic development rarely wins with smokestack chasing or capturing the next "gazelle" (Chun & Griffin, 1996), but instead is achieved by making local decisions to use existing resources (the local businesses). The development must look at both the breadth and depth of the business community.

A diversity of businesses becomes the soil from which the next step is to grow a "critical mass" within a certain area (Bradshaw & Blakely, 1999). At that point, the critical mass can follow the suggestions of Lichtenstein and Lyons (2001) of taking a systemized approach customized to each community by drawing on that communities identified strengths and resources. Along with the critical mass, an interlocking network of support for each other, for prospective entrepreneurs and for the local community is helpful.

Extension can play a key role in each and every step towards such efforts and may be the "glue" that holds the system together. The local Extension office can be a key player in both identifying the small business community and providing education and support to further develop the segment of the local economy.

References

Barta, S. (2003). New metropolitan counties for Oklahoma. Blueprints, 13(2). Stillwater, OK: Rural Development, Oklahoma Cooperative Extension Service.

Bradshaw, T. K., & Blakely, E. J. (1999). What are third wave state economic development efforts? From incentives to industrial policy. Economic Development Quarterly, 13(3), 229-244.

Chun, J., & Griffin, C. E. (1996, Sept.). The mouse that roared. Entrepreneur, 118-122.

Devins, D. (1999). Supporting established micro businesses: Policy issues emerging from an evaluation. International Small Business Journal, 18(1), 86-97.

Family Economics and Nutrition Review. (2001). Small business: Evidence from the 1998 survey of small business finances. Family Economics and Nutrition Review, 14(1), 84-85.

Gorodesky, R., & McCarron, E. (2003), Independent restaurant survival in a mega-chain world, Restaurant Report, [On-line], http://www.restaurantreport.com/features/ft_megashain.html.

Hopkins, J. (2005, March 9). Small firms were economy's '01 salvation. USA Today, A1.

ICMA. (2005). Where do economic development dollars go? Retrieved June 1, 2005 from http://icma.org/upload/bc/attach/%7B34A39D05-637E-4BA6-9D9F-BB9EEDD48AB7%7Ded2004web.pdf.

Korsching, P. F., & Allen, J. C. (2004). Locality based entrepreneurship: A strategy for community economic vitality. Journal of the Community Development Society, 39(4); 385.

Koven, S. G., & Lyons, T. S. (2003). Economic development : Strategies for state and local practice. Washington, DC: International City/county Management Association.

Levy, M., & Weitz, B. W. (2001), Retailing management, New York, NY: McGraw-Hill Irwin.

Lichenstein, G. A., & Lyons, T. S. (2001). The entrepreneurial development system: Transforming business talent and community economics. Economic Development Quarterly, 15(1), 3-20.

Lyons, T. S. (2003). Policies for creating an entrepreneurial region. In Main streets of tomorrow: Growing and financing rural development (pp. 97-105). Kansas City, MO: Kansas City Federal Reserve Bank, Center for Study of Rural America.

Lowe, P., & Talbot, H. (2000). Policy for small business support in rural areas: A critical assessment. Regional Studies, 34(5), 479-499.

Morrison, A., Breen, J., & Ali, S. (2003). Small business growth: Intention, ability, and opportunity. Journal of Small Business Management. 4; 417.

Muske, G., & Woods, M. (2004). Micro businesses as an economic development tool: What they bring and what they need. Journal of the Community Development Society, 35(1), 97-116.

Nolan, A. (2003). Entrepreneurship and local economic development: policy innovations in industrial countries. In Main streets of tomorrow: Growing and financing rural development (pp. 77-90). Kansas City, MO: Kansas City Federal Reserve Bank, Center for Study of Rural America.

Sexton, L. A. (1999). Small business is good business. Arkansas Business and Economic Review, 32(3), 18-19.

Small Business Administration. (2004, March). Small business resources for faculty, students, and researchers: Answers to frequently asked questions. Washington, DC: Small Business Administration.

Small Business Administration. (2001). Statistics of U. S. Businesses: Firm size data. http://www.sbaonline.sba.gov/advo/state/data.html.

