United States 

Department of 

Agriculture

Economic

Research

Service

Faqir Singh Bagi

Richard Reeder

Farm Activities Associated 

With Rural Development 

Initiatives

Economic 

Research 

Report 

Number 134

May 2012
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Recommended citation format for this publication: 

Bagi, Faqir Singh and Richard Reeder. Farm Activities Associated With

Rural Development Initiatives, ERR-134, U.S. Department of Agriculture, 

Economic Research Service, May 2012.

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United States

Department

of Agriculture

www.ers.usda.gov

A Report from the Economic Research Service

Abstract

Since 2002, USDA’s Rural Business and Industry (B&I) Loan Guarantee program 

has increased its emphasis on farm-related business activities associated with renewable energy, local/regional food, and value-added agriculture. Other new programs and 

program modifications also have focused on these and other farm activities and related 

industries, including the use of farm and ranch natural resources. This trend represents 

a relatively new direction for USDA’s Rural Development programs, which have historically focused on nonfarm-related business. This report improves our understanding of the 

farm and farmer characteristics that may influence farm operator involvement in development-related activities, specifically by focusing on five farm activities: organic farming, 

value-added agriculture, direct marketing, agritourism, and energy/electricity production. 

The findings are based on descriptive data from USDA’s 2007 Agricultural Resource 

Management Survey (ARMS) and estimates from logit models used to identify statistically significant factors associated with involvement in certain farm activities.

Keywords: Rural development, regional foods, local foods, direct marketing, organic 

farming, agritourism, value-added agriculture, renewable energy, electricity

Acknowledgments

The authors wish to thank the following reviewers for their helpful comments: Steven 

Deller, professor and community development economist, University of Wisconsin; 

David W. Hughes, professor, Department of Applied Economics and Statistics at 

Clemson University; and Thomas W. Gray in USDA’s Rural Development Cooperatives 

Programs. We thank Catherine Greene, Michael Hand, Sarah Low, and Charlie 

Hallahan in USDA’s Economic Research Service (ERS) for their insightful comments 

and guidance. Thanks also to ERS’s Angela Anderson and Priscilla Smith for editorial 

support and Wynnice Pointer-Napper for design and production assistance.

Farm Activities Associated With 

Rural Development Initiatives

Economic 

Research 

Report 

Number 134

May 2012

Faqir Singh Bagi, fsbagi@ers.usda.gov

Richard Reeder, rreeder@ers.usda.gov
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Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Recent USDA Rural Development Program and Policy Changes . . . . . . 1

The Organization of This Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Comparative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Identifying Farm Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Number of Farms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Economic Research Service’s Farm Typology Categories . . . . . . . . . . . . 8

Revenues From Specified Farm Activities . . . . . . . . . . . . . . . . . . . . . . . . 9

Geographic Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Acreage Operated and Land Tenure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Total Gross and Net Farm Income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Farm Household Net Worth and Household Income . . . . . . . . . . . . . . . 13

Source of Household Income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Age and Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Use of External Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Factors Associated With Farmer Involvement in Selected 

 Onfarm Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Factors That May Affect Farmer Involvement . . . . . . . . . . . . . . . . . . . . . 18

The Econometric Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
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Summary

What Is the Issue?

Over the last 10 years, several USDA Rural Development programs, 

including the Business and Industry (B&I) Loan Guarantee program, have 

put increased emphasis on funding farm-related business activities associated with renewable energy, local/regional food industries, and the use 

of farm and ranch natural resources. In this study, we identified five farm 

activities: organic production; value-added agriculture (activities that add 

value to a farm product or service, such as the production of jam, jelly, 

wine, or cheese); direct marketing to consumers; agritourism; and renewable energy/electricity production. We examined farm and farm operator 

characteristics to explore factors that may be related to a farmer’s decision 

to participate in these activities. 

Previous research provided some information about specific farm activities, 

but it varied from one activity to another and mostly offered descriptive information with no attempt to identify factors that play an independent and statistically significant role in farm (and farmer) activities. This report represents 

an opportunity to gain a better understanding of the five types of farms that 

provide a product or perform a function related to USDA’s recent rural development initiatives. 

What Did the Study Find?

Farms whose owners pursue activities associated with rural development 

policy initiatives tended to differ from others in terms of several farm and 

farm operator characteristics, including farm size, net farm income, household income and net worth, and geographic location. For example: 

•	Farm operators involved with most of these activities were, on average,

better educated, better advised, and had greater access to the Internet. 

•	Among the five activities evaluated, average farm household net worth

was highest for agritourism farms ($2.0 million) and lowest for direct 

marketing farms ($631,000). Total household income was highest for 

energy/electricity farms ($164,000 annually) and lowest for direct 

marketing ($71,000 annually), on average.

•	While a substantial percentage (39-60 percent) of the farms involved

in these five farm activities qualify as rural residence farms, a higher 

percentage (65 percent) of all other farms fall into this rural residence 

farm category.

•	Farms that focused on agritourism and energy/electricity production were

typically larger in size, while direct marketing farms averaged fewer acres.

•	Farm operator age and education, family net worth, farm size, farm

ownership characteristics, farmland use and practices, farm typology, 

geographic location, farm management advice, and Internet access 

contribute to farmer involvement in one or more of the onfarm rural 

development activities. 
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•	A rural or urban setting is statistically related to farmer involvement only in

the direct marketing activity and not in the other onfarm activities studied.

While this study did not attempt to evaluate the costs and benefits to society 

resulting from growth in these farm-related activities, the findings may still 

have policy relevance. Public- and private-sector initiatives may have some 

influence on education, farm management advice, and Internet availability, all 

of which were found to be significantly related to the farm activities studied. 

The findings also indicate the types and location of farms engaged in one 

or more of the five farm activities covered in this report, or that would most 

likely be receptive to involvement if incentives were provided to do so.

How Was the Study Conducted?

Our data were based on USDA’s 2007 Agricultural Resource Management 

Survey (ARMS), conducted jointly by the Economic Research Service (ERS) 

and the National Agricultural Statistics Service (NASS) and on the 2007 

Census of Agriculture, conducted by USDA/NASS. ARMS collected data 

from 18,709 U.S. farms and identified farms involved in farm activities associated with local/regional foods, renewable energy, and other rural development 

emphases. These data provided descriptive information about farm and farm 

operator characteristics. A logit analysis identified characteristics and locations 

that were independently and significantly related to farmer involvement in each 

of the activities studied. Data on farm location, reflecting the geographic region 

and urban/rural characteristics of the county in which each farm is located, 

came from the decennial U.S. Census and other related sources.
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Introduction

In this report, we examine several types of U.S. farms involved in economic 

activities that recently have been given special consideration in Federal 

rural development policies. These farms included those participating in 

organic farming, value-added agriculture (activities that add value to a 

farm product or service, such as the production of honey, wine, or cheese), 

and direct marketing—all of which contribute to the local/regional foods 

industry. Other farm types, including farms that produce energy or electricity and farms that provide agritourism,1 are also of interest to USDA’s 

Rural Development programs.

The local and regional foods industry has been the subject of several recent 

research projects, including studies by ERS. The most comprehensive study, 

Local Food Systems: Concepts, Impacts, and Issues (Martinez et al., 2010), 

includes a thorough review of local foods industry literature, including claims 

made regarding associated health, energy savings, and economic benefits from 

locally grown foods. Other recent ERS studies examined organic farming 

(Green et al., 2009) and agritourism (Brown and Reeder, 2007).2 A 2011 

report (Low and Vogel, 2011) examined farmer direct sales, including sales 

to local intermediaries, and provided a county-level econometric analysis of 

factors affecting direct sales levels. In addition, the findings of a 2009 survey 

of onfarm renewable energy production were recently published and provided 

additional support for our research (USDA/NASS, 2011). While we do not 

systematically restate the findings of these studies, we provide a list of selected 

research on each type of activity in the appendix (see appendix table 3). Our 

study is meant to complement previous research with a comprehensive descriptive analysis of farms involved in a select group of rural development-related 

onfarm activities and an econometric analysis that identifies factors associated 

with farmer involvement in each of these activities.

Recent USDA Rural Development Program 

and Policy Changes

Traditionally, most rural development programs in the United States have 

focused on the nonfarm economy, where most rural employees work.3

Farm-related development benefited from only a few relatively small USDA 

Rural Development programs.4 Several recent changes in USDA’s Rural 

Development programs, most notably those involving treatment of renewable 

energy and the local/regional foods industries, have increased the focus on 

farm-related rural development.

The Farm Security Act of 2002 (the 2002 Farm Act) reauthorized the 

Nation’s farm programs, and several new provisions were added to 

encourage more value-added agricultural production.5 Rules were liberalized to allow value-added producers, firms, and cooperatives greater 

participation in the USDA Rural Development Rural Business and Industry 

(B&I) Loan Guarantee Program. Onfarm renewable energy systems, such 

as wind energy and anaerobic digesters, became eligible for B&I loans, and 

value-added agricultural businesses were made eligible to receive Rural 

Business Enterprise Grants.

1Farm-based recreation refers to a 

wide variety of onfarm recreational 

and entertainment-related activities, 

including hunting, fishing, petting zoos, 

horseback riding, and onfarm rodeos.

2For more information, see the ERS 

briefing room on Organic Agriculture 

http://www.ers.usda.gov/Briefing/

Organic/. 

3Between 1950 and 2000, total 

U.S. farm employment (including 

self-employed) dropped from about 

8 million to 4 million (Torgerson, 

2003, p. 49). As of 2007, 5.9 percent 

(1.5 million) of rural (nonmetropolitan) workers were employed in 

agriculture, with a somewhat smaller 

share of workers employed in closely 

related industries, such as agricultural 

services, processing, marketing, and 

inputs (USDA/ERS, 2009).

4For example, the Farm Labor 

Housing Loans and Grants program 

and the Rural Cooperative Development 

Grant program (established in 1990).

5In this section, the material 

covering the 2002 and 2008 farm acts 

was obtained from http://www.ers.

usda.gov/FarmBill/2008/.
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In the same legislation, the Value-Added Agricultural Product Marketing 

Development Grants pilot program that received some funding in 2001 was 

expanded and provided with annual funding. The 2002 Farm Act liberalized 

this program’s eligibility provisions to increase farm participation and also 

contained provisions that benefited organic agriculture.6

The 2002 Farm Act also included several new or expanded programs 

promoting greater production and use of renewable energy: a competitive 

grant program to support the development of biorefineries; a renewable 

energy system and energy efficiency improvement loan program; and a 

biomass research and development program. These programs are administered by USDA Rural Development.

In the Food, Conservation, and Energy Act of 2008 (the 2008 Farm Act), 

more farm-related changes were made in the B&I program, giving special 

consideration to locally or regionally produced food projects with components benefiting underserved communities. At the same time, new provisions were made to facilitate the sale and use of renewable energy, and 

the renewable energy programs were modified, expanded, and extended. 

In addition, several new multi-State regional development commissions 

were authorized; the projects eligible for funding included renewable 

energy systems, recreation and tourism, resource conservation, and the 

preservation of open spaces. Farm-based recreation was promoted by the 

newly created Voluntary Public Access and Habitat Incentive Program 

(VPA-HIP). 

