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Extension Support for Organic Farmers in the South: A Function of Attitude, Knowledge, or Confidence?

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EXTENSION SUPPORT FOR ORGANIC FARMERS IN THE SOUTH: A FUNCTION OF ATTITUDE, KNOWLEDGE, OR CONFIDENCE? By KENDALL L. SANDERSON A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2004

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ii ACKNOWLEDGMENTS I would like to express my deepest gratitude to my family and friends who stood by me, supported me, and encouraged me to pursue a master’s degree. I would also like to thank my thesis chair, Dr. Marilyn Swisher, for her open door, patient help, guidance, and encouragement. Special thanks also go to my committee members, Dr. Rose Koenig and Dr. Robert McSorley. It has been a pleasure working with them. Finally, I thank the grant team members and participants who made this study possible; and Dr. Humphrey, Meisha Wade, and Cathy Ritchie from the School of Natural Resources and Environment.

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iii TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................................ii LIST OF TABLES.......................................................v LIST OF FIGURES....................................................vii ABSTRACT..........................................................viii CHAPTER 1INTRODUCTION ....................................................1 Need for the Study....................................................8 Purpose and Objectives................................................9 Research Questions...................................................9 Research Hypotheses .................................................10 2LITERATURE REVIEW.............................................11 Theory of Reasoned Action............................................11 Self-Efficacy Theory .................................................15 Adult Education.....................................................17 3METHODOLOGY ..................................................21 Research Design....................................................21 Sampling Frame.....................................................21 Instrument Development..............................................22 Attitude Measurement ................................................23 Standardized Preand Posttest..........................................26 Measurement of Intention.............................................29 Demographic Data...................................................31 Data Collection.....................................................31 Limitations.........................................................32 Data Analysis.......................................................33

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iv 4FINDINGS.........................................................35 Demographic Information.............................................35 Attitude ...........................................................41 Knowledge.........................................................42 Confidence.........................................................46 Attitude, Knowledge, and Confidence Together ............................49 Determining Variables................................................51 5DISCUSSION......................................................52 Research Question A.................................................52 Research Question B.................................................56 Research Question C.................................................58 Hypotheses .........................................................60 6CONCLUSION.....................................................65 APPENDIX AWORKSHOP AGENDA..............................................71 BLIKERT SCALE FOR ATTITUDE.....................................73 CSTANDARDIZED PREAND POSTTEST ...............................75 DINDEX OF CONFIDENCE............................................80 EINDEX OF INTENTION.............................................82 FDEMOGRAPHIC QUESTIONNAIRE ...................................84 REFERENCES........................................................86 BIOGRAPHICAL SKETCH..............................................90

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v LIST OF TABLES Table page 1-1Change in acreage, livestock and poultry, and number of farms under organic certification in the United States, 1992-2001 .......................3 1-2Certified organic acreage in the United States Department of Agriculture southern region, by state, 1997-2001 ...................................3 1-3Certified organic acreage in each of the four geographic regions of the United States Department of Agriculture, 2001 ...........................4 4-1College degree earned by test subjects according to United States Department of Agriculture geographic regions ..........................36 4-2Percentage of test subjects and comparison group members who contribute to publications and mass media as part of job performance.................37 4-3Perception by test subjects and comparison group of peer, supervisor, administrator and local leaders’ attitudes about working with organic producers........................................................37 4-4Measures of importance of organic producers as clients of test subjects and comparison group.................................................38 4-5Relative importance of demographic characteristics of test subjects in relation to intention to perform selected behaviors on the job...............39 4-6Explanatory power of selected demogr aphic variables on intent to perform outcome behaviors, Spearman Rank Order Correlation....................40 4-7Summary of multiple regression analysis, relationship between intent to perform outcome behaviors and selected demographic variables.............41 4-8Results of t -tests for differences between test subjects’ pretest scores and comparison group scores on Likert scale to measure attitude about organic agriculture.......................................................41 4-9Paired t -test for differences between pretest and posttest scores on Likert scale used to measure attitude toward organic agriculture ..................42

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vi 4-10Results of t-tests for differences between test subjects pretest scores and comparison group scores on standardized test of knowledge about the National Organic Standards..........................................44 4-11Paired t -test for differences between pretest and posttest scores on standardized test used to measure knowledge about the National Organic Standards........................................................44 4-12Results of t -tests for differences between test subjects’ pretest scores and comparison group scores on index used to measure confidence in ability to perform outcome behaviors..........................................46 4-13Paired t -test for differences between pretest and posttest scores on index used to measure confidence in ability to perform outcome behaviors .........47 4-14Summary of multiple regression analysis, relationship between intent to perform outcome behaviors and predictor variables of attitude about organic agriculture, knowledge of the National Organic Standards and confidence in ability to perform outcome behaviors ..................................49 4-15 Summary of multiple regression analysis, relationship between intent to perform outcome behaviors and predictor variables of knowledge of the National Organic Standards and confidence in ability to perform outcome behaviors........................................................49 4-16Summary of regression analysis*, relationship between intent to perform outcome behaviors and predictor variables of knowledge of the National Organic Standards and confidence in ability to perform outcome behaviors ....50 4-17Correlation matrix for predictor variables of preand posttest scores on Likert scale used to measure attitude about organic agriculture, standardized test of knowledge about the National Organic Standards, and index used to measure confidence in ability to perform outcome behaviors and outcome variable, intent to perform behaviors..................................50 4-18Spearman rank order correlations for the determining predictor variables (posttest score on Likert scale, posttest score on standardized test of knowledge, post-test score on index of confidence, educational level and doctoral major) for outcome variable of intent to perform behaviors..........51

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vii LIST OF FIGURES Figure page 2-1Theory of reasoned action...........................................12 4-1Distribution of pretest scores of test subjects on Likert scale used to measure attitude toward organic agriculture, test for normality .....................42 4-2Distribution of pretest scores of test subjects on Likert scale used to measure attitude toward organic agriculture, test for normality .....................43 4-3Mean, standard error, and standard deviation of preand posttest scores on Likert scale used to measure attitude about organic agriculture .............43 4-4Distribution of pretest scores of test subjects on standardized test used to measure knowledge about the National Organic Standards, test for normality ........................................................45 4-5Distribution of posttest scores of test subjects on standardized test used to measure knowledge about the National Organic Standards, test for normality ........................................................45 4-6Mean, standard error and standard deviation of preand posttest scores on standardized test used to measure knowledge about the National Organic Standards........................................................46 4-7Distribution of pretest scores of test subjects on index used to measure confidence in ability to perform outcome behaviors .......................47 4-8Distribution of posttest scores of test subjects on index used to measure confidence in ability to perform outcome behaviors .......................48 4-9Mean, standard error and standard deviation for preand posttest scores of test subjects on index used to measure confidence in ability to perform outcome behaviors.................................................48

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viii ABSTRACT 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 EXTENSION SUPPORT FOR ORGANIC FARMERS IN THE SOUTH: A FUNCTION OF ATTITUDE, KNOWLEDGE, OR CONFIDENCE? By Kendall L. Sanderson December 2004 Chair: Marilyn Swisher Department: School of Natural Resources and the Environment My study examined the effectiveness of a workshop about the United States Department of Agriculture’s National Organic Program. The workshop, conducted in July 2004, trained agricultural service providers. My study measured changes in attitude and gains in knowledge and confidence, before and after the workshop, and focused on measuring intent to perform key behaviors. Data were collected through preand posttest scales, questionnaires, and indices (n = 24). Pretest scores and demographic data were compared with a quasi-control group (n = 26) of cooperative extension agents attending an organic agriculture seminar at a national convention in July. My study used the theory of reasoned action to examine relationships among knowledge, attitude, confidence, and change in behavior. This model proposes that behavioral beliefs and outcome expectations, on one hand, and normative beliefs and motivations to comply, on the other, affect attitude and inform the individual’s subjective

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ix norms. The latter variables, in turn, affect intention and, ultimately, change in behavior. The concept of self-efficacy (an individual’s confidence in his/her ability to complete a task) is central to any change in behavior. Therefore, increasing self-efficacy is a key to successful training in this theoretical framework. Amounts of knowledge and confidence gained through the workshop were statistically significant (p<0.05). Mean score of participants for knowledge increased an average of 12% after the training, while mean confidence scores increased 19%. Confidence, educational level, and doctoral major proved to be significant indicators of the outcome variable, intent. According to theoretical findings, confidence was the most important factor in predicting behavior. Surprisingly, the workshop did not significantly affect participants’ attitudes toward organic agriculture. This may be due to the high attitudinal scores at the onset of the workshop (3.91 of 5). The workshop focused on problem-solving in peer groups and experiential learning strategies targeting four different levels of cognition. These instructional techniques were extremely successful, and should be used in future trainings for agricultural service providers. Future studies should include a larger sample size to clarify the effects of certain demographic variables (age, gender, ethnicity, and when university education was completed) on the outcome variable. A study with a longer time frame should include a posttraining follow-up to determine if participants performed the behaviors they intended to perform after completing the training program.

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1 CHAPTER 1 INTRODUCTION My study explored the effects of an intensive workshop for agricultural service providers, including cooperative extension agents, on knowledge, attitude, and confidence. Goals of the workshop were to increase knowledge about the National Organic Standards and to improve the attitude and confidence of local service providers who may advise farmers interested in organic production. My study examined preand posttest scores of participants to determine if a change in attitude, knowledge, or confidence did take place; and to see what effect these factors had in influencing participants’ intentions to perform certain behaviors related to advising and educating farmers interested in organic certification. The National Organic Standards Board defines organic agriculture as "an ecological production management system that promotes and enhances biodiversity, biological cycles and soil biological activity. It is based on minimal use of off-farm inputs and on management practices that restore, maintain and enhance ecological harmony" (Alternative Farming Systems Information Center [AFSIC], 2004). The Organic Food and Production Act of 1990 established the role of the federal government in regulating organic food and fiber produc tion. Through the 1990s, the National Organic Program (NOP) worked with the National Organic Standards Board, which serves as an advisory board to the NOP, to develop the National Organic Regulation. The regulation was fully implemented on October 21, 2002. The National Organic Standards are

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2 enforced by the U.S. Department of Agriculture (USDA) and their accredited certifying agencies. Consumers can buy agricultural products anywhere in the United States that are certified organic and be assured that they have been produced and processed under a uniform set of standards. Since 1990, organic agriculture has been one of the fastest-growing segments of agriculture in the United States. Organic farmers, mostly small-scale producers, numbered 12,200 in 2000; and the USDA estimates the number of organic farmers to increase by about 12% each year. Cropland has also increased proportionately with the growing demand. Retail sales have grown 20% per year over the last decade, evolving into a multi-billion dollar sector of the food and fiber industry. Organic sales accounted for 2% of total U.S. food sales in 2001, reaching $7 billion. Organic food is now sold not only in health food stores but also in 73% of conventional grocery stores (Jordan, 2004). As of 2001, every U.S. state except Delaware and Mississippi had some certified organic cropland. Table 1-1 shows organic acreage and animal production from 1992 to 2001. After full implementation of the National Organic Standards in 2002, the organic industry is expected an annual growth rate of 20% to 25% into the next decade (Sustainable Agriculture Network, 2004). According to the 2003 study by Greene and Kremen, most southern states had very little certified cropland, pasture, or operations. Table 1-2 shows certified organic acreage by state, the USDA’s southern region. The last column shows each southern state’s percentage of the total certified acreage in the U.S. The South accounted for only 13.8% of all certified organic acreage in 2001. Table 1-3 compares distribution of certified organic acreage the four USDA regions Data are from the 2001 totals of U.S. certified organic acreage (Greene & Kremen, 2003).

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3 Table 1-1.Change in acreage, livestock and poultry, and number of farms under organic certification in the United States, 1992-2001 1992199720002001 U.S. certified farmland Total (pasture and cropland)935,4501,346,5582,029,0732,343,924 U.S. certified livestock Total (beef, milk cows, hogs, pigs, sheep, lambs) 11,64718,51356,02871,216 Total Poultry (layer hens, broilers, turkeys) 61,363802,9663,159,0505,014,015 Total certified operations3,5875,0216,5926,949 Change (percent) 1992-971997-012000-01 Total farmland447416 Total livestock 5928527 Total Poultry1,20952459 Total certified operations40385 Source: Economic Research Service, USDA (Greene & Kremen, 2003) Table 1-2.Certified organic acreage in the United States Department of Agriculture southern region, by state, 1997-2001 Total Certified Acreage State199720002001% of Total* Alabama1495350.0020 Arkansas99720,10724,8481.0600 Florida32,745 **5,13612,0590.5200 Georgia5726335460.0200 Kentucky5,6666,2916,5520.2800 Louisiana371161960.0040 Mississippi---North Carolina9801,4741,3770.0600 Oklahoma3,9923,2063,9220.1700 South Carolina41168140.0006 Tennessee1,3511,4343000.0130 Texas30,880100,726266,32011.3600 Virginia4,4169,5207,4280.3200 Total82,012149,351323,49713.8 Percentage of total certified organic acreage (2,343,924 acres) in the U.S. in 2001 In 1997, Florida reported 25,000 acres in the category “wild-crafted acreage” Data not available for Puerto Rico and the U.S. Virgin Islands. Source: Economic Research Service, USDA (Greene & Kremen, 2003) Despite strong growth in organic agriculture since 1992, overall organic production is still only a small percentage of agricultural production in the U.S. Farmers surveyed by Greene (2000) and Greene and Kremen (2003) identified lack of technical

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4 infrastructure as a main obstacle to adopting of certified organic practices. The Organic Farming Research Foundation (OFRF, 2003; Lohr & Park, 2003) conducted a nationwide survey of organic producers in 1997. Respondents were asked to specify to what degree certain constraints inhibited organic produc tion. Specifically, farmers were asked to indicate the usefulness and number of contacts they had with 12 sources of information regarding organic production. Sources included private and public entities. Cooperative Extension Advisers were ranked 10th of 12, rated only slightly more useful than state agricultural departments and the USDA national or regional offices. Furthermore, Extension faculty received the highest percentage of "never useful" ratings at 6% (Lohr & Park, 2003). Topping the list as the most useful sources of information were other farmers, organic certification personnel, and input suppliers. These findings clearly indicate that private sector information is more highly valued in the organic industry and more widely used than public sources such as universities and Extension. Table 1-3.Certified organic acreage in each of the four geographic regions of the United States Department of Agriculture, 2001 Region% of Total* Southern13.8 North East5.2 North Central29.7 Western51.4 *Total certified acreage in 2001 was 2,343,924 Data not available for Washington, D.C. and U.S. territories Source: Economic Research Service, USDA (Greene & Kremen, 2003) Using OFRF data, Lohr and Park (2003) found that small organic farmers with 5 acres or less showed the most dissatisfaction with Extension faculty, as did those who had been involved in organic farming for longer periods of time. Farmers with at least 13 years of experience gave the lowest ratings to Extension faculty. Conversely, among those who ranked Extension as "very useful" were farmers with less than 5 years

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5 experience. The data, however, suggest that dissatisfaction with Extension faculty increases as experience increases. Income was also a significant variable in the study conducted by Lohr and Park (2003). Their findings suggest that Exte nsion advisors provided more relevant information to the most economically viable and top-selling producers. Overall, part-time farmers with higher incomes who also used many private-sector sources of information, rated Extension as useful and effective. Farmers from the western and northeastern regions of the U.S. rated Extension personnel more favorably than producers in the southern and north central regions. The western and northeastern regions are home to the oldest organic farms and certifying agents. Government spending in those regions has historically made greater commitments to organic research and education. The southern and northern central regions of the U.S. have the majority of all farmers nationally (both traditional and organic), with 39% each. The western and northeastern regions have only 14% and 7%, respectively. Of respondents to the OFRF survey from the southern region, 80% ranked Extension as a barrier to organic agriculture. Since 39% of all U.S. farmers live in the South, one would expect the number of organic farmers to grow in this region. Yet the South has some of the lowest overall numbers of certified organic acreage and certified organic operations (Table 1-2). Greene (2000) shows that organic agriculture is growing rapidly in the South, increasing by more than 50% between 2000 and 2001. The South has some of the lowest levels of institutional support for organic research, extension, and education within the Land Grant Universities (OFRF, 2003). Why is so little funding spent on organic research in the South? McDowell (as cited in Lohr & Park, 2003) characterizes Extension as being "held hostage by traditional

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6 audiences, unable to effectively inform its clientele on important emerging agricultural issues and lacking the vision to broaden its client and program portfolio" (p. 635). The organic sector needs greater technical support from Extension, generally; and with the establishment of national standards, it is inevitable that more organic farmers will turn to Cooperative Extension for advice and technical support. Lohr and Park (2003) argue that Extension must meet the needs of nontraditional audiences, and organic producers represent a relatively easy target to develop as a clientele group. They also argue that Extension must understand and meet the needs of the newer organic farmers and tailor advice to the level of these producers. They suggest that newer farmers with less experience will seek more information from Cooperative Extension, especially in areas of regulation and local production problems. Extension can increase its organic clientele by developing credible and appropriate advice, requiring "research that leads, rather than follows, the organic information curve" (Lohr & Park, 2003, p. 642). In a study done by the North Carolina Cooperative Extension Service in 2000, 97.5% of the Extension faculty surveyed agreed that a “proactive perspective was necessary when developing Extension programs” (Minarovic & Mueller, 2000, p. 5). Furthermore, Worstell’s State of the South Project (as cited in Minarovic & Mueller, 2000) revealed that Extension faculty need more training in sustainability ideas, practices, and technologies. According to a study done by OFRF (2003), the southern region has some of the lowest levels of institutional support for organic research, extension, and education within the Land Grant Universities. What is preventing the southern USDA region from devoting more funding to organic research and bolstering the organic knowledge base within Extension?

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7 According to Kraus (as cited in Pooley & O’Connor, 2000), one of the greatest determinants of behavior is attitude. Organizations need commitments from members to a shared goal. When one thinks of an organization, one thinks of a group of people working towards a unified vision. In reality, organizations are made up of many individuals, each with their own goals, visions, beliefs and intentions. This diversity adds richness to an organization by giving members the opportunity to experience different viewpoints and to access different areas of expertise. However, one way in which an organization can work towards a common goal is through a shared vision where members have similar attitudes. Attitudes are mental images a person forms about a concept based on their knowledge, feelings and actions towards it (Alreck & Settle, 1985, as cited in Minarovic & Mueller, 2000). There is need, therefore, for more institutional support for organic farmers in the South. In 2003, SARE (Sustainable Agriculture Research and Education), a USDA program, awarded a grant to the Center for Organic Agriculture at the University of Florida to develop a workshop for agricultural service providers. The workshop, entitled What Service Providers Need to Know About the Organic Rule and Regulation aims to train Cooperative Extension faculty and other local service agents about the National Organic Program (NOP). Cooperative Extension Agents function as the link between farmers and researchers at Land Grant Institutions. Extension faculty members disseminate research findings, assist farmers who seek more scientific knowledge or instruction, and provide educational programs that target the needs of the local population. They bridge the gap between science, farming, and administration, creating a two-way flow of information between research and education. In a given year,

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8 Cooperative Extension employees work with three million volunteers and reach 48 million others (Mayeske, 1991). Collaborators on the grant include Land Grant University faculty, organic farmers, and Cooperative Extension agents from Florida, Kentucky, and the U.S. Virgin Islands. The long-term goal of the grant is to increase the acreage and number of certified organic producers in Florida, Kentucky, and the U.S. Virgin Islands. In order for this to happen there must be an increase in the number of field service providers who understand the National Organic Program, its rules and regulations, and who can accurately advise farmers interested in organic production. This, in turn, will lead to an increase in land under organic production practices. It is hoped that this grant project, which will sponsor seven trainings over the next year and a half, will blossom into a standard training offered to Cooperative Extension agents in the South. Need for the Study Organic agriculture is a relatively new industry, and it is important that Extension programs provide growers with appropriate and timely information about organic production methods. The 2002 Farm Act allocates $3 million yearly to the USDA to fund grants dealing with organic agriculture. The Agricultural Research Service (ARS) of the USDA has more than 125 scientists conducting research on organic systems. Also, the USDA’s Sustainable Agriculture Research and Education (SARE) allocates about 19% of its budget to organic research (Greene & Kremen, 2003). Nonetheless, knowledge and support for organic production among Extension faculty is limited within the southern region, even though their knowledge about organic production is essential in moving the organic industry forward. This workshop trained Cooperative Extension agents and other agricultural service providers about the National Organic Standards and equipped them with the

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9 knowledge and confidence needed to assist producers interested in organic farming. The results of my study can be used to develop, improve, or design future educational programs for Cooperative Extension faculty and other professional groups. My study will provide baseline information about attitudes, knowledge, and confidence of Extension agents in the South both before and after the training. These results can be compared with data from other organic trainings that take a more passive approach to learning. Furthermore, this study provides methods and standardized instruments to assess the effectiveness of other curricula or teaching methods. Results of this study can be used as a basis for requesting that more research funding be allocated to organic research projects in the South. Purpose and Objectives The purpose of this study was to determine the effects of the workshop, What Service Providers Must Know About the Organic Rule and Regulation on participants’ knowledge, attitude, and confidence. These fact ors will influence participants’ intentions to perform or not perform key behaviors related to educating interested farmers about the National Organic Standards. I propose that by looking at the effects of this training in its early stages, I will be able to predict the eff ectiveness of the training in reaching its goals, to increase competency of Extension faculty who advise consumers and farmers, and ultimately increase the amount of acreage under organic production. Research Questions My research addresses the following questions: •How effective was the workshop in improving attitudes, increasing knowledge, and increasing confidence in advising farmers about the National Organic Standards? •How do attitudes, knowledge, and confidence affect the intention of agricultural service providers to perform key behaviors?

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10 •Does a relationship exist between the intent to perform the key behaviors and any of the measured demographic variables? Research Hypotheses Following are the hypotheses I formulated for this study: Hypothesis 1.I predict a positive relationship between increased knowledge about the National Organic Standards and the attitude of agricultural service providers. Hypothesis 2.I predict a positive relationship between increased knowledge about the National Organic Standards and the confidence of agricultural service providers to advise farmers about organic production and conduct organic trainings. Hypothesis 3.I predict a positive relationshi p between the confidence of agricultural service providers and their intention to advise farmers about organic production and conduct organic trainings.

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11 CHAPTER 2 LITERATURE REVIEW Theory of Reasoned Action The theory of reasoned action, developed by Icek Ajzen and Martin Fishbein (1980) is one of the most influential theories describing the attitude-behavior relationship. The theory is based on the premise that people are rational and decide what to do based on available information. They argue that people consider the implications of their actions and then choose a behavior. Ajzen and Fishbein (1980) suggest that attitudes are a favorable or unfavorable evaluation of an object. Attitudes are formed through life experiences, including both direct and indirect experiences and observations. These experiences, also called knowledge, are “behavioral beliefs.” They have been gathered over time and form the basis of opinion or attitude. This implies that attitudes are learned and can be changed. They can be viewed as an overall evaluation of a behavior and can be measured on a bipolar dimension. The more favorable a person’s attitude toward a behavior, the more they intend to perform that behavior. Attitudes are influenced by beliefs (knowledge) and may change over time, as knowledge changes. The immediate determinant of a behavior is intent, the intention to perform or not perform this action. Intention is determined by two basic functions, attitude toward the behavior and subjective norm. Subjective norms are the perceived social pressures to perform or not perform the behavior. In general, a person will intend to perform a behavior if his knowledge about the behavior is positive and if he feels others important

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12 to him want him to perform the behavior. This subjective norm may influence a person to perform or not perform a behavior regard less of his own personal behavioral beliefs. Other factors, or external variables, can also affect behavior. These include personality characteristics and demographic variables. In this theory, external variables may influence the importance of behavioral and normative beliefs. Beliefs influence attitudes and subjective norms; these two components influence intentions; and intentions influence behavior (Ajzen & Fishbein, 1980, p. 80). Figure 2-1.Theory of reasoned action This theory assumes that most social behaviors are under a persons volitional control and therefore can be predicted by inte ntions. Ajzen and Fishbein (1980) explain that, intention is the immediate determinant of behavior, and when an appropriate measure of intention is obtained it will provide the most accurate prediction of behavior (p. 41). To be an accurate measure of behavior, the measure of intention must correspond highly with the behavior in four areas: action, target, context, and time. For example, in order for us to predict if the agricultural service providers will hold their own organic training after attending our workshop, an appropriate measure of intention will be to ask if the service providers intend to conduct (action) their own organic training

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13 workshop (target) for other agricultural service providers or farmers (context) within the next six months (time). Ajzen and Fishbein (1980) argue that the more an intention directly corresponds to a behavior, the more accurate the prediction will be. Predictions will also be stronger within a shorter time frame because outside variables will have a less direct influence on the intention to perform the behavior. For example, an Extension agent who completes the organic training workshop and declares she will conduct her own training within the next year, may be more susceptible to outside variables such as changing jobs, family illness, or governmental changes that may prevent her from completing the behavior. Generally speaking, the longer the time frame, the less accurately intention will predict behavior. Or put more positively, an intention is more likely to predict a behavior within a short time frame. In response to the argument that outside variables such as personal experiences with the action, influence of important people, skills needed to perform the behavior, or unforeseen events, can weaken the intention-behavior relationship, Ajzen and Fishbein (1980) argue that intentions always predict behavior if the two have a high level of correspondence, as long as the intent to perform the behavior has not changed before the behavior is measured. Instead of weakening the behavior, they claim that external variables may moderate the strength of the intention-behavior relationship itself. According to the theory, behavioral change is a function of changing beliefs about an object or behavior. Many assumptions are made with this theory. By analyzing Figure 2-1, you will see that an assumption is made with each step in the diagram. It is assumed that if a change is started at the left hand side of the diagram, it will produce a change along each step. Start with assuming a change in beliefs will produce a change in

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14 attitude or subjective norm, which will produce a change in intention, which will produce a change in behavior. Training could be a catalyst that starts the process of change in Figure 2-1. It is an exposure to new information about an issue. It is an attempt to influence or change beliefs, or knowledge, in order to cause change down the line, ultimately resulting in a behavioral change. Iozzi (as cited in Pooley & O’Connor, 2000) argues that environmental education programs must address both attitude and knowledge in order to induce changes in behavior. There are two basic strategies to induce change, active participation and persuasive communication. A study by Stephen Sapp (2002) looked at the hierarchy of effects principle developed by Ajzen and Fishbein and others. The hierarchy of effects principle claims that behavior is the rational product of knowledge, attitude and intentions. People make rational or logical decisions to perform or not perform a behavior based on their knowledge, attitude and intentions. There is a “logical consistency between beliefs [knowledge] and attitudes, attitudes and intentions, and intentions and behavior” (Sapp, 2002, p. 43). Results of Sapp’s (2002) study show that a lack of knowledge can make one unable to perform certain behaviors, even though his attitude and intentions make performance of that desired behavior the ne xt logical step. Sapp found that in order to perform a behavior, the performer must be above a certain threshold of knowledge. Otherwise the result would be an inability to perform the expected behaviors. In his study, different levels of knowledge were measured, including basic command of facts (knowledge), accurate assessment of facts (comprehension), and the ability to understand linkages between different key aspects (analysis). Sapp’s minimum threshold combined

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15 knowledge and comprehension. Without mastery of these two basic levels, one would be unable to perform the behavior. Self-Efficacy Theory Recent versions of the theory of reasoned action have added self-efficacy to the model (DeJoy, 1996). Self-efficacy theory is an individual’s belief in his or her ability to successfully accomplish a specific task. It is how confident someone feels that he or she can achieve the goal. It is a dynamic, future-oriented judgment, changing over time, as new information, experiences, and feedback are acquired (Gist & Mitchell, 1992). Selfefficacy motivates. It influences a person’s choice of activities and goals, the amount of effort put into a task, and how long he or she will persevere (Bandura, 1989; Gist & Mitchell, 1992; Lent, Brown, & Larkin, 1986). Someone with high self-efficacy views difficulties as challenges to be mastered. Self-efficacy stimulates cognitive functioning and performance, and reduces stress (Bandura, 1989). Self-efficacy is an important mediator of behavioral change. Self-efficacy appears to be especially important for long-term behavioral change and maintenance. Enhancing self-efficacy increases a person’s sense of control, which is critical to successful adherence to a behavior over time (DeJoy, 1996). “When self-efficacy is enhanced, attendant increases in performance are noted” (Gist & Mitchell, 1992, p. 183). Self-efficacy is developed through traini ngs and education, problem-solving and other skill building exercises, actual experience, and modeling of the desired behaviors by coworkers and peers. It is viewed as having generative capabilities, growing over time with a cycle of successful performan ces of the desired behavior, followed by an increase in confidence, followed by further attempts at the desired task (DeJoy, 1996; Gist & Mitchell, 1992).

