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Finding Fun in Food Farming - Characteristics of U.S. Agritourism Industry

Permanent Link: http://ufdc.ufl.edu/UFE0024632/00001

Material Information

Title: Finding Fun in Food Farming - Characteristics of U.S. Agritourism Industry
Physical Description: 1 online resource (136 p.)
Language: english
Creator: Bondoc, Irina
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: activities, agri, agricultural, agritainment, agritourism, agrotourism, industry, states, tourism, united
Food and Resource Economics -- Dissertations, Academic -- UF
Genre: Food and Resource Economics thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Agritourism, also referred to as agrotourism, agritainment or agricultural tourism can be defined as the allocation of leisure time to visit a working farm or agricultural operation to enjoy the rural setting, participate in farm activities, or learn about agriculture production. Agritourism examples include farm tours, hayrides, corn mazes, self-harvest (u-pick), and farm festivals. In the United States, agritourism is among the fastest growing sectors. According to Travel Industry Association of America, between 1999 and 2003, 87 million individuals have taken a trip to a rural destination. According to a study by United States Department of Agriculture (USDA), more than 62 million people age 16 years and older have visited farms during a one-year period in 2000 and 2001. Previous studies have examined farmer motivations for initiating agritourism activities. Lacking in the literature, however, is a comprehensive look at the industry from a national perspective. While individual states such as California, Virginia, Vermont, Hawaii, New York or Colorado have compiled information on agritourism operations, similar statistics for the United States as whole simply do not exist. In the Spring of 2007, a survey instrument using internet sampling methods was developed and in Fall 2007, the survey instrument was posted to the online site SurveyMonkey. The response rate for completed surveys was 22.31%. This study offers a first glimpse at the industry via a nationwide survey of agritourism operators in the United States. The objective of this work is to characterize the U.S. agritourism industry and offer insight on the range of activities under the umbrella of agritourism. In addition to the descriptive statistics, qualitative choice models are used to measure the major drivers of the demand for agritourism. These models will be presented and ranking of the relative importance of the explanatory variables shown. For many agricultural operators, agritourism activities have allowed them to expand operations, supplement income, and broaden employment opportunities. From a public perspective, agritourism activities have help convey an understanding and appreciation of agriculture and rural lifestyles. This work offers a first look at the United States agritourism industry as a whole. In the United States, agritourism is a broad and diverse industry. Characterization of the industry can help the public to better understand this industry, its potential for growth, and how it contributes to local economies, agricultural appreciation, and conservation of rural lands.
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 Irina Bondoc.
Thesis: Thesis (M.S.)--University of Florida, 2009.
Local: Adviser: Ward, Ronald W.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0024632:00001

Permanent Link: http://ufdc.ufl.edu/UFE0024632/00001

Material Information

Title: Finding Fun in Food Farming - Characteristics of U.S. Agritourism Industry
Physical Description: 1 online resource (136 p.)
Language: english
Creator: Bondoc, Irina
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: activities, agri, agricultural, agritainment, agritourism, agrotourism, industry, states, tourism, united
Food and Resource Economics -- Dissertations, Academic -- UF
Genre: Food and Resource Economics thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Agritourism, also referred to as agrotourism, agritainment or agricultural tourism can be defined as the allocation of leisure time to visit a working farm or agricultural operation to enjoy the rural setting, participate in farm activities, or learn about agriculture production. Agritourism examples include farm tours, hayrides, corn mazes, self-harvest (u-pick), and farm festivals. In the United States, agritourism is among the fastest growing sectors. According to Travel Industry Association of America, between 1999 and 2003, 87 million individuals have taken a trip to a rural destination. According to a study by United States Department of Agriculture (USDA), more than 62 million people age 16 years and older have visited farms during a one-year period in 2000 and 2001. Previous studies have examined farmer motivations for initiating agritourism activities. Lacking in the literature, however, is a comprehensive look at the industry from a national perspective. While individual states such as California, Virginia, Vermont, Hawaii, New York or Colorado have compiled information on agritourism operations, similar statistics for the United States as whole simply do not exist. In the Spring of 2007, a survey instrument using internet sampling methods was developed and in Fall 2007, the survey instrument was posted to the online site SurveyMonkey. The response rate for completed surveys was 22.31%. This study offers a first glimpse at the industry via a nationwide survey of agritourism operators in the United States. The objective of this work is to characterize the U.S. agritourism industry and offer insight on the range of activities under the umbrella of agritourism. In addition to the descriptive statistics, qualitative choice models are used to measure the major drivers of the demand for agritourism. These models will be presented and ranking of the relative importance of the explanatory variables shown. For many agricultural operators, agritourism activities have allowed them to expand operations, supplement income, and broaden employment opportunities. From a public perspective, agritourism activities have help convey an understanding and appreciation of agriculture and rural lifestyles. This work offers a first look at the United States agritourism industry as a whole. In the United States, agritourism is a broad and diverse industry. Characterization of the industry can help the public to better understand this industry, its potential for growth, and how it contributes to local economies, agricultural appreciation, and conservation of rural lands.
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 Irina Bondoc.
Thesis: Thesis (M.S.)--University of Florida, 2009.
Local: Adviser: Ward, Ronald W.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0024632:00001


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1 FINDING FUN IN FOOD FARMING CHARACTERISTICS OF U.S. AGRITOURISM INDUSTRY By IRINA BONDOC A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2009

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2 2009 Irina Bondoc

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3 To my son, Aidan

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4 ACKNOWLEDGMENTS This thesis could not have been com pleted wi thout the help, encouragement and support of all members of my supervisory committee: Dr. Ronald Ward, Dr. Charles Moss and Dr. Allen Wysocki. I would especially like to thank my committee chair, Dr. Ronald Ward, for his advisement, mentorship and the knowledge he shared while completing my masters program. His valuable guidance and patience are deeply appreciated. My thanks go to Dr. Jeffrey Burkhardt, Dr Rick Weldon and Jessica Clackum Herman for allowing me the flexibility to pursue my MS. while enjoying the wonders of the motherhood. I owe my deepest gratitude to my family, especially my parents Angelica and Filip, who believed in me and encouraged me throughout the program, making the milestones possible and to my husband Lucian, who during these two years gave me all his love, support and understanding.

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5 TABLE OF CONTENTS Page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........8 LIST OF FIGURES.........................................................................................................................9 LIST OF ABBREVIATIONS........................................................................................................ 12 ABSTRACT...................................................................................................................................13 CHAP TER 1 INTRODUCTION..................................................................................................................15 Background of the Study........................................................................................................15 Why Agritourism? Problem Motivating this Study............................................................. 16 Agritourism around the World........................................................................................ 16 Agritourism in the United States.....................................................................................17 Study Objectives.....................................................................................................................19 Methodology...........................................................................................................................20 Data and Scope.......................................................................................................................22 2 LITERATURE REVIEW.......................................................................................................23 Definition of Agritourism.......................................................................................................23 Research on Agritourism........................................................................................................24 Probability Models............................................................................................................. .....27 3 SURVEY................................................................................................................................30 Survey Method........................................................................................................................30 Web survey Advantages and Disadvantages................................................................ 30 Survey Design and Data collection................................................................................. 31 Survey Results................................................................................................................. .......33 Summary of Survey Results............................................................................................33 Gross Value of Agritourism Sales and the Percentage of Total Farm Income From Agritourism..................................................................................................................34 Primary Farm Commodity Produced on the Farm.......................................................... 35 Types of Agritourism Activities...................................................................................... 35 Reasons for Operating an Agritourism Enterprise.......................................................... 36 Management Characteristics........................................................................................... 37 Demographic Profile....................................................................................................... 39 Visitor Characteristics and Preferences...........................................................................41 Information about the Current State of the Business and Plans...................................... 43

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6 4 ECONOMETRIC MODEL....................................................................................................46 Probit Model and Ordered Probit Model................................................................................ 46 Ordered Probit Agritourism Model Specifications................................................................. 49 Ordered Probit Agritourism Model Results............................................................................ 54 5 ORDERED PROBIT MODEL SIMULATIONS...................................................................62 Simulating the Probabilities of Agritouris m : Gross Value of Agritourism Sales..................62 Gross Value of Agritourism Sales: Farm Type............................................................... 63 Primary Farm Commodity....................................................................................... 63 Acreage: Size of the Farm........................................................................................ 64 Employment: Full Time versus Part Time............................................................... 65 Gross Value of Agritourism Sales: Services and Fees .................................................... 65 Agritourism Activities..............................................................................................65 Fee Charged..............................................................................................................67 Gross Value of Agritour ism Sales: Management............................................................ 67 Years of Experience.................................................................................................67 Business Role........................................................................................................... 68 Seasonality............................................................................................................... 69 Reason of Operating an Agritourism Operation...................................................... 69 Gross Value of Agritouris m Sales: Demographics......................................................... 70 Educational Level..................................................................................................... 70 Age Category............................................................................................................71 Operator Gender.......................................................................................................72 Simulating the Probabilities of Agritourism : Shares of Agritourism Income........................ 72 Shares of Agritourism Income: Farm Type..................................................................... 72 Primary Farm Commodity....................................................................................... 73 Acreage: Size of the Farm........................................................................................ 73 Employment: Full Time versus Part Time............................................................... 74 Shares of agritourism income: Services and Fees........................................................... 74 Agritourism Activities..............................................................................................74 Fee Charged..............................................................................................................75 Shares of Agritourism Income: Management.................................................................. 77 Years of Experience.................................................................................................77 Business Role........................................................................................................... 77 Seasonality............................................................................................................... 78 Reason of Operating an Agritourism Operation...................................................... 78 Shares of Agritourism Income: Demographics...............................................................79 Educational Level..................................................................................................... 79 Age Category............................................................................................................80 Operator Gender.......................................................................................................81 Ranking of the Probabilities of Farm Income from Agritourism........................................... 81 Ranking by Gross Value of Agritourism Sales............................................................... 82 Ranking by Shares of Income..........................................................................................83 A Finalized Ranking of Average Farm Agritourism Earnings........................................ 85

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7 6 SUMMARY AND CONCLUSIONS.....................................................................................95 APPENDIX A AGRITOURISM SURVEY..................................................................................................102 B TSP CODE....................................................................................................................... .....110 LIST OF REFERENCES.............................................................................................................133 BIOGRAPHICAL SKETCH.......................................................................................................136

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8 LIST OF TABLES Table Page 1-1 Percentage of income from agritourism............................................................................. 184-1 Ordered probit model variables and descriptions.............................................................. 504-2 Ordered probit model parameter estimates: Gross value of agritourism sales.................. 554-3 Ordered probit model parameter estimates: Share of agritourism income........................ 58

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9 LIST OF FIGURES Figure Page 1-1 Ordered probit model: Gross value of agritourism sales................................................... 211-2 Ordered probit model: Share of agritourism income......................................................... 213-1 Summary of survey results................................................................................................. 333-2 Gross value of agritourism sales........................................................................................ 343-3 The percentage of total fa rm income from agritourism.....................................................343-4 Primary farm commodi ty produced on the farm................................................................353-5 Types of agritourism activities........................................................................................... 363-6 Reasons for involvement in agritourism............................................................................ 373-7 Charged fee for services/attractions................................................................................... 373-8 Total years of experience.................................................................................................. .373-9 Sources of funding.............................................................................................................383-10 Number of employees by seasonality................................................................................ 393-11 Operation schedule........................................................................................................ .....393-12 Agritourism operator educational level.............................................................................403-13 Agritourism operator age category.................................................................................... 403-14 Agritourism operator gender..............................................................................................403-15 Year 2006 compared with previous years.......................................................................... 413-16 Visitor point of origin................................................................................................... .....423-17 Types of visitor groups................................................................................................... ...423-18 Dollar amount spent per visitor.......................................................................................... 423-19 Marketing and distribution of agritourism products.......................................................... 433-20 Difficulties encountered in expanding the business........................................................... 443-21 Environmental concerns with agritourism......................................................................... 44

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10 3-22 Agritourism future trends................................................................................................. ..445-1 Gross value of agritourism sales by farm type: Primary farm commodity........................ 645-2 Gross value of agritourism sales by se rvice and fee: Agri tourism activities..................... 665-3 Gross value of agritourism sales by service and fees: Fee charged................................... 675-4 Gross value of agritourism sales by management: Years of experience........................... 685-5 Gross value of agritourism sales by management: Business role...................................... 695-6 Gross value of agritourism sales by mana gement: Reason of operating an agritourism operation............................................................................................................................705-7 Gross value of agritourism sales by demographics: Educational level.............................. 715-8 Gross value of agritourism sales by demographics: Age category.................................... 725-9 Shares of agritourism income by farm type: Primary farm commodity............................ 73 5-10 Shares of agritourism income by se rvice and fee: Fee charged........................................ 755-11 Shares of agritourism income by se rvice and fee: Agritourism activities........................ 765-12 Shares of agritourism income by management: Years of experience................................ 775-13 Shares of agritourism income by management: Business role.......................................... 785-14 Shares of agritourism income by mana gement: Reason of operating an agritourism activity................................................................................................................................795-15 Shares of agritourism income by demographics: Educational level..................................805-16 Shares of agritourism income by demographics: Age category........................................ 815-17 Ranking of the probabilities of farm in come from agritourism by gross value of agritourism sales.............................................................................................................. ..845-18 Ranking of the probabilities of farm inco me from agritourism by shares of income........ 855-19 Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Primary farm commodity.......................................................... 865-20 Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Farm size................................................................................... 875-21 Ranking of the variables impacting the income from agritourism (Minimum and maximum index valu es): Employment.............................................................................. 87

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11 5-22 Ranking of the variables impacting the incom e from agritourism (Minimum and maximum index values): Agritourism activities................................................................ 885-23 Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Fee charged............................................................................... 885-24 Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Years of experience...................................................................895-25 Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Bussiness role............................................................................ 895-26 Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Seasonality................................................................................ 905-27 Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Reason for operating an agritourism operation......................... 905-28 Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Educational level....................................................................... 915-29 Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Age category............................................................................. 915-30 Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Operator gender......................................................................... 925-31 Ranking of the variables impacting the income from agritourism (Minimum and maximum index values).....................................................................................................92

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12 LIST OF ABBREVIATIONS TIA Travel Industry Association of America USDA United States Department of Agriculture ARMS Agricultural Resource Management Survey USFS United States Forrest Service ERS Economic Research Service LPM Linear Probability Model CDF Cumulative Distribution Function

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13 Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science FINDING FUN IN FOOD FARMING CHARACTERISTICS OF U.S. AGRITOURISM INDUSTRY By Irina Bondoc August 2009 Chair: Ronald Ward Major: Food and Resource Economics Department Agritourism, also referred to as agrotourism, agritainment or agricultural tourism can be defined as the allocation of leisure time to visit a working farm or agricultural operation to enjoy the rural setting, participate in farm activities, or l earn about agriculture production. Agritourism examples include farm tours, hayrides, corn mazes, self-harvest (u-pick), and farm festivals. In the United States, agritourism is among the fastest growing sectors. According to Travel Industry Association of America, between 1999 and 2003, 87 million individuals have taken a trip to a rural des tination. According to a study by United States Department of Agriculture (USDA), more than 62 million people age 16 years and older have visited farms during a one-year period in 2000 and 2001. Previous studies have examined farmer motivations for initiating agritourism activities. Lacking in the literature, however, is a comp rehensive look at the industry from a national perspective. While individual states such as Calif ornia, Virginia, Vermont, Hawaii, New York or Colorado have compiled information on agritouris m operations, similar statistics for the United States as whole simply do not exist.

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14 In the Spring of 2007, a survey instrument using internet sampling methods was developed and in Fall 2007, the survey instrument was posted to the online site SurveyMonkey. The response rate for completed surveys was 22.31%. This study offers a first glimpse at the indus try via a nationwide su rvey of agritourism operators in the United States. The objective of this work is to characterize the U.S. agritourism industry and offer insight on the range of activities under the umbrella of agritourism. In addition to the descriptive statistics, qualita tive choice models are used to measure the major drivers of the demand for agritourism. These models will be presented and ranking of the relative importance of the explanatory variables shown. For many agricultural operators, agritouris m activities have allowed them to expand operations, supplement income, and broade n employment opportunities. From a public perspective, agritourism activit ies have help convey an unde rstanding and appreciation of agriculture and rural lifestyles. This work offers a first look at the United St ates agritourism industr y as a whole. In the United States, agritourism is a broad and divers e industry. Characterizatio n of the industry can help the public to better understa nd this industry, its potential for growth, and how it contributes to local economies, agricultural apprecia tion, and conservation of rural lands.

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15 CHAPTER 1 INTRODUCTION Background of the Study Agritourism is a field that is growing in popularity as producers try to diversify and incre ase profits. By combining the two major sectors in United States, agriculture and tourism, agritourism offers new sources of revenue but also presents potential problems and legal complications to agritourism operators. Agritourism, also referred to as agrotourism, agritainment or agricultural tour ism can be defined as the alloca tion of leisure time to visit a working farm or agricultural opera tion to enjoy the rural settings, pa rticipate in farm activities, or learn about agricultural production. Agritourism examples include farm tours, hay rides, corn mazes, self-harvest (u-pick), and farm festivals, just to name few. According to Pittman (2006), agritourism can be summarized as an activity that includes the following four factors: Combines the essential elements of the tourism and agricultural industries Attract members of the public to visit agricultural operations It is designed to increase farm income It provides recreation, entertainment, and/ or educational experiences to visitors The link between agriculture and tourism is not new. Farm-related recreation and tourism in America dates back to the early 1900s, when fa milies visited relatives from countryside in an attempt to escape the heat of the city summer. Pe ople began buying fresh fruits and vegetables as part of a recreational outing a nd this activity became more popular afte r 1920 and 1930 when the use of automobile spread widely (Holland and Wolf, 2000). The list of agritourism activities continues to grow and includes a variety of participant, recreational and educationa l activities including: farm retail/d ining (roadside stand, farm market, Christmas tree farm, u-pick operations), educa tional experiences (schoo l tours, farm-related

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16 museums, winery/brewery tours, garden/nur sery tours, crop iden tification programs), entertainment and outdoor activities (rodeo, petti ng zoo, horseback riding, corn maze, hayrides, sleigh rides, fishing, hunting, cro ss county skiing), and hospitality services (bed and breakfast, dude/guest ranch, camping). According to Jane Eckert of Eckert AgriMark eting there are three le vels of agritourism. Level I simply involve selling wh at you grow from a farm stand, te nt or small store. Level II operation creates an authentic farm experience th at can be simple or involved and may include an expanded retail area in a permanent building and activities that offer a va riety of options such as play areas, pick-your-own produce, corn mazes, workshops, petting zoos, concessions, etc. Level III operation gra duates to a large retail shopping destina tion/facility that is open yearround with a restaurant, permanent restrooms, paved parking and major special events. Agritourisms recent growth is both dema nd and supply driven. On the supply side, cost/price pressures have forced farmers to increase their income through diversification. Agritourism helps farmers protect their businesses against fluctuating markets, expand on-farm employment, provide off-season income and im prove business sustainability. On the demand side, increases in income and demand for more specialized forms of vacation experiences have stimulated the growth for tourism and recreational activities in rural areas. Why Agritourism? Problem Motivating this Study Agritourism around the World The agritourism industry has been a major segm ent of the rural economy in parts of Europe and Asia for decades (Bernando et al., 2004). In some regions of Canada, agritourism is well developed and attracts a large numbe r of visitors. The British Columbia Ministry of Agriculture, Food and Fisheries, as well as the Ministry of Small Business, Tourism and Culture recognize the benefits to be gained from agritourism (W illiams et al., 2001). In Alberta, Canada, there are

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17 over 200 farm-based agritourism businesses according to Williams, approximately 120 approved Farmers Markets and over 160 market gard eners and fruit growers in operation. In Ontario, agritourism already exists in a wi de range of agritouris m products and activities that include tours themed around animals and certa in products like sheep or cheese or apples, visits to restaurants and museums, and activit ies related to the produc tion of agricultural products. In 2003, an estimated 304,434 intern ational tourists visited a fa rm while in Australia, along with 1,145 million domestic tourists, making agritourism, one of the most sought alternatives for inbound visitors. About one third of all farm businesses in the United Kingdom are engaged now in nontraditional agricultural enterprises and contribut e on average about 11% of the total business income. Agritourism and other forms of farm -related recreation has become an increasing requirement for financial stability for farm s across Western Europe (Bernando et al., 2004). Agritourism in the United States In the United States, agritourism is among the fastest growing sectors. According to Travel Industry Association of America, between 1999 and 2003, 87 million individuals have taken a trip to a rural destination. According to TIA (Travel Industry Association of America), the travel trends support the growth of agritourism because: tourists are increasingly traveling by cars and are taking shorter trips and planning at the last minute, travelers are looking for new experiences as part of their trips, and families want to strengthen their relationships by being together. According to a study by United States Department of Agriculture (USDA), more than 62 million people age 16 years and older have visite d farms during a one-year period in 2000 and 2001 (USFS National Survey on Recreation and the Environment). The survey described the characteristics of agritourism industry from th e travelers perspectiv e: the average distance

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18 traveled is 80 miles, the average family income is greater than $50,000 and one average trip cost is about $45. Another important element to cons ider is the motivation behind agriculture-based tourism. The same survey investigated the re asons people visited farms and responses included enjoy rural scenery, learning where the food comes from, visit family/or friends, watch/participate in farm activities, purch asing agricultura l products pick fruit or produce, to hunt and fish or spend a night. In addition, US Census data indicates that about 4.5 percent of American farms report some degr ee of agritourism activity (Ference Weicker & Company, 1999). In 2004, farm-based recreat ion or agritourism provided $955 million in income to about 52,000 US farms representing 2.5 % of total U.S. farms (Agricultural Resource Management Survey USDA 2004). The 2004 Ag ricultural Resource Management Survey (ARMS) provides data on farm operator inco me received for agritourism, but does not distinguish between differe nt types of recreation. At this time, nationwide statistics on agritour ism are limited, but diffe rent state statistics provide us with information about the revenue generated from agritourism activities. Hawaii Agricultural statistics said that the value of agritourism related activit ies was 33.9 million dollars in 2003, while the income from Vermont agrito urism was 19.5 million dollars in 2002 (USDA ERS Agricultural Resource Marketing Survey, 2004). In Iowa, tourism was the third largest industry in 2003, with revenues to taling $4.6 billion (see Table1-1). Table 1-1. Percentage of income from agritourism State Percentage of Farms Participating in Agritourism Total Agritourism Income Agritourism Income per Farm Average Gross Income per Farm Percentage of Total Income from Agritourism Vermont Hawaii New York 33% 3% 5% $19.5 M $33.9 M $25.7 M $8,864 $181,283 $12,347 $71,970 $99,882 $80,687 12% 181% 15% Source: New England Ag Statistics Service, 2002; Agritourism Profile, AgMRC, 2003; New York State Agritourism Bu siness Study, Community Food a nd Agriculture Program, 1999; USDA ERS Agricultural Re source Marketing Survey, 2004

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19 Agritourism businesses can yield important benefits for both agricu ltural operators and rural communities. For agricultural operators, agritourism may provide a means to expand existing operations and supplement the familys income, provide employment opportunities for family members and more important, it may be an excellent tool to provide the public a better understanding of the importance of agriculture. For rural communities, agritourism may help local development by creating jobs, increasi ng community income and attracting other businesses and small industries On the other hand, agritourism may increase costs of living for local people; raise environmental concerns and may lead to loss of privacy, extra responsib ilities and interference with the main farm operations. Study Objectives Today, agritourism presents a rising opportunity for tourism and agricultural industries in United States, in spite of that, at this time, nationwide statistics on agritourism are limited. Lacking in the literature, however, is a comp rehensive look at the industry from a national perspective. While individual states su ch as California, Virginia, Vermont, Hawaii, New York or Colorado have compiled information on agritouris m operations, similar statistics for the United States as whole simply do not exist. This st udy offers a first glimpse at the industry via a nationwide survey of agritourism operators in the United States. The general objective of this wo rk is to characterize the U.S. agritourism industry, identify regional differences, and offer insight on the range of activities unde r the umbrella of agritourism. More specifically, the objectives of this study are: To measure the major drivers of the supply of agritourism

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20 To model the importance of agritourism usi ng Ordered Probit Mode ls and to show the relative impacts of different factors on the deci sion to add new agritourism activities to the farm To draw broader implications a bout the future of agritourism Methodology The theoretical fram ework is based on random utility models, in which each farm maximizes its expected utility through the decision of whether or not to pa rticipate in agritourism activities, given a set of factors (characteristics of the operator, characteristics of the farm and of the agritourism). Assuming that a farmer beco mes involved in running an agritourism operation it is possible to identify a set of key factors asso ciated with the amount of income the farmer will earn from it and the proportion of income from agritourism. General models appear as suggested below a nd illustrated with Figure 1-1 and 1-2. These figures are to be used more fully later in the conceptual sections of the analyses: Gross value of agritourism sales = f (X1, X2, X3, X4) X1 = Type X2 = Service and fee X3 = Management X4 = Demographics Share of agritourism income = f (X1, X2, X3, X4) X1 = Type X2 = Service and fee X3 = Management X4 = Demographics

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21 Figure 1-1. Ordered probit model: Gr oss value of agritourism sales Figure 1-2. Ordered probit model: Share of agritourism income The inclusion of both, economic characteristics (such as the productive orientation of the farm, the financial situation of the farm, the size, years of experience) as well as sociodemographic characteristics (the gender, the age and the education of the main farmer operator)

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22 will help us identify th e relative importance of of these f actors on the decision of adopting an agritourism activity. Using the Ordered Probit estimates and si mulation models, simulated changes in the likelihood (probability) of offering agritourism pr ovide insight into the conditions that truly influence this dimension of the U.S. farming sector. The model allows for comparison and ranking of factors positively or negatively affecting the d ecision to add new agritourism activities to the farm. Data and Scope A total of 273 agritourism enterprises were successfully contacted in the fall of 2007 to participate in a national survey by the University of Florida. The scope of the survey was to identify characteristics of U.S. agritourism indus try and regional differences, and offer insight on the range of activities under the umbrella of agritourism. The data set contains information regarding the general ch aracteristics of the operation, spec ific questions about agritourism business, visitor characteristic s and preferences, information about the current state of the business and plans and demographic information. The survey is a cross sec tional study and is not limited at certain types of agritourism, cont aining more than 80 types of activities. The thesis is divided into six chapters with Chapters 1 and 2 setting the stage with the literature and industry data. Chapte r 3 describes the survey method and reports the survey results. Chapter 4 sets forth the Ordered Probit methodol ogy and econometric models and then in the latter half of this chapter th e complete econometric models and estimates are reported. Chapter 5, using the Ordered Probit estimates and simula tion models, shows changes in the likelihood (probability) of offering agritouris m and provides insight into the c onditions that truly affect this dimension of the U.S. farming sector. Fi nally, Chapter 6 highlights the conclusions.

