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Preferred Information Channels of Small Farm Operators in Florida for Receiving Educational Materials

Permanent Link: http://ufdc.ufl.edu/UFE0041650/00001

Material Information

Title: Preferred Information Channels of Small Farm Operators in Florida for Receiving Educational Materials
Physical Description: 1 online resource (100 p.)
Language: english
Creator: Landrum, Kyle
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: demographics, farmers, florida, operators, small
Agricultural Education and Communication -- Dissertations, Academic -- UF
Genre: Agricultural Education and Communication thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: PREFERRED INFORMATION CHANNELS OF SMALL FARM OPERATORS IN FLORIDA FOR RECEIVING EDUCATIONAL MATERIAL In today's agricultural industry and in rural communities across the country, survival often depends on having an edge on information related to the specific markets, efficient allocation of available resources and use of new or innovative farming practices are essential for small farmer operators (Fedale, 1987, p. 7, paragraph 1). There are more than 49,000 farms in Florida; the majority is classified as small farms (University of Florida IFAS Extension, 2008). The Economic Research Service refers to any farm with less than $250,000 annual gross sales as a ?small farm,? (USDA, 2009). The current situation for Florida small farm operators is that the preferred information channels for accessing educational material pertaining to farming practices are unknown. Since many of these small farms are located in rural areas around Florida, reaching the farmers with relevant and timely educational material can be challenging. This study utilized a quantitative approach; a descriptive survey design was used to determine the preferred information channels of Florida small farm operators. The researcher sampled 859 participants for the study. Results revealed certain demographics such as age, operation size, and education level has influence on how small farmers prefer to receive information. Farmers? who earned 50% or more of their gross annual income from farming efforts were less likely to engaged in the Cooperative Extension Service (CES) statewide small farm programs and farmer to farmer networking. Small farm operators? with more education were more likely to engage in the CES statewide small farms programs and farmer-to-farmer networking. Small farmers? with more diverse operations (i.e., more enterprises within one operation) were more likely to use the CES websites to gain information. Future research should be conducted to better understand he small farm operator population in the state of Florida.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Kyle Landrum.
Thesis: Thesis (M.S.)--University of Florida, 2010.
Local: Adviser: Carter, Hannah Sewell.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-04-30

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0041650:00001

Permanent Link: http://ufdc.ufl.edu/UFE0041650/00001

Material Information

Title: Preferred Information Channels of Small Farm Operators in Florida for Receiving Educational Materials
Physical Description: 1 online resource (100 p.)
Language: english
Creator: Landrum, Kyle
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: demographics, farmers, florida, operators, small
Agricultural Education and Communication -- Dissertations, Academic -- UF
Genre: Agricultural Education and Communication thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: PREFERRED INFORMATION CHANNELS OF SMALL FARM OPERATORS IN FLORIDA FOR RECEIVING EDUCATIONAL MATERIAL In today's agricultural industry and in rural communities across the country, survival often depends on having an edge on information related to the specific markets, efficient allocation of available resources and use of new or innovative farming practices are essential for small farmer operators (Fedale, 1987, p. 7, paragraph 1). There are more than 49,000 farms in Florida; the majority is classified as small farms (University of Florida IFAS Extension, 2008). The Economic Research Service refers to any farm with less than $250,000 annual gross sales as a ?small farm,? (USDA, 2009). The current situation for Florida small farm operators is that the preferred information channels for accessing educational material pertaining to farming practices are unknown. Since many of these small farms are located in rural areas around Florida, reaching the farmers with relevant and timely educational material can be challenging. This study utilized a quantitative approach; a descriptive survey design was used to determine the preferred information channels of Florida small farm operators. The researcher sampled 859 participants for the study. Results revealed certain demographics such as age, operation size, and education level has influence on how small farmers prefer to receive information. Farmers? who earned 50% or more of their gross annual income from farming efforts were less likely to engaged in the Cooperative Extension Service (CES) statewide small farm programs and farmer to farmer networking. Small farm operators? with more education were more likely to engage in the CES statewide small farms programs and farmer-to-farmer networking. Small farmers? with more diverse operations (i.e., more enterprises within one operation) were more likely to use the CES websites to gain information. Future research should be conducted to better understand he small farm operator population in the state of Florida.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Kyle Landrum.
Thesis: Thesis (M.S.)--University of Florida, 2010.
Local: Adviser: Carter, Hannah Sewell.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-04-30

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0041650:00001


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1 PREFERRED INFORMATION CHANNELS O F SMALL FARM OPERATORS IN FLORID A FOR RECE I VING EDUCATIONAL MATERIAL By KYLE FINN LANDRUM A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF T HE REQUIREMENTS FO R THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2010

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2 2010 Kyle Finn Landrum

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3 To my parents, Larry and Linda Landrum

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4 ACKNOWLEDGMENTS I thank my parents for always believing in me and supporting my dreams and aspirations both emotionally and financially. Also, my sister Abigail for helping me realize the person I aspire to be in life. My wife Katelyn for the continued support and d edication graduate school takes (without her constant reality checks I am not sure if I would have been able to endure the process as painless as I did). I thank my second Parents, Mark and Janora Crow for the continued support of my career and life goals throughout this process. I thank Dr. Ed Osborne for allowing me t he opportunity to be apart of the Department of Agricultural Education and Communication. All the faculty and staff in which provided me constant attention and answered an endless amount of my questions. I thank Dr. Hannah Carter, and Dr. Glenn Israel as w ell, they were instrument al in my success as a Master s student and without them I would have not been successful. Also Dr. Won Suk Lee for providing me insight in the Agricultural Operations Management program, in which I received my m inor from. I thank all the graduate students in the Agricultural Education and Communication Department. The bonds that were made with some of my fellow graduate students will last a lifetime. Without everyones support and consistent question answering the process of gradua te school would not have been a successful as it was. I thank all the teachers in which impacted me through my academic career and gave me the knowledge to finish what I started. I thank everyone who has ever taught me a lesson, whether it be a formal clas sroom lecture or general life lesson. Finally, the most thanks is given to God to allow me the capacity to intellectually problem solve and the strength to never give up no matter how many times it takes me to get something right.

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5 TABLE OF CONTENTS pag e ACKNOWLEDGMENTS .................................................................................................. 4 LIST OF TABLES ............................................................................................................ 8 LIST OF FIGURES ........................................................................................................ 10 ABSTRACT ................................................................................................................... 11 CHAPTER 1 INTRODCUTION .................................................................................................... 13 Introduction ............................................................................................................. 13 Background ............................................................................................................. 14 Small Farms Sector .......................................................................................... 14 Land Grant Universities .................................................................................... 15 Cooperative Extension Service ........................................................................ 17 Communication Strategies in Agriculture.......................................................... 18 Justification/Rationale ............................................................................................. 20 Problem Statement ................................................................................................. 21 Purpose/Objectives ................................................................................................. 21 Significance of Study .............................................................................................. 22 Definition of Terms .................................................................................................. 22 Small Family Farms .......................................................................................... 23 Other Family Farms .......................................................................................... 23 Nonfamily Farms .............................................................................................. 23 Limitations ............................................................................................................... 24 Assumptions ........................................................................................................... 24 Chapter Summary ................................................................................................... 24 2 LITERTURE REVIEW ............................................................................................. 26 Overview ................................................................................................................. 26 Theoretical Framework ........................................................................................... 26 Conceptual Framework ........................................................................................... 28 Relevant Small Farms Studies ......................................................................... 31 Communication Channels Among Farmers ...................................................... 32 Industry Related Research ............................................................................... 33 Summary ................................................................................................................ 37 3 RESEARCH METHODS ......................................................................................... 39 Introduction ............................................................................................................. 39 Research Design .................................................................................................... 39

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6 Population and Sample ........................................................................................... 40 Instrumentation ....................................................................................................... 41 Data Collection ....................................................................................................... 44 Data Analysis .......................................................................................................... 44 Chapter Summary ................................................................................................... 45 4 RESULTS ............................................................................................................... 47 Introduction ............................................................................................................. 47 Objective One: Identify Demographics of Small Farm Operators ........................... 47 Age of Respondents ......................................................................................... 48 Farming Years of Experience ........................................................................... 48 Education ......................................................................................................... 49 Ge nder ............................................................................................................. 49 Race ................................................................................................................. 50 Gross Income ................................................................................................... 50 Farm Income .................................................................................................... 51 Number of Enterprises ...................................................................................... 51 Mix of Enterprises ............................................................................................. 52 Farm Size ......................................................................................................... 52 Objective Two: To Determine the Preferred Educational Material Formats/Source and Small Farm Operators Demographic Characteristics ......... 53 Patterns of Pre ferred Information Formats/Sources ......................................... 55 Objective Three: To Examine the Relationship Between Preferred Educational Material Formats/ Sources and Small Farm Operators Demographic Characteristics ....................................................................... 62 Summary .......................................................................................................... 70 5 CONCLUSIONS AND RECCOMANDATIONS ....................................................... 71 Purpose and Objectives .......................................................................................... 71 Methodology ........................................................................................................... 71 Summary of Findings .............................................................................................. 72 Objective 1: Identify Demographics of Small Farm Operators in Florida. ................ 72 Objective 2: To Determine the Preferred Format and Source of Educational Materials by Florida Small Farm Operators. ........................................................ 73 Objective 3: To Examine the Relationship between Preferred Educational Material Formats/Sources and Small Farm Operators Demographic Characteristics. .................................................................................................... 73 Conclusions ............................................................................................................ 74 Discussion and Implications .................................................................................... 75 Recommendations .................................................................................................. 78 Practitioners Recommendations ....................................................................... 78 Future Research Recommendations ................................................................ 79

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7 APPENDIX A PROCEED TO SMALL FARMS SURVEY .............................................................. 81 B 2008 SMALL FARMS SURVERY ........................................................................... 85 LIST OF REFERENCES ............................................................................................... 97 BIOGRAPH ICAL SKETCH .......................................................................................... 100

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8 LIST OF TABLES Table page 4 1 Age of participants (n=304) ................................................................................ 48 4 2 Small f armers years of experience farming ....................................................... 48 4 3 Participants education level ............................................................................... 49 4 4 Participants reported gender .............................................................................. 50 4 5 Respondents race .............................................................................................. 50 4 6 Gross sales of small farm operators in 2007 ...................................................... 51 4 7 Farmers w ith 50% of income resulting from farming efforts ................................ 51 4 8 Number of enterprises per small farm ................................................................ 51 4 9 Farmers who have a mix of enter prises ............................................................. 52 4 10 Acers in current production ................................................................................. 53 4 11 Frequency count of respondents education attainment and choice of extension service ................................................................................................ 53 4 12 Frequency count of university extension service that respondents typically use by gender ..................................................................................................... 55 4 13 Frequency count o f university extension service that respondents typically use by race ......................................................................................................... 55 4 14 Variable themes of preferred information formats/sources ................................. 56 4 15 Factor loading and explained variance for Have you ever participated in or used any of the following UF IFAS/FAMU Extension programs, activates and resources? ......................................................................................................... 57 4 16 Component corr elations for Have you ever participated in or used any of the following UF IFAS/FAMU Extension programs, activities and resources? ......... 58 4 17 Factor loading and explained variance for In the pas t 2 years, how much did you rely on the following sources to get information about farming or ranching? ........................................................................................................... 59 4 18 Component correlations for In the past 2 years, how much did you rely on the fol lowing sources to get information about farming or ranching? ................. 60

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9 4 19 Mean scores and standard deviations of F1F7 variables representing themes ................................................................................................................ 61 4 20 Correlations between independent variables ...................................................... 64 4 21 Correlations between study variables and variable themes ................................ 66 4 22 Regression of information themes from question Have you ever participated in or used any of the following UF IFAS/FAMU Extension programs, activates and resources (n=304) ....................................................................................... 68 4 23 Reg ression of information themes for question In the past 2 years how much did you rely on the following sources to get information about farming or ranching (n=304) .............................................................................................. 69

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10 LIST OF FIGURES Figure page 1 1 Innovationdecision process. Source: Rogers Diffusion on Innovation, p. 163, .. 30

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11 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science PREFERRED INFORMATION CHANNELS OF SMALL FARM OPERATORS IN FLORI DA FOR RECE I VING EDUCATIONAL MATERIAL By Kyle Finn Landrum May 2010 Chair: Hannah Carter Major: Agric ultural Education and Communication In today's agricultural industry and in rural communities across the country survival often depends on having an edge on information related to the specific market s, efficient allocation of available resources and use of new o r innovative farming practices are essential for small farmer operators (Fedale, 1987 p. 7, 1). There are more than 49,000 farms in Florida; the majority is classified as small farms (University of Florida IFAS Extension, 2008). The Economic Res earch Service refers to any farm with less than $250,000 annual gross sales as a small farm, (USDA, 2009). The current situation for Florida small farm operators is that the pre ferred information channels for accessing educational material pertaining to farming practices are unknown. Since many of these small farms are located in rural areas around Florida, reaching the farmers with relevant and timely educational material can be challenging. This study ut ilized a quantitative approach; a descriptive surv ey design was used to determine the preferred information channels of Florida small farm operators. T he researcher sampled 859 participants for the study. R esults revealed certain demographics such as age, operation size, and education level has influence on how small farmers prefer to receive information. F armers who earned 50% or more of their gross annual income from

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12 farming efforts were less likely to engaged in the Cooperative Extension Service (CES) statewide small farm programs and farmer to farmer networking S mall farm operators with more education were more likely to engage in the CES statewide small farms programs and farmer to farmer networking. S mall farmers with more diverse operations (i.e. more enterprises within one operation) were more likely to use the CES websites to gain information. Future research should be conducted to better understand he small farm operator population in the state of Florida.