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16 Many studies have been conducted with students and self-efficacy, linking selfefficacy to academic performance and persistence. Students who self reported high levels of self-efficacy achieved higher grades and were more likely to persist in technical or scientific studies (Lent et al., 1986). Self-efficacy accurately predicted writing and math performances (Gist & Mitchell, 1992; Schunk, 1991). Efficacy was also correlated with the ability to quit smoking or stick to a diet, sports performance, work related performance, political participation, adapting to new technology (Bandura, 1997, as cited in Goddard, Hoy, & Hoy, 2004; Garcia, Schmitz, & Doerfler, 1990; Gist & Mitchell, 1992) and behaviors to detect breast cancer (Luszczynska & Schwarzer, 2003). It has been studied in other fields such as business, management, sociology, and education, linking groups’ collective efficacy beliefs to group outcomes (Goddard et al., 2004). Molnar and DeLauretis (as cited in Lent et al., 1986) found that self-efficacy is useful in predicting academic achievements of intellectually homogenous groups of students. Bouffard et al. (as cited in G oddard et al., 2004) found that students with the same level of mathematical abilities had significant differences in their ability to solve math problems based on the strength of their self-efficacy. Students with higher efficacy consistently applied what they knew while those with low levels of efficacy gave up early. According to Luszczynska and Schwarzer (2003), attitude and confidence are the direct cause of behavior, however confidence (self-efficacy) is the best single predictor of intention, which leads to behavior. Bandura (as cited in Gist & Mitchell, 1992; Garcia et al., 1990) has proposed that self-efficacy predicts future behavior more consistently than does past or concurrent behavior. Bandura argues that people are more influenced by how they interpret experiences than by how much they actually achieve. Therefore,

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17 previous behavior largely affects levels of self-efficacy, which then influences future behavior. Like measurement of intention in the theory of reasoned action, accurate measurement of self-efficacy must have a high correspondence to the task. It must be measured for a very specific action or task and must be measured as closely as possible in time to that task (Garcia et al., 1990; Multon, Brown, & Lent, 1991; Pajares & Miller, 1994). If not measured under these strict criteria, ambiguous and false results will be found which confound relationships instead of clarifying them. Self-efficacy advocates warn that self-efficacy is not the sole determinant of behavior, although it is extremely valuable in predictions. They suggest that outcome expectations and incentives also influence the behavioral result. Adult Education Personalities change and develop as adults mature. Adulthood is a combination of wisdom, experience, and knowledge. Successful adult training programs utilize the experience that participants bring with them and focus on enhancing the professionals’ expertise (Tennant & Pogson, 1995). Utilizing peer groups is a very successful method of adult learning (Eisen, 2001; Saltiel, 1998; Schunk, 1991). In fact, “many educators have found collaborative learning to be more successful in promoting achievement than either individualized or competitive learning experiences” (Gerlach, as cited in Saltiel, 1998, p. 8). Peer groups motivate members by fostering relationships between indi viduals of comparable status who share a similar learning objective. Participants use their expertise to learn from and teach others. Observing a peer succeed at a task bolsters the confidence of others within the group, motivating them to attempt and succeed at the task. Schunk (1991) found that by

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18 observing peers model the desired behavior increased skill and efficacy better than watching the teacher model the behavior or having no model at all. Transformative or experiential learning emphasizes learning by doing and stresses the importance of reflection or critical analysis through discourse (Eisen, 2001; Miettinen, 2000; Pruneau, Gravel, Bourque, & Langis, 2003). Education’s role is to empower, to give learners the knowledge and skills they need to effect change and impact their future. Transformative and experiential learning fosters interest and empowerment in participants (Pruneau et al., 2003). Empowerment facilitates behavioral changes by enabling individuals to make and implement plans to solve problems (Becker, Kovach, & Gronseth, 2004). The grant team members from Florida, Kentucky, and the U.S. Virgin Islands met in January of 2004 to discuss workshop content and structure. They outlined different key topics to be covered, which were later developed into training modules that could be used as a full workshop or as individuals lessons (Appendix A). Different modules were also developed for distinctive needs of different states; for example, citrus or livestock modules were discussed for Florida. The team members decided on the importance of each topic and how much time would be devoted to activities in these areas. Next, the team built the activities for each module based on models of adult education, targeting the four main learning styles and four of the five cognitive levels of learning. These cognitive levels include the most basic level of knowledge, or fact retention, the second level of comprehension, or basic understanding of the topic, application, skills and usage of materials learned, and the fourth level of analysis, or understanding of the content and structure of the material. The team wanted to increase

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19 the knowledge and levels of confidence of the participants through experiential learning, engaging all learning styles and challenging participants through higher cognitive levels. The workshop was a two-day, dynamic, hands-on, intensive training about organic agricultural production practices and the National Organic Standards. It provided participants with the necessary tools to advise farmers and train others about organic agriculture and the National Organic Standards. The workshop aimed at empowering participants through discovery learning and skill development. The workshop was designed to be completely participatory, with essentially no lecture. Adults worked in small peer groups to tackle new problems, drawing on the wealth of experience and knowledge that they brought to the workshop. Working in peer groups is an excellent method of teaching adults because it is nonthreatening and encourages participation. The training style focused on experiential learning, whereby adults were given a problem to solve and resources to consult. Each group worked to discover a solution to the problem and then shared their result with the class. The class discussed the findings and solutions, synthesizing this new knowledge with previous job experiences and knowledge. Discovery learning helped participants gain the knowledge and skills they needed to advise farmers interested in organic agriculture. One example of hands-on learning from the training was an exercise about organic labeling. There are different degr ees of organically labeled products, from “100% organic” to “made with organic ingredients,” according to the amount of organic ingredients in the product. Instead of a lecture describing the tiers of organic labeling, participants were directed to consult the National Organic Standards, then to buy three products from a grocery store that could fit th ree levels of organic labeling. The next day

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20 each group presented their products and explained in which labeling category they belonged. This elicited a lively, in-depth discussion about labeling and marketing. Another training example dealt with organic seed stock. Each group was given a list of seeds that a farmer wanted to use on her organic farm. Using the National Organic Standards and seed catalogs, the groups had to determine which seeds were allowed in certified organic production. If a seed was not allowed they had to explain why it was not allowed and recommend another variety that was acceptable. This seed stock discussion evolved into a discussion about core principles in organic agriculture that differ from conventional agriculture, such as the use of genetically modified seeds. Activities such as these stimulated interest in the participants, challenged them, and rewarded their efforts. This process of experiential learning, discussion, and reflection is essential for adult learners. This workshop gave participants the opportunity to learn concepts and skills in a peer group setting, enabling them to more effectively perform work related behaviors pertaining to organic agriculture. The training was very fast paced and intensive, yet many participants claimed it was the most effective training they had ever attended. This participatory learning style is found to be an extremely effective method for educating adult learners.

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21 CHAPTER 3 METHODOLOGY This chapter discusses the research design used in this study. It also describes the development of test instruments, data collection, and data analysis. Research Design I conducted an ex-ante factorial quasi-experiment (de Vaus, 2001). QuasiExperimental designs generally have high internal validity, but may have low external validity. This means results can be confidently generalized to many test subjects who were not actually included in the study, but findings cannot be extrapolated to other groups under different conditions. Therefore, my results can be confidently generalized within the theoretical population, county Extension faculty and other local service providers for the farm population. The major weakness of a quasi-experiment is that random sampling and random assignment of test subjects are not guaranteed. Bias is introduced because the test subjects who attended the workshop chose to do so. This bias, as we will see in the test results, may include a generally more favorable attitude towards organic production by the test subjects who attended the workshop than the attitude held by members of the theoretical population as a whole. However, the instrumentation used in this study was reliable, valid and precise. Sampling Frame The population for this study is Cooperative Extension faculty and other local service providers who work with farmers. The sample consisted of Cooperative

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22 Extension agents and other agricultural service providers who participated in the organic training workshop, What Service Providers Must Know About the Organic Rule and Regulation held on July 26-27, 2004 in Ft. Pierce, Florida (Appendix A). There were 26 subjects in the sample. A quasi-control comparison group was established with 26 Cooperative Extension agents who attended the annual meeting of the National Association of County Agricultural Agents held in July, 2004 in Orlando, Florida. The quasi-control group consisted of Extension faculty from all four USDA regions who selfselected to attend a seminar about forging relationships between organic farmers and land grant universities. Instrument Development There were no suitable test measures already existing for this study. I therefore developed three research tools to measure the change in the independent variables of attitude, knowledge, and confidence, a fourth tool to measure intent to perform job related behaviors, and a demographic questionnaire (Appendix F). I developed a Likert scale to measure attitude (Appendix B), a standardized preand posttest to measure knowledge (Appendix C), and an index to measure confidence (Appendix D). The index to measure intention (Appendix E) was based on Ajzen and Fishbein’s (1980) theory of reasoned action. I followed standard protocol in the development of the instruments and appropriate measures of validity, reliability, and precision were employed. The test instruments were developed from the workshop training materials with the help of the grant team members, consis ting of professional researchers, Cooperative Extension agents, organic certifying agents, and certified organic farmers from the states of Florida, Kentucky, and the US Virgin Islands. The test materials were pilot tested

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23 before use in the training workshop. The steps involved in the development of each instruments are explained in the following sections. Attitude Measurement I developed a scale to measure the attitudes of local service providers about organic production (Appendix B). Attitudes ar e psychological constructs or ways of conceptualizing intangible elements (Mueller, 1986). Attitude has been defined in many ways. Gordon Allport defined it in 1935 as “a me ntal or neural state of readiness” (as cited in Mueller, 1986, p. 3). In 1974 Gagn and Briggs described an attitude as “an internal state which affects an individual’ s choice of action toward some object, person, or event” (in Aiken, 1996, p. 226). In plain terms, attitude is the extent of liking or disliking something. An attitude scale is designed to evaluate the intensity and direction of the subject’s feelings about a concept or practice. The scale is constructed so that all items address only one specific issue or concept. For this study a uni-dimensional Likert scale was developed (Rossi & Freeman, 1993). Likert scales present a range of statements about a topic and subjects rate how they feel about the statement on a scale of 1 to 5 where 1 indicates strong disagreement and 5 shows strong agreement. Statements in a Likert scale are not neutral. They are meant to elicit an opinion from the respondent who indicates to what extent she or he agrees with the statement. Statements used in this study range from “Extension should do more to help organic farmers” to “We need to pay attention to our mainstream clientele, not waste time with organic hobby farmers.” The frequency of positive and negative statements is balanced to minimize bias. Coding is simple with scales because the value of each answer is determined when the scale is constructed. For example, a respondent answering “Disagree” to the statement, “Organic food is the only safe food” receives 2 points. Answers range in

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24 value from 1 to 5 corresponding to the answer choice, strongly disagree (1 point), disagree (2 points), indifferent (3 points), agr ee (4 points), or strongly agree (5 points). It is necessary to reverse the score on questions containing negatives in them, such as “Learning about organic farming is a waste of time.” Someone who answers “strongly agree” with that statement actually has a negative opinion of organic agriculture and is awarded a score of 1 (the reverse score of 5, strongly agree). Likert scales use a summative scoring procedure. Therefore, when the sum of the scores for each individual response is tallied, a higher score indicates greater approval of organic agriculture. Attitude rating scales are easy to use because they provide a single score that indicates both the direction (positive or negative) and the intensity (very positive or very negative) of a person’s attitude (Henerson, Morris, & Fitz-Gibbon, 1987). Precision is the exactness of a tool. By assigning one number value to a respondent’s scale, precision is increased, especially when compared to other more subjective research methods. Higher internal consistency, measured by inter-item consistency, increases precision. The validity of a scale is the degree to which it measures the specific attitude of interest (Sommer & Sommer, 2002) and its appropriateness. The main argument against attitudinal scales is that people’s attitudes are complex and may not be measurable along a single dimension. According to Mueller (1986), validity is the most serious weakness in attitudinal scales. Responses can be faked or adjusted, especially if self-scoring. This is a universal problem in affective measurement. Reliability of a scale indicates its consistency and accuracy in measurement. Originally I generated 134 statements regarding organic agriculture. I asked colleagues to rank the strength of the statements on a seven point scale. In this way I eliminated the

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25 statements where the judges disagreed on the strength, if it was strong, weak, or any degree in between. This narrowed the statements down to 60. Next, I conducted a preliminary test to further narrow down the number of statements to be used in the final scale. Groups of people were asked to indicate what were their general opinions of organic agriculture (negative, indifferent, favorable) before they began the scale. These participants were asked to complete the scale, indicating how each statement made them feel, ranking each question from 1 to 5 as described above (strongly disagree to strongl y agree). It was necessary to obtain a balance of respondents who felt negatively, i ndifferently, and favorably about organic agriculture in order to determine how well each item under consideration for inclusion in the scale differentiated among subjects. I eliminated all questions that elicited a neutral response because they did not discriminate between the respondents who claimed they were in favor of organic agriculture and those who felt negatively about it. I wanted to find statements that elicited a strong response, either for or against organic agriculture. I ran tests to determine the inter-item correlation, Cronbach’s alpha, and the standardized alpha for each statement. Any statement with low scores was removed. To discriminate those whose opinions were favorable about organic agriculture and those who felt negatively towards it, a t -test was run using the top 10% and bottom 10% of responses. Normally, 25% is used to compare responses in a t -test but in my pretest, the results showed strong positive and negative opinions. It was difficult to discriminate the average positive answer from the very strong positive answer using the traditional 25% approach. Using the top and bottom 10% did allow me to discriminate the strength of the answer. The statements were narrowed down to 20.

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26 Cronbach’s alpha was 0.96 for the final 20 items selected for the scale as a result of preliminary testing. After the workshop was completed, I ran another internal consistency test using the raw data from the respondents’ pretest and posttest and the data from the quasi-control group’s pretest. I wanted to determine if any items should be rejected. Cronbach’s alpha was 0.84, the standardized alpha was 0.84 and the inter-item consistency was 0.22. I decided to drop statement 4 (a negative statement) and number 6 (a positive statement) from the final scale because both statements had low inter-item consistency scores and brought the average down considerably. I also chose to eliminate statement number 9, “People who buy organic food are gullible” because the word gullible had to be explained to some participants who did not speak English as a first language. I considered removing statements 1 and 17 (both negative statements) but was concerned about the balance between positive and negative and decided to keep them in the final instrument. The final instrument had a Cronbach’s alpha of 0.85, a standardized alpha of 0.85 and an average inter-item consistency of 0.26. This process of developing the Likert scale for attitude took 8 months. Standardized Preand Posttest I developed a standardized self-completion test based on the key components of the National Organic Standards and the Or ganic Production System Plan required for organic certification (Appendix C). The test measured changes in knowledge about organic production and the National Organic Standards before and after the organic training workshop for agricultural service providers. I ensured a high content validity by developing questions directly from the training modules developed by the team of experts. The final test was approved by team members of the grant.

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27 A meeting was held in January, 2004 for the tri-state team members. Here the training team decided the content matter of the training and ranked the criticality and frequency of the topics (Appendix A). I grouped the questions they suggested for the preand posttest of knowledge by these topics or modules, eliminating repetitive questions and adding more when needed. Eighty percent of the questions were divided into the categories of the Organic Production System Plan, Water Quality, Soil Quality and Crop Fertility, Crop Management, and Organi c Integrity. Six percent of the content was devoted to the Overview of Orga nic Production, Planting StockSeeds and Transplants, and Livestock. Four percent was devoted to Handling and Processing and the remaining 10% to Organic Resources. I developed the questions for the standardized test based on the four cognitive levels of learning that were targeted in the training; knowledge, comprehension, application and analysis. The fifth level, synt hesis, is very difficult to achieve in a single training event so the team decided not to test for it. Knowledge is the lowest level of cognition and only reflects basic retention of the subject matter. An example from the test is, “The transition period from conventional to organic agriculture is __ years.” The second level, comprehension, is the lowest level of understanding and shows if the participant actually understands the meaning of the material. The third level is application, allowing the respondent to show if she or he can use what was learned. An example from the test is “List three disease control methods that organic farmers can use.” Analysis is the final level addressed in the pretest and posttest and requires an understanding of the content and structure of the learned material. An example is, “True or False. A storage box or bin originally used for conventional crops can be reused for organic crops as long as both crops are not stored together in it at the same time.”

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28 Discriminatory power, or the ability to detect who really knows the answer from those who do not, is increased as more and higher levels of testing are included. The preliminary test was administered to people knowledgeable about the subject matter, but not necessarily experts. From these tests I eliminated invalid questions. Invalid questions were ones that fewer than 10% answered correctly or more than 90% answered correctly. This test was then administered to a class of college students learning about organic agriculture. The same process as above was used to determine invalid questions. Backup questions were substituted at this time for questions that either too few people answered correctly, or too ma ny people answered correctly, taking care to retain the balance of cognitive levels. The questions were weighted for higher cognitive levels since that was the aim of the training. Determining a scoring system and answer key before administering the test increases precision and reliability and is also required protocol in the development of a standardized test. This test was based on a 50-point scoring system with each correct answer worth one point. I also calculated a weighted score for each test by multiplying each correct answer by the cognitive level of the question. For example, knowledge level questions were worth one point, comprehe nsion questions were worth two points, application questions were worth three points, and analysis questions were worth four. Thus a correct answer to an analysis level question was worth four points in the weighted score. It took 8 months to develop the sta ndardized preand posttest of knowledge. After gathering the preand posttest data at the workshop I decided to remove a question about organic livestock because time did not allow that module to be covered during the training. Two other questions were largely missed in the posttest but I decided to keep them in the final results because not everyone answered them incorrectly. After I

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29 dropped the livestock question, the final preand posttests were scored out of 49 points instead of the original 50 points. Each preand posttest was given two different scores, a raw score out of 49 points and a weighted score. Measurement of Confidence I developed an index to measure the confidence of participants to perform certain job related behaviors prior to and followi ng the training (Appendix D). The index was developed with the help of Cooperative Extension faculty who suggested behaviors related to organic agriculture that one might expect to perform on the job. They also suggested what weighted score to assi gn each behavior, depending upon the frequency and difficulty of performance. Participants were asked to rate their confidence with a scalar response indicating how confident they felt about performing certain tasks related to organic agriculture. Tasks ranged from answering questions from homeowners and consumers about organic products and options, to including organic farm s on field tours. The index scores were weighted to reflect the difficulty levels of performing certain tasks. A task such as highlighting organic farms as a source for fresh food for consumers would not be as difficult a task for Extension faculty as making field visits to an organic farm for troubleshooting. Twenty-four test subject pretest scores were valid while only 19 posttest scores were completed by test subjects. All 26 pretest scores of the quasi control group were valid. Measurement of Intention One goal of the training, What Service Providers Must Know About the Organic Rule and Regulation was to enable agricultural service providers to assist farmers who want to meet the National Organic Standards. The training proposed to increase the real

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30 number of agricultural service providers in the field who understand the National Organic Standards and who can advise organic farmers and farmers interested in transitioning to or beginning organic production. The workshop emphasized increasing participants’ knowledge of the National Organic Standards and therefore their ability to advise farmers, providing them with training manuals and lesson plans they can utilize to develop their own training sessions for farmers. Ajzen and Fishbein’s (1980) theory of reasoned action states that intentions are an accurate measure of behavior, as long as certain criteria are met. Namely, there must be a high correspondence between the intention and the behavior in four areas: action, target, context and time. A shorter time period between the measurement of the intention and the behavior is more accurate than a longer period, largely due to the consequences of unforeseen circumstances. Following these prescriptions and with the assistance of the grant team members, I developed an index with scalar response questions to measure participants’ intentions to perform job related behaviors (listed below and also Appendix E). •Provide organic advice and options to homeowners. •Answer questions from homeowners and consumers about organic products. •Highlight organic farms as a source of farm fresh food for consumers. •Seek out organic producers in your county. •Include organic farm tours on field days. •Include organic techniques in demonstrations. •Make field visits to organic farms for troubleshooting. •Educate yourself about organic production (i.e. attend other trainings, seek out useful sources of information, etc.) •Include organic farming as an alternative for farmers who call you for advice. •Respond to organic producers questions about production practices and the National Organic Program. •Add organic producers to your advisory council. •Hold a training workshop about organic practices and standards. •Advise producers about where to get organic supplies. •Include information about organic production and standards in media such as a website, newsletter, radio or TV communication.

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31 Workshop participants were asked to indicate how likely they were to conduct these activities in the next 6 months. Test participants checked their answer along a continuum of answers from probable to improbable. This measure of intent was included in the posttest and was only given to those test subjects who had completed the 2-day workshop. This data was not collected from the quasi-control comparison group. Demographic Data Demographic data were collected for all participants at the workshop and the quasi-control comparison group. The demographic data (Appendix F) includes information such as age, gender, name of school, years of formal education, type of course work, years in current employment, and how key coworkers and supervisors feel about organic agriculture. The statistical averages of the quasi-control and the workshop participants were compared to give some sense of how the workshop participants compare to the average Cooperative Extension agent. Tests were conducted with the demographic variables to determine which, if any, influenced the outcomes of the training and the intent to perform the key behaviors. The numbers and results of these tests will be discussed in Chapter 4. Data Collection Participants at the organic training seminar were given a pretest packet that included the University of Florida’s Internal Review Board (IRB) consent form, the scale for attitude, the test of knowledge, the confidence index, and the demographic data questions. They were directed to label their test with a number found on the back of their name tag. This number allowed the test to be anonymous but enabled me to correlate the pretest scores with the posttest scores. Participants were assured that the test results

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32 would remain confidential and that I was interested in the aggregate group score, not individual scores. Most respondents generally completed the packet in half an hour. After the 2-day, hands-on workshop about th e National Organic Standards and organic production, the participants were given the posttest. The posttest packet included the same attitude scale, test of knowledge and confidence index, but with the question order scrambled. Also included in the posttest was the intention index. Most posttests were completed in 30 minutes. The quasi-control comparison group was given the pretest packet only, with all tests and demographic information except for the intention index. Their tests were given prior to the seminar they attended at the national convention. Their tests were also anonymous, and I assigned a number to each test to track test results. Out of the 26 tests from the workshop participants, only 24 tests were used in the data analysis of the attitude scale, knowledge test, and demographic data. Two tests were disqualified because the test subjects were unable to attend the full 2 days of training. Twenty-four pretests were used for the confidence index, but only 19 were completed in the posttest. The index for intention was the last test to be given and suffered from test participant attrition. Only 18 indices were completed and could be used in the final analyses. The quasi-control comparison group yielded 26 valid pretests of attitude, knowledge, confidence, and demographic data. Although the sample size was small, the instruments were valid, reliable, and precise. Measuring with a preand posttest gave tight control of the data. Limitations Administering a posttest at the end of a training workshop can lead to bias. Recall and knowledge are at a peak immediately following training and a posttest at that time

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33 1All statistics were analyzed using StatSoft Statistica version 6.1. may tend to overestimate changes in knowledge. I chose to administer the test immediately following the workshop because there was a better probability of getting the completed tests back. Research shows that mailed posttests have low response rates. I believe that the need for more responses from participants outweighed the bias presented. Data Analysis I consulted Sheskin (2000) to determine which statistical tests to run. I used paired t -tests1 to examine differences in preand posttraining scores for attitude, knowledge and confidence among workshop participants. I used t -tests to examine differences between pretraining scores for workshop participants and the quasi-control group. For the Likert scale measuring attitudes about organic farmers and farming, the t -tests were performed for both the raw summative score and the mean response for each participant. The mean response was easier to interpret and understand. Therefore, given that no differences were found between p -values for the two scores, I used the mean score for the final analyses. Similarly, for the standardized test of knowledge and the index of confidence, I performed t -tests for both unweighted and weighted responses. There were no differences, and I used the weighted responses for the final analyses. I used multiple regression to determine the relationships between the three predictor variables (attitude, knowledge, and confidence) and the outcome variable, intent to change practice. The initial model included all three predictor variables. Additional models including only knowledge and confidence and finally a model using only confidence were also performed. For categorical data, I used one-way ANOVA or t -tests, depending on the number of response categories, to examine the affect of demographic variables on the outcome variable. For continuous data, I used simple

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34 regression to examine the relationships between the demographic variables and the outcome variable. The final analysis was a Spearman Rank Order Correlation that included predictor variables identified in the statistical analyses described above with a p -value of 0.05 or less. The predictor variables were attitude, knowledge, confidence, educational level, and major area of study for the doctoral degree.