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23 CHAPTER 2 LITERATURE REVIEW Definition of Agritourism Today agritourism is being seen as a na tionwide economic development strategy. The growing popularity of agritourism businesses can be explained by the important potential benefits for both, agricultural ope rators and public and by the fact that agritourism is seen as a means for enhancing the quality of life and economic viability of rural communities. A review of existing literature shows that there is no univers al definition of agritourism. Most definitions of agritourism use a concept that combines elements of the tourism and agriculture industries and invol ve bringing public to farms. In 2004, American Farm Bureau Federation gave an extended definition of agritourism: Agritourism refers to an enterprise at a work ing farm, ranch or agricultural plant conducted for the enjoyment of visitors that generate s income for the owner. Agricultural tourism refers to the act of visiting a working farm or any horticultural or agricultural operation for the purpose of enjoyment, educa tion or active involvement in the activities of the farm or operation that also adds to the economic viability of the site. The Small farm Center, a special working group formed in 1999 by the University of California, with the purpose of researching and promoting agr itourism, offers the following definition of agricultural tourism: As the act of visiting a working farm or a ny agricultural, horticultural or agribusiness operation for the purpose of enjoyment, educa tion, or active involveme nt in the activities of the farm or operation. According to the University of Californias Small Farm Center, agritourism is seen as a subset of a larger industry called rural tourism that: Includes resorts, off-site farmers markets, non -profit agricultural t ours, and other leisure and hospitality businesses that attr act visitors to the countryside. The New England Agricultural Statistics Se rvice used the following definition of agritourism in a 2002 report presenting agritourism industry in Vermont:

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24 Agri-tourism is a commercial enterprise on a working farm conducted for the enjoyment, education, and/or active involveme nt of the visitor, generating supplemental income for the farm. Another report prepared for the New Jersey Department of Agriculture by a study team adopted a simple and encompassing definition of agritourism: As the business of making farms travel des tinations for educati onal and recreational purposes. For purposes of this study, the research team considered that only activities offered on a farm were considered as agritourism and on-farm recreational or educational activities did not need to generate revenue to be cons idered agritourism (Schilling et. al.). Hilchey (1993) defines agritourism: As any business conducted by a farmer for the enjoyment or education of the public, to promote the products of the farm and to generate additional farm income. No matter what definition is offered, agritour ism is a growing industry that includes the following common aspects: combines the essential elements of the tourism and agricultural industries and attracts members of the public to visit agricultural operations, it is designed to increase farm income and provides recreation, ente rtainment, and/or educational experiences to visitors (Pittman, 2006). Research on Agritourism In spite of this industrys developm ent nati onwide, agritourism was, and still is poorly understood, consisting mostly of case studies and how-to guides. Most of the studies were conducted in Europe or Canada (Polovitz, 2001). Agritourism has been a major segment of rural economy in parts of Europe fo r several decades. Oppermann agrees that, research on agritourism has focused mostly on bed and breakfast and lacks a comprehensive body of knowledge (1995).

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25 Previous studies about agritourism have focuse d on the motivations of farmers to start an agritourism operation, which can be economic reasons (Nickerson, 2004) or educational purposes Telfer (2000) and on the role played by the income obtained from diversified activities outside the farm (Fall and Magnac, 2004). Mc Gehee and Kim (2004) found that both economic and socio-cultural factors are important, however, based on Mace (2005) and Nickerson (2001) no clear consensus currently exists on whether economic factors or soci al factors are more important in determining farmer involvement in agritourism. Bernardo et al., (2004) suggested that two types of factors are im portant in determining who is likely to start an agritourism operation. The first set of factors refers to farm characteristics (operato rs farming experience, access to capital and the size of the farm operatio n) and the other set of factors concerns the farming community. The size of the farm relating to decision to operate an agritourism operation was analyzed also, by Evans and Ilbery (1992) who noticed that th ere are two types of motivations of farm operators: a survival strategy of small farms and an accumulation strategy of larger farms. Rising incomes and budding interests in al ternative recreation and vacation experiences are believed to have stimulated the demand for recreational and leis ure activities in rural areas. Recently, there have been several studies fo cused on the factors affecting the demand for agritourism in United States (Car pio, Wohlgenant and Boonsaeng, 2006) and on the valuation of the nonmarket benefits of the rural landscape to rural visitors (Bergstrom, 1985 and Rosenberger and Loomis, 1999). Carpio, Wohlgenant and Boons aeng (2006) used data from a 2000 National Survey on Recreation to determine the effect of different factor s affecting American population visits to farms and to explore the economic valu e of the rural landscape for farm visitors. Race, gender and location of residents were found to be the most impor tant characteristics explaining

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26 the decision to visit a farm. The total consumer surplus from the agricultural landscape was estimated to be 24.6 billion dollars. The study develops two different methods to analyze consumer behavior of individuals when time is an important component of the decision process. The models are used to analyze customers decision to buy pick-your-own versus pre-harvested fruit at North Carolina pick-your-own fruit ope rations. Elasticity estimates showed pick-yourown fruit being less price elastic than pre-harvested fruit. A research conducted by Colorado State Univ ersity on the agritourism data from USDA found that natural amenities and urban influence are significantly affecting the recreational income at the county level. The study states that counties with higher amenity values have increased opportunities to e xpand recreational services. Another recent study found out that there is a positive rela tionship between household net worth and recreation income, indi cating wealthier operators make more money in agritourism than those with fewer resources. Also, the study found that a positive relati onship exists between a countys population density a nd recreation income and a nega tive relationship exists for percent population change, indi cating slower growth countie s have greater potential for recreational income than areas gr owing more rapidly (Dennis M. Brown and Richard J. Reeder Farm Based Recreation, A Statis tical Profile, A report from th e Economic Research Service USDA, 2007). A sizable literature exists on case studies a nd how-to guides. The main purpose of these studies is to identify opportunitie s for expanding the agritourism sector and to identify challenges and obstacles facing this industry, and to identify the main entrep reneurial characteristics that define success of the agritouris m operator (Rilla, 1998). In 1997 Ellen Rilla, Director of the University of California Coope rative Extension service for Marin and Sonoma Counties,

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27 interviewed more than 100 persons active in the agritourism field in England and in Marin and Sonoma Counties. Rilla identified the most im portant ingredients for success as having an outgoing personality that enjoyed interacting with the public, a property that was attractive and organized, a product (activity based, object, or service) that people desired, a customer base that was available and consistent, and most important the support of the local community. An Interim Report presented to the Vermont De partment of Agriculture, Food and Markets by Todd Comen and Dick Foster, focused on id entifying the critical success factors for agritourism operations, which have been found to be location or proximity to area attractions), social skills, product/service quality, the ability to understand customer requirements, financial analysis and propensity to learn or change. According to Aaron Blacka, from Virginia Cooperative Extension, the purpose of how-to guides and handbooks is to provide farmers with basic information on how to use tourism as an additional product offering on the farm. The information provided helps farms and ranch operators who are interested on every aspect of finding, starti ng, and operating an agritourism operation like Bed&Breakfast, working ranch or similar services, and those farmers who are marketing their products and serv ices directly to the consum er. Many of these guides cover background, marketing, law, organization and planning, and include a "forms" checklist. Probability Models The qualitative response regression models, in which the regressand is qualitative in nature, are often known as probability models (Guj arati, 2004). According to Gujarati, there are three approaches to developing a probabil ity model for a binary response variable: The Linear Probability Model The Logit model The Probit model

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28 A linear regression model with a dependent variable that is either zero or one is called the Linear Probability Model, or LPM. The LPM pred icts the probability of an event occurring, and, like other linear models, says that the effects of Xs on the probabilities are linear. One situation where the linear probability model is commonly used is when the data set is so large that maximum likelihood estimation of a logit or probit m odel is computationally difficult. The Logit model is an important developm ent on the LPM, overcoming many of these problems. The Probit is similar to the Logit model but assumes a different cumulative distribution function (CDF). Carpio, Wohlgenant and Boonsaeng (2006) us ed a Probit Model for estimating factors affecting the demand for agritourism in United States. The economic framework consisted of a two stage study: the first stage was the decision to visit a farm operati on and the second stage analyzed the number of subseque nt visits to farm. The decision to visit or not to visit an agritourism operation was analyzed using a univariate Probit m odel. The study found out that When comparing the average farm visitor and the average non-visitor, the average farm visitor is more educated, has a higher fa mily income, is younger and belongs to a household with more family member s than the average non-visitor. Loureiro and Jervell-Moxnes (2004) analyzed the role played by socio-demographic factors, the financial situati on of the farm household and th e physical location and productive orientation of the farm on the adoption of agro -tourism activities in Norway. They used Logit models in order to estimate the probability th at a farm is engaged or not on agro-tourism activities and an Ordered Probit model to analyze the intensity of participation in agro-tourism activities. The findings showed th at among factors which affect the decision of whether or not to participate in agro-tourism activities, are size and the rural or semi-rural location of the farm and the presence of a female partner ( with positive signs), the age of the head of the farm and more labor intense productive orientat ions (with negative signs).

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29 Dennis Brown and Richard Reeder (2007) focu sed on logistic regression analysis to identify which factors are associated with th e likelihood that a farmer is involved in farm recreation business, and they used a weighted least squares multiple regression analysis to measure the amount of income earned from farm-based recreation.

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30 CHAPTER 3 SURVEY Survey Method Today, the word survey is used m ostly to describe a method of gathering information from a sample of individuals, sample that is usually a fraction of th e population being studied. Survey data can be collected in several mode s: in person, by mail, telephone, or through the internet. Survey modes differ in cost, time, quality of data, sample control and the quality and amount of information that can be presented to the respondent. Each type of survey has advantages and disadvantages. Mail surveys are frequently used fo r social research, have a low cost and eliminate potential interviewer bias, but at the same time may result in biased sample and have a low response rate and time constraints. Compared to mail surveys, telephone and in person interviews have a high response rate and can offer additional details as the interviewers can ask for clarification on responses. Web survey Advantages and Disadvantages Web surveys have recently been recognized as a valuable instrum ent for collecting data (Dillman, 2000). The rapid development of web surv eys is leading some to argue that soon web surveys will replace traditional methods of su rvey data collection (Couper, 2000). There is a trend toward web-based and other self-administere d forms of survey resear ch due to the steadily increasing cost of interviewer-administered data collection, including the increased time interviewers need to locate, screen, pers uade, and interview respondents ( Couper, 2005). Web surveys are often the least expensive to administer, can be fast in terms of data collection and survey software simplifies the compilation and the analysis of data collected. Web surveys can be administered to a vast geogra phically diverse pool of potential respondents

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31 (Alvares et al., 2003). They support complex que stioning that assures better data and the anonymity of respondents result in more honest answers to sensitive topics. On the other hand, making general statements about large populations based on Internet survey results is currently problematic as th is survey mode faces important methodological issues (Alvares et al., 20 03). It is widely agreed that the ma jor sources of error in web surveys include sampling, coverage, age, nonresponse bi as and estimation errors. Because it is difficult to draw representative samples from among Internet users, the nature of the samples can be questionable. A paper version of a survey obtained higher a nd more accurate response rates than did a computer version (Beebe, Harrison, Park, McR ae, & Evans, 2006). The web survey studied exhibited significant coverage bias that was not correctable by usual st atistical post-survey adjustments such as ratio-ranking (Lee, 2006). Survey Design and Data collection In the spring of 2007, a survey instrum ent us ing internet-sampling methods was developed and pre-tested with a group of 30 people in orde r to identify any problems with the length, content and to be easily underst ood by the respondents. In the su mmer of 2007, a composite list of 1447 U.S. agritourism operators was co mpiled using the following references: http://www.farmstop.com/fssearch.asp http://www.agritourismworld.com/search_results.php ?page=6&&logic=all®ion=5006&sid=2491700 http://www.agriscape.com/agritourism/ Before being administered, the questionnaire was reviewed and approved by the University of Florida Institutional Review Board (UF-IRB) for compliance with ethical standards for human subject research. In fall 2007, th e survey instrument was posted to the online site Survey Monkey. Introductory emails were sent to 1228 agri tourism operators. Three days later, a followup email was sent to each operator containing a brief description of the survey and a web link to

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32 the survey site. The survey included a short st atement on the purpose of the questionnaire, the importance of providing information, completi ng instructions, confidentiality guarantee and contact information in case the respondents ha d any questions. A reminder email was issued three weeks later. By December 2007, 301 surv eys were received and of those 273 were complete. The response rate for completed surv eys was 22.23 percent. Despite the small sample size and response rate, the results provide useful insights to understandi ng agritourism in US. The survey had five sections a nd covered the following topics: General characteristics of the operation: farm commodities/ primary farm commodity produced on the farm types of attractions, operation schedule, fee charged years of experience role and reason for operating an agritourism enterprise Specific questions about agritourism business: number of employees gross value of ag ritourism sales size of the farm and size of the agritourism operation Visitor characteristics and preferences: number of visitors and visitor information dollars spent by visitors length of stay Information about the current state of the business and future plans: issues and obstacles faced future plans/trends in agritourism identification of negative impacts of agritourism Demographic information: numbers and locations of agritourism operations education level/age/gender of the agritourism operator

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33 The basic goal of the survey was to gain a better understanding of U.S. agritourism industry characteristics, and to s how the relative impact of diffe rent factors on the decision to add new agritourism activities to the farm. See Appendix A for a complete description of the questionnaire posted on the website. Survey Results The scope of the survey was to identify ch aracteristics of U.S. ag ritourism industry and regional differences, and offer insight on the range of activities unde r the umbrella of agritourism. Drawing from the 273 completed su rveys, the following sections provide insight into the descriptive aspect of agritourism. Summary of Survey Results Three hundred and one surveys were received and of those 273 surveys w ere complete. The response rate for completed surveys was 22.23 %. Two hundred and nineteen contacts from the initial list of 1447 operators could not be reached. The remaining 927 were unable to respond, refused, were out of business or did not have agritourism activities (see Figure 3-1). The initial sample was drawn from enterprises known to have or ha ve had agritourism activities. Hence, the following uses of the survey data are clearly based on sample selection among all farms. Since we do not have information on farms not providing agritourism, all inferences must be made recognizing the selection process. Figure 3-1. Summary of survey results

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34 Gross Value of Agritourism Sales and the Percentage of Total Farm Income From Agritouris m Sales revenues per farm ranged from $0 to over $1 million per year, with median revenues around $25,000 annually. While some operations (26.8%) earned nearly all of their income from agritourism activities for the majo rity (47.3%), agritourism contribu ted to less than one-fourth of total operations income (see Figu res 3-2 and 3-3). The primary obj ective of the farms with the largest average revenues over $100,0 00 (26.1%) was income. States with the largest average revenues were Maine, Oklahoma, Idaho, Wisconsin and Michigan. Figure 3-2. Gross value of agritourism sales Figure 3-3. The percentage of total farm income from agritourism

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35 Primary Farm Commodity Produced on the Farm Those farm s more likely to offer agritourism activities were fruit and nuts growers (29.0%) and livestock operations (17.3%). Less likely were poultry operations (2.2%), Christmas tree growers (5.6%), and dairy farms (6.3%). Fi gure 3-4 shows the complete distribution. Figure 3-4. Primary farm comm odity produced on the farm Types of Agritourism Activities The survey findings revealed a large range of activities offered. An agritourism operation may offer more than one attraction or activity to visitors. Of the more than 80 types of activities offered, farm tours (58.4%), school trips (53.2%), and festiv als (39%) were among the most common (see Figure 3-5). Pick-your-own was the activity most widely offered (34.8%), followed by retail farm stands (33.4%) and hayrides (33.4%). More than a quarter of respondents also reported an attraction not f itting the options included in the survey. Other activities reported included historic facility tour, buffalo viewing, Alpaca birth day/shearing day, Noah's Ark play land, gardening seminars or workshops on dyeing, felting and knitting lessons. Thus, visitors express a high level of interest in a variet y of agritourism activities and attractions.

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36 Figure 3-5. Types of agritourism activities Reasons for Operating an Agritourism Enterprise As showed in Figures 3-6 and 3-7 supplem en tal income was the primary motivation for offering agritourism activities according to 84.8% of survey respondents, and 72.1% of respondents charged fees for ac tivities. Others earned income by selling goods and 13.8% of respondents indicated that they offered activities as a personal hobby and for that reason did not

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37 charge a fee. Fee price range s from few dollars a day to $1800 per week, depending on the activity or attraction. Figure 3-6. Reasons for involvement in agritourism Figure 3-7. Charged fee for services/attractions Management Characteristics The survey asked respondents to provide the nu mber of years they operated an agritourism enterprise. The number of years of experience ra nged from 1-3 years (18.0%) to more than 10 years of experience (40.8%). Figure 3-8. Total years of experience

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38 The survey asked the respondents to indicate their business role and the modality they funded their enterprise. As seen in Figure 3-9, the majority (76.8%) indicated that they are both owner and operator, 14.0% own the business and 9.2% consider themselves operators. Both annual income/cash flow (68.7%) and personal savings (60.0%) we re considered when funding the agritourism business. Other modalities includ ed grants (13.8%), loans (25.1%), and volunteer efforts. Figure 3-9. Sources of funding Agritourism operations are opened seasonally (4 9.3%) or yearround ( 50.7%). Related to the seasonality of the business is the employment. The study has four classifications: full time year-round, part time year round, full time season ally and part time seasonally. Figure 3-10 shows us the distribution of number of em ployees by seasonality. The number of employees ranges from one to 125 for full time year-round and from one to 50 for part time year-round, with an average of 3.88 full time and 4.36.part ti me. The number of employees ranges from one to 100 for full time seasonally, with an average of 16.3 and from one to 60 for part time seasonally with an average of 8.43.

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39 Figure 3-10. Number of employees by seasonality Figure 3-11. Operation schedule Demographic Profile The survey helped to provide answers about the dem ographic profile of the agritourism operators. According to the agri tourism survey, 52.6 % of farmers were female and 47.4 % male. While the agritourism operators ranged in age from 18 to 65 or more, the average farmer was between 50-64 years old. The majority of operators were college graduate (57.9%) or some technical school graduate (20.3%).

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40 Figure 3-12. Agritourism operator educational level Figure 3-13. Agritourism operator age category Figure 3-14. Agritourism operator gender

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41 Visitor Characteristics and Preferences The survey asked respondents to estim ate the number of visitors during 2006. The number of customers reported ranged from zero to 767,1 01 for 273 farms, with more than 3 million visitors total and an average of approximately 10,000 visitors per en terprise. Twelve enterprises did not make an estimate/ did not tr ack the number of customers in 2006. Respondents were asked to compare the year 2006 with the previous years, and 43.8% of the respondents estimate 2006 visitation about th e same as previous years, 47.9% estimated 2006 higher than average and 8.3% lower than average. The visitor point of origin is diverse and includes local less than 50 miles (72.9%), regiona l greater than 50 miles (69.3%), outside the state (57.0%) and interna tional (36.7%). The majority of agri tourism operators said that more than 60% of their visitors were local and regional travelers. The point of origin is important in categorizing the sources of tourism at each level. Almost 90% of visitors were singles/couples/families and 62% were organized groups (e.g., school groups, senior citizen groups, or church youth groups). The visitor length of stay ranged from one hour to 17 days, with an average of 2-3 days. The average dollar amount spent by visitor per trip ranged from $1 to $49 (67.2%). Almost 17% spent between $50 a nd $99, while 4.9% spent between $100 and $149 and 10.8% of visitors spent over $150 per visit (see Figure 3-15). Figure 3-15. Year 2006 compared with previous years

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42 Figure 3-16. Visitor point of origin Figure 3-17. Types of visitor groups Figure 3-18. Dollar amount spent per visitor

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43 Information about the Current State of the Business and Plans Respondents were asked to respond how they m a rket and distribute th eir product(s). It is interesting to note that word of mouth plays a greater marketi ng role in agritourism industry according to 90.9% of the survey respondents. Ot her ways to promote the agritourism business included email (43.9%), direct marketing (62.1 %) or mailing lists (3.2%) (see Figure 3-19). The majority of entrepreneurs have a plan for future expansion (59.9%), almost 17.6% are unsure as to their future expansion plans a nd 22.8% of entrepreneurs do not have a future expansion plan regarding their business. Agritourism operators were aske d to indicate a variety of di fficulties they encountered in expanding their business. The most significant issues were findi ng qualified employees (48.4%), insurance issues (43.0%), iden tifying new markets (32.6%), obt aining financing (29.0%) and competition from other businesses (18.6%) (Figure 3-20). Respondents identified their main agritourism environmental concerns, with the main concern being the increased building devel opment (60.0%), followed by the disruption of wildlife/livestock (32.7%). Approximately 48.1% of the respondents said that they foresee a moderate growth in agritourism trends and the ma jority agreed that agritourism is important to the economic viability of their county (Figures 3-21 and 3-22). Figure 3-19. Marketing and distri bution of agritourism products

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44 Figure 3-20. Difficulties encountered in expanding the business Figure 3-21. Environmental co ncerns with agritourism Figure 3-22. Agritourism future trends

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45 In this chapter, general descriptive statistic s showed a profile of farms having some level of agritourism activities. Each of these charact eristics is expected to have some impact on the level of agritourism activities initially identifi ed in Figures 1-1 and 1-2. The extent of the impacts, if any, are addressed and measured in the next chapter. Th at is, what affects the level of activity measured in terms of gross agritourism sales and share of income from agritourism?