This legislation also modified the value-added agricultural product market 

development grant program. The program allows funding for projects developing mid-tier food chains, which are local/regional supply networks that 

link independent producers with businesses and cooperatives that market 

value-added agricultural products. Funding priority was given to projects that 

increased opportunities for operators of small and medium farms and ranches 

structured as “family farms.”7 A portion of the funding was allocated specifically for beginning or socially disadvantaged farmers and ranchers. The 2008 

Farm Act did not include any provisions for organic production in the Rural 

Development title, but it did provide organic provisions in six other titles and 

increased mandatory funding for organic research.8

Subsequent Rural Development policy initiatives—agency activities that typically 

use discretionary authority over existing programs to target assistance to achieve 

specific objectives—have also provided assistance to the farm-related business 

activities examined here. Most of these initiatives can be separated into two categories: farmer and food initiatives and regional innovation initiatives. 

The farmer and food initiatives are not specific to Rural Development but 

include other agencies inside and outside USDA. For example, USDA’s Know 

Your Farmer, Know Your Food (KYF2) aims to expand local and regional 

food systems, using Rural Development programs as well as other programs, 

such as those that support farmers markets, to achieve its objectives. 

USDA’s Rural Development mission area recently began two regional innovation initiatives: the Great Regions initiative and the Stronger Economies 

Together (SET) initiative. The Great Regions initiative makes use of the 

6The organic provisions were part of 

the Miscellaneous Title of the legislation (USDA/ERS, January 2008).

7Family farms are farms in which 

ownership and control of the farm 

business is held by a family of individuals related by blood, marriage, or 

adoption.

8For more information on the 

National Organic Program, see http://

www.ams.usda.gov/AMSv1.0/nop/.
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existing Rural Business Opportunity Grants (RBOG) program to target intermediaries (nonprofits, tribes, etc.) that provide assistance to development 

strategies employing regional approaches to rural development objectives, 

with special consideration given to five USDA priority themes, including 

renewable energy and local/regional food systems.9 The SET initiative 

encourages communities to use regional, collaborative approaches and shared 

assets to stimulate regional economic growth. The SET offers technical assistance by four USDA Regional Rural Development Centers that may benefit 

the farm-related activities examined here. 

USDA is also involved in other regional initiatives, including the 

Partnership for Sustainable Communities (PSC)10 and the Jobs and 

Innovation Accelerator Challenge (JIAC).11 Although these initiatives do 

not focus specifically on agriculture or any other particular industry, both 

the renewable energy and local/regional foods industries might, in theory, 

benefit from them.

Another new multi-agency initiative promotes agritourism. America’s Great 

Outdoors Initiative, undertaken jointly by USDA, the U.S. Department of 

the Interior, the U.S. Environmental Protection Agency, and the Council 

of Environmental Quality, emphasizes, “...bringing together farmers and 

ranchers, land trusts, recreation and conservation groups, sportsmen, community park groups, governments and industry, and people from all over the 

country to develop new partnerships and innovative programs to protect and 

restore our outdoors legacy” (White House, 2010). First among the initiative’s 

goals is to “reconnect Americans, especially children, to America’s rivers and 

waterways, landscapes of national significance, ranches, farms and forests, 

great parks, and coasts and beaches...” The VPA-HIP program provides a 

direct funding mechanism to support this initiative. 

The Organization of This Study

Because these farm-related rural development initiatives are relatively 

recent, it may be helpful to gain a better understanding of the five types of 

farm activities that play an important role in local/regional foods, renewable 

energy industries, and other farm-related industries:

•	Organic farming,

•	Value-added processing,

•	Direct marketing to the consumers,

•	Agritourism, and

•	Renewable energy/electricity production.

The first three farm activities may involve the local/regional foods industry, 

to the extent that they are concerned with the production and sale of farm 

products to consumers within local or regional markets. Agritourism capitalizes on the natural resources and amenities available on farms and ranches. 

The fifth activity involves farms that produce energy or electricity from 

renewable energy sources, such as the sun, wind, and methane.12

9The other three priority themes 

include broadband and other infrastructure to help entrepreneurs and 

expand markets, access to capital, and 

innovative use of natural resources.

10PSC is an interagency partnership with the U.S. Department of 

Housing and Urban Development, U.S. 

Department of Transportation, and 

U.S. Environmental Protection Agency.

11JIAC is a multi-agency 

program funded by the U.S. Small 

Business Administration; the U.S. 

Department of Commerce’s Economic 

Development Administration, National 

Institute of Standards and Technology, 

and Minority Business Development 

Agency; the International Trade 

Administration; U.S. Department 

of Labor; U.S. Department of 

Agriculture; U.S. Department of 

Defense; U.S. Department of Energy; 

U.S. Environmental Protection 

Agency; U.S. Department of Health 

and Human Services; U.S. Department 

of Housing and Urban Development; 

U.S. National Science Foundation; and 

U.S. Department of the Treasury.

12Farms that produce commodities 

for biofuels, such as ethanol, or sell 

crops or feedstock to companies that 

actually produce such biofuels at their 

industrial locations, were excluded 

because farmers themselves do not 

produce energy/electricity onfarm.
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Our main objective is to present descriptive data from the 2007 Agricultural 

Resource Management Survey (ARMS) and provide a profile of the 

economic, geographic, and individual characteristics of the average farm or 

farmer involved in one or more of these targeted activities, as well as to make 

comparisons with farms not involved in any of these activities.13 The purpose 

of this comparative analysis, and of the subsequent econometric analysis, is to 

gain insight into the unique characteristics of farms and farm operators that 

provide each kind of onfarm rural-development-related activity. 

Although other farm-related activities could have been included in this study, 

we restricted our research to farmers who might have a pivotal role in the 

growth and innovation taking place in local/regional foods, recreation, and 

renewable energy industries. For example, we included farmers who produced 

renewable energy (such as those operating wind-generated electricity equipment) but excluded farmers who merely provided inputs (land, labor, capital, 

or crops) to those who produced the energy. In a similar fashion, we included 

farmers who operated agritourism activities on their farms, but excluded 

farmers who merely leased part of their farms to other organizations that 

undertook these activities.14 We assumed that the farms and farmers who 

initiated these activities were, in some sense, unique, and that a better understanding of their behavior might be useful.

We included organic producers in our analysis because of their relationship 

with local/regional foods industries. Organic foods are sold in national and 

international markets as well as in local/regional foods markets. Although 

current data sources did not allow for us to say how much of this product 

is sold to consumers in local and regional markets, a 2004 ERS report 

concluded that “the demand for organic products is substantial and growing 

in many U.S. farmers’ markets” and that this type of local/regional food 

market represents an important marketing outlet for many organic farmers 

(Kremen et al., 2004). We also included organic producers because organic 

foods have been covered in a number of special provisions in recent farm 

legislation, gaining the interest of USDA and Rural Development. 

We examined farmers who produce value-added items, such as processed food, 

because of consumer interest in the many processed food items—ranging from 

wine to jelly—that are produced locally or regionally. Value-added agriculture 

also has received special consideration in recent farm legislation.

13When this report was first 

prepared, the 2007 ARMS data were 

the most current data available. We 

considered updating the analysis when 

the 2008 data became available, but 

the number of survey responses for our 

five farm categories was significantly 

higher in 2007 than in 2008 (only 83 

farms involved in renewable energy/

electricity production in 2008, and the 

number of agritourism farms dropped 

362 to 195), making the 2007 data 

better for our analysis. We considered 

aggregating the data for the 2 years, but 

ruled this out because some of the key 

explanatory variables used in the logit 

analysis were not available in 2008. 

The number of renewable energy/electricity farms was only 121 in 2009, and 

this survey question was not asked in 

2010. The survey question about valueadded activity was ambiguous in the 

2009 ARMS questionnaire and was not 

asked in 2010.

14This study also excluded off-farm 

activities by farmers or their families, 

because we were mainly interested in 

activities directly related to the farm.
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Comparative Analysis

In this section, we present descriptive information on the farms and farm 

operators engaged in the onfarm rural development-related activities examined in this study, using data from the 2007 ARMS. The data we use are 

based on survey responses from 18,709 farms, representing about 2.2 

million farms nationwide. (For more information about how the survey was 

conducted, see the appendix box, “Brief Description of ARMS Data.”)

Identifying Farm Types

Before we can compare one farm type with another, we must first understand 

how the ARMS questions identified farms that fit our five categories. If the 

response to the survey question indicated that any amount of the specified 

activity was performed, then the farm was classified as that farm type. The 

questions used for this identification process are presented in the box, “2007 

ARMS Questions Used To Identify the Five Farm Types,” p. 6.

The language of these questions was particularly important when considering 

which farms to include. Most of the ARMS questions used in this report were 

fairly broad in scope. For researchers and policymakers who have a more 

narrow focus, this may be a potential drawback.

For example, the 2007 ARMS organic foods question asked if the farmer 

produced foods that met the official National Organic Standards definition of organic foods. This broad scope question resulted in a larger sample 

of organic producers than we would have seen if the survey asked which 

farmers had an official organic certification.15 Consequently, researchers 

interested only in certified organic farmers may wish to examine other data.

The value-added question was also broad, including both food and nonfood 

items (e.g., floral arrangements). Researchers focused on value-added food 

items will require a more targeted question for their purposes.

Similarly, the agritourism question covered a wide variety of activities, 

ranging from hay rides to hunting and fishing. In some parts of the country, 

agritourism focuses on specific activities, so our analysis may appear overly 

broad. Additionally, farms that only sold food items to their visitors (e.g., 

pick-your-own operations) and did not offer other forms of recreation or 

entertainment would not be counted as agritourism providers. Such specialized agritourism operations would be identified in our study as direct 

marketers. Nevertheless, many agritourism farms providing recreational 

activities, such as hunting and fishing, often sell fruits and vegetables as well 

as value-added food items to visitors. These farms would be included in our 

study as agritourism farms, as direct marketing farms, and as value-added 

farms. In other words, a farm can be identified as more than one farm type in 

this study. 

The question on renewable energy explicitly included farms generating power 

through methane digesters, wind and solar technologies, and some other 

possible (unspecified) sources. This ambiguity produced a significantly larger 

number of farms than what was obtained in a more recent NASS survey that 

15The 2007 Census of Agriculture 

used a similar question to that found 

in the 2007 ARMS, yielding a total of 

20,437 self-identified organic producers 

in that year. However, the 2008 Census 

follow-up Organic Production Survey 

counted 14,540 farms that were legally 

certified (or exempt) organic farms 

(http://www.agcensus.usda.gov/).
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sought to identify only those farms that used wind turbines, solar panels, and 

methane digesters, and excluded farms that only used small solar powered 

devices, such as “fence chargers” (USDA/NASS, 2011). The 2009 On-Farm 

Renewable Energy Production Survey also reported other information, such 

as year the technology was installed, generating capacity, installation costs, 

and dollar savings on utility bills, that was not available in the 2007 ARMS.