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13 CHAPTER 1 INTRODCUTION Introduction In today's agricultural industry, survival oft en depends on having an edge on information related to the market, efficient allocation of available resources and use of new or innovative farming practices, (Fedale, 1987, p. 7, 1). Of the 49 ,000 + regis tered farms in Florida, 90 percent, are classif ied as small farms (University of Florida IFAS Extension, 2008). The decreasing availability of viable farmland in the state of Florida might be a contributing factor to the overwhelming percentage of small farmers (2007 Census of Agriculture, 2009). Even though the numbers of acres available for production continues to decrease, the total number of farms in the state increased by 8% from 2002 to 2007 (USDA, 2009). As the average size of farming operations continue to decrease and the numbers of small farm operators continue to increase, the need for quick and effective education, due mostly to the fact of the great diversity among small farm operators, has become increasingly important to ensure the sustainability of agriculture as an industry in Florida (USDA, 2009). Israel (1991) suggested The effectiveness of delivering extension programs can be increased by matching the information sources and channels used by Extension to those preferred segments of the clientele (p. 1 2). The idea of using different information channels, more specifically different sources and formats to meet the farmers educational needs, has become increasingly important in Extension education. With the number of new farms increasing annually in Florida, it is important that E xtension programs disseminate educational material in several different channels to meet the small farm owners capabilities and preference s for obtaining information

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14 (USDA, 2009). Many small farm operators, who have experienced success profitability and sustainability, are continually advancing their farming knowledge through different educational venues. Florida small farmers are relying on not just cutting edge farming practices but rather sound farming practices, which have been around for generations, to maximize their farming efforts resulting in sustainable agriculture in this state (University of Florida, 2009). Background Small Farms Sector The most common way to define the small farm sector of agriculture is by annual gross sales. Previous literature defines the small farm as something with annual gross sales of less than $1,000 or less than $40,000 (Ingram, 1999). The minifarm term has been coined to represent farms with annual gross sales of $1,000 to $2,499. Another sector that it is included under the small farms umbrella is called a limitedresource farm, the classification it operates under is annual gross sales of $10,000 to less than $ 20,000 (Ingram, 1999). The USDAs Economic Research Service (ERS) refers to any farm with less than $250, 000 annual gross sales as a small farm, with the only official definition being found in the Rural Development act of 1972. The definition as amended states : Small farmer means any farmer with gross sales from farming of $250,000 or less per year, ( USDA, 2009). A farm typology was developed by the ERS which categorizes farms into fairly homogenous groups for policy development and evaluation purposes. Since farms vary widely in size and other identifying characteristics, ranging from very small retir ement and residential farms to establishments with sales in the millions of dollars annually,

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15 several different grouping have been created to accommodate such farm characteristics (USDA, 2009). Land Grant Universities Justin Morrill lobbied the federal government to grant land to colleges that would be devoted to teaching practical subjects, such as agriculture, thus the landgrant university was created (McKinney, 2001). As a result in 1862, Congress passed the Morrill Act, giving each state 30,000 acres of federal land for every senator and representative serving the state. States were then charged to parcel the land and sell it, using the receipts to establish a university. These landgrant universities were born out of a need to provide practical agri cultural and technical education. Until the Morrill Act, university education did little to reach and teach the layman (Seevers et al., 1997). Landgrant universities were established with the goal of providing public education that focused on agricultural and technical skills, with the idea that teaching agriculture and mechanics would better serve America (McKinney, 2001, p.1 2). The landgrant university was built with a threepronged mission: teaching, research and extension. The teaching mission wa s to provide education that was both useful and focused on the goal of increasing higher education throughout the United States (McKinney, 2001). The goal of research was to bring logical solutions to practical problems faced by farmers across the United S tates which was a result of the Hatch Act of 1887. Lastly, the SmithLever Act of 1914 in add ed an extension component to the system, which meant u niversities w ere given the task of extending the research and education to local famers and citizens in a simple, straightforward manner (McKinney, 2001). While the University of Florida traces its roots to back to1853 and the establishment of the statefunded East Florida Seminary, University of Floridas Institute

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16 of Food and Agricultural Sciences (UF/IFAS) traces its roots to the Morrill Act of 1862 (IFAS, 2009). The 1914 SmithLever Act provided federal support for landgrant institutions to offer educational programs to enhance the application of useful and practical information beyond their campuses through cooperative extension efforts wi th states and local communities (IFAS, 2009). Dedicated to provide e xtension education and practical skills, Floridas governing body for higher education created I FAS in 1964, by reorganizing UFs College of Agricult ure, School of Forestry, Agricultural Experiment Station, and the Cooperative Extension Service (CES) into a single unit (IFAS, 2009). Currently, The UF/IFAS is a federal state county partnership dedicated to developing knowledge in agriculture, human and natural resources, and the life sciences, and enhancing and sustaining the quality of human life by mak ing that information accessible (IFAS, 2009). Sinc e IFAS Extension agents are located throughout the state of Florida, small farm operator s receive co ntinue education for their farming venture. Even though the CES is responsible for several sources and formats in which information is disseminated to the small farm sector, the CES is considered to be a reliable and relevant source of information of small farmers efforts annually (IFAS, 2009). Additionally, IFAS is the research and development center for Floridas agricultural and natural resources industries, which has a $93 billion annual impact, (IFAS, 2009). The UF/IFAS research mission is to invent discover and develop knowledge to enhance the agriculture and natural resources of Florida (IFAS, 2009). With the mission remaining fundamentally unchanged for nearly 150 years, small farm operators have the opportunity to attend educational workshops o n not just sound farming

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17 practices but also new technologies, such as the use of the Global Positioning System (GPS) for the use in precision agricultural practices. Through UF/IFAS combination of research and e xtension education, Floridians are able to h ave the university brought to their local communities in all 67 Florida counties. Cooperative Extension Service The Cooperative Extension Service (CES) was formalized in 1914, with the SmithLever Act. This act established the partnership between th e agricultural colleges and the U.S. Department of Agriculture to provide for cooperative agricultural extension work (USDA, 2009). The CES focuses on the applied dimension of traditional education by extending applied knowledge and problem solving to addr ess individual s issues or problems. The main goal of extension education in the United States has been to meet the needs of its clientele and this overarching focus has not changed in over 100 years. The foundation of e xtension is responding to priority needs by taking the university to the people (Texas A & M University Extension Service, 2008). As Extension educators become familiar with various delivery methods, inquiries should be made concerning the usefulness and effectiveness of these methods, more specifically sources and format of the information, type of audience, educational level, skills of Extension agents and their educational goals. These inquiries become even more critical in the context of budget cuts, increased accountability requirement a nd the need for efficient use of human and financial resources (Radhakrishna & Thomson, 1996). The CES has helped small farm operators by providing research and educational programs to help individuals learn new ways to generate income through alternative enterprises (IFAS, 2009). In addition, Extension programs have contributed to improved

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18 marketing strategies and sitespecific management skills necessary for agricultural sustainability. Another way in which small farm owners have benefited from Extension efforts is improved productivity through resource management, soil testing, livestock production practices and many other skills (USDA, 2009). Communication Strategies i n Agriculture Over one hundred years ago, several communication channels were used to convey the message of advancing technology am ong the agricultural industry. Methods such as Movable Schools, were created to travel from townto town to educate agricultural communities on farming practices relevant to the time (Seevers, Graham, Gamon, & Conklin, 1997). In addition to Movable Schools, agricultural demonstrations were also an effective means of communicating information to the farming community. Usually, these demonstrations consisted of farm visits which brought neighboring f armers to gether and demonstrated new technologi es as well as overall sound farming procedures at the time. Traveling railroad exhibits were also used in the early 1900s as an effect ive means for disseminating agricultural innovations across the United States (Seevers, Graham, Gamon, & Conklin, 1997). All of these formats had the common goal of advancing farming knowledge and adoption of new technologies among American farmers of the 1900s. In 1920, KDKA was the first radio station to have a daily program in whic h farm market reports where broadcasted in the mid West. In addition, Frank Mullen was accredited with being the first full time farm broadcaster in 1923. He was a member of the National Association of Farm Broadcasters (NAFB), which is dedicated to servi ng the interests of the agricultural community and has not deviated from its mission in over

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19 89 years (NAFB, 2009). Over time f armers soon became reliant on these radio broadcasts, which contributed to farmers success in the agricultural industry. Prese nt day information channels being used by the Extension Service and other agricultural information providers include but are not limited to: formal classroom education, nonformal education, online tutorial s, hands on workshops, industry specific conferenc es, newspaper/magazine articles and individual instruction via face to face contact or telephone. Several of these sources are used in conjunction with another to better disseminate information to the agricultural sector, thus meeting their needs more effe ctively. S tudies by Licht & Martin (2007) and Israel (1991), have indicated that the idea of effec tive communication between the U niversity and clientele located in Florida communities is still important. An example in which this has been done is the A gent Performance and Customer Satisfaction study conducted by Terry and Israel (2004). This type of survey helps to provide two way interaction among clientele and the planning sector of Extension programs around the state. The feedback from clients helps the CES plan for the future and develop relevant workshops and programs. Programs such as this allow for a continuing collaboration between the U niversity and local communities. Along with the previous studies, Radhakrishna and Thomson (1996) discussed t he importance of what, when, and how infor mation is gathered and used by extension agents. A n example of the different types of sources and formats extension agents typically use was another agent in the office, another agent in another county, e xtension specialists, their immediate supervisor, local news agencies, local business

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20 organizations, state, and federal agencies, and local sch ool teachers and administrators ( Radhakrishna & Thomson, 1996). Understanding of this research and the use of information s ources will go a long way in improving delivery methods for extension education. Justification/Rationale The current situation for Florida small farm operators is that the preferred information channels for accessing educational material pertaining to far ming practices are unknown. Today, more than ever, a wide range of information sources on new or innovative farming practices is available to farmers. However, there is little evidence the increased availability of information sources has been effectively used by farmers, (Lionberger & Gwin, 1982, p. 1 1). When trying to disseminate information to small farm operators, Rogers Diffusion model outlines four major components which contribute to the success of an innovation (Rogers, 2003). The four parts of the model are: innovation, communication channels, time and the social system in which the innovation is being introduced (Rogers, 2003). These components play a major part in the successful dissemination of farming practices to small farmers. Quicker diffusion of valuabl e information to clientele can lead to higher profitability on the individual farm level along with greater farm efficiency, and to a more sustainable Florida agriculture. The delivery method and format in which information is presented can have an important influence on the impact Extension programs have on clientele (Israel, 1991). With the help of appropriate sources and formats of information, small farm owners will continue to increase their knowledge and contribution to the $7.7 bil lion agricultural industry (2007 Census of Agriculture, 2009).

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21 There is a lack of empirical research, however, on how small farm operators prefer to receive educational material based upon demographic attributes such as age, specific industry, and educati onal level. The study can impact the CES by helping e xtension educators target specific groups of small farm operators with their preferred information channels. The studys findings will allow e xtension to refine educational efforts and thus reach more sm all farm operators. Without help from the Florida land gran t universities, in the form of extension programs and private educational consultants, many Floridas small farmers will struggle to contribute to Floridas agricultural industry (University of Flo rida 2009). Problem Statement The current preferences for sources and formats by which Florida small farmers receive educational material are unknown. Demographic attributes such as age, race, educational level and specific agricultural commodity affiliat ion may have an effect on the source and format in which farmers prefer to receive educational material. The diversification among the small farm sector warrants further investigation in the variables above, which have an overarching effect on how educational information is disseminated among the small farms sector. Purpose/Objectives The purpose of this study was to determine the preferred information sources and formats of small farm owners in Florida for receiving educational information. Additionally, this study examined the relationship between preferred educational material formats/ sources and small farm owner demographic characteristics.

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22 The objectives of this study are the following: 1. Identify demographics of small farm operators in Florida 2. To determine the preferred format and source of educational materials by Florida small farm operators for receiving information. 3. To examine the relationship between preferred educational material formats/ source s and small farm o perators demographic character istics. Significance of Study The goal of this study was to determine the preferred information channels of small farm owners for receiving educational material. As a result of this study, both e xtension educators and other information providers will be able to disseminate educational material more effectively and at a minimum cost. Educators of all types will also gain knowledge of small farmers demographics, which will facilitate a better understanding of small farmers in Florida. Also, the st udy will refine the efforts of e xtens ion education and better target farmers with specific characteristics Since many of the e xtension programs currently operate on a tight budget, timely and cost effective information becomes increasingly important (Licht & Mart in, 2007). The last small farm operator study was conducted over 20 years ago, which further demonstrates the need for the research. A knowledge of preferred information channels will not only sustain current small farming operations but will provide researchers with the tools to encourage the expansion of small farms throughout the region, state, nation and globe. Definition of Terms 1. Small Farms Farms with less than $250,000 in gross receipts annually ( USDA, 2009) The small farm typology groups are as follows: (USDA, 2009).