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46 CHAPTER 4 ECONOMETRIC MODEL Probit Model and Ordered Probit Model Figures 1-1 and 1-2 set the stage for m easuri ng the levels of agri tourism activities among farms indicating some level of involvement within the base agricultural enterprise. For both the income levels and shares of income from agricult ural tourism, the values were categorized into discrete groups of measures w ith the measures being exhaustive and mutual exclusive. That is, every farm fits within one of the categories of income or share and only one category for each measure. Given these measures, the fundamental i ssue of interest is to determine the probability of each level of activity and how these probabiliti es differ across the characteristics of the farm. For analyses purposes, two importance aspects of the measure are that the classifications are binary, may be multiple, and ordered in terms of intensity of importance to the farm. Hence, rather than measuring and predicting the level, the goal is to predict the probability of each level. This in turn, dictates the type of modeling requirements needed for the modeling of agritourism importance. Linear regression analysis is a statistical method comm only used by social science researchers that assumes a continuous dependent variable. Because the nature of many social phenomena is discrete rather than continuous (Pampel 2000), lin ear regression analysis proves inappropriate for the analysis of many behavi ors or decisions measured in a non-continuous manner (Liao 1994). The Probit model extends the pr inciples of generalized linear regression to better treat the case of dichotomous and categoric al variables. The model focuses on association of categorical or grouped data, looking at al l levels of possible interaction effects. The Probit model is a popul ar specification of a generalized linear mode l with two categories in the dependent variable (Liao 1994). The dependent random variable, y is assumed

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47 to be binary, taking on but two values, say 0 and 1 and depends on K observable variables xk where k=1, ,K. The outcomes on y are assumed to be mutually exclusive and exhaustive (Aldrich and Nelson 1984). Probit parameters are typically estimated by MLE Maximum likelihood estimation by picking parameter estimates that imply the highest probab ility or likelihood of ha ving obtained the observed sample Y (Aldrich and Nelson 1984). Let Y be a binary outcome variable, and let X be a vector of regressors. The Probit model assumes that: )()/1Pr('xxXY (4-1) where is the cumulative distribution function of the standard normal distribution. The parameters are typically maximum likelihood estimates. The Probit model can be generated by a simple latent unobserved continuous variable*y Suppose that: k k k kx y1 *, where: ) ,0(2 (4-2) The dummy variable, y, is observed and determined by y* as follows: otherwise yif y ,0 0 ,1* (4-3) From the above relations we have: )0 Prob()1Prob(1 k k kkx y =) Prob(1 k k k kx (4-4) = ) (11 k k k kx where = cumulative distribution function of (Liao 1994).

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48 When the dependent variable takes a range of va lues that have order as initially shown in Figures 1-1 and 1-2, then Probit models are no l onger useful. Instead, th e probability of each value must be estimated while recognizing that the ordinal ranking of the values exist. Ordered Probit models serves as an appropriate framework for statistical analysis whenever survey responses are ordinal as distinct from numerical. The Ordered Probit model is a model in which both the dependent variable and the explanatory va riables take values that are ordered across the descriptive categories. Let k k k kx y1 *, where: )1,0( (4-5) where the latent continuous variable, y is a linear combination of some predictors, x, plus a disturbance term that has a st andard Normal distribution. Y is the observed ordinal variable that take s on values 0 through m according to the following scheme: y = 1 if y* 1 (= 0), (4-6) y = 2 if 1 < y* 2, y = 3 if 2 < y* 3, y = 4 if 3 < y* 4, y = J if j-1 < y*. where: j = 0,.,m and the unknown threshold levels ( ) are estimated with the s. Prob(y=1) =)(1 k k k kx, (4-7) Prob(y=2) = ) (1 2 k k k kx )(1 k k k kx

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49 Prob(y=3) = ) (1 3 k k k kx ) (1 2 k k k kx Prob(y=4) = ) (1 4 k k k kx ) (1 3 k k k kx .. Prob(y=J) =) (11 1 k k k k jx where: Prob(y=J) is the probability that the observed y is in category j. The threshold values m show the probability differences in combination with the effects of the parameters associated with the explanatory variables (x). The significance of the s determine the importance of each of the right-hand-side variables first illustrated with Figures 1-1 and 1-2. Ordered Probit Agritourism Model Specifications Ordered Probit m odels are used to measure th e likelihood of offering agritourism activities with the broader farm organization. Using the Or dered Probit estimates and simulation models, simulated changes in the likelihood or (probability) of offering ag ritourism provide insight into the conditions that truly affect this di mension of the U.S. farming sector. Ordered Probit Models are used to analyze the effects of explanatory variables on the dependent variable, the gross value of agritourism sales (Model 1) and the percentage of the total farm operation income estimated from agritourism (Model 2) as first set forth in Figures 1-1 and 1-2. Several categories of variables are included in the Ordered Probit Model: farm commodities, type of activities, charged fee, years of experience, business role, seasonality, reason for becoming involved in agritourism, number of employees, size of the farm number of visitors, education, age and gender. A number of qualitativ e measures of the motivation for involvement in agritourism are included as explanatory vari ables in the Ordered Prob it models. These factors are the gross value of agritourism sales and the percentage of the total farm operation income

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50 estimated from agritourism. The distributions of each of these variables were shown in Chapter 3. Ordered Probit model variables and de scriptions are defined in Table 4-1. Table 4-1. Ordered probit model variables and descriptions Variable Description Beef Beef Cattle, Hogs, Sheep/Other Livestock 1=yes 0=no Xtree Christmas Tree 1=yes 0=no Dairy Dairy 1=yes 0=no F_N Fruits&Nuts 1=yes 0=no Grain Grain(wheat, corn, soybean)/Other Field Crops 1=yes 0=no Horse Horse/Other Equine 1=yes 0=no Nur Nursery/Greenhouse 1=yes 0=no Poultry Poultry 1=yes 0=no Vege Vegetables, Melons, Potatoes 1=yes 0=no Bird Bird watching 1=yes 0=no CCN Cross county skiing 1=yes 0=no Fish Fishing 1=yes 0=no Game Game/wildlife preserve 1=yes 0=no Hunt Hunting 1=yes 0=no Hike Hiking and scenery 1=yes 0=no Horse Horseback riding 1=yes 0=no Tour Farm tours 1=yes 0=no Work Farm work experience 1=yes 0=no Schl School trip 1=yes 0=no Wine Winery tours 1=yes 0=no Make Made on-site food products Pick Pick-your-own 1=yes 0=no Pump Pumpkin picking 1=yes 0=no Stand Retail farm stands 1=yes 0=no Cut You cut Christmas trees 1=yes 0=no B_B Bed and breakfast 1=yes 0=no Camp Camping 1=yes 0=no Vacat Farm vacations 1=yes 0=no Picnic Picnicking 1=yes 0=no Wedd Weddings and receptions 1=yes 0=no Maze Corn mazes 1=yes 0=no Trail Dog trails/training 1=yes 0=no

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51 Table 4-1. Continued Fest Festivals/special events 1=yes 0=no Hay Hay rides 1=yes 0=no Zoo Petting zoo 1=yes 0=no Yes Fee 1=yes 0=no ATour1 years of experience 1-3 1=yes 0=no ATour2 years of experience 3-5 1=yes 0=no ATour3 years of experience 5-7 1=yes 0=no ATour4 years of experience 7-10 1=yes 0=no ATour5 years of experience >10 1=yes 0=no Own Owner 1=yes 0=no Oper Operator 1=yes 0=no Both Owner/Operator 1=yes 0=no Season Year-round= 1 Seasonally=0 Inc Income 1=yes 0=no Hob Hobby 1=yes 0=no Ple Pleasure 1=yes 0=no Pub Public Service 1=yes 0=no Edu Education 1=yes 0=no Full Full time 1= yes 0=no Part Part time 1= yes 0=no Inc1 Income $0-$2500 1=yes 0=no Inc2 Income $2500-$4999 1=yes 0=no Inc3 Income $5000-$99999 1=yes 0=no Inc4 Income $10000-$24999 1=yes 0=no Inc5 Income $25000-$49000 1=yes 0=no Inc6 Income $50000-$99000 1=yes 0=no Inc7 Income $100000-$249000 1=yes 0=no Inc8 Income $250000-$999000 1=yes 0=no Inc9 Income $1000000+ 1=yes 0=no Sh1 Share of income from agritourism 0% 1=yes Sh2 Share of income from agritourism 1%-24% 1=yes Sh3 Share of income from agritourism 25%-49% 1=yes Sh4 Share of income from agritourism 50%-74% 1=yes Sh5 Share of income from agritourism 75%-99% 1=yes Sh6 Share of income from agritourism 100% 1=yes Tot Size of the farm Educ1 Education Less than High School 1=yes 0=no

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52 Table 4-1. Continued Educ2 Education Some College/Tehnical School 1=yes 0=no Educ3 Education Post Graduate Degree 1=yes 0=no Educ4 Education High Sc hool Graduate 1=yes 0=no Educ5 Education College graduate 1=yes 0=no Age1 Age 18-24 1=yes 0=no Age2 Age 25-34 1=yes 0=no Age3 Age 35-49 1=yes 0=no Age4 Age 50-64 1=yes 0=no Age5 Age >65 1=yes 0=no Female Gender-Female 1=yes 0=no Male Gender-Male 1=yes 0=no All explanatory variables de fined in Table 4-1 entering the Ordered Probit model are binary, except Tot Size of the farm. Given ther e are so many binary variables in the model, a convenient approach for dealing with the mutually exclusive a nd exhaustive properties of each dummy category is to restrict a unweighted sum of the coefficients to zero for each discrete variable. The intercept represents the unweighted average and all coefficients are expressed relative to this average, instead of a base set of characte ristics usually adopted when dropping one dummy variable from a discrete classification. To illustrate, let a discrete variable (Age) take five values, which are represente d by five dummy variables (D). Then restrict the unweighted sum of the coefficients to zero: 5 10j j or 4 1 5 j j (4-8) 5 1 4 1 5)()(jj jj jjDD D It is important to recall that each T-value is e xpressed relative to the average. A statistically significant T-value means that the coefficient is statistically different from the mean or 0.

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53 For example in Table 4-2., horseback riding, farm tours and winery tours are statistically different from zero, meaning they are different from average. See Appendix B for the computer codes to show exactly how each variable was created relative to the average. The Ordered Probit model for the gross value of agritourism sales is specified below with all of the variables being dummies except for the fa rm size (Tot): (4-9) Y*i = 0 + 1 dbeefk + 2 dxtreek + 3ddairyk + 4 df_nk + 5 dgraink + 6 dhorsek + 7 dnurk+ 8 dpoultryk + 9 dvegek + 10 dbirdk + 11 dccnk + 12 dfishk + 13 dgamek + 14 dhuntk+ 15 dhikek + 16 dhorsek+ 17 dtourk+ 18 dworkk+ 19 dschlk+ 20 dwinek + 21 dmakek+ 23 dpickk + 23 dpumpk + 24 dstandk + 25 dcutk + 26 db_bk + 27 dcampk+ 28 dvacatk + 29 dpicnick + 30 dweddk + 31 dmazek + 32 dtrailk + 33 dfestk + 34dhayk+ 35 dzook + 36 dothk+ 37 dyesk+ 38 datour1k+ 39 datour2k+ 40 datour3k+ 41 datour4k + 42 datour5k + 43 downk + 44 doperk + 45 dbothk + 46 dseasonk + 47 dinck+ 48 dhobk + 49 dplek + 50 dpubk + 51 dtotk + 52 deduc1k + 53 deduc2k + 54 deduc3k+ 55 deduc4k + 56 deduc5k+ 57dage1k+ 58 dage2k+ 59 dage3k+ 60 dage4k+ 61 dage5k + 62 dfemalek + 63 dmalek The Ordered Probit model for the percentage of the total farm operation income estimated from agritourism is specified as follows: (4-10 ) Y*i = 0 + 1 dbeefk + 2 dxtreek + 3ddairyk + 4 df_nk + 5 dgraink + 6 dhorsek + 7 dnurk+ 8 dpoultryk + 9 dvegek + 10 dbirdk + 11 dccnk + 12 dfishk + 13 dgamek + 14 dhuntk+ 15 dhikek + 16 dhorsek+ 17 dtourk+ 18 dworkk+ 19 dschlk+ 20 dwinek + 21 dmakek+ 23 dpickk + 23 dpumpk + 24 dstandk + 25 dcutk + 26 db_bk + 27 dcampk+ 28 dvacatk + 29 dpicnick + 30 dweddk + 31 dmazek + 32 dtrailk + 33 dfestk + 34dhayk+ 35 dzook + 36 dothk+ 37 dyesk+ 38 datour1k+ 39 datour2k+ 40 datour3k+ 41 datour4k + 42 datour5k + 43 downk + 44 doperk + 45 dbothk + 46 dseasonk + 47 dinck+ 48 dhobk + 49 dplek + 50 dpubk + 51 dtotk + 52 deduc1k + 53 deduc2k + 54 deduc3k+ 55 deduc4k + 56 deduc5k+ 57dage1k+ 58 dage2k+ 59 dage3k+ 60 dage4k+ 61 dage5k + 62 dfemalek + 63 dmalek

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54 Ordered Probit Agritourism Model Results Survey result provided insight into factors con tributing to the decision of adopting an agritourism activity. The Ordered Probit model was estimated using the survey data and maximum likelihood procedures. The parameter estimates, reported in Tables 4-2 and 4-3, correspond to the coefficients in Equations 4-9 and 4-10 and represent factors positively or negatively affecting the decision to add new agritourism activities to the farm. The R2 reveals that over 56 % of the variation in the gross value of agritouris m sales and over 42% of the variation in the percentage of the total farm operation income estimated from agritourism is explained by the models. The estimates show that several factors have a statistically significant impact on the gross value of agritourism sales. The agritourism sale s are grouped into four categories as follows: $0 $ 9,999, $10,000 $49,999, $50,000 $99,999 $100,000 $1,000,000 or more. The following activities were found to be statistically different from the average at the 99% confidence level: horseback riding with a coefficient of 0.4160 (t-value equal to 3.0282), farm tours with a coefficient of -0.2458 (t-value equal to -2.7152), and winery tours with a coefficient of 0.6879 (t-value equal to 4.2149). At the 95% confidence level we found that fa rm work experience (a coefficient of -02392 and t-value equal to -2.1739), re tail farm stands (a coefficient of 0.2132 and t-value equal to 2.2318) and hay rides (a coeffici ent of 0.2135 and t-value equal to 2.1080) have a statistically significant impact on the gross value of agritourism sales relative to the average. Festivals or special events and corn mazes have a positive impact at a 90% confidence level with estimates of 0.1724 (t-value equal to 1.8696), respectiv ely 0.2793 (t-value equal to 1.9076).

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55 Charging a fee has a negative statistically significant impact on the gross value of agritourism sales at the 95% level with a coefficient of -0.1896 and t-value of -2.0272. Survey respondents total years of experience also appears to play a significant role on the gross value of agritouris m sales. This relationship between agritourism sales and number of years of experience is consistent with the previous st udies, showing that less ex perience (1-3 years and 3-5 years) has a negative impact and more experi ence (7-10 years or more than 10 years) has a positive impact on the agritourism sales. Being open seasonally also has a positive impact on the agritourism sales at the 95% confidence level. Season has a coefficient of 0.1740 with a t-value of 2.0496. The reason of operating an agrit ourism enterprise also appears to play a significant role on the gross value of agritourism sales. Income was found to play a positive impact at a 99% confidence level with estimates of 0.5486 (t-val ue equal to 3.006), while agritourism as a hobby has a negative impact with estimates of -0.6423 (t-value equal to -2.7733). Age, education and gender were found not to ha ve a statistically sign ificant impact on the agritourism sales. Table 4-2. Ordered probit model parameter es timates: Gross value of agritourism sales Variable Parameter estimate T-Value Intercept 2.5198 3.8629*** ZQ02_BEEF 0.0040 0.0170 ZQ02_XTREE -0.0637 -0.1268 ZQ02_DAIRY -0.0062 -0.0165 ZQ02_F_N 0.2949 1.3685 ZQ02_GRAIN -0.1287 -0.4762 ZQ02_HORSE -0.1747 -0.4989 ZQ02_NUR -0.5071 -1.6053 ZQ02_POULTRY 0.1576 0.3189 ZQ02_VEGE 0.3052 1.3359 ZQ03_BIRD -0.0134 -0.1080 ZQ03_CCN -0.1373 -0.7816

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56 Table 4-2. Continued ZQ03_FISH -0.1193 -0.8880 ZQ03_GAME 0.1101 0.7880 ZQ03_HUNT 0.0722 0.6149 ZQ03_HIKE -0.1214 -0.8884 ZQ03_HORSE 0.4160 3.0282*** ZQ03_TOUR -0.2458 -2.7152*** ZQ03_WORK -0.2392 -2.1738** ZQ03_SCHL -0.0574 -0.6236 ZQ03_WINE 0.6880 4.2150*** ZQ03_MAKE 0.1202 1.2631 ZQ03_PICK 0.0548 0.5626 ZQ03_PUMP -0.0253 -0.1762 ZQ03_STAND 0.2132 2.2318** ZQ03_CUT -0.0736 -0.2652 ZQ03_B_B 0.0294 0.2069 ZQ03_CAMP 0.2314 1.2641 ZQ03_VACAT 0.0949 0.6192 ZQ03_PICNIC -0.1050 -1.0246 ZQ03_WEDD 0.0416 0.3946 ZQ03_MAZE 0.2794 1.9076* ZQ03_TRAIL -0.3723 -1.2671 ZQ03_FEST 0.1724 1.8696* ZQ03_HAY 0.2135 2.1080** ZQ03_ZOO 0.1705 1.5365 ZQ04_YES -0.1896 -2.0272** ZQ05_ATOUR1 -0.6330 -3.2106*** ZQ05_ATOUR2 -0.1131 -0.5560 ZQ05_ATOUR3 0.0809 0.3724 ZQ05_ATOUR4 0.5082 2.4145** ZQ05_ATOUR5 0.6375 3.8515*** ZQ06_OWN -0.2018 -0.8843 ZQ06_OPER 0.3134 1.1448 ZQ06_BOTH -0.1843 -0.9677 ZQ07_SEASON 0.1740 2.0496** ZQ08_INC 0.5486 3.0059*** ZQ08_HOB -0.6424 -2.7733*** ZQ08_PLE 0.2625 1.4488 ZQ08_PUB -0.0508 -0.2420 ZQ08_EDU -0.1770 -0.9080 ZQ11_FULL 0.0247 0.2523 Q14_TOT 0.0000 1.4949

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57 Table 4-2. Continued ZEDUC1 2.2074 0.0009 ZEDUC2 1.0322 0.0004 ZEDUC3 0.5356 0.0002 ZEDUC4 0.8509 0.0004 ZEDUC5 0.6726 0.0003 ZAGE1 -0.0108 0.0000 ZAGE2 -1.2432 -0.0005 ZAGE3 -0.8676 -0.0004 ZAGE4 -0.9590 -0.0004 ZAGE5 -1.2743 -0.0005 ZFEMALE -0.2352 -0.6245 ZMALE -0.0758 -0.2006 MU3 0.4235 4.7385*** MU4 0.7179 6.6703*** MU5 1.4067 10.6314*** MU6 2.0324 13.7186*** MU7 2.5194 15.7544*** MU8 3.2165 17.5950*** MU9 4.2361 16.9310*** The regression coefficient is statistically different from zero: *** at the 0.01 level of significance ** at the 0.05 level of significance at the 0.1 level of significance Several factors were also found to have a stat istically significant impact on the percentage of total farm operation income estimated from agritourism. The agritourism shares were grouped into four categories as follows: 0% %24, 25% 49%, 50 % 74%, 75% 100% The only choice of farm commodity that has been found to have a statistically negative impact at the 95% confidence level on agritouris m shares was Vegetables, Melons, and Potatoes with a coefficient of -0.4529 and a t-value of -2.0372. We also found that farm tours

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58 (a coefficient of -0.1965 and t-va lue equal to -2.1756), school trip s (a coefficient of -0.2919 and t-value equal to -3.0727), retail farm stands (a coefficient of 0.1977 and t-value equal to 2.0386) and hay rides (a coefficient of 0.2035 and t-value equal to 1.9768) have a statistically significant impact on the percentage of total farm opera tion income estimated from agritourism. Agritourism operators total years of experience appears to pl ay a significant role on the shares of agritourism income as well as for the agritourism sales, and the relationship is consistent with the previous fi ndings, meaning less experience (1-3 years) has a negative impact and more experience (7-10 years or more than 10 years) has a positive impact on the shares of agritourism income. Being both owner and operato r also has a positive impact on the share of agritourism income at the 90% confidence leve l. Both has a coefficient of 0.3289 with a tvalue of 1.6849. In addition, the size of farm has a statistically significant negative effect on the percentage of total farm operation income estimat ed from agritourism, as well as operating an agritourism farm for a public service reason. It is interesting to note that education of the farm operato r is the only socio-demographic characteristic statistically significant from the av erage. Being a high school graduate or a college graduate appears to play a significant role in th e decision to add new agri tourism activities to the farm. Parameter estimates for Educ4 and Educ 5 are 0.6696 and 0.5479 respectively with t-values of 1.8120 and 1.9070. The rest of the included vari ables are not statis tically significant. Coefficients and T-values of the Ordere d Probit model are presented in Table 4-3. Table 4-3. Ordered probit model parameter estimates: Share of agritourism income Variable Parameter estimate T-Value Intercept 1.9362 2.9060 *** ZQ02_BEEF 0.0043 0.0179 ZQ02_XTREE 0.1213 0.0240 ZQ02_DAIRY -0.2047 -0.5301 ZQ02_F_N -0.1423 -0.6559

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59 Table 4-3. Continued ZQ02_GRAIN -0.3728-1.3622 ZQ02_HORSE 0.4152 1.1293 ZQ02_NUR -0.0430 -0.1452 ZQ02_POULTRY 0.5497 1.0797 ZQ02_VEGE -0.4529 -2.0372 ** ZQ03_BIRD -0.0740 -0.5923 ZQ03_CCN -0.0694 -0.3821 ZQ03_FISH 0.1144 0.8134 ZQ03_GAME 0.0202 0.1356 ZQ03_HUNT -0.1412 -1.1608 ZQ03_HIKE -0.0896 -0.6536 ZQ03_HORSE 0.1764 1.2498 ZQ03_TOUR -0.1965 -2.1760 ** ZQ03_WORK -0.0881 -0.7986 ZQ03_SCHL -0.2919 -3.0727*** ZQ03_WINE 0.1079 0.6828 ZQ03_MAKE -0.1131 -1.1726 ZQ03_PICK 0.0975 0.9953 ZQ03_PUMP -0.2133 -1.4996 ZQ03_STAND 0.1977 2.0386** ZQ03_CUT 0.3332 1.2344 ZQ03_B_B 0.0320 0.2292 ZQ03_CAMP 0.1120 0.6277 ZQ03_VACAT 0.1921 1.2275 ZQ03_PICNIC 0.0383 0.3715 ZQ03_WEDD 0.0816 0.7687 ZQ03_MAZE 0.0336 0.2323 ZQ03_TRAIL -0.0928 -0.2955 ZQ03_FEST 0.0586 0.6300 ZQ03_HAY 0.2035 1.9768** ZQ03_ZOO 0.1122 0.9907 ZQ04_YES 0.1042 1.0920 ZQ05_ATOUR1 -0.4218 -2.1477 ** ZQ05_ATOUR2 -0.0173 -0.0817 ZQ05_ATOUR3 0.4473 1.9615** ZQ05_ATOUR4 0.6921 3.2058*** ZQ05_ATOUR5 0.2422 1.4113 ZQ06_OWN -0.2943 -1.2717 ZQ06_OPER 0.3229 1.1491 ZQ06_BOTH 0.3289 1.6849*

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60 Table 4-3. Continued ZQ07_SEASON -0.1646-1.8523* ZQ08_INC 0.2542 1.3366 ZQ08_HOB -0.2117 -0.9197 ZQ08_PLE 0.1379 0.7756 ZQ08_PUB -0.5046 -2.3711** ZQ08_EDU 0.0578 0.3021 ZQ11_FULL 0.1360 1.3231 Q14_TOT 0.0000 -2.5779 ** ZEDUC1 -1.3748 -1.2916 ZEDUC2 0.4047 1.3431 ZEDUC3 0.3632 1.1280 ZEDUC4 0.6696 1.8120* ZEDUC5 0.5479 1.9070* ZAGE1 -0.8219 -0.7183 ZAGE2 0.4203 0.9893 ZAGE3 0.0168 0.0473 ZAGE4 0.2273 0.6640 ZAGE5 0.3774 0.9861 ZFEMALE -0.4043 -1.2600 ZMALE 0.0344 0.1072 MU3 1.9145 11.6212*** MU4 2.3318 13.4977*** MU5 2.8626 15.6552*** MU6 3.3970 17.4532*** The regression coefficient is statistically different from zero: *** at the 0.01 level of significance ** at the 0.05 level of significance at the 0.1 level of significance In this chapter, we set forth the Or dered Probit methodology and developed the econometric models for measuring the major driver s of the supply of agr itourism. Two measures were used: gross value of agritourism sales and the percentage of total farm operation income estimated from agritourism. Then, in the latter half of this chapter the complete econometric models and estimates were reported. Hence, th e next chapter concentrates on using the rich results from this chapter to numerically show changes in the probability of offering agritourism and the impacts of the factors that affect U.S. agritourism.