U. S. Bureau of Census (2005). Metropolitan and micropolitan statistical areas. Retrieved from http://www.census.gov/population/www/estimates/metroarea.html.

U. S. Bureau of Census (2001). Statistics about business size. www.census.gov/epcd/www.smallbus.html.

Venkatraman, N., & Ramanujam, V. (1986), Measurement of business performance in strategy research: A comparison of approaches, Academy of Management Review 11(Oct), 801-814.

Winter, M, Fitzgerald, M. A., Heck, R. K. Z., Haynes, G. W., & Danes, S. M. (1998). Revisiting the study of family businesses: Methodological challenges, dilemmas, and alternative approaches. Family Business Review, 11(3), 239-252.

Horse and Human Labor Estimates for Amish Farms

Randall E. James
Professor
Ohio State University Extension
Burton, Ohio
james.7@osu.edu

Extension workers are increasingly being called on to assist Amish farm families. The Amish population more than doubles every 20 years, and farming has always been one of the foremost occupations. There are over 1,400 congregations, or church districts, in at least 33 states and one Canadian province. These church districts are clustered together into more than 250 settlements (Kraybill & Hostetter, 2001; Kraybill, 1989). New settlements are constantly being established in areas where Amish have never lived before, which means an ever-increasing number of Extension educators need relevant materials to assist these new communities.

The economic efficiencies of large farms and the cost-size relationships of farms have long been important areas of research for agricultural economists (Castle, 1989). Extension educators have sometimes advocated that farms need to both get bigger and specialize in order to survive. Against this backdrop, it is easy to view Amish farms as an anachronism--a part of our rural past. However, Extension educators need to view Amish farms as important, valid clientele. Small, diversified Amish farms, using traditional farming methods and draft horses, or mules, as a major power source, are surprisingly successful, sustainable, and profitable (James, 2005; Bender, 2001; Stinner, Moore, & Stinner, 1999; Stinner, Paoletti & Stinner, 1989).

The proceedings of recent national conferences focused on Amish communities suggest that Extension workers are now routinely asked to help Amish farmers with a wide variety of agricultural problems. Finding relevant, accurate, up-to-date information to assist horse-powered Amish farms represents a particular challenge to Extension workers serving these communities (Conferences for Extension Educators 1998, 2001, 2004). The methods and equipment used on Amish farms are largely dictated by the ordnung (spoken rules of the church district) of each church district (Kraybill & Olshan, 1994; Drake & James, 1993). While rules vary somewhat between church districts within a settlement and more widely between different settlements, almost all old-order Amish farms use horses and horse-drawn equipment.

Farm management information designed to help Extension educators and farmers estimate the costs and returns for crop production on Amish farms was identified as an important educational need. A recent study found the mean purchase price for all of the crop production equipment on a typical Amish farm to be approximately $24,000. The total annual cost of the equipment was found to be slightly over $2,000. The purchase price of a draft horse was reported to be approximately $1,100, and the total cost of owning and maintaining a draft horse was $2.30/day.

This information on machinery and horse costs was useful in beginning to calculate the cost of Amish crop production. However, a major limitation preventing the development of realistic Extension crop enterprise budgets and other educational tools was the lack of information on the amount of horse and human hours needed to produce crops on Amish farms (James, 2004).

Methodology

In 2003, two county Extension workers facilitated discussions with three small groups of Amish farmers in the Geauga Amish settlement. The settlement is centered in Geauga County, Ohio, and is the fourth-largest Amish settlement in the world, with approximately 1,800 families and over 80 church districts (Kraybill & Hostetter, 2001; Miller, 2001). The interviews took place on three separate days in three different Amish homes. Approximately six to 10 Amish farmers participated in each group interview.

Utilizing a modified focus group interview process and a set interview guide, each group was asked to discuss and agree upon the amount of acres/day a typical horse hitch would be able to work for various field operations. Based on discussions with Amish farmers, a normal horse working day was set at 6 hours, consisting of 3 hours in the morning, a noon break, and 3 hours in the afternoon. The number of horses in a typical horse hitch for each field operation was established in the first group interview and held constant for the next two group interviews.