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23 Small Family Farms 1. Limited resource Sales less than $100,000, total farm assets less than $150,000, and total operator household income less than $20,000. Limitedresource farmers may report farming, a nonfarm occupation, or retir ement as their major occupation. 2. Retirement Operators report that they are retired (excludes limitedresource farms operated by retired farmers). 3. Residential/lifestyle Small farms whose operators report a major occupation other than farming (excludes l imited resource farms with operators reporting a nonfarm major occupation). 4. Farming occupation/low sales Sales less than $100,000 whose operators report farming as their major occupation (excludes limitedresource farms with operators reporting farming as their major occupation). 5. Farming occupation/highsales Sales between $100,000 and $249,999 whose operators report farming as their major occupation. Other Family Farms 1. Large family farms Sales between $250,000 and $499,999. 2. Very large family farms Sales of $500,000 or more. Nonfamily Farms 1. Nonfamily farms Farms organized as nonfamily corporations or cooperatives, as well as fa rms operated by hired managers (USDA, 2009). The researcher chose to operationally define the following words in a manner in which fit the specific study to eliminate any confusion on how the words were used in the study. 1. Agriculture the science, art, or practices of cultivating the soil, producing crops, and raising livestock and in varying degrees the preparation and marketing of the resulting products (Merriam Webster, 2009). 2. Diffusion The process in which an innovation is communicated through certain channels over time among the members of a social system (Rogers, 2003). 3. Information Channels A channel is the mea ns by which a message gets from the source to the receiver (Vergot III, Israel, & Mayo, 2005). 4. IFAS Extension A partnership between state, federal, and county governments to provide scientific knowledge and expertise to the public (UF/IFAS Extension, 2009).

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24 5. FAMU CESTA Florida Agricultural and Mec hanical University/ College of E ngineering Science, Technology and Agriculture (FAMU CESTA Extension, 2010) 6. Landgrant University A land grant college or university is an institution that has been designated by its state legislature or Congress to receive unique federal support. The landgrant university was built with a threepronged mission: teaching, research and extension (USDA, 2009) 7. Sustainability 1. Relating to, or being a method of harvesting or using a resource so that the resource is not depleted or permanently damaged. 2 Of relation to social or economic viability over a long term period (Merriam Webster, 2009). Limitations Since the diversity of small farmers across the United States currently is even more demographically different than Florida farmers, generalizability of this study to other states is a limitation. A nother limitation of the study is bias in the existing data, prima rily due to nonresponse error because of a lack of willingness to participate among some small farm o perators A final limitation is that there is currently not a master list with small farmers contact information available, therefore a limitation of the study is how contact information for the participants was generated Assumptions Identifying key assumptions on preferred information channels of small farm owners in Florida is important to understanding the study. The first assumption is that people receiving the instrument will answer honestly, thus this self reporting method will yield accurate results. Another assumption is that participants of the study will have adequate knowledge of such terms as small farm, educational program and information channels. Chapter Summary This chapter discussed the need for further investigation of information channels preferred by small farmers, more specifically sources and formats of educational

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25 information. This chapter also stated the problem, which is the current preferences for sources and formats by which Florida small farmer s receive educational material are unknown. A summary of the university land grant system, which was started by the Morrill Act of 1862, was provided, as well as an explanation of the origin of the Cooperative Extension Service, which still exists today. Furthermore, this chapter discussed previous studies, which have been done on preferred information channels of certain industries. A brief history was provided outlining the origins of Extensions use of communication channels, from the first daily radio broadcast on which farmers relied heavily to the adaptation of using the Internet as an inf ormation channel. Additionally, key definitions, limitations, and assumptions were discussed for the study.

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26 CHAPTER 2 LITERTURE REVIEW Overview The purpose of this study was to determine the preferred information channels of small farm owners for receiving educational material. To better understand the inform ation acquisition process Roger s Diffusion of Innovation was used as the theoretical framework for t he study. An overview of the four phases of the Rogers adoption model is discussed in this chapter. The impact and usability of Rogers Diffusi on of Innovation pertaining to extension education, along with any other form of education is also examined. The discussion then turns to a review of relevant research findings pertaining to small farms, communication channels, industry specific relevant studies, and the U niversity o f Florida Institute o f Food and Agricultural Sciences (UF/IFAS) efforts. Theoretic al Framework The theoretical framework chosen for the study was Rogers Diffusion of Innovations. Researchers recognize that extension clientele use different/multiple information channels during the adoption process (Rogers, 2003). Diffusion is the proces s in which an innovation is communicated through certain channels over time among the members of a social system (Rogers, 2003). Rogers stated, Getting a new idea adopted, even when it has obvious advantages, is difficult. Many innovations require a lengt hy period of many years from the time when they become available to the time when they are widely adopted. ( 2003 p. 1 1). Even though obvious advantages exist in the use of technologies, such as online education and nonformal education, the road to adoption can be lengthy in nature. Rogers Diffusion model has four major

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27 elements: the innovation, communication channels, time, and the social system in which the innovation is being introduced. Innovations are ideas, practices, or objects that are seen as new to an individual or social system (Rogers, 2003). Rogers states, Innovations that are perceived by individuals as having greater relative advantage, compatibility, trialability, and observability and less complexity will be adopted more rapidly than other innovations (Rogers, 2003, p. 16 1). When diffusing an innovation these attributes are important to consider when setting realistic time goals for adoption. For example, a grower tests a new variety on a trial plot for a growing season to see the results before converting his/her entire acreage to the variety for the next season. The particular communication channel in which the innovation is diffused can vary depending on audience demographics and other case specific variables. Even though the particular innovat ion could be beneficial, if appropri ate communication channels are not employed to diffuse the innovation, the adoption process might be slowed or stopped (Rogers, 2003). The third aspect of the diffusion model is the actual time in which i t takes an innovation to be diffused to a target audience. Depending on the appropriateness or effectiveness of the communication channel in which the innovation is diffused, the amount of time required to adopt an innovation can vary widely. The social sy stem in which the particular innovation is being interjected has the potential to have a great bearing on how quickly, if at all, the inn ovation is adopted (Rogers, 2003). Through methods employed using the diffusion model, Extension education programs and additional sources of farming information can better understand how to effectively communicate with small farm operators through individually preferred

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28 information channels. A study conducted by King and Rollins focused on the adoptioninnovation proces s which Extension educators use for planning educational programming (1993). The study employed Rogers Diffusion of Innovation model to better understand programming issues and meet clienteles educational needs (King & Rollins, 1993). The researchers concluded that Rogers and Shoemakers (1971) generalizations about innovations are still useful for profiling categories of adopters (King & Rollins, 1993). The researchers also concluded that not all potential adopters of new technology use just one source o f information when deciding to adopt an innovation. The research reports more often than not multiple sources are used in conjunction with one another to facilitate an adoption of an innovation. Through Roger s diffusion model a realistic plan for future E xtension programming was implemented in Pennsylvania communities (King & Rollins, 1993). Demographics such as age, education level, and specific agricultural industry can have a strong influence on the adoption rate of innovations in the agricultural industry (King & Rollins, 1993). The innovationdecision process is the process through which an individual (or other decisionmaking unit) passes from first knowledge of an innovation, to forming an attitude toward the innovation, to a decision to adopt or reject, to implementation of the new idea, and to confirmation of this decision (Rogers, 2003, p. 170). Conceptual Framework Previous studies have identified motivating aspects, economic or personal gratification, and demographic variables as influencing the source and format in which small farmers acquire educational material. These variables as contributing factors to how small farm operators acquire education information are depicted in Figure 11 This

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29 conceptual model represents the infor mation acquisition process of small farm operators, beginning with an individual farmer s motivation and the specific preferred communication channels in which information is disseminated. The small farm operator travels through a series of events conduciv e to acquiring information in the innovation decision process. Farmer demographics, trustworthiness of source and other confounding factors all influence the operators decision to implement a change. Even though a farmer might enter the innovationdecisio n process using a certain information channel it is understood the preferred channel could be replaced with another one while moving through the process, so using the same information channel is not imperative to a successful adoption of an inn ovation throughout the process The first phase of the conceptual model is the knowledge stage, or when the farmer first learns about a new innovation. Such things as socioeconomic characteristics, p ersonality variables and communication behaviors influence the knowl edge stage of the innovationdecision process. These char acteristics of the decisionmaking process can influence the knowledge stage by determining how and where a small farmer acquires information. Many smal l farm operators are limited by either time or capital which c an have an effect of the knowledge phase. The second phase of the process is the persuasion stage. In this particular stage, the perceived characteristics of the innovation are considered. The five components include the innovations perceiv ed relative advantage, compatibility, complexity, trialability, and observability. Small farm operator demographics such as age, education level, and industry affiliation have the ability to influence the persuasion stage.

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3 0 The third phase of the process is the decision stage. Here the decision to either adopt the innovation or reject the innovation occurs. S mall farmer characteristics and previous experiences can affect the choice to adopt or reject the innovation. Later, small farmers can decide to either continue adopting the innovation, adopt at a later, discontinue adoption, or continue to reject the innovation. After the decision has been made to either adopt or reject the innovation, the implementation phase is entered. In this phase the small farmer c hanges his/her behavior according to the innovation or continues to reject the innovation. Figure 11 Innovationdecision process. Source: Rogers Diffusion on Innovation, p. 163, In the final stage of the innovation decision process the farmer confirm s the decision to adopt or reject. Here the farmer confirms the innovation has a perceived

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31 relative advantage to adopting or continues rejection of the innovation because it is more beneficial than adopting. Relevant Small Farms Studies Research conducted by Ingram (1999), titled Small Farms Extension Programs in Southern States, provides an overview of what classifies a small farm. Understanding the motivating factors of why small farmers chose to farm, whether it is farming for economic reasons or a lo ve of the rural lifestyle, provides insight to how regularly these farmers need to receive continued education on farming practices (Ingram, 1999). Ingram concluded, The tremendous diversity in the agricultural enterprises referred to as the small farms is reflected by the diversity in definitions for this sector of U.S. farms (1999). Ingram (1999) makes key conclusions in regards to the small farm sector in the southern states. The first conclusion was small farm extension audiences are diverse within the southern region a nd even between specific states. Extension programs directed efforts to the general small farm audience, which has a gross annual income of $5,000 up to $40,000 in most cases (Ingram, 1999). Another identifying factor of the small fa rm sector was oneon one contact. Personal interaction was the preferred information dissemination method (Ingram, 1999) Israel and Ingram conducted a small farm operator study in the North and North central region of Florida in 1989. Based on responses of 382 small farm operators the researchers concluded the sample to be diverse in many attributes such as age, race, education level and number of years farming (Israel & Ingram, 1989). The authors described this particular study to be a snapshot in ti me of the current small farm operator situation. Israel and Ingram (1989) reported that the small farm segment has a

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32 variety of different educational needs. The results varied by county and whether the county was considered metro or nonmetro (Israel & Ing ram, 1989). Another finding of the study was the Cooperative Extension Service was used as a source of information by two thirds of small farm operators, and bulletins and newsletters are the most preferred way to receive information, (Israel & Ingram, 1989, p. 45 1). The researchers also reported threefourths of respondents earned less than $5,000 per year for their farming efforts. Additionally, 30 percent were losing money as a result of their farming efforts. A majority of participants reported a weak or moderate level of agricultural knowledge regarding farming practices, marketing, and business management (Israel & Ingram, 1989). Communication Channels Among Farmers More than ever, a wide range of information sources on new or innovative farmi ng practices is available to farmers. However, there is little evidence that the increased availability of information sources has been effectively used by farmers, (Lionberger & Gwin, 1982, p. 1 2). The researchers found there are currently several dif ferent communication channels in which Extension educators, along with private sources of information, use to educate small farm operators. Lionberger and Gwin concluded that of all the individual communication strategies studied, the one most associated with success is involvement of people in planning programs and strategies. Changeagent success isn't so much a question of how people are involved in the communication/decisionmaking process; it's more a question of whether they're involved, (1982, p. 2 3). For the small farm sector specific communication channels can be separated into two categories, traditional educational methods and nontraditional educational