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61 The major contribution of chapte r 4 is getting the econometric responses while chapter 5 is more useful for truly showing the changes in th e likelihood of offering agritourism. To recap, it is important to recognize that all T-values are ex pressing differences relati ve to the average farm and not between any two levels of an explanat ory variable. Clearly, le vel differences may be statistically significant while diffe rences to the average may not.

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62 CHAPTER 5 ORDERED PROBIT MODEL SIMULATIONS Chapter 5 uses estimates from previous chap ters to show simulations over each variable included in the models. In chapter 4, we develope d the models for measuring the major drivers of the supply of agritourism. Our analysis was lim ited at two measures: gro ss value of agritourism sales and the percentage of total farm ope ration income estimated from agritourism. The models were designed to show change s in the likelihood (pr obability) of offering agritourism and provide insight into the conditions that a ffect the decision to add new agritourism activities to the farm. The changes in likelihood can be estimated by three modalities: calculating the marginal responses calculating the odds of ratios simulating the probabilities of a certain even t occurring when given a particular set of conditions of explanatory variables. In order to estimate changes in the probability of adding new agritourism activities a base is set. The base fixes all the explanatory variables at their average value. Each simulation is based on first estimating the probabilities acro ss all farms and then getting the average probabilities for either the four income earning levels or four income shares. Then each variable is simulated across the farms using the actual co nditions except for the variable under control such as education, etc. Again, the probabilities are averaged over the farms with just the controlled variable being changed. Hence, each simulation can be compared to the overall average or the average for any othe r controlled level or variable. Simulating the Probabilities of Agritouris m: Gross Value of Agritourism Sales From our Ordered Probit models (see Equati ons 4-9 and 4-10) the probability of each category is estimated with both the base (and average) probabilities calculated and then the

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63 probabilities shown for each controll ed variable (e.g. education). In all of the subsequent figures each level of agritourism is shown on the horizont al axis and the predicted probability of each level occurring is on the vertical axis. When the level is on the horizontal axis, there will be as many figures as needed depending on the variable(s) being simulated. For example, if the effect of education is being simulated a nd there are five levels of educa tion, then five graphs would be needed. For some simulations the horizontal includes the simulated variable, then nine figures are needed to show the probability for each level. Fo r either type horizontal axis, the vertical axis always shows the probabilities of the agritourism levels. Each figur e also includes the percentage distribution of the variable being simulated with these values shown in the bottom of each figure. In the following discussion there ar e two agritourism levels as first developed in Chapter 1 and estimated in Chapter 4 (see Table 4-2 and 4-3). Fi rst, the simulated results are presented for the gross value of agritouris m sales and followed with the agritour ism shares of gross farm income. For both agritourism measures, simulations ar e completed over the following controlled variables: type (primary farm commodity, acreage, employment), service and fee (activities, fee charged), management (years of experience, business role, seasonality, reason of operating an agritourism operation) and demographi cs (education, age and gender). Gross Value of Agritourism Sales: Farm Type Primary Farm Commodity Figure 5-1 shows the impact of the prim ary farm commodity on the gross value of agritourism sales grouped into four categor ies as follows: (a) $0 $ 9,999, (b) $10,000 $49,999, (c) $50,000 $99,999 and (d) $100,000 $1,000,000 or more. Figure 5-1 shows that 44% of the Nursery agritourism earnings are in the first group $0 $ 9,999, 14% are in income group +$100,000 and less than 9% are in the th ird group $50,000 $99,999. Vegetables and Fruit&Nuts had the greatest positive impact (30%) on agritourism for categories $10,000

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64 $49,999 and $100,000 $1,000,000 and the greatest negati ve impact (12%) on the category of agritourism sales with gross value of ag ritourism sales of $50,000 $99,999. The most significant variation of the pr obabilities was observed in th e income group $0 $ 9,999 and +$100,000 and the least variation was observed in middle-income groups ($10,000 $49,999 and $50,000 $99,999). Interesting to note that the ranking of agritourism activities has a reverse pattern for the two extreme income groups: $0 $ 9,999 and +$100,000. Figure 5-1. Gross value of agritourism sale s by farm type: Primary farm commodity Acreage: Size of the Farm The base probabilities for the av erage farm were compared to the probabilities resulting from a simulation in which the farm size variable s were grouped into th ree categories: 75% of the average farm size, 100% of the average farm size and 125% of the average farm size. The simulated probabilities results are the same meaning that farm size has little or no impact to the gross value of agritourism sales.

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65 Employment: Full Time versus Part Time The probabilities of having full tim e employme nt versus part time employment remain about the same and there is a very slightly difference between groups, meaning that a certain form of employment does not produce a great influe nce on the gross value of agritourism sales. Gross Value of Agritourism Sales: Services and Fees Agritourism Activities In Figure 5-2, the base probabilities for the average farm are compared to the probabilities resulting from a simulation in whic h the gross value of agritourism sales are grouped into four categories. Th e horizontal axis shows the agr itourism activities offered by the farm, grouped into 26 categories: Bird watching, Cross country skiing, Fishing, Game/wildlife, Hunting, Hiking and scenery, Horseback riding, Farm tours, Farm work, School trips, Winery tours, Made on-site food products, Pick-your-o wn, Pumpkin picking, Retail farm stands, You Cut Christmas trees, Bed and breakfast, Ca mping, Farm vacations, Picnicking, Weddings and receptions, Corn mazes, Dog trails/training, Fest ivals / special events, Hay rides and Petting zoos. As the gross value of agritourism sale s increases from $0 to + $100,000, there is a corresponding shift in the probabilities. For income group $0 $9,999 there are not noticeable differences between simulated values for activities, Wedding and Receptions having the greatest impact on simulated probabilities and Horseb ack riding activity at the opposite side. The distribution for income group $10,000-$49,999 is approxi mately the same for all activities. The magnitude of this increase differs, though, with probabilities higher. Th e probabilities of having agritourism activities are reduced for income group $50,000 $99,999, with Horseback riding activity having the greatest impact, and Wedding and Receptions the lowest impact. The upward trend is the same for income group $100,000+, this group having the highest positively influence

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66 the probability of having agritourism activities. W e d d i n g s a n d r e c e p t i o n s G a m e / w i l d l i f e H a y r i d e s B i r d w a t c h i n g F e s t i v a l s / s p e c i a l e v e n t s C a m p i n g F a r m t o u r s P i c n i c k i n g H i k i n g a n d s c e n e r y C o r n m a z e s B e d a n d b r e a k f a s t P u m p k i n p i c k i n g D o g t r a i l s / t r a i n i n g M a d e o n s i t e f o o d p r o d u c t s Y o u C u t C h r i s t m a s t r e e s R e t a i l f a r m s t a n d s C r o s s c o u n t r y s k i i n g H u n t i n g P e t t i n g z o o s W i n e r y t o u r s P i c k y o u r o w n F a r m w o r k S c h o o l t r i p s F a r m v a c a t i o n s F i s h i n g H o r s e b a c k r i d i n g W e d d i n g s a n d r e c e p t i o n s G a m e / w i l d l i f e H a y r i d e s B i r d w a t c h i n g F e s t i v a l s / s p e c i a l e v e n t s C a m p i n g F a r m t o u r s P i c n i c k i n g H i k i n g a n d s c e n e r y C o r n m a z e s B e d a n d b r e a k f a s t P u m p k i n p i c k i n g D o g t r a i l s / t r a i n i n g M a d e o n s i t e f o o d p r o d u c t s Y o u C u t C h r i s t m a s t r e e s R e t a i l f a r m s t a n d s C r o s s c o u n t r y s k i i n g H u n t i n g P e t t i n g z o o s W i n e r y t o u r s P i c k y o u r o w n F a r m w o r k S c h o o l t r i p s F a r m v a c a t i o n s F i s h i n g H o r s e b a c k r i d i n g Farm Services (Ranked within each earning group) 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 Distributions for each farm services by earning group Farm Income from Agritourism $0-$9,999 $10,000 $49,999 W e d d i n g s a n d r e c e p t i o n s G a m e / w i l d l i f e H a y r i d e s B i r d w a t c h i n g F e s t i v a l s / s p e c i a l e v e n t s C a m p i n g F a r m t o u r s P i c n i c k i n g H i k i n g a n d s c e n e r y C o r n m a z e s B e d a n d b r e a k f a s t P u m p k i n p i c k i n g D o g t r a i l s / t r a i n i n g M a d e o n s i t e f o o d p r o d u c t s Y o u C u t C h r i s t m a s t r e e s R e t a i l f a r m s t a n d s C r o s s c o u n t r y s k i i n g H u n t i n g P e t t i n g z o o s W i n e r y t o u r s P i c k y o u r o w n F a r m w o r k S c h o o l t r i p s F a r m v a c a t i o n s F i s h i n g H o r s e b a c k r i d i n g W e d d i n g s a n d r e c e p t i o n s G a m e / w i l d l i f e H a y r i d e s B i r d w a t c h i n g F e s t i v a l s / s p e c i a l e v e n t s C a m p i n g F a r m t o u r s P i c n i c k i n g H i k i n g a n d s c e n e r y C o r n m a z e s B e d a n d b r e a k f a s t P u m p k i n p i c k i n g D o g t r a i l s / t r a i n i n g M a d e o n s i t e f o o d p r o d u c t s Y o u C u t C h r i s t m a s t r e e s R e t a i l f a r m s t a n d s C r o s s c o u n t r y s k i i n g H u n t i n g P e t t i n g z o o s W i n e r y t o u r s P i c k y o u r o w n F a r m w o r k S c h o o l t r i p s F a r m v a c a t i o n s F i s h i n g H o r s e b a c k r i d i n g Farm Services (Ranked within each earning group) 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 Distributions for each farm services by earning group Farm Income from Agritourism$50,000 $99,999$100000 + Figure 5-2. Gross value of agritourism sales by service and fee: Agritourism activities

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67 Fee Charged In both cases, Fee/No fee, agritourism sales group $50,000-$99,000 presents the lowest probability of having agritou rism, close to 0.12 percentages. The effects of the variable Fee/No fee on the different agritourism sales categorie s are a decrease in the probability of having agritourism activities for no char ging a fee for the group less than $ 9,999 and a small increase in the probability of no charging a fee for groups with agritourism sales between $10,000 $49,999 and +$100,000. For those agritourism operators ch arging a fee, the pr obability of having agritourism is decreasing when the most negative impact for $50,000-$99,000 income group. 0 3 4 0 3 4 0 1 1 0 2 1 0 2 6 0 3 3 0 1 2 0 2 9 $ 0 $ 9 9 9 9 $ 1 0 0 0 0 $ 4 9 9 9 9 $ 5 0 0 0 0 $ 9 9 9 9 9 $ 1 0 0 0 0 0 + $ 0 $ 9 9 9 9 $ 1 0 0 0 0 $ 4 9 9 9 9 $ 5 0 0 0 0 $ 9 9 9 9 9 $ 1 0 0 0 0 0 +Farm income from agritourism 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 Distributions for each farm type by earning group Farm Income from Agritourism No FeesFees Figure 5-3. Gross value of agritourism sales by service and fees: Fee charged Gross Value of Agritourism Sales: Management Years of Experience Figure 5-4 illustrates the sim ulated probabil ities for ATour, or y ears of experience, a statistically significant variable from the Ordered Probit model. This simulation reveals that for the first group of agritourism sa les: less than $ 9,999 as the numb er of years of experience rises

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68 the probabilities of having agritourism activities decreases. For the last group with agritourism sales higher than $100,000, the probab ilities of having agritourism ac tivities is increasing as the number of years of experi ence is higher. For middle group sales between $10,000 and $99,000, the distribution is approximately the same with very little differen ces in percentages. 0 0 9 0 1 7 0 2 1 0 3 0 0 3 4 0 0 7 0 1 0 0 1 1 0 1 3 0 1 4 0 3 3 0 3 7 0 3 7 0 3 5 0 3 4 0 5 1 0 3 6 0 3 1 0 2 1 0 1 8 1 3 3 5 5 7 7 1 0 > 1 0 1 3 3 5 5 7 7 1 0 > 1 0 1 3 3 5 5 7 7 1 0 > 1 0 1 3 3 5 5 7 7 1 0 > 1 0Years of farm experience 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 Distributions for each farm by experience farming group (e.g. 51% of low experience farmers are inunder $10,000) Farm Income from Agritourism $0-$9,999$10,000 $49,999$50,000 $99,999 $100,000 + Figure 5-4. Gross value of agritourism sa les by management: Years of experience Business Role In Figure 5-5, the sim ulated probabilities fo r variables corresponding to Business role are compared to the base probabilities of averag e farms. For the first two income groups, under $9,999 and $10,000 $49,999 the probability of having ag ritourism activities being an owner or both owner/operator is greater than being an ope rator. The third and the fourth income groups $50,000 $99,999 and over $100,000 showed an increase in probability of having agritourism activities when being an operato r versus being an owner or bo th owner/operator, which makes sense since owning an agritourism operation necessitates capital resources, including land properties.

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69 0 2 4 0 3 6 0 2 4 0 1 1 0 1 3 0 1 1 0 3 4 0 3 1 0 3 4 0 3 1 0 2 0 0 3 1 O w n e r O p e r a t o r O w n e r / O p e r a t o r O w n e r O p e r a t o r O w n e r / O p e r a t o r O w n e r O p e r a t o r O w n e r / O p e r a t o r O w n e r O p e r a t o r O w n e r / O p e r a t o rOperation type 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 Distributions for each farm type operation by earning group Farm Income from Agritourism $0-$9,999$10,000 $49,999$50,000 $99,999$100,000 + Figure 5-5. Gross value of agritouris m sales by management: Business role Seasonality Seasonality, a statistically significant variable from the Ordered Probit model has a positive influence on probabilities for agritourism sales under $44,999 and a negative influence for agritourism sales over $50,000. Being open year round is more profitable for businesses with agritourism sales bigger than $50,000, the great er impact being observed for sales over $100,000. Reason of Operating an Agritourism Operation In Figure 5-6, the base probabilities f or the av erage farm are compared to the probabilities resulting from a simulation in which different reasons of operating an agritourism operation are shown. For agritourism sales category betw een $0 and $9,999, the reasons are primarily nonmonetary, the main motivations being hobby, e ducation, public service and pleasure, with income being on the last place. For agritouris m sales over $50,000, income was the main reason of operating an agritourism ope ration and hobby was the least possible. The amount of income

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70 received by the farmer is linked to participa tion in the business for earnings between $10,000 and $49,999, but pleasure, public services and educati on are following very closely with differences in percentages of 0.01. 0 2 7 0 2 1 0 1 5 0 1 3 0 0 8 0 1 2 0 1 1 0 0 9 0 0 9 0 0 6 0 3 6 0 3 5 0 3 5 0 3 4 0 2 9 0 5 7 0 4 4 0 4 0 0 3 2 0 2 5 H o b b y E d u c a t i o n P u b l i c S e r v i c e P l e a s u r e I n c o m e P l e a s u r e I n c o m e P u b l i c S e r v i c e E d u c a t i o n H o b b y I n c o m e P l e a s u r e P u b l i c S e r v i c e E d u c a t i o n H o b b y I n c o m e P l e a s u r e P u b l i c S e r v i c e E d u c a t i o n H o b b yReasons for operating 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 Distributions for each reason for operating by earning group Farm Income from Agritourism $0-$9,999$10,000 $49,999$50,000 $99,999 $100,000 + Figure 5-6. Gross value of agritourism sales by management: Reas on of operating an agritourism operation Gross Value of Agritourism Sales: Demographics Educational Level Educational level was grouped into five categor ies: high school graduate, som e college or technical school, college graduate and postgra duate degree. Category less than high school graduate is not representative for our simulati on study because it counted only 0.4% of the total number of respondents, respectively one re sponse. For both middle income groups ( $10,000 $49,999 and $50,000 $99,999) the trend is the same, meaning that for the income group the probability of having agritourism is about the sa me regardless of educational level attended. For

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71 the first group the propensity of having agritourism activities is in creased when the operators are college graduates or have a post graduate de gree. For income group +$100,000, being a college graduate or having a post gradua te degree had a negative impact on the propensity of adding agritourism activities on farm comp ared to the first income group. Figure 5-7. Gross value of agritourism sa les by demographics: Educational level Age Category While the agritourism operators ranged in age from 18 to 65 or more, almost 83 percents of the interviewed farmers were in 35 and 64-ag e category. Category 18-24 is not representative for our simulation study because it counted only 0.4% of the total number of respondents. Figure 5-8 shows the age probability levels for different agritourism sales categories. When compared to the average value, there is not huge difference between simulated values for the age categories probabilities, with a peak in the middle for income group +$100,000.

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72 Figure 5-8. Gross value of agritouris m sales by demographics: Age category Operator Gender Operators gender was not strongly linked with the variations in the am ount of agritourism sales. When compared to the average value, there are not noticeable differences between simulated values for gender. Both males and women had a negative impact on agritourism activities for income group $50,000 $99,999, with probabilities closed to 12 percent. Simulating the Probabilities of Agritourism: Shares of Agritourism Income Shares of Agritourism Income: Farm Type This part of the chapter deals with th e impact of the different variables on the probabilities of farm adding new agritourism activ ities with the respect to the average farm, showing the impact of studied variables on th e percentage of total farm operation income estimated from agritourism (shares of agrit ourism income) grouped into four categories as follows: (a) 0% %24, (c) 25% 49 %, (d) 50 % 74%, (e) 75% 100%.

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73 Primary Farm Commodity Figure 5-9 shows that 57% of the Vegetable ag ritourism earnings are in the first group 0% 24%, 18% are in agritourism sh ares group 75% 100% and less th an 13% are in the third group 50% 74%. Poultry had the greatest positive im pact (44%) on agritouris m for categories 75% 100% and the greatest negative impact (29% and 11%) on the first two agritourism shares categories. The most significan t variation of the probabilities was observed in the agritourism shares group 0% 24%, and 75% 100% and the least variation was observed in middle-income groups (25% 49% and 50 % 74%). 0 4 4 0 4 0 0 3 2 0 2 9 0 2 8 0 2 5 0 2 4 0 2 0 0 1 8 0 1 6 0 1 6 0 1 5 0 1 5 0 1 5 0 1 4 0 1 4 0 1 3 0 1 3 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 1 1 0 5 7 0 5 5 0 5 0 0 4 8 0 4 5 0 4 4 0 4 0 0 3 2 0 2 9 V e g e t a b l e s G r a i n D a i r y F r u i t s & N u t s N u r s e r y B e e f X t r e e H o r s e P o u l t r y N u r s e r y B e e f F r u i t s & N u t s D a i r y X t r e e G r a i n V e g e t a b l e s H o r s e P o u l t r y H o r s e P o u l t r y X t r e e B e e f N u r s e r y F r u i t s & N u t s D a i r y G r a i n V e g e t a b l e s P o u l t r y H o r s e X t r e e B e e f N u r s e r y F r u i t s & N u t s D a i r y G r a i n V e g e t a b l e sPrimary Farm Type (Ranked within each earning group) 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 Distributions for each farm type by earning group (e.g. 57% of vegetables agritourims earnings are in the 0-24% group) Share of Farm Income from Agritourism 0%-24%25%-49%50%-74%75%-100% Figure 5-9. Shares of agritourism income by farm type: Primary farm commodity Acreage: Size of the Farm The base probabilities for the av erage farm were compared to the probabilities resulting from a simulation in which the farm size variable s are grouped into three categories: 75% of the average farm size, 100% of the average farm size and 125% of the average farm size. The simulated probabilities results are the same for each category meaning that farm size does not affect the shares of agritouris m income, similar with precedent results.

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74 Employment: Full Time versus Part Time Forty seven percent of the full tim e employ ment is under 24% shares of agritourism income category, while 39% of the part time employment is under the same category. Middleincome groups (25% 49% and 50 % 74%) show s no variation in the distribution of the probabilities when respondents have full time em ployment versus part time employments for their farms. As the percent of agritourism sh ares increases, we can observe a shift in the distribution of the probab ilities, meaning that 26% of the fu ll time employment and 33% of the part time employment are in the 75% 100% sh ares of agritourism income category. Shares of agritourism income: Services and Fees Agritourism Activities In Figures 5-11, the base probabilities for the average farm are compared to the probabilities resulting from a simulation in whic h the gross value of agritourism sales are grouped into six categories: (a ) 0% 24%, (c) 25% 49%, (d ) 50 % 74%, (e) 75% 100%. The horizontal axis shows the agritourism activities offered by the farm, grouped into 26 categories: Bird watching, Cro ss country skiing, Fishing, Game /wildlife, Hunting, Hiking and scenery, Horseback riding, Farm tours, Farm work, School trips, Winery tours, Made on-site food products, Pick-your-own, Pumpki n picking, Retail farm stands, You Cut Christmas trees, Bed and breakfast, Camping, Farm vacations, Pi cnicking, Weddings and receptions, Corn mazes, Dog trails/training, Festivals / special events, Hay rides and Petting zoos. The distribution of agritourism activities as the percentage of total income farm estimated from agritourism increases to 49% is the sa me. For group 25% 49%, the impact of different activities on the probabilities of offering agritourism is reduced when compared with the first group 0% 24%. School trips had the greatest po sitive impact on total income farm estimated from agritourism for both groups and You cut Christmas tree had the most negative impact.