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35 CHAPTER 4 FINDINGS Demographic Information Twenty-six individuals participated in my study, which was the pilot training workshop of the grant project. Of these 26 participants, only the preand posttest data from 24 participants was used. The data from the other two participants was disqualified because the participants were unable to attend the full two days of training. The preand posttest data included the Likert test for attitude, a test for knowledge, and an index of confidence. An index of intention was included as a posttest only. Eighteen completed indices were used to measure the outcome variable, intent. The eight disqualified tests included the two test subjects who did not complete the full training plus blank data sheets. Out of the valid 24 tests, 7 of the participants were female (29%) and 17 (71%) of the test subjects were male. Three participants were black (12.5%), 3 were Hispanic (12.5%), and 18 were white (75%). The ages of the participants ranged from 27 to 69 with a mean age of 47 years. Ninety-six percent of the quasi-control comparison group were white males with an average age of 52 years. Three test subjects worked in the US Virgin Islands, one in Kentucky, and 20 in Florida. All three of these states/territories are located within the southern USDA region. In the comparison group, 54% worked in the southern USDA region, 12% worked in the northeastern USDA region, and 17% worked in each of the north central and western regions.

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36 Half (12) of the test subjects were county Extension faculty (50%). Three participants (12.5%) were Natural Resource Conservation Service employees, three were university professors (12.5%), two participants were farm managers (8%), one was an agricultural consultant (4%), one worked with 4-H community gardens (4%), one was an organic inspector (4%), and one conducted postdoctoral research (4%). Nine test subjects (37.5%) had prior training about or ganic production or the National Organic Standards. The comparison group was exclusively county Extension faculty, and 37.5% also had previous training about organic production or the National Organic Standards. The levels of education within the test group varied from no college education to doctoral degrees (Ph.D.). One test subject had no college level education (4%), 4 had a bachelors (BA or BS) degree (17%), 10 participants had a masters (MA or MS) degree (42%), eight participants had a doctoral degree (33%), and one test had missing data. The comparison group of Extension faculty consisted of 9% undergraduates, 74% with a masters degree, and 17% with a doctoral degree. Fifty-two percent of the degrees earned within the test group were from universities within the southern USDA region. This compares to 30% from the comparison group. Table 4-1 explains where test study participants attended university. The schools are grouped according to USDA regions. Table 4-1.College degree earned by test subjects according to United States Department of Agriculture geographic regions USDA region SouthernNorth EastNorth CentralWestern Other (non-US ) Undergraduate94225 Graduate105101 Doctoral511

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37 Table 4-2 reflects the number of participants who had a website, contributed to a newsletter, radio or TV program. Of the 24 test subjects involved in the workshop, 15 had a website (62.5%), 16 contributed to a newsletter (67%), and 7 contributed to a radio or TV program (29%). The comparison group reflected that 37.5% had a website, 83% contributed to a newsletter, and 67% contributed to a radio or TV program. Table 4-2.Percentage of test subjects a nd comparison group members who contribute to publications and mass media as part of job performance WebsiteNewsletterRadio/TV Test subjects63%67%29% Comparison group38%83%67% The test subjects were asked a series of questions to determine how peers and influential decision makers felt about them working with organic producers. Participants in the test group accorded an indifferent/positive score towards peers, supervisors, and administrators. Members of the comparison group gave similar ratings of indifferent/positive for peers, supervisors, and administrators. The attitude of county leaders toward organic agriculture, as judged by the test subjects, is depicted as somewhat indifferent (2.48/4). The comparis on group gave the attitude of county leaders a score of 2.44 (out of 4). Table 4-3 shows the results from the questionnaire. Table 4-3.Perception by test subjects and comparison group of peer, supervisor, administrator and local leaders’ attitudes about working with organic producers Mean score Rating*Test subjectsComparison group PeersIndifferent/positive2.742.64 SupervisorIndifferent/positive2.862.86 AdministratorIndifferent/positive2.822.77 County leadersIndifferent2.482.44 *Categories and scores awarded for each category: 0 = Very negative; 1 = Negative, 2 = Indifferent, 3 = Positive, and 4 = Very positive

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38 1All data analysis was done using StatSoft Statistica version 6.1. Participants indicated that their overall experience with organic growers has leaned more toward positive (2.88/4) than indifferent. The comparison group’s score was slightly lower than the test subjects’ score but was also more positive than indifferent with a score of 2.68 (out of 4). On average, the test participants rarely (1.48/4) advised organic growers and indicated that they worked with organic farmers or producers expressing an interest in organic farming less that 10% of the time. The comparison group also rarely advised organic growers ( 1.36/4) and worked with organic farmers or farmers interested in organic production less than ten percent of the time (Table 4-4). Table 4-4.Measures of importance of organic producers as clients of test subjects and comparison group Test subjectsComparison group Previous experience with organic growers2.88*2.68* Frequency of advising organic growersRareRare Percentage of clients who are organic producers or are interested in organic production <10%<10% *Categories and scores awarded for each category: 0 = Very negative; 1 = Negative, 2 = Indifferent, 3 = Positive, and 4 = Very positive Screening t -tests1 and one-way ANOVA tests were run on the categorical demographic variables, and regressions were run on the continuous variables, to reduce the dimensionality of the model used to predict intent to change behavior. This was an exploratory activity in dimension reduction to determine which demographic variables statistically influenced the outcome variable, or intent to perform key behaviors. Table 4-5 shows the p -values for all of the dimensions measured by the demographic questionnaire. Only those variables with a p -value less than 0.05 were considered to be significant. These included the undergraduate university ( p < 0.018), graduate university ( p < 0.012), doctoral university ( p < 0.018), doctoral major ( p < 0.029), and if they had a newsletter ( p < 0.037).

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39 Table 4-5.Relative importance of demographic characteristics of test subjects in relation to intention to perform selected behaviors on the job Variable p -valueType of test** Location of MS/MA university by USDA region0.012a Location of undergraduate university by USDA region0.018a Location of Ph.D. university by USDA region0.018a Ph.D. major0.029a Contributes to newsletter0.037t Ethnicity0.057a Age0.107r Graduate major0.110a Year completed undergraduate degree0.142r Year completed terminal graduate degree0.153r Education level*0.156a Frequency of advising organic growers0.196a Undergrad minor0.221a Number of courses in soil ecology0.249r Number of courses in general ecology0.260r Years with employer0.266r Number of courses about genetic engineering0.293r Sex0.330t Previous experience with organic growers0.348a State where employed0.410a Has professional website0.442t Perception of supervisor’s attitude about working with organic farmers 0.483a Perception of county leaders’ attitude about working with organic farmers 0.528a Number of courses in pesticide technology0.545r Number of courses including agricultural ecology as major topic 0.569r Perception of administrator’s attitude about working with organic farmers 0.603a Number of courses in agricultural ecology0.604r Number of courses about organic practices0.620r Year completed Ph.D. (if applicable)0.639r Number of courses including pesticide technology as major topic 0.640r Previous training about or ganic production or NOP0.689t Job title0.737a Perception of peer’s attitude about working with organic farmers 0.758a Number of courses including organic practices as major topic 0.791r Number of courses including general ecology as major topic 0.818r

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Table 4-5. Continued 40 Variable p -valueType of test** Number of courses in IPM0.829r Has pesticide applicator’s license0.862t Number of courses including IPM as major topic0.866r Contributes to radio or TV program0.900t Undergraduate major0.904a Number of courses including soil ecology as major topic0.925r % of clients who are organic growers0.937a Number of courses including genetic engineering as major topic 0.958r Nature of previous employment0.984a *0 = no college, 1 = bachelor’s degree, 2 = master’s degree, 3 = Ph.D ** r= regression, t= t-test, a= ANOVA When a Spearman Rank Order Correlation was performed on the outcome variable, intent to perform job related behaviors, and the significant demographic variables, undergraduate university, graduate university, doctoral university, educational level, doctoral major, and if they had a newsletter, only two were significant at the p < 0.05 level. The two significant demographic variables include educational level ( p < 0.009) and doctoral major ( p < 0.047) (Table 4-6). Table 4-6.Explanatory power of selected de mographic variables on intent to perform outcome behaviors, Spearman Rank Order Correlation Demographic variableValid NSpearman R p -value Undergraduate university170.1270.628 MS/MA university180.2910.241 Ph.D. university180.3430.163 Educational level180.5940.009 Graduate major180.4010.099 Ph.D. major180.4750.047 Contributes to newsletter180.4430.066 I ran a regression summary on three demographic variables that I suspected would play a role in determining intent to perform the key behaviors. Although none were significant at the p < 0.05 level, I did discover a negative relationship between age and intent and undergraduate year and intent (Table 4-7).

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41 Table 4-7.Summary of multiple regression analysis, relationship between intent to perform outcome behaviors and selected demographic variables N=10BetaStd Err of Beta p -value Intercept0.580 Age-0.4181.7710.821 Undergraduate year-2.1281.9370.314 Graduate year 1.2402.0440.566 Note R = 0.334 p < 0.454 Std. Error of estimate: 0.179 Attitude I measured the attitude of the test subjects before and after the two day training with a Likert scale. The purpose of the test was to determine the impact of the workshop on participant’s attitudes about organic production and the role of Extension in promoting the National Organic Program. As you can see in Table 4-8, participants in the test group had a mean average attitudinal score of 3.91 (out of 5) on the pretest. This was slightly higher than the mean pretest attitudinal score of 3.58 (out of 5) for the comparison group. A p < 0.039, p was significant at the 0.05 level. Table 4-8.Results of t -tests for differences between test subjects’ pretest scores and comparison group scores on Likert scale to measure attitude about organic agriculture Test subjectsComparison group N2426 Mean score3.913.58 Standard deviation0.480.59 P 0.039 Table 4-9 shows that there was no significant increase in the attitudinal score of the test subjects from pretest to posttest. Scores increased from 3.91 to 3.98, which was not significant at the 0.05 level (p<0.17). Figures 4-1 and 4-2 show that the preand posttest scores of the test group were normally distributed. Figure 4-3 is a box and whis ker plot of the gain in attitude from the pretest to the posttest.

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42 Table 4-9.Paired t -test for differences between pretest and posttest scores on Likert scale used to measure attitude toward organic agriculture Pretest scorePosttest score N2424 Mean score3.913.98 Standard deviation0.480.53 Difference-0.07 Standard deviation difference0.24 p 0.17 Figure 4-1.Distribution of pretest scores of te st subjects on Likert scale used to measure attitude toward organic agriculture, test for normality Knowledge Knowledge about the National Organic Program was measured before and after the workshop with a preand posttest. Each question was worth between one to four points depending upon the level of difficulty of the question. Multiplying the test answer by the level of difficulty resulted in a weighted score indicating not only how much knowledge was gained but at what cognitive level knowledge was gained (knowledge,

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43 Figure 4-2.Distribution of pretest scores of te st subjects on Likert scale used to measure attitude toward organic agriculture, test for normality Figure 4-3.Mean, standard error, and standard deviation of preand posttest scores on Likert scale used to measure attitude about organic agriculture

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44 comprehension, application, and analysis). Weighted pretest scores indicate that there was no statistically significant difference between the test group and the comparison group (Table 4-10). Table 4-10.Results of t-tests for differences between test subjects pretest scores and comparison group scores on standardized test of knowledge about the National Organic Standards Test subjectsComparison group N2426 Mean73.4869.46 Standard deviation13.6310.15 p 0.24 Table 4-11 shows the increase in test subject scores from the pretest to the posttest. The mean gain was 12.11 points and is statistically significant at the 0.05 level, with p < 0.001. The standard deviation decreased from 13.63 on the pretest to 7.86 on the posttest. Figures 4-4 and 4-5 show that th e preand posttest scores of the test group were normally distributed. Figure 4-6 is a box and whisker plot of the gain in attitude from the pretest to the posttest. Notice that not only did the mean increase significantly, but the standard deviation, or variance of scores narrowed. Table 4-11.Paired t -test for differences between pretest and posttest scores on standardized test used to measure knowledge about the National Organic Standards Pretest scoresPosttest score N2424 Mean73.4885.58 Standard deviation13.637.86 Difference-12.11 Standard deviation difference9.50 p <0.001

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45 Figure 4-4.Distribution of pretest scores of test subjects on standardized test used to measure knowledge about the National Organic Standards, test for normality Figure 4-5.Distribution of posttest scores of test subjects on standardized test used to measure knowledge about the National Organic Standards, test for normality

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46 Figure 4-6.Mean, standard error and standard deviation of preand posttest scores on standardized test used to measure knowledge about the National Organic Standards Confidence Test participants also had a higher average score on the confidence index than the comparison group but there it was not significant at p < 0.33. The test group had a mean weighted score of 59.92 while the comparison group had a mean weighted score of 54.63. The confidence index tasks were weighted according to the degree of difficulty for Extension faculty (Table 4-12). Table 4-12.Results of t -tests for differences between test subjects’ pretest scores and comparison group scores on index used to measure confidence in ability to perform outcome behaviors Test subjectsComparison group N2426 Mean score59.9254.63 Standard deviation0.2020.89 p 0.33

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47 The weighted confidence preand posttest scores of the test subjects revealed a statistically significant average gain of 13 points with p < 0.001. Only 19 confidence posttests were completed. Table 4-13 shows the gains from pretest levels to posttest levels. Figures 4-7 and 4-8 show the normal distribution of the data points for a linear equation. Figure 4-9 is a box and whisker plot showing the weighted confidence preand posttest scores and variance of the test group. Table 4-13.Paired t -test for differences between pretest and posttest scores on index used to measure confidence in ability to perform outcome behaviors Pretest scorePosttest score N2419 Mean score62.3775.62 Standard deviation16.2014.57 Difference-13.25 Standard deviation difference13.84 p 0.0006 Figure 4-7.Distribution of pretest scores of test subjects on index used to measure confidence in ability to perform outcome behaviors

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48 405060708090100110Confidence Post-Test Score -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0Expected Normal Value Figure 4-8.Distribution of posttest scores of test subjects on index used to measure confidence in ability to perform outcome behaviors Figure 4-9.Mean, standard error and standard deviation for preand posttest scores of test subjects on index used to measure confidence in ability to perform outcome behaviors

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49 Attitude, Knowledge, and Confidence Together The next step was to run a multiple regression analysis to flesh out the relationship between three factors (attitude, knowledge, and confidence) and the outcome, intent. Together these three variables had an R = 0.5097 and p < 0.012. Only confidence was significant at p < 0.001. Table 4-14 shows the results of the regression. It is interesting to note that the attitude and knowledge scores are essentially the reverse of each other (beta column in Table 4-14) and may influence each other’s scores. The next multiple regression analysis was run without the Likert attitude scale (R = 0.4998 and p < 0.0039; Table 4-15). Table 4-14.Summary of multiple regression analysis, relationship between intent to perform outcome behaviors and predictor variables of attitude about organic agriculture, knowledge of the National Organic Standards and confidence in ability to perform outcome behaviors N=19BetaStd. err. of betap-value Intercept0.830 Posttest Likert score0.1010.1830.589 Posttest knowledge score-0.1100.1830.555 Posttest confidence score0.7300.1850.001 Note R = 0.5097; p < 0.012; std. error of estimate: 0.134 Table 4-15. Summary of multiple regression analysis, relationship between intent to perform outcome behaviors and predictor variables of knowledge of the National Organic Standards and confidence in ability to perform outcome behaviors N=19BetaStd. err. of beta p -value Intercept0.463 Posttest knowledge score-0.1130.1790.537 Posttest confidence score0.7140.1790.001 Note R = 0. 4998; p < 0. 0039; std. error of estimate: 0.131 A third multiple regression analysis was run, this time without the Likert attitude scale and without the knowledge test. Again, R decreased slightly to 0.487 with a p value of p < 0.001 (Table 4-16).

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50 Table 4-16.Summary of regression analysis*, relationship between intent to perform outcome behaviors and predictor variables of knowledge of the National Organic Standards and confidence in ability to perform outcome behaviors N=19BetaStd. err. of beta p -value Intercept0.682 Posttest confidence score0.6980.1740.0009 R = 0. 487; p < 0. 00089; std. error of estimate: 0.128 The next step was to run a full correlation matrix with the preand posttest scores. Table 4-17 shows that the attitude held before training is strongly negatively correlated with the knowledge posttest and with the confidence posttest (Columns 1, 4, and 6). In Column 2 the slight increase in posttest attitude resulted in a weaker negative correlation with posttest confidence and knowledge posttest. Columns 3 and 5 show a high correlation between pretest knowledge and pretest confidence. Column 7 shows that the relationship between the confidence preand posttests increased. This change in confidence is due to the training and is strongly correlated to the outcome, intent. Table 4-17.Correlation matrix for predictor variables of preand posttest scores on Likert scale used to measure attitude about organic agriculture, standardized test of knowledge about the National Organic Standards, and index used to measure confidence in ability to perform outcome behaviors and outcome variable, intent to perform behaviors Likert preLikert postKnowledge preKnowledge post Likert pre1.000 Likert post0.8941.000 Knowledge pre-0.171-0.2011.000 Knowledge post-0.173-0.1080.7481.000 Confidence pre0.1340.2740.3490.339 Confidence post-0.425-0.1460.0360.183 Intent-0.176-0.028-0.072-0.040 Confidence preConfidence postIntent average Likert pre-0.176 Likert post-0.028 Knowledge pre-0.072 Knowledge post-0.040 Confidence pre1.0000.568 Confidence post0.6341.0000.714 Intent0.5680.7141.000

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51 Determining Variables The final step in the statistical analysis of the data was to combine the statistically important demographic variables with the statistically important preand posttest variables. In a Spearman rank order correlation three variables came out as the most important indicators of intent to apply the outcome. These three variables are confidence (Spearmans R 0.664, p < 0.003), educational level (Spearmans R 0.594, p < 0.009) and doctoral major (Spearmans R 0.475, p < 0.047; Table 4-18). These three variables are the key factors in determining whether count y Extension faculty will perform the desired outcome behaviors after attending the 2-day workshop. Table 4-18.Spearman rank order correlations for the determining predictor variables (posttest score on Likert scale, posttest score on standardized test of knowledge, post-test score on index of confidence, educational level and doctoral major) for outcome variable of intent to perform behaviors Predictor variableValid NSpearman R p -value Posttest Likert score18-0.0180.945 Posttest knowledge score180.0200.938 Posttest confidence score180.6640.003 Educational level180.5940.009 Ph.D. major180.4750.047

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52 CHAPTER 5 DISCUSSION The purpose of mytudy was to determine any changes in attitude, knowledge, or confidence in participants who attended the 2-day workshop about the National Organic Standards. I examined differences in attitude towards organic production before and after the workshop, assessed any changes in knowledge about organic production, and especially the rules and regulations of the National Organic Standards, and evaluated any pretraining and posttraining differences in confidence in educating others about the National Organic Standards. I was also in terested in exploring what demographic variables played role in determining intention to perform key measurable behaviors. This chapter presents an interpretation and discussion of the findings of my study. It also offers theoretical implications of the results. Research Question A •How effective was the workshop in improving attitudes, increasing knowledge, and increasing confidence to advise farmers about the National Organic Standards? The workshop, What Service Providers Must Know About the Organic Rule and Regulation was very successful in increasing the knowledge and confidence of the participants. There were no statistical gains in attitude although there was a slight real gain. The attitudinal pretest scores indicated that the test subjects entered the training with a positive attitude about organic agriculture (3.91 of 5). This numerical average,

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53 although slightly higher than the mean of the quasi-control comparison group, was not statistically significant. The posttest scor e did improve 0.07 points to 3.98 (of 5), but it was not a statistically significant gain. In other words, it was hard for the scale to significantly differentiate between someone who liked organic agriculture and someone who really liked organic agriculture. The lack of a statistical change in attitude could be due to the high pretest score which showed the participants already had a favorable inclination towards organic agriculture when they came into the workshop, or it may have been influenced by the small sample size. In any event, the raw attitudinal score did improve and would likely improve more significantly if the pretest scores reflected a lower attitude towards organic production. The training was statistically effective in increasing knowledge and confidence levels. The mean gain for knowledge was 12 points (12%) while confidence increased an average of 13 points (19%). The test subjects who participated in the 2-day workshop had higher levels of knowledge, confidence, and a more favorable attitude toward organic production and the National Organic Program at the beginning of the workshop than the quasi-control group (Cooperative Extension faculty at the National Association of County Agricultural Agents who also self-selected to attend a seminar about organic production). It appears that the test subjects who chose to attend the intensive training were already interested in the organic movement and had some background knowledge. Nonetheless, after the workshop, large gains appeared in levels of knowledge and confidence. This implies that we would see even larger gains in participants who did not have as much knowledge or confidence at the onset of the training. In fact, the results show that those participants who knew the least coming into the workshop learned more than participants who had higher pretest levels of knowledge. Participants with a lower

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54 pretest score had higher point gains on the posttest. The variance on knowledge posttest scores decreased significantly. It stands to reason that all future test subjects will have the opportunity to increase their knowledge base. The questions on the preand posttest for knowledge were developed to address four cognitive levels of learning. The lowest level, knowledge, reflects retention of fact. Comprehension, the next lowest level, begins to address basic understanding of the subject. This is followed by application, where respondents are able to demonstrate that they are able to use what they have learned. The highest level tested was analysis. Analysis requires an understanding of the content and structure of the learned material. In analyzing the content of the preand posttests, I looked at the incorrectly answered questions on the pretest which were subsequently correct on the posttest. Knowledgelevel questions, or basic retention of the subject matter constituted 35% of the gain. This was followed by a 24% gain in the highest tested cognitive level, analysis. Applicationlevel questions were in third place with 23% and comprehension at 18%. These results are interesting because the main gains were in the lowest cognitive level, knowledge, or retention of basic facts about the National Organic Standards and, in analysis, the highest tested level of understanding of the content of the program. This gain in basic knowledge level is not surprising because the National Organic Standards are complex and very detailed. Many participants who only had a cursory understanding of the Standards could learn many small details that would enable them to do better on the posttest. For example, many participants who incorrectly answered the pretest question, “A farmer must have __ years of records for his/her land for organic certification” were able to respond correctly on the posttest. What is more interesting, though, is that the next

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55 highest gain in cognitive levels was found at the analysis level. This implies that participants truly learned concepts taught in the workshop. A danger with conducting a posttest immediately following a workshop is that retention levels are higher than they would be a few weeks later. This may be reflected in the high knowledge-level scores, but correct answers at the analysis level are more indicative because they show true learning and comprehension of the subject matter. Participants were able to digest what they had learned and reason out the correct answer about the National Organic Standards. Following is an example of a true/false analysis question that was frequently incorrect on the pretest but correct on the posttest. “A storage box or bin originally used for conventional crops can be reused for organic crops as long as both crops are not stored together in it at the same time.” Another interesting gain in understanding of the workshop content was illustrated with a comprehension-level question dealing with livestock. The livestock module was not included in the workshop because of time constraints, and the question was thrown out of the statistical analysis. However, most participants who missed the question on the pretest answered it correctly on the posttest! This signals that although the question was registered at the comprehension level, it reinforces the gains in analysis-type learning because participants were able to use what they had learned and reason out a correct answer. It appears that participants obtained a general sense of understanding of the NOP Standards and could make accurate assumptions even in areas that they had not yet learned about. Twenty-five percent of the test subjects had a 10-point or more gain from pretest levels to posttest (50 points total possible). Broken down by cognitive level, they gained the most pretest to posttest scores in knowledge (31%), application (26%), followed by

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56 analysis (24%), and comprehension (19%). Application-level questions were reflected in the measure of confidence and intent becau se they dealt with “how-to” questions or skills. For example, participants learned alternative ways to fertilize organic croplands and learned organic disease control methods, which are frequently different from the standard conventional methods. DeJoy (1996) stresses that education must first start with learning of facts, or knowledge, but then must focus on skill development. This will enhance confidence, or self-efficacy, and result in the desired behaviors. Participants who had the highest pretest scores (40 points and above of 50) showed the least gains in application-level questions. They showed the highest gains in knowledge and comprehension. This suggests that they already understood the skills necessary for organic production, but did not know all the rules and regulations imposed by the National Organic Standards. By targeting these four levels of cognitive learning, all participants, no matter what level of understanding they came with, were able to benefit from the workshop. This is an important result which can have a significant impact in increasing knowledge and understanding of the National Organic Standards and improving skills related to organic agricultural practices. Research Question B •How do attitudes, knowledge, and confidence affect the intention of agricultural service providers to perform key behaviors? According to the theory of reasoned action and self-efficacy theory, knowledge forms the skeletal framework for attitude. Attitude, in conjunction with confidence, influences intention, which directly leads to the desired behavior. However confidence (self-efficacy), is the most consistent (Gist & Mitchell, 1992) and best single predictor of

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57 intention (Luszczynska & Schwarzer, 2003). Worsley (2002) said that knowledge alone is not sufficient in determining behavior. He listed attitudes, skills, confidence, motivation/motivators, and the outside environment as important factors in determining intention and desired behaviors. I ran multiple regressions to try to understand the nature of the relationship of the factors, knowledge, attitude, and confidence in affecting the outcome, intention. A multiple regression analysis showed that these three factors influenced 51% of the outcome variable, intention. The scores of knowledge and attitude were essentially the reverse of each other, and so I tried to discover what influence they might have on each other. I ran a second multiple regression analysis without attitude. Fifty percent of the outcome was influenced by knowledge and confidence. Forty-nine percent of the outcome was influenced by confidence alone. This implies that knowledge and attitude were each responsible for influencing only 1% of the outcome. Confidence had a Spearmen’s R of 0.664 when run against intent and was significant at p < 0.003. This result is in line with other studies done by Luszczynska and Sc hwarzer (2003), Lent et al. (1986), and Goddard et al. (2004), who found self-efficacy to be the strongest indicator of intention. The results of the regression analyses are very interesting because they seem to imply that although someone’s attitude could be negative toward organic production, they could still intend to advise farmers about the National Organic Standards as long as they feel confident in their ability to do so. Before conducting the study, I thought that attitude would play the key role in determining the outcome behavior. However, the results show that the main determinant for the outcome variable is confidence. So what

