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Factors Influencing the Adoption of Best Management Practices by Row Crop Farmers in the Suwannee Valley of North Centra...

Permanent Link: http://ufdc.ufl.edu/UFE0042936/00001

Material Information

Title: Factors Influencing the Adoption of Best Management Practices by Row Crop Farmers in the Suwannee Valley of North Central Florida
Physical Description: 1 online resource (105 p.)
Language: english
Creator: BRITTON,ALLISON H
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2011

Subjects

Subjects / Keywords: ADOPTION -- AGRICULTURE -- BEST -- ROW -- SUWANNEE
Agricultural Education and Communication -- Dissertations, Academic -- UF
Genre: Agricultural Education and Communication thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Best management practices (BMPs) were developed because of the association between agricultural activities and the contamination of watersheds with nutrients such as phosphorus and nitrogen. BMPs are designed to reduce the amount of nutrients entering water systems. In the Suwannee Valley of North Central Florida, BMPs have been implemented to decrease nitrogen concentrations in rivers and springs associated with the Lower Suwannee River Basin. The Suwannee Valley is largely agricultural and dominated by row crops. Some farmers in the Suwannee Valley have chosen to implement BMPs but adoption has not been widespread. The purpose of this research was to determine factors which influence the implementation of BMPs by row crop farmers in the Suwannee Valley of North Central Florida. A quantitative study was used to gather row crop farmers? opinions on factors which influenced the adoption decision process. A questionnaire was developed by the researcher and examined by a panel of experts before being distributed to row crop growers in the Suwannee Valley. The results of this study found that interaction with other farmers, family and extension agents is related to the decision to adopt BMPs. Farmers also believe that greater incentives will lead to increased adoption rates among agricultural producers. A key finding of this study was the relationship between interaction with extension personnel and a farmer?s willingness to accept risk regarding their farming operation. As a result, extension should facilitate gatherings for farmers and their families as well as extension personnel in order to increase adoption rates among row crop growers in the Suwanee Valley.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by ALLISON H BRITTON.
Thesis: Thesis (M.S.)--University of Florida, 2011.
Local: Adviser: Gifford, Gregory Tim.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2011
System ID: UFE0042936:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042936/00001

Material Information

Title: Factors Influencing the Adoption of Best Management Practices by Row Crop Farmers in the Suwannee Valley of North Central Florida
Physical Description: 1 online resource (105 p.)
Language: english
Creator: BRITTON,ALLISON H
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2011

Subjects

Subjects / Keywords: ADOPTION -- AGRICULTURE -- BEST -- ROW -- SUWANNEE
Agricultural Education and Communication -- Dissertations, Academic -- UF
Genre: Agricultural Education and Communication thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Best management practices (BMPs) were developed because of the association between agricultural activities and the contamination of watersheds with nutrients such as phosphorus and nitrogen. BMPs are designed to reduce the amount of nutrients entering water systems. In the Suwannee Valley of North Central Florida, BMPs have been implemented to decrease nitrogen concentrations in rivers and springs associated with the Lower Suwannee River Basin. The Suwannee Valley is largely agricultural and dominated by row crops. Some farmers in the Suwannee Valley have chosen to implement BMPs but adoption has not been widespread. The purpose of this research was to determine factors which influence the implementation of BMPs by row crop farmers in the Suwannee Valley of North Central Florida. A quantitative study was used to gather row crop farmers? opinions on factors which influenced the adoption decision process. A questionnaire was developed by the researcher and examined by a panel of experts before being distributed to row crop growers in the Suwannee Valley. The results of this study found that interaction with other farmers, family and extension agents is related to the decision to adopt BMPs. Farmers also believe that greater incentives will lead to increased adoption rates among agricultural producers. A key finding of this study was the relationship between interaction with extension personnel and a farmer?s willingness to accept risk regarding their farming operation. As a result, extension should facilitate gatherings for farmers and their families as well as extension personnel in order to increase adoption rates among row crop growers in the Suwanee Valley.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by ALLISON H BRITTON.
Thesis: Thesis (M.S.)--University of Florida, 2011.
Local: Adviser: Gifford, Gregory Tim.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2011
System ID: UFE0042936:00001


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FACTORS INFLUENCING THE ADOPTION OF BEST MANAGEMENT PRACTICES BY ROW CROP FARMERS IN THE SUWANNEE VALLEY OF NORTH CENTRAL FLORIDA By ALLISON HOPE BRITTON 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 2011 1

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2011 Allison Hope Britton 2

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To Brandon, who always knew I could do it 3

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ACKNOWLEDGMENTS I would like to thank the many individuals who contributed to my success as a graduate student at the University of Florida. My family, friends and colleagues have all played an instrumental role during this time in my life. Without all of you, I would not be where I am today. First I would like to thank Dr. Greg Giffo rd who was my advi sor, committee chair and ultimately, friend. Thank you for answe ring my hundreds of questions and allowing me to panic on occasion. Without you allowing me to work through my own difficulties and problems, I would never have been able to complete this research project. I would also like to thank Dr. Paul Monaghan and Dr. Eric Simonne who served as members of my committee. Dr. Monaghan pr ovided me with the resources needed to contact members of the Suwann ee River Partnership, as well as explain the important role BMPs play in agriculture. Dr. Simonne helped me to understand the development of best management practices as well as the rules and regulations regarding their use. Their input contributed great ly to this research. My parents, grandparents and little brother have supported me every step of the way. At a young age, my parents, David and Susan Britton, instilled upon my brother and me the importance of education. My par ents have always supported my desire to continue my education to the highest level. I am so grateful not onl y for their support of my school work but also for planning my dr eam wedding. Without you, I would not be the social scientist I am today. My little brother Alex Britton has serv ed as motivation to finish school already and get a real job. I have used that as inspiration to have a successful finish. My grandparents, Henry and Julia Britton and Stan and Sue Stafford have provided encouraging phone ca lls and letters that have gotten me through the past 4

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two years. Somehow, they always knew when I needed a little encouragement to get through the day and for that, I thank them. During my masters career, I was fortunat e to marry into an amazing family who supported me as if I was one of their own. Eric and Tamra Wager have been the most supportive in-laws I could ever ask for. Thank you for your words of encouragement and support during this journey. I could not have completed this research project or the last two years of school without my Florida Friends Andrea Andrews, Adri enne Gentry and Melissa Mazurkewicz. I feel so grateful to have completed this jour ney all of you. Our tears, laughter, and Girls Nights hav e provided a much needed break from thesis writing. I have been lucky to have four best friends who I cannot imagine not having in my life Sasha Kaohi, Jennifer Spivey, Lauren White and Mandy Whitley. Without these four ladies, I would never have made it this far. They have provided constant support through phone calls and letters. I love each of you dearly and feel incredibly lucky to have you in my life. Lastly, I would like to thank my husband and best friend, Brandon Wagner. Thank you for allowing me to pursue my goals and supporting me along the way. I will always be grateful for the sacrifices you made so that I could achieve my goals. I feel so lucky to have you in my life and I look forward to our future together. 5

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TABLE OF CONTENTS page ACKNOWLEDG MENTS .................................................................................................. 4LIST OF TABLES ............................................................................................................ 9LIST OF FI GURES ........................................................................................................ 10LIST OF ABBR EVIATION S ........................................................................................... 11ABSTRACT ................................................................................................................... 12 CHAPTER 1 INTRODUC TION .................................................................................................... 14Agriculture and the Environm ent ............................................................................. 15Soil Eros ion ...................................................................................................... 15Point vs. NonPoint S ources of Po llution ........................................................... 15Total Maximum Daily Loads ............................................................................. 16Best Management Practices ................................................................................... 17Best Management Practice Implement ation ..................................................... 20Cost-Shar e ....................................................................................................... 23North Florida Best Management Practices ....................................................... 23Suwannee Va lley .............................................................................................. 24Topography and soil char acterist ics ........................................................... 24The Suwannee River Partnersh ip .............................................................. 26Research Pr oblem .................................................................................................. 26Purpose and Objectives .......................................................................................... 27Significance of Study .............................................................................................. 27Definition of Terms .................................................................................................. 28Limitations ............................................................................................................... 29Assumpti ons ........................................................................................................... 29Chapter Su mmary ................................................................................................... 302 REVIEW OF LI TERATURE .................................................................................... 31Overview ................................................................................................................. 31Theoretical Framework Roger s Innovation Decision Process .............................. 31Innovatio n ......................................................................................................... 31Communication Channels ................................................................................ 33Time ................................................................................................................. 34Social System ................................................................................................... 35The Innovation-Decision Proc ess ........................................................................... 36Adopter Cat egories ................................................................................................. 38Innovators ......................................................................................................... 38 6

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Early Adop ters .................................................................................................. 39Early Majority .................................................................................................... 39Late Majori ty ..................................................................................................... 39Laggards .......................................................................................................... 39Previous Re search ................................................................................................. 40Beef Cattle Pr oduction ..................................................................................... 40Dairy Prod ucers ................................................................................................ 41Conceptual Model ................................................................................................... 41Farmer Characteristics ............................................................................................ 44Age ................................................................................................................... 44Communication wit h Peer s ............................................................................... 45Educati on ......................................................................................................... 45Family Inte raction ............................................................................................. 46Farm Size ......................................................................................................... 46Interaction with Ex tension Ag ent ...................................................................... 47Risk Aver sion ................................................................................................... 48Land Characteri stics ............................................................................................... 49Land Ownership ............................................................................................... 49Presence of Riv er/Stream ................................................................................ 50Plot Characte ristics .......................................................................................... 50Soil Ty pe .......................................................................................................... 51Financial Inc entive .................................................................................................. 51Fines/Stronger Enforcement ............................................................................. 51Growing Regul ation .......................................................................................... 52Environmental Attitudes .......................................................................................... 52Moral Obligat ions ............................................................................................. 53Organization In volvement ................................................................................. 53Chapter Su mmary ................................................................................................... 543 RESEARCH DESIGN AND METHOD OLOGY ....................................................... 55Research Design .................................................................................................... 55Non-Response Error ........................................................................................ 56Sampling E rror ................................................................................................. 57Validity .............................................................................................................. 57Procedures ............................................................................................................. 59Instrument ation ....................................................................................................... 60Validity .............................................................................................................. 61Reliabilit y .......................................................................................................... 61Data Anal ysis .......................................................................................................... 62Chapter Su mmary ................................................................................................... 624 RESULT S ............................................................................................................... 64Overview ................................................................................................................. 64Demographi cs ......................................................................................................... 65Row Crops Grown ............................................................................................ 65 7

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Age ................................................................................................................... 66Highest Level of Formal Education Co mpleted ................................................ 66Reliability of Assessment s ...................................................................................... 67Findings .................................................................................................................. 69Objective: 1 Identify the factors that row crop fa rmers in North Central Florida perceive as influential in their decision to adopt (or not adopt) best management prac tices. ................................................................................. 69Objective 2: Determine the self-perceiv ed relative importance of each factor in the adoption decision. ............................................................................... 73Objective 3: Examine the relationship of demographic characteristics on the adoption decision. ......................................................................................... 75Objective 4: Identify the fact ors that are most important in predicting rates of best management practi ce adopti on. ............................................................ 75Chapter Su mmary ................................................................................................... 765 CONCLUS IONS ..................................................................................................... 77Overview ................................................................................................................. 77Statement of t he Problem ................................................................................. 77Purpose and Objectives ................................................................................... 77Methods ............................................................................................................ 78Summary of Result s................................................................................................ 78Conclusi ons ............................................................................................................ 80Discussions and Implicati ons .................................................................................. 81Recommendat ions .................................................................................................. 87Recommendations fo r Practi ce ........................................................................ 87Recommendations for Fu rther Inquiry .............................................................. 88 APPENDIX A SURVEY ................................................................................................................. 91B IRB APPRO VAL ..................................................................................................... 96C INFORMED CONSENT .......................................................................................... 97D COVER LE TTER .................................................................................................... 98E FOLLOWUP PO STCARD ....................................................................................... 99F SECOND COVER LETTER .................................................................................. 100LIST OF REFE RENCES ............................................................................................. 101BIOGRAPHICAL SK ETCH .......................................................................................... 104 8

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LIST OF TABLES Table page 4-1 Percentages of crops gr own by row crop farmers in the Suwannee Valley of North Central Florida .......................................................................................... 664-2 Percentages of re spondents age ....................................................................... 664-3 Percentages of highest level of educ ation completed by respondents ............... 674-4 Mean and standard deviation of eac h subscale and re liability ............................ 694-5 Analysis of interact ions between farmers ........................................................... 704-6 Analysis of influence on adopt ion by family member s ........................................ 714-7 Analysis of interacti ons with extens ion agent s ................................................... 724-8 Analysis and categor ization of farmer willing ness to acc ept risk ........................ 734-9 Farmers opinions of voluntar y BMP adoption and in centives ............................ 734-10 Bivariate correlation analysis on fa rmer interaction, family influence, extension interaction, risk aver sion and voluntary BMP adoptio n ....................... 744-11 Bivariate correlation analysis between farmer interaction, family influence, extension interaction, risk aversion, voluntary BMP and types of row crops grown .................................................................................................................. 744-12 Bivariate correlation analysis of farmer interaction, family influence, extension interaction, risk aversion, voluntary BM P adoptio n ............................................. 76 9

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LIST OF FIGURES Figure page 1-1 BMP decision tr ee flowc hart. .............................................................................. 211-2 Boundary of the Su wannee River Water Man agement Dist rict. .......................... 252-1 Rogers (2003) adopt er categor ies ...................................................................... 382-2 Factors influencing t he adoption of BMPs by row crop farmers in the Suwannee Valley of Nort h Central Fl orida .......................................................... 43 10

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LIST OF ABBREVIATIONS BMP Best Management Practices FCWA Federal Clean Water Act FDACS Florida Department of Agri culture and Consumer Services OAWP Office of Agricu ltural Water Policy SRWMD Suwannee River Water Management District TMDL Total Maximum Daily Loads UF-IFAS University of Florida Instit ute of Food and Agricu ltural Sciences 11

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Abstract of Thesis Pres ented to the Graduate School of the University of Florida in Partial Fulf illment of the Requirements for t he Degree of Master of Science FACTORS INFLUENCING THE ADOPTION OF BEST MANAGEMENT PRACTICES BY ROW CROP FARMERS IN THE SUWANNEE VALLEY OF NORTH CENTRAL FLORIDA By Allison Hope Britton May 2011 Chair: Gregory Gifford Major: Agricultural Education and Communication Best management practices (BMPs) were developed because of the association between agricultural activities and the cont amination of watersheds with nutrients such as phosphorus and nitrogen. BMPs are desi gned to reduce the amount of nutrients entering water systems. In the Suwannee Valley of North C entral Florida, BMPs have been implemented to decrease nitrogen concentrati ons in rivers and springs associated with the Lower Suwannee River Basin. The Suwannee Valley is largely agricultural and dominated by row crops. Some farmers in the Suwannee Valley have chosen to implement BMPs but adopti on has not been widespread. The purpose of this research was to determine factors which influence the implementation of BMPs by row crop farmers in the Suwannee Valley of North Central Florida. A quantitative study was used to gat her row crop farmers opinions on factors which influenced the adoption decision process. A questionnaire was developed by the researcher and examined by a panel of expe rts before being distributed to row crop growers in the Suwannee Valley. 12

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13 The results of this study found that in teraction with other farmers, family and extension agents is related to the decision to adopt BMPs. Farmers also believe that greater incentives will lead to increased adoption rates am ong agricultural producers. A key finding of this study was the relati onship between interaction with extension personnel and a farmers willingness to accept risk regarding their farming operation. As a result, extension should facilitate gatherin gs for farmers and their families as well as extension personnel in order to increase ado ption rates among row crop growers in the Suwannee Valley.