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75 The rest of the 26 categories of ac tivities are slightly differentiated. As the shares of agritourism increase from 75% to100%, the probability of having agritourism activities is increased, with the highest positive impact for You cut Christmas tree, Hay rides, Retail farm stands, Farm vacations and Horseback riding act ivities. The least significant variation of probabilities was observed in middle-income groups (25% 49% and 50 % 74%). Fee Charged In both cases, Fee/No fee, agritourism shar es group 25% 49% and 50 % 74% presents the lowes t probability of having agritourism, close to 12%-15%. The effects of the variable Fee/No fee on the different agritourism sales cate gories are an increase in the probability of having agritourism activities for charging a fee fo r the shares of agritourism income group 0%24% and a small decrease in the probability of charging fee for group 75% 100%. The same pattern is observed for variable No fee. The magnitude of the increase differs, though, with Fee variable having a smaller impact (31%) on group 75% 100%, and a more significant impact (43%) on group 0% 24%. 0 4 3 0 1 2 0 1 5 0 3 0 0 4 9 0 1 2 0 1 4 0 2 5 0 % 2 4 % 2 5 % 4 9 % 5 0 % 7 4 % 7 5 % 1 0 0 % 0 % 2 4 % 2 5 % 4 9 % 5 0 % 7 4 % 7 5 % 1 0 0 %Share of farm income from agritourism 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 Distributions for each farm type by earning group Share of Farm Income from Agritourism No FeesFees Figure 5-10. Shares of agritourism in come by service and fee: Fee charged

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76 S c h o o l t r i p s P u m p k i n p i c k i n g F a r m t o u r s H u n t i n g M a d e o n s i t e f o o d p r o d u c t s D o g t r a i l s / t r a i n i n g H i k i n g a n d s c e n e r y F a r m w o r k B i r d w a t c h i n g C r o s s c o u n t r y s k i i n g G a m e / w i l d l i f e B e d a n d b r e a k f a s t C o r n m a z e s P i c n i c k i n g F e s t i v a l s / s p e c i a l e v e n t s W e d d i n g s a n d r e c e p t i o n s P i c k y o u r o w n W i n e r y t o u r s C a m p i n g P e t t i n g z o o s F i s h i n g H o r s e b a c k r i d i n g F a r m v a c a t i o n s R e t a i l f a r m s t a n d s H a y r i d e s Y o u C u t C h r i s t m a s t r e e s S c h o o l t r i p s P u m p k i n p i c k i n g F a r m t o u r s H u n t i n g M a d e o n s i t e f o o d p r o d u c t s D o g t r a i l s / t r a i n i n g H i k i n g a n d s c e n e r y F a r m w o r k B i r d w a t c h i n g C r o s s c o u n t r y s k i i n g G a m e / w i l d l i f e B e d a n d b r e a k f a s t C o r n m a z e s P i c n i c k i n g F e s t i v a l s / s p e c i a l e v e n t s W e d d i n g s a n d r e c e p t i o n s P i c k y o u r o w n W i n e r y t o u r s C a m p i n g P e t t i n g z o o s F i s h i n g H o r s e b a c k r i d i n g F a r m v a c a t i o n s R e t a i l f a r m s t a n d s H a y r i d e s Y o u C u t C h r i s t m a s t r e e s Farm Services (Ranked within each earning group) 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 Distributions for each farm services by earning group Share of Farm Income from Agritourism 0%-24%25%-49% S c h o o l t r i p s P u m p k i n p i c k i n g F a r m t o u r s H u n t i n g M a d e o n s i t e f o o d p r o d u c t s D o g t r a i l s / t r a i n i n g H i k i n g a n d s c e n e r y F a r m w o r k B i r d w a t c h i n g C r o s s c o u n t r y s k i i n g G a m e / w i l d l i f e B e d a n d b r e a k f a s t C o r n m a z e s P i c n i c k i n g F e s t i v a l s / s p e c i a l e v e n t s W e d d i n g s a n d r e c e p t i o n s P i c k y o u r o w n W i n e r y t o u r s C a m p i n g P e t t i n g z o o s F i s h i n g H o r s e b a c k r i d i n g F a r m v a c a t i o n s R e t a i l f a r m s t a n d s H a y r i d e s Y o u C u t C h r i s t m a s t r e e s S c h o o l t r i p s P u m p k i n p i c k i n g F a r m t o u r s H u n t i n g M a d e o n s i t e f o o d p r o d u c t s D o g t r a i l s / t r a i n i n g H i k i n g a n d s c e n e r y F a r m w o r k B i r d w a t c h i n g C r o s s c o u n t r y s k i i n g G a m e / w i l d l i f e B e d a n d b r e a k f a s t C o r n m a z e s P i c n i c k i n g F e s t i v a l s / s p e c i a l e v e n t s W e d d i n g s a n d r e c e p t i o n s P i c k y o u r o w n W i n e r y t o u r s C a m p i n g P e t t i n g z o o s F i s h i n g H o r s e b a c k r i d i n g F a r m v a c a t i o n s R e t a i l f a r m s t a n d s H a y r i d e s Y o u C u t C h r i s t m a s t r e e s Farm Services (Ranked within each earning group) 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 Distributions for each farm services by earning group Share of Farm Income from Agritourism 50%-74%75%-100% Figure 5-11. Shares of agritourism income by service and fee: Agritourism activities

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77 Shares of Agritourism Income: Management Years of Experience Figure 5-12 illus trates the simulated probabilit ies for ATour, or years of experience, a statistically significant variable from the Orde red Probit model. Sixty-four percent of low experience farmers (1-3 years) and 43% of the high experience group (+10 years) are in the 024% group. Forty one percent of the farmers with 7-10 years of experience are in the agritourism shares group 75% 100%. 0 1 4 0 2 2 0 3 4 0 4 1 0 2 8 0 1 1 0 1 4 0 1 6 0 1 6 0 1 5 0 1 1 0 1 3 0 1 3 0 1 2 0 1 3 0 6 4 0 5 1 0 3 7 0 3 0 0 4 3 1 3 3 5 5 7 7 1 0 > 1 0 1 3 3 5 5 7 7 1 0 > 1 0 1 3 3 5 5 7 7 1 0 > 1 0 1 3 3 5 5 7 7 1 0 > 1 0Years of farm experience 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 Distributions for each farm by experience farming group (e.g. 64% of low experience farmers are in the 0-24% group) Share of Farm Income from Agritourism 0%-24%25%-49%50%-74%75%-100% Figure 5-12. Shares of agritourism inco me by management: Years of experience Business Role In Figure 5-13, business role of the respondent presents a slightly range of variations for all f our categories of agritourism shares. Being owner presents a positive impact for category 0% 24%, with 62% of the owners being in this category. Being operator and both owner/operator impact positively the probability of having an agritourism activity for those farmers that have 100% agrit ourism activities on the farm, wi th 29% of both operator and owner/operator being in this category. The least significant variation of probabilities was observed in middle-income groups (25% 49% and 50 % 74%).

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78 0 1 6 0 2 9 0 2 9 0 1 1 0 1 5 0 1 5 0 1 1 0 1 2 0 1 2 0 6 2 0 4 3 0 4 3 O w n e r O p e r a t o r O w n e r / O p e r a t o r O w n e r O p e r a t o r O w n e r / O p e r a t o r O w n e r O p e r a t o r O w n e r / O p e r a t o r O w n e r O p e r a t o r O w n e r / O p e r a t o rOperation type 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 Distributions for each farm type operation by earning group Share of Farm Income from Agritourism 0%-24%25%-49%50%-74%75%-100% Figure 5-13. Shares of agritourism income by management: Business role Seasonality Fifty-one percent of being open year round and 41% of being open seasonally is in the 0%24% category, while only 23% of being open year round and 31% of being open seasonally is in the 75%-100% category. Middle-incom e groups (25% 49% and 50 % 74%) have equal distribution of probabiliti es of both being year round open or seasonal. Reason of Operating an Agritourism Operation In figure 5-14, the simulated probabilities of the respondents present a wide range of variation for all categories of income shares. As seen in Figure 5-14, for agritourism shares income group 0%-24% the main reasons of operating and agritourism operation are nonmonetarily ( 65% public service, 57% hobby, 48% education, 46% pleasure and 42% income). For middle-income groups (25% 49% and 50 % 74%) the distribution of probabilities is not showing much variation, with income being the main reason of ope rating and agritourism operation (13% and 15%, respectively). Category 75% 100% has the same distribution as

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79 middle-income groups, with 30% income, 27% pleasure, 25% education, 19% hobby and 13% public service. 0 3 0 0 2 7 0 2 5 0 1 9 0 1 3 0 1 5 0 1 5 0 1 4 0 1 3 0 1 1 0 1 3 0 1 3 0 1 3 0 1 2 0 1 1 0 6 5 0 5 7 0 4 8 0 4 6 0 4 2 P u b l i c S e r v i c e H o b b y E d u c a t i o n P l e a s u r e I n c o m e I n c o m e P l e a s u r e E d u c a t i o n H o b b y P u b l i c S e r v i c e I n c o m e P l e a s u r e E d u c a t i o n H o b b y P u b l i c S e r v i c e I n c o m e P l e a s u r e E d u c a t i o n H o b b y P u b l i c S e r v i c eReasons for operating 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 Distributions for each reason for operating by earning group Share of Farm Income from Agritourism 0%-24%25%-49%50%-74%75%-100% Figure 5-14. Shares of agritourism income by management: Reason of operating an agritourism activity Shares of Agritourism Income: Demographics Educational Level Across Education categories, Less Than High School Graduate category is not representative for our simulation study because it counted only 0.4% of the total number of respondents, respectively one response. For bot h middle-income groups (25% 49% and 50 % 74%) the trend is the same, meaning that regardle ss of educational level attended the probability of having agritourism is about the same (13% and 15%). Forty six percent of the some college or technical school graduates, 48% of the post grad uate degree graduates and 42% of the college graduates are in the first category (0%-24%) whil e 26% of the some colle ge or technical school graduates, 25% of the post graduate degree gradua tes and 30% of the college graduates are in the

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80 last category of agritourism shares of inco me (75% 100%). High sc hool graduates counted 39%, respectively 33% for both categories mentioned. Figure 5-15. Shares of agritourism inco me by demographics: Educational level Age Category As we m entioned before, age category 18-24 is not representative for our simulation study because it counted only 0.4% of the total number of respondents, while almost 72% of the interviewed farmers were in 35 and 64-age catego ry. Age of the agritourism operator presents a significant range of variations for the first (0% 24%) and the last (75% 100%) agritourism shares of income categories. Figure 5-16 shows that 50% of people age 35-49 and 44% of people age 50-64 were in the first category (0 % 24%). Middle-income groups (25% 49% and 50 % 74%) again show no much variation with probabilities of 12% and 16%, regardless of the age category. Category 75% 100% shows no huge difference between simulated values for the

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81 age categories probabilities, with 23% of peopl e age 35-49 and 29% of people age 50-64 being in this category. Figure 5-16. Shares of agritourism income by demographics: Age category Operator Gender Although gender is not a statisti cally significant vari able, the sim ulation results reveal the specific probabilities for each gender category. S imulations show that 53% of men and 40% of women were in the 0% 24% category and 22% of men and 32% of women were in the agritourism shares group 75% 100%. Middle-income groups (25% 49% and 50 % 74%) have an equal distribution of probabilities, meani ng that gender does not make a difference in the propensity of adding new agritour ism activities to the farm. Ranking of the Probabilities of Farm Income from Agritourism In the next section, the relative effects of those variables included in the Ordered Probit model will b e ranked by the variable for both gross value of agritourism sales and the percentage of the total farm operation income estimated from agritourism.

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82 Difference between the maximum and the minimu m range for the average probability are ranked for farm type, size of the farm, employment activities, fee charge d, years of experience, business role, seasonality, reasons for ag ritourism, education, age and gender. Each of the next figures (Figures 5-17 and 5-18) shows the relative impact of specific variables on each category of agr itourism income and shares of agritourism income. This way, one can easily see, for example, where Employme nt is more or less important. Importance is determined by ranking the variables effect acr oss different categorie s of gross value of agritourism sales and shares of agritourism income from the greatest range to the minimal range. Ranking by Gross Value of Agritourism Sales Figure 5-17 shows ranking of the variables by gr oss value of agritourism sales taking into consideration maximum and minimum values ba se for probabilities. Ranking variables by $0 $9,999 category of agritourism sales, Farm Expe rience and Reasons for Agritourism had the highest effect on the probability of adding new agritourism activities on the farm, with both having a range of 34%, respectively 32%. Gender, Employment, and Farm Size had the lowest impacts on $0 $9,999 category of agritourism sales. Figure 5-17 shows the $10,000 $49,999 category pr obabilities pattern with Reasons for Agritourism having the highest impact followed by Farm Experience and Agritourism Service, with both having a range of nearly 5%. Charge d fee, Seasonality, and Gender had the lowest difference between the maximum and the minimu m, with a very small range over the $10,000 $49,999 category. When ranking the probabilities by $50,000 $99,999 category of agritourism sales, Farm Experience and Reasons for Agritourism are the ones with the highest difference between the maximum and the minimum values. Charged fee, Seasonality, and Gender have the least effect on the probability of adding new agritourism act ivities and show the lowest gap between the

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83 maximum and the minimum values with 1 percent. Employment and Farm Size show no effect on the probabilities of adding new ag ritourism activities on the farm. In terms of ranking the probabilities by $100,000+ category, Agritourism Services has obviously the highest difference between th e minimum and the maximum value. Farm experience and Reasons for Agritourism are followi ng closely with changes in probabilities of 25% and 24 %. Gender and Employme nt are the last variables ranke d in terms of influencing the probabilities of adding new agritourism activities. For all categories of gross valu e of agritourism sales, farm size showed no influence on the probabilities of adding new agritourism activities on the farm. Ranking by Shares of Income For all agritourism shares categories, by far Fa rm Experience had the largest effect on the probability of adding new agritourism activities, followed by Farm Type and Reasons for Agritourism (see Figure 5-18). Fee Charged showed the least effect on the probability of adding new agritourism activities to the farm and farm size had shown influence at all. Ranking base on the 0%24% category, the vari able with the largest difference between the maximum and the minimum probability of adding agritourism activities is Farm Experience. A change in this category causes the probability of adding agritourism activities to change by 34 percent. Farm Experience is followed by Farm type, Reasons for Agritourism, Ownership and Gender. Education, Employment and Charged Fe e have the lowest di fference between the maximum and the minimum. Category 25%-49% has a very small range over the category levels. Farm Experience is followed by Farm type, Reas ons for Agritourism and Ownership. The rest of the variables do not present any difference at all. Ranking the variables by 50%-74% reveals that th e variable with the highest impact on the probability of adding new agritourism is Farm E xperience, followed by Reasons for Agritourism,

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84 Ownership and Farm type. Seasonality, Education, Employment and Charged Fee show the smallest differences with changes of 1 percent. Across 75%-100% category, Farm Experience is again the variable showing the highest difference between the minimum and the maximu m range. Farm Type, Reasons for Agritourism and Agritourism Services, follows Farm Experience with 26 %, 17% and respectively 16 % range in the probability of adding new agritouris m activities to the farm. Education and Charged fee shows the least impact on the probabilities. Figure 5-17.Ranking of the probabilities of farm income from agritourism by gross value of agritourism sales

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85 Figure 5-18.Ranking of the probabilities of farm income from agritourism by shares of income A Finalized Ranking of Average Farm Agritourism Earnings In Figure 5 -17, the importance of each variable was ranked for each of the four agritourism income earning levels. Clearly, the interpretati on is compounded somewhat in that the ranking have to be shown for each earning level. A si mplifying extension to the earning graph would be to estimate an average earning level per farm based on the probabilities for each of the four earning levels. From the simulations, we know th e average probability for each of the four earning levels and for illustration purposes, let us define those to be Prob01-Prob04. Next instead of using the earning ranges, defined the levels to be $10,000, $30,000, $75,000, and $100,000. Then the average farm income is (Pr ob01 x $10,000) + (Prob02 x $30,000) + (Prob03 x $75,000)

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86 + (Prob04 x $100,000). For the estimate d probabilities, this gives an average farm agritourism income of $46,685. Following the same simu lations used in deriving Figure 5-31, the probabilities for any controlled se tting are estimated and then average farm agritourism earnings are again calculated by multiplying the new probab ilities by the four ear ning levels above. For example, earnings from agritourism from prim ary vegetable farms are $51,481 while for nursery the farm agritourism is predicted to be $34,713. Now instead of four earning levels, we have the average farm agritourism revenue. Since the exact earning level is influe nced by how we define the dollar level for each earnings group, a useful way around that scaling issue is to index each agritourism level to the base of $46,685. For exampl e, over the primary type of farm, vegetables have a score of 1.103 while nurser y score 0.744. With this method, th e scores are calculated for each variable initially shown in Figure 5.17. Then the maximum, minimum, and range are derived for each variable and ra nked by the range from the largest to the smallest. In Figures 519 to 5-31, the impact variables are ranked with the minimum and maximum scores shown with the pivotal point being 1.0 based on the average earnings of $46,685. Each of the next several figures shows the relative impact of specific variables on farm income from agritourism. Figure 5-19.Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Primary farm commodity

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87 Figure 5-20.Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Farm size Figure 5-21.Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Employment

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88 Figure 5-22.Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Agritourism activities Figure 5-23.Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Fee charged

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89 Figure 5-24.Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Years of experience Figure 5-25.Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Bussiness role

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90 Figure 5-26.Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Seasonality Figure 5-27.Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Reason for operating an agritourism operation

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91 Figure 5-28. Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Educational level Figure 5-29. Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Age category

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92 Figure 5-30. Ranking of the variables impacting the income from agritourism (Minimum and maximum index values): Operator gender Figure 5-31.Ranking of the variables impacting the income from agritourism (Minimum and maximum index values)

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93 In Figure 5-31, the variables have been ranked with one slight adjustment. For Education and Age variables, the lowest education level and age category have less than a 1% chance of occurring so the simulated impacts of that edu cation level and age category were excluded from the education and age scoring. These were the only variables where the likelihood of occurring was so small that it should not be considered when doing the rankings. With this slight adjustment, Figure 5-31 clearly shows that Fa rm Experience and Reasons of operating an agritourism operation are the two most importa nt drivers influenci ng the average farm agritourism earnings. For example, low experien ce produces average agri tourism earning of only 63% of the mean earnings while the most experien ce gives earnings of 120% of the average. The range of impact of Farm Experience, Reasons, Primary Farm Function, and Services are quite similar even though the upper and lower scores differ. Then, starting with Management (Ownership) the range of impact drops off quickly. Finally as seen with Figure 5-31, the impact of Gender, Employment and Farm size are negligible. This chapter used estimates from previous chapters to show simulations over each variable included in the models and has focused on which variables had the most and least impact on the probability of adding new agritourism activities. Our analysis was limited at two measures: gross value of agritourism sales and the percentage of total farm operation income estimated from agritourism. Rankings across occasions were based on the probability of farm type, size of the farm, employment, activities, fees char ged, years of experi ence, business role, s easonality, reasons for agritourism, education, age and gender, whic h considered the minimum and the maximum impact of each variable.

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94 Tables 5-16, 5-17 and 5-31 presented in summ ary which variable had the highest and the lowest effect on each category of agritourism income and shares of agritourism income.

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95 CHAPTER 6 SUMMARY AND CONCLUSIONS Agritourism, also referred to as agrotourism agritainment or agricultural tourism can be defined as the allocation of leisure time to visit a working farm or agricultural operation to enjoy the rural setting, participate in farm activities, or learn abou t agriculture production. In the United States, agritourism is am ong the fastest growing sectors. Agritourisms recent growth is both dema nd and supply driven. On the supply side, cost/price pressures have forced farmers to incr ease their income through diversification. On the demand side, increases in income and demand for more specialized forms of vacation experiences have stimulated the growth for tour ism and recreational activities in rural areas. The general objective of this wo rk is to characterize the U.S. agritourism industry, identify regional differences and offer insight on the range of activities under the umbr ella of agritourism. More specifically, the pu rpose of this study is to provide in formation about major drivers of the supply of agritourism, to model the importance of agritourism using an Ordered Probit model and to show the relative impacts of different factors on the decision to add new agritourism activities to the farm. In order to achieve these objectives, two Orde red Probit models are used to analyze the effects of explanatory variables on the dependent variable, the gr oss value of agritourism sales (Model 1) and the percentage of the total farm operation income estimated from agritourism (Model 2). Using the Ordered Probit estimates a nd simulation models, simulated changes in the likelihood or (probability) of offe ring agritourism provide insight into the conditions that truly affect this dimension of the U.S. farming sector.

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96 Using maximum likelihood procedures, Ordered Probit model parameter estimates revealed several variables significantly affecting the decision to add new agritourism activities to the farm. Factors that impact and are st atistically significant in influe ncing farm operator gross value of agritourism sales include th e following agritourism activities: horseback riding, farm tours, winery tours, farm work experiences, retail farm stands, hay rides, festivals and corn mazes. Charging a fee has a significant negative impact on gross value of agritourism sales, while being open seasonally and having income as the principal reason of operating an agritourism operation, both have positive impacts on gross value of agritourism sales. The relationship between agritour ism sales and number of years of experience is consistent with previous studies, showing that less experience (1-3 years and 3-5 years) has a negative impact and more experience (7-10 years or more than 10 years) has a positive impact on both the agritourism sales and the shares of agritourism income. Ordered Probit model parameter estimates reveal ed several variables significantly affecting the percentage of total farm operation income es timated from agritourism (shares of agritourism income). The only choice of farm commodity that has been found to have a negative statistically influence on agritourism shares was Vegetable, Melons and Potatoes. We also found that farm tours and school trips have a negative impact on the percentage on total farm operation income estimated from agritourism, while retail farms and hayrides have a significant positive impact. Being both owner and operator proved to be a significant positive factor across shares of agritourism.

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97 In addition, operating an agritourism farm for a public service reason has a statistically significant negative effect on the percentage of total farm operation income estimated from agritourism. It is interesting to note that education of the farm operato r is the only socio-demographic characteristic statistically signifi cant. Being a high school graduate or a college graduate appears to play a significant role in the decision to add new agritourism activities to the farm. Ordered Probit estimates were incorporated into simulation analyses in order to illustrate the effects of explanatory variables on the deci sion of adding new agritourism activities on the farm. Ordered Probit model revealed that in add ition to Age, Years of experience (7-10 years), commodities like Nursery/Greenhouse, Vegetables (Melons, Potatoes) and Poultry have a substantial positive effect on the probabilities of adding new agritourism activities to the farm. Non-monetary reasons for gross income sale s under $50,000 and monetary reasons for gross agritourism income sales greater than $50,000 show ed positive effects on the probabilities of adding new agritourism activities to the farm. Simulations also showed that activities like Weddings and Horseback riding exhibited th e highest probabilities on 0$-9,999$ category, respectively $100,000+ category of gro ss values of agritourism sale s, while School trips and You Cut Christmas Tree exhibited the highest prob abilities on the 0%-25% category, respectively 75%-100% category of shares of agritourism income. Being an owner proved to be a significant positive factor acro ss 0$ -9,999$ category of gross agritourism income sales, and being an operator had the same effect for 100,000+ category. Being an owner presents a positive impact for category 0%24% of sh ares of agritourism income, while being an operator or both owner/ operator impacts positively the probability of

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98 having an agritourism activity for those farmer s that have 100% agrit ourism activities on the farm. In what concerns seasonality, being open year round is more profitabl e for businesses with agritourism sales bigger than $50,000, the greater impact being observed for sales over $100,000. Employment did not seem to make a differe nce for gross values of agritourism sales simulations, but the same variable was shown to have a significant impact on the probabilities of adding new agritourism activities to the farm for shares of agritourism income categories. Part time employment proved to affect positively 75%-100% category while full time employment had the same effect for 0% 25% category. Next, the relative effects of those variables included in Ordered Probit model have been ranked by the variable for both gros s value of agritourism sales a nd the percentage of the total farm operation income estimated from agritourism. Using the rankings allows one to visually see the relative importance of differe nt drivers of the agritourism supply. All variables have been ranked by their importance from the highest to the lowest values ba sed on deviations from their respective means. The most important drivers influencing the probability of adding new agritourism activities on the farm were Farm Experience and Reasons of operating an agritourism operation, while Gender and Employment had the lowest impact on these categories of agritourism and Farm Size does not have any influence at all. A change in Farm Experience (years of expe rience) can change the probability of adding new agritourism activities on the farm by 57 % in come points, while a change in Reasons of offering an agritourism operation can change the probability of adding new agritourism activities

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99 by 56 % income points. Employment form had the least eff ect on the probability of adding new agritourism activities on the farm with changes in this variable causing the probabi lity to differ by 2 points. In the latter part of Chapter 5, the averag e agritourism operation generated an estimated $46,000 in annual income. While relative small in total, those supplemental incomes can be extremely important to farms operating on the margins of profitability. Clearly, farming experience is a key factor in the income genera ting capacity of an agrito urism operation with the income range differing by as much as nearly 100 % between those w ith little experience compared with the more tested producers. Public policies cannot directly impact experience but thought appropriate extension type programs, educational efforts could be a potentially useful tool to counter the less experien ce factor. Given the range of earni ngs across experience, the role of knowledge seems clear and the ability to offset the lack of experience though training likely has considerable potential. Likewise, the analys is establishes the earning potential across the types of farms with vegetable farming having the largest relative earning potential while nurseries show the least potential The farm type ordering in Figur e 5-19 gives clear insight into the earning potential over different farm operatio ns with many of the farm types have very similar agritourism earnings potential. These earni ngs across farm types suggest the feasibility of agritourism earnings in many farm settings. Subj ectively, we were a litt le surprised with the lower earnings from the horse farms since horseback riding was ranked number one in terms of agritourism activities as an income generator. It must be that many of the other primary farms functions offer horseback riding. Note that this horse riding produced a 32 % increase over the average agritourism earnings. Regardless of the farm primary function, this study points to the earning points from offering certain services such as riding and fishing. The levels from Figure

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100 5-22 should be particularly useful to any farm operation who is cont emplating starting an agritourism facility. Similarly, the lower earning functions are equally insightful. Analyses show farm size to be of little consequence to the earning potential and similarly for seasonality. This is important from a public polic y standpoint in that a ny efforts to encourage agritourism should generally be broad in scope (i.e., across all farm sizes and time). Earning potentially is little effected by either. In a sim ilar way, those farms motivated to offer agritourism as an incoming generator are far more financially successful than when the agritourism is just for a hobby. Recall that farms with agritourism as a hobby had earnings of about 57 % of the average. The relationship from agritourism as a business to one of just pleasure/hobby is strongly negative with the income earning potential differing by nearly 50 %. While there is considerable technical details in the research, one can take the figures in the last portion of Chapter 5 as gui des to the potential ear nings depending on the nature of the farm and the agritourism services considering. Likewise, the analysis provide insight into the earning potential if public agencies attempt to encourage (or discourage) entry into this market. Finally, most of the tables and figures show a one-dimen sional impact relative to the average farm. Most farms are not one-dimensional and th e models developed can be easily used to show the gains (or losses) from a combination of activities. That aspect of the research will be part of a forthcoming paper. For many agricultural operators, agritouris m activities have allowed them to expand operations, supplement income, and broade n employment opportunities. From a public perspective, agritourism activit ies have help convey an unde rstanding and appreciation of agriculture and rural lifestyles.