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33 methods. Some of these traditional communication strategies are audio (tapes/CDs), videotape, CD ROM, letters, memos, reports, newspaper subscriptions, trade publications, oneon one farm visits, group meetings, and telephone calls. Other nontraditional communication strategies would consist of email announcements, Internet list servs, online classes/tutorials, and interactive video conferencing (Fastrak Consulting, 1998). Nudell, Roth, and Saxowsky (2005) concluded such diversity among communication channels allows access to the landgrant system even when people live a great distance from educati onal centers or research farms The researchers also found further use of these different communication channels in conjunction with one another would be preferred. The researchers also concluded that this diversification among communication channel s will help Extension education efforts as well as those from private sources of information available to small farmers. In addition, Nudell, Roth, and Saxowsky found potential information users indicate they are constrained by financial and time pressu res, commitments to family and jobs, along with the responsibilities of operating the family farm, (2005, p. 3 4). Finally, the use of online tutorials, web conferencing, and distance learning programs were intended to accommodate non traditional client s (Nudell, Roth, & Saxowsky, 2005). Industry Related Research A study of Iowa corn and soybean farmers sought to find out the preferred information channels of grain farmers in Iowa to better disseminate agricultural information statewide (Licht & Martin, 2007) The researchers found the needs o f the farmers were very diverse and although the information needs pertained to just two crops, the farmers preferred several different methods in which to receive different types

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34 of agricultural informa tion (Licht and Martin, 2007). Based on focus group interviews, f armers preferred mass media channels for general information, but for information on specific applications at the individual farm level, individual interaction wich Extension personal was a key for suc cess. The researchers also found producers preferred to obtain agricultural information through personal consultations to all other forms of communication methods. Producers like consultations because they provide reliable, timely, and local information s pecific to their operation and problems, said Licht and Martin (2007, p. 8 4). Producers also prefer red communication channels that are quick to access, easy to use, and specific to individual farmer information needs (Licht and Martin, 2007). This research suggested d epending on which stage the small farm operator is in the innovation decision process, the preferred information channels are going to be different. Vergot Israel, and Mayo (2005) conducted a study in Nort hwest Florida in, to examine the sources and channels of information used by beef cattle producers. This study sought to address the impact of such sources of information on Extension programming in Florida. Given that t he effectiveness of delivering Extension programs can be increased by matching the information sources and channels used by Extension to those preferred by segments of the clientele, (Isr ael, 1991, p. 2 2), Vergot et. al. identified the importance of understanding the sources used by clientele. The se researchers suggest ed that the use of appropriate information channels can facilitate a widespread coverage of the target audience and, subsequently found survey respondents used several different methods of receiving educational material and information within the agricultural community.

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35 E thnicity, age, and educational level can contribute to preferences for information channels in which to r eceive educational material by beef cattle farmers. Education, for example, influence s how a farmer prefers to receive information bec ause people have different reading levels in which they comprehend or higher levels of technical skill when operating computers f or online tutorials. Such demographic factors influence the effectiveness of the information delivery and the use of such sourc es/ formats. County Extension agents were rated relatively highly overall as a source of reliable infor mation, according to Vergot and colleagues Similar to Licht and Martins (2007) findings, Vergot Israel, and Mayo also concluded that individual consul tations were the best method of disseminating information to Extension clientele, assuming the service was being utilized in their respected local communities. These researchers also found that the typical Extension user relies on several information sourc es and formats to acquire educational information (Vergot III, et al., 2005 ). In addition, Radhakrishna, Nelson, Franklin, and Kessler (2003) identified reaching forest landowners with new information as a notable problem in South Carolina. Furthermore the authors identify several factors that should be considered in the delivery of educational information, such as target audience, educational objective, type of content in the message, and characteristics of the delivery method (Radhakrishna, et al., 2 003). The researchers found th at newsletters, publications and field tours were viewed as the most useful of all the sources available to the longleaf pine tree farmers. Conversely, the worst rated sources of i nformation among the farmers were the use of short course, formal education, and the Internet. These findings suggest that landowners place value on differ ent information channels and this can cr eate a more

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36 efficient means of communication. In this particular study, the findings reinforced the need to modify existing efforts in educating forest landowners (Radhakrishna, Nelson, Franklin, & Kessler, 2003). As mentioned before, similar studies have been conducted on other sector s of the agricultural industry besides the small farm sector. In a Tennessee study Jensen, English, and Menard (2009) found the most commonly used source of animal/health information by livestock producers was the local veterinarian. The researchers finding s were similar to Vergot et al. (2005) study which stated oneonone interaction was the format in which cattle farmers most preferred to receive ed ucational material. Veterinarians can be def ined as oneonone interaction. Jensen, Engli sh, & Menard (2009) also suggested the types of information sources used may be particular to the type of livestock enterprise (p. 5 3). Although one onone interaction may be linked to the agricultural sector in which the study was conducted, other demographics such as educational level or age might also be factors in other contexts Jensen, E ngli sh, & Menard (2009) also found farmers were managing more types of livestock, such as cows, pigs, horses, and turning to multiple sour ces for information acquisition. Even though the findi ngs cannot be generalized to other groups the study demonstrates the diversity of needs not just across industries but also even within specific industries, in this case the livestock sector (Jensen, English, & Menard, 2009). Cartmell II, Orr, and Kelemen ( 20 06) explored the preferred methods of receiving info rmation by li mi ted scale landowners, as well as examined the role demographics played on preferred information channels of limitedscale farmers. If information is to be used, it must be disseminated in a way that best facilitates its use by agricultural

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37 producers, said Cartmell II, Orr, and Kelemen (2006, p. 2 8). The authors also stated that knowing where people look for information is only half the battle for Extension communicators and where people find information is the other half of the struggle (Cartmell II, Orr, & Kelemen, 2006). I nterviews of farmers who owned 50 acres or l ess in Lincoln County, Oklahoma, revealed that direct mail was the preferred method of information dissemination among the small farmers. O neon one interaction was not as im portant as it was in the previously mentioned studies; rather mass medi a techniques were preferred for receiving educational information. The research also found the audience most often sought agricultural information from the Cooperative Extension Service or the Internet (Cartmell II, Orr, & Kelemen, 2006). Demographics such as age, education level, and ethnicity, had an effect in determining the preferred information channels of farmers ( Cartmell II, Orr, & Kelemen, 2006). A conclusion of the study was a ge of the user is directly affecting the willingness to use technologies, such as the internet. Considering the diversity among groups and personal preferences, information providers cannot assume there is likely one preferred method in which information i s disseminated amongst clientele (Richardson, 1995). Summary The theory of Roger s Diffusion of Innovation offers insight into the dissemination of innovations among all levels of adopters. Such things as the particular innovation, communication ch annel in which the message is being sent, actual time of the adoption process, and social system in which the innovation is being interjected into all have a bearing on how successful information reaches its intended audience, in this particular study smal l farm operators in Florida. Research findings indicate many sectors of agricultural prefer a oneonone method of receiving education information. This can be

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38 achieved from farmer to farmer interactions farm visits, telephone calls, and so forth. With th e advance of technology, these communication channels will become better developed and will be relied on more in future of the agricultural industry. In conclusion, information sources, demographic characteristics, and information formats can affect the source/format of the diffus ion of educational material, whether the providers be from either the Extension service or private educational source. The conceptual model also depicts the farm operators motivation behind choosing to seek information, which could be either economic reasons or simply for personal gratification.

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39 CHAPTER 3 RESEARCH METHODS Introduction This study was designed to determine the preferred information channels of Florida small farm operators for receiving educational material. The researcher sought to investigate the relationship between demographic variables and the source/format in which small farmers receive educational materials. To achieve the purpose of the study, the following objectives are investigated : 1. Iden tify demographics of small farm operators in Florida. 2. To determine the preferred format and source of educational materials by Florida small farm operators for receiving information. 3. To examine the relationship between preferred educat ional material formats/ source s and small farm o perators demographic characteristics. This chapter describes the descriptive survey design and the process used to address the studys objectives. The researcher defined the population and master list from which the sample was drawn. The instrumentation that was used in the study was also discussed in this chapter. Validity and reliability and the procedures in which the instrument was administered were also discussed. Finally, data analysis techniques were outlined and elaborated upon. Research Design This study utilized a quantitative approach. Quantitative research typically aims to classify features, count them, along with constructing statistical models in an attempt to explain what is observed (Ary, Jacobs, Razavieh, and Sorenson, 2006). More specifically, the researcher chose to use a descriptive survey design approach to ascertain the preferred information channels of Florida small farm operators. Ary,

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40 Jacobs, Razavieh, and Sorenson (2006) defined the descriptive research as using instruments and questionnaires to ascertain information, which can be used to generalize characteristics or measure attitudes and opinions of a group of subjects (p. 31). In addition, these designs result in a descripti on of the data, whether in words, pictures, charts, or tables, and whether the data analysis shows statistical relationships or is merely descriptive, (Washington State U niversity, 2009, p. 56). A research team used a mailed questionnaire to collect infor mation on small farm operators preferred information channels, as part of a larger study of small farm operations. Ary, Jacobs, Razavieh, and Sorenson define validity as the extent to which an instrument measured what it claimed to measure, (2006, p. 243, 1). A survey research design approach should address five major types of validity (Messick, 1995): face, content, construct, concurrent, and predictive. The research team used a panel of experts to address face validity and also used a pilot study to h elp ensure content and construct validity. The research team chose to use stati stical procedures to create internal validity among concurrent and predicative validity as well (Messick, 1995). Population and Sample The population included in the study is 49,000+ small farm operators in Floridas agricultural industry (2007 Census of Agriculture, 2009). The sample was drawn from the following workshops/programs held around the state of Florida: 1. Agri tunity Regional S mall F arm Conference in January 2007 2. Volusia County S mall F arm mailing list 2007 3. Marion County S mall F arm mailing list 2007 4. Brevard County S mall F arm mailing list 2007 5. FAMU Regional Goat Conference in 2007 6. South West Fl orida, Working Group members in 2007 7. Local Food Guide, published in Alachua Co. in 2007 8. UF/IFAS Organic Production Field Day in Fall 2006 9. Hillsborough Co S mall F arm Regional Conference in 2006

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41 The research team the n reduced the number down to 856 usable addresses. Duplicates, nonparticipators, farmers who were known t o have ceased farming efforts were removed from the master list. The research team considered this procedure to be valid in the sense demographic information was compared and checked for similarities/differences among the sample and the 2007 Census of A gri culture demographic information (Gaul et al, 2009). A review by this researcher found the demographic information of the survey respondents to be closely related to the 2007 Census of Agriculture. As a member of the research team, this researcher assisted by inputting 150 of the 304 returned surveys. Instrumentation The researcher used an instrument designed by the small farms focus team, lead by program coordinator Robert Hochmuth and Dr. Danielle Tredwell Additionally, Dr. Glenn Israel served as the sur vey director. The 12page questionnaire was composed of 41 questions and included Likert scale, multiple choice, and openended question types. These questions allowed the researcher to determine demographic information on the small farms group. Additional ly, opinions and barriers were also measured through the questionnaire. The instrument design team paid close attention to the overall appeal of the instrument in the design phase. For example, the pictures on the front cover of the survey were carefully s elec ted and arranged. The research team also use d the University of Florida and Florida A&M University logos in order to create a sense of legitimation among participants. A simple format and general organization of questions were taken into consideration in the design process. Dillman, Smyth, and Christian

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42 (2009) suggested the overall visual appeal, wording, and format in which the instrument is designed has a significant bearing on subjects willingness to participate. The research team submitted a proposal to conduct this study to the University of Florida Institutional Review Board (IRB 02) before any collection of data occu rred. Once the study was approved by the University of Florida IRB 02 a pilot test of 18 small farm operators in Live Oak, FL and Ocala, FL was conducted. The goal of the pilot test was to minimize the t wo most common types of survey error which are defined as measurement and non response errors (Dillman et. al., 2009). The research team implemented changes suggested by the two pilot study groups into the final draft of the survey (Gaul et. al., 2009). We hope that you will enjoy completing this survey about your farm or ranch and we appreciate your help, was used by the research team to provide encouragement and let the participants know his or her response was valuable. The first section of the survey addressed whether or not the farmer was indeed currently farming and how many years/acres were currently in production (S ee Appendix A). The second section of the instrument collec ted information on reasons for farming in order to identify which reasons were most important to small farm operators. Thirdly, a section was included to also gain knowledge on what specific crops were being produced and the diversity of each production operation. The specific resources in which small farm operators were receiving educational information and how reliable those sources were perceived to be was the fourth section of the instrument. Lastly, 13 demographic questions were used to collect inform ation on years of farming experience, current occupation, age, race, educational level and on/ off farm

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43 income levels. The researcher used these specific demographic questions to determine whether relationships existed between small farmers characteristic s and specific sources/formats of receiving educational material. In addition to determining the preferred information channels of small farm operators, the instrument was also designed to provide a snapshot of small farm owners at a particular point in time, 2008. Dillman, Smyth, and Christian (2009) identified four types of errors, which must be addressed in research. Coverage error is defined as all members of the population not having a known, nonzero chance of being included in the sample and from t hose excluded differing from those included, (p.19). In this study no measure of coverage error existed but it should be noted that the available list included only 856 of the 49,000 small farm operators in Florida. The second type of error is sampling er ror. This type of error occurs when results from only some, rather than all, members of the population are reported (Dillman, Smyth, and Christian, 2009). In this study sampling error was not an issue because all of the potential participants were given the opportunity to respond to the survey. Non response error was defined, as people who do not respond are different in a way that is important to the study from those who do respond, (p.19). Both coverage and non response error was addressed in the study by comparing 2007 Census of Agriculture demographic data to the demographic data from the respondents. Finally, measurement error refers to an inaccurate answer to questions and stems from poor question wording, survey mode effects, or aspects of the res pondents behavior, (p.19). The research team worked to minimize measurement error by using a pilot test for the study. The researcher addressed reliability and validity through minimizing these