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CHAPTER 1 INTRODUCTION The period after the Civil War resulted in a shift in American agriculture. The War devastated the infrastructure of agricultur e in the south, and overall, the industry declined. However, until the 1900s, the Un ited States experienced a rise in mechanization of the agricultural industry. Agriculture began expanding to meet the increasing demands for food and fiber. Low analys is fertilizers were also introduced but not widely used due to costs. This prosperity diminished, however after World War I and during the Depression as the stock mark et crashed and commodity prices fell (Lilly, n. d). The outlook for agriculture during and a fter the Depression was grim. Intensive agricultural practices diminished soil quality and erosion was prevalent. In 1933 the Soil Erosion Service was formed in response to large amounts of soil loss, especially in the Midwest. During this time period, the number of horses and mules used for plowing and tillage also decreased by 13,500, 000 head (Lilly, n. d.), as tr actors and other machinery were developed. At the end of World War II, significant advances were made in the field of agriculture to increase and modernize food production (University of California Davis Sustainable Agricultural Research and E ducation Program (UCDavis SAREP), 1997). As the United States population exploded after the war (Lil ly, n. d.), the demand for more agricultural products (food and fiber) increased. Over the years, increased mechanization and government subsidies gave rise to lower-priced and larger food supplies (Earles, 2005). Pesticide use, t opsoil depletion and ground water contamination 14

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from agricultural practice, have led to questi ons a reexamination of previously accepted agriculture practices ( UC-Davis SAREP, 1997). Agriculture and the Environment Soil Erosion The erosion of soil has long been a problem in agriculture in the United States (Pimentel, 2000; Pimentel et al ., 1995). However, the rate of soil erosion has increased in recent decades (Pimentel et al., 1995) due to an increase in human population and expansion in agricultural pr oduction (Pimentel, 2000). Approxim ately 75 billion tons of topsoil are lost worldwide from agricultura l production (Pimentel et al., 1995; OGeen & Schwankl, 2006). In the United States, 6.9 billi on tons of soil is lost each year through erosion (OGeen & Schwankl, 2006). The topsoil ends up as sediment in lakes, ponds, and other water sources (Pimentel et al ., 1995) blocking currents and streams. According to OGeen and Schwankl (2006), the loss of top soil at these rates is increasing dependence on fertilizers and soil am endments in an attemp t to compensate for nutrients lost through erosion. Point vs. NonPoint Sources of Pollution Nonpoint sources of pollution are a major concern for the agricultural industry. Nonpoint sources of pollution are caused by rainfall or snowmelt which moves through and over the ground. As the r unoff migrates, it picks up and carries with it natural and human-made pollutants. These sediments are then deposited into lakes, rivers, wetlands, coastal waters and ground waters (Environmental Protection Agency (b), 2010). The agricultural industry is the main s ource of groundwater contamination in rural areas with contaminants coming predominately from nitrates and pesticides (Cogger & MacConnell, 1991). Other sources of nonpoint pollution from agriculture include: 15

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sediment from improperly managed crop lands, salt from irrigation practices, and excess fertilizers from agricultural lands (Environmental Protection Agency (b), 2010). Land used for agricultural production also pr oduces nonpoint sources of pollution when large amounts of vegetative cover are re moved which expose the soil to erosion. When the soil erodes, it becomes sediment, in streams, rivers, ponds, and lakes which creates the potential for water degradation (B ianchi & Harter, 2002). When fertilizers, wastes, and crop residues are added to the soilwater-plant system, they begin to break down, and nutrients are carried into nearby str eams, lakes, and reservoirs where they can accumulate in excessive amounts. Even in small amounts, nitrates and pesticides pose health risks (Cogger & MacConnell, 1991) when contaminated with water supplies. High concentrations of pesticides have been lin ked to a variety of acute toxic effects such as nausea, vomiting, and blood disorders. Total Maximum Daily Loads The Federal Water Pollution Control Act was enacted in 1948 to regulate the discharges of pollutants into US waters and regulate quality standards for surface waters. The Act was significantly modi fied and expanded in 1972 and its name was changed to the Federal Clean Water Act (FCWA) (Environmental Protection Agency (a), 2010). The Federal Clean Water Act gave the Environmental Protection Agency the authority to regulate water quality and determi ne loads water bodies could receive of certain pollutants while still meeting water quality standards (Migliaccio & Boman, 2009). These load estimates determined for each water body are referred to as Total Maximum Daily Loads (TMDLs). TMDLs are defined as the maxi mum amount of a pollutant that a water-body can receive and still meet the water quality standards as 16

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established by the 1972 Federal Clean Water Act. Section 303(d) of the FCWA requires states to submit lists of surface waters that do not meet applicable water quality standards and to establish TMDLs for t hese waters on a prioritized schedule (Migliaccio & Boman, 2009, p. 1). One important element of the FCWA was the establishment of total maximum daily loads (TMDLs). TMDLs det ermine the amount of pollutants allowed to enter a body of water wh ile still meeting designated use. In 1999, the Florida legislature passed the 1999 Fl orida Watershed Restoration Act, which allowed FDACS to regulate the amount of pollutants en tering the watershed (Florida Department of Agricultural and Cons umer Services (FDACS), 2006). When a water basin is identified and TMDLs have been determined, pollutant loads are then divided among two groups of stakeholders, agricultural and urban. Each stakeholder implements a set of management practices which are intended to reduce pollutant loads. These practices are referr ed to as Best Management Practices (BMPs) and are defined as: A practice or combination of practices determined by the coordinating agencies, based on research, field-testing, and exper t review, to be the most effective and practical on-location means, including ec onomic and technological considerations, for improving water quality in agricul tural and urban discharges. (Migliaccio & Boman, 2009, p. 2) Best Management Practices The Office of Agricultural Water Policy (OAWP) was established in 1995 and is housed within the Florida Department of Ag riculture and Consumer Sciences (FDACS). OAWP was developed in order to facilitate communications among federal, state and local agencies as well as the agricultural industry on water quanti ty and water quality issues involving agriculture. The OAWP is involved in the development of best 17

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management practices in addressing water quality and water conservation issues involving agriculture (Office of Agri cultural Water Po licy (b), n.d.). Best management practices are designed to guide approaches to agriculture operations from a sustainable perspective. Agricultural BMPs are designed to be practical and cost-effective methods producers can use in order to reduce the amount of pesticides, fertilizers, animal wastes and ot her pollutants from entering waterways. BMPs were developed to not only benefit water quality but also to maintain and possibly enhance agricultural production. FDACS has developed a series of BMP rules for different agricultural operations in order to minimize agricultur es impact on Floridas water sources (Office of Agricult ural Water Policy (a), n.d.). Best management practices were develop ed because of links between agricultural activity and contaminated watersheds with nutrients such as nitrogen and phosphorus. BMPs can assist in reducing the amount of nutrients, sediments, pesticides and other pollutants that enter the wa ter system. Specifically in Florida, BMPs are needed because much of the state is built on limestone. The limestone allows water to return in an unpurified state to the aquifer. This allows pollutants to enter the water supply quickly causing possible danger to not only humans but also ecosystems (University of Florida Institute for Food and Agricultur al Sciences (UF-IFAS, 2008). Best management practices are currently divided into six main categories: pesticide management, conservation practi ces and buffers, erosion control and sediment management, nutrient and irri gation management, water resources management and seasonal or temporary farming. Each category contains a number of specific practices which relate to proper implementation of each structural BMP. 18

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Pesticide management: In the late 1940s, the agrichemical industry boomed. Thousands of compounds were developed to control pests, diseases, insects and weeds in order to improve yields. Over t he next 50 years however, many lessons were learned about pesticide resistance and the environmental and human consequences that can result from the use and misuse of pes ticides. As a result, in the past 20 years, advances have been made in the development of target-specific pesticides which are less harmful to the environment and safer fo r those who handle them. Integrated Pest Management (IPM) has become a common approach to dealing with pests. IPM integrates biological controls, chemical controls and management practices as a method of reducing pests as well as dec reasing dependence on agrichemicals (FDACS, 2006). Conservation practices and buffers: As stated previously, runoff water from agricultural lands is a growing concern for ru ral water supplies. Runoff from agricultural lands may pick up sediments, nutrients, pesticides and other po llutants. If these contaminants reach groundwater, they can impact drinking water systems. As a result, BMPs have been developed to help protect wate r quality by obstructing runoff (FDACS, 2006). Erosion control and sediment management: The loss of soil erosion can alter drainage patterns and cause excess runoff of water during rain events or during irrigation. These sediments can then drain and contaminate surface and ground waters. BMPs have been developed to prevent erosion and sediment transport from agricultural lands. These BMPs are aimed at reducing la rge amounts from reac hing water-bodies as well as improving overall water quality (FDACS, 2006). 19

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Nutrient and irrigation management: Many row crops require some degree of fertilization. In row crop production, nitrogen, phosphorus, and potassium constitute the major macronutrient in many fertilizers. In Florida, nitrogen is needed because the soils are largely sandy and have low organic matter c ontent. The soils retain little water or nitrogen and during intense rainfall or excess ive irrigation, nitrogen can leach from the root zone into ground water. BMPs hav e been developed to maximize nitrogen efficiency in agricultural production so that little nitrogen is lost through runoff (FDACS, 2006). Water resources management: Floridas water resour ces include springs, wetlands, rivers, lakes, estuaries, l agoons, coastal marshes and underground aquifers. Many of these ecosystems are d ependent on one another and depend upon seasonal rainfalls. Current BMPs aim to educate t he industry on the most common irrigation and storm-water management (FDACS, 2006). Seasonal or temporary farming operations: Seasonal or temporary operations primarily involve using crop rotations. Crop ro tations not only allow farmers to break disease cycles but also provide the opportuni ty to plant high residue cover crops which help improve overall soil hea lth and tilth. BMPs have been developed which utilize crop rotations, agrichemicals and livestock (FDACS, 2006). Best Management Practice Implementation Implementing best management practices is a complex process. When deciding to implement BMPs, a farmer mu st perform an inventory of existing or current farm practices. This inventory may show that a farmer is already engaging in some best management practices as well as determining future BMPs which could be implemented. The FDACS is available to assist growers with on-farm assessments. 20

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Upon an inventory of current and existing farm practices, farmers as instructed by extension agents to use and follow the BMP Decision Tree Flowchart as depicted below (FDACS, 2006). Figure 1-1. BMP decis ion tree flowchart. 21

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Figure 1-1. Continued Farmers are also instructed to have a vi able Quality Assuranc e (QA) program to ensure that BMP implementati on is occurring as planned. The QA program also serves 22

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to build credibility and provi de documentation that BMPs are being designed and used in accordance with the Notice of Intent to Implement form. A N otice of Intent to Implement form follows Flori da Statute 403.067(7)(c)2 and Rule 5M-8.004 F. A. C.. It provides proof that the farm owner or leaseholder intends to implement BMPs (FDACS, 2006). Cost-Share Agricultural producers can receive federal cost-share money to implement BMPs. In order to receive funds, pr oducers are often required to have a Conservation Plan developed by the United Stat es Department of Agricultural Natural Resource Conservation Service (USDA-NRCS) or an approved third party (FDACS, 2006). There are currently three cost-share pr ograms available to farmers. SOUTHWEST FLORIDA WATER MANAGEMENT DISTRICT (SWFWMD) FACILITATING AGRICULTURAL RESOURCE MANAGEMENT SYSTEMS (FARMS). Cost-share reimbursement program which provides funds to promote surface water and groundwater sustainability (Unive rsity of Florida, n.d.). SWFWMD MINI-FARMS. Cost share program which reimburses growers up to 85% of their costs and up to a maximu m of $8,000 per approved water resource project (University of Florida, n.d.). USDA EQIP. Offers financial and technica l assistance to producers implement structural and management pr actices on eligible agricultural land (University of Florida, n.d.). North Florida Best Management Practices The region used for this study was the Su wannee Valley which is located in North Central Florida. Counties in the Suwannee Valle y which were included in this study are: Jefferson, Madison, Taylor, Hamilton, Suwannee, Lafayette, Dixie, Columbia, Gilchrist, Levy, Union, Bradford, and Al achua. The majority of farmers in this area grows agronomic crops, have implemented some form of conservation tillage and crop 23

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rotations. BMPs for this region have been s ubdivided into three categories: non-irrigated crop land, irrigated non-plasticulture croplan d, and plasticulture farming standards (FDACS, 2006). Suwannee Valley The Suwannee River is a major water res ource which originates in Georgia and flows through North Florida until reaching the eastern Gulf of Mexi co (Obreza & Means, 2006). Two-thirds of the basin is located in Georgia while the other one-third, which is known as the Lower Suwannee River, is located in Flori da (Figure 1-1). The Lower Suwannee River compromises The Suwann ee River Water Management District. The Lower Suwannee Basin is also classified as a Showcase Watershed by the Environmental Protection Agency (EPA) becaus e it contains the highest concentration of first magnitude freshwater springs in the world (Obreza & Means, 2006, p. 1). A first magnitude spring is classified as a spring t hat discharges at least 100 cubic feet of water per second (Obreza & Means, 2006) Since the mid-1980s the nitrate concentration in some rivers and springs associated with the Lower Suwannee River Basin has increased significantly and caused environmental concerns. As a result, the state governments of Flori da and Georgia, along with other organizations have determined that the Suwannee River Basin is an ecosystem in need of protection because of its unique biota and important wate r resources (Obreza & Means, 2006, p. 2). Figure 1-1 shows the boundary of the Suwannee River Water Management District. Topography and soil characteristics The area of the Suwannee River Basin is ge nerally flat with an occasional rolling topography (Obreza & Means, 2006 p. 4). Elevations range fr om 180 feet above sea 24

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level to sea level. In some areas, where agric ultural fields slope, soil erosion can occur. However, this tends to be localized pr oblem. Some areas wit hin the Suwannee River Basin are subject to soil erosion due to the water-holding-capacity of soils in this area. When compared to other ar eas in the United States, t he water-holding capacity of soils in the north Florida area is low. In general, the water-holding capacity of soils in this area is two to three times lower than those in the Midwest. Most soils in the Suwannee River Basin are classified in t he soil order, Entisol. Entisol soils are vulnerable to leaching of nutrients and agrichemicals because they are sandy throughout, contain little organic matter, are hi ghly conductive to water flow, and have no sub-surface layer that can slow wate r drainage (Obreza & Means, 2006, p. 7). Figure 1-2. Boundary of the Suwannee River Water Management District. 25

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The Suwannee River Partnership With concerns seen in the Suwannee River Basin region regarding soil erosion and leaching, the Suwannee River Partnership was created in 1999 as a coalition of state, federal and r egional agencies, local govern ments, and private industry representatives working toget her to reduce nitrate levels in the surface waters and groundwater within the basin, or watersheds (Suwannee River Partnership (a), n.d., para. 2). The mission of the Partnership is to provide solutions which protect and conserve water resources within the Suwannee River Water Management District by reducing nitrate levels in surface and ground waters (Suwannee River Partnership (b), n.d.). The Partnership has been instrumental in the voluntary adoption of best management practices among area farmers. The Partnership provides several cost share programs to help farmers offset the costs associated with t he implementation of BMPs. Divine (2010) found that t he Partnership was instrumental in the decision to implement BMPs in the SRWMD. Specifically Divine (2010) found that growers in the SRWMD appreciated the knowle dge staff the Partnership had regarding agricultural history. Members of the Part nership also had a homophilous relationship with farmers. This homophilous relationship allowed Pa rtnership members to build a trusting relationship with producers and disseminate in formation about BMPs more effectively (Divine, 2010). Research Problem Significant advances have been made in agriculture since World War II. While many of these advances have been positive, there have also been negative implications. These include an increase in the amount of pesticides used, groundwater 26

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contamination, and the incr easing loss of topsoil. Best management practices for sustainable agriculture have the potential to address many of the issues associated with conventional agriculture but, not everyone in volved in production agriculture has chosen to adopt these practices. The process fa rmers use and the factors influencing BMP implementation are critic al elements of the adopti on decision process. Purpose and Objectives The purpose of this research is to determine factors which influence the implementation of best management practice s by row crop farmers in the Suwannee Valley of North Central Florida. The objectives of this study were to: Identify the factors that ro w crop farmers in the Suwannee Valley of North Central Florida perceive as influential in their decision to adopt (or not adopt) best management practices. Determine the self-perceived relative im portance of each fact or in the adoption decision. Examine the relationship of demographic characteristics on the adoption decision. Identify the factors that ar e most important in predict ing rates of best management practice adoption. Significance of Study While the need for more sustainable agricultural programs has been widely known for some time, they have yet to be fully integrat ed (UC-Davis SAREP, 1997). Sustainable agriculture may address many pr oblems associated with current agricultural practices such as top soil depletion, ground water contamination, and increased pesticide resistance. By determining motivating factors of row crop farmers to adopt best management practices, strategies for encouraging non-adopters to adopt these practices may be 27