The direct marketing question may not be broad enough for some purposes 

because it only included direct sales to consumers—not direct sales to foodservice institutions, such as schools, hospitals, or restaurants. In addition, the 

question excluded processed foods. As a result, it only accounted for part of 

the local food industry. More generally, none of the food-related questions 

Organic Farms

Question: Did this operation produce organic products (according to the 

National Organic Standards) for sale in 2007?

Farms responding affirmatively to this question were identified as organic farms.

Value-Added Farms

Question: At any time during 2007, did this operation produce and sell 

value-added crops, livestock, or products, such as beef jerky, fruit jams, 

jelly, preserves, floral arrangements, etc.? 

Farms responding affirmatively to this question were identified as valueadded farms.

Direct Marketing

Question: During 2007, did you produce, raise, or grow any crops, livestock, 

poultry, or agricultural products that were sold directly to individual consumers 

for human consumption? (Include sales from roadside stands, farmers markets, 

pick your own, door to door, direct Internet sales, etc. Exclude craft items and 

processed products, such as jellies, sausages, and hams).

Farms responding affirmatively to this question were identified as direct 

marketing farms.

Agritourism

Question: What was the amount received from agritourism and recreational 

services? (Such as farm or winery tours, hay rides, hunting, fishing, etc.)

Farms indicating positive income on this question were identified as 

agritourism farms.

Energy/Electricity

Question: At any time during 2007, did this operation generate energy or 

electricity on the farm using wind or solar technology, methane digester, etc.?

Farms responding affirmatively to this question were identified as energy/

electricity farms.

2007 ARMS Questions Used To Identify the Five Farm Types
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accounted for whether the agricultural products were sold locally or regionally.16 Nevertheless, we believe that three of our basic farm types (direct 

marketers, organic producers, and value-added producers) were more likely 

to be involved in the local/regional food industry than most other farms. 

Hence, the information provided here is still relevant to the recent Rural 

Development policy initiatives related to this industry. For more information 

about these types of farm activities, including related activities not specifically identified by the ARMS, see appendix table 3 for select references.

Number of Farms

Using 2007 Census of Agriculture data, we found that relatively few farms 

were involved in the onfarm rural development-related activities that are 

the focus of this study. The number of farms in each activity is as follows: 

18,211 organic farms, 78,418 value-added farms, 136,817 direct marketing 

farms, 23,350 agritourism farms, and 23,451 energy/electricity farms (fig. 1). 

Of the five activities, direct marketing to consumers was the most common, 

accounting for about 6.2 percent of the total number of U.S. farms. The other 

four farm types accounted for substantially fewer farms. Organic farms 

accounted for 0.8 percent of all U.S. farms, while energy/electricity farms 

accounted for 1.1 percent, agritourism farms for 1.1 percent, and value-added 

farms for 3.6 percent of all U.S. farms.

Many farms undertook more than one of these activities, according to data 

from the 2007 ARMS, which produces somewhat different counts of farms.17

This aspect was most notable for organic and value-added activities, which 

were both frequently involved with direct marketing to consumers; 46 percent 

of organic farms and 37 percent of value-added farms were involved with 

direct marketing of food for human consumption (table 1). Value-added 

farms actually accounted for a larger share of direct marketing farms (13.5 

percent) because value-added farms significantly outnumbered organic 

farms. Agritourism farms (as we identified them) and energy/electricity 

farms were less likely to be involved in direct marketing (7.1 and 9.0 percent, 

16For example, many organic and 

value-added food items are sold in the 

national and international market. It is 

worth noting that all of these questions 

referred to activities that occurred on 

the farm and represented a product of 

the farm business operation. If a farmer 

had a business that operated separately 

from the farm, including businesses that 

operate off the farm, these businesses 

were excluded from our analysis. We 

also excluded activities by individuals or 

businesses that rented the farm’s land or 

equipment to others.

17Despite the relatively small 

percentages of farms undertaking 

these activities, our ARMS estimates 

of the number of farms in each activity 

are considered reasonably accurate, 

as indicated by the coefficients of 

variation (CV) (table 1; see also note in 

table 1 on CV). However, they do differ 

(particularly in the case of value-added 

producers) from the counts provided by 

the 2007 Census of Agriculture.

Figure 1

Number of U.S. farms by farm activity, 2007

Number of farms (1,000)

Source: USDA, National Agricultural Statistics Service, Census of Agriculture, 2007.

Organic Value

added

Direct

marketing

Agritourism Energy/

electricity

0

20

40

60

80

100

120

140

160
8

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respectively), however, both were more involved with direct marketing than 

farms not engaged in any of these development-related activities.

Considerable overlap can be seen among other farm types (see table 1). 

Value-added farms accounted for a fairly high proportion (over 12 percent) 

of organic, direct marketing, and energy/electricity farms. Agritourism farms 

were generally the least likely to overlap significantly with any of the other 

farm types. 

Economic Research Service’s 

Farm Typology Categories

To determine how each of our five farm types differed from other farms, we 

first see where they fit into the ERS farm typology. The ERS farm typology 

identifies three basic categories of family farms: rural residence, intermediate, and commercial (fig. 2).18 The first two categories include small farms 

(less than $250,000 in annual gross sales or cash farm income) and the third 

includes large farms ($250,000 or more in annual gross sales or cash farm 

income). Rural residence farm operators are either retired or report a primary 

occupation other than farming. Intermediate farms are small family farms 

where the principal operators report farming as their primary occupation.

Rural residence farms accounted for over half of the farms identified as 

value-added, direct marketing, and energy/electricity farms. Intermediate 

farms accounted for a larger share of farms defined as organic and agritourism. Compared with other farms, all five of our farm types were more 

likely to be intermediate farms and less likely to be residential farms. Direct 

18This is the “collapsed” farm 

typology. Each of these three types 

includes subtypes. The typology also 

includes nonfamily farms. For more 

information about these typologies, 

see the glossary in ERS Briefing Room 

on Farm Household Economics and 

Well-Being at http://www.ers.usda.gov/

Briefing/WellBeing/glossary.htm/. 

Table 1

Farm category overlap, 2007

Organic production Value added Direct marketing Agritourism Energy/electricity

Number1 (percent) of farms

Organic production 21,669 2,755 9,896 526 788

(100) (6.1) (8.0) (1.5) (2.9)

Value added 2,755 45,474 16,721 1,550 4,559

(12.7) (100) (13.5) (4.5) (16.6)

Direct marketing 9,896 16,721 124,092 2,429 2,483

(45.7) (36.8) (100) (7.1) (9.0)

Agritourism 526 1,550 2,429 34,417 976

(2.4) (3.4) (2.0) (100) (3.5)

Energy/electricity 788 4,559 2,483 976 27,498

(3.6) (10.0) (2.0) (2.8) (100)

Notes: Numbers in bold represent the total number of farms engaged in each type of farm activity. Numbers in parentheses are column 

percentages. Numbers not in parentheses represent the number of farms in each category. For example, in the first row under the valueadded column, 2,755 represents the number of farms involved in both value-added and organic production, while (6.1) indicates that 6.1 

percent of value-added farms were involved in organic production.

1Estimated number of farms in the 48 contiguous States. Organic, value-added, direct marketing, agritourism, and energy-producing farms 

accounted for 1.0, 2.1, 5.6, 1.6, and 1.3 percent of all farms, respectively. The coefficients of variation (CV = (standard error/estimate)*100) for 

the total number of farms for these five types of farms were 15.4, 14.5, 7.4, 13.6, and 14.5, respectively, or well below the 25 (or less) CV value 

generally considered a good level of accuracy.

Source: USDA, Economic Research Service and National Agricultural Statistics Service, Agricultural Resource Management Survey, 2007.
9

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marketing farms stood out, having the highest share of residential farms and 

the lowest share of commercial farms.

Because farm characteristics often vary among the farm typology categories, 

most of the tables in this report provide corresponding statistics for each of 

the three farm typology categories to facilitate comparisons with our five 

farm types.

Revenues From Specified Farm Activities

Research by Low and Vogel (2011) established that a farm’s total sales revenues were directly related to the farm’s direct sales; the larger the farm’s total 

sales, the larger its revenues from direct sales. We found a similar pattern to 

be true in the 2007 ARMS data, where commercial farms (those with relatively large total sales revenues) had more direct marketing revenues than 

intermediate and rural residence farms (those with relatively small total sales 

revenues). We also found this relationship holds true for two other activities examined in this report: agritourism and organic production (fig. 3). 

Intermediate farms raised more revenues than rural residence farms, for all 

three of these activities. ARMS data on revenues for the value-added and 

energy/electricity activities were not available, so we could not show a similar 

relationship for these activities. 

The relatively high gross cash income received by organic producers, especially for commercial farms, has several possible explanations. First, large 

organic farms are very large operations in general. Second, as measured 

by activity-related farm cash income as a percentage share of total farm 

cash income, these farms tend to be more specialized in organic production 

than direct-marketing farms and agritourism farms are specialized in direct 

marketing and agritourism, respectively (ARMS, 2007). Third, organic 

products often fetch premium prices (Greene et al., 2009). Fourth, organic 

Figure 2

Share of farms, by Economic Research Service farm typology, 2007

Percent of farms

Source: USDA, Economic Research Service and National Agricultural Statistics Service, 

Agricultural Resource Management Survey, 2007.

Value

added

Direct

marketing

Agritourism Energy/

electricity

Organic All

others

0

20

40

60

80

100

Farm type

Rural residence Intermediate Commercial
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products are sold in the local, regional, national, and even international 

markets, while markets for agritourism and direct marketing to consumers 

are primarily local or regional (Greene et al., 2009; Park and Lohr, 1996; 

Park and Lohr, 2010).

Geographic Location

The concentration of farms in specific regions of the country varied by 

activity, with organic farms being highly concentrated in the Northeast and 

Pacific regions and value-added and direct marketing farms being more 

geographically dispersed (table 2). The Mountain region had the highest 

concentration of energy/electricity production, while the Southern Plains had 

a high percentage of agritourism activity.19

Over half of the organic and direct marketing farms were in a metropolitan 

(metro) county, whereas the majority of the other three farm types were 

outside of metro counties. Energy/electricity and agritourism activities were 

most likely to be in noncore (more rural) nonmetropolitan (nonmetro) counties.20 However, over half of the farms in all five farm types were within 5 

miles of a city with a population of at least 10,000. 

Acreage Operated and Land Tenure

Agritourism and energy/electricity farms stood out as being relatively large, as 

measured by total farm acreage (fig. 4). Agritourism farms were about six times 

as large as the “all other farms” category, which includes farms not belonging to 

any of our five farm types.21 Only direct marketing farms averaged fewer acres 

than the all other farms category, probably reflecting their focus on fruit and 

vegetable production, which often occurs on smaller plots of land. 

19Some of the most extreme variations in regional concentrations, such 

as the high percentage of organic 

farms in the Northeast and energy/

electricity farms in the Mountain 

region, may reflect a higher degree of 

statistical error, as indicated by the 

relatively high coefficients of variation 

(CVs) for these estimates.