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58 variables account for the remaining 49% of the outcome? Do demographic variables have a large influence? Research Question C •Does a relationship exist between the intent to perform the key behaviors and any of the measured demographic variables? These results were surprising. Only educational level and doctoral major showed any significance in the Spearman Rank Order Correlation. This may be largely due to the small population size. There was a negative correlation between undergraduate year and intent. Likewise there was a negative correlation between age and intent. This implies that the older the participants were, or the longer ago they finished their bachelor’s degree, the less likely they were to change their ways and try a new behavior. This brings to mind the clich, “You can’t teach an old dog new tricks.” As we become older we become more set in our ways and without many years left ahead professionally, many people don’t see the need to take on new challenges or change their way of doing things. On the contrary, the outcome variable and the year participants received a graduate degree had a positive correlation. So did the level of education. This implies that the more educated the test subjects became, the more likely they were to perform the outcome behaviors. “Education encourages a different set of beliefs and values (or interests) among its participants” (Worsley, 2002, p. S583). Advanced education makes participants more capable of performing new behaviors or taking on new tasks. It was surprising to me that so few demographic variables played a role in determining outcome. For instance, why did not age play a more significant role especially if I can determine that the relationship with the outcome variable was

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59 negative? The p -value for ethnicity was relatively low at 0.057. This would be interesting to examine in a larger sample size. The test subject population was predominately white, with only three Hispanics and three blacks, so the importance of ethnicity may change with a larger sample size. Another demographic variable, “attended a previous organic training,” had no significant effect on the intent to perform target behaviors. Is this because previous trainings were inadequate? Were they dull and uninteresting and little was learned? If previous trainings were inadequate, then perhaps attendees did not gain enough confidence from them to significantly impact the outcome variable. Where test participants attended school was originally significant in the first paring down of demographic variables ( t -tests and ANOVA). The schools were coded according to location in USDA regions. The idea was to see if regional schools had different influences on attitudes or knowledge about organic production. The reason for this is that the western region is by far and away much more advanced in promoting organic agriculture that the rest of the nation. The western region has more organic acreage, more organic producers and more governmental (and nongovernmental) funding spent in organic research and education. The southern region has some of the lowest levels of organic acreage, producers, and governmental spending. I was trying to understand if there was a deeper underlying cause for the lack of research spending or promotion of organic agriculture by Cooperative Extension in the South. Does some core difference, perhaps in attitude, extend all the way back to university teachings? I did not find any significant answers to these questions. The number and types of courses also had no significant impact on the outcome variable. I suspected that typical “anti-organic” courses such as pesticide technology or

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60 genetic engineering may negatively affect attitude. Likewise, “pro-organic” courses such as integrated pest management or agricultural ecology might positively affect attitude, but these demographic variables showed no significant impact on the outcome. One section of the demographic questionnaire measured certain behaviors in which participants were currently engaged. I asked them if they had a website, newsletter, or contributed to a radio or TV program. At first analysis, having a newsletter appeared to be significant. This may appear again in a larger sample. I suspect that perhaps having a newsletter, in which one writes articles of interest to his readership and of which the writer feels confident in discussing a topic, may be linked to some of the outcome variables. The outcome variables, or intent to perform certain behaviors, address issues such as advisi ng consumers and organic growers, and even including information about organic production in mass media. Hypotheses •I predict a positive relationship between increased knowledge about the National Organic Program and the attitude of agricultural service providers. Knowledge is power. However, knowledge is more than just a catalogue of facts, according to Worsley (2002). It is a system of beliefs, or framework, upon which we base our beliefs and facts. According to the theory of reasoned action by Ajzen and Fishbein (1980), knowledge determines attitude. Attitude, with the influence of important others, determines intent, which is reflective of future behavior. I did not see the causal relationship described in the theory of reasoned action in this study. In fact, two things stand out. First, I did not find a positive relationship between an increase in knowledge and an improvement in attitude. In fact, the correlation matrix shows a negative relationship between attitude and knowledge. As

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61 knowledge increased from the pretest scores to the posttest scores, attitude decreased or didn’t improve as much. However the strength of the negative relationship weakens from pretest to posttest. As attitude improves over the course of the workshop, change in knowledge decreases, or doesn’t improve as much. Secondly, participants were asked to rate how their peers, supervisors, administrators and county leaders felt about organic agriculture. The mean rating was indifferent/positive. This does not support the theory of reasoned action. The theory attributes greater significance to the opinion of others than I see in the results of this study. Even though these significant others of the test population have only an indifferent or slightly positive attitude towards organic agriculture, the test subjects still responded favorably in intent to perform the job related behaviors. This may correspond with the high percentage of minorities found w ithin the training. The agricultural service field is dominated by Caucasian males, yet the workshop had a high percentage of women and racial minorities (29% and 25%, respectively). Perhaps minorities who selfselect to work in a field dominated by one demographic type are more able to resist normative pressures, including the opinions of others. In a sample with different demographic characteristics, attitude and subjective norms may be more positively attributed to influencing intention. In this case, attitude seemed to more strongly influence the intent to perform the behaviors than the influence of important others. But why was attitude negatively correlated with knowledge? I expected that participants would begin the training with a positive attitude towards organic production. After all, they chose to attend the workshop. I saw a small but statistically insignificant gain in attitude after the workshop was completed, but the gains in knowledge were so large that I expected to see more gain

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62 in attitude. If knowledge truly determines attitude, why weren’t the gains proportional? I believe there must be some other determinant of attitude besides knowledge alone. And in my study I did not see the importance of the role of significant others to influence outcome. •I predict a positive relationship between increased knowledge about the National Organic Standards and the confidence of agricultural service providers to advise farmers about organic production and conduct organic trainings. A common finding in nutrition studies (Worsley, 2002) is that increased knowledge corresponds with an increase in the desired behavior. But knowledge does not seem to directly lead into performance of the desired behavior. Instead, two other variables played a key role in the link between knowledge and outcome: interest and confidence. Britten (as cited in Worsley, 2002) found that knowledge is a predictor of confidence. And confidence, or self-effi cacy, is directly linked to performance. Heightened self-efficacy yields higher behavi oral achievements, such as improved skill development, and motivation to continue the behavior (Bandura, 1997, as cited in Goddard et al., 2004; Lent et al., 1986; Luszczynska & Schwarzer, 2003; Schunk, 1991). According to Bandura (1977), individuals with higher levels of self-efficacy are seen to work harder and have greater persistence in the face of challenges than those who doubt their capabilities. However, self-efficacy alone cannot produce competent behavioral outcomes if the skills needed to perform the behavior are lacking (Schunk, 1991). Participants at the training gained knowledge across four cognitive levels of learning, including basic retention of facts, comprehension, application, and analysis. Application and analysis focus on skill development. According to Schunk (1991), students obtain information

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63 about how well they are performing as they work on tasks. When they recognize that they are performing well and understanding the material, their self-efficacy and motivation are improved. Therefore, it follows that as we saw large gains in knowledge levels, we saw a proportionate gain in confidence levels. •I predict a positive relationship between the confidence of agricultural service providers and their intention to advise farmers about organic production and conduct organic trainings. The results showed a very strong correlation between confidence and the outcome variable, intent. There was also a strong correlation between the pretest confidence score and intent, however the posttest score had a much stronger correlation. It should not surprise anyone that what people believe they can do affects what their actual actions are. After all, without confidence, where are we? Success raises selfefficacy (Bandura, 1986). Initially, self-efficacy is a function of abilities, attitudes, and prior experience (Schunk, 1991). But as students learn and achieve, self-efficacy grows. This begins a cycle of motivation towards further successes. More of the desired behavior is initiated, success breeds confidence, confidence leads to further persistence and more of the desired behavior. I feel confident projecting that participants who attended the workshop, What Service Providers Must Know About the Organic Rule and Regulation will perform the behaviors that they stated they would perform on their intention indices. Research has shown an extremely high correlation between self-efficacy and performance of the desired behaviors. This workshop has equipped participants with the knowledge, skills, and confidence to successfully advise farmers about the National Organic Standards. This knowledge and confidence was earned through real-life, hands-on problem solving with peer groups. This was a transformative learning process targeting experienced adult

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64 professionals. Test results have shown a st atistically significant gain in self-efficacy (p<0.003). This is highly predictive of performance of the outcome variable.

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65 CHAPTER 6 CONCLUSION The chapter closes the study with a discussion of future research directions and limitations of this study. The workshop, What Service Providers Must Know About the Organic Rule and Regulation was successful in augmenting participants’ knowledge about the National Organic Standards and organic production practices. It was not successful in raising attitudinal levels. However it was highly successful in raising the confidence of agricultural service providers. This is extremely important because confidence has been shown in both the literature and the results of my study to be the single most important factor in predicting a successful completion of the desired behaviors. The results of my study show that participants increased their knowledge and understanding of the National Organic Standards and organic agriculture across all cognitive learning levels. No matter with how much or how little knowledge the participants entered the training, they learned something. I believe this is due largely to the structure of the training and the transformative and experiential learning process. Many of these participants had previous organic training, yet this was not found to correlate significantly with the outcome variable, intent to perform the desired behaviors. I believe that this was due to the nature of the previous trainings. Adult training must utilize the wealth of knowledge and experience that the professionals bring with them. Adults want to tackle hands-on, real-world situations

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66 using their previous experiences and wisdom. Peer groups have been found to be a highly successful means of adult learning. Peers are nonthreatening because they have similar experiences and levels of knowledge. Watching peers model the desired behavior is very motivating and helps build self-efficacy of both the performer and the observer. Adults also need discussion and time to reflect upon their decisions and actions. Small groups work with peers, and large group discussions following the exercises give participants that opportunity for reflection. This workshop curriculum was designed to address four cognitive levels of learning. It focused not only on conveying ba sic facts, but on teaching skills and critical thinking of the issues involved. According to the literature, this is essential for success. Participants must pass a threshold of basic knowledge, then learn the skills necessary to successfully perform the desired behaviors. All of these methods were used in the training. The training was intensive and fast-paced, yet participants were stimulated and challenged. Of course, there is something missing in the discussion about gaining knowledge and skills to elicit the performance of the desired outcome. That missing factor is confidence. Research has shown that basic knowledge and skills are not sufficient by themselves to elicit the desired behavioral response, but confidence can elicit that response. Confidence is gained through problem solving and reflection. As adults learn new topics and are challenged with legitimate scenarios in small group settings, their confidence increases. Bandura (as cited in Gist & Mitchell, 1992) said it is not so much a success that increases confidence, but the process of learning and reflection. Confidence can be gained by realizing that one can do better the next time, by watching peers succeed, and by learning skills to enable success.

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67 The tests were also useful in showing where the training lacked clarity. Two questions were largely missed on both the pretest and posttest, indicating that the training needed to be refined in those areas. Future training for adults and especially Extension could emulate the successful teaching strategies found in this workshop. An interesting study would be to compare the gains in knowledge, attitude, and confidence of participants who attend this training with participants who attend other organic trainings of a more passive design. I suspect that the test subjects who participate in the experiential learning process will retain what they have learned longer and be more skilled and motivated to perform the desired behaviors in the workplace. There were some demographic variables within my study that may become more significant with a larger sample size. First, gender did not appear significant in my study; but I suspect that it would within a larger sample. There was a greater proportion of women at this organic workshop (29%) than found within Extension and within the agricultural service field. There is also a high percentage of women farmers engaged in organic production. Little research has been done on gender issues within organic agriculture and would be an interesting tract for future study. I suspect that gender makes a significant difference in the organic agricultural movement. Lent et al. (1986) found that self-efficacy theory may influence career decisions and achievements, especially for that of women. They claim that efficacy is related to perceived career options, persistence, and success in their fields. A second demographic variable worth future study is that of ethnicity. This sample was too small to make accurate assumptions; but like the proportions of gender, there was a higher percentage of minorities represented at the training than one finds

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68 within the agricultural service field. Although the quasi-control group was 96% Caucasian, the workshop participants were 13% Hispanic and 13% black. The p -value for ethnicity in this study was 0.057. I suspect that this will become more significant with a larger sample size. A third interesting demographic aspect for future studies would be to look at the negative correlation of age and completion of college contrasted to the positive correlation of completion of a master’s degree and doctoral major. Although a doctoral major turned out to be significant in this study, there were only seven with doctorates in the sample. This was a large percentage (33%) of test subjects, and this should be looked at within the context of a larger sample size. Besides the limitations of sample size and the time constraints, which did not allow for a follow-up on behavior, future studi es might consider having the quasi-control group fill out the intent index. This gives a mean for comparing the intent of the test subjects with the intent (or actions) of the control group. I was unable to make some generalizations about the test subjects and the quasi-control due to the lack of intention data by the quasi-control group. Finally, I suggest a deeper look at regional discrepancies. This study did not get at the root cause of why the South allocates less money to organic research and development than other regions. I suspected that educational and professional attitudes might be significant, but this was not corroborated in the current study. Future research might take a more in-depth look at the attitude of significant others. The attitudes of coworkers, supervisors, and county officials were found to be lukewarm both in the test subjects and the quasi-control (54% of the quasi-control worked in the Southern region). This may have significance and merits further research. Also, comparing attitudes of

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69 significant others in the South with attitudes found in other regions might be illuminating. My study was limited by its small sample size and also time constraints of the researcher. Future research would be strengthened with a larger sample size and a follow-up 6 months after the training, to determine what behaviors have truly been realized. Six more trainings will be conducted in Florida, Kentucky, and the U.S. Virgin Islands over the next year and a half. The compiled data from all of these workshops would strengthen and expand the findings of the current study. Finally, I can confidently say that there is sufficient evidence to assume that the six future trainings to be held in Florida, Kentucky, and the U.S. Virgin Islands over the next year and a half will be as equally successful as the first, and that the grant project will indeed be on its way to a successful completion of its goal, to increase the acreage and number of certified organic producers in Florida, Kentucky, and the U.S. Virgin Islands. This workshop increased knowledge and confidence of participants, key factors in motivating behavior.

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APPENDIX A WORKSHOP AGENDA

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71 The National Organic Standards What Service Providers Must Know About the Organic Rule and Regulation July 26-27, 2004 Ft Pierce REC Monday, July 26, 2004 9:00 a.m.Welcome Pre-tests Module 1: Overview of Organic Production 10:30 a.m.Break 10:45 a.m.Module 2: The Organic Production System Plan (The Farm Plan) 12:00 p.m.Lunch 1:00 p.m.Module 3: Planting Stock – Seeds and Transplants 1:45 p.m.Break 2:00 p.m.Module 4: Water Quality, Soil Quality and Crop Fertility 5:00 p.m.Adjourn Tuesday July 27, 2004 8:30 a.m.Opening 9:00 a.m.Module 5: Crop Management 10:15 a.m.Break 10:30 a.m.Module 6: Organic Integrity 12:00 p.m.Lunch 1:00 p.m.Module 7: Handling and Processing 2:00 p.m.Break 2:15 p.m.Post-tests Closure 4:00 p.m.Adjourn

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APPENDIX B LIKERT SCALE FOR ATTITUDE

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73

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APPENDIX C STANDARDIZED PREAND POSTTEST

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75

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76

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77

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78

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APPENDIX D INDEX OF CONFIDENCE

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80

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APPENDIX E INDEX OF INTENTION

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82

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APPENDIX F DEMOGRAPHIC QUESTIONNAIRE

PAGE 93

84

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85

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86 REFERENCES Aiken, L. R. (1996). Rating scales and checklists. Evaluating behavior, personality, and attitudes. New York: John Wiley. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall. Alternative Farming Systems Information Center. (2004). Organic food production. Retrieved October 28, 2004, from http://www.nal.usda.gov/afsic/ofp/ Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191-215. Bandura, A. (1986). Social foundations of thought and action; A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall. Bandura, A. (1989). Regulation of cognitive processes through perceived self-efficacy. Developmental Psychology, 25 (5),729-735. Becker, J., Kovach, A. C., & Gronseth, D. L. (2004). Individual empowerment: How community health workers operationalize self-determination, self-sufficiency, and decision-making abilities of low-income mothers. Journal of Community Psychology, 32 (3), 327-342. DeJoy, D. M. (1996). Theoretical models of health behavior and workplace selfprotective behavior. Journal of Safety Research, 27 (2), 61-72. de Vaus, D. (2001). Research design in social research. London: Sage. Eisen, M. J. (2001). Peer-based professional development viewed through the lens of transformative learning. Holistic Nursing Practice, 16 (1), 30-42. Garcia, M. E., Schmitz, J. M., & Doerfler, L. A. (1990). A fine-grained analysis of the role of self-efficacy in self-initiated attempts to quit smoking. Journal of Consulting and Clinical Psychology, 58 (3), 317-322.

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87 Gist, M. E., & Mitchell, T. R. (1992). Self-efficacy: A theoretical analysis of its determinants and malleability. Academy of Management Review, 17 (2), 183-211. Goddard, R. D., Hoy, W. K., & Hoy, A. W. (2004). Collective efficacy beliefs: Theoretical developments, empirical evidence, and future directions. Educational Researcher, 33 (3), 3-13. Greene, C. (2000). U.S. organic agriculture gaining ground (Economic Research Service, USDA, Agricultural Outlook, AGO-270). Retrieved April 28, 2004, from http://www.ers.usda.gov/publications/agoutlook/apr2000/ao270d.pdf Greene, C., & Kremen, A. (2003). U.S. organic farming in 2000-2001: Adoption of certified systems (Agriculture Information Bulletin No. 780, USDA Economic Research Service, Resource Economics Division). Retrieved (DATE) from http://www.ers.usda.gov/publications/aib780/ Henerson, M. E., Morris, L. L., & Fitz-Gibbon, C. T. (1987). How to measure attitudes. Newbury Park, CA: Sage. Jordan, C. F. (2004). Organic farming and agroforestry: Alleycropping for mulch production for organic farms of Southeastern United States. Agroforestry Systems, 61, 79-90. Lent, R. W., Brown, S. D., & Larkin, K. C. (1986). Self-Efficacy in the prediction of academic performance and perceived career options. Journal of Counseling Psychology, 33 (3), 265-269. Lohr, L., & Park, T. A. (2003). Improving ex tension effectiveness for organic clients: Current status and future directions. Journal of Agricultural and Resource Economics, 28 (3), 634-651. Luszczynska, A., & Schwarzer, R. (2003). Pla nning and self-efficacy in the adoption and maintenance of breast self-examination: A longitudinal study on self-regulatory cognitions. Psychology and Health, 18 (1), 93-108. Mayeske, G. W. (1991). Program design: An evaluative approach (Library ID # S544.M391 1991) Washington, DC: Cooperative Extension System, USDA. Miettinen, R. (2000). The concept of experiential learning and John Dewey’s theory of reflective thought and action. International Journal of Lifelong Education, 19 (1), 54-72. Minarovic, R. E., & Mueller, J. P. (2000). North Carolina Cooperative Extension service professionals’ attitudes toward sustainable agriculture. Journal of Extension, 38 (1), 2-11.

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88 Mueller, D. J. (1986). Measuring social attitudes. A handbook for researchers and practitioners. New York: Teachers College Press. Multon, K. D., Brown, S. D., & Lent, R. W. (1991). Relation of self-efficacy beliefs to academic outcomes: A meta-analytic investigation. Journal of Counseling Psychology, 38 (1), 30-38. Organic Farming Research Foundation. (2003). State of the states: Organic Systems Research at Land Grant Institutions, 2001-2003 (2nd ed.). Retrieved (DATE) from http://www.ofrf.org/publications/SoS/SoS2.overview.page.html Pajares, F., & Miller, D. M. (1994). Role of self-efficacy and self-concept beliefs in mathematical problem solving: A path analysis. Journal of Educational Psychology 86 (2), 193-203. Pooley, J. A., & O’Connor, M. (2000). Environmental Education and attitudes, emotions and beliefs are what is needed. Environment and Behavior 32( 5 ): 711-723. Pruneau, D., Gravel, H., Bourque, W., & La ngis, J. (2003). Experimentation with a socio-constructivist process for climate change education. Environmental Education Research, 9 (4), 429-446. Rossi, P. H., & Freeman, H. E. (1993). Evaluation: A systematic approach 5 Newbury, Park, CA: Sage. Saltiel, I. M. (1998). Defining collaborative partnerships. New Directions for Adult and Continuing Education, 79, 5-11. Sapp, S. G. (2002). Incomplete knowledge and attitude-behavior inconsistency. Social Behavior and Personality, 30 (1), 37-44. Schunk, D. H. (1991). Self-efficacy and academic motivation. Educational Psychologist 26 (3/4), 207-231. Sheskin, D. J. (2000). Handbook of parametric and nonparametric statistical procedures (2nd ed.). Boca Raton, FL: Chapman & Hall/CRC. Sommer, R., & Sommer, B. (2002). A practical guide to behavioral research tools and techniques (5th ed.). New York: Oxford University Press. Sustainable Agriculture Network. (2004). Transitioning to Organic production. Burlington, VT: Sustainable Agriculture Publications. Retrieved July 9, 2004, from http://www.sare.org/bulletin/organic Tennant, M., & Pogson, P. (1995). Learning and change in the adult years. A developmental perspective. San Francisco: Jossey-Bass.

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89 Worsley, A. (2002). Nutrition knowledge and food consumption: Can nutrition knowledge change food behavior? Asia Pacific Journal of Clinical Nutrition 11 (Suppl), S579-S585.

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90 BIOGRAPHICAL SKETCH Kendall Louise Sanderson was born on August 20, 1972 in Winter Park, Florida. She is the second of four children born to Catherine H. and Carlton B. Sanderson. She attended elementary through high school in Satellite Beach, Florida. In high school, Kendall’s parents began fostering her love of travel and introduced her to exchange programs in France and Colombia. In 1994, she completed her bachelor’s degree at Florida State University, majoring in French with a minor in international relations. After college, Kendall traveled extensively through Europe and the United States and lived in Puerto Rico, Miami, Florida, and California. She taught French and ESOL at a middle school in Redlands, Florida. In the spring of 1998, Kendall was accepted to the Peace Corps and sent to Kenya, fulfilling a life-long dream of living in Africa. Kendall was a Water and Sanitation Volunteer in a rural village. She learned to speak Kiswahili and some Kikuyu, the local tribal language, while working with local schools, women’s groups and farmers. After two years in Kenya, Kendall spent some time traveling throughout Eastern, Central, and Southern Africa. Impacted by the work she did in Africa, Kendall decided to continue her studies of the environment, sustainable agriculture, and sustainable development through the School of Natural Resources and Environment at the University of Florida. Currently, Kendall lives in St. Petersburg, Florida.


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Title: Extension Support for Organic Farmers in the South: A Function of Attitude, Knowledge, or Confidence?
Physical Description: Mixed Material
Copyright Date: 2008

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Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
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EXTENSION SUPPORT FOR ORGANIC FARMERS IN THE SOUTH:
A FUNCTION OF ATTITUDE, KNOWLEDGE, OR CONFIDENCE?
















By

KENDALL L. SANDERSON


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA


2004















ACKNOWLEDGMENTS

I would like to express my deepest gratitude to my family and friends who stood

by me, supported me, and encouraged me to pursue a master's degree. I would also like

to thank my thesis chair, Dr. Marilyn Swisher, for her open door, patient help, guidance,

and encouragement. Special thanks also go to my committee members, Dr. Rose Koenig

and Dr. Robert McSorley. It has been a pleasure working with them. Finally, I thank the

grant team members and participants who made this study possible; and Dr. Humphrey,

Meisha Wade, and Cathy Ritchie from the School of Natural Resources and

Environment.
















TABLE OF CONTENTS

page

ACKNOW LEDGM ENTS ........................................ . ii

LIST OF TABLES ..................................................... v

LIST OF FIGURES ................... ................................. vii

ABSTRACT ....... ............................................ viii

CHAPTER

1 INTRODUCTION ....................................................1

Need for the Study .................................................. 8
Purpose and Objectives ................... ................... ...... ... 9
Research Questions ................... ................... ............. 9
Research Hypotheses ................... ................... ......... 10

2 LITERATURE REVIEW . .................. ............. .11

Theory of Reasoned Action ................... ................... ... 11
Self-Efficacy Theory ................... ................... ......... 15
Adult Education ................... .................................. 17

3 METHODOLOGY ................... ................... ............ 21

Research Design ................... ................................. 21
Sampling Frame ................................................... 21
Instrument Development ................... ................... ..... ... 22
Attitude M easurement ................... ................... ....... 23
Standardized Pre- and Posttest ................... ................... ....26
Measurement of Intention ................. ................. ... 29
Demographic Data ................. ........................... 31
Data Collection ................. ............................... 31
Limitations ................. .................................. 32
Data Analysis ................. ................................ 33











4 FINDINGS ................... ................... ................... 35

Demographic Information ................... ................... ....... 35
Attitude .........................................................41
Knowledge ...................................................... 42
Confidence ................... ................... ................... 46
Attitude, Knowledge, and Confidence Together ................... . 49
Determining Variables ................... ................... ....... 51

5 DISCUSSION .................................................... 52

Research Question A ................... ................... ......... 52
Research Question B ................... ................... ......... 56
Research Question C ................... ................... ......... 58
Hypotheses ........ ........................................... 60

6 CONCLUSION ................... ..........................65

APPENDIX

A WORKSHOP AGENDA .............................................. 71

B LIKERT SCALE FOR ATTITUDE ......................... . 73

C STANDARDIZED PRE- AND POSTTEST ................... .......... 75

D INDEX OF CONFIDENCE ......... ......... ...... 80

E INDEX OF INTENTION ............................... ............. 82

F DEMOGRAPHIC QUESTIONNAIRE ................... ........ . 84

REFERENCES ...................................................... 86

BIOGRAPHICAL SKETCH .............. ...........................90















LIST OF TABLES


Table page

1-1 Change in acreage, livestock and poultry, and number of farms under
organic certification in the United States, 1992-2001 ........... . 3

1-2 Certified organic acreage in the United States Department of Agriculture
southern region, by state, 1997-2001 ................ ................ 3

1-3 Certified organic acreage in each of the four geographic regions of the
United States Department of Agriculture, 2001 ................... ..... 4

4-1 College degree earned by test subjects according to United States
Department of Agriculture geographic regions ................... 36

4-2 Percentage of test subjects and comparison group members who contribute
to publications and mass media as part of job performance ................. 37

4-3 Perception by test subjects and comparison group of peer, supervisor,
administrator and local leaders' attitudes about working with organic
producers ...................................................... 37

4-4 Measures of importance of organic producers as clients of test subjects and
comparison group ................ ............................ 38

4-5 Relative importance of demographic characteristics of test subjects in
relation to intention to perform selected behaviors on the job ........... ... 39

4-6 Explanatory power of selected demographic variables on intent to perform
outcome behaviors, Spearman Rank Order Correlation .............. 40

4-7 Summary of multiple regression analysis, relationship between intent to
perform outcome behaviors and selected demographic variables ............. 41

4-8 Results of t-tests for differences between test subjects' pretest scores and
comparison group scores on Likert scale to measure attitude about organic
agriculture ................... ............................41

4-9 Paired t-test for differences between pretest and posttest scores on Likert
scale used to measure attitude toward organic agriculture ............. 42









4-10 Results of t-tests for differences between test subjects pretest scores and
comparison group scores on standardized test of knowledge about the
National Organic Standards ................... ................... ....44

4-11 Paired t-test for differences between pretest and posttest scores on
standardized test used to measure knowledge about the National Organic
Standards ...................................................... 44

4-12 Results of t-tests for differences between test subjects' pretest scores and
comparison group scores on index used to measure confidence in ability to
perform outcome behaviors . .................. ....... ... 46

4-13 Paired t-test for differences between pretest and posttest scores on index
used to measure confidence in ability to perform outcome behaviors ......... 47

4-14 Summary of multiple regression analysis, relationship between intent to
perform outcome behaviors and predictor variables of attitude about organic
agriculture, knowledge of the National Organic Standards and confidence in
ability to perform outcome behaviors ................... ....... ..... 49

4-15 Summary of multiple regression analysis, relationship between intent to
perform outcome behaviors and predictor variables of knowledge of the
National Organic Standards and confidence in ability to perform outcome
behaviors ...................................................... 49

4-16 Summary of regression analysis*, relationship between intent to perform
outcome behaviors and predictor variables of knowledge of the National
Organic Standards and confidence in ability to perform outcome behaviors .... 50

4-17 Correlation matrix for predictor variables of pre- and posttest scores on
Likert scale used to measure attitude about organic agriculture, standardized
test of knowledge about the National Organic Standards, and index used to
measure confidence in ability to perform outcome behaviors and outcome
variable, intent to perform behaviors ................... ........ 50

4-18 Spearman rank order correlations for the determining predictor variables
(posttest score on Likert scale, posttest score on standardized test of
knowledge, post-test score on index of confidence, educational level and
doctoral major) for outcome variable of intent to perform behaviors .......... 51















LIST OF FIGURES

Figure pae

2-1 Theory of reasoned action ........... .. . ..................... 12

4-1 Distribution of pretest scores of test subjects on Likert scale used to measure
attitude toward organic agriculture, test for normality ............... 42

4-2 Distribution of pretest scores of test subjects on Likert scale used to measure
attitude toward organic agriculture, test for normality ............... 43

4-3 Mean, standard error, and standard deviation of pre- and posttest scores on
Likert scale used to measure attitude about organic agriculture ............. 43

4-4 Distribution of pretest scores of test subjects on standardized test used to
measure knowledge about the National Organic Standards, test for
normality ...................................................... 45

4-5 Distribution of posttest scores of test subjects on standardized test used to
measure knowledge about the National Organic Standards, test for
normality ...................................................... 45

4-6 Mean, standard error and standard deviation of pre- and posttest scores on
standardized test used to measure knowledge about the National Organic
Standards ...................................................... 46

4-7 Distribution of pretest scores of test subjects on index used to measure
confidence in ability to perform outcome behaviors ............... .... 47

4-8 Distribution of posttest scores of test subjects on index used to measure
confidence in ability to perform outcome behaviors ............... .... 48

4-9 Mean, standard error and standard deviation for pre- and posttest scores of
test subjects on index used to measure confidence in ability to perform
outcome behaviors ....... ........................... ......... 48
















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

EXTENSION SUPPORT FOR ORGANIC FARMERS IN THE SOUTH:
A FUNCTION OF ATTITUDE, KNOWLEDGE, OR CONFIDENCE?