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44 four common types of errors, through visual survey design, c reating a master list of small farm operators and specific question formatting/ wording. Data Collection Following the pilot test, the data was collected following the procedures recommended by Dillman, Smyth, and Christians Tailored Design Method (2009). The overall premise of the Tailored Design Method is to make the participants feel that the y are important to the study rather than just a survey number (Dillm an et al., 2009). The research team use d principles of Dillmans Tailored Design Method to design personalized correspondence for the participants of the study including use of real stamps ( instead of business correspondences ) and signatures that were sig ned in blue ink. The research team used multiple contacts as a method to maximizing the number of participants and also as a method of reducing nonresponse error (2009). The data collection procedures for the mail survey include a presurvey letter, w hich was mailed on July 18, 2008, alerting participants to be on the lookout for the letter and actual instrument (See Appendix B). Following the preletter, the instrument was sent with a cover letter conveying the importance of the survey, which was to be returned by July 25, 2008. To address nonrespondents, the research team sent a remi nder post c ard on August 1, 2008 in order to increase the response rate and reduce nonresponse error. Finally, a second survey accompanied with another cover letter, specifically tailored to nonrespondents and again conveying the importance of the study was mailed on August 21, 2008 to the population. Data Analysis The researcher used the Statistical Package for the Social Sciences (SPSS) software to analyze the data. The researcher use d a four step process for analyzing the

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45 survey data collected. Step one was t o calculate descriptive statistics on each variable such as age, acres producing, race, education level, etc. Frequencies were also used to report how often small farm operators use a source of information and the specific format in which the educational mat erial was transferred. With descriptive statistics you are simply describing what is or what the data shows, said Trochim (2006). Descriptive statistics provide simple summaries about the sample and the measures. The second step was to create cross tab ulations or correlation coefficients between variables that were under investigation in the analysis. An example of this is a cross tabulation between age of the farmer and the format in which they prefer to receive educational information. These types of cross tabulations and correlations were used to describe the relationships both, positive and negative, that lie in specific sources/formats of information and demographics. In addition, Trochim defined correlation as As a single number that describes the degree of relationship between two variables (2006, p. 4 3). The third step in the analysis was to conduct a factor analysis of the set of information sources and formats A factor analysis reveals patterns of interrelationships among variables and detects clusters of variable in which contain variable that are strongly intercorrelated (Agresti & Finlay, 2009). The final s tep in the analysis procedure was to compare demographics to specific groupings in the data using regression analysis. Chapter Summ ary This chapter described the descriptive survey process, which was used to complete this research. The population of small farm operators was defined along with the procedures the researcher used to gather the survey sample. The researcher

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46 described the instrument, which was designed to identify the small farm operator demographics and measure the source/ format in which educational material acquired. The researcher addressed Dillman, Smyth, and Christians (2009) four threats to the validity and reliability of a study. The research modeled procedures after the Tailored Design Method (Dillman, Smyth, & Christian, 2009). Finally, the researcher discussed the use of frequencies, descriptive statistics, and correlations to examine relationships among demog raphic characteristics and source/ format in which educational information is acquired.

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47 CHAPTER 4 RESULTS Introduction The purpose of this study was to determine the preferred information sources and formats of small farm owners in Florida when receiv ing educational information. This research aimed to determine the relationship, if any, of communication methods and user demographics such as age, education level and gender. In order to meet the purpose of this study, the following objectives were invest igated: 1. Identify demographics of small farm operators in Florida. 2. To determine the preferred format and source of educational materials by Florida small farm operators for receiving information. 3. To examine the relationship between preferred educational ma terial formats/ source s and small farm o perators demographic characteristics. This chapter presents the findings of the study from the results of the survey questionnaires. The sample for this study consisted of 859 Florida small farm operators. At the c onclusion of the data collection procedures outlined in Chapter three, 304 (35.3%) of small farmers responded. This chapter presents the demographic characteristics of the population studied and the results of the analysis of preferred information formats/ sources Cross tabulations were computed to determine if relationship existing among varying small farmer demographics and preferred communication channels followed by regression analysis results Objective One: Identify Demographics of Small Farm Operato rs The questionnaire contained 13 demographic questions that asked general information about the respondent. These demographic questions addressed age, gender, education level, and income level of their small farm. D escriptive statistics and

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48 frequency counts were calculated for applicable demographic questions in order to answer the objectives of the study. Easton and McColl (2009) define frequency as a record of how often each value (or set of values) of the variable in question occurs. Age of Respondents The mean age of the small farm operator was 58 years old with a standard deviation of 11.5 years. The group with the greatest amount of participants was that of 5160 years of age (n=93), 30.5% of the sample (Table 4.1) Con versely, the group with the l east amount of participants is the 2130 year range (n=2) only .6% of the sample. Table 4 1. Age of p articipants (n=304) Category Number Percent 21 30 2 .6 31 40 15 4.9 41 50 66 21.7 51 60 93 30.5 61 70 79 25.9 71 80 46 15.1 Over 80 3 .9 Farming Years of Experience Small farmers reported between 0 and 65 years of experience operating a farm or ranch. The largest group represented was the 6 10 year s of experience group, with 21.8% being repr esented in the sample (Table 42) Table 4 2. Small farmers years of experience farming Number o f years farming Number Percent 0 5 45 17.2 6 10 57 21.8 11 15 32 12.2 16 20 34 13.0 21 25 22 8.4 26 30 20 7.6 30+ 51 19.5 Total 261 100.0

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49 The smallest group represented by the sample was the 2630 years of experienc e, with 7.6% of small farmers reporting this experience level. Education The small farm operators were asked to report their education level in the survey. Small farmer operators who had less than a 12th grade education represented 2.6% of respondents (T able 43) Operators who had a high school education totaled to 12.5% of the population studied. Following participants who had a high school education were 26.6% respondents reporting having some collage training. Additionally, 57% of small farm operator s reporting having at least a twoyear degree or some type vocational certificate from an institution With 20.1% of small farm operators reporting having a four year degree from an institution, either a Bachelors of Science or a Bachelors of Arts. Addi tionally, 24.3% (n=74) of small farmers reported obtaining some sort of professional or graduatelevel training. Table 4 3. Participants education l evel Number Percent Less than 12th grade High S chool diploma Some college 2 y ear. degree 4 y ear degree Professional school 8 2.6 38 12.5 81 26.6 40 13.2 61 20.1 74 24.3 Total 302 100.0 Gender Of the 304 respondent s who reported their gender 66.1% were male and the remaining 33.9 % were female small farm operators (Table 44)

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50 Table 4 4. Participants reported gender Number Percent Female 103 33.9 Male 201 66.1 Total 304 100.0 Race Of the 304 respondents 93.8% classified themselves as white (Table 45) The second highest ethnic group being represented was African American at 2.3% T he remaining 3 groups, which were American Indian/Alaskan Native, Asian, and Other all had a 1.3% overall share of the percentage. Table 4 5. Respondents race Number Percent A merican I ndian & A l askan N ative 4 1.3 Asian 4 1.3 Black 7 2.3 Other 4 1.3 White 285 93.8 Total 304 100.0 Gross Income Of the 280 responses from the questionnaire 53.2% small farm operators reported gros sing $010,000 dollars in sales in 2007 (Table 46) For the cate gory $10,00125,0 00, 14.2% reported having gross sales in this range. The next category was $25,00150,000, and 10.6% reported gross sales in 2007 in this range. This is followed by 7.1% of small farmer operators with s ales of $50,000$100,000 and 8.5% wit h sales of $100,001 $250,000. A total of 6.4 % of small farm operators reported having over $250,000 in gross sales in 2007.

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51 T able 4 6. Gross sales of small farm operators in 2007 Number Percent $0 10,000 149 53.2 $10,001 25,000 40 14.2 $25,001 50, 000 29 10.6 $50,001 100,000 20 7.1 $100,001 250,000 24 8.5 Over $250,000 18 6.4 Total 280 100.0 Farm Income Respondents were asked if more than 50% of their income resulted from farming efforts, and 42% reported making less than 50% of their income from the farm (table 47). The other 58% reported more than 50% of their income was a result of their farming efforts. Table 4 7. Farmers with 50% of income resulting from farming efforts Number Percent Farm i ncome less than 50% 128 42% Farm income greater than 50% 176 58% Total 304 100% Number of Enterprises P articipants reported the number of enterprises per small farm operation. The operations ranged from having one enterprise to eleven. Having just one enterprise was Tabl e 4 8. Number of enterprises per small farm Number of e nterprises Number Percent 1 106 30.7 2 79 22.9 3 54 15.7 4 25 7.2 5 15 4.3 6 10 2.9

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52 Table 4 8. Continued 7 4 1.2 8 5 1.4 9 1 .3 10 4 1.2 11 1 .3 Total 304 100.0 the most common and was reported by 30.7% of respondents (Table 48). With 66% of small farmers reporting having three enterprises or less in their operation, in this particular sample the number of operations decreased as enterprises increased. Mix of Enterprises The number of small farm operators in which had a mix of enterprises totaled 45.1% of the s ample population. The other 54.9% reported not having a mix of enterprises in their operation. In this particular case a mix of enterprises is defined by having different types of enterprises (i.e., row crops and livestock). Table 4 9. Farmers who have a mix of enterprises Number Percent Does not have a mix 163 54.9 Does have a mix 134 45.1 Total 297 100.0 Farm Size The participants were asked how many acres they currently had in production. Of the 297 responses, 34% reported having one to ten acres currently in production (Table 4 10) The sample ranged from 110000 acres, with 67% reporting having less than 50 acres in production currently. The average small farm size of the respondents was 174 acres, with a standard deviation of 842 acres (Table 410).

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53 Table 4 10. Acers in current production Number Percent 1 to 10 101 34.0 11 to 20 51 17.1 21 to 30 19 6.3 31 to 40 13 4.3 41 to 50 15 5.0 51 to 100 45 15.1 101 to 250 26 8.7 251 to 500 12 4.0 501 to 1000 5 1.6 1000 or more 10 3.3 Total 297 100.0 Mean size of farm in acr es 173.7 Standard deviation in acres 842.0 Objective Two : To Determine the Preferred Educational Material Formats/Source and Small Farm Operators Demographic Characteristics The particip ants were asked which Cooperative E xtension Servic e (CES) they typically choose for obtaining information regarding their operation, either UF IFAS Table 4 11 Frequency count of respondents education attainment and cho ice of extension service Respondents Educational Attainment Extension Service used Less than 12th grade education High school diploma or GED Some college but no degree 2 Year degree or vocational program Complete four year degree (BA or BS) Graduate or professional school Total Both 2 7 10 4 7 13 43 CESTA 0 2 3 1 1 1 8 IFAS 4 18 49 22 46 48 187 None 2 11 19 15 7 12 66 Total 8 38 81 42 61 74 304 Extension, FAMU CESTA, n either, or both. Of the responses 61.8% used the UF/IFA S CES for educational information (Table 411) Of the small farmer sample population 2.6% reported using the service provided by FAMU. Along with 14.1% of participants

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54 reported using both UF IFAS Extension and FAMU CESTA. With 21.7% reported not using eit her of the Extension services for information acquisition of any kind. The research found 78.2% of respondent reported using some kind of University Extension Service (UES). A frequency count was computed between the previously discussed education level of respondents and the particular UES small farm operators use. The frequency count revealed 88% of participants obtaining a four year degree are the most likely to take advantage of the University Extension Service in the form of seeking educational inform ation when needed (Table 411 ) The second group most likely willing to seek information from the Extension service was small farm operators having professional schoo l training, which was 83%. This 83% was g enerated from adding both, IFAS, and CESTA sectio ns. The group least likely to use the Extension Service as an information resource was the group having a twoyear degree or completed other vocational degr ee program, which was 69% Frequencies count between the University Extension Service participants typically uses and the relationship between respondents gender were computed The findings showed 61.1% of males typically used the University of Florida Extension Service; additionally 2.5% of males used the Florida Agricultural and Mechanical University Service (T able412) Of the smal l farm respondents 62.2% of female small farm operators reported using the University of Florida Extension Service. Conversely, 2.8% of female small farm operators turned to the Florida Agricultural and Mechanical Universit y Extension Service for information. In short, there was little differences in the use of CES between men and women.