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created. By voluntarily engaging in best management practices, farmers will have additional time to become accustomed to the farming methods and requirements associated with BMPs. Since best management practices may become regulatory in the future, it is important to determine why farmers adopt BMPs in order to make the transition process for later adopters more effective. This information may also be helpful in developing effective methods to persuade row crop farm ers to adopt best management practices. Three groups of individuals will be able to best utilize this information: extension agents, row crop farmers, and policy makers. Extension agents will use this information to help farmers overcome barriers associ ated with the adoption of best management practices. Farmers will use this informati on to determine whether adoption of BMPs would be beneficial on their farming operatio n. Lastly, policy makers will use this information to determine whether regulator y adoption would be more beneficial than current incentive-based methods. Definition of Terms SUSTAINABLE AGRICULTURE an integrated system of plant and animal production practices having a site-specific applicati on that will, over the long term: satisfy human food and fiber needs; enhance envir onmental quality and the natural resource base upon which the agricultural economy depends; make the most efficient use of nonrenewable resources and on-farm resources and integrate, where appropriate, natural biological cycles and controls; sustain the economic viability of farm operations; and enhance the quality of life for farmers and society as a whole (United States D epartment of Agriculture, 2007). BEST MANAGEMENT PRACTICES guidelines advising producers on how to manage the water, nutrients, and pesticides they use in order to minimize agricultures impact on the states natural resources (U F/IFAS, 2008). For the purpose of this study, a best management practice for fiel d crop producers were defined as those listed in the Florida Department of Agricu ltures and Consumer Services Manual of Water Quality/Quantity Best Management Practices for Florida Vegetable and Agronomic Crops (2006) (FDACS, 2006). 28

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ROW CROP FARMER In this study, row crop farmers were individuals involved in the following areas of production: potat oes, corn, soybeans, peanuts, cotton and tobacco. Limitations Because this study utilizes a target ed population, certain limitations were considered. Generalizations wi ll only apply to row crop farmers in the Suwannee Valley of North Central Florida since this is wher e the population was drawn. This study utilized a questionnaire which was administered to subj ects. As a result, because some of the survey questions are free-response, participant fatigue may set in or, these questions may be skipped entirely by participants. Al though a questionnaire was used, survey research itself is a limitation. When using a questionnaire, there is a constraint on the information being gathered which is imposed by the questionnaire itself. Lastly, subjects self-select to participate in this study. Theref ore, subjects could choose to complete or not complete the questionnaire. Assumptions This study operates under the assumption that there are agronomic row crop farmers who utilize best management practi ces in The Suwannee Valley of North Central Florida. There is also the assump tion that those surveyed will be willing to answer a questionnaire. Participants will also need to be able to give accurate and honest reasons why they chose to engage in best management practices. Lastly, this study was conducted under the assumption that the rates of adoption of best management practices among agronomic row crop farmers in the Suwannee River Water Management District will vary. 29

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Chapter Summary This chapter has explained the rela tionship between sustainability and best management practices as related to agriculture Advances in agriculture since World War II have created the need for agricultural practices which are economically feasible, environmentally healthy, and socially acceptabl e. While the need for these practices has been thoroughly documented, reasons why producers chose to adopt sustainable practices have not been fully researched. Research in this area is needed in the Suwannee Valley of North Florida because this region is dominated by agronomic crops, and producers in this area have previ ously adopted sustainable practices. This population will provide an adequate sample of those involved in best management practices and be useful in determining the motivational factors behind why row crop farmers chose to adopt BMPs. 30

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CHAPTER 2 REVIEW OF LITERATURE Overview With negative impacts seen wit hin the agriculture industry since the end of World War II, a need has arisen for sustainable agricult ural practices. In t he state of Florida, best management practices developed to help agricultural producers manage the water, nutrients, and pesticides they use in order to minimize agricultures impact on the states natural resources (UF-IFAS 2008, para 1). The purpose of this research is to determine and evaluate influences which affect a row crop farmers decision to adopt best management practices. Theoretical Framework Rogers Innovation Decision Process Rogerss (2003) Diffusion of Innovation Model was the guiding framework for this study. According to Rogers (2003), diffusion is defined as a process which begins with an innovation that is passed through comm unication channels over time through members of a social system. The diffusion pr ocess is a special type of communication, where messages deal with new ideas. Rogers (2003) defined four main elements of diffusi on: 1) innovation, 2) communication channels, 3) time, and the 4) t he social system. For the purpose of this research, the innovation was the adoption of best management practices. Diffusion was the process by which BMPs were co mmunicated through communication channels among row crop farmers in the Suwannee River Water Management District. Innovation According to Rogers (2003), an innovation is an idea, practice, or object that is perceived as new by an individual (p. 12) However, it does not matter that the 31

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innovation was developed some time ago, only t hat the innovation is viewed as new to the individual. Rogers stated that innovations often in volve technology, and that technology encompasses two components: a hardware aspect and a software aspect. The hardware aspect consists of the tool that embodies the technology as a material or physical object (p. 13). The software aspect consists of the information base for the tool (p. 13). For the purpose of this res earch the hardware aspect consisted of best management practice technology. The software aspect of best management practices is the individuals who educate row crop fa rmers on the benefits and components of the program. According to Rogers (2003), there are five characteristics of innovations. These characteristics help explain why individual s differ in their adopt ion rates. Rogers characteristics of adoption include: 1) relative advantage, 2) compatibi lity, 3) complexity, 4) trialability, and 5) observability. Relati ve advantage is the extent to which the new innovation is perceived to be better than the original idea. However, it does not matter how advantageous the new innovation actually is but rather the perception of its advantages. When an innovation is perceived to be highly advantageous, it will be adopted much more quickly. Compatibility is how similar the innovation is to existing values, past experiences, and the needs of pot ential adopters. The more compatible an innovation is, the sooner it is adopted into a social system. Complexity is defined as the degree of difficulty of a new innovation. I nnovations which are easier to understand are adopted more rapidly than innovations which are complex and require the adopter to develop new skills and understandings. Trialabili ty refers to experimentation of a new 32

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innovation. A new idea, which can be implemented over a trial period, is likely to be adopted more quickly than innovations which are implemented at once. Lastly, observability is the extent to which the resu lts of the innovation ar e seen by individuals within the social system. Indivi duals are more likely to adopt if positive results of the innovation can be seen. Communication Channels Rogers (2003) defined a communication ch annel as the means through which a message is relayed from one individual to anot her. Mass media channels such as radio, television, newspapers, and t he Internet are quick and effect ive methods of delivering information to large audiences of potential adopters. However, Rogers stated that interpersonal channels, such as face-to-face meetings are more effective in persuading individuals to adopt a new innovation. Interaction between two individuals was a crucial step in the decision to adopt BMPs. Rogers (2003) stated t hat more effective communication occurs when two or more individuals are homophilious. Homophily is measured as the extent to which two individuals who interact are similar in certain attributes. While it is ideal that individuals interacting are homophilious, this is rarely the case. In general, co mmunication between two or more individuals is heterophilous. Heter ophily is the extent to which two or more individuals who interact differ in some a ttributes. The nature of diffusion requires heterophily to a certain extent between t he two individuals in the communication process so there is new information to shar e. Ideally, however, t he individuals would be homophilious regarding a ll other attributes. 33

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Time The time aspect of the diffusion process has three components: 1) the innovationdecision process, 2) the innovativeness of the individual when compared with other members of the social system and 3) t he innovators rate of adoption. The innovation-decision process is how an individual progresses from learning about an innovation to forming an attitude, deciding to reject or adopt the idea, implementation, and confirmation of the deci sion. These steps are summarized as 1) knowledge, 2) persuasion 3) decision, 4) implementation, and 5) confirmation. The innovativeness of an individual is the degree to which the individual is relatively early in adopting new ideas t han the other members of a system (Rogers, 2003, p. 22). Therefore, more innovative individuals more quickly adopt a new innovation. Adopter categorie s classified members of a so cial system based on their degree of innovativeness. These categories include: 1) innovat ors, 2) early adopters, 3) early majority, 4) late majori ty, and 5) laggards. These ca tegories will be discussed in further detail in relation to row crop farme rs in the Suwannee River Water Management District. The third component of time, the rate of adoption, is defined as the speed in which the innovation is adopted by individuals in th e social system. The rate of adoption is measured by the length of time required fo r a certain percentage of individuals in a social system to adopt an innovation (R ogers, 2003). The system is defined as a community, organization or ot her structure where the i nnovation is taking place. Depending on the social system, the rate of a doption may vary for the same innovation. 34

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Social System A social system is defined as a set of interrelated units that are engaged in joint problem solving to accomplish a goal (Rogers, 2003, p. 23). The interrelated units of a social system can be individuals, informa l groups, organizations, or subsystems. The structure of the social system dictates the innovatio ns diffusion. The structure consists of the pattern of arrangements of the units in a system (Rogers, 2003). A clearly defined system allows for stability wit hin a system and the ability to predict (to some degree) human behavior. For the purpose of this research, the social system is defined as the behaviors and guidelines (from The Florida Department of Agriculture and Consumer Services) used by row crop farmers in the Suwannee River Water Management District. The norms of a system can also affect t he rate of diffusion. Norms are established behavior patterns for the members of a so cial system (Rogers, 2003, p. 26). Norms help define tolerable behaviors in a social system and serve as a set of guidelines for members. Those involved in the education of best managem ent practices hope that in future, the adoption of these practices wil l be considered a norm among row crop farmers in the Suwannee Valley. Opinion leaders and change agents can also a ffect the rate of diffusion within a social system (Rogers, 2003). These indivi duals provide information concerning the innovation to members in the system. Once an individual has established a relationship with those in the social system and has demonstrated competence in the subject area, he or she can influence the adoption of an innovation. Opinion leaders and change agents are crucial in the adoption process in that they affect members decisions to adopt. In the current research, opinion leader s were extension agents who worked with 35

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row crop famers in the dissemination of information concerning best management practices. The Innovation-Decision Process As discussed previously, the innovation-de cision process involves five steps knowledge, persuasion, decision, implementation and confirmation. The decision to adopt best management practices is not an instantaneous decision. The decision to adopt often requires time and consideration on the part of the pr oducer. When deciding to adopt, a farmer goes through an intensive process which begins with learning about an innovation, deciding to adopt, implementation, and choosing to continue the use of the innovation. In the knowledge stage, the individual learns of the inno vation and understanding of how it functions (Rogers, 2003). In t he adoption of best management practices, the knowledge phase consists of row crop farmers learning about best management practices. This knowledge could be gained fr om mass media channel s, interaction with other farmers, or through an extension agent. Row crop farme rs could chose to actively pursue information regarding BMPs or rece ive the information involuntarily. During the persuasion stage, the adopter forms a favorable or unfavorable opinion regarding the adoption (Rogers, 2003). The decision to adopt is based on the relative advantage, compatibility, complexity, trialability, and observability of the innovation. The more an innovation matches these criteria, t he more likely the innovation is to be adopted. During the stage, row crop farmers wil l determine their attitudes toward the adoption of best management practices. The third stage in the innovation-decision pr ocess is the decision stage. In this phase, the adopter decides whether to adopt or reject the innovation (Rogers, 2003). 36

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During this stage, a row crop farmer in t he Suwannee Valley will decide to adopt some BMPs while rejecting others (based on the characteristi cs of the farming operation). Based on Rogers (2003) theorie s, row crop farmers will be more willing to adopt best management practices if they are able to test the farming methods on a trial basis. This trial basis would allow the farmer to make an informed dec ision regarding the innovation. During the implementation st age, the innovation is put into practice (Rogers, 2003). During this phase, the individual c an physically see the change taking place. Implementation of an innovation happens qui ckly after a decision is made but while consequences still linger regarding adoption. This phase of the Innovation-Decision Process may occur for an extended period of time, as the innovation becomes a habit for the adopter. For the purpose of this research, implementation is when a farmer adopts best management practices. The farmer may chose to temporarily or permanently adopt these practices. The last phase of the Innovation-Deci sion Process is confirmation. During confirmation, the individual seeks reinforcement of the innovation-decision but reserves the right to reverse the decision which was made (Rogers, 2003). Dissonance can also occur during this phase if the individual re ceives conflicting information regarding the innovation. During this phase, a row crop fa rmer will seek additional reinforcement of the decision which was previously made. Fa rmers may also choose to discontinue use of the innovation if they receive informa tion that contradicts previous information received. 37

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Adopter Categories Rogers defined five ideal types of adopt ers (2003): innovator s, early adopters, early majority, late majority, and laggards. While these categories attempt to define adopters, they are not inclusive, and all mem bers of a social system may not clearly fit into one category. Figure 2-1 shows a vi sual representation of Rogerss adoption categories. Exceptions are possible, but in general, adopters can be identified by one of the following categories. The percentages estimate the proportion of the population which can be classified by each category. Figure 2-1. Rogers (2003) adopter categories Innovators Innovators are classified as individuals who are venturesome and generally the first to adopt a new innovation (Rogers, 2003) These individuals are interested in new ideas and have the available capi tal to invest in new opport unities. They are generally of a higher socioeconomic stat us, have obtained a higher education, and understand the degree of risk associated with adopting new i nnovations (Rogers, 2003). A farmer would be classified as an innovator if he or she adopted best management practices soon after receiving material r egarding the farming methods. I nnovative farmers are often younger; more educated, and actively seek information (Habron, 2004). 38

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Early Adopters Early adopters are well respected and serve as a role model for others in the social system (Rogers, 2003). Others look to early adopters for advice and information regarding the innovation. Change agents and opinion leaders actively seek early adopters to help trigger a critic al mass in the adoption of an i nnovation. In this research, early adopters were considered farmers who had many years of experience in the row crop industry. Early Majority Members of the early majority categor y adopt new innovations just before the average member of a social system (Rogers, 2003). While these individuals interact frequently with their peers, they are seldom seen as opini on leaders or change agents. They debate for a lengthy period of time bef ore deciding to adopt a new idea. The early majority is typically seen as followers and rarely lead others to adoption. Late Majority Members of the late majority categor y adopt a new innovation just after the average member of a social system (Rogers, 2003). Individuals within this category approach new ideas with skepticism are ofte n critical of potent ial benefits. These individuals are often motivated by their peers to adopt and lack the resources to adopt earlier. Laggards Laggards are the last indivi duals within a social system to adopt a new innovation (Rogers, 2003). These individuals often have li ttle to no communication with others in the social system, and therefore, lack the point of reference to make decisions on their own. They are typically suspicious of new ideas and use information from the past to 39

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guide future decisions. Laggards, for the purpose of this research, were classified as farmers who were the last to adopt best management practices even after hearing of potential benefits or having an onsite visit to assess adoption practices on their farm. Previous Research Previous research has shown a gap in the information regarding the adoption of best management practices within the row cr op industry. Currently, information is available regarding the adoption of best m anagement practices in other areas of agriculture production such as cattle (K im, Gillespie, and Paudel 2005), dairy (Rahelizatovo & Gillespie, 2004), and general agric ultural practices (Lamba, Filson, and Adekunle, 2009). The following sections wi ll describe the adoption of best management practices within other areas of agricultural production. Beef Cattle Production U.S. livestock and poultry production facilities were classified as sources of nonpoint source pollution (Kim, et al., 2005). Wh ile this area of pollution has received little attention in previous years, an effort to reduce sources of nonpoint pollution has brought the practices of livestock and poultry production under scrutiny. Kim, Gillespie, and Paudel (2005) studied the adoption rates of best management practices among Louisiana cattle producers. Fi ndings revealed that having a greater number of crops or livestock enterprises, a greater proportion of owned land, and contact with Natural Resources Conservation Services (NRCS) personnel at least once a year had a positive and significant impact (p. 117) on the adoption of best management practices. Beef cattle producer s who held a college bachelors degree, raised purebred cattle, and had greater financial resources were also more likely to 40

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adopt (Kim, et al., 2005). Producers, who were cl assified as risk-averse were less likely to adopt best management practices. Dairy Producers Since the 1980s, concern has arisen regar ding concentrations of fecal coliform bacteria in U.S. streams and other bodies of water (Rahelizatovo & Gillespie, 2004). The Louisiana dairy industry has been cited as a source of this contamination. As a result, 21 best management practices were implemented to help reduce runoff and contamination and improve wate r quality overall in areas w here large numbers of dairy cattle are present. A survey conducted in 2001 of Louisiana da iry producers found th at farmers who had knowledge of the Clean Wate r Act, operated a larger farm with a higher milk yield and had contact with the Cooperative Extension Service personnel were significantly associated with the adoption of a greater number of BMPs (Rahelizatovo & Gillespie, 2004, p. 237). The study also found that BM Ps with the greatest adoption rate were more highly promoted or determined to be economically viable by the producers. The researchers called for additional study in this area to determine the costs and benefits of adoption in order to provide producers with information needed to make an effective decision regarding adoption for t heir specific operation. Conceptual Model Adopting models by Lamba Filson, and Adekunle (2009) and Rogers Innovation Decision Process (Rogers, 2003), Figure 2-2 was developed to describe factors which can affect a farmers decision to adopt bes t management practices. These influences are divided into four broad categories: farmer characteristics, land, financial incentives, 41

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42 and environmental attitudes. The four ca tegories are then broken down into sub headings which further delineate reasons for adoption of BMPs.