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101 This work offers a first look at the United St ates agritourism industr y as a whole. In the United States, agritourism is a broad and divers e industry. Characterizatio n of the industry can help the public to better understa nd this industry, its potential for growth, and how it contributes to local economies, agricultural apprecia tion, and conservation of rural lands. This analysis presented should be viewed as a first step in assessing the effects of different factors on agritourism. Future research might focus on a number of que stions, including what combination of factors are affecting more or less agritourism (inc luding visitors related factors) or whether agritourism increases farming survival rate, or if on the oppos ite, is a step towards quitting agricultural production.

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102 APPENDIX A AGRITOURISM SURVEY

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110 APPENDIX B TSP CODE COMMAND *************************************************************** 1 OPTIONS MEMORY=250; 2 TITLE 'AGRITOURISM STUDY IRINA BONDOC'; 3 ?========================================; 3 ? CREATE THE TSP DATASET UNDER AGTOUR.TLB; 3 ?========================================; 3 proc xxxx; 4 OUT 'c:\zagritour\agtour'; 5 READ(FORMAT=LOTUS,FI LE='c:\zagritour \agtour.WK1'); 6 List ZVARZ IDCode Year Month Date2 Q01_Cattle Q01_Xmas Q01_Dairy Q01_FR_Nut Q01_Grains Q01_Horse Q01_Nurs 6 Q01_Poultry Q01_Vege Q01_Oth Q01_Total Q02_Beef Q02_Xtree Q02_Dairy Q02_F_N Q02_Grain Q02_Horse Q02_Nur 6 Q02_Poultry Q02_Vege Q02_Oth Q02_Total Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse 6 Q03_Tour Q03_Work Q03_Schl Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q03_Cut Q03_B_B Q03_Camp 6 Q03_Vacat 6 Q03_Picnic Q03_Wedd Q03_ Maze Q03_Trail Q03_ Fest Q03_Hay Q03_Zoo Q03_Oth Q03_Tot Q04_Yes Q05_ATour1 6 Q05_ATour2 Q05_ATour3 Q05_ATour4 Q05_ATour5 Q05_ATour Q06_Own Q06_Oper Q06_Both Q06_Type Q07_Season 6 Q08_Inc 6 Q08_Hob Q08_Ple Q08_Pub Q08_Edu Q08_Total Q09_Bus Q09_Mkg Q10_Inc Q10_Grant Q10_Loan Q10_Pers Q10_Oth 6 Q11_FullA Q11_PartA Q11_FullS Q11_PartS Q12_Inc1 Q12_Inc2 Q12_Inc3 Q12_Inc4 Q12_Inc5 Q12_Inc6 Q12_Inc7 6 Q12_Inc8 Q12_Inc9 Q12_Total Q13_Sh1 Q13_Sh2 Q13_Sh3 Q13_Sh4 Q13_Sh5 Q13_Sh6 Q13_Total Q14_Tot Q15_Used 6 Q16_Visitors Q17_Same Q17_Higher Q17_Lower Q18_Local Q18_Region Q18_State Q18_Inter Q19_Bus Q19_Single 6 Q19_School Q19_Tour Q19_Oth Q20 Q21 Q22 Q23_Amt1 Q23_Amt2 Q23_Amt3 Q23_Amt4 Q24_Promo Q25_Email Q25_Dir 6 Q25_Mail Q25_Word Q25_Oth Q26_Exp1 Q26_Exp2 Q26_Exp3 Q27_Diff1 Q27_Diff2 Q27_Diff3 Q27_Diff4 Q27_Diff5 6 Q27_Diff6 Q28_Env1 Q28_Env2 Q28_Env3 Q28_Env4 Q28_Env5 Q28_Env6 Q29_Sign Q29_Mod Q29_Little Q29_None 6 Q29_NotS Q30_VImport Q30_SIimport Q30_NImport Q30_NotS State Educ1 Educ2 Educ3 Educ4 Educ5 6 Age1 Age2 Age3 Age4 Age5 Female Male; 7 DOC IDCode 'FARM IDENTIFICATION'; 8 doc Year 'Year'; 9 doc Month 'Month'; 10 doc Date2 'Date'; 11 doc Q01_Cattle 'Beef Cattle, Hogs, Sheep/Other Livestock 1=yes 0=no'; 12 doc Q01_Xmas 'Christmas Tree 1=yes 0=no'; 13 doc Q01_Dairy 'Dairy 1=yes 0=no'; 14 doc Q01_FR_Nut 'Fruits&Nuts 1=yes 0=no'; 15 doc Q01_Grains 'Grain(wheat, corn, soybean)/Other Field Crops 1=yes 0=no'; 16 doc Q01_Horse 'Horse/Other Equine 1=yes 0=no'; 17 doc Q01_Nurs 'Nursery/Greenhouse 1=yes 0=no';

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111 18 doc Q01_Poultry 'Poultry 1=yes 0=no'; 19 doc Q01_Vege 'Vegetables, Melons, Potatoes 1=yes 0=no'; 20 doc Q01_Oth 'Other 1=yes 0=no'; 21 doc Q01_Total 'Total 0 to 10'; 22 doc Q02_Beef 'Beef Cattle, Hogs, Sheep/Other Livestock 1=yes 0=no'; 23 doc Q02_Xtree 'Christmas Tree 2=yes 0=no'; 24 doc Q02_Dairy 'Dairy 3=yes 0=no'; 25 doc Q02_F_N 'Fruits&Nuts 4=yes 0=no'; 26 doc Q02_Grain 'Grain(wheat, corn, soybean)/Other Field Crops 5=yes 0=no'; 27 doc Q02_Horse 'Horse/Other Equine 6=yes 0=no'; 28 doc Q02_Nur 'Nursery/Greenhouse 7=yes 0=no'; 29 doc Q02_Poultry 'Poultry 8=yes 0=no'; 30 doc Q02_Vege 'Vegetables, Melons, Potatoes 9=yes 0=no'; 31 doc Q02_Oth 'Other 10=yes 0=no'; 32 doc Q02_Total 'Total 0 to 10'; 33 doc Q03_Bird 'Bird watching 1=yes 0=no'; 34 doc Q03_CCN 'Cross county skiing 1=yes 0=no'; 35 doc Q03_Fish 'Fishing 1=yes 0=no'; 36 doc Q03_Game 'Game/wildlife preserve 1=yes 0=no'; 37 doc Q03_Hunt 'Hunting 1=yes 0=no'; 38 doc Q03_Hike 'Hiking and scenery 1=yes 0=no'; 39 doc Q03_Horse 'Horseback riding 1=yes 0=no'; 40 doc Q03_Tour 'Farm tours 1=yes 0=no'; 41 doc Q03_Work 'Farm work experience 1=yes 0=no'; 42 doc Q03_Schl 'School trip 1=yes 0=no'; 43 doc Q03_Wine 'Winery tours 1=yes 0=no'; 44 doc Q03_Make 'Made on-site food products'; 45 doc Q03_Pick 'Pick-your-own 1=yes 0=no'; 46 doc Q03_Pump 'Pumpkin picking 1=yes 0=no'; 47 doc Q03_Stand 'Retail farm stands 1=yes 0=no'; 48 doc Q03_Cut 'You cut Christmas trees 1=yes 0=no'; 49 doc Q03_B_B 'Bed and breakfast 1=yes 0=no'; 50 doc Q03_Camp 'Camping 1=yes 0=no'; 51 doc Q03_Vacat 'Farm vacations 1=yes 0=no'; 52 doc Q03_Picnic 'Picnicking 1=yes 0=no'; 53 doc Q03_Wedd 'Weddings and receptions 1=yes 0=no'; 54 doc Q03_Maze 'Corn mazes 1=yes 0=no'; 55 doc Q03_Trail 'Dog trails/training 1=yes 0=no'; 56 doc Q03_Fest 'Festivals/special events 1=yes 0=no'; 57 doc Q03_Hay 'Hay rides 1=yes 0=no'; 58 doc Q03_Zoo 'Petting zoo 1=yes 0=no'; 59 doc Q03_Oth 'Other 1=yes 0=no'; 60 doc Q03_Tot 'Total 0 to 27'; 61 doc Q04_Yes 'Fee 1=yes 0=no'; 62 doc Q05_ATour1 'years of experience 1-3 1=yes 0=no'; 63 doc Q05_ATour2 'years of experience 3-5 2=yes 0=no'; 64 doc Q05_ATour3 'years of experience 5-7 3=yes 0=no'; 65 doc Q05_ATour4 'years of experience 7-10 4=yes 0=no'; 66 doc Q05_ATour5 'years of experience >10 5=yes 0=no'; 67 doc Q05_ATour 'years of experience total 0 to 5'; 68 doc Q06_Own 'Owner 1=yes 0=no'; 69 doc Q06_Oper 'Operator 2=yes 0=no'; 70 doc Q06_Both 'Owner/Operator 3=yes 0=no'; 71 doc Q06_Type 'Type total 0 to 3'; 72 doc Q07_Season 'Year-round= 1 Seasonally=0'; 73 doc Q08_Inc Income 1=yes 0=no';

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112 74 doc Q08_Hob 'Hobby 1=yes 0=no'; 75 doc Q08_Ple 'Pleasure 1=yes 0=no'; 76 doc Q08_Pub 'Public Service 1=yes 0=no'; 77 doc Q08_Edu 'Education 1=yes 0=no'; 78 doc Q08_Total 'Total 0 to 6'; 79 doc Q09_Bus 'Business Plan 1=yes 0=no'; 80 doc Q09_Mkg 'Marketing Plan 1=yes 0=no'; 81 doc Q10_Inc 'Annual Income/cash flow 1=yes 0=no'; 82 doc Q10_Grant 'Grants 1=yes 0=no'; 83 doc Q10_Loan 'Loans 1=yes 0=no'; 84 doc Q10_Pers 'Personal Savings 1=yes 0=no'; 85 doc Q10_Oth 'Other 1=yes 0=no'; 86 doc Q11_FullA 'Full time year round'; 87 doc Q11_PartA 'Part time year round'; 88 doc Q11_FullS 'Full time seasonally'; 89 doc Q11_PartS 'Part time seasonally'; 90 doc Q12_Inc1 'Income $0-$2500 1=yes 0=no'; 91 doc Q12_Inc2 'Income $2500-$4999 2=yes 0=no'; 92 doc Q12_Inc3 'Income $5000-$99999 3=yes 0=no'; 93 doc Q12_Inc4 'Income $10000-$24999 4=yes 0=no'; 94 doc Q12_Inc5 'Income $25000-$49000 5=yes 0=no'; 95 doc Q12_Inc6 'Income $50000-$99000 6=yes 0=no'; 96 doc Q12_Inc7 'Income $100000-$249000 7=yes 0=no'; 97 doc Q12_Inc8 'Income $250000-$999000 8=yes 0=no'; 98 doc Q12_Inc9 'Income $1000000+ 9=yes 0=no'; 99 doc Q12_Total 'Income Total 0 to 9'; 100 doc Q13_Sh1 'Share of income from agritourism 0% 1=yes'; 101 doc Q13_Sh2 'Share of income from agritourism 1%-24% 2=yes'; 102 doc Q13_Sh3 'Share of income from agritourism 25%-49% 3=yes'; 103 doc Q13_Sh4 'Share of income from agritourism 50%-74% 4=yes'; 104 doc Q13_Sh5 'Share of income from agritourism 75%-99% 5=yes'; 105 doc Q13_Sh6 'Share of income from agritourism 100% 6=yes'; 106 doc Q13_Total 'Share of income from agritourism total 0 to 5'; 107 doc Q14_Tot 'Size of the farm'; 108 doc Q15_Used 'Size of agritourism operation'; 109 doc Q16_Visitors 'Number of visitors'; 110 doc Q17_Same '2006 compared to previous years same 1=yes 0=no'; 111 doc Q17_Higher '2006 compared to previous years higher 1=yes 0=no'; 112 doc Q17_Lower '2006 compared to previous years lower 1=yes 0=no'; 113 doc Q18_Local 'Local visitors<50 miles 1=yes 0=no'; 114 doc Q18_Region 'Regional>50 miles 1=yes 0=no'; 115 doc Q18_State 'Outside the state 1=yes 0=no'; 116 doc Q18_Inter 'International 1=yes 0=no'; 117 doc Q19_Bus 'Business group 1=yes 0=no'; 118 doc Q19_Single 'Singles/Couples/Families 1=yes 0=no'; 119 doc Q19_School 'School groups 1=yes 0=no'; 120 doc Q19_Tour 'Tour groups 1=yes 0=no'; 121 doc Q19_Oth 'Other 1=yes 0=no'; 122 doc Q23_Amt1 'Visitor expenses $1-$49 1=yes 0=no'; 123 doc Q23_Amt2 'Visitor expenses $50-$99 1=yes 0=no'; 124 doc Q23_Amt3 'Visitor expenses $100-$149 1=yes 0=no'; 125 doc Q23_Amt4 'Visitor expenses >$150 1=yes 0=no'; 126 doc Q24_Promo '% of annual agritourism revenue spent on promotional activities'; 127 doc Q25_Email 'Email newsletter 1=yes 0=no'; 128 doc Q25_Dir 'Direct marketing 1=yes 0=no'; 129 doc Q25_Mail 'Purchased mailing list(s) 1=yes 0=no';

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113 130 doc Q25_Word 'Word of mouth 1=yes 0=no'; 131 doc Q25_Oth 'Other 1=yes 0=no'; 132 doc Q26_Exp1 'Plan for future expansion yes'; 133 doc Q26_Exp2 'Plan for future expansion no'; 134 doc Q26_Exp3 'Plan for future expansion not sure'; 135 doc Q27_Diff1 'Difficulties competition from other business 1=yes 0=no'; 136 doc Q27_Diff2 'Difficulties fi nding qualified employesss 1=yes 0=no'; 137 doc Q27_Diff3 'Difficulties identifying new markets 1=yes 0=no'; 138 doc Q27_Diff4 'Difficulties Insurance issues 1=yes 0=no'; 139 doc Q27_Diff5 'Difficulties Obtaining financing 1=yes 0=no'; 140 doc Q27_Diff6 'Difficulties Other 1=yes 0=no'; 141 doc Q28_Env1 'Disruption of wildlife/livestock 1=yes 0=no'; 142 doc Q28_Env2 'Incresed building development 1=yes 0=no'; 143 doc Q28_Env3 'Loss of farmland to tourism use 1=yes 0=no'; 144 doc Q28_Env4 'Soil erosion, clearance of vegetation 1=yes 0=no'; 145 doc Q28_Env5 'Water or air pollution 1=yes 0=no'; 146 doc Q28_Env6 'Other 1=yes 0=no'; 147 doc Q29_Sign 'Significant growth 1=yes 0=no'; 148 doc Q29_Mod 'Moderate growth 1=yes 0=no'; 149 doc Q29_Little 'Little growth 1=yes 0=no'; 150 doc Q29_None 'No growth 1=yes 0=no'; 151 doc Q29_NotS 'Not sure 1=yes 0=no'; 152 doc Q30_VImport 'Very important 1=yes 0=no'; 153 doc Q30_SIimport 'Somewhat important 1=yes 0=no'; 154 doc Q30_NImport 'Not at all important 1=yes 0=no'; 155 doc Q30_NotS 'Not sure 1=yes 0=no'; 156 doc State 'State'; 157 doc Educ1 'Education Less than High School 1=yes 0=no'; 158 doc Educ2 'Education Some College/Tehnical School 1=yes 0=no'; 159 doc Educ3 'Education Post Graduate Degree 1=yes 0=no'; 160 doc Educ4 'Education High School Graduate 1=yes 0=no'; 161 doc Educ5 'Education College graduate 1=yes 0=no'; 162 doc Age1 'Age 18-24 1=yes 0=no'; 163 doc Age2 'Age 25-34 1=yes 0=no'; 164 doc Age3 'Age 35-49 1=yes 0=no'; 165 doc Age4 'Age 50-64 1=yes 0=no'; 166 doc Age5 'Age >65 1=yes 0=no'; 167 doc Female 'Gender-Female 1=yes 0=no'; 168 doc Male 'Gender-Male 1=yes 0=no'; 169 OUT; 170 endproc xxxx; 171 ? dblist 'm:\ZSTUDENT\BONDOCIRINA\AGTOUR'; 171 ? IN 'c:\zagritour\agtour'; 171 IN 'm:\ZSTUDENT\BONDOCIRINA\AGTOUR'; 172 ?========================================; 172 ? BASIC STATISTICS ; 172 ?========================================; 172 HIST(DISCRETE) Q13_TOTAL; ? SHARE OF INCOME FROM AGRI TOURISM; 173 HIST(DISCRETE) Q12_ TOTAL; ? GROSS VALUE OF AGRI TOURISM; 174 Q6ALL= Q06_Own + Q06_Oper +Q06_Both; 175 HIST(DISCRETE) Q6ALL; 176 LIST ZEXGZ Q02_Beef Q02_Xtree Q02_Dairy Q02_F_N Q02_Grain Q02_Horse Q02_Nur Q02_Poultry Q02_Vege Q02_Oth Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q03_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Pi cnic Q03_Wedd Q03_Maze Q03_ Trail Q03_Fest Q03_Hay Q03_Zoo Q03_Oth Q04_Yes Q05_ATour1 Q05_ATour2 Q05_ATour3 Q05_ATour4 Q05_ATour5 Q06_Own Q06_Oper Q06_Both Q07_Season

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114 Q08_Inc Q08_Hob Q08_Ple Q08_Pub Q08_Edu Educ1 Educ2 Educ3 Educ4 Educ5 Age1 Age2 Age3 Age4 Age5 Female Male; 177 DOT ZEXGZ; .=(.>0); ENDDOT; 180 ? Q02_NRP =( (Q02_Beef + Q02_Xtree +Q02_Dairy +Q02_F_N +Q02_Grain +Q02_Horse +Q02_Nur + Q02_Poultry + Q02_Vege + Q02_Oth)=0) ; 180 ? HIST(DISCRETE) Q02_NRP; 180 Q02_OTH =( (Q02_Beef + Q02_Xtree +Q02_Dairy +Q02_F_N +Q02_Grain +Q02_Horse +Q02_Nur+ Q02_Poultry + Q02_Vege) =0) ; 181 HIST(DIS CRETE) Q02_OTH 182 DOT Q02_Beef Q02_Xtree Q02_Dairy Q02_F_N Q02_Grain Q02_Horse Q02_Nur Q02_Poultry Q02_Vege ; 183 Z. = (. Q02_OTH); ENDDOT; 185 Q03_OTH = ( (Q03_Bird + Q03_CCN + Q03_Fish + Q03_Game + Q03_Hunt + Q03_Hike + Q03_Horse + Q03_Tour 185 + Q03_Work + Q03_Schl + Q03_Wine + Q03_Make + Q03_Pick + Q03_Pump + Q03_Stand + Q03_Cut + Q03_B_B 185 + Q03_Camp + Q03_Vacat + Q0 3_Picnic + Q03_Wedd + Q03_Maze + Q03_Trail + Q03_Fest + Q03_Hay + Q03_Zoo )=0); 186 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 186 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q03_Cu t Q03_B_B Q03_Ca mp Q03_Vacat Q03_Picnic 186 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_Hay Q03_Zoo ; 187 Z. = (. =1) + (.^=1)*-1; ENDDOT; 189 HIST(DIS CRETE) Q03_OTH; 190 Q05_NRP= ((Q05_ATour1 +Q05_ATour2 +Q05_ATour3 +Q05_ATour4 +Q05_ATour5)=0); 191 DOT Q05_ATour1 Q05_ATour2 Q05_ATour3 Q05_ATour4 Q05_ATour5 ; 192 Z. = (. Q05_NRP); ENDDOT; 194 HIST(DIS CRETE) Q05_NRP; 195 Q06_NRP= (( Q06_Own + Q06_Oper +Q06_Both)=0); 196 DOT Q06_Own Q06_Oper Q06_Both; 197 Z. = (. Q06_NRP); ENDDOT; 199 HIST(DIS CRETE) Q06_NRP; 200 Q07_NRP= ((Q07_SEASON)=0); 201 DOT Q07_SEASON; 202 Z. = (. Q07_NRP); ENDDOT; 204 HIST(DIS CRETE) Q07_NRP; 205 Q08_NRP= ((Q08_Inc + Q08_Hob + Q08_Ple + Q08_Pub +Q08_Edu)=0); 206 DOT Q08_Inc Q08_Hob Q08_Ple Q08_Pub Q08_Edu; 207 Z. = (. Q08_NRP); ENDDOT; 209 HIST(DIS CRETE) Q08_NRP; 210 EDUC6 = (EDUC1=0 & EDUC2=0 & EDUC3=0 & EDUC4=0 & EDUC5=0); 211 DOT 1-5; 212 ZEDUC.= EDUC. EDUC6; 213 ENDDOT; 214 AGE6 = (AGE1=0 & AGE2=0 & AGE3=0 & AGE4=0 & AGE5=0); 215 DOT 1-5; 216 ZAGE.= AGE. AGE6; 217 ENDDOT; 218 OTHGEN= (FEMALE=0 & MALE=0); 219 ZFEMALE=FEMALE-OTHGEN; 220 ZMALE = MALE OTHGEN; 221 Q11_FULL=(Q11_FullA=1 | Q11_FullS=1); 222 ZQ11_FULL= (Q11_FULL=1) + (Q11_FULL^=1)*-1; 223 ZQ04_YES=(Q04_YES=1) + (Q04_YES^=1)*-1; 224 MSD Q14_TOT Q15_Used Q16_Visitors ;