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55 Table 4 12 Frequency count of university extension service that respondents typically use by gender Extension Service Used Female Male Total Both 14 29 43 CESTA 3 5 8 IFAS 66 121 187 None 23 43 66 Total 106 1 98 304 A frequency count between t he University Extension Service used and respondents reported race. The findings suggest white and nonwhite respondents reported prefer ring the UF IFAS/ Extension Service. Of the 289 participants who reported being of white decent 61.9% reported using th e UF IFAS Extension service as the preferred service in which they receive inf ormation (Table 413) Also, 2.7% of white respondents reported using FAMU as their preferred Extension Service provider. With 13.1% reported using both services and 22.1% report ed using n either service to obtain educational information on farming and farming practices. Table 4 13 Frequency count of university extension service that respondents typically use by race Extension Service Used Non White White Total Both 5 38 43 CESTA 0 8 8 IFAS 8 179 187 None 2 64 66 Total 15 289 304 Patterns of Preferred Information Formats/Sources Another part of the analysis in volved using factor analysis to identify patterns of preferred formats/sources and to create varia ble s representing trends among the items for the question Have you ever participated in or used any of the following UF -

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56 IFAS/FAMU Extension programs, activates and resources and In the past 2 years, how much have you rely on the following sources to get information about farming or ranching. Finally, a factor analysis is used to reduce a large number of variables to a smaller number of variables, such as the factors of the analysis (Agresti & Finlay, 2009) The factor analysis revealed seven themes and groupings of different sources/formats in which small farm operators prefer to receive education material ( Table 414 ). Table 4 14. Variable t hem es of preferred information formats/sources F1= One on one/attending workshops F2= Website(s)/technology F3= Traditional print/county meetings F4= Internet/TV/Radio programs/list serve e mails F5= Other farmers/family F6= Ag business professions/lender/USDA agencies F7= USDA Agencies/IFAS events The process of creating the mean scores for variables F1F7 was of that of the following: each item of the survey questions Have you ever participated in or used any of the following UF IFAS/FAMU Extension programs, activates and resources and In the past 2 years, how much have you rely on the following sour ces to get information about farming or ranch ing was subjected to SPSS factor analysis procedure, using an oblique rotation. Once the factors in which very significant a function was identified, factor loadings were used to generate the mean scores for e ach theme for all 304 respondents. The factor loading s were used to determine which items of the survey question Have you ever participated in or used any of the following UF IFAS/FAMU Extension pro grams, activates and resources were grouped together to form the first through third

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57 Table 4 15. Factor loading and explained variance for Have you ever participated in or used any of the following UF IFAS/FAMU Extension programs, activates and resources? Component Eigen v alue Percent Variance Cumulative P ercent One on one/attending workshops 3.270 29.726 29.726 Website(s)/technology 1.439 13.086 42.812 Traditional print/county meetings 1.137 10.339 53.150 Factor Loadings One on one/ attending workshops Websites/ t echnology Traditional print / county meetings Multi state or national conferences & workshops .694 .197 .186 Regional & State wide meetings, workshops, etc. .661 .109 .040 One on one extension visits to the farm .633 .085 .120 Educational farm tours or farmer networking .617 .057 .255 FAMU CESTA cooperative extension website .057 .751 .102 Florida small farms & alternative enterprises website .008 .736 .194 UF IFAS solutions for your life website .123 .649 .062 Local county extension websites .193 .560 .554 Local coun ty extension newsletters .079 .009 .749 Extension information in 3rd party newsletters or magazines .011 .060 .713 County meetings, workshops, conferences, or field days .270 .078 .531 themes. The bolded numbers in Table 415 represent strong loadings of an item on the component. Additionally, note the Eigen values of all three components were all over 1.000, and the cumulative percentage of the three components was above 50%. A correlation matrix of the components was c omputed to determine if any significant relationships existed among the themes A positive correlation with strength

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58 of .194 was found between the theme o ne on one/attending workshops and websites /technology (Table 416). Also, a negative correlation was found between the theme, o neon one/attending workshops and t raditional print/county meetings and theme w ebsite(s)/technology and t raditional print/county meetings This suggests component one and two might be used in conjuncture tog ether but three tended to be a standalone theme of communication. A factor analysis procedure also was used to determine which items of the question In the past 2 years, how much have you rely on the following sources to get information about farming or ranch ing were grouped together thus forming the variables for the fourth through seventh themes Again, t he bolded numbers in Table 417 represent st rong loadings of an item on the component. Additionally, note the Eigen values of all four components were all over 1.000, and the cumulative percentage of the four components was above 50%. A correlation matrix of components was computed to determine if any significant relationships existed among the themes. The matrix suggest that if a small farmer is using theme four, which is Internet/TV/Radio programs/list serve emails he or she is more likely to use theme Ag business professions lender/USDA agenc ies and USDA Agencies/ IFAS events. Conversely, if a small farm operator is using theme Other Table 4 16. Component correlation s for Have you ever participated in or used any of the following UF IFAS/FAMU Extension programs, activities and resources? Component One on one/attending workshops Website(s)/technology Tr aditional print/county meetings One on one/attending workshops 1.000 Website(s)/technology .194 1.000 Traditional print/county meetings .28 0 .183 1.000

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59 Table 4 17 Factor loading and explained variance for In the past 2 years, how much did you rely on the following sources to get information about farming or ranching? Component Eigen value Percent Variance Cumulative Percent Internet/TV/Radio programs/list serve e mails 4.298 30.697 30.697 Other farmers/family 1.665 11.892 42.589 Ag business professions/lender/USDA agencies 1.308 9.344 51.933 USDA Agenc ies/IFAS events 1.090 7.783 59.716 Factor Loadings Internet/TV /Radio programs/ list serve e mails Other farmers/ family Ag business professions lender/ USDA agencies USDA Agencies/ IFAS events Rely on IFAS/CESTA internet classes .898 .007 .031 .031 Rely on IFAS/CESTA internet list servs .811 .070 .150 .196 Rely on IFAS/CESTA interactive video conferences .784 .037 .212 .108 Rely on TV or radio with IFAS/CESTA hosts .601 .006 .076 .181 Rely on other farmers or ranchers for information 111 .791 .092 .067 Rely on commercial publications for information .238 .714 .050 .257 Rely on family members for information .285 .589 .000 .229 Rely on farm organizations for information .090 .524 .296 .152 Rely on lenders for information .053 .091 .788 .044 Rely on certified crop advisors or consultants .005 .039 .733 .175 Rely on agribusiness representatives for information .059 .035 .617 .295 Rely on USDA agencies for information .167 .158 .414 .378 Rely on IFAS/CESTA events .045 .0 60 .067 .816 Rely on IFAS/CESTA internet websites & printed pubs .109 .052 .007 .733 Rely on IFAS/CESTA Extension agent .172 .047 .068 .696

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60 farmers/family he or she is less likely to use them e Ag business professions lender/USDA agencies and US DA Agencies/ IFAS events as a means of communication Table 4 18. Component correlation s for In the past 2 years, how much did you rely on the following sources to get information about farming or ranching? Component Internet/TV / Radio programs/ l ist serve e mails Other farmers/family Ag business professions/ lender/USDA agencies USDA Agencies/ IFAS events Internet/TV/Radio programs/list serve emails 1.000 Other farmers/family .259 1.000 Ag business professions/ lender/USDA ag encies .289 .286 1.000 USDA Agencies/IFAS events .250 .083 .149 1.000 Mean scores, standard deviations, minimums, and maximums scores for va riables themes F1F7 were reported in Table 4 19. With the first three themes of the study relating to the question, Have you ever participated in or used any of the following UF IFAS/FAMU Extension programs, activities and resources? the mean scores between oneon one/ attending workshops and websites/technology are similar with the same being true for the standard deviation. It can be seen in Table 417 there is positive correlation between one onone/ attending workshops and websites/technology, which explains the similarities in mean and standard deviation. Conversely, between the first two themes and t radit ional print/county meetings the mean and standard deviation are

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61 very different; the previous correlation in Table 417 shows a negative relationship among the themes. It can be understood the smaller the number for variable theme traditional print/count y meetings the more reliant the small farm operator is on those particular sources. So if the small farm operator is reliant on variable theme one onone/attending workshops and websites/technology he or she is less likely to use traditional print/cou nty meetings. The additional four themes of the study were associated with the question, In the past 2 years, how much did you rely on the following sources to get information about farming or ranching? For variable themes Internet/TV/Radio programs/l ist serves e mails, Other family/farmers, and Ag business professionals/lenders/USDA agencies, the higher the number the greater the use by the small farm operator. Conversely, the Table 4 19. Mean scores and standar d deviations of F1 F7 variables r epresenting themes Variable Mean Score Standard Deviation Minimum Maximum One on one/attending workshops 2.775 1.694 0.000 5.210 Website(s)/technology 2.480 1.600 0.000 4.094 Traditional print/county meetings .351 1.631 5.094 5.392 Internet/ TV/Radio programs/list serve emails .886 2.134 3.094 9.282 Other farmers/family 3.804 1.701 0.000 7.845 Ag business professions/lender/US DA agencies .0346 1.811 2.552 5.518 USDA Agencies/IFAS events 2.589 2.648 6.440 2.623

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62 lower the value for variable theme USDA agencies/IFAS events, the more small farm operators depend on the source for information. Objective Three: To Examine the Relationship Between Preferred Educational Material Formats/ S ource s and Small Farm Operators Demograp hic C haracteristics. The final stage of the data analysis was using correlation analysis and regression models to distinguish the significant demographic variables for predicting use of the thematic sets of information channels Salkind defines regression as statistical technique in which one variable is used to predict another, (2004). Similarly, multiple regressions uses multiple variables of a study to predict just one, ( Salkind 2004). Several variables were found to be significant and can be explained by certain demographical variables. A further explanation will be provided below for each variable of the study progressing from theme one through seven ( see table 4 13) In order to be considered significant the p value must be less than .05 at a 95% c onfidence interval (Agresti & Finlay, 2009). In order to determine if any of the independent variables were related to one another a correlation matrix was created between nine of the variables used in the regression model (Table 420). It is important to note all of the bolded values in the table were found to have significant using a p value of .05. The variable years farming wa s found to have a significant positive correlation with age, mix of enterprises and farm size. Also years farming was significant ly associated with number of enterprises but had a negative correlation, which means small fa rm operators with many years of experience are less likely to have multiple numbers of enterprises. Another pair of variables in wh ich was found to be s ignificant was between age and more than 50% of income comes from farming efforts. A positive correlation between these two variables existed;

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63 as age increases the more likely the small farm operator depends on their farming efforts to generate 50% or more of their household income. P articipants gender and number of enterprises in an operation also had a significant positive correlation. For this case a female small farmer is somewhat more likely to have multiple enterprises in her operation rather than a male farmer In the correlation of variables, education and mix on enterprises were found to be significant with a negative correlation. This can be interpreted the higher level of formal education a s mall farm operator has received, the less likely the s mall farmer is to have a mix of enterprise, which is defined by having both crops and animals among their operation. An example might be a small farmer who has received a higher level of formal education would have either a traditional crop or some form of animal livestock but not both. Finally, having a larger number of enterprises showed a strong, positive correlation with having a mix of enterprises, as one would expect. A correlation was computed between the independent variables and the seven communi cation channel themes (Table 421). The variable years farming was found to be significant with the theme of w ebsite(s)/technology and a g ricultural business professions/lender and USDA agencies Meaning the more years of experience the less likely you ar e willing to use websites and technology as a source of information. The theme, agricultural business professional, lenders, and USDA agencies, was found to have a positive correlation with years farming. Thus, confirming this is the preferred source in which farmers with many years of experience prefer to use. The relationship between the theme w ebsite(s)/technology and age was found significant but also to have a negative correlation. Since years farming and age are

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64 Table 4 20. Correlations between inde pendent variables Years farming Age Gender Race 50% of income Education Mix of enterprises # of enterprise Farm size Years farming 1.000 Age .415 1.000 Gender .089 .015 1.000 Race .060 .036 .076 1.000 50% of income .026 .130 .023 .008 1.000 Education .054 .005 .079 .024 .010 1.000 Mix of enterprise 234 .039 .014 .037 .037 .119 1.000 Number of enterprise .128 .077 .147 .034 .029 .110 .589 1.000 Farm size 144 .068 .016 .012 .015 .060 .048 .01 4 .012 Note: Bold indicates p<.05

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65 closely related, this paints the same picture as in the previous correlation, resulting in small farmers who are older do not prefer to use websites and technology as a means of receiving information. The next variable in which was found to be significant was greater than 50% of income is generated from farming efforts with the theme t raditional print/county meetings It can be concluded these farmers who rely on farming efforts to generate more than 50% of household i ncome do not rely on the use traditional print such as newsletters, trade publications, and magazines to receive information about their industry. Education was found to be significant with a positive correlation with the theme, o neon one/attending worksh ops It can be concluded the higher level of education the more likely the small farmer is to prefer this oneo n one type of communication while also attending c ounty/state workshop. In addition to this type of preferred format of communication education w as also found to be signific ant with theme six as well. Sinc e the correlation was positive among these variables it can be concluded the higher level o f education a small farmer them the more likely they are going to use agricultural business professional, lenders, and USDA agencies as sources of information. Having a mix of enterprises was found to be significant with the theme Internet, TV, radio programs, and lists serve e mails With a positive correlation it can be concluded small farmers who have a m ix of enterprises prefer to use these types of information technologies to obtain educational material. Additionally, the number of enterprises was found to be significant with the theme s w ebsite(s)/technology and traditional prints/county meetings Much l ike having a mix of