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Figure 2-2. Factors influencing the adoption of BMPs by row crop farmers in t he Suwannee Valley of North Central Florida 43

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Farmer Characteristics Many factors can affect a farmers dec ision to adopt best management practices, including characteristics of the innovation and innovator and the environmental context where the innovation will take place (Wejnert, 2002). Previous research has revealed that several farmer characterist ics are related to adoption. Age The age of a farmer can significantly impact the adoption of best management practices. Many previous studies concl uded that as a farmer ag es, he/she become less likely to adopt new practices, especially thos e involving conservation (Soule, Tegene, & Wiebe, 2000). Previous research has found that adoption rates among older producers are lower because they have shorter planning horizons and may not fully realize the long-term benefits of adoption (Kim, et al., 2005, p. 114) Older producers may also choose not to adopt because the change in technology would represent an untried practice that would have uncertain results, as well as that large investments in capital and knowledge are unlikely to be viewed as feasible when the planning horizon is limited (Rahelizatovo & Gillespie, 2005, p. 238) Similarly, Kassie, Zikhali, Manjur, and Edwards (2009) found young farmers in Ethiopia were more likely to try new innovations with the belief that costs c ould be justified over time. Results from Lamba et al. (2009) showed that farmers who were born after 1970 were more likely to adopt BMPs. While y ounger farmers stated they would be more willing to adopt, farmers participating in environmental programs were older or middle aged. A disconnect exists between those who would be willing to adopt and those who actually participate in c onservation programs. 44

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While previous studies have found younger farmers are more willing to adopt sustainable practices, Ki m et al. (2005) studied beef cattle producers and found older producers were more likely to adopt best m anagement practices. This is likely due to the fact that BMPs have been promoted for an extended period of time and older producers have had time to adjust. In the cattl e industry, upon retirement, many farmers still choose to actively engage in areas of production. Therefore, older producers choosing to adopt BMPs seem to be unique to this industry. Communication with Peers Communication with peers can also be influe ntial in the adoption-decision process. Habron (2004) found landowners who regularly spoke with peers about conservation practices were more likely to adopt off-stream livestock water developments (a best management practice for livestock producers in Oregon). While communication between farmers is im portant in the decision to adopt, the trust created through these relationships is also important. Habron (2004) found that landowners enjoy communica ting with other landowners r egarding the adoption of conservation practices. Landowners feel as though developing a relationship built on trust with other landowners is important in order identify and discuss successes and complications arising from the adoption of BMPs. Likewis e, Lamba et al. (2009) found that famers were more will ing to adopt BMPs if they had witnessed other farmers success. Education A farmers educational status can al so impact adoption of best management practices. Research determined that t hese individuals have an enhanced ability to process information and can clearly see the benefits of adoption (Kim et al., 2005). Wu 45

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and Babcock (1998) found that as the level of college education level increased, so did the adoption of conservation tillage, crop rotation, and soil nitrogen testing. Highly educated producers are generally able to make more informed decisions, and therefore, are more likely to be aware of production alte rnatives (Kim et al., 2005). Lamba et al. (2009) found that farmer s with work experience or educati on greater than that of a high school equivalency had higher adoption rates than those with only a primary school education. Family Interaction The passing of a farm from one generation to the next is a tradition often seen in agriculture (Errington, 2002). Often in agricultural settings, family members work together to better the farming operation. Multiple generat ions may work together simultaneously and therefore provide a variety of differe nt opinions. As one generation retires, another generation continues fa rming. Warriner and Moul (1992) found a positive connection between the adoption of cons ervations practices and farming with a family member. The relationship between a farmer and a spouse has also been studied as a factor in the adoption-decision pr ocess. Habron (2004) f ound that sharing management decisions with a spouse can increase the pr obability of adopting sustainable agricultural practices. Since adopting more sustainable practices can require additional capital, farmers may feel it necessary to consult with a spouse to make a decision which can impact the farming operation, and therefore, income generated. Farm Size Studies have shown that farm size has a significant effect on technology adoption within the agricultural industry (Feder, 1980). Research has found that a farmer who is 46

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engaged in a variety of farmi ng enterprises will be more likely to adopt BMPs. Farmers with larger herds or with higher crop produc tion have a greater ability to adopt new technologies and spread the costs associated ov er more units of productions (Kim et al., 2005). As farm size increases, farmers become mo re likely to adopt BMPs (Lamba et al. 2009). Rahelizatovo and Gillespie (2004) found that Louisiana dairy producers with large farms and higher milk yields were more likely to adopt best management practices. Similarly, Kim et al. (2005) found having a greater number of livestock enterprises or crops had a significant impact on the adoption of BMPs. Farmers who receive a large portion of their income from the farming operation are also more willing adopt best management pr actices, showing concern for long-run economic efficiency (Kim et al., 2005, p. 114). Farmers who ar e also more diversified in their production enterprises are also more likely to adopt BMPs because of advantages seen in all areas of the operation (Kim et al., 2005). Interaction with Extension Agent Exposure to new technologies played a cruc ial role in the adoption process. In order for farmers to make informed decisions, farmers must understand the risks and benefits associated with adoption (Feder, 1980). A relationship between Natural Resource Agents and/or Cooperative Extensi on Agents must be established by the farmer. These individuals help in the di ssemination of information and can inform farmers on how to make the best decis ion regarding their farming enterprise. Rahelizatovo and Gillespie ( 2004) found a positive corre lation between interaction with the Cooperative Extension Service and adopt ion of BMPs. More specifically they found that having contact with t he Natural Resource Conservation Service at least once 47

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a year had a positive and significant effect on the adoption of BMPs. However, contact with xxtension personnel more than four ti mes a year did not necessarily lead to adoption (Rahelizatovo & Gillespie, 2004). Even in remote areas of Ethiopia, farmers believe access to agricultural extension services was crucial to the adoption of su stainable agricultural practices (Kassie, Zikhali, Manjur, & Edwards, 2009). When fa rmers had interaction with an extension agent, the likelihood of adopting new technologi es (i.e. sustainable practices) was positive. While interaction between the farmer and ex tension agent is important, trust is also an important factor in this relationship (Lamba et al., 2009). Trust between a farmer and extension agent is not easily gained but must be earned for an easier transition to adoption. For adoption to occur, farmers mu st see that conservation/extension agents make significant commitments to program l ongevity and funding (Lamba et al., 2009). If a trust relationship does not develop, farmers will be less likely to trust the program being implemented, and therefore, not be willing to adopt (Marshall, 2004). Extension agents must also help estab lish a positive relationship between the government and the farmer. Extension agents represent the agencies in which they are involved, and therefore, represent a br anch of government. Ensuring a positive relationship between government agencies and farmers helps increase compliance with future programs (Marshall, 2004). Risk Aversion The levels of risk famers are willing to accept when adopting new technologies has also been documented (Feder, Just, & Zilbe rman, 1985). A risk-averse farmer chooses technologies which ensure a positive net return. Rahelizatovo and Gillespie (2004) 48

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found that the positive effect of a riskaverse attitude suggest that risk-averse producers may view BMP adoption as a riskreducing strategy (p. 237). This shows that some farmers see the long-term financial benefits of adopting best management practices. In a contradictory study, Ki m et al., (2005) found risk-av erse producers were less likely to adopt BMPs. Their results suggest that cattle producers see the adoption of BMPs as risky, possibly due to the lack of information available regarding costs and benefits. Land Characteristics While the characteristics of a farmer can dictate the adoption de cision process, the flow of the land can also determine if adopt ion occurs. Best management practices are developed based upon the characte ristics of the regions in which they will be adopted. In order for sustainable practi ces to be utilized by farmers, the practices must address site-specific characteristics (Kassie et al., 2009, p. 196). Land Ownership Ownership of land used for farming can also impact a farmers adoption of best management practices. Previous studies f ound a negative association between renting land and BMP adoption (Soule, et al., 2003). In general, farmers were unwilling to adopt BMPs on rented land (Cardona, 1999), but share-renters were more likely to adopt conservation practices than cash-renters. Plot ownership revealed a significant relationship in the adoption of best management practices in Ethiopia (Kassie et al ., 2009). A study of the Tigray region in Ethiopia found that plot ownership had a posit ive impact on the adopt ion of sustainable practices. 49

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Findings by Rahelizatovo and Gillespie ( 1998) contradicted previous studies. Rahelizatovo and Gillespie (199 8) found the greater the percentage of land owned, the fewer the number of best management pr actices were adopted. This is believed because it is relatively inexpensive for a landlord to include conditions in a lease requiring the use of sustainable practices. When a farmer is producing on his/her own land, the farmer is responsible for costs associated with conservation practices, whereas when land is rented, the landlord covers co sts not paid by government programs. Presence of River/Stream A stream is generally expected to increase the adoption of best management practices (Rahelizatovo & Gillespie, 2004). However, Kim et al., (2005) found mix results when a stream or rive r ran through the farm. The pres ence of a stream or river was important only when the adopt ion practices included required the use of a water source. However, Rahelizatovo and Gillesp ie (2004) found the pr esence of a stream was crucial, no matter whether the adoption included the use of a water source (2004). Plot Characteristics Prokopy, Floress, Klotthor-Weinkauf and Baumgart-Getz (2008) stated farmers with steep slopes on their farms would be more willing to adopt best management practices because of problems with soil erosi on. Similarly, Kassie et al., (2009) found plot characteristics, such as slope, si gnificantly influenced the adoption of best management practices. Specifically, Kassie et al., (2008) determined that the likelihood to adopt practices is less on land whic h is flat to moder ately sloped. 50

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Soil Type The soil quality on a farm can also affect a farmers decision to implement best management practices. Prokopy et al., ( 2008) stated the better the soil quality on a farm, the greater the likelihood a farmer will not adopt best management practices. However, farmers with highly erodible land s are more likely to adopt best management practices in order to increase production an d reduce erosion (Soule et al., 2000). The color of soils can also impact a fa rmers decision to adopt best management practices. A study of Ethiopi an famers in the semi-arid r egion of Tigray found farmers were less likely to engage in conservation t illage where soils were predominately black (Kassie et al., 2009). Financial Incentive Financial incentives can also play a ro le in a farmers decision to adopt best management practices. While characteristi cs of the farmer and land can determine which practices farmers chose to adopt, financ ial incentives can serve as one of the leading factors as to why farmers adopt best management practices. Fines/Stronger Enforcement Fines for not adopting best management prac tices can serve as a motivational factor for farmers to adopt. Lamba et al., (2009) studi ed farmers in Southern Ontario and sought to determine what participants belie ved to be the most useful incentive governments could provide to encourage the adoption of best management practices. According to farmers in the study, monetar y rewards were the most frequently sought after incentive. Similarly, Cooper and Keim (1996) also found that financial incentives increase the voluntarily adoption rate of BMPs. However, the suggested amount of the financial incentive to provide to farmers was not determined in this study. 51

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While previous studies have shown incentiv es can increase adoption rates, a fear of higher fines and stronger enforcements can also increase adoption rates. Lamba et al., (2009) also suggested that stronger enforcements and fi nes from state and federal levels would encourage more farmers to adopt. Similarly, conservation agents in this study believed that financial incentives, along with tax r ebates, would also increase adoption rates among farmers in sout hern Ontario (Lamba et al., 2009). Growing Regulation While Lamba et al., (2009) found farmers be lieved financial incentives were a component to encouraging adoption, the farmers in this study stated that there was too much government involvement in the adopti on of BMP decisions. Many participants in this study were found to be resentful to wards government interaction and stated less government regulation was needed in the fu ture. However, fa rmers stated the governmental agencies were needed to provide t he means for financial incentives. Regulation in the area of BMP adoption has becoming a growing concern in the agricultural industry. While currently, BM P adoption is voluntary, many believe regulations will be necessary to increase t he adoption rate (Lamba et al., 2009). A voluntary approach to adoption is also preferred, but accordi ng to Lamba et al., (2009) this approach is not effective in increasing adoption rates. Environmental Attitudes The attitude a farmer has not only towards the environment but also towards the role of best management practices in conserva tion can also play a role in the adoptiondecision process. Involvement in environmental organizations and moral obligations can also play a factor in fa rmers decision to adopt. 52

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Moral Obligations Theories suggest that farmers chose to adopt best management practices for a variety of reasons, including environment al concerns, financial incentives, environmental regulation, and pressure from peer s. Lamba et al., ( 2009) found that nonspecific agricultural producers in sout hern Ontario, Canada ranked environmental responsibility as their primary reason for adopting BMPs. Similarly, Kaiser and Shimoda (1999) found that a moral obligation caused many farmers to adopt voluntarily. One reason farmers were believed to adopt BMPs is because of a desire to be seen as good stewards of the environment. While many farmers understand the environmental implications their farming operations pose, these individuals also choose to actively engage in practices which are envi ronmentally responsible (Wall, Weersink, & Swanton, 2001). Organization Involvement Previous research has found that famer associations and unions are important sources of information in the decision to adopt best management practices (Caviglia & Kahn, 2001). Specifically, environment al organizations have often promoted conservation practices and can serve as re -enforcement of decisions to adopt by farmers. Research has shown involvement in an environmental organization means that farmers are more likely to adopt agricult ural programs (Smith ers and Furman, 2003; Morris and Potter, 1995). Farmers involved in these types of organizations often see the need for agricultural practices which benefit t he environment. Similarly, Kassie et al. (2009) found that if at least one member of a farming household was involved in a farming organization, the fa rmer was more likely to adopt best management practices. 53

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Chapter Summary This chapter provided an analysis of Rogers Adoption Diffusion Model as the theoretical framework for this research. The diffusion of innovations process begins with an innovation which is communicated, over time, through the social system. In this research, the innovation under investigat ion was the adoption of best management practices by row crop farmers in the Su wannee Valley. According to Rogers, the adoption of best management practices begins with the knowledge stage, followed by persuasion, decision, implementation, and confirmation. For the purpose of this research, farmers were classified as innovators, early adopters, early majority, late majority, and laggards based on t he time frame of adoption. Following a model by Lamba et al., (2009) Figure 2-2 was developed to describe influences which can affect a farmers dec ision to adopt best management practices. These influences include farmer characteri stics, such as age, communication with peers, education, farm size, interacti on with conservation agent, land ownership, and organization involvement. A farmers level of risk aversion can also affect their decision to adopt. Other reasons for adoption include characteristics of the land, financial incentives, and environmental attitudes. 54

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CHAPTER 3 RESEARCH DESIGN AND METHODOLOGY This research focused specifically on best management practices within the row crop industry. While many in the row crop industry have chosen to adopt best management practices, the reason why adop tion has occurred has not been fully documented. A quantitative study is needed in this area to help understand why row crop farmers in the Suwannee River Wate r Management District adopt BMPs. This quantitative study was selected in an attemp t to assist extension agents and policy makers regarding the adoption of BMPs. Research Design According to McMillan and Schumacher (2010), quantitative research measures objectivity and describes phenomena. As a re sult, data were analyzed using numbers, statistics, structure, and contro l. Quantitative research is divided into two subcategories experimental and non-experimental research Non-experimental research describes phenomena and studies relationships between different phenomena without directly manipulating any of the condi tions that ar e expected. This study used descriptive research, which is a form of non-experimental research. Descriptive research provides a summary of an existing phenomenon by using numbers to characterize individual s or groups (McMillan & Schumacher, 2010, p. 22). As a result, descriptive research is limited to describing a phenomenon in its current state. Specifically, this research utilized descrip tive research. In th is type of research, the investigator selects a sample fr om a target population and administers a questionnaire via the Internet, mail, phone or in person. Surveys can be used to gather 55

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opinions, attitudes, beliefs, values, and behaviors. The questionnaire administered in this study asked row crop farmers to identif y the factors which influenced their decision to adopt BMPs. While a descriptive survey was chosen as the instrument in this study, the method does have its weaknesses. With this type of research design, the researcher must ensure a large number of the selected sample will repl y, it may be difficult for participants to recall information, and survey research is difficult in addressing context (Colorado State University, 2010). To ensure a higher response rate, the researcher spoke with extension personnel in areas w here the survey was administered. These extension agents promoted the questionnaire and encouraged row crop farmers to complete and return the questi onnaire to the researcher. It was also determined that questions where participants were asked to recall information may have a low response rate. Lastly, the context of the questions wa s addressed by a panel of experts to ensure the questions were related to the focus of the study. A descriptive questionnaire (Appendix A) was used in this study because previous research showed a lack of quantitativ e research in the area of adoption of best management practices by row crop farmers. Th is type of research was also selected because it was believed participants w ould be more willing to answer a brief questionnaire than schedule an interv iew or attend a focus group. Non-Response Error With survey studies, a percentage of the population does not usually respond to the questionnaire (McMillan & Schumacher, 2010). To address non-response error in this study, a second contact was made on Nove mber 18, 2010 in the form of a reminder postcard to non-responders. A third contact and final contact was made on December 1, 56