20Noncore nonmetropolitan means 

outside of both metropolitan (a county 

having a city with population of at least 

50,000 or with significant commuting 

to such a city) and micropolitan areas. 

See footnote 36, p. 23, for a more 

detailed explanation of these terms.

21As shown earlier, however, most 

agritourism farms are small in terms of 

total agricultural sales. Thus, how you 

measure farm size (acres or agricultural sales) makes a difference.

Figure 3

Gross income from selected development-related activities 

by farm typology, 2007

Average gross income, dollars

Source: USDA, Economic Research Service and National Agricultural Statistics Service, 

Agricultural Resource Management Survey, 2007.

Rural residence Intermediate Commercial

Organic Direct marketing Agritourism

7,926

44,600

487,411

5,039 14,083

92,929

6,325 20,767 29,905
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Table 2

Geographic distribution of farm types, 2007

Five types of farms All farms, excluding five types of farms

Location

Organic 

production

ValueaddedDirect 

marketing Agritourism Energy/electricity

Rural 

residence 

farms

Intermediate 

farms

Commercial

farms

Percent

U.S. geographic regions1

Northeast *34.3 14.1 17.6 *12.3 7.5 6.0 7.8 5.2

Lake States 7.0 *8.5 18.1 4.8 2.4 8.7 10.7 11.4

Corn Belt 8.7 14.6 14.8 8.1 *14.9 19.2 17.8 23.9

Northern Plains 3.2 5.3 *2.6 8.0 *7.9 6.5 10.1 15.5

Appalachia 1.0 *8.9 7.5 #4.9 *12.8 16.1 10.8 6.9

Southeast 1.4 *4.8 4.8 #8.3 8.0 8.3 7.8 6.8

Delta 0.0 4.2 2.8 2.8 2.2 6.3 3.9 6.7

Southern Plains 2.0 10.4 6.0 28.9 4.7 17.0 15.1 9.6

Mountain 7.8 16.9 10.3 17.9 *29.4 5.8 8.7 5.7

Pacific 34.5 12.3 15.4 *4.1 10.4 6.0 7.3 8.3

ERS county type2

Nonmetro noncore 28.4 29.7 25.2 44.1 48.7 35.4 37.2 41.9

Nonmetro 

micropolitan 19.8 21.8 22.7 21.5 *12.9 26.0 23.3 23.2

Metropolitan 51.8 48.6 52.1 34.5 39.1 38.6 39.4 34.9

Distance to nearest city 

 with population of at 

 least 10,000

Less than 5 miles 50.4 60.7 52.0 54.2 51.7 48.5 61.3 66.4

5-20 miles 19.4 *14.5 22.5 21.8 18.3 23.9 17.3 13.4

20 or more miles 30.2 24.8 25.5 *24.0 *30.0 27.6 21.4 20.2

Average miles from 

city with population 

of at least 10,000 22 31 22 25 36 21 22 25

Notes: Based on 18,709 observations (17,465 households and 1,244 nonhouseholds). All 48 contiguous States were included in the sample. 

ARMS 2007 estimates possibly differ from other years because 2007 ARMS weights were calibrated to the 2007 Census of Agriculture. 

Coefficient of variation (CV) = (standard error/estimate)*100. The higher the CV, the less precise the estimate. Rounded percents may not add 

to exactly 100. Unless othewise indicated, the CV is 25 or less.

* = Indicates that CV is greater than 25 and less than or equal to 50. 

# = Indicates that CV is greater than 50 and less than or equal to 75. 

1Northeast includes Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode 

Island, and Vermont; Lake States include Michigan, Minnesota, and Wisconsin; Corn Belt includes Illinois, Indiana, Iowa, Missouri, and Ohio; 

Northern Plains includes Kansas, Nebraska, North Dakota, and South Dakota; Appalachia includes Kentucky, North Carolina, Tennessee, 

Virginia, and West Virginia; Southeast includes Alabama, Florida, Georgia, and South Carolina; Delta includes Arkansas, Louisiana, and 

Mississippi; Southern Plains includes Oklahoma and Texas; Mountain includes Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, 

and Wyoming; and Pacific includes California, Oregon, and Washington.

2Metropolitan counties are those associated with cities of more than 50,000 people. Nonmetropolitan (nonmetro) counties include smaller 

cities or no cities. Micropolitan counties are nonmetro counties associated with cities (urban clusters) of between 10,000 and 50,000 people. 

Noncore nonmetro counties had no cities (urban cluster) as large as 10,000 people. For further information, see the ERS Briefing Room on 

Measuring Rurality: Urban Influence Codes, at http://www.ers.usda.gov/Briefing/Rurality/UrbanInf/.

Source: USDA, Economic Research Service and National Agricultural Statistics Service, Agricultural Resource Management Survey, 2007.
12

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These five types of farms generally rented (or leased) a significant proportion 

of their farmland, but the amount varied considerably among them (see fig. 4). 

Agritourism and organic farms rented only about 30 percent of their land as a 

group while direct marketing farms rented 57 percent.22

All five types of farms were somewhat less likely to be full owners (and more 

likely to be part owners) than were operators in the all other farms category 

(fig. 5). Value-added farms were almost twice as likely to be full tenants as 

were operators of all other farms.

22Note that the statistics cited here 

and elsewhere in this section refer to 

the entire farm, not just the part of the 

farm devoted to a particular activity. 

In addition, acreage rented out by farm 

operators is not reflected in rented 

acres unless it is rented to other farm 

operators.

Figure 4

Average number of owned and rented acres, by farm activity, 2007 

Average number of acres operated

Source: USDA, Economic Research Service and National Agricultural Statistics Service, 

Agricultural Resource Management Survey, 2007.

Value

added

Direct

marketing

Agritourism Energy/

electricity

Organic All

others

Farm activity

Owned Rented

0

500

1,000

1,500

2,000

2,500

3,000

Figure 5

Land tenure characteristics, by farm activity, 2007 

Percent of farms

Source: USDA, Economic Research Service and National Agricultural Statistics Service, 

Agricultural Resource Management Survey, 2007.

Value

added

Direct

marketing

Agritourism Energy/

electricity

Organic All

others

Farm activity

0

20

40

60

80

100

Full owners Part owners Full tenants
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Total Gross and Net Farm Income

Average gross and net farm incomes were highest for organic farms, with 

energy/electricity ranked second on both measures (table 3).23 At the other 

extreme, direct marketing farms had the lowest gross and net farm income. 

Net farm income varied significantly among agritourism farms, making their 

estimated average net farm income less precise than those for other farm 

types.

Farm Household Net Worth and Household Income

Household wealth and income are standard economic measures of household 

well-being. They are also important indicators of financial capacity, or the 

ability to make financial investments in farm activities. As seen in table 3, 

average farm household net worth was highest for agritourism farms ($2.0 

million) and lowest for direct marketing farms ($631,000).24

Total household income exhibited a different pattern and was highest for 

energy/electricity farms ($165,000 annually) and value-added farms ($90,000 

annually), on average. The other three farm types had substantially lower 

average household incomes (ranging from $71,000 to $75,000 annually) 

compared with about $88,000 for all other farms (fig. 6).

Source of Household Income

All five farm types typically received most of their household income from 

off-farm sources (fig. 7). Off-farm wages were the largest source of income 

for organic, direct marketing, and energy/electricity farms, on average, while 

“nonwage off-farm income” was typically more important to value-added 

and agritourism farms.25 Farm income provided only a small fraction of total 

household income to these farm types, on average, and except for organic 

farms, farm income was less important (as a percentage of household income) 

than for all other farms. The amount of household income from farming was 

actually negative for most agritourism farms.26

Age and Education

With the exception of agritourism farms, younger farmers (under 45 years of 

age) were more likely to operate development-related farms than they were 

for all other farms (fig. 8). Young operators were most common on energy/

electricity farms (26 percent). Older farmers (65 years of age and older) 

played a larger role as operators of agritourism farms (40.4 percent) than for 

the other farm activities. 

With the exception of direct marketing farms, the operators of the development-related farms we examined were generally more educated than operators of all other farms (fig. 9).27 Roughly 70 percent of farmers in agritourism 

had at least some college education, and the other three types of developmentrelated farm operators accounted for a higher share of farmers with college 

degrees (5-7 percentage points higher) than all other farms. 

23Net farm income equals gross 

farm income minus cash and noncash 

production expenses.

24Agritourism farms’ average net 

worth was comparable with that of 

commercial farms, reflecting the fact 

that both were relatively large, on 

average.

25“Nonwage off–farm income” 

consists of nonfarm business income, 

Social Security and other nonfarm 

transfer payments, off-farm interest 

and dividends, and other sources 

(undefined).

26Farm income to the household 

(FARMHHI) is different from net 

farm income (NFI) in various ways, 

including how wages paid to operators 

and other family members are counted, 

plus income households receive from 

operating other farms and from renting 

out farm land (excluded from NFI; 

included in FARMHHI). However, the 

factor that explains the substantially 

lower FARMHHI than NFI is that 

depreciation expenses and decline 

in asset values are subtracted from 

FARMHHI but not from NFI.

27The proportion of direct marketing 

farm operators with a college degree 

was 2 percentage points lower than the 

proportion of all other farm operators.
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Use of External Resources

The ARMS asked a number of questions regarding the farmer’s use of (or 

access to) external resources, such as information from the Internet, management advice, and Government program assistance. These indicators may be 

useful to assess a farmer’s capacity to use available resources to thrive in 

today’s complex economy.

All five of our farm types had greater access to the Internet (including highspeed Internet), than did residential or intermediate farms in general. Organic 

farms had the most Internet access (table 4). Three of these farm types also 

made more use of purchased farm management advice than the typical residential or intermediate farm. Energy/electricity farms used free management 

advice most,28 while direct marketers made the least use of both the Internet 

and free farm management advice.

28This form of external management advice was more commonly 

used by organic and agritourism farms 

than by the other three farm types we 

examined.

Table 3

U.S. farm economic indicators, by farm activity, 2007

Five farm activities All farms, excluding five types of farms

Indicator

Organic Valueadded

Direct 

marketing Agritourism Energy/electricity

Rural 

residence 

farms

Intermediate 

farms

Commercial

farms

Total number of farms 21,669 45,474 124,092 34,417 27,498 1,285,165 460,566 236,156

Average number of acres 

farm operated *394 504 318 2,439 1,534 149 391 1,436

Average family/unpaid labor 

(FTE days) 572 521 420 481 450 202 423 670

Dollars (average)

Farm household net worth 1,163,475 900,063 631,255 2,020,308 1,477,992 484,229 794,103 2,145,882

Gross farm income *342,598 206,606 88,459 184,876 242,659 22,264 74,906 849,031 

Variable cash expenses on 

production 225,166 127,732 53,824 123,919 *137,426 11,783 41,160 464,171

Total fixed cash expenses *37,808 28,441 13,996 29,560 35,967 5,189 12,800 93,794

Net farm income *63,046 *38,269 14,224 #18,453 53,920 3,161 14,483 241,945

Government payments *2,200 4,499 1,799 7,403 5,053 1,264 3,503 20,200

Household farm income #12,685 3,260 412 -1,531 #12,701 -5,344 920 149,513

Total off-farm income 62,600 86,983 70,712 73,895 *151,824 91,729 47,804 45,165

Total household income1 75,285 90,243 71,123 72,364 164,525 86,385 48,724 194,678

1Total household income equals household farm income plus total off-farm income.