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66 enterprises, small famers with multiple enterprises prefer to use information technologies as a format to receive educational material. With theme three being the use of traditional prints, trade publications, and magazines, it can be c oncluded small farm operators with m ultiple enterprises do not prefer to use these types of traditional print. The final variable in which was found to be significant between the studies themes was farm size in acres. Themes one onone interaction, trad itional print/ county Table 4 21. Correlations between study variables and variable themes One onone/ Work sho p Website / T ech nology Tradition prints/ county meeting Internet/TV/ Radio programs/ list serve e mails Other farmers/ family Ag p rofession al/ lenders/ USDA agencies USDA Agenc y/ IFAS events Years farming .085 .137 .006 066 .117 .247 .073 Age .052 .156 .079 .10 1 .008 .073 .055 Gender .076 .092 .061 .046 .021 .112 .032 Race .025 .057 .005 .027 .051 .038 .051 G reater than 50% of Income .127 .103 .157 .042 .066 .090 .104 Educati on .130 .040 .054 .012 .017 .122 .062 Mix of enterpri se .063 .003 .092 .143 .067 .0 76 .087 # of enterpri se .012 .175 .164 .094 .032 .045 .072 # of acres .128 .007 .118 .03 5 .159 .153 .029 Note: Bold indicates p<.05 meetings, other farmers/ family, and agricultural business professional/ lenders/ USDA agencies were all found to be significant with the size of the farm. It can be concluded

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67 the larger the operation the more likely the small farm operator prefers to use oneon one interaction, also with also attending workshops as being an effective means of communication. This is consistent with theme five, which is using family, friends, agricultural business professional, l enders, and USDA agencies as a source of information. Also, the negative relationship between farm size and theme three, which is traditional prints, trade publications, and magazines shows a contrasting preference with the use of oneonone interactions and workshops among small farm operators with multiple acre operations. Multiple Regressions M ultiple regressions were computed on the thematic constructs for the items to question have you ever participated in or used any of the following UF IFAS/FAMU Ext ension programs, activates and resources, of the instrument in conjunction with nine demographic variables in the study. The values are bold ed in Table 422, when the parameter estimate was found to be significant. Using o neon one/attending workshops as the dependent variable in this multiple regression analysis, the variable s 50% of the income is a result of farming efforts and education level were considered significant with farmers who have less income from the farm or more education being more likely to use oneonone consultation or attending workshops. The regression model for this theme had a adjusted R2 of .034 Using the w ebsite(s)/technology theme as the dependent variable, the number of enterprises had a significance level of .044, whereby thos e with more enterprises tended to prefer websties and the adjusted R2 of the model was .041.

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68 Table 4 22 Regression of information themes from question Have you ever participated in or used any of the following UF IFAS/FAMU Extension pro grams, activates and resources ( n=304) Independent Variables One on one/attending workshops Website(s)/ technology Traditional prints/ county meetings B Sig. B Sig. B Sig. Intercept 2.224 2.043 2.958 Over 50% of household income is from farm .431 .049 .303 .139 .504 .014 Education .140 052 .013 .847 .038 .568 Respondent's race .296 .607 .512 .341 .044 .934 Respondent's sex .133 .572 .297 .178 .240 .276 Respondent's age .012 .272 .011 .250 .004 .667 Years farming .014 .147 .012 .190 .004 657 Number of e nterprises .008 .884 .105 044 .139 .008 Number of acres .000 .139 .003 .775 .000 .086 Has a mix of enterprises .231 .298 .218 .294 .365 .080 Adjusted R 2 .034 .041 .058 Model F Value 1.950 2.180 2.687 Sig. .046 .024 .00 5 With the same principle from the previous multiple regression as above but rather using the question In the past 2 years, how much did you rely on the following sources to get information about farming or ranch ing there were variables in which can be predicted using the fourth through seventh theme The values are bolde d in Table 423 when it is concluded to be significant. The first being years of experience farming or ranching in the model for using internet/TV/radio programs/list servs which had a parameter estimate of .03 and was significant at .007

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69 Table 4 23 Regression of information themes for question In t he past 2 years h ow much did you rely on the following sources to get information about farming or ranching (n=304) Independent V ariables Internet/TV/Radio programs/list serve e mails Other farmers/ family Ag professional/ lenders/ USDA agencies USDA Agencies/ IFAS events B Sig. B Sig. B Sig. B Sig. Intercept 3.003 3.186 1.181 2.339 Greater than 50% of Income .012 .963 .227 .291 .326 .158 .385 .271 Education .029 .737 .027 .700 .177 020 .122 .288 Respondent's race .511 .458 .451 .425 .306 .614 .681 .459 Respondent's sex .032 .908 .008 .973 .326 .190 .073 .845 Respondent's age .021 .096 .000 .990 .006 586 .014 .400 Years farm or ranching .030 .007 .014 .133 .025 .012 .017 .263 Number of e nterprises .093 .159 .077 .159 .013 .829 .113 .204 Acres in the operation 3.723 .795 .000 4 .023 .000 9 .051 1.143 .952 Has a mix of enterprises .498 .062 .103 636 .551 .020 .490 .168 Adjusted R 2 .068 020 .102 .000 Model F Value 3.002 1.554 4.088 .999 Sig. .002 .130 .000 .441 The adjusted R2 for the model was .068. When using the theme of other farmers/family as the dependent variable it was c oncluded the independent variable of number of acres in production was proven to be statically significant at the .023 levels. The models adjusted R2 was .020. The size of the operation and scale to which the small farm is operating plays a role in which educational source and format they prefer to receive

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70 information when using the theme of agricultural business professional/lenders/USDA agencies, the regression had three other independent variables in which are stati sti cally significant. The firs t being educational attainment (b= .177 ), as well as number of years farming or experience (b=.025) and finally having a mix of enterprises (b=.551). This model has an adjusted R2 of .102. Finally, no predictions were significant in distinguishing preferences for the theme USDA Agencies/IFAS events. Overall, the demographic variables accounted for only a small amount of variation in each of the seven dependent variables used in the regression analysis. Summary This chapter presented the results gathered from the 20 08 Small Farms Survey. The research reported frequency and other descriptive statistics in which provide demographic information on the population under research. Also, a wide range of ages was represented in this study varying from 21 81 years of age. A dditionally, the analysis used a factor analysis to extract variables within the study to group multiple items into sets that had similar themes of information sources and formats. A correlation analysis was conducted to determine if certain demographics such as age, education level, and farm size played a role in the pattern of preferred sources and formats wh ich small farm operators sought educational informat ion. Finally, a multiple regression approach was used to test nine variables in the study agains t mean scores of the constructed variables Statistical significances were found when analyzing the nine variables together to which source/format small farm operators prefer to receive educational material.

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71 CHAPTER 5 CONCLUSIONS AND RECCOMANDATIONS Pu rpose and Objectives The purpose of this study was to determine the preferred information sources and formats of small farm operators in Florida for receiving educational information. Additionally, the study examined the relationship between preferred educ ational material form ats/ source and small farm operators demographic characteristics. In order to meet the purposes of this study, the following objectives were investigated: 1. Identify demographics of small farm operators in Florida. 2. To determine the pr eferred format and source of educational materials by Florida small farm operators for receiving information. 3. To examine the relationship between preferred educational material formats/ sources and small farm operators demographic characteristics. Method ology Quantitative research typically aims to classify features, count them, along with constructing statistical models in an attempt to explain what is observed (Ary, Jacobs, Razavieh, and Sorenson, 2006). More specifically, the researcher chose to use a descriptive survey design approach to ascertain the preferred information channels of Florida small farm operators. The sample was drawn from addresses gathered from both the Cooperative Extension Service (CES) Small Farms mailing list and workshop atten dee list around the state of Florida, which served as the frame for the study. The research team mailed a total of 859 questionnaires to small farm operators around Florida according to Dillmans Tailored Design Method (2009). A total of 304 usable responses were obtained and t he research team found the sample data to be

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72 comparable with the 2007 Census of Agriculture demographical information (Gaul et. al, 2009). Statistical Package for the Social Sciences (SPSS) 17.0 for Windows was used to analyze the questionnaire data. Descriptive statistics were calculated along with frequencies and factor analys es were computed. Finally, a regression and multiple regression analyzes was conducted to determine relationships among the studys variables. Summary of Fi ndings Objective 1: Identify Demographics of Small Farm Operators in Florida. Demographic findings were reported on age, years of farming experience, education, gender, race, and yearly gross income. The mean age of the small farm operator was 58 years old. Of the respondents nearly 75% reported having ten plus years of experience operating a farm or ranch. The participants were well educated, with nearly 70% having at least college education and nearly onefourth had a graduate or professional degree. Of the 304 respondents who reported their gender 66.2% were male and the remaining 33.8% were female small farm operators. Nearly all respondents 93.5% classified themselves as white. A few were African American, American Indian, Alaskan Native, Asian, or o ther. Most small farms did not generate a lot of income, with 49% of small farm operators reporting $010,000 in total sales in 2007.

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73 Objective 2: To Determine the Preferred Format and Source of Educational Materials by Florida Small Farm Operators. Fact or analyse s were used to create variables which represent trends among information sources/formats for Have you ever participated in or used any of the following UF IFAS/FAMU Extension programs, activates and resources and In the past 2 years, how much have you rely on the following sources to get information about farming or ranching. S even variables were created to represent information channel themes The first theme that was found was the oneon one interaction and attending workshops held by educa tors. The second theme in which was found was the use of information technologies. The third theme in which was found was that of traditional print and magazine/newspaper publications. The fourth theme in which existed was the use of Internet, TV, radio pr ograms, and list serv e mails The fifth theme in which was found was the use of other farmers and family in the farming community. The sixth theme in which was found was the use of ag riculture business professions, lender, and USDA agencies The final theme that was found was the use of USDA agencies and IFAS events. Objective 3: To Examine the Relationship between Preferred Educational Material Formats/Sources and Small Farm Operators Demographic Characteristics. 1. A series of regression analyses were conducted to identify the significant demographic predictors of information sources and formats It was concluded, education level, number of enterprises, mix of enterprises, and size of operati on were found to be significant in explaining variation in inf ormation use. A negative correlation between the number of enterprises per operation and the variable with the theme of using traditional prints/publications was found to be significant. A positive correlation was between the number of years farming or ranching and

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74 the variable with the theme of using information technologies/ Ag professionals and USDA agencies. A correlation between the number of acres in operation and the variable theme of using other farmers/family also had a positive correlation. A conc lusion from the multiple regression was s mall farm operators who had larger acreage operations were more likely to use family, peer s, and farm organizations as a source of information. Conclusions Based on the demographic comparison of the 2008 Small Farms Survey and the 2007 Census of Agriculture, the findings and conclusions can be generalized to the 85 9 small farm operators studied. The following conclusions were drawn from the study: 2. Small farmer operators who earned 50% or more of their gross annual income from farming efforts were less likely to be engaged in the Cooperative Extension Service (CES) statewide small farms programs; instead these farmers use other farmers/family as a resource for information. 3. Small farm operators with more education were more likely to engage in the CES statewide small farms programs and farmer to farmer networking. 4. Small farmers wi th more diverse operations (i.e., more enterprises within one operation) were more likely to use the CES websites to gain information. 5. Small farm operators who had more years of experience were less likely to use information technologies as a source of information. In addition, small farm operators with a mix of enterprises (i.e., both row crops and livestock) were more likely to use the se information technologies as sources of information. 6. Small farm operators who had larger acreage operations were more likely to use family, peer s, and farm organizations as a source of information. 7. The traditional small farm operator is most likely to use family, peers, and farm organizations as a source of information.