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2010 to non-respondents. The third cont act included a second copy of the questionnaire, cover letter, informed consent and a pr e-addressed postage paid envelope. Sampling Error Sampling error is defined as the extent to which the precision of the survey estimates is limited because not every person in the population is sampled (Dillman, Smyth, and Christian, 2009, p. 17). However, because this study was a census, there was no sampling error. Validity This research design also addressed eleven different areas of internal validity. According to McMillan and Schumacher (2010), hi story is defined as threats to internal validity through uncontrolled events or incident s that can affect the dependent variable. For the purpose of this research, history wa s evaluated by determining if a major event occurred during the data collection process which would have threatened the validity of the data gathered. Selection is referred to as a series of threats related to participant characteristics (McMillan & Schumacher, 2010). To address selecti on in this study, all participants were selected from a list of row crop farmers in the Suwannee River Water Management District. The list was obtained from an extension agent in the Suwannee River Water Management District. The list contained the mo st up to date information regarding row crop farmers in this area. Statistical regression refers to the tenden cy of participants who score very high or very low on the pretest to score closer to the mean on the posttest (McMillan & 57

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Schumacher, 2010). To prevent statistical r egression in this study, no extreme groups were studied. Pretesting is a threat to internal validit y in which taking a pretest can affect the results (McMillan & Schumacher, 2010). To addr ess affects of pretesting, no pretest was given in this study. Instrumentation refers to changes in the in strument or persons used to collect data which may affect results (McMillan & Schum acher, 2010). To address problems with the instrument in this research, the instrument was reviewed by a panel of experts in survey design and those involved in the area of best management practices. The panel of experts consisted of extension agents within the Suwa nnee River Water Management District and the Florida Department of Agriculture and Consumer Services. Attrition, also referred to as mortality involves the loss of participants in a study (McMillan & Schumacher, 2010). To prevent attr ition in this study, there was only one collection of data. Maturation refers to the natural and biological changes in participants during the course of the study (McMillan & Schumacher 2010). To prevent maturation in this study, there was only one collection of data. Diffusion of intervention is a threat to in ternal validity in wh ich the subjects are influenced by other conditions of the indep endent variable (McMillan & Schumacher, 2010). To prevent diffusion of interventi on, no interventions were given. Experimenter effects refer to the influence the researcher can have on the results (McMillan & Schumacher, 2010). To avoid experim enter effects the researcher was not present when the surveys were completed. 58

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Intervention replications can occur when the number of intervention replications is not equivalent to the number of subjects (McMillan & Sc humacher, 2010). To prevent intervention replications in this st udy, no replications were taken. Subject effects refer to the influence par ticipants can have on the results (McMillan & Schumacher, 2010). Since there was only one oppor tunity to collect data, subjects did not have an opportunity to change behaviors. Theref ore, there were so subject effects. Procedures A descriptive survey research design wa s used in this study. The questionnaire was created by the researcher and evaluated for validity by a panel of experts. The panel of experts consisted of those who had experience in research design and those who had previously worked with row crop fa rmers. The questionnaire was then testpiloted to a group of row farmers in Jackson County, Florida who had similar characteristics of the tar get population. The data gathered from the test pilot were analyzed to determine the length of time required to complete the survey and whether directions and items were cl ear. Reliability coefficients we re also determined using data from the pilot test. Due to the small size of the population (N = 105) studied, a census was used (Israel, 2009). According to Israel, a census is ideal when populations are 200 or less. A census also provides data on all of the individuals in the population and eliminates sampling error. The outline of the study, procedures and instrument were submitted to the Institutional Review Board (I RB) at the University of Florida. IRB final approval was granted on August, 31, 2010 (Approval of Pr otocol # 2010-U-0779) (Appendix B). An 59

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informed consent (Appendix C) which included the protocol number, risks, benefits, and purpose of the study was sent to participants. The questionnaire was distributed using the United States Postal Service to row crop farmers in the Suwannee River Wate r Management District in the fall of 2010. Access to this population was provided by an extension agent who worked closely with row crop farmers in this area. Included with the survey was a letter (A ppendix D) which described the research. Participants were given two weeks to res pond to the survey. A reminder postcard (Appendix E) was then mailed to non-respondent s two weeks after the initial contact reminding participants to mail the completed survey back as soon as possible. A replacement questionnaire (Appendix A) and cover letter (Appendix F) was then sent to non-respondents one month after the in itial questionnaire mailing. Instrumentation The instrument used for this study wa s a survey developed by the researcher and consisted of 15 questions. This method was chosen because it was determined to be the most effective way to reach a large number of individuals with little amount of interaction. Questions were divided into 5 categories, including a demographic section. The first set of questions pertained to areas of row crop production in which the producer was involved. Questions also asked how long after hearing about BMPs farmers decided to adopt. The format of questions in th is section included multiple choice, check all that apply, and open ended questions, Section two of the instrument as ked questions which pertained to farmer interactions. This section was divided into thr ee areas 1) farmer interaction, 2) family 60

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influence and 3) interaction with an extens ion agent. There were seven items per section which were on a 5-point Likert scale except for the sect ion regarding family influence which was on a 6-point Liker scale. The third section of the survey cont ained questions pertaining to the farmers level of risk aversion. This section contai ned seven questions on a 5-point Likert scale. The fourth section of the survey cont ained questions pertaining to the farmers opinions of voluntary BMP adoption and ince ntives. This section contained seven questions on a 5-point Likert scale. The final section of the survey included three demographic questions. These questions pertained to participants age, t he amount of land leas ed/rented or owned, and the highest level of education achieved. The format of the questions consisted of multiple choice and short answer. Validity The face and content validity of the surv ey questions created by the researcher were assessed by a panel of experts. Questions were evaluated by those with best management practice experience, as well as those with survey design experience. The external validity of this research wa s also considered. External validity is the extent to which results can be generalized (McMillan & Schumacher, 2010). According to McMillan and Schumacher (2010), the results in this study can be generalized to all row crop farmers in the Suwannee River Water Management District, since this was a census survey. Reliability Reliability refers to the extent results ar e similar when using different forms of the instrument or at differ ent data collection times (McMillan & Schumacher, 2010). 61

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Reliability is often measured by the extent to which a measurement is free from error. Therefore, if an instrument has little error, it is considered to be more reliable. In this study, Cronbachs alpha was us ed to assess reliability. Cronbachs alpha determines the agreement of answers as re lated to a specific trait (McMillan & Schumacher, 2010). It is associated with the variation accounted for by the true score of the underlying construct (Santos, 1999, para. 7) with the construct being the variable measured. McMillan and Schumacher ( 2010) stated pilot testing is essential in evaluating the instructions and questions on a questionnai re. A pilot test should also be conducted with respondents who are similar to the target population. For th is study, a pilot test was conducted with 10 row crop farmers in Jacks on County, Florida. Participants were asked to evaluate the instructions for each questions as well as the formatting of the question. As a result of the pilot test, f eedback was evaluated and t he original questions were revised. Data Analysis Data were analyzed using SPSS: An IB M Company (version 18). Descriptive statistics were calculated including mean and standard deviation. Bivariate correlations were calculated for each predicted rela tionships. Demographic variables were calculated make group comparisons and describe the population. Chapter Summary A quantitative research method was used in this study to determine influences which effect a row crop farmers decisi on to adopt best management practices. Specifically, a descriptive survey was used because it was determined to be the most effective method of reaching a large number of individuals. The questionnaire was 62

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administered and data were collected during the fall of 2010. Data were then analyzed using SPSS: An IBM Company (version 18) to determine correlations and make comparisons among groups. 63

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CHAPTER 4 RESULTS Overview The purpose of this research was to det ermine factors which influence the use of best management practices by row crop farm ers in the Suwannee Valley of North Central Florida. To achieve the purpose of the study, the research was guided by the following objectives: Identify the factors that row crop farmers in North C entral Florida perceive as influential in their decision to adopt (o r not adopt) best managem ent practices. Determine the self-perceived relative im portance of each fact or in the adoption decision. Examine the relationship of demographic characteristics on the adoption decision. Identify the factors that ar e most important in predic ting rates of best management practice adoption. To achieve the stated research objecti ves, a census was taken of row crop farmers in the Suwannee Valley of North Central Florida. The original sample size was 125. Twenty were either deceased or no longer active farmers and were excluded from the sample. One hundred and five surveys were sent to current row crop farmers in the Suwannee Valley of North Cent ral Florida via first class ma il. Participants signed a consent form and were then asked to comp lete a six page survey. Surveys were returned via first class mail in a preaddressed postage paid envelope each with a unique identification number. Thirty three questionnaires were returned yielding a response rate of 31.43%. Of the 33 retu rned questionnaires, 24 were completed. Participants indicated on the remaining ni ne questionnaires that they did not engage in Best Management Practices. 64

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Following Dillmans five compatible cont acts (Dillman, Smith & Christian, 2009) questionnaire mailing was sent via first cl ass mail to each member of population on November 1, 2010. The mailing contained the questionnaire, consent form, letter describing the research and a self-addr essed stamped envelope to return the completed survey and consent form. A se cond contact was made on November 18, 2010. Reminder postcards were mailed to participants who had not yet responded. A replacement mailing containing the questionnai re, consent form, letter describing the research and a self-addressed stamped envel ope were sent to non-respondents on December 1, 2010. Demographics The demographic questions included in th is study were row crops grown, age, and highest level of formal education completed. Demographic results were reported in Table 4-1, Table 4-2 and Table 4-3. Row Crops Grown Survey respondents were given the following choices of row crops to choose from: potatoes, corn, soybeans, peanuts, cotton and tobacco. Some respondents produced multiple crops which are reflected in Table 4-1. Respondents who grew any other type of row crop were asked to list t he other crops grown in the space provided. Twelve percent ( n = 3) of respondents grew pot atoes. Seventy-two percent (n = 18) of respondents grew corn. Thirty two percent ( n = 8) of respondents grew soybeans. Eighty percent of respondents ( n = 20) grew peanuts. Ninety two percent ( n = 23) of respondents grew cotton. Eight percent ( n = 2) of respondents grew tobacco. Thirty six percent of respondents ( n = 9) grew other types of ro w crops. This information can be seen in Table 4-1. 65

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Table 4-1. Percentages of crops grown by row crop farmers in the Suwannee Valley of North Central Florida Crops Number grown Percentage Potato 3 12 Corn 18 72 Soybean 8 32 Peanut 20 80 Cotton 23 92 Tobacco 2 8 Other 9 36 n = 24 Age Respondents were asked to indicate thei r age in years. The following age ranges were provided: less than 20 years old, 21-30 years old, 31-40 years old, 41-50 years old, 51-60 years old, 61-70 years old and 71 years old or older. Four percent ( n = 1) of respondents were between the ages of 21 30 years old. Eight percent ( n = 2) were between the ages of 31 40. Twenty nine percent ( n = 7) of respondents were between the ages of 41 50 years old. Twenty five percent ( n = 6) of respondents were between the ages of 51 60 years old. Twenty nine percent (n = 7) of respondents were 61 70 year s of age. Four percent ( n = 1) of respondents were 71 years of age and older. This information can be seen in Figure 4-2. Table 4-2. Percentages of respondents age Age (in years) Number of respondents Percentage 21-30 1 4 31-40 2 8 41-50 7 29 51-60 6 25 61-70 7 29 71 and older 1 4 n = 24 Highest Level of Formal Education Completed Participants were asked to indicate their highest level of formal education completed from a provided list. The following education levels were provided: less than 66

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12th grade, high school diploma or GED, some college but no degree, completed 2 year degree (AA) or other vocati onal degree program, completed 4 year degree (BA or BS) and graduate school or professional school. Thirty three percent ( n = 8) of respondents had a high school diploma or GED equivalent. Twenty five percent ( n = 6) of respondents had so me college credit but no degree. Twenty one percent ( n = 5) of respondents had comp leted a 2 year degree (AA) or other vocational degree program. Twenty one percent ( n = 5) had completed a 4 year degree (BA or BS). This information can be seen in Figure 4-3. Table 4-3. Percentages of highest level of education completed by respondents Highest level of education completed Number of respondents Percentage High school diploma or GED 8 33 Some college but no degree 6 25 Completed 2 year degree 5 21 Completed 4 year degree 5 21 n = 24 Reliability of Assessments To measure farmer attitudes towards best management practices adoption, five subscales were included in this study farmer interaction, family influence, interaction with extension agents, and risk aversion. Table 4-4 shows the average of the means and standard deviations of each subscale as well as Cronbachs alpha (reliability) for each subscale. Farmer interaction refers to a farmers interaction with other farmers. A sample question from this subscale is I interact on a weekly basis with ot her farmers. Farmer interaction was measured on a 5-point Likert -type scale ranging from strongly disagree 67

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(1) to strongly agree (5). This subscale met an acceptable reliability with a Cronbachs alpha = 0.86. Family influence refers to a farmers inte raction with other family members who are also involved in a farming operation. A samp le question from this subscale is The interactions I have with members of my fam ily who also farm are important. Family influence was measured on a 6-point Likert-ty pe scale ranging from strongly disagree (1) to strongly agree (5) and not applicable (6). This subscale met an acceptable reliability with a Cronbachs alpha = 0.94. Interaction with extension agents refers to a farmers interaction with extension agents. A sample question from this subscale is I regul arly interact with an extension agent. Interaction with extension agents was measured on a 5-point Likert-type scale ranging from strongly disagree (1) to strongly agree (5). This subscale met an acceptable reliability with a Cronbachs alpha = 0.93. Risk aversion refers to a fa rmers willingness to accept risk. A sample question from this subscale is I believe I acc epted some risk when deciding to adopt BMPs. Risk aversion was measured on a 5-point Li kert-type scale ranging from strongly disagree (1) to strongly agree (5). This s ubscale met an acceptable reliability with a Cronbachs alpha = 0.79. Voluntary BMP adoption and incentives refers to a farm ers attitude to voluntary and mandatory adoption. A sample question from this subscale is I adopted BMPs on my farming operation because of a fear of growing regulation and fines. Voluntary BMP adoption and incentives was measured on a 5point Likert-type scale ranging from 68

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strongly disagree (1) to strongly agree (5). This subscale met an acceptable reliability with a Cronbachs alpha = 0.73. Table 4-4. Mean and standard deviation of each subscale and reliability Subscale Mean Standard dev iation Reliability Farmer interaction 3.97 0.76 0.86 Family influence 4.10 1.06 0.94 Interaction with extension agents 3.79 0.95 0.93 Risk aversion 3.68 0.80 0.79 Voluntary BMP adoption and incentives 3.74 0.79 0.73 Findings Objective: 1 Identify the factors that row crop farmers in North Central Florida perceive as influential in their deci sion to adopt (or not adopt) best management practices. The majority of respondents agreed or stro ngly agreed (63%) to interaction on a weekly basis with other farmer s. Sixty seven percent of fa rmers also agreed or strongly agreed that they were willing to discuss their farming operation while 88% of farmers agreed or strongly agreed they were willing to discuss BMPs with other farmers. The majority of respondents (83%) were also willi ng to try new farming innovations based on advice received from peers. Ninety six percent of farmers also stated that building a trusting relationship with other farmers is important. Seventy one percent of respondents agreed or strongly agreed that inte ractions with other farmers influence decisions made regarding BMPs. Ninety two per cent of respondents agreed or strongly agreed they would encourage pe ers who have not adopted BMPs to adopt in the near future. This information can be seen in Table 4-5. 69

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Table 4-5. Analysis of interactions between farmers Strongly disagree Disagree Neutral Agree Strongly agree Weekly basis 0 8.3% (2) 29.2% (7) 37.5% (9) 25% (6) Farming operation 0 16.7% (4) 16.7% (4) 45.8% (11) 20.8% (5) Discussion of BMPs 0 4.2% (1) 8.3% (2) 75% (18) 12.5% (3) New farming innovations 0 0 16.7% (4) 54.2% (13) 29.2% (7) Trusting relationships 0 0 4.2% (1) 58.3% (14) 37.5% (9) Influence decisions 0 12.5% (3) 16.7% (4) 62.5% (15) 8.3% (2) Encourage BMP adoption 0 4.2% (1) 4.2% (1) 58.3% (14) 33.3% (8) n = 24 The majority of farmers (89%) agreed or strongly agreed to discussion of farming practices with members of their family who also farm. Surprisingly, 94% of respondents agreed or strongly agreed to working with fa mily members on the farming operation. Seventy two percent of respondents agreed or strongly agreed that they would be passing their farm to a member of their family upon retirement. Fifty percent of respondents agreed or strongly agreed that their spouse is involved in the day-to-day operations of the farm. Sev enty eight percent of respond ents agreed or strongly agreed that interactions with family members w ho also farm influences decisions made regarding BMPs. Ninety four percent of respondents indicat ed interactions with other family members involved in farming are important. This information can be seen in Table 4-6. 70