The participants, in each of the group interviews, were encouraged to discuss each field operation individually and reach a group consensus on the various values. In two cases, field operations were not estimated in acres/day. Manure spreading was estimated in hours/day, and cutting firewood was estimated in hours/cord.

Results and Discussion

Mean values of the information gleaned from all three interviews are presented in Table 1. The final column in Table 1 is a calculated estimate of the amount of human labor involved in each operation. Because each team, regardless of the number of horses in the team, is driven by one farmer, it is possible to divide the 6-hour horse and driver day by the mean acres/day to determine the amount of farmer labor required for each operation. In a few cases, such as corn silage hauling, small grain hauling, and hay hauling, two people are required for each horse team, and the human labor values are adjusted accordingly.

Information from this study and an earlier study on Amish machinery costs (James, 2004) was used to produce a series of crop enterprise budgets for horse-drawn/Amish practices. The format for each budget was made to be similar to the format used for non-Amish crop enterprise budgets available through the Ohio State University Extension. Because the format for both the Amish and the non-Amish budgets is similar, direct comparisons between these two radically different agricultural systems can be made.

Comparisons of the budgets for the two systems found that on a per acre basis, return to labor and management (net return above all costs except labor and management) was consistently higher for Amish farms. Return to labor and management for the Amish farming system was estimated to be $126/acre for small grains, $233/acre for alfalfa hay, and $65/acre for corn. The return to labor and management for the conventional farming budgets was only $28/acre for small grains, $124/acre for alfalfa, and a loss of $9/acre for corn.

Operator labor/acre was consistently higher on Amish farms compared to non-Amish farms. On Amish farms, approximately 12, 25, and 17 hours of labor/acre were required for small grains, alfalfa hay, and corn, respectively. Non-Amish farms required approximately 3.5, 6.5, and 3.6 hours of labor/acre for the same crops.

However, Amish farm far less acres. A typical Amish farm rotation of 15 acres of small grains, 20 acres of alfalfa hay, and 15 acres of corn has an estimated total labor requirement of only 920 hours/year, or 23 40-hour work weeks, and the labor requirement is spread throughout the spring, summer and fall seasons. In most cases, this labor requirement can easily be met within the Amish family. In contrast, just the 1000 acres of corn on which the non-Amish budget is based requires 3600 hours, or 90 forty-hour work weeks. In this case, most of the labor requirement is compacted into the spring and fall and often necessitates hiring labor, which reduces the return to the farm operator by the total cost of the hired labor.

Implications for Extension

Most of U.S. agriculture gave up farming with horses at least a generation ago, and most Extension information is geared toward assisting modern tractor-powered farms. Yet while farm numbers nationally are declining, the Amish continue to establish new successful farming communities. Specific data is not available, but it is likely that in terms of farm numbers, the Amish represent one of the fastest growing segments of the national agricultural industry.

Therefore, Extension educators anywhere in the country may suddenly find themselves scrambling to find valid, up-to-date information to assist a new Amish settlement in their region. The crop enterprise budgets discussed in this article, and available online at <http://aede.osu.edu/programs/farmmanagement/budgets/amish.index.htm> may be one educational tool that will help to open a dialogue and demonstrate that Extension is seriously interested in working with this fast-growing, horse-powered segment of the U.S. farm community.

References

Bender, M. H. (2001). An economic comparison of traditional and conventional agricultural systems at a county level. American Journal of Alternative Agriculture, 16 (1).

Castle, E. (1989). Is farming a constant cost industry? American Journal of Agricultural Economics, 71 (3).

Conference for Extension Educators--Extension Education in Amish and Other Anabaptist Communities (1998). Proceedings.

Conference for Extension Educators--Serving Amish and Anabaptist Communities (2001). Proceedings.

Conference for Extension Educators--Enhancing the Health and Well-Being of Plain Communities (2004). Proceedings.

Drake, B., & James, R. (1993). Extension in religious communities. Journal of Extension [On-line], 31(1). Available at: http://www.joe.org/joe/1993spring/a6.html

James, R. (2004). Machinery cost estimates for Amish farms. Journal of Extension [On-line], 42(5). Available at: http://www.joe.org/joe/2004october/rb8.shtml

James, R. (2005). Why cows learn Dutch and other secrets of Amish farms. Kent, OH: Kent State University Press.