By

Kendall L. Sanderson

December 2004

Chair: Marilyn Swisher
Department: School of Natural Resources and the Environment

My study examined the effectiveness of a workshop about the United States

Department of Agriculture's National Organic Program. The workshop, conducted in

July 2004, trained agricultural service providers. My study measured changes in attitude

and gains in knowledge and confidence, before and after the workshop, and focused on

measuring intent to perform key behaviors. Data were collected through pre- and

posttest scales, questionnaires, and indices (n = 24). Pretest scores and demographic data

were compared with a quasi-control group (n = 26) of cooperative extension agents

attending an organic agriculture seminar at a national convention in July.

My study used the theory of reasoned action to examine relationships among

knowledge, attitude, confidence, and change in behavior. This model proposes that

behavioral beliefs and outcome expectations, on one hand, and normative beliefs and

motivations to comply, on the other, affect attitude and inform the individual's subjective









norms. The latter variables, in turn, affect intention and, ultimately, change in behavior.

The concept of self-efficacy (an individual's confidence in his/her ability to complete a

task) is central to any change in behavior. Therefore, increasing self-efficacy is a key to

successful training in this theoretical framework.

Amounts of knowledge and confidence gained through the workshop were

statistically significant (p<0.05). Mean score of participants for knowledge increased an

average of 12% after the training, while mean confidence scores increased 19%.

Confidence, educational level, and doctoral major proved to be significant indicators of

the outcome variable, intent. According to theoretical findings, confidence was the most

important factor in predicting behavior. Surprisingly, the workshop did not significantly

affect participants' attitudes toward organic agriculture. This may be due to the high

attitudinal scores at the onset of the workshop (3.91 of 5).

The workshop focused on problem-solving in peer groups and experiential

learning strategies targeting four different levels of cognition. These instructional

techniques were extremely successful, and should be used in future training for

agricultural service providers. Future studies should include a larger sample size to

clarify the effects of certain demographic variables (age, gender, ethnicity, and when

university education was completed) on the outcome variable. A study with a longer

time frame should include a posttraining follow-up to determine if participants performed

the behaviors they intended to perform after completing the training program.















CHAPTER 1
INTRODUCTION

My study explored the effects of an intensive workshop for agricultural service

providers, including cooperative extension agents, on knowledge, attitude, and

confidence. Goals of the workshop were to increase knowledge about the National

Organic Standards and to improve the attitude and confidence of local service providers

who may advise farmers interested in organic production. My study examined pre- and

posttest scores of participants to determine if a change in attitude, knowledge, or

confidence did take place; and to see what effect these factors had in influencing

participants' intentions to perform certain behaviors related to advising and educating

farmers interested in organic certification.

The National Organic Standards Board defines organic agriculture as "an

ecological production management system that promotes and enhances biodiversity,

biological cycles and soil biological activity. It is based on minimal use of off-farm

inputs and on management practices that restore, maintain and enhance ecological

harmony" (Alternative Farming Systems Information Center [AFSIC], 2004). The

Organic Food and Production Act of 1990 established the role of the federal government

in regulating organic food and fiber production. Through the 1990s, the National Organic

Program (NOP) worked with the National Organic Standards Board, which serves as an

advisory board to the NOP, to develop the National Organic Regulation. The regulation

was fully implemented on October 21, 2002. The National Organic Standards are







2

enforced by the U.S. Department of Agriculture (USDA) and their accredited certifying

agencies. Consumers can buy agricultural products anywhere in the United States that

are certified organic and be assured that they have been produced and processed under a

uniform set of standards.

Since 1990, organic agriculture has been one of the fastest-growing segments of

agriculture in the United States. Organic farmers, mostly small-scale producers,

numbered 12,200 in 2000; and the USDA estimates the number of organic farmers to

increase by about 12% each year. Cropland has also increased proportionately with the

growing demand. Retail sales have grown 20% per year over the last decade, evolving

into a multi-billion dollar sector of the food and fiber industry. Organic sales accounted

for 2% of total U.S. food sales in 2001, reaching $7 billion. Organic food is now sold not

only in health food stores but also in 73% of conventional grocery stores (Jordan, 2004).

As of 2001, every U.S. state except Delaware and Mississippi had some certified organic

cropland. Table 1-1 shows organic acreage and animal production from 1992 to 2001.

After full implementation of the National Organic Standards in 2002, the organic

industry is expected an annual growth rate of 20% to 25% into the next decade

(Sustainable Agriculture Network, 2004).

According to the 2003 study by Greene and Kremen, most southern states had

very little certified cropland, pasture, or operations. Table 1-2 shows certified organic

acreage by state, the USDA's southern region. The last column shows each southern

state's percentage of the total certified acreage in the U.S. The South accounted for only

13.8% of all certified organic acreage in 2001. Table 1-3 compares distribution of

certified organic acreage the four USDA regions. Data are from the 2001 totals of U.S.

certified organic acreage (Greene & Kremen, 2003).









Table 1-1. Change in acreage, livestock and poultry, and number of farms under organic
certification in the United States, 1992-2001
1992 1997 2000 2001
U.S. certified farmland
Total (pasture and cropland) 935,450 1,346,558 2,029,073 2,343,924
U.S. certified livestock
Total (beef, milk cows, hogs, pigs, 11,647 18,513 56,028 71,216
sheep, lambs)
Total Poultry (layer hens, broilers, 61,363 802,966 3,159,050 5,014,015
turkeys)
Total certified operations 3,587 5,021 6,592 6,949
Change (percent)
1992-97 1997-01 2000-01
Total farmland 44 74 16
Total livestock 59 285 27
Total Poultry 1,209 524 59
Total certified operations 40 38 5
Source: Economic Research Service, USDA (Greene & Kremen, 2003)

Table 1-2. Certified organic acreage in the United States Department of Agriculture
southern region, by state, 1997-2001
Total Certified Acreage
State 1997 2000 2001 % of Total*
Alabama 1 495 35 0.0020
Arkansas 997 20,107 24,848 1.0600
Florida 32,745 ** 5,136 12,059 0.5200
Georgia 572 633 546 0.0200
Kentucky 5,666 6,291 6,552 0.2800
Louisiana 371 161 96 0.0040
Mississippi
North Carolina 980 1,474 1,377 0.0600
Oklahoma 3,992 3,206 3,922 0.1700
South Carolina 41 168 14 0.0006
Tennessee 1,351 1,434 300 0.0130
Texas 30,880 100,726 266,320 11.3600
Virginia 4,416 9,520 7,428 0.3200
Total 82,012 149,351 323,497 13.8
Percentage of total certified organic acreage (2,343,924 acres) in the U.S. in 2001
In 1997, Florida reported 25,000 acres in the category "wild-crafted acreage"
Data not available for Puerto Rico and the U.S. Virgin Islands.
Source: Economic Research Service, USDA (Greene & Kremen, 2003)

Despite strong growth in organic agriculture since 1992, overall organic

production is still only a small percentage of agricultural production in the U.S. Farmers

surveyed by Greene (2000) and Greene and Kremen (2003) identified lack of technical









infrastructure as a main obstacle to adopting of certified organic practices. The Organic

Farming Research Foundation (OFRF, 2003; Lohr & Park, 2003) conducted a nationwide

survey of organic producers in 1997. Respondents were asked to specify to what degree

certain constraints inhibited organic production. Specifically, farmers were asked to

indicate the usefulness and number of contacts they had with 12 sources of information

regarding organic production. Sources included private and public entities. Cooperative

Extension Advisers were ranked 10th of 12, rated only slightly more useful than state

agricultural departments and the USDA national or regional offices. Furthermore,

Extension faculty received the highest percentage of "never useful" ratings at 6% (Lohr

& Park, 2003). Topping the list as the most useful sources of information were other

farmers, organic certification personnel, and input suppliers. These findings clearly

indicate that private sector information is more highly valued in the organic industry and

more widely used than public sources such as universities and Extension.

Table 1-3. Certified organic acreage in each of the four geographic regions of the United
States Department of Agriculture, 2001
Region % of Total*
Southern 13.8
North East 5.2
North Central 29.7
Western 51.4
*Total certified acreage in 2001 was 2,343,924
Data not available for Washington, D.C. and U.S. territories
Source: Economic Research Service, USDA (Greene & Kremen, 2003)

Using OFRF data, Lohr and Park (2003) found that small organic farmers with 5

acres or less showed the most dissatisfaction with Extension faculty, as did those who

had been involved in organic farming for longer periods of time. Farmers with at least 13

years of experience gave the lowest ratings to Extension faculty. Conversely, among

those who ranked Extension as "very useful" were farmers with less than 5 years









experience. The data, however, suggest that dissatisfaction with Extension faculty

increases as experience increases.

Income was also a significant variable in the study conducted by Lohr and Park

(2003). Their findings suggest that Extension advisors provided more relevant

information to the most economically viable and top-selling producers. Overall,

part-time farmers with higher incomes who also used many private-sector sources of

information, rated Extension as useful and effective. Farmers from the western and

northeastern regions of the U.S. rated Extension personnel more favorably than producers

in the southern and north central regions. The western and northeastern regions are home

to the oldest organic farms and certifying agents. Government spending in those regions

has historically made greater commitments to organic research and education.

The southern and northern central regions of the U.S. have the majority of all

farmers nationally (both traditional and organic), with 39% each. The western and

northeastern regions have only 14% and 7%, respectively. Of respondents to the OFRF

survey from the southern region, 80% ranked Extension as a barrier to organic

agriculture. Since 39% of all U.S. farmers live in the South, one would expect the

number of organic farmers to grow in this region. Yet the South has some of the lowest

overall numbers of certified organic acreage and certified organic operations (Table 1-2).

Greene (2000) shows that organic agriculture is growing rapidly in the South, increasing

by more than 50% between 2000 and 2001. The South has some of the lowest levels of

institutional support for organic research, extension, and education within the Land Grant

Universities (OFRF, 2003).

Why is so little funding spent on organic research in the South? McDowell (as

cited in Lohr & Park, 2003) characterizes Extension as being "held hostage by traditional









audiences, unable to effectively inform its clientele on important emerging agricultural

issues and lacking the vision to broaden its client and program portfolio" (p. 635). The

organic sector needs greater technical support from Extension, generally; and with the

establishment of national standards, it is inevitable that more organic farmers will turn to

Cooperative Extension for advice and technical support. Lohr and Park (2003) argue that

Extension must meet the needs of nontraditional audiences, and organic producers

represent a relatively easy target to develop as a clientele group. They also argue that

Extension must understand and meet the needs of the newer organic farmers and tailor

advice to the level of these producers. They suggest that newer farmers with less

experience will seek more information from Cooperative Extension, especially in areas

of regulation and local production problems. Extension can increase its organic clientele

by developing credible and appropriate advice, requiring "research that leads, rather than

follows, the organic information curve" (Lohr & Park, 2003, p. 642).

In a study done by the North Carolina Cooperative Extension Service in 2000,

97.5% of the Extension faculty surveyed agreed that a "proactive perspective was

necessary when developing Extension programs" (Minarovic & Mueller, 2000, p. 5).

Furthermore, Worstell's State of the South Project (as cited in Minarovic & Mueller,

2000) revealed that Extension faculty need more training in sustainability ideas,

practices, and technologies. According to a study done by OFRF (2003), the southern

region has some of the lowest levels of institutional support for organic research,

extension, and education within the Land Grant Universities.

What is preventing the southern USDA region from devoting more funding to

organic research and bolstering the organic knowledge base within Extension?









According to Kraus (as cited in Pooley & O'Connor, 2000), one of the greatest

determinants of behavior is attitude.

Organizations need commitments from members to a shared goal. When one

thinks of an organization, one thinks of a group of people working towards a unified

vision. In reality, organizations are made up of many individuals, each with their own

goals, visions, beliefs and intentions. This diversity adds richness to an organization by

giving members the opportunity to experience different viewpoints and to access

different areas of expertise. However, one way in which an organization can work

towards a common goal is through a shared vision where members have similar attitudes.

Attitudes are mental images a person forms about a concept based on their knowledge,

feelings and actions towards it (Alreck & Settle, 1985, as cited in Minarovic & Mueller,

2000).

There is need, therefore, for more institutional support for organic farmers in the

South. In 2003, SARE (Sustainable Agriculture Research and Education), a USDA

program, awarded a grant to the Center for Organic Agriculture at the University of

Florida to develop a workshop for agricultural service providers. The workshop, entitled

What Service Providers Need to Know About the Organic Rule and Regulation, aims to

train Cooperative Extension faculty and other local service agents about the National

Organic Program (NOP). Cooperative Extension Agents function as the link between

farmers and researchers at Land Grant Institutions. Extension faculty members

disseminate research findings, assist farmers who seek more scientific knowledge or

instruction, and provide educational programs that target the needs of the local

population. They bridge the gap between science, farming, and administration, creating a

two-way flow of information between research and education. In a given year,









Cooperative Extension employees work with three million volunteers and reach 48

million others (Mayeske, 1991).

Collaborators on the grant include Land Grant University faculty, organic

farmers, and Cooperative Extension agents from Florida, Kentucky, and the U.S. Virgin

Islands. The long-term goal of the grant is to increase the acreage and number of

certified organic producers in Florida, Kentucky, and the U.S. Virgin Islands. In order

for this to happen there must be an increase in the number of field service providers who

understand the National Organic Program, its rules and regulations, and who can

accurately advise farmers interested in organic production. This, in turn, will lead to an

increase in land under organic production practices. It is hoped that this grant project,

which will sponsor seven training over the next year and a half, will blossom into a

standard training offered to Cooperative Extension agents in the South.

Need for the Study

Organic agriculture is a relatively new industry, and it is important that Extension

programs provide growers with appropriate and timely information about organic

production methods. The 2002 Farm Act allocates $3 million yearly to the USDA to

fund grants dealing with organic agriculture. The Agricultural Research Service (ARS) of

the USDA has more than 125 scientists conducting research on organic systems. Also,

the USDA's Sustainable Agriculture Research and Education (SARE) allocates about

19% of its budget to organic research (Greene & Kremen, 2003). Nonetheless,

knowledge and support for organic production among Extension faculty is limited within

the southern region, even though their knowledge about organic production is essential in

moving the organic industry forward.

This workshop trained Cooperative Extension agents and other agricultural

service providers about the National Organic Standards and equipped them with the







9

knowledge and confidence needed to assist producers interested in organic farming. The

results of my study can be used to develop, improve, or design future educational

programs for Cooperative Extension faculty and other professional groups. My study

will provide baseline information about attitudes, knowledge, and confidence of

Extension agents in the South both before and after the training. These results can be

compared with data from other organic training that take a more passive approach to

learning. Furthermore, this study provides methods and standardized instruments to

assess the effectiveness of other curricula or teaching methods. Results of this study can

be used as a basis for requesting that more research funding be allocated to organic

research projects in the South.

Purpose and Objectives

The purpose of this study was to determine the effects of the workshop, What

Service Providers Must Know About the Organic Rule and Regulation, on participants'

knowledge, attitude, and confidence. These factors will influence participants' intentions

to perform or not perform key behaviors related to educating interested farmers about the

National Organic Standards. I propose that by looking at the effects of this training in its

early stages, I will be able to predict the effectiveness of the training in reaching its goals,

to increase competency of Extension faculty who advise consumers and farmers, and

ultimately increase the amount of acreage under organic production.

Research Questions

My research addresses the following questions:

* How effective was the workshop in improving attitudes, increasing knowledge, and
increasing confidence in advising farmers about the National Organic Standards?

* How do attitudes, knowledge, and confidence affect the intention of agricultural
service providers to perform key behaviors?









* Does a relationship exist between the intent to perform the key behaviors and any of
the measured demographic variables?

Research Hypotheses

Following are the hypotheses I formulated for this study:

Hypothesis 1. I predict a positive relationship between increased knowledge about the
National Organic Standards and the attitude of agricultural service
providers.

Hypothesis 2. I predict a positive relationship between increased knowledge about the
National Organic Standards and the confidence of agricultural service
providers to advise farmers about organic production and conduct organic
training.


Hypothesis 3.


I predict a positive relationship between the confidence of agricultural
service providers and their intention to advise farmers about organic
production and conduct organic training.















CHAPTER 2
LITERATURE REVIEW

Theory of Reasoned Action

The theory of reasoned action, developed by Icek Ajzen and Martin Fishbein

(1980) is one of the most influential theories describing the attitude-behavior

relationship. The theory is based on the premise that people are rational and decide what

to do based on available information. They argue that people consider the implications

of their actions and then choose a behavior. Ajzen and Fishbein (1980) suggest that

attitudes are a favorable or unfavorable evaluation of an object. Attitudes are formed

through life experiences, including both direct and indirect experiences and observations.

These experiences, also called knowledge, are "behavioral beliefs." They have been

gathered over time and form the basis of opinion or attitude. This implies that attitudes

are learned and can be changed. They can be viewed as an overall evaluation of a

behavior and can be measured on a bipolar dimension. The more favorable a person's

attitude toward a behavior, the more they intend to perform that behavior. Attitudes are

influenced by beliefs (knowledge) and may change over time, as knowledge changes.

The immediate determinant of a behavior is intent, the intention to perform or not

perform this action. Intention is determined by two basic functions, attitude toward the

behavior and subjective norm. Subjective norms are the perceived social pressures to

perform or not perform the behavior. In general, a person will intend to perform a

behavior if his knowledge about the behavior is positive and if he feels others important









to him want him to perform the behavior. This subjective norm may influence a person

to perform or not perform a behavior regardless of his own personal behavioral beliefs.

Other factors, or external variables, can also affect behavior. These include personality

characteristics and demographic variables. In this theory, external variables may

influence the importance of behavioral and normative beliefs. "Beliefs influence

attitudes and subjective norms; these two components influence intentions; and intentions

influence behavior" (Ajzen & Fishbein, 1980, p. 80).

Behavioral Beliefs & > 7 tt 7
Outcome Expectations Aiue I


Relative Importance of Intention > Behavior
Attitude & Subjective Norm


Normative Beliefs & > Subjective Norm
Motivations to Comply


Figure 2-1. Theory of reasoned action

This theory assumes that most social behaviors are under a person's volitional

control and therefore can be predicted by intentions. Ajzen and Fishbein (1980) explain

that, "intention is the immediate determinant of behavior, and when an appropriate

measure of intention is obtained it will provide the most accurate prediction of behavior"

(p. 41). To be an accurate measure of behavior, the measure of intention must

correspond highly with the behavior in four areas: action, target, context, and time. For

example, in order for us to predict if the agricultural service providers will hold their own

organic training after attending our workshop, an appropriate measure of intention will be

to ask if the service providers intend to conduct (action) their own organic training







13

workshop (target) for other agricultural service providers or farmers (context) within the

next six months (time).

Ajzen and Fishbein (1980) argue that the more an intention directly corresponds

to a behavior, the more accurate the prediction will be. Predictions will also be stronger

within a shorter time frame because outside variables will have a less direct influence on

the intention to perform the behavior. For example, an Extension agent who completes

the organic training workshop and declares she will conduct her own training within the

next year, may be more susceptible to outside variables such as changing jobs, family

illness, or governmental changes that may prevent her from completing the behavior.

Generally speaking, the longer the time frame, the less accurately intention will predict

behavior. Or put more positively, an intention is more likely to predict a behavior within

a short time frame.

In response to the argument that outside variables such as personal experiences

with the action, influence of important people, skills needed to perform the behavior, or

unforeseen events, can weaken the intention-behavior relationship, Ajzen and Fishbein

(1980) argue that intentions always predict behavior if the two have a high level of

correspondence, as long as the intent to perform the behavior has not changed before the

behavior is measured. Instead of weakening the behavior, they claim that external

variables may moderate the strength of the intention-behavior relationship itself.

According to the theory, behavioral change is a function of changing beliefs about

an object or behavior. Many assumptions are made with this theory. By analyzing

Figure 2-1, you will see that an assumption is made with each step in the diagram. It is

assumed that if a change is started at the left hand side of the diagram, it will produce a

change along each step. Start with assuming a change in beliefs will produce a change in







14

attitude or subjective norm, which will produce a change in intention, which will produce

a change in behavior.

Training could be a catalyst that starts the process of change in Figure 2-1. It is

an exposure to new information about an issue. It is an attempt to influence or change

beliefs, or knowledge, in order to cause change down the line, ultimately resulting in a

behavioral change. Iozzi (as cited in Pooley & O'Connor, 2000) argues that

environmental education programs must address both attitude and knowledge in order to

induce changes in behavior. There are two basic strategies to induce change, active

participation and persuasive communication.

A study by Stephen Sapp (2002) looked at the hierarchy of effects principle

developed by Ajzen and Fishbein and others. The hierarchy of effects principle claims

that behavior is the rational product of knowledge, attitude and intentions. People make

rational or logical decisions to perform or not perform a behavior based on their

knowledge, attitude and intentions. There is a "logical consistency between beliefs

[knowledge] and attitudes, attitudes and intentions, and intentions and behavior" (Sapp,

2002, p. 43). Results of Sapp's (2002) study show that a lack of knowledge can make

one unable to perform certain behaviors, even though his attitude and intentions make

performance of that desired behavior the next logical step. Sapp found that in order to

perform a behavior, the performer must be above a certain threshold of knowledge.

Otherwise the result would be an inability to perform the expected behaviors. In his

study, different levels of knowledge were measured, including basic command of facts

(knowledge), accurate assessment of facts (comprehension), and the ability to understand

linkages between different key aspects (analysis). Sapp's minimum threshold combined







15

knowledge and comprehension. Without mastery of these two basic levels, one would be

unable to perform the behavior.

Self-Efficacy Theory

Recent versions of the theory of reasoned action have added self-efficacy to the

model (DeJoy, 1996). Self-efficacy theory is an individual's belief in his or her ability to

successfully accomplish a specific task. It is how confident someone feels that he or she

can achieve the goal. It is a dynamic, future-oriented judgment, changing over time, as

new information, experiences, and feedback are acquired (Gist & Mitchell, 1992). Self-

efficacy motivates. It influences a person's choice of activities and goals, the amount of

effort put into a task, and how long he or she will persevere (Bandura, 1989; Gist &

Mitchell, 1992; Lent, Brown, & Larkin, 1986). Someone with high self-efficacy views

difficulties as challenges to be mastered. Self-efficacy stimulates cognitive functioning

and performance, and reduces stress (Bandura, 1989).

Self-efficacy is an important mediator of behavioral change. Self-efficacy

appears to be especially important for long-term behavioral change and maintenance.

Enhancing self-efficacy increases a person's sense of control, which is critical to

successful adherence to a behavior over time (DeJoy, 1996). "When self-efficacy is

enhanced, attendant increases in performance are noted" (Gist & Mitchell, 1992, p. 183).

Self-efficacy is developed through training and education, problem-solving and

other skill building exercises, actual experience, and modeling of the desired behaviors

by coworkers and peers. It is viewed as having generative capabilities, growing over

time with a cycle of successful performances of the desired behavior, followed by an

increase in confidence, followed by further attempts at the desired task (DeJoy, 1996;

Gist & Mitchell, 1992).