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Table 4-6. Analysis of influence on adoption by family members Strongly disagree Disagree Neutral Agree Strongly agree N/A Discussion 5.6% (1) 0 5.6% (1) 38.9% (7) 50% (9) 0 Assistance 5.6% (1) 0 0 50.0% (9) 44.4% (8) 0 Passing of farm 5.6% (1) 0 16.7% (3) 33.3% (6) 38.9% (7) 5.6% (1) Spouse involvement 11.1% (2) 5.6% (1) 33.3% (6) 16.7% (3) 33.3% (6) 0 Importance 5.6% (1) 0 0 55.6% (10) 28.9% (7) 0 Decision 5.6% (1) 5.6% (1) 11.1% (2) 55.6% (10) 22.4% (4) 0 Importance of interactions 5.6% (1) 0 0 44.4% (8) 50% (9) 0 n = 24 (Some participants did not fully complete this question.) Only 61% of respondents agreed or st rongly agreed that they had regular interaction with an extension agent. Eight y three percent of respondents agreed or strongly agreed that an extension agent in the area provided information regarding BMPs. Contrary to expectations, only 58% of respondents agreed or strongly agreed that interactions with ext ension agents are infl uential in the decision to adopt BMPs. Eighty three percent of respondents agreed or strongly agreed that the effectiveness of an extension agent can affect a farmers decision to adopt BMPs. Surprisingly, 21% of respondents disagreed or strongly disagreed t hat their extension a gent did not provide the necessary information regarding the adopt ion of BMPs. A majority of respondents (86%) agreed or strongly agr eed that extension agents have th eir best interest in mind. Similar to previous research, 87% of respondents trust the information given by extension agents regarding BMPs. This in formation can be seen in Table 4-7. 71

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Table 4-7. Analysis of interactions with extension agents Strongly disagree Disagree Neutral Agree Strongly agree Regular interaction 8.7% (2) 4.3% (1) 26.1% (6) 43.5% (10) 17.4% (4) Information 4.2% (1) 8.3% (2) 4.2% (1) 70.8% (17) 12.5% (3) Decisions 4.2% (1) 8.3% (2) 29. 2% (7) 54.2% (13) 4.2% (1) Effectiveness 0 4.3% (1) 13.0% (3) 60.9% (14) 21.7% (5) Necessary information 4.2% (1) 16.7% (4) 16.7% (4) 50.0% (12) 12.5% (3) Best interest 4.3% (1) 0 8.7% (2) 56.5% (13) 30.4% (7) Trust 4.3% (1) 0 8.7% (2) 65.2% (15) 21.7% (5) n = 24 (Some participants did not fully complete this question) A majority of respondents (55%) agreed or strongly agreed to being a risk taker. Seventy three percent of respondents agreed or strongly agreed that they were willing to take risks with their fa rming operation. Ninety one percent of respondents agreed or strongly agreed that accepting some risk in r egards to a farming operation is important. Surprisingly, 52% of respondents agreed or strongly agreed that they accepted risk when deciding to adopt BMPs. Eighty two percent of respondents saw the risk in adopting BMPs as beneficial to their fa rming operation. Surprisingly, 14% of respondents disagreed that if the risk asso ciated with BMPs was minimized, more farmers may decide to adopt. This info rmation can be seen in Figure 4-8. Only 38% of respondents adopted BMPs because of a fear of growing regulations and fines. Sixty three percent of respondents agreed or st rongly agreed that financial incentives played a role in the decision to adopt BMPs. Fifty five percent of respondents agreed or strongly agreed that BMP adoption may become m andatory in the future. Similar to previous findings, 79% of res pondents agreed or strongly agreed that more farmers would adopt BMPs voluntarily if ther e were a larger financial incentive. Eighty three percent of respondents al so agreed or strongl y agreed that incentives, other than 72

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financial should be provided to farmers who adopt BMPs. This information can be seen in Table 4-9. Table 4-8. Analysis and categorizati on of farmer willingness to accept risk Strongly Disagree Disagree Neutral Agree Strongly Agree Risk taker 0 9.1% (2) 36.4% (8) 27.3% (6) 27.3% (6) Farming operation 0 9.1% (2) 18.2% (4) 50% (11) 22.7% (5) Importance 0 4.5% (1) 4.5% (1) 72.7% (16) 18.2% (4) Risk of BMPs 0 4.8% (1) 42.9% (9) 47.6% (10) 4.8% (1) Beneficial 0 4.5% (1) 13.6% (3) 77.3% (17) 4.5% (1) Risk nonadoption 4.5% (1) 9.1% (2) 36.4% (8) 45.5% (10) 4.5% (1) Minimal risk 0 14.3% (3) 28.6% (6) 47.6% (10) 9.5% (2) n = 24 (Some participants did not fully complete this question) Table 4-9. Farmers opinions of vo luntary BMP adoptio n and incentives Strongly Disagree Disagree Neutral Agree Strongly Agree Growing fear 8.3% (2) 20.8% (5) 33.3% (8) 29.2% (7) 8.3% (2) Financial incentives 0 8.3% (2) 29.2% (7) 50% (12) 12.5% (3) Mandatory 0 9.1% (2) 36.4% (8) 50% (11) 4.5% (1) Larger incentives 0 20.8% (5) 54.2% (13) 25% (6) Other incentives 0 0 16.7% (4) 54.2% (13) 29.2% (7) n = 24 (Some participants did not fully complete this question). Objective 2: Determine the self-perceived relative importa nce of each factor in the adoption decision. A bivariate correlation analysis was conducted on all variables (Table 4-10). Results indicated a significant positive relationship between extension interaction and family influence (0.65). This indicates that farmers who interact wit h their family also interact with extension personnel. Results also indicated a significant positive relationship between interaction with extension personnel and a farmers willingness to 73

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accept risk on his/her farming operation (0.66). This indicates that as a farmer interacts with extension personnel, he/she becomes more willing to accept risk on their farming operation. For the purpose of this study, willingness to accept risk is referred to as risk aversion. A significant positive relationship was found between farmer interaction and voluntary BMP adoption (0.43). Th is indicates that as farme r interaction increases so does voluntary adoption. Table 4-10. Bivariate correlation analysis on farmer interaction, family influence, extension interaction, risk av ersion and voluntary BMP adoption Farmer Interaction Family Influence Extension Interaction Risk Aversion Voluntary BMP adoption Farmer Interaction 0.26 0.05 0.32 0.43 Family Influence 0.26 1.00 0.65 0.40 -0.11 Extension Interaction 0.05 0.65** 1.00 0.66 -0.05 Risk Aversion 0.32 0.40 0.66** 1.00 0.27 Voluntary BMP Adoption 0.43* -0.11 -0.05 0.27 1.00 *. Correlation is significant at the 0.05 level (2-tailed) **. Correlation is significant at the 0.01 level (2-tailed) Table 4-11. Bivariate correlation analysis bet ween farmer interaction, family influence, extension interaction, risk aversion, voluntary BMP and types of row crops grown Farmer Interaction Family Influence Extension Interaction Risk Aversion Voluntary BMP adoption Potatoes 0.20 0.23 0.14 -0.38 -0.12 Corn 0.19 0.42 0.25 0.27 -0.13 Soybeans 0.03 0.12 0.30 0.26 -0.29 Peanuts 0.20 0.00 -0.03 -0.21 -0.26 Cotton 0.03 0.08 0.42* 0.41 -0.01 Tobacco 0.08 0.08 0.16 0.48* 0.49* Other 0.23 0.14 -0.11 0.24 0.07 *. Correlation is significant at the 0.05 level (2-tailed) 74

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A bivariate correlation analysis was also conducted on the five variables and types of row crops grown (Table 4-11). Re sults indicated a significant positive relationship between those who grow cotton and extension interaction (0.42). This indicates that cotton growers regularly interact with extension personnel. Results also indicated a significant positive relationship between those who grow tobacco and his/her willingness to accept risk (0.48). This indicate s that tobacco farmers are more likely to engage in risky practices when it comes to their farming operation. Results also indicated a significant positive relations hip between voluntary BMP adoption and those who produced tobacco (0.49). This indicates that tobacco growers are more likely to voluntarily adopt BMPs. Objective 3: Examine the relationship of demographic characteristics on the adoption decision. No significant relationships were found in regards to demographic characteristics and the decision to adopt BMPs. A negat ive trend was found between extension interaction and age and risk aversion and age but the trend was not significant. This was not expected in that previous resear ch had found significant relationships between age and BMP adoption and highest level of education completed and BMP adoption. Objective 4: Identify the factors that are most important in predicting rates of best management practice adoption. A bivariate correlation analysis was conduc ted on the five variables and methods of receiving information about BMPs (Table 4-12). Results indicated a significant positive relationship between risk aversion and receiving information about BMPs from an extension agent (0.49). This indicates t hat as farmers interact with extension personnel they become more willing to accept risk on their farming operation. 75

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Table 4-12. Bivariate correlation analysis of farmer interaction, family influence, extension interaction, risk av ersion, voluntary BMP adoption Farmer Interaction Family Influence Extension Interaction Risk Aversion Voluntary BMP adoption Extension agent 0.05 0.10 0.34 0.49* -0.27 Family member -0.22 0.01 0.14 0.05 0.21 Another farmer -0.04 a -0.06 0.14 -0.14 UF sponsored event 0.21 0.38 0.30 -0.15 0.14 Internet a a a a a Magazine/newspaper -0.04 a -0.06 0.14 -0.14 Suwannee River Partnership 0.05 0.42 -0.01 -0.11 0.01 Florida Dept of Ag. -0.14 -0.43 -0.23 -0.38 -0.04 Environmental organization a a a a a Other 0.39 0.08 0.26 0.43 0.25 *. Correlation is significant at the 0.05 level (2-tailed) a. indicates there was not enough res ponses to calculate a correlation. Chapter Summary This chapter outlined the findings from a quantitative descriptive survey. An N of 105 farmers was used for this study. Questi onnaires were mailed via first class U.S. mail. Thirty three questionnaires were return ed yielding a response rate of 31.43%. The results were then analyzed using SPSS: An IBM Company (Version 18) and analysis of relationships between influential fact ors and farmers decision to adopt best management practices. 76

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CHAPTER 5 CONCLUSIONS Overview Statement of the Problem Advances made within c onventional agriculture have been both positive and negative. Best management practices have the potential to address many issues associated with conventional agricultural pr oduction but not everyone associated with production agriculture has chosen to adopt thes e practices. Currently, there is a gap in the research regarding reasons row crop fa rmers adopt BMPs. Identifying these factors could help eliminate barriers associ ated with BMP adoption and increase adoption rates. Purpose and Objectives The purpose of this research was to deter mine factors which influence the use of best management practices by row crop farm ers in the Suwannee Valley of North Central Florida. The research aimed to determine if farmer interacti on, family influence, interaction with extension agents, level of risk aversion, and voluntary BMP adoption and incentives played a role in the decisio n to adopt Best Management Practices by row crop farmers in the Suwannee Valley of North Central Florida. To achieve the purpose of this study, the research wa s guided by the following objectives: Identify the factors that row crop farmers in North C entral Florida perceive as influential in their decision to adopt (o r not adopt) best managem ent practices. Determine the self-perceived relative im portance of each fact or in the adoption decision. Examine the relationship of demographic characteristics on the adoption decision. Identify the factors that ar e most important in predict ing rates of best management practice adoption. 77

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Methods To achieve the stated research objectives, a descriptive survey was sent via first class mail to 105 row crop farmers in the Suwannee Valley of North Central Florida. Thirty three questionnaires were retur ned with 24 being complete. Results were analyzed using SPSS: An IBM Company (version 18) and correlations were used to determine factors influential in the adoption decision process of BMPs. Summary of Results Objective 1: Identify the fa ctors that row crop farmers in North Central Florida perceive as influential in their decision to adopt (or not adopt) best management practices. The majority of respondents (63%) agreed or strongly agreed that they interact on a weekly basis with other farmers. Ninet y six percent of farme rs agreed or strongly agreed that building a trusting relationship with other farmers is important. Seventy one percent agreed or strongly agreed that interactions wit h other farmers influence decisions made regarding BMPs and 92% of farmers agreed or strongly agreed they would encourage peers who have not adopted to adopt in the near future. In regards to family influence, 89% of farmers agreed or strongly agreed to having discussion of farming practices with family members who also farm and 94% stated they work with family members on the farming oper ation. A majority of respondents (78%) agreed or strongly agreed that interactions with family members who also farm influences decisions made regarding BMPs. Sixty one percent of farme rs agreed or strongly agreed they had regular interaction with an extension agent wh ile 58% agreed or strongly agreed that interactions with extension agents are influenti al in the decision to adopt BMPs. Twenty 78

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one percent of respondents disagreed or strongl y disagreed that their extension agent did not provide the necessary informa tion regarding the adoption of BMPs. In regards to risk aversion, 55% of re spondents agreed or strongly agreed that they were risk takers while 73% of farmer s agreed or strongly agreed that they were willing to take risks in regards to their fa rming operation. Fifty tw o percent of farmers agreed or strongly agreed to accepting risk w hen deciding to implement BMPs. Eighty two percent of respondents agreed or strongly agreed that the risk in adopting BMPs was beneficial to their farming operation. Only 38% of farmers agreed or strongl y agreed that they adopted BMPs because of a fear of growing regulation and fines. Sixty three per cent of respondents agreed or strongly agreed that the role of financial incentives was a fact or in the decision to adopt BMPs. Seventy nine percent of farmers believe more farmers would adopt BMPs voluntarily if there were a larger financ ial incentive and 83% of farmers believe incentives other than financ ial should be provided to farmers who adopt BMPs. Objective 2: Determine the self-perceived re lative importance of each factor in the adoption decision. A significant positive relationship wa s found between extension interaction and family influence (0.65). A significant positive relationship was also found between interaction with extension personnel and a farme rs level of risk aversion (0.66). Lastly, a significant positive relationship was f ound between farmer interaction and voluntary BMP adoption (0.43) A significant positive relationship wa s found between those who grow cotton and extension interaction (0.42). A significant positive relationship was also found between 79

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those who grow tobacco and a farmers level of risk aversion (0.49). Lastly, results indicated a significant positive relati onship between volunt ary BMP adoption and tobacco farmers (0.49). Objective 3: Examine the relationship of demographic characteristics on the adoption decision. Contrary to expectation s, no significant relationships were found between demographics and the adoption decision. A negative trend was found between extension interaction and age as well as risk aversion and age bu t the trend was not significant. Objective 4: Identify the fact ors that are most important in predicting rates of best management practice adoption. A significant positive relationship was found between risk aversion and receiving information about BMPs from an extension agent (0.49) Conclusions Based on the results of this stud y and previous research the following conclusions were drawn. Famer interaction is positively related to the adoption decision process of BMPs. Family interaction is positively related to the adoption decision process of BMPs. Interaction with extension agents is pos itively related to the adoption decision process of BMPs. Farmers believe the risks associated wit h the adoption of BMPs are beneficial to their farming operation. Greater incentives (i.e. monetary or tax rebates) will lead to increased adoption rates. Farmers who interact with their family also interact with extension personnel. 80

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As farmers interact with extension personnel they become more willing to accept risk on their farming operation (risk aversion). As farmer interaction between other farme rs increases so does voluntary adoption. Cotton farmers have regular inte raction with extension personnel. Tobacco producers are willing to accept risk regarding their farming operation compared to other ro w crop growers. Tobacco producers are more likely to vo luntarily adopt BMPs than other row crop growers. Age and highest level of education completed are not related to a farmers decision to adopt BMPs. Discussions and Implications Conclusion 1: Famer interaction is pos itively related to the adoption decision process of BMPs. Results showed that interacti on with other farmers is influential in the decision to adopt BMPs. This indicates that farmers who make time to interact socially and/or professionally with other farmers are more likely to adopt best management practices on their farming operation. This is similar to findings by Habron (2004) who found that regular interaction amongst farmers led to higher adoption rates of BMPs for livestock production. The results of this study also correspond with Habrons (2004) finding that developing a trusting relationship amongst other farmers is important in order to discuss the successes and complications that may arise with BMP adoption. Conclusion 2: Family interaction is positively related to the adoption decision process of BMPs. Based on the findings of this study, in teractions made with family members who also farm influences decisions made regarding BMPs. This indicates that farmers who make time to interact with t heir family socially and/or prof essionally are more likely to 81