Kraybill, D. (1989). The riddle of Amish culture. Baltimore: The John Hopkins University Press.

Kraybill, D., & Hostetter, C. (2001). Anabaptist world. Scottsdale, PA: Herald Press.

Kraybill, D., & Olshan, M. (1994). The amish struggle with modernity. Hanover, NH: University Press of New England.

Miller, A. (2001). Ohio Amish directory, Geauga County and vicinity. Sugarcreek, OH: Carlisle Printing.

Stinner, D., Moore, R., & Stinner, B. R. (1999). Integrating quality of life, economic and environmental issues: An agroecosystem analysis of amish farming. Extension and education materials for sustainable agriculture, 10. University of Nebraska-Lincoln, Center for Sustainable Agricultural Systems.

Stinner, D., Paoletti, M. G., & Stinner, B. R. (1989). In search of traditional farm wisdom for a more sustainable agriculture: A study of Amish farming and society. Agriculture, Ecosystems and Environment, 27:77-90. Amsterdam: Elsevier Science Publishers B.V.

Effect of Age-at-Weaning and Post-Weaning Management on Performance and Carcass Characteristics of Angus Steers

John F. Grimes
Extension Educator, Agriculture and Natural Resources
Ohio State University Extension
Hillsboro, Ohio
grimes.1@osu.edu

Francis L. Fluharty
Research Scientist
Department of Animal Sciences
The Ohio State University
Wooster, Ohio
fluharty.1@osu.edu

Thomas B. Turner
Assistant Professor
Department of Animal Sciences
The Ohio State University
Columbus, Ohio
turner.15@osu.edu

Henry N. Zerby
Assistant Professor
Department of Animal Sciences
The Ohio State University
Columbus, Ohio
zerby.8@osu.edu

Gary D. Lowe
Data Control Technician
Department of Animal Sciences
The Ohio State University
Wooster, Ohio
Lowe.7@osu.edu

Introduction

According to the 2002 Census of Agriculture, there were 16,104 farms in Ohio with beef cattle, having 260,702 beef cows for an average herd size of 16 cows (USDA-NASS, 2002). A majority of the herds are located in the Appalachian region of eastern and southern Ohio and provide a vital segment of the economic infrastructure to the local communities. Many of the forage-producing areas of the eastern U.S. are in relatively close proximity to both grain-producing regions and eastern U.S. markets for fed cattle. Therefore, the choice of selling feeder cattle or retaining ownership of the feeders and selling fed cattle is an economically viable option to many beef producers.

Rising land costs and the emergence of grid marketing systems that assign value to an individual carcass based on its USDA Yield and Quality Grade have made production decisions at the farm level much more complex in recent years. Many small-scale producers in the beef cattle industry need information that is applicable to diet and management situations that use minimally processed feed grains in order to optimize farm income. The objective of the study reported here was to determine the effects of management systems on the relative differences in feed inputs, final harvest weight, and carcass characteristics. These results can be used by Extension professionals to help producers to make economically sound management decisions.

Materials and Methods

Seventy-five, non-implanted Angus steers born in 2001 and 2002 were used to determine the effects of age-at-weaning and post-weaning management on performance and carcass characteristics. The study was conducted at the Southern Agricultural Research Station of the Ohio Agricultural Research and Development Center located in Ripley, Ohio.

Purebred Angus bulls were mated to commercial Angus females in both years for a calving season of approximately 90 days in early February through early May. Females were mated through artificial insemination in the first cycle of the breeding season and then exposed to two paternal brothers for the last 60-75 days of the breeding season.

Calf birth weight and gender were recorded within 24 hours postpartum. Prior to the date of early weaning, male calves were castrated, and all calves were vaccinated with Clostridial and respiratory complex vaccinations. Animals were weaned at 100 or 200 days of age (DOA) and managed using one of three systems: 1) weaned at 100 DOA and fed a high-grain diet immediately: early-weaned (EW), 2) weaned at 200 DOA and fed a high-grain diet immediately: normal-weaned (NW), and 3) weaned at 200 DOA and backgrounded on pasture and hay until 400 DOA: yearling (YR), before being fed a high-grain diet. No calves received creep feed while nursing their dams.