Notes: Based on 18,709 observations (17,465 households and 1,244 nonhouseholds). All 48 contiguous States were included in sample. 

Coefficient of variation = (standard error/estimate)*100. The higher the CV, the less accurate the estimate. Unless otherwise indicated, the CV 

is 25 or less.

* = Indicates that CV is greater than 25 and less than or equal to 50. 

# = Indicates that CV is greater than 50 and less than or equal to 75.

FTE = Full-time equivalent.

Source: USDA, Economic Research Service and National Agricultural Statistics Service, Agricultural Resource Management Survey, 2007.
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Involvement in Federal commodity payment programs can provide access to 

financial assistance and risk management options. With the exception of agritourism, our five farm types were less involved in commodity programs than 

other intermediate or commercial farms.29 Conversely, agritourism farms had 

the highest participation rate (20 percent) in the USDA Conservation Reserve 

Program, while organic and direct marketers had the lowest (5-6 percent). 

Relatively few of our five farm types bought crop insurance.

29A lower percentage of organic 

farms had base acreage in Government 

programs, and program participation 

by direct marketing farms was comparable with that of other residential 

farm categories, but energy/electricity, 

value-added, and agritourism farms all 

had higher program participation rates.

Figure 6

Farm household income, by farm activity, 2007 

Average annual income, 1,000 dollars

Source: USDA, Economic Research Service and National Agricultural Statistics Service, 

Agricultural Resource Management Survey, 2007.

Value

added

Direct

marketing

Agritourism Energy/

electricity

Organic All

others

Farm activity

Nonwage off-farm income

Off-farm wage income

Household farm income

-20

0

20

40

60

80

100

120

140

160

180

Figure 7

Sources of farm household income, by farm activity, 2007 

Percent of farm household income

Source: USDA, Economic Research Service and National Agricultural Statistics Service, 

Agricultural Resource Management Survey, 2007.

Value

added

Direct

marketing

Agritourism Energy/

electricity

Organic All

others

Farm activity

Household farm income Off-farm wage income Nonwage off-farm income

-20

0

20

40

60

80

100
16

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Figure 8

Percent of farms by operator age class, 2007 

Percent of principal farm operations

Source: USDA, Economic Research Service and National Agricultural Statistics Service, 

Agricultural Resource Management Survey, 2007.

Value

added

Direct

marketing

Agritourism Energy/

electricity

Organic All

others

Farm activity

Younger than 45 45 to 64 65 and older

0

20

40

60

80

100

Figure 9

Farm operator education, by farm activity, 2007 

Percent of principal farm operations

Source: USDA, Economic Research Service and National Agricultural Statistics Service, 

Agricultural Resource Management Survey, 2007.

Value

added

Direct

marketing

Agritourism Energy/

electricity

Organic All

others

Farm activity

High school or less Some college College degree

0

20

40

60

80

100
17

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Table 4

External resource and program participation indicators, by farm activity, 2007 

Five types of farms All farms, excluding five types of farms

Indicator

Organic

production

ValueaddedDirect 

marketing Agritourism Energy/electricity

Rural 

residence 

farms

Intermediate 

farms

Commercial

farms

Percent

Access to the Internet 86.5 80.1 66.6 73.2 73.4 60.2 54.1 75.0

Access to high-speed 

 Internet 61.5 53.3 38.5 49.0 53.4 35.1 30.0 51.2

Purchased farm 

 management advice 72.5 57.5 54.3 70.4 45.6 45.8 56.5 73.7

Free farm management 

 advice from NRCS 8.8 *15.0 6.0 *11.5 *18.7 3.2 4.1 9.5

Free management advice 

 from other sources 10.1 11.3 5.3 *6.7 *13.7 2.0 2.6 5.4

Base acreage in 

 Government programs 4.2 11.7 6.7 19.5 10.0 6.5 13.3 21.2

Enrolled in USDA 

 Conservation Reserve

 Program 4.7 *13.1 5.7 20.3 13.3 14.7 15.5 23.1

Purchased crop insurance *11.5 *10.7 9.5 14.0 18.3 6.3 24.6 54.8

NRCS = USDA, Natural Resources Conservation Service. 

Notes: Based on 18,709 observations (17,465 households and 1,244 nonhouseholds). All 48 contiguous States were included in the sample. 

Coefficient of variation = (standard error/estimate)*100. The higher the CV, the less accurate the estimate. Unless otherwise indicated, the CV is 

25 or less.

* = Indicates that CV is greater than 25 and less than or equal to 50. 

Source: USDA, Economic Research Service and National Agricultural Statistics Service, Agricultural Resource Management Survey, 2007.
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Factors Associated With Farmer 

Involvement in Selected Onfarm Activities

Factors That May Affect Farmer Involvement

Three kinds of factors may affect a farmer’s decision to use farm-based 

resources to diversify: 

•	The farmer’s personal characteristics (net worth, age, education, experience, and use of management advice); 

•	Farm characteristics (farm size and type, farm practices, farm amenities);

and 

•	Geographic factors (multistate region and urban/rural location).

Personal Characteristics

Research on farm diversification and entrepreneurship indicated that personal 

or household characteristics varied from the monoactive farmers (those 

involved only in normal production agriculture) to farmers involved in diversification activities and niche specializations similar to those examined in 

this study. For example, Carter (1998) found that “a gradation can be seen 

between groups based on training, business experience, available resources, 

and managerial, entrepreneurial, and strategic sophistication” (Carter, 1998, 

p. 27). Monoactive farmers were generally older and lacked training and 

experience outside agriculture, while the more diversified farm operators 

were younger and had more training in both agriculture and management. 

The more diversified farmers also tended to have a greater resource base (e.g., 

finances, land, equipment, etc.).

Consistent with our understanding of how farmers adopt a more diverse 

array of activities, our study hypothesized that farm operator household net 

worth was related to farmer involvement in these activities because household 

wealth helps farmers raise start-up capital. Household net worth might significantly contribute to farmer involvement, particularly for activities that require 

substantial up-front capital. Household wealth can also serve as a financial 

cushion to help the farm overcome short-term financial downturns (Brown 

and Reeder, 2007). 

Formal education, skill, training, and agricultural and/or management experience might increase a farmer’s ability to search for, interpret, analyze, and 

adapt information for practical economic decisionmaking (Schultz, 1975; 

Becker, 1975). Similarly, acquired skills related to innovation, management, 

and accessing the market can enhance decisionmaking skills and reduce the 

risk associated with adopting new activities. Internet access aids searching 

for, processing, and comprehending relevant information. Farmers can also 

utilize additional farm management information from public and private 

sources. Thus, we hypothesize that all of these factors could improve the 

probability that a farmer might become involved in our five farm activities.

Farm operator age may have a variety of effects. A relatively young farmer may 

have more robust health and a longer planning horizon and pay-off period for 
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personal and farm investments. A young farmer might also be more optimistic 

and enthusiastic about future possibilities, more aggressive about starting and 

expanding the farm business, more willing to take risks, and more receptive 

to new ideas, technologies, and opportunities. Older farmers, however, usually 

have more experience and practical knowledge and, as a result, may be more 

efficient decisionmakers and managers. Older farmers may have had more time 

to pay off loans and accumulate wealth, so farm household net worth could 

work in their favor, enabling them to finance more investment for new activities.30 Because of these potentially different effects, age-related factors may 

not offer a clear picture of farmers adopting our five farm activities. Based on 

Carter’s research, however, younger farmers may be more likely to be involved 

with most of these activities.

Farm Characteristics

Farm size (measured by acres, sales, or gross farm income) may affect adoption of new technology or activity for various reasons. For example, a larger 

farm may benefit from economies of scale that reduce costs per unit of 

output, making some activities more profitable than on a smaller farm. But 

it is unclear how these particular activities would compare with other activities available to large farm operators or to predict if these activities would be 

adopted. In contrast, for small farm operators, farm survival is more likely 

to be the main issue when an operator chooses to adopt a new activity. If a 

new activity is necessary for the farm to survive, then the farmer may have 

little choice but to adopt. We hypothesize that small farm operators are more 

likely to adopt these activities, particularly those with households dependent 

primarily on farm income.

The share of farmland in crops or livestock may also affect the operator’s 

decision to adopt a new activity (e.g., producing fruits and vegetables may 

be associated with using direct marketing). How a farm is defined based on 

ERS farm typology (rural residential, intermediate, or commercial) might 

also be associated with the likelihood of participating in an activity because 

this typology tends to be associated with a variety of different farm characteristics. A farm’s involvement in Government programs, such as Federal 

conservation programs, could be associated with the adoption of some activities, such as agritourism/recreation based on wildlife and a natural setting. 

Other factors may be associated with agritourism and natural amenities, such 

as sales of forest products and whether the farmer allows public access to the 

farm. Other farm characteristics, such as the share of farmland that is rented 

or owned or whether the farmer is a sole proprietor or tenant farmer, could 

make a difference for some of these activities, though it is not obvious what 

the effects would be.31

Geographic Characteristics

Location (geographic region and proximity to urban areas) can affect the 

economic viability of farm activities, thus affecting a farmer’s decision to 

adopt a new activity. A farm’s geographic location reflects soil quality, natural 

amenities, and climate, which in turn can affect a variety of farm activities, 

including the farmer’s choice of crops and livestock production. Location also 

affects farm access to local and regional transportation systems, exposure to 

State and local taxes, cooperative extension service activities, and regional 

30We separated out this wealth factor 

for our analysis. 

31Recent research by Park and Lohr 

(2010) included several of the factors 

expected to contribute to an organic 

farmer’s participation in local sales, 

including farm typology, percentage of 

land leased, the mix of farm products 

produced, use of Internet and farm 

management advice, and whether 

a farm was a sole proprietorship or 

family owned.
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development strategies. Geographic regions may therefore be associated with 

varying likelihood of adopting each of the five activities. Urban versus rural 

location can also make a difference, particularly for farm activities that rely 

heavily on urban consumers. For example, a more urban location could be an 

advantage for farm activities related to the local foods industry.

The Econometric Analysis

We estimate logit models encompassing the various factors that we hypothesize might be associated with a farmer’s participation in each of the activities highlighted in this report. Each logit model and analysis (explained in 

more detail in the appendix) includes all the farms in the ARMS and estimates the statistical relationship of various factors with the probability of 

participation in each of the five farm activities. The results are summarized 

in tables 5, 6, and 7 (more detailed tables, including estimated coefficients, 

measures of the estimated models’ performance, and odds ratios can be found 

in the appendix). It should be noted, however, that not all of the models were 

equally successful in predicting whether a farmer was involved in a specified 

activity. For organic farms, the model correctly predicted farmer involvement 

about 85 percent of the time, while only 63 percent of the predictions were 

correct for both the value-added and energy/electricity models. Our discussion of all five activities focuses on the relationships we found to be statistically significant (referred to as “significant” throughout).