Many studies have been conducted with students and self-efficacy, linking self-

efficacy to academic performance and persistence. Students who self reported high

levels of self-efficacy achieved higher grades and were more likely to persist in technical

or scientific studies (Lent et al., 1986). Self-efficacy accurately predicted writing and

math performances (Gist & Mitchell, 1992; Schunk, 1991). Efficacy was also correlated

with the ability to quit smoking or stick to a diet, sports performance, work related

performance, political participation, adapting to new technology (Bandura, 1997, as cited

in Goddard, Hoy, & Hoy, 2004; Garcia, Schmitz, & Doerfler, 1990; Gist & Mitchell,

1992) and behaviors to detect breast cancer (Luszczynska & Schwarzer, 2003). It has

been studied in other fields such as business, management, sociology, and education,

linking groups' collective efficacy beliefs to group outcomes (Goddard et al., 2004).

Molnar and DeLauretis (as cited in Lent et al., 1986) found that self-efficacy is

useful in predicting academic achievements of intellectually homogenous groups of

students. Bouffard et al. (as cited in Goddard et al., 2004) found that students with the

same level of mathematical abilities had significant differences in their ability to solve

math problems based on the strength of their self-efficacy. Students with higher efficacy

consistently applied what they knew while those with low levels of efficacy gave up

early.

According to Luszczynska and Schwarzer (2003), attitude and confidence are the

direct cause of behavior, however confidence (self-efficacy) is the best single predictor of

intention, which leads to behavior. Bandura (as cited in Gist & Mitchell, 1992; Garcia et

al., 1990) has proposed that self-efficacy predicts future behavior more consistently than

does past or concurrent behavior. Bandura argues that people are more influenced by

how they interpret experiences than by how much they actually achieve. Therefore,









previous behavior largely affects levels of self-efficacy, which then influences future

behavior.

Like measurement of intention in the theory of reasoned action, accurate

measurement of self-efficacy must have a high correspondence to the task. It must be

measured for a very specific action or task and must be measured as closely as possible

in time to that task (Garcia et al., 1990; Multon, Brown, & Lent, 1991; Pajares & Miller,

1994). If not measured under these strict criteria, ambiguous and false results will be

found which confound relationships instead of clarifying them.

Self-efficacy advocates warn that self-efficacy is not the sole determinant of

behavior, although it is extremely valuable in predictions. They suggest that outcome

expectations and incentives also influence the behavioral result.

Adult Education

Personalities change and develop as adults mature. Adulthood is a combination

of wisdom, experience, and knowledge. Successful adult training programs utilize the

experience that participants bring with them and focus on enhancing the professionals'

expertise (Tennant & Pogson, 1995).

Utilizing peer groups is a very successful method of adult learning (Eisen, 2001;

Saltiel, 1998; Schunk, 1991). In fact, "many educators have found collaborative learning

to be more successful in promoting achievement than either individualized or competitive

learning experiences" (Gerlach, as cited in Saltiel, 1998, p. 8). Peer groups motivate

members by fostering relationships between individuals of comparable status who share a

similar learning objective. Participants use their expertise to learn from and teach others.

Observing a peer succeed at a task bolsters the confidence of others within the group,

motivating them to attempt and succeed at the task. Schunk (1991) found that by









observing peers model the desired behavior increased skill and efficacy better than

watching the teacher model the behavior or having no model at all.

Transformative or experiential learning emphasizes learning by doing and stresses

the importance of reflection or critical analysis through discourse (Eisen, 2001;

Miettinen, 2000; Pruneau, Gravel, Bourque, & Langis, 2003). Education's role is to

empower, to give learners the knowledge and skills they need to effect change and

impact their future. Transformative and experiential learning fosters interest and

empowerment in participants (Pruneau et al., 2003). Empowerment facilitates behavioral

changes by enabling individuals to make and implement plans to solve problems (Becker,

Kovach, & Gronseth, 2004).

The grant team members from Florida, Kentucky, and the U.S. Virgin Islands met

in January of 2004 to discuss workshop content and structure. They outlined different

key topics to be covered, which were later developed into training modules that could be

used as a full workshop or as individuals lessons (Appendix A). Different modules were

also developed for distinctive needs of different states; for example, citrus or livestock

modules were discussed for Florida. The team members decided on the importance of

each topic and how much time would be devoted to activities in these areas.

Next, the team built the activities for each module based on models of adult

education, targeting the four main learning styles and four of the five cognitive levels of

learning. These cognitive levels include the most basic level of knowledge, or fact

retention, the second level of comprehension, or basic understanding of the topic,

application, skills and usage of materials learned, and the fourth level of analysis, or

understanding of the content and structure of the material. The team wanted to increase







19

the knowledge and levels of confidence of the participants through experiential learning,

engaging all learning styles and challenging participants through higher cognitive levels.

The workshop was a two-day, dynamic, hands-on, intensive training about

organic agricultural production practices and the National Organic Standards. It

provided participants with the necessary tools to advise farmers and train others about

organic agriculture and the National Organic Standards. The workshop aimed at

empowering participants through discovery learning and skill development.

The workshop was designed to be completely participatory, with essentially no

lecture. Adults worked in small peer groups to tackle new problems, drawing on the

wealth of experience and knowledge that they brought to the workshop. Working in peer

groups is an excellent method of teaching adults because it is nonthreatening and

encourages participation.

The training style focused on experiential learning, whereby adults were given a

problem to solve and resources to consult. Each group worked to discover a solution to

the problem and then shared their result with the class. The class discussed the findings

and solutions, synthesizing this new knowledge with previous job experiences and

knowledge. Discovery learning helped participants gain the knowledge and skills they

needed to advise farmers interested in organic agriculture.

One example of hands-on learning from the training was an exercise about

organic labeling. There are different degrees of organically labeled products, from

"100% organic" to "made with organic ingredients," according to the amount of organic

ingredients in the product. Instead of a lecture describing the tiers of organic labeling,

participants were directed to consult the National Organic Standards, then to buy three

products from a grocery store that could fit three levels of organic labeling. The next day









each group presented their products and explained in which labeling category they

belonged. This elicited a lively, in-depth discussion about labeling and marketing.

Another training example dealt with organic seed stock. Each group was given a

list of seeds that a farmer wanted to use on her organic farm. Using the National Organic

Standards and seed catalogs, the groups had to determine which seeds were allowed in

certified organic production. If a seed was not allowed they had to explain why it was

not allowed and recommend another variety that was acceptable. This seed stock

discussion evolved into a discussion about core principles in organic agriculture that

differ from conventional agriculture, such as the use of genetically modified seeds.

Activities such as these stimulated interest in the participants, challenged them,

and rewarded their efforts. This process of experiential learning, discussion, and

reflection is essential for adult learners. This workshop gave participants the opportunity

to learn concepts and skills in a peer group setting, enabling them to more effectively

perform work related behaviors pertaining to organic agriculture. The training was very

fast paced and intensive, yet many participants claimed it was the most effective training

they had ever attended. This participatory learning style is found to be an extremely

effective method for educating adult learners.















CHAPTER 3
METHODOLOGY

This chapter discusses the research design used in this study. It also describes the

development of test instruments, data collection, and data analysis.

Research Design

I conducted an ex-ante factorial quasi-experiment (de Vaus, 2001). Quasi-

Experimental designs generally have high internal validity, but may have low external

validity. This means results can be confidently generalized to many test subjects who

were not actually included in the study, but findings cannot be extrapolated to other

groups under different conditions. Therefore, my results can be confidently generalized

within the theoretical population, county Extension faculty and other local service

providers for the farm population. The major weakness of a quasi-experiment is that

random sampling and random assignment of test subjects are not guaranteed. Bias is

introduced because the test subjects who attended the workshop chose to do so. This bias,

as we will see in the test results, may include a generally more favorable attitude towards

organic production by the test subjects who attended the workshop than the attitude held

by members of the theoretical population as a whole. However, the instrumentation used

in this study was reliable, valid and precise.

Sampling Frame

The population for this study is Cooperative Extension faculty and other local

service providers who work with farmers. The sample consisted of Cooperative







22

Extension agents and other agricultural service providers who participated in the organic

training workshop, What Service Providers Must Know About the Organic Rule and

Regulation, held on July 26-27, 2004 in Ft. Pierce, Florida (Appendix A). There were 26

subjects in the sample. A quasi-control comparison group was established with 26

Cooperative Extension agents who attended the annual meeting of the National

Association of County Agricultural Agents held in July, 2004 in Orlando, Florida. The

quasi-control group consisted of Extension faculty from all four USDA regions who self-

selected to attend a seminar about forging relationships between organic farmers and land

grant universities.

Instrument Development

There were no suitable test measures already existing for this study. I therefore

developed three research tools to measure the change in the independent variables of

attitude, knowledge, and confidence, a fourth tool to measure intent to perform job

related behaviors, and a demographic questionnaire (Appendix F). I developed a Likert

scale to measure attitude (Appendix B), a standardized pre- and posttest to measure

knowledge (Appendix C), and an index to measure confidence (Appendix D). The index

to measure intention (Appendix E) was based on Ajzen and Fishbein's (1980) theory of

reasoned action. I followed standard protocol in the development of the instruments and

appropriate measures of validity, reliability, and precision were employed.

The test instruments were developed from the workshop training materials with

the help of the grant team members, consisting of professional researchers, Cooperative

Extension agents, organic certifying agents, and certified organic farmers from the states

of Florida, Kentucky, and the US Virgin Islands. The test materials were pilot tested









before use in the training workshop. The steps involved in the development of each

instruments are explained in the following sections.

Attitude Measurement

I developed a scale to measure the attitudes of local service providers about

organic production (Appendix B). Attitudes are psychological constructs or ways of

conceptualizing intangible elements (Mueller, 1986). Attitude has been defined in many

ways. Gordon Allport defined it in 1935 as "a mental or neural state of readiness" (as

cited in Mueller, 1986, p. 3). In 1974 Gagne and Briggs described an attitude as "an

internal state which affects an individual's choice of action toward some object, person,

or event" (in Aiken, 1996, p. 226). In plain terms, attitude is the extent of liking or

disliking something. An attitude scale is designed to evaluate the intensity and direction

of the subject's feelings about a concept or practice. The scale is constructed so that all

items address only one specific issue or concept. For this study a uni-dimensional Likert

scale was developed (Rossi & Freeman, 1993). Likert scales present a range of

statements about a topic and subjects rate how they feel about the statement on a scale of

1 to 5 where 1 indicates strong disagreement and 5 shows strong agreement. Statements

in a Likert scale are not neutral. They are meant to elicit an opinion from the respondent

who indicates to what extent she or he agrees with the statement. Statements used in this

study range from "Extension should do more to help organic farmers" to "We need to

pay attention to our mainstream clientele, not waste time with organic hobby farmers."

The frequency of positive and negative statements is balanced to minimize bias.

Coding is simple with scales because the value of each answer is determined

when the scale is constructed. For example, a respondent answering "Disagree" to the

statement, "Organic food is the only safe food" receives 2 points. Answers range in









value from 1 to 5 corresponding to the answer choice, strongly disagree (1 point),

disagree (2 points), indifferent (3 points), agree (4 points), or strongly agree (5 points). It

is necessary to reverse the score on questions containing negatives in them, such as

"Learning about organic farming is a waste of time." Someone who answers "strongly

agree" with that statement actually has a negative opinion of organic agriculture and is

awarded a score of 1 (the reverse score of 5, strongly agree). Likert scales use a

summative scoring procedure. Therefore, when the sum of the scores for each individual

response is tallied, a higher score indicates greater approval of organic agriculture.

Attitude rating scales are easy to use because they provide a single score that indicates

both the direction (positive or negative) and the intensity (very positive or very negative)

of a person's attitude (Henerson, Morris, & Fitz-Gibbon, 1987).

Precision is the exactness of a tool. By assigning one number value to a

respondent's scale, precision is increased, especially when compared to other more

subjective research methods. Higher internal consistency, measured by inter-item

consistency, increases precision.

The validity of a scale is the degree to which it measures the specific attitude of

interest (Sommer & Sommer, 2002) and its appropriateness. The main argument against

attitudinal scales is that people's attitudes are complex and may not be measurable along

a single dimension. According to Mueller (1986), validity is the most serious weakness

in attitudinal scales. Responses can be faked or adjusted, especially if self-scoring. This

is a universal problem in affective measurement.

Reliability of a scale indicates its consistency and accuracy in measurement.

Originally I generated 134 statements regarding organic agriculture. I asked colleagues

to rank the strength of the statements on a seven point scale. In this way I eliminated the









statements where the judges disagreed on the strength, if it was strong, weak, or any

degree in between. This narrowed the statements down to 60.

Next, I conducted a preliminary test to further narrow down the number of

statements to be used in the final scale. Groups of people were asked to indicate what

were their general opinions of organic agriculture (negative, indifferent, favorable)

before they began the scale. These participants were asked to complete the scale,

indicating how each statement made them feel, ranking each question from 1 to 5 as

described above (strongly disagree to strongly agree). It was necessary to obtain a

balance of respondents who felt negatively, indifferently, and favorably about organic

agriculture in order to determine how well each item under consideration for inclusion in

the scale differentiated among subjects.

I eliminated all questions that elicited a neutral response because they did not

discriminate between the respondents who claimed they were in favor of organic

agriculture and those who felt negatively about it. I wanted to find statements that elicited

a strong response, either for or against organic agriculture. I ran tests to determine the

inter-item correlation, Cronbach's alpha, and the standardized alpha for each statement.

Any statement with low scores was removed. To discriminate those whose opinions

were favorable about organic agriculture and those who felt negatively towards it, a t-test

was run using the top 10% and bottom 10% of responses. Normally, 25% is used to

compare responses in a t-test but in my pretest, the results showed strong positive and

negative opinions. It was difficult to discriminate the average positive answer from the

very strong positive answer using the traditional 25% approach. Using the top and

bottom 10% did allow me to discriminate the strength of the answer. The statements

were narrowed down to 20.







26

Cronbach's alpha was 0.96 for the final 20 items selected for the scale as a result

of preliminary testing. After the workshop was completed, I ran another internal

consistency test using the raw data from the respondents' pretest and posttest and the data

from the quasi-control group's pretest. I wanted to determine if any items should be

rejected. Cronbach's alpha was 0.84, the standardized alpha was 0.84 and the inter-item

consistency was 0.22. I decided to drop statement 4 (a negative statement) and number 6

(a positive statement) from the final scale because both statements had low inter-item

consistency scores and brought the average down considerably. I also chose to eliminate

statement number 9, "People who buy organic food are gullible" because the word

gullible had to be explained to some participants who did not speak English as a first

language. I considered removing statements 1 and 17 (both negative statements) but was

concerned about the balance between positive and negative and decided to keep them in

the final instrument. The final instrument had a Cronbach's alpha of 0.85, a standardized

alpha of 0.85 and an average inter-item consistency of 0.26. This process of developing

the Likert scale for attitude took 8 months.

Standardized Pre- and Posttest

I developed a standardized self-completion test based on the key components of

the National Organic Standards and the Organic Production System Plan required for

organic certification (Appendix C). The test measured changes in knowledge about

organic production and the National Organic Standards before and after the organic

training workshop for agricultural service providers. I ensured a high content validity by

developing questions directly from the training modules developed by the team of

experts. The final test was approved by team members of the grant.









A meeting was held in January, 2004 for the tri-state team members. Here the

training team decided the content matter of the training and ranked the criticality and

frequency of the topics (Appendix A). I grouped the questions they suggested for the

pre- and posttest of knowledge by these topics or modules, eliminating repetitive

questions and adding more when needed. Eighty percent of the questions were divided

into the categories of the Organic Production System Plan, Water Quality, Soil Quality

and Crop Fertility, Crop Management, and Organic Integrity. Six percent of the content

was devoted to the Overview of Organic Production, Planting Stock- Seeds and

Transplants, and Livestock. Four percent was devoted to Handling and Processing and

the remaining 10% to Organic Resources.

I developed the questions for the standardized test based on the four cognitive

levels of learning that were targeted in the training; knowledge, comprehension,

application and analysis. The fifth level, synthesis, is very difficult to achieve in a single

training event so the team decided not to test for it. Knowledge is the lowest level of

cognition and only reflects basic retention of the subject matter. An example from the

test is, "The transition period from conventional to organic agriculture is years." The

second level, comprehension, is the lowest level of understanding and shows if the

participant actually understands the meaning of the material. The third level is

application, allowing the respondent to show if she or he can use what was learned. An

example from the test is "List three disease control methods that organic farmers can

use." Analysis is the final level addressed in the pretest and posttest and requires an

understanding of the content and structure of the learned material. An example is, "True

or False. A storage box or bin originally used for conventional crops can be reused for

organic crops as long as both crops are not stored together in it at the same time."









Discriminatory power, or the ability to detect who really knows the answer from those

who do not, is increased as more and higher levels of testing are included.

The preliminary test was administered to people knowledgeable about the subject

matter, but not necessarily experts. From these tests I eliminated invalid questions.

Invalid questions were ones that fewer than 10% answered correctly or more than 90%

answered correctly. This test was then administered to a class of college students

learning about organic agriculture. The same process as above was used to determine

invalid questions. Backup questions were substituted at this time for questions that either

too few people answered correctly, or too many people answered correctly, taking care to

retain the balance of cognitive levels. The questions were weighted for higher cognitive

levels since that was the aim of the training.

Determining a scoring system and answer key before administering the test

increases precision and reliability and is also required protocol in the development of a

standardized test. This test was based on a 50-point scoring system with each correct

answer worth one point. I also calculated a weighted score for each test by multiplying

each correct answer by the cognitive level of the question. For example, knowledge level

questions were worth one point, comprehension questions were worth two points,

application questions were worth three points, and analysis questions were worth four.

Thus a correct answer to an analysis level question was worth four points in the weighted

score. It took 8 months to develop the standardized pre- and posttest of knowledge.

After gathering the pre- and posttest data at the workshop I decided to remove a

question about organic livestock because time did not allow that module to be covered

during the training. Two other questions were largely missed in the posttest but I decided

to keep them in the final results because not everyone answered them incorrectly. After I









dropped the livestock question, the final pre- and posttests were scored out of 49 points

instead of the original 50 points. Each pre- and posttest was given two different scores, a

raw score out of 49 points and a weighted score.

Measurement of Confidence

I developed an index to measure the confidence of participants to perform certain

job related behaviors prior to and following the training (Appendix D). The index was

developed with the help of Cooperative Extension faculty who suggested behaviors

related to organic agriculture that one might expect to perform on the job. They also

suggested what weighted score to assign each behavior, depending upon the frequency

and difficulty of performance.

Participants were asked to rate their confidence with a scalar response indicating

how confident they felt about performing certain tasks related to organic agriculture.

Tasks ranged from answering questions from homeowners and consumers about organic

products and options, to including organic farms on field tours. The index scores were

weighted to reflect the difficulty levels of performing certain tasks. A task such as

highlighting organic farms as a source for fresh food for consumers would not be as

difficult a task for Extension faculty as making field visits to an organic farm for

troubleshooting. Twenty-four test subject pretest scores were valid while only 19

posttest scores were completed by test subjects. All 26 pretest scores of the quasi control

group were valid.

Measurement of Intention

One goal of the training, What Service Providers Must Know About the Organic

Rule and Regulation, was to enable agricultural service providers to assist farmers who

want to meet the National Organic Standards. The training proposed to increase the real









number of agricultural service providers in the field who understand the National

Organic Standards and who can advise organic farmers and farmers interested in

transitioning to or beginning organic production. The workshop emphasized increasing

participants' knowledge of the National Organic Standards and therefore their ability to

advise farmers, providing them with training manuals and lesson plans they can utilize to

develop their own training sessions for farmers.

Ajzen and Fishbein's (1980) theory of reasoned action states that intentions are an

accurate measure of behavior, as long as certain criteria are met. Namely, there must be

a high correspondence between the intention and the behavior in four areas: action,

target, context and time. A shorter time period between the measurement of the intention

and the behavior is more accurate than a longer period, largely due to the consequences

of unforeseen circumstances. Following these prescriptions and with the assistance of

the grant team members, I developed an index with scalar response questions to measure

participants' intentions to perform job related behaviors (listed below and also

Appendix E).

* Provide organic advice and options to homeowners.
* Answer questions from homeowners and consumers about organic products.
Highlight organic farms as a source of farm fresh food for consumers.
Seek out organic producers in your county.
* Include organic farm tours on field days.
* Include organic techniques in demonstrations.
* Make field visits to organic farms for troubleshooting.
Educate yourself about organic production (i.e. attend other training, seek out
useful sources of information, etc.)
* Include organic farming as an alternative for farmers who call you for advice.
Respond to organic producers questions about production practices and the
National Organic Program.
* Add organic producers to your advisory council.
* Hold a training workshop about organic practices and standards.
Advise producers about where to get organic supplies.
Include information about organic production and standards in media such as a
website, newsletter, radio or TV communication.









Workshop participants were asked to indicate how likely they were to conduct

these activities in the next 6 months. Test participants checked their answer along a

continuum of answers from probable to improbable. This measure of intent was included

in the posttest and was only given to those test subjects who had completed the 2-day

workshop. This data was not collected from the quasi-control comparison group.

Demographic Data

Demographic data were collected for all participants at the workshop and the

quasi-control comparison group. The demographic data (Appendix F) includes

information such as age, gender, name of school, years of formal education, type of

course work, years in current employment, and how key coworkers and supervisors feel

about organic agriculture. The statistical averages of the quasi-control and the workshop

participants were compared to give some sense of how the workshop participants

compare to the average Cooperative Extension agent. Tests were conducted with the

demographic variables to determine which, if any, influenced the outcomes of the

training and the intent to perform the key behaviors. The numbers and results of these

tests will be discussed in Chapter 4.

Data Collection

Participants at the organic training seminar were given a pretest packet that

included the University of Florida's Internal Review Board (IRB) consent form, the scale

for attitude, the test of knowledge, the confidence index, and the demographic data

questions. They were directed to label their test with a number found on the back of their

name tag. This number allowed the test to be anonymous but enabled me to correlate the

pretest scores with the posttest scores. Participants were assured that the test results









would remain confidential and that I was interested in the aggregate group score, not

individual scores. Most respondents generally completed the packet in half an hour.

After the 2-day, hands-on workshop about the National Organic Standards and organic

production, the participants were given the posttest. The posttest packet included the

same attitude scale, test of knowledge and confidence index, but with the question order

scrambled. Also included in the posttest was the intention index. Most posttests were

completed in 30 minutes.

The quasi-control comparison group was given the pretest packet only, with all

tests and demographic information except for the intention index. Their tests were given

prior to the seminar they attended at the national convention. Their tests were also

anonymous, and I assigned a number to each test to track test results.

Out of the 26 tests from the workshop participants, only 24 tests were used in the

data analysis of the attitude scale, knowledge test, and demographic data. Two tests were

disqualified because the test subjects were unable to attend the full 2 days of training.

Twenty-four protests were used for the confidence index, but only 19 were completed in

the posttest. The index for intention was the last test to be given and suffered from test

participant attrition. Only 18 indices were completed and could be used in the final

analyses. The quasi-control comparison group yielded 26 valid protests of attitude,

knowledge, confidence, and demographic data. Although the sample size was small, the

instruments were valid, reliable, and precise. Measuring with a pre- and posttest gave

tight control of the data.

Limitations

Administering a posttest at the end of a training workshop can lead to bias. Recall

and knowledge are at a peak immediately following training and a posttest at that time









may tend to overestimate changes in knowledge. I chose to administer the test

immediately following the workshop because there was a better probability of getting the

completed tests back. Research shows that mailed posttests have low response rates. I

believe that the need for more responses from participants outweighed the bias presented.

Data Analysis

I consulted Sheskin (2000) to determine which statistical tests to run. I used

paired t-tests' to examine differences in pre- and posttraining scores for attitude,

knowledge and confidence among workshop participants. I used t-tests to examine

differences between pretraining scores for workshop participants and the quasi-control

group. For the Likert scale measuring attitudes about organic farmers and farming, the

t-tests were performed for both the raw summative score and the mean response for each

participant. The mean response was easier to interpret and understand. Therefore, given

that no differences were found between p-values for the two scores, I used the mean

score for the final analyses. Similarly, for the standardized test of knowledge and the

index of confidence, I performed t-tests for both unweighted and weighted responses.

There were no differences, and I used the weighted responses for the final analyses.

I used multiple regression to determine the relationships between the three

predictor variables (attitude, knowledge, and confidence) and the outcome variable,

intent to change practice. The initial model included all three predictor variables.

Additional models including only knowledge and confidence and finally a model using

only confidence were also performed. For categorical data, I used one-way ANOVA or

t-tests, depending on the number of response categories, to examine the affect of

demographic variables on the outcome variable. For continuous data, I used simple

1All statistics were analyzed using StatSoft Statistica version 6.1.







34

regression to examine the relationships between the demographic variables and the

outcome variable. The final analysis was a Spearman Rank Order Correlation that

included predictor variables identified in the statistical analyses described above with a

p-value of 0.05 or less. The predictor variables were attitude, knowledge, confidence,

educational level, and major area of study for the doctoral degree.















CHAPTER 4
FINDINGS

Demographic Information

Twenty-six individuals participated in my study, which was the pilot training

workshop of the grant project. Of these 26 participants, only the pre- and posttest data

from 24 participants was used. The data from the other two participants was disqualified

because the participants were unable to attend the full two days of training. The pre- and

posttest data included the Likert test for attitude, a test for knowledge, and an index of

confidence. An index of intention was included as a posttest only. Eighteen completed

indices were used to measure the outcome variable, intent. The eight disqualified tests

included the two test subjects who did not complete the full training plus blank data

sheets.

Out of the valid 24 tests, 7 of the participants were female (29%) and 17 (71%) of

the test subjects were male. Three participants were black (12.5%), 3 were Hispanic

(12.5%), and 18 were white (75%). The ages of the participants ranged from 27 to 69

with a mean age of 47 years. Ninety-six percent of the quasi-control comparison group

were white males with an average age of 52 years.

Three test subjects worked in the US Virgin Islands, one in Kentucky, and 20 in

Florida. All three of these states/territories are located within the southern USDA region.

In the comparison group, 54% worked in the southern USDA region, 12% worked in the

northeastern USDA region, and 17% worked in each of the north central and western

regions.









Half (12) of the test subjects were county Extension faculty (50%). Three

participants (12.5%) were Natural Resource Conservation Service employees, three were

university professors (12.5%), two participants were farm managers (8%), one was an

agricultural consultant (4%), one worked with 4-H community gardens (4%), one was an

organic inspector (4%), and one conducted postdoctoral research (4%). Nine test

subjects (37.5%) had prior training about organic production or the National Organic

Standards. The comparison group was exclusively county Extension faculty, and 37.5%

also had previous training about organic production or the National Organic Standards.

The levels of education within the test group varied from no college education to

doctoral degrees (Ph.D.). One test subject had no college level education (4%), 4 had a

bachelors (BA or BS) degree (17%), 10 participants had a masters (MA or MS) degree

(42%), eight participants had a doctoral degree (33%), and one test had missing data.