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adopt best management practices on their farming operation. This is similar to findings by Warriner and Moul (1992) who found that families who farm together were more likely to adopt environmental innovations. For th is study, 50% of farmers indicated their spouse was involved in the day-to-day operations of the farm. This is similar to findings by Habron (2004) who found t hat sharing management decis ions with a spouse can increase the probability of adopting sustainable agricultural practices. Conclusion 3: Interaction with extension ag ents is positively related to the adoption decision process of BMPs. Results from this study showed that inte raction with extension agents influences a row crop farmers decision to adopt BMPs. This indicates that farmers who have regular interaction with extension agents are more likely to adopt best management practices on their farming operation. This is similar to findings by Rahelizatovo and Gillespie (2004) who found a positive correlation between interaction with the Cooperative Extension Service and adoption of BMPs. This study also found that row crop farmers believe establishing a trusting relationship with extension personnel is important. This corresponds to previous studies which found t hat in order for adoptio n to occur, farmers must establish a trusting relationship with extension personnel (Lamba et al., 2009). Conclusion 4: Farmers belie ve the risks associated wit h the adoption of BMPs are beneficial to their farming operation. Results from this study show that fa rmers believe the risks associated with the adoption of BMPs are beneficial to their farming operation. This indicates that although farmers believe there are risks associated with adopting BMPs, the risks outweigh the consequences of not adopting BMPs. Surprising ly, 52% of farmers in this study agreed 82

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or strongly agreed that t hey accepted risk when deciding to adopt BMPs. This contradicts findings by Rahe lizatovo and Gillespie (2004) who found that farmers who adopted BMPs believed they were engaging in a risk-reducing strategy. This difference could be due to the types of row crops grown and the practices that can be implemented with each. Practices associated with some row crops may be considered more risky. Conclusion 5: Greater incentives (i .e. monetary or tax rebates) will lead to increased adoption rates. Results of this study found only 38% of farmers adopted BMPs because of fear of growing regulations and fines. This indicates that farmers believe if greater incentives were offered, adoption rates of best m anagement practices would increase. The majority of farmers indicated that financial incent ives played a role in the decision to adopt BMPs. This corresponds with findings by Cooper and Keim (1996) who found that financial incentives increased the voluntarily adoption rate of BMPs. This study also found that farmers believe that more farmers would voluntarily adopt BMPs if there were larger financial incentives as well as other non-financial incentives. This is similar to previous research which suggested that financial incentives, along with tax rebates would increase adoption rates among farmers in Southern Ontario (Lamba et al., 2009). Conclusion 6: Farmers who in teract with their family also interact with extension personnel. This study found a significant positive relationship between interaction with extension agents and family influence. This in dicates that farmers wh o interact with their family also interact with extension agents. Th is is a reasonable conclusion in that if 83

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farmers are willing to interact with one another they would also be willing to interact with extension personnel. Farmers who interact wit h their family and extension personal can be described as extroverted and are t herefore by nature, sociable. Conclusion 7: As farmers interact wit h extension personnel, they become more willing to accept risk on their farming operation (risk aversion). Results of this study found that as a farmer interacts wit h extension personnel, they become more willing to accept risk on t heir farming operation. This indicates that as farmers interact with extension personnel they bec ome more comfortable with experimenting with new farming techniques on their operation. This is a reasonable conclusion since the mission of extension is to provide information to its clientele. Here, the clientele is row crop fa rmers. Therefore, extension a gents in the Suwannee Valley should provide all of the necessary informa tion, guidelines and procedures for adopting BMPs. The interaction should allow the fa rmer to make an informed and confident decision regarding BMP adoption. Conclusion 8: As farmer interaction between other farmers increases so does voluntary adoption. The results of this study found a signifi cant positive relationship between farmer interaction and voluntary BMP adoption. This indicates that farmers who interact with other farmers socially and/or professi onally encourage non-adopter s to adopt BMPs. This is a reasonable conclusion in that farmers who interact with one another may encourage their peers to adopt BM Ps. This study found that 92% of farmers would encourage peers to volunt arily adopt BMPs. 84

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Conclusion 9: Cotton grow ers have regular interaction with extension personnel. The results of this study found a signifi cant positive relationship between cotton producers and interaction with extension personnel This indicates that cotton farmers have regular interaction (professionally or socially) with extension personnel. For all other row crops grown, no significant rela tionship was found bet ween the crop grown and interaction with extension personnel. This could be because cotton was the most predominant crop grown by those in this study. However this relationship could also be because cotton farmers regularly visit ex tension agents or extension agents make a point to regularly visit cotton farmers. Anot her possible explanation is that there is a well-qualified cotton extens ion agent in the Suwannee Valley who provides the necessary information regarding BMPs to cotton farmers in the area. Conclusion 10: Tobacco producers are willing to accept risk regarding their farming operation compared to other row crop growers. The results of this study found a signific ant positive relationship between tobacco farmers and their willingness to accept. This indicates that tobacco farmers are more willing to engage in risky practices on their farming operation than other row crop growers. This is a reasonable conclusion in that tobacco fa rmers often accept risk when deciding to grow tobacco. Tobacco is a hi gh risk commodity because the government is raising taxes on tobacco products in order to reduce the demand these items. As a result, there is less of a demand for tobacco. Conclusion 11: Tobacco producers are more likely to voluntarily adopt BMPs than other row crop growers. 85

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The results of this study found a signific ant positive relationship between tobacco growers with the voluntarily adoption of BMPs. This is a reasonable conclusion because of the fluctuating tobacco industry (i.e. 2004 Tobacco Buyout). The 2004 Tobacco Buyout program was legislation to end the federal tobacco program and compensate tobacco quota growers and owners (Brown 2005). Tobacco growers and quota holders were given payments to transition to new tobacco production methods, out of tobacco production to other areas of agr icultural production or out of tobacco production into a non-farming occupation (Brown, 2005). As a result of the buyout, tobacco farmers may feel that voluntarily adopting BMPs can lead to stability. Conclusion 12: Age and highest level of educ ation completed are not related to a farmers decision to adopt BMPs. The results of this study found no signifi cant relationship between age and highest level of education achieved and the decision to adopt BMPs. This indicates that for this study, no relationships were found between age and highest level of education completed and the decision to adopt BMPs. This is significant in that previous research found that adoption rates among older producers are lower (Kim et al., 2005). Lamba et al., (2009) found that farmers born after 1970 were more likely to adopt BMPs. Previous research also found that as an individuals level of education increased so did their willingness to adopt conservati on practices (Wu & Babcock, 1998). Lamba et al. (2009) found similar conclusions in that farmers with education greater than that of a high school equivalency had higher adoption rates than those with only a primary school education. 86

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Recommendations Recommendations for Practice Based on the results and conclusi ons of this study the following recommendations for practice should be considered. Extension personnel should facilitate gather ings for farmers, extension agents and family members of the farmers. The Florida Department of Agriculture and Consumer Services should increase monetary incentives and offer other incentives (i.e. tax rebates) in order to increase adoption rates of BMPs. Recommendation for Further Practice 1: Extension personnel should facilitate gathering of farmers, ex tension agents and family members of the farmers. The results of this study show that in teraction among other farmers, extension personnel and family members who also farm pl ays a role in adoption decision process for row crop farmers. Therefor e, extension agents should facilitate meetings between agents, farmers and their families. These meet ings can be formal or informal as long as they aid in discussion amongst participants. Recommendation for Further Practice 2: The Florida Department of Agriculture and Consumer Services should increase monetar y incentives and offer other incentives (i.e. tax rebates) in order to increase adoption rates of BMPs. The Florida Department of Agriculture should increase monetar y incentives and offer other incentives (i.e. tax rebates) in order to increase adoption rates of BMPs. Farmers in this study indicated increased incentives would lead to adoption among nonadopters. Therefore, the Flor ida Department of Agricult ure and Consumer Services should develop incentives which are appealing to farmers in order to increase adoption rates. Higher adoption rates could lead to more effective and e fficient farming on 87

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Floridas economy. Currently, agriculture is the second largest industry in Florida (Hodges & Rahmani, 2004) but with increased adoption rates, agriculture could become Floridas leading industry. Recommendations for Further Inquiry Based on the results and conclusions the following recommendations should be an emphasis for future inquiry. A larger population fr om which to sample. According to this study, age and highest level of education completed are not related to a farmers decision to adopt BMPs. This contradicts previous findings and should be examined further. Further examination of the relations hip between extension personnel and cotton farmers. Further examination of the level of risk t obacco farmers are willing to accept on their farming operation. Further examination of why tobacco farmers are more willing to voluntarily adopt BMPs than other row crop farmers. In future studies, age should be collected as a continuous va riable rather than a categorical variable. Questions four, five and six on the ques tionnaire were problematic and produced unexpected results. In future studies, t hese questions should be revised in order to produce more accurate results. Future studies should be conducted to determine how to interact with laggards as defined by Rogers (2003). Recommendation for Further Inquiry 1: A larger population from which to sample. This study did not have enough respondents to determine if demographics (i.e. age and highest level of education achieved) were a factor in the adoption decision process. A larger sample may have yi elded a relationship between age and highest level of education achieved and the adoption decision pr ocess of BMPs. 88

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Recommendation for Further Inquiry 2: A ccording to this study, age and highest level of education completed are not related to a farmers decision to adopt BMPs. This contradicts previous findings and should be examined further. Because the finding in this study was contr adictory to previous research, additional empirical inquiry is needed. Previous res earch indicated significant relationships between demographics (i.e. age and highest level of education achieved) and the adoption decision process of BMPs. Further research is needed to determine if this lack of relationship is unique to the Suwannee Valley or an anomaly. Recommendation for Further Inquiry 3: Fu rther examination of the level of risk tobacco farmers are willing to a ccept on their farming operation. The results of this study indicated that cotton farmers have regular interaction with extension personnel. Further research is needed to determine if this relationship is unique to the Suwannee Valley or an anomaly. Specifically research is needed to determine if this relationship applies only to co tton farmers in this area or if it can be applied to all cotton farmers. Recommendation for Further Inquiry 4: Fu rther examination of the level of risk tobacco farmers are willing to a ccept on their farming operation. Research is needed to determine if tobacco farmers in the Suwannee Valley are more willing to accept risk than other tobacco farmers within the state of Florida and within the United States. Tobacco farmers may be willing to accept higher levels of risk because of the possible higher rewards (i.e. payments) associated with the commodity. Recommendation for Further Inquiry 5: Further examination of why tobacco farmers are more willing to voluntarily adopt BMPs than other row crop farmers. 89

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Further research is needed to determine wh y tobacco farmers are more willing to voluntarily adopt BMPs than other row crop farmers. Specific ally, research is needed to determine if this conclusion is unique to the Suwannee River Water Management District or an anomaly. Tobacco farmers ma y be willing to voluntarily adopt BMPs in order to receive financial incentives as a method of offsetting the high costs associated with the commodity. Recommendation for Further Inquiry 6: In future studies, age should be collected as a continuous variable rather than a categorical variable. In future studies age should be collected as a continuous variable so a more thorough analysis of age can be conducted. By collecting as a continuous variable, relationships among factors influencing adoption of BMPs and age can be determined. Recommendation for Further Inquiry 7: Questions four, five and six on the questionnaire were problematic and produced unexpected results. In future studies, these questions should be revised in order to produce more accurate results. Questions four, five and six were open ended questions and were skipped by many participants. As a result, there wa s insufficient data collected to determine relationships. Recommendation for Further Inquiry 8: Future studies should be conducted to determine how to interact with laggar ds as defined by Rogers (2003). The participation for this study was low. This could be attributed to Rogers (2003) Categories of Innovativeness wh ich states non-early adopt ers may not be willing to participate in a research study. As a resu lt, future studies s hould examine how to interact with non-early adopters in terms of participating in research studies. 90

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APPENDIX A SURVEY Please answer the following questions to the best of your ability based on your use of best management practices. 1) Have you filed a Notice of Intent (N OI) with the Florida Department of Agriculture? 2) Of the following row crops, wh ich do you currently produce? Check all that apply. ___ Potatoes ___ Cotton ___ Corn ___ Tobacco ___ Soybeans ___ Other (please list ) _______________________ ___ Peanuts 3) How did you first hear about best management practices for the row crop industry? Check one. ___ Extension/Conservation Agent ___ Family Member ___ Another farmer ___ University of Florida sponsored event ___ Internet ___ Magazine/Newspaper Article ___ Suwannee River Partnership ___ Florida Department of Agriculture ___ Environmental Organization ___ Other (please list ) __________________ _______________ 4) Upon hearing about best management practices, how long did it take for you to decide to implement these farming techniques on your farm? Please respond with an approximate estimation. Responses can be in days, weeks, months, or years. ___ days, weeks months, years (circle one). If no, please return this survey. If yes, when? ____________ ___________ FACTORS INFLUENCING THE ADOPTION OF BEST MANAGEMENT PRACTICES BY ROW CROP FARMERS IN NORTH CENTRAL FLORIDA 91

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5) For those BMPs that you have im plemented, please list the two factors that most influenced this decision. __________________________________________ ______________________ __________________________________________ ______________________ ________________________ _____________________ ___________________ 6) For those BMPs that you have not implemented, please list the two factors that most infl uenced this decision. ____________________________________ __________________________ ____________________________________ __________________________ ____________________________________ __________________________ 7) Please rate the degree to which you agree or disagree with the following statements regarding farmer interaction. Check one for each item. Strongly Disagree Disagree Neutral Agree Strongly Agree 1 2 3 4 5 a. I interact on a weekly basis with other farmers. b. I discuss my farming operation with other farmers. c. I am willing to discuss BMPs with other farmers. d. I am willing to try new farming innovations based on the advice I receive from peers. e. I believe building a trusting relationship with other farmers is important. f. My interaction with other farmers influences the decisions I make regarding BMPs g. I would encourage peers who have not adopted BMPs to adopt in the near future. 8) Are any of your family members farmers? Check one. Yes If yes, how many ___________ No If no, skip to question 10. 92

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9) Please rate the degree to which you agree or disagree with the following statements regarding family influence. Check one for each item. Strongly Disagree Disagree Neutral Agree Strongly Agree N/A 1 2 3 4 5 6 a. I discuss farming practices with members of my family who also farm. b. Members of my family assist me with my farming operation. c. I intend to pass on my farm to a member of my family when I retire. d. My spouse is involved in the day-today operation of my farm. e. The interactions I have with members of my family who also farm are important. f. My interaction with family members who also farm influences the decisions I make regarding BMPs g. I believe interacting with family members who are also involved in a farming operation is important. 10) Please rate the degree to which you agree or disagree with the following statements regarding your interaction with extension agents. Check one for each item. Strongly Disagree Disagree Neutral Agree Strongly Agree 1 2 3 4 5 a. I regularly interact with an extension agent. b. The extension agent in my area provides information regarding BMPs. c. My interaction with extension agents influences the decisions I make regarding BMPs. d. The effectiveness of a extension agent can affect a farmers decision to adopt BMPs. e. An extension agent provided me with the necessary information regarding adoption of BMPs. f. I believe the extension agents I work with have my best interest in mind. 93

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g. I trust the information given to me by extension agents regarding BMPs. 11) Please rate the degree to which you agree or disagree with the following statements regarding your level of risk aversion. Check one for each item. Strongly Disagree Disagree Neutral Agree Strongly Agree 1 2 3 4 5 a. I believe I am a risk taker. b. I am willing to take risks when it comes to my farming operation. c. I believe accepting some risk on my farming operation is important. d. I believe I accepted risk when deciding to adopt BMPs. e. I believe the risk I accepted when deciding to adopt BMPs was beneficial to my farming operation. f. I believe the risk associated with BMP adoption may cause some farmers not to adopt. g. If the risk level associated with BMP adoption was minimized, more farmers may decide to adopt. 12) Please rate the degree to which you agree or disagree with the following statements regarding voluntary BMP adoption and incentives. Check one for each item. Strongly Disagree Disagree Neutral Agree Strongly Agree 1 2 3 4 5 a. BMP adoption should be voluntary. b. BMP adoption should be mandatory. c. I adopted BMPs on my farming operation because of a fear of growing regulation and fines. d. Financial incentives played a role in my decision to adopt BMPs. 94

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95 e. BMP adoption may become mandatory in the future. f. I believe more farmers would adopt BMPs voluntarily if there were a larger financial incentive. g. Incentives, other than financial, should be provided to farmers who adopt BMPs. 13) What amount of land do you lease/rent and own? ____ Acres Leased/rented ____ Acres Owned 14) What is your age in years? Please check one. ___ Less than 20 years old ___ 21-30 years old ___ 31-40 years old ___ 41-50 years old ___ 51-60 years old ___ 61-70 years old ___ 71 years old or older 15) What is your highest level of formal education completed? Check one. __ Less than 12 th grade __ High school diploma or GED __ Some college but no degree __ Completed 2 year degree (AA) or other vocational degree program __ Completed 4 year degree (BA or BS) __ Graduate school or professional school Other comments: ________________________ _____________________ _________________________ ________________________ _____________________ _________________________ ________________________ _____________________ _________________________ ________________________ _____________________ _________________________ ________________________ _____________________ _________________________ ________________________ _____________________ _________________________ ________________________ _____________________ _________________________ Thank you for completing this survey Please return this questionnaire and informed consent statement in the self-addressed envelope as soon as possible. Your input is greatly appreciated.