Farmer Effects

The characteristics of individual farmers and their families, including access 

to and use of information, were significant factors for farm involvement in all 

five activities, though the significant factors varied depending on the activity 

(table 5).

Table 5

Estimated effects of farmer characteristics on farm activity involvement, 2007

Farm activity

Farmer characteristics

Organic

production Value-added Direct marketing Agritourism Energy/electricity

Age (-) (-) - + (-) 

Has at least some college education (+) + + + + 

Family net worth - # + + 

Access to the Internet + + + # (+)

Purchased advice + + # 

Received free advice1 + + + 

Notes: The signs + and – indicate positive and negative effect, respectively. Unless otherwise indicated, these effects are statistically significant 

at the 5-percent or less probability of error level. Signs followed by # indicate only marginal statistical significance (10-percent level). Signs in 

parentheses indicate that factors were included in the estimated models but the coefficients were not significantly different from zero. Blanks 

in each model show that these variables were included in the early stages of analysis but because their t-vales were less than 1.0 in the final 

estimation, these variables were left out of the estimated equations.

1Advice came from advisory groups other than USDA, Natural Resources Conservation Service.

Source: USDA, Economic Research Service and National Agricultural Statistics Service, Agricultural Resource Management Survey, 2007.
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Table 6

Estimated effect of farm characteristics on involvement in farm activity, 2007 

Farm activity

Farm characteristics

Organic

production Value-added Direct marketing Agritourism Energy/electricity

Farm size (acres) (+) (-) (+) + (+)

Farm ownership:

Sole proprietor (+) -

Full owner of farmland - # 

Rented share of total land -

Farmland use/practices: 

Cropland share of total land - - - #

Allows public access to farm + 

Conservation program use + #

Sells forest products + # + + +

Farm typology:

Intermediate + + + +

Rural residence +

Note: The signs + and – indicate positive and negative effect, respectively. Unless otherwise indicated, these effects were statistically significant 

at the 5-percent or less probability of error level. Signs followed by # indicate only marginal statistical significance (10-percent level). Signs in 

parentheses indicate that factors were included in the estimated models but the effects were not significantly different from zero. Blanks in each 

model show that these variables were included in the early stages of analysis, but because their t-vales were less than 1.0 in the final estimation, these variables were left out of the estimated equations.

Source: USDA, Economic Research Service and National Agricultural Statistics Service, Agricultural Resource Management Survey, 2007.

Table 7

Estimated effect of location on involvement in farm activity, 2007

Farm activity

Location

Organic

production Value-added Direct marketing Agritourism Energy/electricity

Rural (noncore) location1 -

Region:2 

 Northeast + + 

 Lake States + + -

 Corn Belt + # 

 Southern Plains - + -

 Mountain + + (+) + 

 Pacific + + +

Note: The signs + and – indicate positive and negative effect, respectively. Unless otherwise indicated, these effects were statistically significant 

at the 5-percent or less probability of error level. Signs followed by # indicate only marginal statistical significance (10-percent level). Signs in 

parentheses indicate that factors were included in the estimated models but the effects were not significantly different from zero. Blanks in each 

model show that these variables were included in the early stages of analysis, but because their t-vales were less than 1.0 in the final estimation, these variables were left out of the estimated equations.

1Rural (noncore) refers to a nonmetropolitan county that is not in a micropolitan area. These counties lack any city or urban cluster with populations as large as 10,000 and do not have a significant share of residents commuting to cities this size or larger. 

2Other regions not determined to be significant location factors were the Northern Plains, Appalachia, Southeast, and Delta. See table 2 for a 

list of States in each region.

Source: USDA, Economic Research Service and National Agricultural Statistics Service, Agricultural Resource Management Survey, 2007.
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•	Age of the farmer was significant only for direct marketing and agritourism (negative for direct marketing and positive for agritourism).32

•	Educational achievement was significant and positive for all activities

except organic farming.

•	Wealth (family net worth) was a significant positive factor for agritourism

and energy/electricity activities but had an inverse relationship with direct 

marketing activities, although only marginally.

•	Internet access had a significant positive relationship with organic and

value-added activities and was marginally positive for agritourism.

•	Farm management advice was significant for involvement in all five activities, although the form of advice differed among activities: purchased 

advice was significant for organic and agritourism activities (only marginally for the latter), while free advice was significant for value-added, 

direct marketing, and energy/electricity activities.

Most of the statistically significant factors had the expected signs, as most of 

the individual factors (education, wealth, access to information) were thought to 

be positively related to adoption of innovative activities. The only exception in 

our analysis was the marginally negative wealth coefficient (family net worth) 

for involvement in value-added activities. This result could reflect situations in 

which farm households attempt to increase the value of their onfarm activities 

when their ability to earn off-farm income or income from the sale of additional farm commodities is constrained. The age coefficients were mixed, and 

this result was not unexpected given ambiguous expectations for this factor.

Farmer involvement in agritourism was associated with the greatest number 

of these farmer characteristics (four out of five), while organic production 

was associated with the least number (two out of five). Our data, however, did 

not provide any information on farmer personality or attitude, which might 

also help explain involvement in some activities.33

Farm Effects

Only a few farm characteristics were significant enough to explain farmer 

involvement in more than one or two of the farm activities examined (table 6):

•	A relatively larger share of cropland was negatively associated with

involvement in all but organic and value-added activities (this effect was 

only marginally negative for energy/electricity).

•	Sales of forest products were positively associated with all but agritourism

activities (this effect was only marginally positive for organic farms).

•	Intermediate farms, as defined by the ERS farm typology, were positively

associated with participation in all activities except energy/electricity 

production.34

Only three farm activities were significantly associated with any of the other 

farm characteristics:

•	Agritourism was associated with five farm characteristics: farm size

(positive effect), sole proprietorship (negative effect), rented share of total 

32The age variable had a negative, 

though nonsignificant, effect on farmer 

participation in the other three activities.

33For example, adoption of these 

activities might be associated with 

a farmer’s attitude toward risk when 

entering a new enterprise; willingness 

to learn how to do something different 

or how to use a new technology; 

willingness to have greater contact 

with the public; and eagerness to make 

more money, either to enable the farm 

to survive or to add to the family’s 

resources in general. Attitudes toward 

altruism may also play a role in some 

cases, such as a desire to provide urban 

and regional residents with more fresh, 

natural, or tasty food (direct marketing, 

organic producers, value-added 

processing); produce less negative 

environmental externalities (organic 

producers, renewable energy); educate 

urban residents about agriculture; and 

provide local and regional residents 

access to the farm’s land for hunting, 

fishing, and other forms of recreation 

(agritourism).

34Use caution when interpreting the 

estimated coefficients associated with 

the ERS farm typology classifications. 

The typology includes three classifications for family farms: rural residence, 

intermediate, and commercial. In 

econometric analyses, such as this one, 

when only one or two of these classifications were used to estimate the 

model, the estimated coefficients were 

interpreted relative to those classifications not included in the model. Thus, 

in the case of organic, value-added, and 

agritourism farms, the significant positive coefficient of the dummy variable 

for the intermediate classification meant 

that involvement in these activities was 

more likely to occur than for rural residence and commercial farms. However, 

the model for direct marketing farms 

included dummy variables for both 

rural residence and intermediate farm 

classifications, and thus the positive 

coefficients for the rural residence and 

intermediate farms were in comparison 

with commercial farms (the only 

excluded farm typology classifications).
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land (negative), public access to the farm (positive), and conservation 

program use (marginally positive).

•	Value-added activity was associated with one other farm factor: being a

full owner of the farmland (marginally negative).

•	Direct marketing was associated with one other farm factor: being a rural

residence farm (positive).

•	Most of the farm characteristic effects either fit with our expectations or

were not very surprising.35

Location Effects

Our findings on the importance of location may be a bit more surprising 

(table 7). Proximity to urban customers was considered an advantage 

for most of these activities. Consequently, being in a remote rural area 

(specified in our model as a noncore nonmetropolitan county, or outside 

metropolitan areas and not in a micropolitan area) would be a significant 

disadvantage for most of our activities.36 We found this to be true, however, 

only for direct marketers, which suggests that many providers of the development-related activities we examined had no particular advantages related 

to urban/rural setting.

Organic farms are highly concentrated in the Northeast and on the Pacific 

Coast, and our statistical analysis confirmed that such locations are associated with farmer involvement in organic farming. However, organic farms 

also appeared to have an advantage when located in the Lake States, Corn 

Belt, and Mountain regions.37 In addition, farms involved in agritourism were 

strongly associated with only one regional location, the Southern Plains. It is 

important to recognize, however, that outdoor recreation activities, including 

hunting and fishing, were the main agritourism-related activities, at least as 

defined in the ARMS (Brown and Reeder, 2007). These activities were quite 

popular in the Southern Plains (Texas and Arkansas), which enjoy some 

climate advantages over other regions for such outdoor recreation activities.

The regional effects for value-added activity seemed similar to those for 

organic production, which was not unexpected given the amount of processed 

organic foods (meat, wine, etc.). The regional effects for energy/electricity 

might correspond to wind and climate patterns. The findings for farms 

marketing directly to consumers were also not unexpected, though we also 

might have expected the Northeast to have a positive coefficient for that activity.

The econometric analysis also provides information about the magnitude of 

the effects. For example, our estimates indicate that the farms with access 

to the Internet compared to farms without access to the Internet have three 

times the likelihood of engaging in organic production, and double the 

likelihood of participating in value-added activity. In addition, some large 

variations in the size of effects are found with regard to regional locations. 

For instance, farms located in the Northeast and Pacific regions have much 

greater likelihood of engaging in organic production than farms located in 

any other region. The appendix provides more detailed information about the 

magnitude of the effects of all the explanatory factors examined in this study. 

35We found that selling forest products 

was positively related to involvement for 

all our farm types except agritourism. 

Selling forest products implies the presence of an amenity—forest land—that 

might be particularly conducive for agritourism. However, the empirical models 

for this study were limited in the number 

of variables that could be used. Because 

many other factors were significant for 

agritourism, the sale-of-forest-products 

variable was left out of the final model.

36Metropolitan counties were associated with cities of more than 50,000 

people. Nonmetropolitan (nonmetro) 

counties included smaller cities or no 

cities. Micropolitan counties included 

nonmetro counties associated with 

cities (urban clusters) of between 

10,000 and 50,000 people. Noncore 

nonmetro counties had no cities (urban 

cluster) as large as 10,000 people. 

For further information, see the ERS 

website Briefing Room for Measuring 

Rurality: Urban Influence Codes at 

http://www.ers.usda.gov/Briefing/

Rurality/UrbanInf/.