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115 225 PRINT @MEAN @MSD; 226 LIST XMODELX 226 ZQ02_Beef ZQ02_Xtree ZQ02_Dairy ZQ02_F_N ZQ02_Grain ZQ02_Horse ZQ02_Nur 226 ZQ02_Poultry ZQ02_Vege 226 ZQ03_Bird ZQ03_CCN ZQ03_Fish ZQ03_Game ZQ03_Hunt ZQ03_Hike 226 ZQ03_Horse ZQ03_Tour ZQ03_Work ZQ03_Schl ZQ03_Wine ZQ03_Make ZQ03_Pick ZQ03_Pump ZQ03_Stand 226 ZQ03_Cut ZQ03_B_B ZQ03_Camp ZQ03_Vacat ZQ03_Picnic ZQ03_Wedd ZQ03_Maze ZQ03_Trail ZQ03_Fest 226 ZQ03_Hay ZQ03_Zoo 226 ZQ04_Yes 226 ZQ05_ATour1 ZQ05_ATour2 ZQ05_ATour3 ZQ05_ATour4 ZQ05_ATour5 226 ZQ06_Own ZQ06_Oper ZQ06_Both ZQ07_Season ZQ08_Inc ZQ08_Hob ZQ08_Ple ZQ08_Pub 226 ZQ08_Edu 226 ZQ11_Full Q14_Tot 226 ZEduc1 ZEduc2 ZEduc3 ZEduc4 ZEduc5 ZAge1 ZAge2 ZAge3 ZAge4 ZAge5 ZFemale ZMale; 227 HIST(DISCRETE) Q12_TOTAL Q13_TOTAL; 228 SELECT Q13_TOTAL>0; 229 ORDPROB Q13_TOTAL C XMODELX; 230 ? SELECT Q12_TOTAL>0; 230 ? ORDPROB Q12_TOTAL C XMODELX; 230 ?========================================================; 230 ? TWO MODELS 13 FIRST SHARE OF INCOME 230 ?========================================================; 230 MMAKE STATS @COEF @T %T; 231 print STATS; 232 SET NU=4; ? NUMBER OF ORDER CATEGORIES LESS 2; 233 FIT=@FIT; 234 SELECT 1; 235 DFIT=( (FIT**2)>=0); 236 MAT BB=@COEF; 237 MAT NR=NROW(BB); SET RR=NR(1)-NU; 239 MFORM(TYPE=GEN,NROW=RR,NCOL=1) AA=0; 240 DO J=1 TO RR; SET AA(J)=BB(J); ENDDO; PRINT AA; 244 DOT(VALUE=K) 2-5; SET L=RR + K -1; 246 SET MU.=BB(L); PRINT K L MU.; ENDDOT; ?==================================================================== 249 ? SETTING FOR THE SIMULATION PORTION OF THE ANALYSIS ; ?==================================================================== 249 LIST ZVAR2Z 249 ZQ02_Beef ZQ02_Xtree ZQ02_Dairy ZQ02_F_N ZQ02_Grain ZQ02_Horse ZQ02_Nur 249 ZQ02_Poultry ZQ02_Vege ZQ03_Bird ZQ03_CCN ZQ03_Fish ZQ03_Game ZQ03_Hunt ZQ03_Hike 249 ZQ03_Horse ZQ03_Tour ZQ03_Work ZQ03_Schl ZQ03_Wine ZQ03_Make ZQ03_Pick ZQ03_Pump ZQ03_Stand 249 ZQ03_Cut ZQ03_B_B ZQ03_Camp ZQ03_Vacat ZQ03 _Picnic ZQ03_Wedd ZQ03_Maze ZQ03_Trail ZQ03_Fest 249 ZQ03_Hay ZQ03_Zoo Q04_Yes ZQ05_ATour1 ZQ05_ATour2 ZQ05_ATour3 ZQ05_ATour4 ZQ05_ATour5 249 ZQ06_Own ZQ06_Oper ZQ06_Both ZQ07_Season ZQ08_Inc ZQ08_Hob ZQ08_Ple ZQ08_Pub 249 ZQ08_Edu ZQ11_Full Q14_Tot ZEduc1 ZEduc2 ZEduc3 249 ZEduc4 ZEduc5 ZAge1 ZAge2 ZAge3 ZAge4 ZAge5 ZFemale ZMale; 250 LIST SVARW

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116 250 WZQ02_Beef WZQ02_Xtree WZQ02_Dairy WZQ02_F_N WZQ02_Grain WZQ02_Horse WZQ02_Nur 250 WZQ02_Poultry WZQ02_Vege WZQ03_Bird WZQ03_CCN WZQ03_Fish WZQ03_Game WZQ03_Hunt WZQ03_Hike 250 WZQ03_Horse WZQ03_Tour WZQ03_Work WZQ03_Schl WZQ03_Wine WZQ03_Make WZQ03_Pick WZQ03_Pump WZQ03_Stand 250 WZQ03_Cut WZQ03_B_B WZQ03_Camp WZQ03_Vacat WZQ03_Picnic WZQ03_Wedd WZQ03_Maze WZQ03_ Trail WZQ03_Fest 250 WZQ03_Hay WZQ03_Zoo WQ04_Yes WZQ05_ATour1 WZQ05_ATour2 WZQ05_ATour3 WZQ05_ATour4 WZQ05_ATour5 250 WZQ06_Own WZQ06_Oper WZQ06_Both WZQ07_Season WZQ08_Inc WZQ08_Hob WZQ08_Ple WZQ08_Pub 250 WZQ08_Edu WZQ11_Full WQ14_Tot WZEduc1 WZEduc2 WZEduc3 250 WZEduc4 WZEduc5 WZAge1 WZAge2 WZAge3 WZAge4 WZAge5 WZFemale WZMale; 251 LIST SVARS 251 SZQ02_Beef SZQ02_Xtree SZQ02_Dairy SZQ02_F_N SZQ02_Grain SZQ02_Horse SZQ02_Nur 251 SZQ02_Poultry SZQ02_Vege 251 SZQ03_Bird SZQ03_CCN SZQ03_Fish SZQ03_Game SZQ03_Hunt SZQ03_Hike 251 SZQ03_Horse SZQ03_Tour SZQ03_Work SZQ03_Schl SZQ03_Wine SZQ03_Make SZQ03_Pick SZQ03_Pump SZQ03_Stand 251 SZQ03_Cut SZQ03_B_B SZQ03_Camp SZQ03_Vacat SZQ03_Picnic SZQ03_Wedd SZQ03_Maze SZQ03_Trail SZQ03_Fest 251 SZQ03_Hay SZQ03_Zoo 251 SQ04_Yes SZQ05_ATour1 SZQ05_ATour2 SZQ05_ATour3 SZQ05_ATour4 SZQ05_ATour5 251 SZQ06_Own SZQ06_Oper SZQ06_Both SZQ07_Season SZQ08_Inc SZQ08_Hob SZQ08_Ple SZQ08_Pub 251 SZQ08_Edu SZQ11_Full SQ14_Tot SZEduc1 SZEduc2 SZEduc3 251 SZEduc4 SZEduc5 SZAge1 SZAge2 SZAge3 SZAge4 SZAge5 SZFemale SZMale; 252 DOT ZVAR2Z; 253 SET S.=0; SET W.=0; SET IHH=1; ENDDOT; 257 SET I=0; 258 ?======================; 258 PROC INIT; 259 ?======================; 259 DOT ZVAR2Z; SET S.=0; SET W.=0; SET IHH=1; ENDDOT; 264 ENDPROC; 265 MFORM(TYPE=GEN,NROW=150,NCOL=12) MSIM=0; 266 SET SIMNUM=0; 267 ?======================; 267 PROC ZSIMZ; 268 ?======================; 268 SELECT 1; 269 IZZ=1; 270 SET IWW=0; 271 SET I=I+1; 272 MMAKE SX1 IZZ ZVAR2Z ; 273 MMAKE SX2 IWW SVARS ; 274 MMAKE SX3 IWW SVARW ; 275 MAT X2= SX1%(IZZ#SX2');

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117 276 MAT X3= IZZ#SX3'; 277 MAT X1 = SX1 X2 + X3; 278 MAT NRX1=NROW(X1); 279 MAT XB=X1*AA; 280 MAT PROB1= CNORM(-XB); 281 MAT PROB2= CNORM( MU2 XB) CNORM(-XB); 282 MAT PROB3= CNORM( MU 3 XB) CNORM( MU2 XB); 283 MAT PROB4= CNORM( MU 4 XB) CNORM( MU3 XB); 284 MAT PROB5= CNORM( MU 5 XB) CNORM( MU4 XB); 285 MAT PROB6= 1CNORM( MU5 XB); 286 MAT NN=NROW(PROB1); 287 DOT 1-6; UNMAKE PROB. LPROB.; DLPROB. = LPROB.*DFIT; ENDDOT; 291 MSD(NOPRINT) DLPROB1 DLPROB2 DLPROB3 DLPROB4 DLPROB5 DLPROB6 ; 292 SET MSIM(I,1)=I; 293 SET MSIM(I,2)=SIMNUM; 294 SET MSIM(I,3)=VARNUM; 295 SET MSIM(I,4)= @MEAN(1); 296 SET MSIM(I,5)= @MEAN(2); 297 SET MSIM(I,6)= @MEAN(3); 298 SET MSIM(I,7)= @MEAN(4); 299 SET MSIM(I,8)= @MEAN(5); 300 SET MSIM(I,9)= @MEAN(6); 301 SET MSIM(I,10)= 0; 302 ENDPROC; 303 ? STARTING THE SIMULATIONS; 303 ?==================================================; 303 ? SIMULATION #1 AVERAGE PERSON RESPONDING ; 303 ?==================================================; 303 SET SIMNUM=1; 304 SET VARNUM=1; 305 INIT; ZSIMZ; 307 ?==================================================; 307 ? SIMULATION #2 TYPE OF BUSINESS ; 307 ?==================================================; 307 SET SIMNUM=2.1; 308 INIT; 309 DOT Q02_Beef Q02_Xtree Q02_Dairy Q02_F_N Q02_Grain Q02_Horse Q02_Nur 309 Q02_Poultry Q02_Vege ; 310 SET SZ.=1; ENDDOT; 312 DOT Q02_Beef; 313 SET VARNUM=1; 314 SET WZ.=0; ZSIMZ; 316 SET VARNUM=2; 317 SET WZ.=1; ZSIMZ; 319 ENDDOT; 320 SET SIMNUM=2.2; 321 INIT; 322 DOT Q02_Beef Q02_Xtree Q02_Dairy Q02_F_N Q02_Grain Q02_Horse Q02_Nur 322 Q02_Poultry Q02_Vege ; 323 SET SZ.=1; ENDDOT; 325 DOT Q02_Xtree; 326 SET VARNUM=1; 327 SET WZ.=0; ZSIMZ; 329 SET VARNUM=2; 330 SET WZ.=1; ZSIMZ; 332 ENDDOT;

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118 333 SET SIMNUM=2.3; 334 INIT; 335 DOT Q02_Beef Q02_Xtree Q02_Dairy Q02_F_N Q02_Grain Q02_Horse Q02_Nur 335 Q02_Poultry Q02_Vege ; 336 SET SZ.=1; ENDDOT; 338 DOT Q02_Dairy; 339 SET VARNUM=1; 340 SET WZ.=0; ZSIMZ; 342 SET VARNUM=2; 343 SET WZ.=1; ZSIMZ; 345 ENDDOT; 346 SET SIMNUM=2.4; 347 INIT; 348 DOT Q02_Beef Q02_Xtree Q02_Dairy Q02_F_N Q02_Grain Q02_Horse Q02_Nur 348 Q02_Poultry Q02_Vege ; 349 SET SZ.=1; ENDDOT; 351 DOT Q02_F_N; 352 SET VARNUM=1; 353 SET WZ.=0; ZSIMZ; 355 SET VARNUM=2; 356 SET WZ.=1; ZSIMZ; 358 ENDDOT; 359 SET SIMNUM=2.5; 360 INIT; 361 DOT Q02_Beef Q02_Xtree Q02_Dairy Q02_F_N Q02_Grain Q02_Horse Q02_Nur 361 Q02_Poultry Q02_Vege ; 362 SET SZ.=1; ENDDOT; 364 DOT Q02_Grain; 365 SET VARNUM=1; 366 SET WZ.=0; ZSIMZ; 368 SET VARNUM=2; 369 SET WZ.=1; ZSIMZ; 371 ENDDOT; 372 SET SIMNUM=2.6; 373 INIT; 374 DOT Q02_Beef Q02_Xtree Q02_Dairy Q02_F_N Q02_Grain Q02_Horse Q02_Nur 374 Q02_Poultry Q02_Vege ; 375 SET SZ.=1; ENDDOT; 377 DOT Q02_HORSE; 378 SET VARNUM=1; 379 SET WZ.=0; ZSIMZ; 381 SET VARNUM=2; 382 SET WZ.=1; ZSIMZ; 384 ENDDOT; 385 SET SIMNUM=2.7; 386 INIT; 387 DOT Q02_Beef Q02_Xtree Q02_Dairy Q02_F_N Q02_Grain Q02_Horse Q02_Nur 387 Q02_Poultry Q02_Vege ; 388 SET SZ.=1; ENDDOT; 390 DOT Q02_NUR; 391 SET VARNUM=1; 392 SET WZ.=0; ZSIMZ; 394 SET VARNUM=2; 395 SET WZ.=1; ZSIMZ; 397 ENDDOT; 398 SET SIMNUM=2.8;

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119 399 INIT; 400 DOT Q02_Beef Q02_Xtree Q02_Dairy Q02_F_N Q02_Grain Q02_Horse Q02_Nur 400 Q02_Poultry Q02_Vege ; 401 SET SZ.=1; ENDDOT; 403 DOT Q02_POULTRY; 404 SET VARNUM=1; 405 SET WZ.=0; ZSIMZ; 407 SET VARNUM=2; 408 SET WZ.=1; ZSIMZ; 410 ENDDOT; 411 SET SIMNUM=2.9; 412 INIT; 413 DOT Q02_Beef Q02_Xtree Q02_Dairy Q02_F_N Q02_Grain Q02_Horse Q02_Nur 413 Q02_Poultry Q02_Vege ; 414 SET SZ.=1; ENDDOT; 416 DOT Q02_VEGE; 417 SET VARNUM=1; 418 SET WZ.=0; ZSIMZ; 420 SET VARNUM=2; 421 SET WZ.=1; ZSIMZ; 423 ENDDOT; 424 SET SIMNUM=2.10; 425 INIT; 426 DOT Q02_Beef Q02_Xtree Q02_Dairy Q02_F_N Q02_Grain Q02_Horse Q02_Nur 426 Q02_Poultry Q02_Vege ; 427 SET SZ.=1; ENDDOT; 429 DOT DOT Q02_Beef Q02_Xtree Q02_Dairy Q02_F_N Q02_Grain Q02_Horse Q02_Nur 429 Q02_Poultry Q02_Vege ; 430 SET VARNUM=1; 431 SET WZ.=-1; 432 ENDDOT; 433 ?==================================================; 433 ? SIMULATION #3 TYPE OF ACTIVITIES ; 433 ?==================================================; 433 SET SIMNUM=3.1; 434 INIT; 435 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 435 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q0 3_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 435 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 436 SET SZ.=1; ENDDOT; 438 DOT Q03_Bird; 439 SET VARNUM=1; 440 SET WZ.=-1; ZSIMZ; 442 SET VARNUM=2; 443 SET WZ.=1; ZSIMZ; 445 ENDDOT; 446 SET SIMNUM=3.2; 447 INIT; 448 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 448 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q0 3_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 448 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 449 SET SZ.=1; ENDDOT;

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120 451 DOT Q03_CCN; 452 SET VARNUM=1; 453 SET WZ.=-1; ZSIMZ; 455 SET VARNUM=2; 456 SET WZ.=1; ZSIMZ; 458 ENDDOT; 459 SET SIMNUM=3.3; 460 INIT; 461 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 461 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q0 3_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 461 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 462 SET SZ.=1; ENDDOT; 464 DOT Q03_FISH; 465 SET VARNUM=1; 466 SET WZ.=-1; ZSIMZ; 468 SET VARNUM=2; 469 SET WZ.=1; ZSIMZ; 471 ENDDOT; 472 SET SIMNUM=3.4; 473 INIT; 474 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 474 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q0 3_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 474 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 475 SET SZ.=1; ENDDOT; 477 DOT Q03_GAME; 478 SET VARNUM=1; 479 SET WZ.=-1; ZSIMZ; 481 SET VARNUM=2; 482 SET WZ.=1; ZSIMZ; 484 ENDDOT; 485 SET SIMNUM=3.5; 486 INIT; 487 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 487 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q0 3_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 487 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 488 SET SZ.=1; ENDDOT; 490 DOT Q03_HUNT; 491 SET VARNUM=1; 492 SET WZ.=-1; ZSIMZ; 494 SET VARNUM=2; 495 SET WZ.=1; ZSIMZ; 497 ENDDOT; 498 SET SIMNUM=3.6; 499 INIT; 500 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 500 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q0 3_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 500 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 501 SET SZ.=1; ENDDOT;

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121 503 DOT Q03_HIKE; 504 SET VARNUM=1; 505 SET WZ.=-1; ZSIMZ; 507 SET VARNUM=2; 508 SET WZ.=1; ZSIMZ; 510 ENDDOT; 511 SET SIMNUM=3.7; 512 INIT; 513 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 513 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q0 3_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 513 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 514 SET SZ.=1; ENDDOT; 516 DOT Q03_HORSE; 517 SET VARNUM=1; 518 SET WZ.=-1; ZSIMZ; 520 SET VARNUM=2; 521 SET WZ.=1; ZSIMZ; 523 ENDDOT; 524 SET SIMNUM=3.8; 525 INIT; 526 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 526 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q0 3_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 526 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 527 SET SZ.=1; ENDDOT; 529 DOT Q03_TOUR; 530 SET VARNUM=1; 531 SET WZ.=-1; ZSIMZ; 533 SET VARNUM=2; 534 SET WZ.=1; ZSIMZ; 536 ENDDOT; 537 SET SIMNUM=3.9; 538 INIT; 539 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 539 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q0 3_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 539 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 540 SET SZ.=1; ENDDOT; 542 DOT Q03_WORK; 543 SET VARNUM=1; 544 SET WZ.=-1; ZSIMZ; 546 SET VARNUM=2; 547 SET WZ.=1; ZSIMZ; 549 ENDDOT; 550 SET SIMNUM=3.10; 551 INIT; 552 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 552 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q0 3_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 552 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 553 SET SZ.=1; ENDDOT;

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122 555 DOT Q03_SCHL; 556 SET VARNUM=1; 557 SET WZ.=-1; ZSIMZ; 559 SET VARNUM=2; 560 SET WZ.=1; ZSIMZ; 562 ENDDOT; 563 SET SIMNUM=3.11; 564 INIT; 565 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 565 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q0 3_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 565 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 566 SET SZ.=1; ENDDOT; 568 DOT Q03_WINE; 569 SET VARNUM=1; 570 SET WZ.=-1; ZSIMZ; 572 SET VARNUM=2; 573 SET WZ.=1; ZSIMZ; 575 ENDDOT; 576 SET SIMNUM=3.12; 577 INIT; 578 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 578 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q0 3_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 578 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 579 SET SZ.=1; ENDDOT; 581 DOT Q03_MAKE; 582 SET VARNUM=1; 583 SET WZ.=-1; ZSIMZ; 585 SET VARNUM=2; 586 SET WZ.=1; ZSIMZ; 588 ENDDOT; 589 SET SIMNUM=3.13; 590 INIT; 591 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 591 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q03_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 591 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 592 SET SZ.=1; ENDDOT; 594 DOT Q03_PICK; 595 SET VARNUM=1; 596 SET WZ.=-1; ZSIMZ; 598 SET VARNUM=2; 599 SET WZ.=1; ZSIMZ; 601 ENDDOT; 602 SET SIMNUM=3.14; 603 INIT; 604 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 604 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q03_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 604 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 605 SET SZ.=1; ENDDOT;

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123 607 DOT Q03_PUMP; 608 SET VARNUM=1; 609 SET WZ.=-1; ZSIMZ; 611 SET VARNUM=2; 612 SET WZ.=1; ZSIMZ; 614 ENDDOT; 615 SET SIMNUM=3.15; 616 INIT; 617 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 617 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q03_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 617 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 618 SET SZ.=1; ENDDOT; 620 DOT Q03_STAND; 621 SET VARNUM=1; 622 SET WZ.=-1; ZSIMZ; 624 SET VARNUM=2; 625 SET WZ.=1; ZSIMZ; 627 ENDDOT; 628 SET SIMNUM=3.16; 629 INIT; 630 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 630 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q03_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 630 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 631 SET SZ.=1; ENDDOT; 633 DOT Q03_CUT; 634 SET VARNUM=1; 635 SET WZ.=-1; ZSIMZ; 637 SET VARNUM=2; 638 SET WZ.=1; ZSIMZ; 640 ENDDOT; 641 SET SIMNUM=3.17; 642 INIT; 643 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 643 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q03_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 643 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 644 SET SZ.=1; ENDDOT; 646 DOT Q03_B_B; 647 SET VARNUM=1; 648 SET WZ.=-1; ZSIMZ; 650 SET VARNUM=2; 651 SET WZ.=1; ZSIMZ; 653 ENDDOT; 654 SET SIMNUM=3.18; 655 INIT; 656 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 656 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q03_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 656 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 657 SET SZ.=1; ENDDOT;

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124 659 DOT Q03_CAMP; 660 SET VARNUM=1; 661 SET WZ.=-1; ZSIMZ; 663 SET VARNUM=2; 664 SET WZ.=1; ZSIMZ; 666 ENDDOT; 667 SET SIMNUM=3.19; 668 INIT; 669 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 669 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q03_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 669 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 670 SET SZ.=1; ENDDOT; 672 DOT Q03_VACAT; 673 SET VARNUM=1; 674 SET WZ.=-1; ZSIMZ; 676 SET VARNUM=2; 677 SET WZ.=1; ZSIMZ; 679 ENDDOT; 680 SET SIMNUM=3.20; 681 INIT; 682 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 682 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q03_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 682 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 683 SET SZ.=1; ENDDOT; 685 DOT Q03_PICNIC; 686 SET VARNUM=1; 687 SET WZ.=-1; ZSIMZ; 689 SET VARNUM=2; 690 SET WZ.=1; ZSIMZ; 692 ENDDOT; 693 SET SIMNUM=3.21; 694 INIT; 695 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 695 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q03_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 695 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 696 SET SZ.=1; ENDDOT; 698 DOT Q03_WEDD; 699 SET VARNUM=1; 700 SET WZ.=-1; ZSIMZ; 702 SET VARNUM=2; 703 SET WZ.=1; ZSIMZ; 705 ENDDOT; 706 SET SIMNUM=3.22; 707 INIT; 708 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 708 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q03_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 708 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 709 SET SZ.=1; ENDDOT;

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125 711 DOT Q03_MAZE; 712 SET VARNUM=1; 713 SET WZ.=-1; ZSIMZ; 715 SET VARNUM=2; 716 SET WZ.=1; ZSIMZ; 718 ENDDOT; 719 SET SIMNUM=3.23; 720 INIT; 721 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 721 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q03_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 721 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 722 SET SZ.=1; ENDDOT; 724 DOT Q03_TRAIL; 725 SET VARNUM=1; 726 SET WZ.=-1; ZSIMZ; 728 SET VARNUM=2; 729 SET WZ.=1; ZSIMZ; 731 ENDDOT; 732 SET SIMNUM=3.24; 733 INIT; 734 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 734 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q03_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 734 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 735 SET SZ.=1; ENDDOT; 737 DOT Q03_FEST; 738 SET VARNUM=1; 739 SET WZ.=-1; ZSIMZ; 741 SET VARNUM=2; 742 SET WZ.=1; ZSIMZ; 744 ENDDOT; 745 SET SIMNUM=3.25; 746 INIT; 747 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 747 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q03_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 747 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 748 SET SZ.=1; ENDDOT; 750 DOT Q03_HAY; 751 SET VARNUM=1; 752 SET WZ.=-1; ZSIMZ; 754 SET VARNUM=2; 755 SET WZ.=1; ZSIMZ; 757 ENDDOT; 758 SET SIMNUM=3.26; 759 INIT; 760 DOT Q03_Bird Q03_CCN Q03_Fish Q03_Game Q03_Hunt Q03_Hike Q03_Horse Q03_Tour Q03_Work Q03_Schl 760 Q03_Wine Q03_Make Q03_Pick Q03_Pump Q03_Stand Q0 3_Cut Q03_B_B Q03_Camp Q03_Vacat Q03_Picnic 760 Q03_Wedd Q03_Maze Q03_Trail Q03_Fest Q03_ Hay Q03_Zoo ; 761 SET SZ.=1; ENDDOT;