The comparison group of Extension faculty consisted of 9% undergraduates, 74% with a

masters degree, and 17% with a doctoral degree. Fifty-two percent of the degrees earned

within the test group were from universities within the southern USDA region. This

compares to 30% from the comparison group. Table 4-1 explains where test study

participants attended university. The schools are grouped according to USDA regions.

Table 4-1. College degree earned by test subjects according to United States Department
of Agriculture geographic regions
USDA region

Other
Southern North East North Central Western (non-US)
Undergraduate 9 4 2 2 5
Graduate 10 5 1 0 1
Doctoral 5 1 1







37

Table 4-2 reflects the number of participants who had a website, contributed to a

newsletter, radio or TV program. Of the 24 test subjects involved in the workshop, 15

had a website (62.5%), 16 contributed to a newsletter (67%), and 7 contributed to a radio

or TV program (29%). The comparison group reflected that 37.5% had a website, 83%

contributed to a newsletter, and 67% contributed to a radio or TV program.

Table 4-2. Percentage of test subjects and comparison group members who contribute to
publications and mass media as part of job performance
Website Newsletter Radio/TV
Test subjects 63% 67% 29%
Comparison group 38% 83% 67%

The test subjects were asked a series of questions to determine how peers and

influential decision makers felt about them working with organic producers. Participants

in the test group accorded an indifferent/positive score towards peers, supervisors, and

administrators. Members of the comparison group gave similar ratings of

indifferent/positive for peers, supervisors, and administrators. The attitude of county

leaders toward organic agriculture, as judged by the test subjects, is depicted as

somewhat indifferent (2.48/4). The comparison group gave the attitude of county leaders

a score of 2.44 (out of 4). Table 4-3 shows the results from the questionnaire.

Table 4-3. Perception by test subjects and comparison group of peer, supervisor,
administrator and local leaders' attitudes about working with organic
producers
Mean score
Rating* Test subjects Comparison group
Peers Indifferent/positive 2.74 2.64
Supervisor Indifferent/positive 2.86 2.86
Administrator Indifferent/positive 2.82 2.77
County leaders Indifferent 2.48 2.44
*Categories and scores awarded for each category: 0 = Very negative; 1 = Negative, 2 =
Indifferent, 3 = Positive, and 4 = Very positive









Participants indicated that their overall experience with organic growers has

leaned more toward positive (2.88/4) than indifferent. The comparison group's score was

slightly lower than the test subjects' score but was also more positive than indifferent

with a score of 2.68 (out of 4). On average, the test participants rarely (1.48/4) advised

organic growers and indicated that they worked with organic farmers or producers

expressing an interest in organic farming less that 10% of the time. The comparison

group also rarely advised organic growers (1.36/4) and worked with organic farmers or

farmers interested in organic production less than ten percent of the time (Table 4-4).

Table 4-4. Measures of importance of organic producers as clients of test subjects and
comparison group
Test subjects Comparison group
Previous experience with organic growers 2.88* 2.68*
Frequency of advising organic growers Rare Rare
Percentage of clients who are organic producers <10% <10%
or are interested in organic production
*Categories and scores awarded for each category: 0 = Very negative; 1 = Negative, 2 =
Indifferent, 3 = Positive, and 4 = Very positive

Screening t-tests' and one-way ANOVA tests were run on the categorical

demographic variables, and regressions were run on the continuous variables, to reduce

the dimensionality of the model used to predict intent to change behavior. This was an

exploratory activity in dimension reduction to determine which demographic variables

statistically influenced the outcome variable, or intent to perform key behaviors.

Table 4-5 shows the p-values for all of the dimensions measured by the demographic

questionnaire. Only those variables with a p-value less than 0.05 were considered to be

significant. These included the undergraduate university (p < 0.018), graduate university

(p < 0.012), doctoral university (p < 0.018), doctoral major (p < 0.029), and if they had a

newsletter (p < 0.037).

1All data analysis was done using StatSoft Statistica version 6.1.









Table 4-5. Relative importance of demographic characteristics of test subjects in relation
to intention to perform selected behaviors on the job
Variable p-value Type of test**
Location of MS/MA university by USDA region 0.012 a
Location of undergraduate university by USDA region 0.018 a
Location of Ph.D. university by USDA region 0.018 a
Ph.D. major 0.029 a
Contributes to newsletter 0.037 t
Ethnicity 0.057 a
Age 0.107 r
Graduate major 0.110 a
Year completed undergraduate degree 0.142 r
Year completed terminal graduate degree 0.153 r
Education level* 0.156 a
Frequency of advising organic growers 0.196 a
Undergrad minor 0.221 a
Number of courses in soil ecology 0.249 r
Number of courses in general ecology 0.260 r
Years with employer 0.266 r
Number of courses about genetic engineering 0.293 r
Sex 0.330 t
Previous experience with organic growers 0.348 a
State where employed 0.410 a
Has professional website 0.442 t
Perception of supervisor's attitude about working with 0.483 a
organic farmers
Perception of county leaders' attitude about working with 0.528 a
organic farmers
Number of courses in pesticide technology 0.545 r
Number of courses including agricultural ecology as major 0.569 r
topic
Perception of administrator's attitude about working with 0.603 a
organic farmers
Number of courses in agricultural ecology 0.604 r
Number of courses about organic practices 0.620 r
Year completed Ph.D. (if applicable) 0.639 r
Number of courses including pesticide technology as major 0.640 r
topic
Previous training about organic production or NOP 0.689 t
Job title 0.737 a
Perception of peer's attitude about working with organic 0.758 a
farmers
Number of courses including organic practices as major 0.791 r
topic
Number of courses including general ecology as maj or 0.818 r
topic









Table 4-5. Continued


Variable p-value Type of test**
Number of courses in IPM 0.829 r
Has pesticide applicator's license 0.862 t
Number of courses including IPM as major topic 0.866 r
Contributes to radio or TV program 0.900 t
Undergraduate major 0.904 a
Number of courses including soil ecology as major topic 0.925 r
% of clients who are organic growers 0.937 a
Number of courses including genetic engineering as major 0.958 r
topic
Nature of previous employment 0.984 a
*0 = no college, 1 = bachelor's degree, 2 = master's degree, 3 = Ph.D
** r= regression, t= t-test, a= ANOVA

When a Spearman Rank Order Correlation was performed on the outcome

variable, intent to perform job related behaviors, and the significant demographic

variables, undergraduate university, graduate university, doctoral university, educational

level, doctoral major, and if they had a newsletter, only two were significant at the

p < 0.05 level. The two significant demographic variables include educational level

(p < 0.009) and doctoral major (p < 0.047) (Table 4-6).

Table 4-6. Explanatory power of selected demographic variables on intent to perform
outcome behaviors, Spearman Rank Order Correlation
Demographic variable Valid N Spearman R p-value
Undergraduate university 17 0.127 0.628
MS/MA university 18 0.291 0.241
Ph.D. university 18 0.343 0.163
Educational level 18 0.594 0.009
Graduate major 18 0.401 0.099
Ph.D. major 18 0.475 0.047
Contributes to newsletter 18 0.443 0.066

I ran a regression summary on three demographic variables that I suspected would

play a role in determining intent to perform the key behaviors. Although none were

significant at thep < 0.05 level, I did discover a negative relationship between age and

intent and undergraduate year and intent (Table 4-7).









Table 4-7. Summary of multiple regression analysis, relationship between intent to
perform outcome behaviors and selected demographic variables
N=10 Beta Std Err of Beta p-value
Intercept 0.580
Age -0.418 1.771 0.821
Undergraduate year -2.128 1.937 0.314
Graduate year 1.240 2.044 0.566
Note. R2 = 0.334 p < 0.454 Std. Error of estimate: 0.179

Attitude

I measured the attitude of the test subjects before and after the two day training

with a Likert scale. The purpose of the test was to determine the impact of the workshop

on participant's attitudes about organic production and the role of Extension in

promoting the National Organic Program. As you can see in Table 4-8, participants in

the test group had a mean average attitudinal score of 3.91 (out of 5) on the pretest. This

was slightly higher than the mean pretest attitudinal score of 3.58 (out of 5) for the

comparison group. Ap < 0.039, p was significant at the 0.05 level.

Table 4-8. Results of t-tests for differences between test subjects' pretest scores and
comparison group scores on Likert scale to measure attitude about organic
agriculture
Test subjects Comparison group
N 24 26
Mean score 3.91 3.58
Standard deviation 0.48 0.59
P 0.039

Table 4-9 shows that there was no significant increase in the attitudinal score of

the test subjects from pretest to posttest. Scores increased from 3.91 to 3.98, which was

not significant at the 0.05 level (p<0.17).

Figures 4-1 and 4-2 show that the pre- and posttest scores of the test group were

normally distributed. Figure 4-3 is a box and whisker plot of the gain in attitude from the

pretest to the posttest.








42

Table 4-9. Paired t-test for differences between pretest and posttest scores on Likert scale
used to measure attitude toward organic agriculture
Pretest score Posttest score
N 24 24
Mean score 3.91 3.98
Standard deviation 0.48 0.53
Difference -0.07
Standard deviation difference 0.24
p 0.17


0 .0 -. ....- .. .. --- ........- -.... ..... ..... .... -... ... ..... .... ..... ...... ......
0 .5

**
z



LU



-2.0

-2.5
2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6 4.8
Likert Pre-Test Score

Figure 4-1. Distribution of pretest scores of test subjects on Likert scale used to measure
attitude toward organic agriculture, test for normality

Knowledge

Knowledge about the National Organic Program was measured before and after

the workshop with a pre- and posttest. Each question was worth between one to four

points depending upon the level of difficulty of the question. Multiplying the test answer

by the level of difficulty resulted in a weighted score indicating not only how much

knowledge was gained but at what cognitive level knowledge was gained (knowledge,









43



*
1.5




w 0.5




o -0.5
z

S -1.0 t . .* *
a.



-2.0 -

-2.5



22 2.4 26 28 30 32 34 36 38 40 42 44 46 48 50

Likert Post-Test Score


Figure 4-2. Distribution of pretest scores of test subjects on Likert scale used to measure
attitude toward organic agriculture, test for normality


4.6



4.4 ---- ---- - ---- -- -



4.2



4.0



S3.8
3. 40........................................................















Pre-Test Post-Test


Figure 4-3. Mean, standard error, and standard deviation of pre- and posttest scores on
Likert scale used to measure attitude about organic agriculture
Likedt scale used to measure attitude about organic agriculture









comprehension, application, and analysis). Weighted pretest scores indicate that there

was no statistically significant difference between the test group and the comparison

group (Table 4-10).

Table 4-10. Results of t-tests for differences between test subjects pretest scores and
comparison group scores on standardized test of knowledge about the
National Organic Standards
Test subjects Comparison group

N 24 26
Mean 73.48 69.46
Standard deviation 13.63 10.15
p 0.24

Table 4-11 shows the increase in test subject scores from the pretest to the

posttest. The mean gain was 12.11 points and is statistically significant at the 0.05 level,

with p < 0.001. The standard deviation decreased from 13.63 on the pretest to 7.86 on

the posttest. Figures 4-4 and 4-5 show that the pre- and posttest scores of the test group

were normally distributed. Figure 4-6 is a box and whisker plot of the gain in attitude

from the pretest to the posttest. Notice that not only did the mean increase significantly,

but the standard deviation, or variance of scores narrowed.

Table 4-11. Paired t-test for differences between pretest and posttest scores on
standardized test used to measure knowledge about the National Organic
Standards
Pretest scores Posttest score

N 24 24
Mean 73.48 85.58
Standard deviation 13.63 7.86
Difference -12.11
Standard deviation difference 9.50
p <0.001






















I 1


2
z

C
a
a0 U

x -1
LU


Figure 4-4.




















C



LU


30 40 50 60 70 80 90 100
Pre-Test Score


Distribution of pretest scores of test subjects on standardized test used to
measure knowledge about the National Organic Standards, test for
normality

2.5






2.0. ......-- ........ ............ ......................... .......... ......
1 .5 ............................................................... .............................. .... .













-1.0







-1.5 07-----0 ----- ---0- ----- -----100------------
-2.5
65 70 75 80 85 90 95 100


Knowledge Post-Test Score


Figure 4-5. Distribution of posttest scores of test subjects on standardized test used to
measure knowledge about the National Organic Standards, test for
normality

















84 ....... .. .. .


74 .. .

72 -.. .. .

70 -.. .. .


Pre-Test Post-Test


Figure 4-6.


* Mean
SE
I +1.96*SE


Mean, standard error and standard deviation of pre- and posttest scores on
standardized test used to measure knowledge about the National Organic
Standards


Confidence

Test participants also had a higher average score on the confidence index than the

comparison group but there it was not significant atp < 0.33. The test group had a mean

weighted score of 59.92 while the comparison group had a mean weighted score of

54.63. The confidence index tasks were weighted according to the degree of difficulty

for Extension faculty (Table 4-12).

Table 4-12. Results of t-tests for differences between test subjects' pretest scores and
comparison group scores on index used to measure confidence in ability to
perform outcome behaviors
Test subjects Comparison group

N 24 26
Mean score 59.92 54.63
Standard deviation 0.20 20.89
p 0.33









The weighted confidence pre- and posttest scores of the test subjects revealed a

statistically significant average gain of 13 points with < 0.001. Only 19 confidence

posttests were completed. Table 4-13 shows the gains from pretest levels to posttest

levels. Figures 4-7 and 4-8 show the normal distribution of the data points for a linear

equation. Figure 4-9 is a box and whisker plot showing the weighted confidence pre- and

posttest scores and variance of the test group.

Table 4-13. Paired t-test for differences between pretest and posttest scores on index
used to measure confidence in ability to perform outcome behaviors
Pretest score Posttest score
N 24 19
Mean score 62.37 75.62
Standard deviation 16.20 14.57
Difference -13.25
Standard deviation difference 13.84
p 0.0006


20 30 40 50 60 70
Pre-Test Confidence Score


Figure 4-7. Distribution of pretest scores of test subjects on index used to measure
confidence in ability to perform outcome behaviors
































0
z




x
w

















Figure 4-8.


z.u



1.5



1.0



0.5



0.0



-0.5



-1.0



-1.5



-2.0


40 50 60 70 80 90 100 110

Confidence Post-Test Score



Distribution of posttest scores of test subjects on index used to measure

confidence in ability to perform outcome behaviors


Mean
S+SE
T +Sn


Pre-Test Post-Test



Figure 4-9. Mean, standard error and standard deviation for pre- and posttest scores of

test subjects on index used to measure confidence in ability to perform

outcome behaviors


------ -- -- - -- -- -- - - -- - - -- - - -


*
------ -- -- - -- -- -- - - -- - -- - - - -





0*
------ -- -- - -- -- -- - - 1 -- - - - - -




S*
0*
------------------------------------------------------- ------------------------------------------------------






*
*
------------------------------------------- ------------------------------------------------------------------

*^

------------------------------- ------------------------------------------------------------------------------




9*


*








*U_ _









Attitude, Knowledge, and Confidence Together

The next step was to run a multiple regression analysis to flesh out the

relationship between three factors (attitude, knowledge, and confidence) and the

outcome, intent. Together these three variables had an R2 = 0.5097 andp < 0.012. Only

confidence was significant atp < 0.001. Table 4-14 shows the results of the regression.

It is interesting to note that the attitude and knowledge scores are essentially the reverse

of each other (beta column in Table 4-14) and may influence each other's scores. The

next multiple regression analysis was run without the Likert attitude scale (R2 = 0.4998

andp < 0.0039; Table 4-15).

Table 4-14. Summary of multiple regression analysis, relationship between intent to
perform outcome behaviors and predictor variables of attitude about organic
agriculture, knowledge of the National Organic Standards and confidence in
ability to perform outcome behaviors
N=19 Beta Std. err. of beta p-value
Intercept 0.830
Posttest Likert score 0.101 0.183 0.589
Posttest knowledge score -0.110 0.183 0.555
Posttest confidence score 0.730 0.185 0.001
Note. R2 = 0.5097; < 0.012; std. error of estimate: 0.134

Table 4-15. Summary of multiple regression analysis, relationship between intent to
perform outcome behaviors and predictor variables of knowledge of the
National Organic Standards and confidence in ability to perform outcome
behaviors
N=19 Beta Std. err. of beta p-value
Intercept 0.463
Posttest knowledge score -0.113 0.179 0.537
Posttest confidence score 0.714 0.179 0.001
Note. R2 = 0. 4998; p < 0. 0039; std. error of estimate: 0.131

A third multiple regression analysis was run, this time without the Likert attitude

scale and without the knowledge test. Again, R2 decreased slightly to 0.487 with a

p value ofp < 0.001 (Table 4-16).









Table 4-16. Summary of regression analysis*, relationship between intent to perform
outcome behaviors and predictor variables of knowledge of the National
Organic Standards and confidence in ability to perform outcome behaviors
N=19 Beta Std. err. of beta p-value
Intercept 0.682
Posttest confidence score 0.698 0.174 0.0009
R2 = 0. 487; p < 0. 00089; std. error of estimate: 0.128

The next step was to run a full correlation matrix with the pre- and posttest

scores. Table 4-17 shows that the attitude held before training is strongly negatively

correlated with the knowledge posttest and with the confidence posttest (Columns 1, 4,

and 6). In Column 2 the slight increase in posttest attitude resulted in a weaker negative

correlation with posttest confidence and knowledge posttest. Columns 3 and 5 show a

high correlation between pretest knowledge and pretest confidence. Column 7 shows

that the relationship between the confidence pre- and posttests increased. This change in

confidence is due to the training and is strongly correlated to the outcome, intent.

Table 4-17. Correlation matrix for predictor variables of pre- and posttest scores on
Likert scale used to measure attitude about organic agriculture, standardized
test of knowledge about the National Organic Standards, and index used to
measure confidence in ability to perform outcome behaviors and outcome
variable, intent to perform behaviors
Likert pre Likert post Knowledge pre Knowledge post
Likert pre 1.000
Likert post 0.894 1.000
Knowledge pre -0.171 -0.201 1.000
Knowledge post -0.173 -0.108 0.748 1.000
Confidence pre 0.134 0.274 0.349 0.339
Confidence post -0.425 -0.146 0.036 0.183
Intent -0.176 -0.028 -0.072 -0.040
Confidence pre Confidence post Intent average
Likert pre -0.176
Likert post -0.028
Knowledge pre -0.072
Knowledge post -0.040
Confidence pre 1.000 0.568
Confidence post 0.634 1.000 0.714
Intent 0.568 0.714 1.000









Determining Variables

The final step in the statistical analysis of the data was to combine the statistically

important demographic variables with the statistically important pre- and posttest

variables. In a Spearman rank order correlation three variables came out as the most

important indicators of intent to apply the outcome. These three variables are confidence

(Spearman's R 0.664, p < 0.003), educational level (Spearman's R 0.594, p < 0.009) and

doctoral major (Spearman's R 0.475,p < 0.047; Table 4-18). These three variables are

the key factors in determining whether county Extension faculty will perform the desired

outcome behaviors after attending the 2-day workshop.

Table 4-18. Spearman rank order correlations for the determining predictor variables
(posttest score on Likert scale, posttest score on standardized test of
knowledge, post-test score on index of confidence, educational level and
doctoral major) for outcome variable of intent to perform behaviors
Predictor variable Valid N Spearman R p-value
Posttest Likert score 18 -0.018 0.945
Posttest knowledge score 18 0.020 0.938
Posttest confidence score 18 0.664 0.003
Educational level 18 0.594 0.009
Ph.D. major 18 0.475 0.047
















CHAPTER 5
DISCUSSION

The purpose of mytudy was to determine any changes in attitude, knowledge, or

confidence in participants who attended the 2-day workshop about the National Organic

Standards. I examined differences in attitude towards organic production before and

after the workshop, assessed any changes in knowledge about organic production, and

especially the rules and regulations of the National Organic Standards, and evaluated any

pretraining and posttraining differences in confidence in educating others about the

National Organic Standards. I was also interested in exploring what demographic

variables played role in determining intention to perform key measurable behaviors.

This chapter presents an interpretation and discussion of the findings of my study.

It also offers theoretical implications of the results.

Research Question A

How effective was the workshop in improving attitudes, increasing knowledge,
and increasing confidence to advise farmers about the National Organic
Standards?

The workshop, What Service Providers Must Know About the Organic Rule and

Regulation, was very successful in increasing the knowledge and confidence of the

participants. There were no statistical gains in attitude although there was a slight real

gain.

The attitudinal pretest scores indicated that the test subjects entered the training

with a positive attitude about organic agriculture (3.91 of 5). This numerical average,









although slightly higher than the mean of the quasi-control comparison group, was not

statistically significant. The posttest score did improve 0.07 points to 3.98 (of 5), but it

was not a statistically significant gain. In other words, it was hard for the scale to

significantly differentiate between someone who liked organic agriculture and someone

who really liked organic agriculture. The lack of a statistical change in attitude could be

due to the high pretest score which showed the participants already had a favorable

inclination towards organic agriculture when they came into the workshop, or it may

have been influenced by the small sample size. In any event, the raw attitudinal score did

improve and would likely improve more significantly if the pretest scores reflected a

lower attitude towards organic production.

The training was statistically effective in increasing knowledge and confidence

levels. The mean gain for knowledge was 12 points (12%) while confidence increased an

average of 13 points (19%). The test subjects who participated in the 2-day workshop

had higher levels of knowledge, confidence, and a more favorable attitude toward organic

production and the National Organic Program at the beginning of the workshop than the

quasi-control group (Cooperative Extension faculty at the National Association of

County Agricultural Agents who also self-selected to attend a seminar about organic

production). It appears that the test subjects who chose to attend the intensive training

were already interested in the organic movement and had some background knowledge.

Nonetheless, after the workshop, large gains appeared in levels of knowledge and

confidence. This implies that we would see even larger gains in participants who did not

have as much knowledge or confidence at the onset of the training. In fact, the results

show that those participants who knew the least coming into the workshop learned more

than participants who had higher pretest levels of knowledge. Participants with a lower







54

pretest score had higher point gains on the posttest. The variance on knowledge posttest

scores decreased significantly. It stands to reason that all future test subjects will have

the opportunity to increase their knowledge base.

The questions on the pre- and posttest for knowledge were developed to address

four cognitive levels of learning. The lowest level, knowledge, reflects retention of fact.

Comprehension, the next lowest level, begins to address basic understanding of the

subject. This is followed by application, where respondents are able to demonstrate that

they are able to use what they have learned. The highest level tested was analysis.

Analysis requires an understanding of the content and structure of the learned material.

In analyzing the content of the pre- and posttests, I looked at the incorrectly answered

questions on the pretest which were subsequently correct on the posttest. Knowledge-

level questions, or basic retention of the subject matter constituted 35% of the gain. This

was followed by a 24% gain in the highest tested cognitive level, analysis. Application-

level questions were in third place with 23% and comprehension at 18%. These results

are interesting because the main gains were in the lowest cognitive level, knowledge, or

retention of basic facts about the National Organic Standards and, in analysis, the highest

tested level of understanding of the content of the program. This gain in basic knowledge

level is not surprising because the National Organic Standards are complex and very

detailed. Many participants who only had a cursory understanding of the Standards could

learn many small details that would enable them to do better on the posttest. For

example, many participants who incorrectly answered the pretest question, "A farmer

must have years of records for his/her land for organic certification" were able to

respond correctly on the posttest. What is more interesting, though, is that the next









highest gain in cognitive levels was found at the analysis level. This implies that

participants truly learned concepts taught in the workshop.

A danger with conducting a posttest immediately following a workshop is that

retention levels are higher than they would be a few weeks later. This may be reflected

in the high knowledge-level scores, but correct answers at the analysis level are more

indicative because they show true learning and comprehension of the subject matter.

Participants were able to digest what they had learned and reason out the correct answer

about the National Organic Standards. Following is an example of a true/false analysis

question that was frequently incorrect on the pretest but correct on the posttest. "A

storage box or bin originally used for conventional crops can be reused for organic crops

as long as both crops are not stored together in it at the same time."

Another interesting gain in understanding of the workshop content was illustrated

with a comprehension-level question dealing with livestock. The livestock module was

not included in the workshop because of time constraints, and the question was thrown

out of the statistical analysis. However, most participants who missed the question on

the pretest answered it correctly on the posttest! This signals that although the question

was registered at the comprehension level, it reinforces the gains in analysis-type

learning because participants were able to use what they had learned and reason out a

correct answer. It appears that participants obtained a general sense of understanding of

the NOP Standards and could make accurate assumptions even in areas that they had not

yet learned about.

Twenty-five percent of the test subjects had a 10-point or more gain from pretest

levels to posttest (50 points total possible). Broken down by cognitive level, they gained

the most pretest to posttest scores in knowledge (31%), application (26%), followed by









analysis (24%), and comprehension (19%). Application-level questions were reflected

in the measure of confidence and intent because they dealt with "how-to" questions or

skills. For example, participants learned alternative ways to fertilize organic croplands

and learned organic disease control methods, which are frequently different from the

standard conventional methods. DeJoy (1996) stresses that education must first start with

learning of facts, or knowledge, but then must focus on skill development. This will

enhance confidence, or self-efficacy, and result in the desired behaviors.

Participants who had the highest pretest scores (40 points and above of 50)

showed the least gains in application-level questions. They showed the highest gains in

knowledge and comprehension. This suggests that they already understood the skills

necessary for organic production, but did not know all the rules and regulations imposed

by the National Organic Standards.

By targeting these four levels of cognitive learning, all participants, no matter

what level of understanding they came with, were able to benefit from the workshop.

This is an important result which can have a significant impact in increasing knowledge

and understanding of the National Organic Standards and improving skills related to

organic agricultural practices.

Research Question B

How do attitudes, knowledge, and confidence affect the intention of agricultural
service providers to perform key behaviors?

According to the theory of reasoned action and self-efficacy theory, knowledge

forms the skeletal framework for attitude. Attitude, in conjunction with confidence,

influences intention, which directly leads to the desired behavior. However confidence

(self-efficacy), is the most consistent (Gist & Mitchell, 1992) and best single predictor of







57

intention (Luszczynska & Schwarzer, 2003). Worsley (2002) said that knowledge alone

is not sufficient in determining behavior. He listed attitudes, skills, confidence,

motivation/motivators, and the outside environment as important factors in determining

intention and desired behaviors.

I ran multiple regressions to try to understand the nature of the relationship of the

factors, knowledge, attitude, and confidence in affecting the outcome, intention. A

multiple regression analysis showed that these three factors influenced 51% of the

outcome variable, intention.