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APPENDIX B IRB APPROVAL 96

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APPENDIX C INFORMED CONSENT 97

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APPENDIX D COVER LETTER November 1, 2010 Allison Britton 408 Rolfs Hall University of Florida Gainesville FL 32611-0540 Telephone: (352)-392-0502 x244 Fax: (352)-392-9585 ahbritton@ufl.edu Dear Grower; I am writing to ask for your help to better understand what influences row crop farmers to adopt best management practices North Central Florida. I am a graduate student in the Agricultural Education and Communication department at the University of Florida and this research is a part of my Masters Thesis. You are being asked to participate in this study based on your location in the Suwannee River Water Management District. Your answers will be used to help UF extension agents work with farmers to overcome problems row crop farmers are dealt with when deciding to adopt BMPs. While I know this time of year can be busy, I would appreciate your participation in this study as you are one of a small number of growers being asked to participate. Your participation is voluntarily and I sincerely hope you can help with this project. You do not have to answer any questions that you do not wish to answer. There is no risk to you from participating in this study. If you have questions about your rights concer ning this study, please contact the UF IRB office, Box 112250, University of Florida, Gainesville, FL 32611-2250. Before completing the questionnaire, please read and sign the informed consent statement which is also included in this packet. Return the informed consent statement with the completed questionnaire in the self-addressed envelope by December 6th, 2010. All answers to the completed questionnaire will be grouped together when the results are presented to Extension faculty and the public. Please note that the identification number on the questionnaire will be used only to check your name off the mailing list when your questionnaire is returned. Please be assured that I will not release information which could identify individuals who participate in the study. If you have any questions, you can contact me by telephone (252-578-3190) or through email at ahbritton@ufl.edu I have provided a paid postage envelope for your convenience, so please put your completed questionnaire and informed consent statement in the enclosed self-addressed envelope and return by mail. Thank you for your help. Sincerely, Allison H. Britton Graduate Student 98

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APPENDIX E FOLLOWUP POSTCARD Dear Grower; Two weeks ago, you received a questionnaire a sking your opinion of factors influencing the adoption of Best Management Practices. My records indicate you have not returned the completed questionnaire and consent form. Please complete the survey and consent form and return in the stamped addre ssed envelope as soon as possible. Your input is essential to the development of best management practices in row crop farming. If a replacement questionnaire is needed, contact Allison by email at ahbrittton@ufl.edu or by phone at (252)-578-3190. Thank you again, Allison Britton 99

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APPENDIX F SECOND COVER LETTER December 3, 2010 Allison Britton 408 Rolfs Hall University of Florida Gainesville FL 32611-0540 Telephone: (352)-392-0502 x244 Fax: (352)-392-9585 ahbritton@ufl.edu Dear Grower; On November 1, 2010, you received a letter and survey asking for your help in better understanding what influences row crop farmers to adopt best management practices North Central Florida. My records indicate you have not returned your completed survey and consent form. Your input is important I am a graduate student in the Agricultural Education and Communication department at the University of Florida and this research is a part of my Masters Thesis. You are being asked to participat e in this study based on your location in the Suwannee River Water Management District. Your answers will be used to help UF extension agents work with farmers to overcome problems row crop farmers are dealt with when deciding to adopt BMPs. While I know this time of year can be busy, I would appreciate your participation in this study as you are one of a small number of growers being asked to participate. Your participation is voluntarily and I sincerely hope you can help with this project. You do not have to answer any questions that you do not wish to answer. There is no risk to you from participating in this study. If you have questions about your rights concer ning this study, please contact the UF IRB office, Box 112250, University of Florida, Gainesville, FL 32611-2250. Before completing the questionnaire, please read and sign the informed consent statement which is also included in this packet. Return the informed consent statement with the completed questionnaire in the self-addressed envelope by December 14th, 2010. All answers to the completed questionnaire will be grouped together when the results are presented to Extension faculty and the public. Please note that the identification number on the questionnaire will be used only to check your name off the mailing list when your questionnaire is returned. Please be assured that I will not release information which could identify individuals who participate in the study. If you have any questions, you can contact me by telephone (252-578-3190) or through email at ahbritton@ufl.edu. I have provided a paid postage envelope for your convenience, so please put your completed questionnaire and informed consent statement in the enclosed self-addressed envelope and return by mail. Thank you for your help. Sincerely, Allison H. Britton Graduate Student 100

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LIST OF REFERENCES Bianchi, M. & Harter, T. (2002). Nonpoint Sources of Pollution in Irrigated Agriculture (Publication 8055) Retrieved from University of California Division of Agriculture and Natural Resources website: http://groundwater.ucdavis.edu Brown, B. (2005). Buyout Background Retrieved from: http://www.cals.ncsu.edu/advancement/tobaccobuyout/buyoutbkgd_new.htm Cardona, H. (1999). Analysis of Policy Awareness in the Implementation of Coastal Nonpoint Pollution Control Program for Agriculture (Doctoral dissertation). Retrieved from: http://etd.lsu.edu/cgi-bin/ETDbrowse/browse?first_letter=C Caviglia, J.K. & Kahn, J.R. (2001). Diffusion of sustainable agriculture in the Brazilian rainforest: A discrete choice analysis. Economic Development and Cultural Change, 49(2), 311-333. Retrieved from: http://www.jstor.org/stable/1154910 Cogger, C. G. & MacConnell, C. (1991, August). Why the Concern about Agriculture Contamination in Groundwater? Retrieved from: http://cru.cahe.wsu.edu/CEPu blications/ eb1632/eb1632.html Colorado State University (2010 ). Advantages and Disadvantages of the Survey Method Retrieved from: http://writing.colostate.edu/guide s/research/survey/com2d1.cfm Cooper, J.C. & Keim, R.W. (1996). Incentive Payments to Encourage Farmer Adoption of Water Quality Protection Practices. American Journal of Agricultural Economics, 78(1), 54-64. doi: 10.2307/1243778 Dillman, D. A., Smyth, J.D., & Christian, L. M. (2009). Internet, Mail, and Mixed-Mode Surveys The Tailored Design Method. Hoboken, New Jersey: John Wiley & Sons, Inc. Divine, R. A. (2010). Factors Associated with Farmer Adoption of Best Management Practices in the Suwannee River Water Ma nagement District of North Florida (Masters thesis). Availabl e from ProQuest Dissertatio ns and Theses database. Earles, R. (2005). Sustainable Agriculture: An Introduction (Publication of ATTRA). Retrieved from ATTRA The National Sust ainable Agriculture Information Service website: http://attra.ncat.org/attra-pub/sustagintro.html Environmental Protection Agency (a). (2009, February 10). EPA Clean Water Act. Retrieved from: http://cfpub.epa.gov/npdes/cwa.cfm?program_id=45 Environmental Protection Agency (b). (2010, April 15). What is Nonpoint Source Pollution? Retrieved from: http://water.epa.gov/polwaste/nps/whatis.cfm 101

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Errington, A. (2002, August). Handing Over the Reins: A Comparative Study of Intergenerational Farm Transfers in England, France and Canada. Paper presented at the Xth EAAE Congress on Exploring Diversity in the European AgriFood System, Zaragoza, Spain. Feder, G. (1980). Farm Size, Risk Aversion and the Adoption of New Technology under Uncertainity. Oxford Economic Papers, 32 (2), 263-283. Retrieved from: http://www.jstor.org/stable/2662685?origin=JSTOR-pdf Feder, G., Just, R.E., & Zilbe rman, D. (1985). Adoption of Agricultural Innovations in Developing Countries: A Survey. Economic Development and Cultural Change, 33(2), 255-298. doi:10.1086/451461 Florida Department of Agricultural and Consumer Services. (2006). Water Quality/Quantity Best Management Practices for Fl orida Vegetable and Agronomic Crops Retrieved from: http://www .floridaagwaterpolicy.com/ Habron, G.B. (2004). A doption of conservation practices by agricultural land owners in three Oregon watersheds. Journal of Soil and Water Conservation 59(3), 109115. Retrieved from: http://www.swcs.org/ Hodges, A. & Rahmani, M. (2004). Economic Impacts of Agricultural, Food, and Natural Resource Industries in Florida in 2004: Marketing Florida Agriculture. Retrieved from: http://www.florida-agriculture.com/economic_impact.htm Israel, G.D. (2009). Determining Sample Size (Publication PEOD6). Retrieved from University of Florida, Institute of Food and Agricultural Science website: http://edis.ifas.ufl.edu/pd006 Kaiser, F.G. & Shimoda, T.A. (1999). Responsibility as predic tor of ecological behavior. Journal of Environmental Psychology, 19 (3), 243-253. doi: 10. 1006/jevp.1998.91 23 Kassie, M., Zikhali, P., Manjur, K., & Ed wards, S. (2009). A doption of sustainable agriculture practices: Evidence fr om a semi-arid region of Ethiopia Natural Resources Forum, 33 (3),189-198. doi: 10. 1111/j.1477-8947.2009.01224.x Kim, S., Gillespie, J. M., & P audel, K. P. (2005). The effect of socioeconomic factors on the adoption of best m anagement practices in beef cattle production. Journal of Soil & Water Conservation, 60(3), 111-120. Retrieved from: http://www.swcs.org/ Lamba, P., Filson, G., Adekunle B. (2009). Environmentalist. Factors affecting the adoption of best management prac tices in southern Ontario, 29 (1), 64-77. doi: 10.1007/s10669-008-9183-3 Lilly, J. P. (n.d.) Agricultural History of North Carolina Retrieved from: http://www.ncagr.gov/stats/general/history.htm 102

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Marshall, G. (2004). From words to deeds: enfor cing farmers conservation cost-sharing commitments. Journal of Rural Studies 20(2), 157-167. doi: 10.1016/S0743-0167 (03)00049-4 McMillan J., & Schumacher, S. (2010). Research in Education: Evidence-Based Inquiry 7th Edition. New Jersey: Pearson Education Inc. Migliaccio, K. W. & Boman, B. J., (2009). Total Maximum Daily Lo ads and Agricultural BMPs in Florida (ABE 362). Retrieved from University of Florida, Institute of Food and Agricultural Science website: http://edis.ifas.ufl.edu/ae388 Morris, C. & Potter C. (1995). Recruiting the new conservati onists: farmers adoption of agri-environmental schemes in the U.K. Journal of Rural Studies, 11(1), 51-63. doi: 10.1016/0743-016 7(94)00037-A Obreza, T. & Means, G. (2006). Characterizing Agriculture in Floridas Lower Suwannee River Basin Area (Publication SL 241) Retrieved from: Univ ersity of Florida, Institute of Food and Agricult ural Sciences website: http://edis.ifas.ufl.edu/ss460 Office of Agricultural Wa ter Policy (a). (n.d.). Agricultural BMPs at a Glance Retrieved from: http://www.floridaagwaterpo licy.com/AtaGlance.html Office of Agricultural Wa ter Policy (b). (n.d.). Office of Agricultural Water Policy Retrieved from: http://www.floridaagwaterpo licy.com/AtaGlance.html OGeen, A.T., & Schwankl, L. J. (2006). Understanding Soil Erosion in IrrigatedAgriculture (Publication 8196). Retrieved from Univer sity of California, Division of Agriculture and Na tural Resources website: http://ucanr.org/ Pimentel, D. (2000). Soil Eros ion and the Threat to Food Security and the Environment. Ecosystem Health 6 (4), 221-226. doi: 10.1007/s10668-005-1262-8 Pimentel, D., Harvey, C., Resosudarmo, P., Si nclair, K., Kurz, D., McNair, M., Crist, S., Shpritz, L., Fitton, L., Saffouri, R., & Blair, R. (1995). Environmental and Economic Costs of Soil Erosion and Conservation Benefits. Science, 267 (5201), 1117-1123. doi:10.1126/scienc e.267.5201.1117 Prokopy, L.S., Floress, K ., Klotthor-Weinkauf, D., & Baumgart-Getz, A. (2008). Determinants of agricultural best managem ent practice adoption: Evidence from the literature. Journal of Soil and Water Conservation 63(5), 300-311. Retrieved from: http://www.swcs.org/ Rahelizatovo, N.C. & Gillespie, J.M. (2004). The Adopti on of Best-Management Practices by Louisiana Dairy Producers. Journal of Agricultural and Applied Economics 36(1), 229-240. Retrieved from: http://purl.umn.edu/43445 Rogers, E.M. (2003). Diffusion of Innovations. 5 Edition. New York: The Free Press. 103

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Santos, J.R.A. (1999). Cronbachs Alpha: A Tool for Assessing the Reliability of Scales. Journal of Extension 37(2). Retrieved from: http://www.joe.org Smithers, J. & Furman, M. ( 2003). Environmental farm planning in Ontario: exploring participation and the endurance of change. Land Use Policy 20(4), 343-356. doi: 10.1016/S0264-8377(03)00055-3 Soule, M.J., Tegene, A. & Wiebe, K.D. (2000). Land Tenure and the Adoption of Conservation Practices American Journal of Agricultural Economics 82(4), 9931005. doi: 10.1111/ 0002-9092.00097 Suwannee River Partnership (a). (n.d.) Background Retrieved from http://www.suwannee.org/background.html Suwannee River Partnership (b). (n.d.). Mission Retrieved from: http://www.suwannee.org/mission.html United States Department of Agriculture. (1999, August). Sustainable Agriculture: Definitions and Terms. Retrieved from http://www.nal.usda.gov University of California Davis Sustainable Agricultural Research & Education Program (UCDavis SAREP). (1997, December). What is sustainable agriculture? Retrieved from: http://www.sarep.ucdavis.edu/Concept.htm University of Florida. (n.d.). Best Management Practices (BMP) Cost Share. Retrieved from: http://bmp.ifas.ufl.edu University of Florida/Institute of Food and Agricultural Sciences (UF/IFAS). (2008). Best Management Practices UF/IFAS Ext ension: Solutions for Your Life Retrieved from: http://solutionsforyourlife.ufl.edu/hot_topics/agriculture/bmps.html Wall, E., Weersink, A., & Swanton, C.J. (2001). Agriculture and the ISO 14,000. Food Policy, 26(1), 35-48. doi:10.1016/ S0306-9192(00)00025-7 Warriner, G.K. & Moul, T. M. (1992). Kinship and personal communication network influences on the adoption of agriculture conservation technology. Journal of Rural Studies 8(3), 279-291. doi: 10.1016/0743-0167(92)90005-Q Wejnert, B. (2002). Integrating models of diffusion of innovation: a conceptual framework. Annual Review of Sociology 28(1), 297-326. doi: 10.1146/annurev .soc.28.110601.141051 Wu, J & Babcock, B.A. (1998). The choice of tillage, rotation, and testing practices: Economic and environment al implications. American Journal of Agricultural Economics 80(3), 494-511. Retrieved from: http://www.jstor.org/stable/1244552 104

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105 BIOGRAPHICAL SKETCH Allison Hope Britton was raised in Galatia, North Carolina. She grew up on her familys farming operation and at a young age developed a love for agriculture. Following in her parents footst eps, she attended North Carolina State University in Raleigh, North Carolina. She graduated Summa cum Laude with a Bachelor of Science degree in Agricultural Business Management and a minor in Agronomy. These degrees gave her an understanding of the busine ss side of production agriculture. Allison completed her masters of science degree in 2011 from the University of Floridas College of Agricultural and Life Sciences in Agricultural Education and Communication with a specialization in Leade rship. Her research examined factors which influence row crop farmers to impl ement best management practices. Allison plans to pursue her Doctorat e of Philosophy and specializ e in Extension Education before returning to her home state. Her ultima te goal is to become a faculty member at North Carolina State University where she can work with extension agents and agronomists on how to increase the use of sustainable agricultural practices across North Carolina.