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126 763 DOT Q03_ZOO; 764 SET VARNUM=1; 765 SET WZ.=-1; ZSIMZ; 767 SET VARNUM=2; 768 SET WZ.=1; ZSIMZ; 770 ENDDOT; 771 ?==================================================; 771 ? SIMULATION #4 CHARGE FEES ; 771 ?==================================================; 771 SET SIMNUM=4.1; 772 INIT; 773 DOT ZQ04_Yes; 774 SET S.=1; ENDDOT; 776 DOT Q04_Yes; 777 SET VARNUM=1; 778 SET W.=-1; ZSIMZ; 780 SET VARNUM=2; 781 SET W.=1; ZSIMZ; 783 ENDDOT; 784 ?==================================================; 784 ? SIMULATION #5 YEARS OF EXPERIENCE ; 784 ?==================================================; 784 SET SIMNUM=5.1; 785 INIT; 786 DOT ZQ05_ATour1 ZQ05_ATour2 ZQ05_ATour3 ZQ05_ATour4 ZQ05_ATour5; 787 SET S.=1; ENDDOT; 789 DOT ZQ05_ATour1; 790 SET VARNUM=1; 791 SET W.=0; ZSIMZ; 793 SET VARNUM=2; 794 SET W.=1; ZSIMZ; 796 ENDDOT; 797 SET SIMNUM=5.2; 798 INIT; 799 DOT ZQ05_ATour1 ZQ05_ATour2 ZQ05_ATour3 ZQ05_ATour4 ZQ05_ATour5; 800 SET S.=1; ENDDOT; 802 DOT ZQ05_ATour2; 803 SET VARNUM=1; 804 SET W.=0; ZSIMZ; 806 SET VARNUM=2; 807 SET W.=1; ZSIMZ; 809 ENDDOT; 810 SET SIMNUM=5.3; 811 INIT; 812 DOT ZQ05_ATour1 ZQ05_ATour2 ZQ05_ATour3 ZQ05_ATour4 ZQ05_ATour5; 813 SET S.=1; ENDDOT; 815 DOT ZQ05_ATour3; 816 SET VARNUM=1; 817 SET W.=0; ZSIMZ; 819 SET VARNUM=2; 820 SET W.=1; ZSIMZ; 822 ENDDOT; 823 SET SIMNUM=5.4; 824 INIT; 825 DOT ZQ05_ATour1 ZQ05_ATour2 ZQ05_ATour3 ZQ05_ATour4 ZQ05_ATour5; 826 SET S.=1; ENDDOT;

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127 828 DOT ZQ05_ATour4; 829 SET VARNUM=1; 830 SET W.=0; ZSIMZ; 832 SET VARNUM=2; 833 SET W.=1; ZSIMZ; 835 ENDDOT; 836 SET SIMNUM=5.5; 837 INIT; 838 DOT ZQ05_ATour1 ZQ05_ATour2 ZQ05_ATour3 ZQ05_ATour4 ZQ05_ATour5; 839 SET S.=1; ENDDOT; 841 DOT ZQ05_ATour5; 842 SET VARNUM=1; 843 SET W.=0; ZSIMZ; 845 SET VARNUM=2; 846 SET W.=1; ZSIMZ; 848 ENDDOT; 849 SET SIMNUM=5.6; 850 INIT; 851 DOT ZQ05_ATour1 ZQ05_ATour2 ZQ05_ATour3 ZQ05_ATour4 ZQ05_ATour5; 852 SET S.=1; ENDDOT; 854 DOT ZQ05_ATour1 ZQ05_ATour2 ZQ05_ATour3 ZQ05_ATour4 ZQ05_ATour5; 855 SET VARNUM=1; 856 SET W.=-1;ENDDOT; 858 ZSIMZ; 859 ?==================================================; 859 ? SIMULATION #6 TYPE OF MANAGEMENT ; 859 ?==================================================; 859 SET SIMNUM=6.1; 860 INIT; 861 DOT ZQ06_Own ZQ06_Oper ZQ06_Both ; 862 SET S.=1; ENDDOT; 864 DOT ZQ06_Own; 865 SET VARNUM=1; 866 SET W.=0; ZSIMZ; 868 SET VARNUM=2; 869 SET W.=1; ZSIMZ; 871 ENDDOT; 872 SET SIMNUM=6.2; 873 INIT; 874 DOT ZQ06_Own ZQ06_Oper ZQ06_Both ; 875 SET S.=1; ENDDOT; 877 DOT ZQ06_OPER; 878 SET VARNUM=1; 879 SET W.=0; ZSIMZ; 881 SET VARNUM=2; 882 SET W.=1; ZSIMZ; 884 ENDDOT; 885 SET SIMNUM=6.3; 886 INIT; 887 DOT ZQ06_Own ZQ06_Oper ZQ06_Both ; 888 SET S.=1; ENDDOT; 890 DOT ZQ06_BOTH; 891 SET VARNUM=1; 892 SET W.=0; ZSIMZ; 894 SET VARNUM=2; 895 SET W.=1; ZSIMZ;

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128 897 ENDDOT; 898 SET SIMNUM=6.4; 899 INIT; 900 DOT ZQ06_Own ZQ06_Oper ZQ06_Both ; 901 SET S.=1; ENDDOT; 903 DOT ZQ06_Own ZQ06_Oper ZQ06_Both ; 904 SET VARNUM=1; 905 SET W.=-1;ENDDOT; 907 ZSIMZ; 908 ?==================================================; 908 ? SIMULATION #7 SEASONAL ; 908 ?==================================================; 908 SET SIMNUM=7.1; 909 INIT; 910 DOT ZQ07_Season; 911 SET S.=1; ENDDOT; 913 DOT ZQ07_Season; 914 SET VARNUM=1; ? FULL SEASON; 915 SET W.=1; ZSIMZ; 917 SET VARNUM=2; ? SEASONAL; 918 SET W.=-1; ZSIMZ; 920 ENDDOT; 921 ?==================================================; 921 ? SIMULATION #8 PURPOSE ; 921 ?==================================================; 921 SET SIMNUM=8.1; 922 INIT; 923 DOT ZQ08_Inc ZQ08_Hob ZQ08_Ple ZQ08_Pub ZQ08_Edu ; 924 SET S.=1; ENDDOT; 926 DOT ZQ08_Inc; 927 SET VARNUM=1; 928 SET W.=0; ZSIMZ; 930 SET VARNUM=2; 931 SET W.=1; ZSIMZ; 933 ENDDOT; 934 SET SIMNUM=8.2; 935 INIT; 936 DOT ZQ08_Inc ZQ08_Hob ZQ08_Ple ZQ08_Pub ZQ08_Edu ; 937 SET S.=1; ENDDOT; 939 DOT ZQ08_HOB; 940 SET VARNUM=1; 941 SET W.=0; ZSIMZ; 943 SET VARNUM=2; 944 SET W.=1; ZSIMZ; 946 ENDDOT; 947 SET SIMNUM=8.3; 948 INIT; 949 DOT ZQ08_Inc ZQ08_Hob ZQ08_Ple ZQ08_Pub ZQ08_Edu ; 950 SET S.=1; ENDDOT; 952 DOT ZQ08_PLE; 953 SET VARNUM=1; 954 SET W.=0; ZSIMZ; 956 SET VARNUM=2; 957 SET W.=1; ZSIMZ; 959 ENDDOT; 960 SET SIMNUM=8.4;

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129 961 INIT; 962 DOT ZQ08_Inc ZQ08_Hob ZQ08_Ple ZQ08_Pub ZQ08_Edu ; 963 SET S.=1; ENDDOT; 965 DOT ZQ08_EDU; 966 SET VARNUM=1; 967 SET W.=0; ZSIMZ; 969 SET VARNUM=2; 970 SET W.=1; ZSIMZ; 972 ENDDOT; 973 SET SIMNUM=8.5; 974 INIT; 975 DOT ZQ08_Inc ZQ08_Hob ZQ08_Ple ZQ08_Pub ZQ08_Edu; 976 SET S.=1; ENDDOT; 978 DOT ZQ08_Inc ZQ08_Hob ZQ08_Ple ZQ08_Pub ZQ08_Edu; 979 SET VARNUM=1; 980 SET W.=-1; ENDDOT; 982 ZSIMZ; 983 ?==================================================; 983 ? SIMULATION #9 FULL VERSUS PARTTIME ; 983 ?==================================================; 983 SET SIMNUM=9.1; 984 INIT; 985 DOT ZQ11_Full; 986 SET S.=1; ENDDOT; 988 DOT ZQ11_Full; 989 SET VARNUM=1; 990 SET W.=-1; ZSIMZ; 992 SET VARNUM=2; 993 SET W.=1; ZSIMZ; 995 ENDDOT; 996 ?==================================================; 996 ? SIMULATION #10 ACERAGE ; 996 ?==================================================; 996 SELECT 1; 997 MSD Q14_TOT; 998 SET MSIZE=@MEAN; 999 SET SIMNUM=10.0; 1000 INIT; 1001 DOT Q14_TOT; 1002 SET S.=1; ENDDOT; 1004 DOT Q14_TOT; 1005 SET VARNUM=1; 1006 SET W.=MSIZE*.75; ZSIMZ; 1008 SET VARNUM=2; 1009 SET W.=MSIZE*1.0; ZSIMZ; 1011 SET VARNUM=3; 1012 SET W.=MSIZE*1.25; ZSIMZ; 1014 ENDDOT; 1015 ?==================================================; 1015 ? SIMULATION #11 EDUCATION ; 1015 ?==================================================; 1015 SET SIMNUM=11.1; 1016 INIT; 1017 DOT ZEduc1 ZEduc2 ZEduc3 ZEduc4 ZEduc5; 1018 SET S.=1; ENDDOT; 1020 DOT ZEduc1;

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130 1021 SET VARNUM=1; 1022 SET W.=0; ZSIMZ; 1024 SET VARNUM=2; 1025 SET W.=1; ZSIMZ; 1027 ENDDOT; 1028 SET SIMNUM=11.2; 1029 INIT; 1030 DOT ZEduc1 ZEduc2 ZEduc3 ZEduc4 ZEduc5; 1031 SET S.=1; ENDDOT; 1033 DOT ZEduc2; 1034 SET VARNUM=1; 1035 SET W.=0; ZSIMZ; 1037 SET VARNUM=2; 1038 SET W.=1; ZSIMZ; 1040 ENDDOT; 1041 SET SIMNUM=11.3; 1042 INIT; 1043 DOT ZEduc1 ZEduc2 ZEduc3 ZEduc4 ZEduc5; 1044 SET S.=1; ENDDOT; 1046 DOT ZEduc3; 1047 SET VARNUM=1; 1048 SET W.=0; ZSIMZ; 1050 SET VARNUM=2; 1051 SET W.=1; ZSIMZ; 1053 ENDDOT; 1054 SET SIMNUM=11.4; 1055 INIT; 1056 DOT ZEduc1 ZEduc2 ZEduc3 ZEduc4 ZEduc5; 1057 SET S.=1; ENDDOT; 1059 DOT ZEduc4; 1060 SET VARNUM=1; 1061 SET W.=0; ZSIMZ; 1063 SET VARNUM=2; 1064 SET W.=1; ZSIMZ; 1066 ENDDOT; 1067 SET SIMNUM=11.5; 1068 INIT; 1069 DOT ZEduc1 ZEduc2 ZEduc3 ZEduc4 ZEduc5; 1070 SET S.=1; ENDDOT; 1072 DOT ZEduc5; 1073 SET VARNUM=1; 1074 SET W.=0; ZSIMZ; 1076 SET VARNUM=2; 1077 SET W.=1; ZSIMZ; 1079 ENDDOT; 1080 SET SIMNUM=11.6; 1081 INIT; 1082 DOT ZEduc1 ZEduc2 ZEduc3 ZEduc4 ZEduc5; 1083 SET S.=1; 1084 SET W.=-1; 1085 ENDDOT; 1086 SET VARNUM=1; 1087 ZSIMZ; 1088 ?==================================================; 1088 ? SIMULATION #12 AGE ; 1088 ?==================================================;

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131 1088 SET SIMNUM=12.1; 1089 INIT; 1090 DOT ZAGE1 ZAGE2 ZAGE3 ZAGE4 ZAGE5; 1091 SET S.=1; ENDDOT; 1093 DOT ZAGE1; 1094 SET VARNUM=1; 1095 SET W.=0; ZSIMZ; 1097 SET VARNUM=2; 1098 SET W.=1; ZSIMZ; 1100 ENDDOT; 1101 SET SIMNUM=12.2; 1102 INIT; 1103 DOT ZAGE1 ZAGE2 ZAGE3 ZAGE4 ZAGE5; 1104 SET S.=1; ENDDOT; 1106 DOT ZAGE2; 1107 SET VARNUM=1; 1108 SET W.=0; ZSIMZ; 1110 SET VARNUM=2; 1111 SET W.=1; ZSIMZ; 1113 ENDDOT; 1114 SET SIMNUM=12.3; 1115 INIT; 1116 DOT ZAGE1 ZAGE2 ZAGE3 ZAGE4 ZAGE5; 1117 SET S.=1; ENDDOT; 1119 DOT ZAGE3; 1120 SET VARNUM=1; 1121 SET W.=0; ZSIMZ; 1123 SET VARNUM=2; 1124 SET W.=1; ZSIMZ; 1126 ENDDOT; 1127 SET SIMNUM=12.4; 1128 INIT; 1129 DOT ZAGE1 ZAGE2 ZAGE3 ZAGE4 ZAGE5; 1130 SET S.=1; ENDDOT; 1132 DOT ZAGE4; 1133 SET VARNUM=1; 1134 SET W.=0; ZSIMZ; 1136 SET VARNUM=2; 1137 SET W.=1; ZSIMZ; 1139 ENDDOT; 1140 SET SIMNUM=12.5; 1141 INIT; 1142 DOT ZAGE1 ZAGE2 ZAGE3 ZAGE4 ZAGE5; 1143 SET S.=1; ENDDOT; 1145 DOT ZAGE5; 1146 SET VARNUM=1; 1147 SET W.=0; ZSIMZ; 1149 SET VARNUM=2; 1150 SET W.=1; ZSIMZ; 1152 ENDDOT; 1153 SET SIMNUM=12.6; 1154 INIT; 1155 DOT ZAGE1 ZAGE2 ZAGE3 ZAGE4 ZAGE5; 1156 SET S.=1; 1157 SET W.=-1; 1158 ENDDOT;

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132 1159 SET VARNUM=1; 1160 ZSIMZ; 1161 ?==================================================; 1161 ? SIMULATION #13 GENDER ; 1161 ?==================================================; 1161 SET SIMNUM=13.1; 1162 INIT; 1163 DOT ZMALE ZFEMALE; 1164 SET S.=1; ENDDOT; 1166 DOT ZMALE; 1167 SET VARNUM=1; 1168 SET W.=0; ZSIMZ; 1170 SET VARNUM=2; 1171 SET W.=1; ZSIMZ; 1173 ENDDOT; 1174 SET SIMNUM=13.2; 1175 INIT; 1176 DOT ZMALE ZFEMALE; 1177 SET S.=1; ENDDOT; 1179 DOT ZFEMALE; 1180 SET VARNUM=1; 1181 SET W.=0; ZSIMZ; 1183 SET VARNUM=2; 1184 SET W.=1; ZSIMZ; 1186 ENDDOT; 1187 SET SIMNUM=13.3; 1188 INIT; 1189 DOT ZMALE ZFEMALE; 1190 SET S.=1; ENDDOT; 1192 DOT ZMALE ZFEMALE; 1193 SET VARNUM=1; 1194 SET W.=0; ZSIMZ; 1196 ENDDOT; 1197 ZSIMZ; 1198 WRITE(FORMAT=EXCEL, FILE='m:\ZSTUDENT\BONDOCIRINA\AGTOURSIM.XLS') MSIM;

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133 LIST OF REFERENCES Aaron Blacka, and others. 2001. Agri-Tourism. Virginia Cooperative Extension, Virginia Tech. Publication Num ber 310-003. http://www.ext.vt.edu/pubs/agritour/310-003/310003.htm l (last accessed September 2008). Aldrich, John H. and Forrest D. Nelson. 1984. Lin ear Probability, Logit, and Probit Models. Newbury Park, CA: Sage Publications, Inc., Alvarez, R.M, R.P. Sherman, and C. VanB eselaere. 2003. Subject Acquisition for WebBasedSurveys. Political Analysis 11(1):23-43. American Farm Bureau Federati on. 2004 Annual Meetings Highlights, www.fb.org (last accessed February 2008 ). Beebe, Timothy J.; Harrison, Patricia A.; Pa rk, Eunkyung ; McRae, James A. Jr.; & Evans, James. 2006. The effects of data collection mode and disclosure on adolescent reporting of health behavior. Social Science Computer Review 24(4), 476-488. Bergstrom, J.C., B.L. Dillman, and J.R. Stoll. 1985. Public Environmental Amenity Benefits of Private Land: The Case of Prime Agricultural Land. Southern Journal of Agricultural Economics 17: 139-149. Bernardo, Dan; Luc Valentin, and John Leathe rman. 2004. Agritourism: If We Build It, Will Come? Paper presented at the 2004 Risk a nd Profit Conference, Manhattan, KS: 19-20. Borooah, Vani K. Logit and Probit, 2002. Ordered and Multinomial Models Thousand Oaks, CA: Sage Publications, Inc. Brian J. Schilling, Lucas J. Marxen, Helen H. Heinrich, Fran J. A. Brooks. 2006. The Opportunity for Agritourism Development in New Jersey. A Report Prepared for the New Jersey Department of Agriculture. http://www.nj.gov/agricult ure/pdf/ATReport.pdf (last accessed July 2008) Brown, Dennis M., Reeder, Richard J. 2007. Farm Based Recreation, A Sta tistical Profile. A report from the Economic Research Service USDA. Carpio,C.E., M.K. Wohlgenant, Boonsaeng,T ., 2006. The Demand for Agritourism in the United States, Selected Paper pr epared for presentation at the Southern Agricultural Economics Association Annual Meetings Orlando, Florida, February 5-8. Couper, M. P. 2000. Web surveys: A Re view of Issues and Approaches. Public Opinion Quarterly 64:464-494. Couper, Mick P. 2005. Technology tre nds in survey data collection. Social Science Computer Review 24(3), 486-501.

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134 Dillman, D.A. 2000. Mail and Internet Survey s: The Tailored Design Methods, 2nd Ed. New York:John Wiley and Sons, Inc. Evans, N.J., and B.W. Ilbery. 1992. Farm-base d Accommodation and th e Restructuring of Agriculture: Evidence from Three English Counties, Journal of Rural Studies Vol. 8, No. 1: 85-96. Fall, M. and T. Magnac. 2004.How Valuab le is On-Farm Work to Farmers? American Journal of Agricultural Economics 86(1) (February):267-281. Ference Weicker & Company. 1999. Agriculture Sect or Strategy for the Dist rict of Chilliwack. Chilliwack: District of Chilliwack. Harrison M. Pittman. 2006. Planting the Seeds fo r a New Industry in Arkansas: Agritourism An Agricultural Law Research Article. The National Agricultural Law Center. Hilchey, Duncan. 1993. Agri-tourism in New York State: Opportunities and Challenges in Farm-Based Recreation and Hosp itality. Farming Alternativ es Program, Department of Rural Sociology, Cornell University. Jane Eckert, 2008. Agritourism Speaker and Di rect Farm Marketing Consultant Eckert Agrimarketing, Cultivating Agritourism with Jane Eckert, Conference presented at Gainesville, Florida, April 8, http://www.eckertagrimarketing.com/index.php (last accessed August 2008). Joshua W ilson, Dawn Thilmany and Martha Sull ins. 2006. Agritourism: A Potential Economic Driver in the Rural West, Department of Agricultural and Resource Economics, Fort Collins, CO 80523-1172 February 2006-EDR 06-01 Lee, Sunghee 2006. An evaluation of nonresponse a nd coverage errors in a pre-recruited probability web panel survey. Social Science Computer Review 24(4), 260-275. Liao, Tim Futig. 1994. Interpreti ng Probability Models: Logit, Probit, and Other Generalized Linear Models. Thousand Oaks, CA: Sage Publications, Inc. Loureiro, Maria L. and Jervell-Moxnes, Anna. 2004. Analyzing Farms Participation decisions in Agro-tourism Activities in Norway: Some Welfare Implications. Selected paper presented at the American Agricultural Economics Association Annual Meetings, Denver, Colorado, August 1-4. Mace, David. 2005. Factors Motivating Agritouris m Entrepreneurs. Paper presented at the 2005 Risk and Profit Conference, Manhattan, KS, August, 11-12. McGehee, Nancy G., and Kyungmi Kim. 2004. Motivation for Agri-Tourism Entrepreneurship, Journal of Travel Research Vol. 43, No. 2: 161-170. New England Agricultural Sta tistics Service Vermont Agri-T ourism. 2002. Agri-Tourism in Vermont, New England Agricultural Statistics Service, NASS, USDA.

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135 Nickerson, Norma Polovitz, Black, Rita J ., McCool, Stephen F., 2001. Agritourism: Motivations behind Farm/Ranch Business Diversification Journal of Travel Research 40: 19-26. http://jtr.sagepub.com/cgi/content/a bstract/40/1/19 (last accessed March 2008). Oppermann, M. 1995. Holidays on the Farm: A Case Study of German Hosts and Guests. Journal of Travel Research, 34 (1): 63-67. Rilla, E. 1997. Unique Niches: Agritourism in Britain and New England Marin and Sonoma Counties, California. University of California Co-operative Extension http://www.sfc.ucdavis.edu/agritourism/agritour.htm l (last accessed July 2008). Rosenberger, R.S. and J.B. Loomis. 1999. The Value of Ranch Open Space to Tourists:Combining Observed and Contingent Behavior Data. Growth and Change (30): 366-383. Telfer, D. J., 2000. Tastes of Niagara: Build ing Strategic Alliances Between Tourism and Agriculture. Co-published simultaneously in International Journal of Hospitality & Tourism Administration (The Haworth Pr ess, Inc., Vol.I, No.1,2000, pp.71-88) and: Global Alliances in Tourism and Hospitality Management, By John C. Crotts, Dimitrios Buhalis, Roger March. Todd Comen, Dick Foster, 2006 Agricultural Divers ification and Agritourism: Critical Success Factors. Interim Report Presented to the Ve rmont Department of Agriculture, Food and Markets. The Institute for Integrated Rural Tourism. University of California Small Farms Center 1999. Agritourism and Nature Tourism in California, Small Farms Center: Davis, California. USDA Forest Service, Interagency National Survey Consortium. 2000-2003. National Survey on Recreation and the Environment (NRSE). USDA Economic Research Service, 2004. Ag ricultural Resource Management Survey. U.S. Department of Agriculture. 2002. Census of Agriculture. National Agricultural Statistics Service, Washington, DC. http://www.nass.usda.gov/Census_of_Agriculture/index.asp (last accessed June 2008). W ilson, Josh & Thilmany, Dawn, 2005. "Exploring Sp illover Effect of Public Investments in Conservation Programs onto Agritour ism," 2005 Annual meeting, July 24-27, Providence, RI 19189, American Agricultural Economics Asso ciation (New Name 2008: Agricultural and Applied Economics Association).

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136 BIOGRAPHICAL SKETCH Irina Bondoc was born in Slobozia, Rom ania, 1978. In 1998, she was accepted at Academy of Economic Studies, where she received in 2002 her diploma as an Economist in Management Department. After obtaining her bachelor degree in economic s, she worked as an Economist for an Audit company and later as a Financial Director within a medical center from Bucharest. After working for a while as a Financial Director, sh e decided to fulfill her dream of earning a higher degree and so she was accepted at University of Florida with an assistantship scholarship. She received her Master of Science degree in Food and Resource Economics Department in August 2009.