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75 Discussion and Implications Farmers who earned 50% or more of their gross annual income from farming efforts were less likely to engaged in the Cooperative Extension Service (CES) statew ide small farm programs and farmer to farmer networking. A negative parameter estimate in the regression model was found between more than 50% of income comes from farm and oneonone meetings and workshops. Income level of the farm was shown to be signifi cant when predicted which format the small farmers preferred to receive educational information. It can be concluded the farmers who matches this criteria rely heavily on farm income and are not classified as those who farm as a hobby or for a s econdary income. These types of farmers are more reliant on individual farmer networks within their community and agri business professionals. As for as practitioners of the research, it would be most effective to target these agri business professionals so they woul d be able to supply this type of farmer with information or serve as a median of information between CES and the farmer. This specific finding supports previous studies findings, such as King and Rollins conducted in 1993, farm size and acres in production have an affect on which information channels is preferred by the farmer. In this particular case the, at least some socioeconomic characteristics previously mention in the conceptual model play a role in the adoption of new information channels. Small farm operators with more education were more likely to engage in the CES statewide small farms programs and farmer to farmer networking. A positive correlation existed between educational level and the utilization of the CES. This correlation can be interpreted that the small farm operators in which tap into the CES programs tend to have a higher educational level than the farmers who do not use the CES as a

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76 resource. These types of small farm operators recognize the r esources the CES offers and chose to take advantage of the professional advice funded by the state of Florida. As Cartmell, Orr, and Kelemen (2006) suggest ed, it is not the individual educational level of the small farmer but rather the perceived self confidence around experts or res earchers. Intimidation among Extension agents and researcher professional can be seen as a barrier to communication to small farm operators. O perators who have a higher level of comprehension and read at a level higher than that of a high school graduate, are thought to be more comfortable to interact with industry professional and experts w ithin their specific industry (Cartmell, Orr, & Kelemen, 2006) The higher level of education can boost perceived self confidence and is congruent with the style of lea rning at a formal collegiate setting. Additionally, t he CES can engage small farmers with a lower education level by designing programs taught at a lower comprehension level or that are overall more basic. Characteristics of the decisionmaking unit such as personality variables and communication behavior are affected by educational level and perceived self confidence, thus affecting the knowledge and persuasion stage in the studys conceptual model (Rogers, 2005) Small farmers with more diverse oper ations (i.e. more enterprises within one operation) were more likely to use the CES websites to gain information. It can be concluded the small farm oper ators with a diverse operation prefers the method of receive information via the CES websites and oth e r information technologies due to quick delivery Diversity among enterprises suggests the need for fast relevant information to stay compe titive in multiple enterprises. Within the conceptual model a prior condition must exist for the farmer to feel there is a relative advantage to use

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77 technology such as sources associated with the Internet. A certain level of innovativeness is associated with the ability to use new technologies without being intimidated (Fastrak Consulting, 1998) It is important to recognize when a small farmer begins the innovationdecision process and when he or she ends multiple information channels can be used (Rogers, 2005; Israel, 1991) If the farmer begins using the Internet as source of information does not mean that is the only source the farmer will use at the end of the process. Having more years of experience actually decreases the use of information technologies and a mix of enterprises (i.e. both row crops and livestock) increases the use of information technologies amon g information channels In this particular case a mix of enterprises is defined by having both crops and some type of livestock. The relationship with a mix of enterprises and multiple enterprises mentioned previously suggest the need for fast relevant inf ormation. Results showed that the older you are the less likely you would use the previously mentioned information technology as an effective communication channel. Again, there must be a perceived relative advantage to adopt these types of information tec hnology sources within the innovationdecision proce ss. C onditions such as past experience perceived advantage and social economic characteristics all have an effect of the decision process. Larger acreage operations were more likely to use family, peers and farm organizations as a source of information. A positive regression parameter suggests that the size of the operation does have an effect on the preferred information channels of small farm operators. Because some small farmers get information via f arm organizations, this can be an effective means of reaching these farmers in charge of

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78 larger operations or more likely to be full time farmers. Here the characteristic of the decisionmaking unit has the most effect on where information is obtained. In this st udy, the socioeconomic characteristics of a large farm operator or one who depends on the farm for more than 50% of the household income lead to different preferences for information formats/sources than a farmer who is operating a small scale operation has This concept has remained relatively unchanged from previous studies such as Nudell, Roth, and Saxowky in 2005, regardless of the specific industry. In general, the use of family, peers and farm organizations are most likely to reflect the traditional Florida small farmer. Much like the farmers who operate large scale operation the information channels of family, peers, and farm organizations typically reflect the traditional small farmer in Florida. It can be concluded this type of oneon o ne interaction among other farmers and family is the most effect means of traditional small farm operators in Florida to receive information regarding specific commodities and industry related news. Recommendations Based on the results and conclusions f rom this study, the researcher has made recommendations for practitioners and researchers. Practitioners Recommendations Based on this study, the researcher suggests that practitioners consider the following recommendation: 1. Agricultural educators should target small farm operators who have a mix of enterprises in their operation with varying information technologies such as website use, list servs, and interactive videos. 2. Agricultural educator should not use information technologies to target older farme rs but rather oneonone interaction as a means of communication.

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79 3. Agricultural educators should target larger acreage operators with oneonone interactions (i.e. telephone calls or farm visits) to disseminate educational information to small farm operators. 4. Agricultural educators using traditional prints, newsletters, and trade publications should be expected to reach small farmers who have diversity among enterprises on their small farm (i.e., several different enterprises among their operation) Addit ionally, small farm operators who have a mix of enterprises among their operation use these types of traditional print, newsletters, and trade publications as an effective means of communication. 5. Agricultural educators should design educational programs t o specific audience education level to ensure learning and retention of material, in such a way there would be levels of knowledge like beginner, intermediate, and an advanced level. Obviously the advanced level programs would have higher technical data an d move at an accelerated rate. 6. Agricultural educators should target small farmers with multiple enterprises or small farm operators who are less experienced with information technologies in order to minimize cost and maximize coverage/dissemination of material to these groups. 7. Agriculturalist s should create an enterprise specific network in which farmers in the state producing the same products can get together and exchange information regarding farming practices. Also, having the ability to determine wha t enterprises are profitable and sustainable in the future would be another perceived advantage of this industry specific network This can be done using current technology such as social networking sites, blogging, Internet, and traditional mail 8. Agri cultural educators should promote the use of information technologies such as interactive classes and interactive video conferencing to groups who are willing to adopt the use of technologies. With the use of technology these groups can receive educ ation m aterial at their leisure and comprehension level with the assumption the small farm operator has a means to connect to the Internet Future Research Recommendations This study has identified the need for research in the following areas: 1. To better under stand Florida small farm operators a study should be conducted to determine the motivational factors to farm, whether it is economic, recreational, or other reasons. Understanding the reason why small farm operators choose to farm might be important to det ermine how active they are seek ing new information. Different motivational factors might lead to different preferred information channels.

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80 2. To better understand the willingness of small farm operators to accept and use information technology sources. It wa s shown that certain demogr aphic characteristics are more accepting of information technologies such as Internet use and online tutorials. Since these resources are currently present, and available to everyone with an Internet connection, it is a underutil ized resource among many of the small farm sector.

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81 APPENDIX A PROCEED TO SMALL FAR MS SURVEY

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84 Small farms Survey Post Card Message Dear Florida Farmer or Rancher, A few days ago, I sent you the 2008 Small Farms Survey. It asks about your f arm or ranch operation, as well as how Extension can be more helpful to you. If you have completed and returned the questionnaire, please accept my sincere thanks. If you have not returned your questionnaire yet, please do so as soon as possible. Becaus e of the small number of people being asked to participate in this survey, it important that each person complete the questionnaire. Thank you for your help. Sincerely, Glenn Israel Survey Director

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85 APPENDIX B 2008 SMALL FARMS SUR VERY

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97 LIST OF REFERENCES Agresti, A., & Finlay, B., (2009). Statistical methods for the social sciences. Upper Saddle River, NJ. Pearson Prentice Hall. Bernan, E., Pingali, P., & Stamoulis, K. (2008). The transformat ion of agri food systems. Sterling, VA. Earthscan Press. Cartmell II, D., Orr, C., & Kelemen, D. (2006). Effectively disseminating information to limited scale landowners in the urban/rural interface. Journal of Extension, 44(1). Colorado State Universi ty Extension (2009). New state and local data. Retrieved on April 5, 2009, from http://www.ext.colostate.edu/cis/0903.html. Darlington, R., Weinberg S., & Walberg, R. (1973). Canonical variate analysis and related techniques. Review of Educational Research 5 453 454. Dillman, D., Smyth J.D., & Christian, L.M. (2009). Internet, mail, and mixedmode surveys: The tailored design method. 3 rd edition. Wiley. Easton, V., & McColl, J. (2009). Statistics Glossary. Retrieved from http://www.stats.gla.ac.uk/step s/glossary/presenting_data.html#freqtab Etllng, A. (1993). What is nonformal education? Journal of Agricultural Education, 39( 4). Fastrak Consulting. (1998). Communication methods compared. Retrieved July 2, 2009 from http://www.fastrak consulting.co.uk /tactix/Features/commopts/comopt07.htm Fedale, S. (1987). Principles and practices 92 extension education: Electronic information technology for extension. Unpublished manuscript, University of Idaho, Agricultural Communications, Moscow, ID. Gaul, S., Hoch muth, R., Israel, G., & Treadwell, D. (2009). Characteristics of small farm operators in Florida: Economincs, demographics, and preferred information channels and sources. Gainesville: University of Florida Institute of Food and Agricultural Sciences. Ret rieved on November 3, 2009 from http://edis.ifas.ufl.edu/WC088. Hargrove, T. & Jones, B. (2004). A qualitative case study analysis of the small farmers outreach training and technical assistance (2501) program fr om 1994 2001: Implications for African American farmers. Journal of Agricultural Education, 45(2). Ingram, D. (2009). Small farms extension programs in southern states. Retrieved on June 3, 2009 from http://edis.ifas.ufl.edu/WO011. Israel, G. & Ingram, D. (1989). Characteristics of small farm operators in the North and N orth C entral Florida. University of Florida Press.

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98 Israel, G. (1991). Reaching Extension's clientele: Exploring patterns of preferred information channels among small farm operators. Southern Rural Sociology, 8. 1 18. Jensen, K., English, B., & Menard, R. (2009). Livestock farmers use of animal or herd health information sources. Journal of Extension, 47(1). King, R. & Rollins, T. (1993). Factors influencing the adoption decision: An analysis of adopters and nonadopters. Journal of Agricultural Education, 36 (4). 1 12 Licht, M. & Martin, R. (2007). Communication channels preferences of corn and soybean producers. Journal of Extension, 45(6). Lionberger, H.F., & Gwin, P.H. (1982). Communication strategies: A guide for agricultural change agents. Danville, IL: The Interstate Printers and Publishers, Inc. McKinney, M. (2001). The Morrill land grant act. Retrieved on June 14, 2009 from http://www.lib.niu.edu/2001/ihy010240.html Merriam Webster, (2009). Retrieved from http://www.merriam webster.com/dictionary/agriculture on May, 31, 2009. National Association of farm Broadcasters (2009). About us Retrieved on June 15, 2009 from http://www.nafb.com/aboutus Nudell, D., Roth, B., & Saxowsky, D. (2005). Nontraditional extension education using video conference. Journal of Extension, 43(1). Radhakrishna, R. & Thomson, J. (1996). Extension agents use of information sources. Journal of Extension, 34 (1). Radhakrishna, R., Nelson, L., Franklin, R., & Kessler, G. (2003). Information sources a nd extension delivery methods used by private longleaf pine landowners. Journal of Extension, 41(4). Riesenberg, L., & Obel Gor, C. (1989). Farmers preference for methods of receiving information on new or innovative farming practices. Journal of Agricul tural Education 43(2) 7 13. Rogers, E. (2003). Diffusion of innovation. New York City, New York. Free Press. Salkind, N. (2004). Statistics for people who think they hate statistics. Thousand oak, California Sage publishing. Small Farms Alternative Enter prises (2009). About us R etrieved on April 5, 2009, from http://smallfarms.ifas.ufl.edu/.

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99 Stat Soft. (2010). Cross tabulation and StubandBanner Tables. Retrieved on January 28, 2010 from http://www.statsoft.com/textbook/basic statistics/#Crosstabulation a Terry, B., & Israel, G. (2004). Agent performance and customer satisfaction. Journal of Extension, 42(6). U.S. Census Bureau. (2007). State profile, Florida. Retrieved April 5, 2009 from http://www.agcensus.usda.gov/Publications/2007/Online_Highlights/C ounty_P rofil es/Flo rida/cp99012.pdf. United States Department of Agriculture, (2009). About us: Extension today. Retrieved on May 24, 2009 from http://www.csrees.usda.gov/qlinks/extension.html#today. United States Department of Agriculture, (2009). Ameri cas diverse family farms: Assorted sizes, types, and situations. Retrieved on July 1, 2009 from http://www.ers.usda.gov/publications/aib769/aib769.pdf University of Florida IFAS/Extension (2009). About extension. Retrieved on April 5,2009 from http://solu tionsforyourlife.ufl.edu/about/. University of Florida IFAS/Extension (2009). IFAS facts. Retrieved on July 1, 2009 from http://www.ifas.ufl.edu/IFAS_facts.html University of Texas A&M Extension (2009). Overview Retrieved June 3, 2009 from http://extens ioneducation.tamu.edu/. Vergot III, P., Israel, G. & Mayo, D. (2005). Sources and channels of information used by beef cattle producers in the 12 counties of the northwest Florida extension district. Journal of Extension, 43(2).

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100 BIOGRAPHICAL SKETCH Kyl e Landrum was raised in a rural community in DeLand, Florida. Growing up, he was active in 4H and around agricultural as his mother was a University of Florida IFAS/ Extension agent. Mr. Landrum attended T he University of Florida where he earned his Bachel or of Science in food and resource economics. Upon graduation from the University of Florida Mr. Landrum knew he wanted to continue his education. Given the opportunity by the Agricultural Education and Communication Department at the University of Florid a Mr. Landrum pursued his Master of Science, with a concentration in leadership development Mr. Landrum plans to obtain a career within the agricultural sector to begin his climb to the top of the corporate ladder with his wife Katelyn by his side.