Steer calves were alternately assigned into the three treatment groups based on chronological birth order. Once placed into their treatment groups, calves were alternately assigned to replication groups based on chronological birth order. In 2001, there were 13 EW steers, 12 NW steers, and 12 YR steers. In 2002, there were 12 EW steers, 13 NW steers, and 13 YR steers. All steers were fed at the Southern Station from weaning until harvest.

Early-weaned and normal-weaned calves were fed a high-grain diet from their respective weaning dates until harvest. Yearling calves were fed a high-forage diet during the backgrounding phase from weaning until approximately 400 DOA. Calves were then placed in the feedlot and received a high-grain diet until harvest. The diet fed during the finishing phase to each group appears in Table 1.

During the feedlot phase for each treatment group, all feed was weighed each day prior to feeding to allow dry matter intake (DMI) to be calculated. Pasture consumption during the backgrounding phase was estimated using Net Energy equations of the National Research Council (NRC, 1984). Energy required to achieve observed gains was calculated. Energy provided by the supplemental corn and hay was subtracted from this total. This difference represents the energy provided by the grazed forage. Pasture composition was estimated to be 60% grass species (predominantly orchardgrass and fescue) and 40% legume (predominantly alfalfa and red clover).

Calves were weighed every 28 days after weaning. Steers were selected for harvest using a combination of visually estimated 12th rib backfat and a minimum live weight of 1075 pounds. The desired combination was a minimum live weight of 1,150 pounds with a backfat between .40 to .55 inches. This combination was expected to generate a carcass of at least 700 pounds with a reasonable chance of achieving the USDA Choice quality grade.

Steers were harvested and carcass measurements were collected at The Ohio State University Animal Science Department's Meat Laboratory in Columbus, Ohio. Live weights also were recorded at the Meat Laboratory (HVSTWT) prior to harvest in order to calculate the shrink percentage (%SHRINK). Carcass measurements included hot carcass weight (HCW); dressing percentage (DRESS%); back fat thickness (BF); ribeye area (REA); kidney, pelvic, and heart fat percentage (KPH%); marbling (MARB); Quality Grade (QG); and Yield Grade (YG).

Steaks were dissected from the 13th rib, frozen after 24 hours, and stored at -20° C until determination of tenderness. Steaks were thawed and cooked to an average internal temperature of 71.7° C, and peak Warner-Bratzler shear force was used as a measure of tenderness according to American Meat Science Association (AMSA, 1995) recommendations.

Results and Discussion

The effects of age at feedlot entry and year on steer performance are shown in Table 2. Due to a severe drought in 2002 (Year 2), calves on the normal-weaned and yearling feedlot entry groups entered the feedlot at a lighter weight than in 2001 (Year 1). The lighter feedlot entry weights in Year 2 resulted in lighter harvest weights compared with Year 1 in each of the three treatment groups (P < .01). Additionally, as the age at feedlot entry increased, average daily gain (ADG), daily concentrate dry matter intake (DMI), total daily DMI, and body weight at harvest increased (P < .01). However, the efficiency of gain decreased as age at feedlot entry increased (P < .01). Nevertheless, total concentrate DMI and total DMI increased age at feedlot entry decreased (P < .01).

Assuming a concentrate weight of 56 pounds per bushel (the average bushel weight of whole-shelled corn), the early-weaned, normal-weaned, and yearling feedlot entry groups consumed an average of approximately 76, 66, and 54 bushels of concentrate feedstuffs, respectively. Therefore, the opportunity to market harvested grains and forage through cattle needs to be balanced with the opportunity to have cattle harvest their own stockpiled forage when making the economic decision as to the appropriate age to place cattle in the feedlot. Growing Angus steers in a backgrounding situation decreases the amount of harvested grains needed, but also decreases the efficiency with which those grains are used for weight gain.