37This may be explained partly by 

a concentration of processed organic 

production in the Lake and Corn Belt 

States (Eades, 2006). Since the coefficients of these regional dummy variables were interpreted as the difference 

from the excluded regions, this may 

reflect the relative absence of organic 

production in some of the excluded 

regions, such as in Appalachia, 

Southeast, and the Great Plains. (See 

appendix table 2 for the coefficients 

and definitions associated with the 

regions used in this analysis).
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Conclusions

Our objective was to provide comparable information about several types of 

farms that are engaged in activities emphasized by recent rural development 

initiatives in the United States. The farm types we examined, using 2007 

ARMS data, were organic producers, value-added producers, direct marketers, 

agritourism farms, and renewable energy/electricity producing farms. 

The findings from our comparative analysis showed that some characteristics 

of the typical farm involved in the five rural development-related activities we 

examined varied greatly, including the number of farms, farm size, household 

income, net farm income, operator age, and education. We also found a great 

deal of variation in the regional distribution of farms involved in the five farm 

activities examined.

The findings from our econometric analysis further illustrated factors with 

the potential to influence a farmer’s decision to undertake one of these 

activities. While we did not attempt to establish causality, the logit analysis 

we present identified statistically significant relationships that may be worth 

further exploration. For example, our econometric analysis indicated that 

farm management information was an important (statistically significant) 

to farmers’ involvement in all five activities, and education was important 

for all but organic farmers. This is understandable, because these activities 

typically involve adoption of distinctly different or complex production and 

marketing practices. While the farm operators involved in these activities 

tended to be more educated and made more use of farm management advice 

from various sources than other farm operators, many still lacked any 

college education. Also, most did not make use of free farm management 

advice, though most paid for relevant advice. 

To the extent that the positive relationship between education/advice and 

involvement in RD-related activities reflected the benefits of information to 

those considering expanding their onfarm activities, improving access to 

managerial advice or technical assistance could affect the number of farmers 

willing to become involved in these new activities. Direct marketers might 

be particularly affected since evidence suggests that they use free management advice less frequently than the other four farm types, but that direct 

marketing was one of three activities found to be statistically related to free 

management advice. 

Among our other findings, access to the Internet was statistically associated with the adoption of most of the farm activities we examined (direct 

marketers and energy/electricity farms are the exceptions). Although most 

of the farmers involved in our five activities had access to the Internet, many 

did not. Hence, ongoing efforts to improve Internet access throughout rural 

America might lead to increased farmer participation in these activities. For 

example, over a quarter of agritourism farms lacked access to the Internet, 

and half lacked access to broadband in 2007.

Our analysis revealed that a farm family’s financial situation appears to affect 

adoption of some, but not all, of these activities. Specifically, we found that 

family net worth was positively associated with agritourism and energy/
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electricity farms, but not organic production and value-added farms, and it 

was negatively related (marginally) to direct marketing farms.

The size and category of a farm also played a part in whether farms were 

involved in these activities. Because it is often difficult for small farm operators to make a living selling farm commodities, we might expect that smallsized farms were more likely than large farms to become involved in some 

of the activities covered in this study. Our analysis found that farm size as 

measured by land area was only a significant factor in the case of agritourism, 

where larger farms were more likely to be involved, other things being equal. 

However, when we defined farm size in terms of sales, we found evidence 

that small farms were more likely than large farms to undertake most of 

these activities. For example, ERS-defined intermediate farms (those with 

annual gross sales below $250,000 with operators that report farming as their 

primary occupation) were more likely than other farms to be involved in 

these activities. Thus, for operators whose primary occupation was farming, 

small (sales) farm operators were more likely to adopt these farm activities. 

Two exceptions must be noted. First, both intermediate and rural residence 

farms were more likely to participate in direct marketing than other farms. 

Rural residence farms, as defined by ERS, had relatively low gross sales, but 

their farm operators did not consider farming their main occupation. So for 

direct marketing farms, the principal occupation of the farm operator did not 

appear to be particularly important. Second, energy/electricity farms were 

not particularly favored by any of the ERS farm types.

Our study also revealed that some location-related advantages appear to exist 

for the five farm activities. Our analysis showed that a rural location was not 

a significant advantage for any of these activities, but an urban location was 

not generally an advantage either. The only exception was found for farms 

located in highly rural (nonmetro noncore) counties; they were significantly 

less likely to participate in direct marketing to individual consumers than 

farms located elsewhere.

The Northeast, Mountain, and Pacific regions appeared to offer locational 

advantages for organic and value-added activities. Each of the other three 

activities seemed to have its own geographic tendency. Direct marketing 

appeared to have a locational advantage in the Lake States and Pacific 

regions and a locational disadvantage in the Southern Plains. In contrast, 

the Southern Plains was a favored location for agritourism farms. Energy/

electricity producers appeared to have a locational advantage in the Mountain 

region and were less likely to be located in Lake States and Southern Plains 

regions. In addition, organic producers appeared to have a locational advantage not only in the Pacific and Northeast, where our descriptive analysis 

showed a high concentration, but also in the Lake States, Mountain, and Corn 

Belt regions, where they were less concentrated.

Information on the magnitude of the estimated effects of various characteristics can also be gleaned from this study. For example, we found 

that Internet access adds more to the probability that a farm is involved 

in organic production than it does for farm involvement in value-added 

production or agritourism. These variations in magnitude of effects can be 
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particularly large for regional location characteristics, as was the case for 

participation in organic production. 

We should emphasize that this study faced certain data limitations due to how 

the 2007 ARMS questions were structured. Our findings did not reflect all 

possible rural development-related activities. For example, we examined only 

farms marketing products directly to individual consumers, excluding those 

who sold to institutions. Our study also excluded forms of agritourism that 

involved only the sales of products to visitors. In other cases, our coverage 

may have been too broad, such as with organic producers, where we examined all who reported themselves as organic producers, though some may 

lack official certification as organic farmers. Nor does this study address all 

of the factors that could affect farmer involvement in any of these activities. 

This report represents an opportunity to gain a better understanding of the 

five types of farms that provided a product or performed a function related to 

USDA’s recent rural development initiatives. Our comparative analysis using 

ARMS data showed that these types of farms were different from “all other 

farms” in various ways. Moreover, the report’s identification and estimation 

of factors related to farmer involvement in these activities could support the 

work of various entities in both the public and private sector as they interact 

with these farms.
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Appendix: The Logit Model, Estimation, and 

Discussion of Results 

Theoretical Framework Underlying the Decision 

To Use the Logit Estimating Model

A farmer’s decision to adopt a new technology or a new activity, such as 

organic production, value-added processing, direct marketing, agritourism, 

and energy and/or electricity production, could be viewed as a choice 

between a new and a traditional technology (activity). Choice models in 

consumer theory provided guidance for understanding innovation (or activity) 

adoption decisions (Fernandez-Cornejo, 1996). In this framework, the farmer 

is supposed to make the choice (to adopt or not to adopt) that will maximize 

the farmer’s expected utility derived from the expected net income or profit 

from the adoption or nonadoption. This choice is affected by various farm 

and farmer characteristics. Since errors of perception, judgment, and optimization occur, the utility function is assumed to be random. 

Following Mishra and Goodwin (2003), we wrote:

 (1) Max {E(U(πi)) =f (Xi) + εi}, i = 1,...,n, 

where U(πi) is ith farmer’s expected or perceived utility of adoption or 

nonadoption. f ( ) is a function of Xi

 = xi1, ......., xik, which is a (1 × k) vector 

of observable characteristics specific to the ith farmer, the farm and the farm 

business, and εi

 is a random term representing a proxy for errors in farmer’s 

perception and measurement of utility, unobserved characteristics or attributes and preferences, and instrumental variables (Ben-Akiva and Lerman, 

1985; Fernandez-Cornejo, 1996). 

Let yi

 = 1 if the ith farmer adopts, and yi = 0 if ith farmer does not adopt new 

technology. As the probability of a given farmer adopting is bounded by zero 

and 1, a limited dependent variable model like logit or probit is relevant. If εi

is a random variable, is independently and identically distributed, and has a 

Weibull density function,1 which is similar to the normal density function, but 

with greater kurtosis, or thicker tails, then the logit model is an appropriate 

choice model (McFadden, 1974 and 1981; Maddala, 1983). In the logit model, 

the probability of the ith farmer adopting an activity is based on:

 (2) Pi = P(yi = 1 Xi) = 1/[1 + exp – f (Xi)],

where Pi

 is the ith farmer’s probability of adopting the activity, given the 

explanatory variables – Xi

 (Amemiya, 1981). Since the exact functional form 

of f(Xi

) is not known, a priori, following Mishra and Goodwin, a linear form 

(f(Xi

) = Xiβ) is assumed, where β is a vector of (1 × k) coefficients. Since the 

β’s have a nonlinear relationship with the probabilities (Pi

), these coefficients 

do not have an easy interpretation. But these coefficients can be estimated 

and interpreted easily by taking the logarithm of the ratio of the probability 

of adoption to the probability of nonadoption:

 (3) ln (Pi / (1-Pi)) = Xi′β, 

1The most general form of the 

Weibull distribution has the threeparameter ( β, η, γ) form, where, β is 

the shape or slope parameter, η is a 

scale parameter, and γ is a location 

parameter on the x-axis representing 

the time of the event or failure of 

occurrence. The parameter γ may 

assume all values and provide an estimate of the earliest time a failure may 

be observed, and may represent units 

such as hours, miles, cycles, actuations, 

etc. The Weibull distribution is versatile, capable of taking on the characteristics of other types of distributions, 

such as, normal distribution, twoparameter exponential distribution, etc. 

(Zanakis and Kyparsis, 1986).
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where Xi′β is the nonstochastic part of the model and is given by: 

 (4) ln (Pi / (1-Pi)) = β0 + β1x1i + β2x2i + ..... + βkxki

We used this form of the empirical model to estimate the farm and farmer’s 

characteristics’ effects on, or association with, the ith farmer’s probability or 

likelihood of adopting an activity.2 The estimates of this model are shown in 

appendix table 1.

Estimating Standard Errors Using 

Jackknife Method

We relied on the jackknife method to estimate standard errors because: 

•	The estimation of the logit model does not provide a log-likelihood function for the fitting criterion and hence no matrix to yield standard errors 

of the estimated coefficients (Greene, 1990). 

•	The Agricultural Resource Management Survey (ARMS) data are based

on a complex survey. 

Both these traits require that standard errors for the coefficients be estimated 

via bootstrapping or jackknife methods.