The scores of knowledge and attitude were essentially the reverse of each other,

and so I tried to discover what influence they might have on each other. I ran a second

multiple regression analysis without attitude. Fifty percent of the outcome was

influenced by knowledge and confidence. Forty-nine percent of the outcome was

influenced by confidence alone. This implies that knowledge and attitude were each

responsible for influencing only 1% of the outcome. Confidence had a Spearmen's R of

0.664 when run against intent and was significant atp < 0.003. This result is in line with

other studies done by Luszczynska and Schwarzer (2003), Lent et al. (1986), and

Goddard et al. (2004), who found self-efficacy to be the strongest indicator of intention.

The results of the regression analyses are very interesting because they seem to

imply that although someone's attitude could be negative toward organic production,

they could still intend to advise farmers about the National Organic Standards as long as

they feel confident in their ability to do so. Before conducting the study, I thought that

attitude would play the key role in determining the outcome behavior. However, the

results show that the main determinant for the outcome variable is confidence. So what









variables account for the remaining 49% of the outcome? Do demographic variables

have a large influence?

Research Question C

Does a relationship exist between the intent to perform the key behaviors and any
of the measured demographic variables?

These results were surprising. Only educational level and doctoral major showed

any significance in the Spearman Rank Order Correlation. This may be largely due to the

small population size.

There was a negative correlation between undergraduate year and intent.

Likewise there was a negative correlation between age and intent. This implies that the

older the participants were, or the longer ago they finished their bachelor's degree, the

less likely they were to change their ways and try a new behavior. This brings to mind

the cliche, "You can't teach an old dog new tricks." As we become older we become

more set in our ways and without many years left ahead professionally, many people

don't see the need to take on new challenges or change their way of doing things.

On the contrary, the outcome variable and the year participants received a

graduate degree had a positive correlation. So did the level of education. This implies

that the more educated the test subjects became, the more likely they were to perform the

outcome behaviors. "Education encourages a different set of beliefs and values (or

interests) among its participants" (Worsley, 2002, p. S583). Advanced education makes

participants more capable of performing new behaviors or taking on new tasks.

It was surprising to me that so few demographic variables played a role in

determining outcome. For instance, why did not age play a more significant role

especially if I can determine that the relationship with the outcome variable was









negative? The p-value for ethnicity was relatively low at 0.057. This would be

interesting to examine in a larger sample size. The test subject population was

predominately white, with only three Hispanics and three blacks, so the importance of

ethnicity may change with a larger sample size.

Another demographic variable, "attended a previous organic training," had no

significant effect on the intent to perform target behaviors. Is this because previous

training were inadequate? Were they dull and uninteresting and little was learned? If

previous training were inadequate, then perhaps attendees did not gain enough

confidence from them to significantly impact the outcome variable.

Where test participants attended school was originally significant in the first

paring down of demographic variables (t-tests and ANOVA). The schools were coded

according to location in USDA regions. The idea was to see if regional schools had

different influences on attitudes or knowledge about organic production. The reason for

this is that the western region is by far and away much more advanced in promoting

organic agriculture that the rest of the nation. The western region has more organic

acreage, more organic producers and more governmental (and nongovernmental) funding

spent in organic research and education. The southern region has some of the lowest

levels of organic acreage, producers, and governmental spending. I was trying to

understand if there was a deeper underlying cause for the lack of research spending or

promotion of organic agriculture by Cooperative Extension in the South. Does some core

difference, perhaps in attitude, extend all the way back to university teachings? I did not

find any significant answers to these questions.

The number and types of courses also had no significant impact on the outcome

variable. I suspected that typical "anti-organic" courses such as pesticide technology or







60

genetic engineering may negatively affect attitude. Likewise, "pro-organic" courses such

as integrated pest management or agricultural ecology might positively affect attitude,

but these demographic variables showed no significant impact on the outcome.

One section of the demographic questionnaire measured certain behaviors in

which participants were currently engaged. I asked them if they had a website,

newsletter, or contributed to a radio or TV program. At first analysis, having a

newsletter appeared to be significant. This may appear again in a larger sample. I

suspect that perhaps having a newsletter, in which one writes articles of interest to his

readership and of which the writer feels confident in discussing a topic, may be linked to

some of the outcome variables. The outcome variables, or intent to perform certain

behaviors, address issues such as advising consumers and organic growers, and even

including information about organic production in mass media.

Hypotheses

I predict a positive relationship between increased knowledge about the National
Organic Program and the attitude of agricultural service providers.

Knowledge is power. However, knowledge is more than just a catalogue of facts,

according to Worsley (2002). It is a system of beliefs, or framework, upon which we

base our beliefs and facts. According to the theory of reasoned action by Ajzen and

Fishbein (1980), knowledge determines attitude. Attitude, with the influence of

important others, determines intent, which is reflective of future behavior.

I did not see the causal relationship described in the theory of reasoned action in

this study. In fact, two things stand out. First, I did not find a positive relationship

between an increase in knowledge and an improvement in attitude. In fact, the

correlation matrix shows a negative relationship between attitude and knowledge. As







61

knowledge increased from the pretest scores to the posttest scores, attitude decreased or

didn't improve as much. However the strength of the negative relationship weakens from

pretest to posttest. As attitude improves over the course of the workshop, change in

knowledge decreases, or doesn't improve as much.

Secondly, participants were asked to rate how their peers, supervisors,

administrators and county leaders felt about organic agriculture. The mean rating was

indifferent/positive. This does not support the theory of reasoned action. The theory

attributes greater significance to the opinion of others than I see in the results of this

study. Even though these significant others of the test population have only an

indifferent or slightly positive attitude towards organic agriculture, the test subjects still

responded favorably in intent to perform the job related behaviors. This may correspond

with the high percentage of minorities found within the training. The agricultural service

field is dominated by Caucasian males, yet the workshop had a high percentage of

women and racial minorities (29% and 25%, respectively). Perhaps minorities who self-

select to work in a field dominated by one demographic type are more able to resist

normative pressures, including the opinions of others. In a sample with different

demographic characteristics, attitude and subjective norms may be more positively

attributed to influencing intention.

In this case, attitude seemed to more strongly influence the intent to perform the

behaviors than the influence of important others. But why was attitude negatively

correlated with knowledge? I expected that participants would begin the training with a

positive attitude towards organic production. After all, they chose to attend the

workshop. I saw a small but statistically insignificant gain in attitude after the workshop

was completed, but the gains in knowledge were so large that I expected to see more gain







62

in attitude. If knowledge truly determines attitude, why weren't the gains proportional?

I believe there must be some other determinant of attitude besides knowledge alone. And

in my study I did not see the importance of the role of significant others to influence

outcome.

I predict a positive relationship between increased knowledge about the National
Organic Standards and the confidence of agricultural service providers to advise
farmers about organic production and conduct organic training.

A common finding in nutrition studies (Worsley, 2002) is that increased

knowledge corresponds with an increase in the desired behavior. But knowledge does

not seem to directly lead into performance of the desired behavior. Instead, two other

variables played a key role in the link between knowledge and outcome: interest and

confidence.

Britten (as cited in Worsley, 2002) found that knowledge is a predictor of

confidence. And confidence, or self-efficacy, is directly linked to performance.

Heightened self-efficacy yields higher behavioral achievements, such as improved skill

development, and motivation to continue the behavior (Bandura, 1997, as cited in

Goddard et al., 2004; Lent et al., 1986; Luszczynska & Schwarzer, 2003; Schunk, 1991).

According to Bandura (1977), individuals with higher levels of self-efficacy are seen to

work harder and have greater persistence in the face of challenges than those who doubt

their capabilities.

However, self-efficacy alone cannot produce competent behavioral outcomes if

the skills needed to perform the behavior are lacking (Schunk, 1991). Participants at the

training gained knowledge across four cognitive levels of learning, including basic

retention of facts, comprehension, application, and analysis. Application and analysis

focus on skill development. According to Schunk (1991), students obtain information









about how well they are performing as they work on tasks. When they recognize that

they are performing well and understanding the material, their self-efficacy and

motivation are improved. Therefore, it follows that as we saw large gains in knowledge

levels, we saw a proportionate gain in confidence levels.

I predict a positive relationship between the confidence of agricultural service
providers and their intention to advise farmers about organic production and
conduct organic training.

The results showed a very strong correlation between confidence and the outcome

variable, intent. There was also a strong correlation between the pretest confidence score

and intent, however the posttest score had a much stronger correlation.

It should not surprise anyone that what people believe they can do affects what

their actual actions are. After all, without confidence, where are we? Success raises self-

efficacy (Bandura, 1986). Initially, self-efficacy is a function of abilities, attitudes, and

prior experience (Schunk, 1991). But as students learn and achieve, self-efficacy grows.

This begins a cycle of motivation towards further successes. More of the desired

behavior is initiated, success breeds confidence, confidence leads to further persistence

and more of the desired behavior.

I feel confident projecting that participants who attended the workshop, What

Service Providers Must Know About the Organic Rule and Regulation, will perform the

behaviors that they stated they would perform on their intention indices. Research has

shown an extremely high correlation between self-efficacy and performance of the

desired behaviors. This workshop has equipped participants with the knowledge, skills,

and confidence to successfully advise farmers about the National Organic Standards. This

knowledge and confidence was earned through real-life, hands-on problem solving with

peer groups. This was a transformative learning process targeting experienced adult







64

professionals. Test results have shown a statistically significant gain in self-efficacy

(p<0.003). This is highly predictive of performance of the outcome variable.















CHAPTER 6
CONCLUSION

The chapter closes the study with a discussion of future research directions and

limitations of this study. The workshop, What Service Providers Must Know About the

Organic Rule and Regulation, was successful in augmenting participants' knowledge

about the National Organic Standards and organic production practices. It was not

successful in raising attitudinal levels. However it was highly successful in raising the

confidence of agricultural service providers. This is extremely important because

confidence has been shown in both the literature and the results of my study to be the

single most important factor in predicting a successful completion of the desired

behaviors.

The results of my study show that participants increased their knowledge and

understanding of the National Organic Standards and organic agriculture across all

cognitive learning levels. No matter with how much or how little knowledge the

participants entered the training, they learned something. I believe this is due largely to

the structure of the training and the transformative and experiential learning process.

Many of these participants had previous organic training, yet this was not found to

correlate significantly with the outcome variable, intent to perform the desired behaviors.

I believe that this was due to the nature of the previous training.

Adult training must utilize the wealth of knowledge and experience that the

professionals bring with them. Adults want to tackle hands-on, real-world situations









using their previous experiences and wisdom. Peer groups have been found to be a

highly successful means of adult learning. Peers are nonthreatening because they have

similar experiences and levels of knowledge. Watching peers model the desired behavior

is very motivating and helps build self-efficacy of both the performer and the observer.

Adults also need discussion and time to reflect upon their decisions and actions. Small

groups work with peers, and large group discussions following the exercises give

participants that opportunity for reflection.

This workshop curriculum was designed to address four cognitive levels of

learning. It focused not only on conveying basic facts, but on teaching skills and critical

thinking of the issues involved. According to the literature, this is essential for success.

Participants must pass a threshold of basic knowledge, then learn the skills necessary to

successfully perform the desired behaviors. All of these methods were used in the

training. The training was intensive and fast-paced, yet participants were stimulated and

challenged.

Of course, there is something missing in the discussion about gaining knowledge

and skills to elicit the performance of the desired outcome. That missing factor is

confidence. Research has shown that basic knowledge and skills are not sufficient by

themselves to elicit the desired behavioral response, but confidence can elicit that

response. Confidence is gained through problem solving and reflection. As adults learn

new topics and are challenged with legitimate scenarios in small group settings, their

confidence increases. Bandura (as cited in Gist & Mitchell, 1992) said it is not so much a

success that increases confidence, but the process of learning and reflection. Confidence

can be gained by realizing that one can do better the next time, by watching peers

succeed, and by learning skills to enable success.









The tests were also useful in showing where the training lacked clarity. Two

questions were largely missed on both the pretest and posttest, indicating that the training

needed to be refined in those areas.

Future training for adults and especially Extension could emulate the successful

teaching strategies found in this workshop. An interesting study would be to compare the

gains in knowledge, attitude, and confidence of participants who attend this training with

participants who attend other organic training of a more passive design. I suspect that

the test subjects who participate in the experiential learning process will retain what they

have learned longer and be more skilled and motivated to perform the desired behaviors

in the workplace.

There were some demographic variables within my study that may become more

significant with a larger sample size. First, gender did not appear significant in my study;

but I suspect that it would within a larger sample. There was a greater proportion of

women at this organic workshop (29%) than found within Extension and within the

agricultural service field. There is also a high percentage of women farmers engaged in

organic production. Little research has been done on gender issues within organic

agriculture and would be an interesting tract for future study. I suspect that gender

makes a significant difference in the organic agricultural movement. Lent et al. (1986)

found that self-efficacy theory may influence career decisions and achievements,

especially for that of women. They claim that efficacy is related to perceived career

options, persistence, and success in their fields.

A second demographic variable worth future study is that of ethnicity. This

sample was too small to make accurate assumptions; but like the proportions of gender,

there was a higher percentage of minorities represented at the training than one finds









within the agricultural service field. Although the quasi-control group was 96%

Caucasian, the workshop participants were 13% Hispanic and 13% black. The p-value

for ethnicity in this study was 0.057. I suspect that this will become more significant

with a larger sample size.

A third interesting demographic aspect for future studies would be to look at the

negative correlation of age and completion of college contrasted to the positive

correlation of completion of a master's degree and doctoral major. Although a doctoral

major turned out to be significant in this study, there were only seven with doctorates in

the sample. This was a large percentage (33%) of test subjects, and this should be looked

at within the context of a larger sample size.

Besides the limitations of sample size and the time constraints, which did not

allow for a follow-up on behavior, future studies might consider having the quasi-control

group fill out the intent index. This gives a mean for comparing the intent of the test

subjects with the intent (or actions) of the control group. I was unable to make some

generalizations about the test subjects and the quasi-control due to the lack of intention

data by the quasi-control group.

Finally, I suggest a deeper look at regional discrepancies. This study did not get

at the root cause of why the South allocates less money to organic research and

development than other regions. I suspected that educational and professional attitudes

might be significant, but this was not corroborated in the current study. Future research

might take a more in-depth look at the attitude of significant others. The attitudes of

coworkers, supervisors, and county officials were found to be lukewarm both in the test

subjects and the quasi-control (54% of the quasi-control worked in the Southern region).

This may have significance and merits further research. Also, comparing attitudes of









significant others in the South with attitudes found in other regions might be

illuminating.

My study was limited by its small sample size and also time constraints of the

researcher. Future research would be strengthened with a larger sample size and a

follow-up 6 months after the training, to determine what behaviors have truly been

realized. Six more training will be conducted in Florida, Kentucky, and the U.S. Virgin

Islands over the next year and a half. The compiled data from all of these workshops

would strengthen and expand the findings of the current study.

Finally, I can confidently say that there is sufficient evidence to assume that the

six future training to be held in Florida, Kentucky, and the U.S. Virgin Islands over the

next year and a half will be as equally successful as the first, and that the grant project

will indeed be on its way to a successful completion of its goal, to increase the acreage

and number of certified organic producers in Florida, Kentucky, and the U.S. Virgin

Islands. This workshop increased knowledge and confidence of participants, key factors

in motivating behavior.















APPENDIX A
WORKSHOP AGENDA









The National Organic Standards
What Service Providers Must Know About the Organic Rule and Regulation
July 26-27, 2004
Ft Pierce REC

Monday, July 26, 2004

9:00 a.m. Welcome
Pre-tests
Module 1: Overview of Organic Production

10:30 a.m. Break

10:45 a.m. Module 2: The Organic Production System Plan (The Farm Plan)

12:00 p.m. Lunch

1:00 p.m. Module 3: Planting Stock Seeds and Transplants

1:45 p.m. Break

2:00 p.m. Module 4: Water Quality, Soil Quality and Crop Fertility

5:00 p.m. Adjourn

Tuesday July 27, 2004

8:30 a.m. Opening

9:00 a.m. Module 5: Crop Management

10:15 a.m. Break

10:30 a.m. Module 6: Organic Integrity

12:00 p.m. Lunch

1:00 p.m. Module 7: Handling and Processing

2:00 p.m. Break

2:15 p.m. Post-tests
Closure

4:00 p.m. Adjourn















APPENDIX B
LIKERT SCALE FOR ATTITUDE










Identification Number


Please indicate how much you agree or disagree with each statement presented below. Check the
box that best describes your feeling.

1 2 3 4 5
Strongly Indifferent Strongly
Disagree Agree
Learning about the organic rules and regulations
is a big waste of time for Extension agents.
Organic food is the only safe food.
Serving organic farmers is an important thing for
Extension to do.

If you ask me, the organic market is the health
food nut market.
We need to pay attention to our mainstream
clientele, not waste time with organic hobby
farmers.
Organic farmers are the only farmers who really
care about what happens to the Earth.

The government should really give organic
farming a big push.
An Extension agent can't afford to waste time
working with small organic farmers.

People who buy organic food are gullible.
Organic farmers are snobs they think they
know it all.
Extension should do more to help organic
farmers.
Organic agriculture protects natural resources.

Learning about organic farming is a waste of
time.

Extension has ignored organic farmers way too
much in the past.
People buy organic food to be trendy.

Buying organic helps save the fabric of local
rural communities.
Organic farming is not real agriculture.
Organic food is a lot better for you.
Organic farmers are just trying to make money
from gullible consumers.
Extension should promote organic farming.















APPENDIX C
STANDARDIZED PRE- AND POSTTEST










Identification Number

Please answer the following questions.

The transition period from conventional to organic agriculture is years.

Which three choices below are sections of the Organic System Plan used to apply for organic certifi-
cation? Check three.

Soil and crop fertility management
Crop management
Biological controls
Maintenance of organic integrity
Planting schedules

Name three ways organic farmers can improve soil fertility and health.

1.

2.

3.

A product such as Milorganite, a sewage sludge product, is acceptable for certified organic
production.
True False

Name two alternatives to chemical fertilizer that the certified organic producer can use.

1.

2.

Processors must ensure that organic and conventional products are shipped in separate trucks
to prevent commingling.
True False

Additional water tests other than those required under the Safe Water Drinking Act are NOT
required of water used for washing and processing organic products.
True False

Plastic may NOT be used for weed control because it is not natural.
True False

Which of the following soil amendments have restrictions on their use. Check all that apply.

Raw manure Chilean nitrate
Animal by-products Sewage sludge
Ash

The USDA NOP is the primary certifying agency for organic farmers.
True False










List three disease control methods that organic farmers can use.

1.

2.

3-

A farmer must have years of records for his/her land for organic certification

Check the one statement below that best describes the core principal underlying pest and disease
management in organic production.

Use of synthetic pesticides is NOT allowed in an organic system.
Organic systems rely on management practices that restore, maintain and
enhance ecological harmony.
Organic systems rely on resistant plant varieties to minimize pest and disease
outbreaks.

Materials labeled "R" on the OMRI (Organic Materials Review Institute) Brand Name Products and
Generic Products Lists mean that they are (check one):
reliable reusable
regulated recommended

Crop rotations are encouraged but NOT required in order to be certified organic.
True False

What information must be included in the field history for the Organic System Plan? Check all that
apply.
Name of the person with legal title to the land
Rate and date of application of inputs
Pest management strategy
Compost and/or manure used on fields
S Water quality test results for irrigation water

When crop rotation, soil and crop nutrient management practices, sanitation, and cultural practices
are NOT sufficient to prevent or control crop pests, weeds and diseases, certain synthetic substances
are allowed for use in organic crop production.
True False

Name two problems that can occur when an organic field is located adjacent to a conventional field.

1.

2.

A storage box or bin originally used for conventional crops can be reused for organic crops as long as
both crops are not stored together in it at the same time.
True False










List two methods for organic weed management.

1.

2.

A grower has a large scale production system with both organic and conventionally produced veg-
etables. What are the National Organic Program (NOP) requirements for sharing equipment? Check
all that apply.

Prevent commingling of organic and non-organic products
Sterilize equipment between uses
Establish clean-out protocols and maintain clean-out logs
Protect organic products from contact with prohibited substances
Use of the same equipment is prohibited for conventional and organic
production.

An organic producer must select and implement tillage and cultivation practices that minimize soil
erosion.


True


False


Name three ways an organic farmer can physically protect his/her field from
conventional field located next door.


contamination from a


1.

2

3.

Farmers can vaccinate organic livestock for endemic diseases.
True False

What is the purpose of the Organic System Plan. Check all that apply.

Need for certification
Record keeping
Tool that gives the strategy for farm management
Proves legal title to the land
Proves to the certifying agency that the farm is organic

Producers who market less than $5,000 of organic products annually are NOT required to apply for
organic certification, but ARE required to comply with the organic production and handling require-
ments of the National Standard.
True False


When may you use non-organic seed for certified organic production?








78

Compost is the only type of fertilizer that an organic farmer can apply to the soil.
True False

Which of the following is the most reliable source for identifying approved substances under the
National Organic Program? Check one.
The National List
ATTRA (Appropriate Technology Transfer for Rural Areas)
OMRI (Organic Materials Resource Institute)
Certifying agencies

The certifying agency is allowed to help the farmer develop his/her Organic System Plan.
True False

An organic potato farmer is allowed to apply raw manure to the field 90 days before harvest.
True False

If a product meets the USDA National Organic Standard it can be sold as an organic product any-
where in the world.
True False















APPENDIX D
INDEX OF CONFIDENCE











Identification Number


Please indicate your level of confidence in conducting each of the following activities on a scale of
1 to 5 where 1 means Not at all Confident and 5 means Very Confident. Check the box that best
describes how confident you are.



1 2 3 4 5
Not at all Not Very Somewhat Fairly Very
Confident Confident Confident Confident Confident

Provide organic advice and options to
homeowners.

Answer questions from homeowners and
consumers about organic products.

Highlight organic farms as a source of
farm fresh food for consumers.

Seek out organic producers in your county.

Include organic farm tours on field days.

Include organic techniques in
demonstrations.

Make field visits to organic farms for
troubleshooting.

Educate yourself about organic production
(i.e., attend other training, seek out useful
sources of information. etc.).

Include organic farming as an alternative
for farmers who call you for advice.

Respond to organic producers' questions
about production practices and the
National Organic Program

Add organic producers to your advisory
council.

Hold a training workshop about organic
practices and standards.

Advise producers about where to get
organic supplies.

Include information about organic
production and standards in media such
as a website, newsletter, or radio or TV
show.















APPENDIX E
INDEX OF INTENTION











Identification Number


Please indicate your level of confidence in conducting each of the following activities on a scale of
1 to 5 where 1 means Not at all Confident and 5 means Very Confident. Check the box that best
describes how confident you are.



1 2 3 4 5
Not at all Not Very Somewhat Fairly Very
Confident Confident Confident Confident Confident

Provide organic advice and options to
homeowners.

Answer questions from homeowners and
consumers about organic products.

Highlight organic farms as a source of
farm fresh food for consumers.

Seek out organic producers in your county.

Include organic farm tours on field days.

Include organic techniques in
demonstrations.

Make field visits to organic farms for
troubleshooting.

Educate yourself about organic production
(i.e., attend other training, seek out useful
sources of information, etc.).

Include organic farming as an alternative
for farmers who call you for advice.

Respond to organic producers' questions
about production practices and the
National Organic Program

Add organic producers to your advisory
council.

Hold a training workshop about organic
practices and standards.

Advise producers about where to get
organic supplies.

Include information about organic
production and standards in media such
as a website, newsletter, or radio or TV
show.















APPENDIX F
DEMOGRAPHIC QUESTIONNAIRE











Please answer the following questions.

What is your age? ____ years What is your sex? Female Male

What is your ethnicity? White Black Hispanic Other

What is the state where you work?

What is your job title?

How long have you been with your current employer? years

What is your previous career history? What kinds of jobs have you held before?


Have you had any training about organic production or the National Organic Program?
Yes No

If yes, please describe briefly:


Please list the university, the year you
and graduate degrees.


graduated, and your major and minor for your undergraduate


Do you have a pesticide applicator's license?

Do you have a website?

Do you contribute to a newsletter?

Do you contribute to a radio or TV program


Yes

Yes

Yes

Yes


No

No

No

No


UniversIty Year Major Minor
Graduated


Under-
graduate
Degree

Graduate
Degree -
Master

Graduate
Degree -
Ph.D.












Subject Matter Areas How many specific courses have you How many courses have you taken where
taken in the subject matter areas listed to these were major topics, but not the total
the eft? subject matter of the course?

Soil ecology

General ecology

Pesticide technology

'ritegrated pest
management

Organic proauclion

Agricultural production

Genetic engineering

AirLcnalural ecology


How do you think the following people would feel about your working with organic producers? Please
check the box that best describes how you think each of these three groups would feel.

Group Very Negative Negative Indifferent Positive Very Positive

Your peers

Your Supervisors

Your Administrators


What is the attitude of leaders in
your county towards organic
agriculture?


How have your experiences with
organic growers been?




How often do you advise organic
growers?


What percentage of the farmers yoi
work with are organic producers or
express an interest in organic produ
tion?


Very Negative Negative Indifferent Positive Very Positive




Very Negative Negative Indifferent Positive Very Positive





Never Rarely Occasionally Commonly Often


<10% 11-25% 26-50% 51-75% >75%


How do you define organic agriculture?


U

c-

















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BIOGRAPHICAL SKETCH

Kendall Louise Sanderson was born on August 20, 1972 in Winter Park, Florida.

She is the second of four children born to Catherine H. and Carlton B. Sanderson. She

attended elementary through high school in Satellite Beach, Florida. In high school,

Kendall's parents began fostering her love of travel and introduced her to exchange

programs in France and Colombia. In 1994, she completed her bachelor's degree at

Florida State University, majoring in French with a minor in international relations.

After college, Kendall traveled extensively through Europe and the United States and

lived in Puerto Rico, Miami, Florida, and California. She taught French and ESOL at a

middle school in Redlands, Florida. In the spring of 1998, Kendall was accepted to the

Peace Corps and sent to Kenya, fulfilling a life-long dream of living in Africa. Kendall

was a Water and Sanitation Volunteer in a rural village. She learned to speak Kiswahili

and some Kikuyu, the local tribal language, while working with local schools, women's

groups and farmers. After two years in Kenya, Kendall spent some time traveling

throughout Eastern, Central, and Southern Africa. Impacted by the work she did in

Africa, Kendall decided to continue her studies of the environment, sustainable

agriculture, and sustainable development through the School of Natural Resources and

Environment at the University of Florida. Currently, Kendall lives in St. Petersburg,

Florida.