ARMS is based on a complex survey design to select a farm sample representative of U.S. farms in the 48 contiguous States. So, the selected farm sample 

is not a simple random sample (see appendix box). This original sample 

of farms from which data have been collected is then divided by National 

Agricultural Statistics Service (NASS) into 15 almost equal and mutually 

exclusive subgroups of farms. First, NASS creates a vector showing the 

full sample weight (W) of the original survey, and then, using a resampling 

method with replacement, creates 15 vectors of replicate weights. These 15 

vectors, plus the vector of full sample weights (W), are then used in 16 runs 

to estimate regression coefficients. The first estimated vector of regression 

coefficients (b) is based on W that provides full sample b of a given variable. The remaining 15 regressions of the same variable are based on the 15 

vectors of the replicate weights, which yield a set of 15 βk, (k=1,...., 15). The 

final step in the derivation of the jackknife variance for each regression coefficient is: 

15

1

Var( )= S ( )( ) , ˆ ˆˆ ˆˆ k k

k

β ββ ββ =

∑ − − ′

where Var( β

ˆ ) is a 15 x 15 matrix, S is a scalar as the adjustment factor to 

the degrees of freedom for the jackknife method and is equal to 14/15, β

ˆ is 

an estimate from the full sample, and β

ˆ k is an estimate of the kth jackknife 

sample. The square roots of the diagonal elements of Var( β

ˆ ) yield the proper 

jackknife standard errors of the regression coefficients of the explanatory 

variables (Dubman, 2000).

2In this appendix, we will use the 

word “effect” to describe the relationship between a farm or farmer 

characteristic and the probability of 

the farmer participating in a particular 

farm activity. Although the term 

“effect” suggests a particular direction of causality, the analysis actually 

only identifies a statistical association 

between the characteristics and the 

probability of participation, hence the 

direction of causality remains somewhat ambiguous. 
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Model Performance Measures

Several model performance measures are presented at the end of appendix 

table 1. These measures indicate that our model performed satisfactorily for 

each of the five activities:

1. The F-value is significant at the 1-percent level for energy/electricity 

farms; at the 5-percent level for organic farms, value-added, and 

direct marketing farms; and at the 10-percent level for agritourism 

farms. A relatively large number of observations3 for each activity 

helps improve the performance measures. 

2. The McFadden R2 serves as a measure of the extent of variation in 

the dependent variable that is explained by the model. Although we 

generally expect a value of 0.2 or above for this measure in a wellperforming model, our model estimation is plagued by a dependent 

variable that is “overwhelmed by zeros,” meaning that relatively few 

farms adopt each of these five activities, so the magnitudes of this 

measure are not too low to be viewed as problematic. 

3. Chi-squared values are also large and highly significant at well below 

the 1-precent level, indicating that the models have performed well.

4. The percent of concordant values equals the correct predictions made 

by the estimated model. “Percent concordant” is the sum of percentages for the 0 and 1 predicted values by the model, and perfectly 

match the observed 0 and 1 values for each farm in the sample. The 

higher the percent of concordant (predicted) values, the more reliable 

the estimated model. Percent concordant values for estimated models 

3The number of usable sample 

responses representing adoption in each 

model is indicated by “Sample” at the 

bottom of the appendix table 1 (p. 38), 

ranging from 333 responses for energy/

electricity production to 1,115 responses 

for direct marketing. The total number 

of usable farm survey responses, 

including those that did not adopt, is 

18,709, so the n is quite large.

The Agricultural Resource Management Survey (ARMS) is 

conducted annually for U.S. farms in the 48 contiguous States by 

USDA’s Economic Research Service and National Agricultural 

Statistics Service. For this survey, a U.S. farm is defined as any 

farm establishment, except institutional farms, that sold or normally 

would have sold at least $1,000 worth of agricultural products during 

the year. 

This report used the 2007 ARMS Phase III survey, which was based on 

a probability-based stratified multiple frame sample of the U.S. farm 

population. Phase III, conducted during late winter and early spring of 

the year following the survey year, collected detailed data on the farm 

business, the farm operator, and the farm operator’s household. ARMS 

also collected data on commodity and farm business practices and the 

production costs of targeted livestock commodities. Phase III included 

a small component “area frame,” which may account for around 5 

percent of the total sample (Banker et al., 2001). In Phase III for 2007, 

about 36,000 contacts were made and, out of those, a little over half 

were targeted for personal visits by enumerators to individual farmers 

for data collection. The rest of the sample was administered through 

mailed questionnaires. Out of these, 18,709 usable questionnaires 

were obtained in 2007 (Banker, 2007).

Brief Description of ARMS Data
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for value added as well as energy/electricity farms is about 63. Future 

research should take a hard look at these models to respecify them 

for higher percent of concordant values.

Interpreting Coefficients of Dummy 

Explanatory Variables

There are a number of 0, 1 dummy variables (variables equal to 1 for meeting 

a condition and 0 for not meeting that condition) in our estimated models. 

For most of these dummy variables, that coefficient is interpreted as the 

effect of meeting the condition compared with not meeting the condition. 

For example, the positive and significant coefficient for the “Conserve” variable in the agritourism model indicates that farms engaged in conservation 

programs, compared with farms not engaged in conservation programs, are 

significantly more likely to be involved in agritourism. In cases where a set of 

three or more dummy variables are mutually exclusive, such as the ERS farm 

typology and geographic regions, the regression coefficient of the dummy 

variable(s) included in the estimated model is interpreted relative to the 

excluded variable(s) from that set of dummy variables. 

For example, there are 10 regional dummy variables—one for each of 10 U.S. 

geographic regions. In the equation for value-added farms, we dropped seven 

regional dummy variables, keeping only three regional dummy variables 

(Northeast, Mountain, and Pacific). Thus, the coefficient for Northeast should 

be interpreted relative to the seven regional dummy variables that were 

dropped from the equation. The same holds for the coefficients of dummy 

variables for Mountain and Pacific regions. 

Interpretation of Odds Ratios

The coefficient estimates ( β

ˆ ’s) presented in appendix table 1 are natural 

logarithms (loge) of the odds of the farmer participating relative to not 

participating in a niche farming activity, such as, organic production, valueadded, direct marketing, agritourism, and energy or electricity production. 

To make it easier to interpret β

ˆ ’s and compare them for various explanatory variables, we transform them into odds ratios, which refer to the effect 

of a change in an explanatory variable on the likelihood (odds) that the 

farmer participates in a given niche activity. Mathematically, the odds ratio 

for variable k X ek

β = the odds (probability) of farmer involvement after 

Xk is increased by one unit / the odds of farmer involvement before the 

unit increase in Xk (Long and Freese, 2006). Odds ratios are presented in 

appendix table 2.

The odds ratio for a continuous explanatory variable represents the amount of 

change in the probability of occurrence for the dependent variable that will be 

caused by a unit change in that explanatory variable, keeping all other explanatory variables at their means. For example, the odds ratio for farm size in 

case of agritourism is 1.007 (see appendix table 2). This means that a 1-unit 

(100 acres) increase in farm size will increase the probability of participating 

in agritourism by 0.007 or 0.7 percent. Similarly, a 1-year increase in farm 

operator’s age will decrease the probability of engaging in direct marketing 

by 0.012 (=1-0.988) or 1.2 percent, other things being equal.
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The interpretation of odds ratios for most of the (0,1) categorical variables 

used in our model is also straightforward.4 In case of organic production, the 

odds ratio for the categorical variable MngtAdvice (=1 if purchased farm 

management advice, = 0 otherwise) is 2.167. This means that a farmer who 

uses purchased management advice is 2.167 times more likely to be involved 

in organic production than a farmer who does not use purchased farm advice. 

In the case of value added activity, the odds ratio of a full owner is 0.662. 

This means a full owner has, other things being equal, two-thirds the probability of engaging in value-added activity compared to those who are not full 

owners (part-owners and full-tenants). 

Interpreting the results for categorical variables involving more than two 

categories, such as the ERS farm typology and the region location variable, 

is slightly different from that of a typical (0,1) categorical variable, in that the 

effect is interpreted in relation to those categories excluded from the empirical model. For example, in the organic model, the intermediate farm type is 

included in the model, but the other two farm types (commercial and residential/lifestyle) are excluded. This means the 2.531 odds ratio for the intermediate farm variable would be interpreted as follows: an intermediate farm has 

2.531 times greater likelihood of engaging in organic production as compared 

with a farm that is not an intermediate farm (a farm that is either commercial 

or residential/lifestyle). However, in the case of the direct marketing model, 

two of the farm types were included in the empirical model: intermediate and 

residential/lifestyle. Hence, the 2.495 odds ratio for the intermediate farm 

variable is interpreted as: an intermediate farm has 2.495 times greater likelihood of engaging in direct marketing compared with a commercial farm 

(since only the commercial farm category was excluded from the analysis).

The regional variables are interpreted in similar fashion. For instance, 

in the organic model, 5 of the 10 regions were included in the model: 

Northeast, Lake States, Corn Belt, Mountain, and Pacific. This means 

that the 24.96 odds ratio for the Northeast region variable is interpreted 

as follows: a farm located in the Northeast, other things being equal, is 25 

times more likely to be engaged in organic production than farms located 

in the regions excluded from the model (Northern Plains, Southern Plains, 

Southeast, Delta, and Appalachia). Because a different set of regions is 

excluded from each model, comparisons of odds ratios of regions from one 

model to another for a particular variable are not recommended. 

The odds ratios that are not significant at least at the 10-percent level are 

not reliable. For example, the odds ratio for the Corn Belt region in the 

model for organic production is quite large (3.02) but it is not significant at 

even the 10-percent level.

Direction for Future Research

Our models provide a good first attempt at a comparative analysis of the 

factors related to adoption of these five onfarm activities. This topic could 

certainly benefit from additional research. 

Instead of the rural county measures used in our study, future research might 

experiment with other county-based demographic measures that correspond 

to rurality. These measures might include such county characteristics as 

4Categorical variables in case of only 

two categories, such as 0 and 1, are 

referred to as dummy variables.
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population density, population size, share of county population that is urban, 

share of workers commuting to metro counties, or even various measures of 

transportation access to large cities. 

Future study could also use pooled data from many years of ARMS, potentially to identify growth in adoption and any structural or technological 

changes over the years. While true panels may be difficult to develop because 

ARMS does not typically survey the same farmers each year, pseudo panels 

may be useful. Additional future research may recognize the importance of 

bundling among the five activities. As noted earlier (see table 1, p. 8), we 

found substantial overlap because some farmers who adopt one activity may 

also adopt one or more of the other four activities. 

Finally, future research may use other data sources. For example, the 2007 

Census of Agriculture includes questions about activities similar to the ones 

asked in the 2007 ARMS. Using these data to estimate spatial econometric 

models, future research may yield a better understanding of the importance 

of various locational factors to explain adoption of some activities. When 

modeling farmer participation in energy or electricity production, another, 

potentially even more useful data source may be found in data from the 

USDA survey of renewable energy producers that was released in 2011. 
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Appendix table 1

Logit model estimates of participation in different types of farm activities, 2007

Farm activity

Organic Value- Direct Agritourism Energy/

Farm/farmer characteristics production added marketing electricity

Estimates of coefficients

Intercept -7.802*** -4.628*** -2.638*** -6.6158*** -4.028*** 

(1.334) (0.533) (0.300) (0.6041) (0.796)

ACRES: farm size in acres (100) 0.00018 -0.0023 0.0012 0.00695** 0.00038

(0.0033) (0.0055) (0.002) (0.0026) (0.0018)

Farm family net worth ($10,000) -0.0011* 0.00027** 0.00026**

 (0.0006) (0.000146) (0.0001)

Share of cropland to total land -0.441*** -1.0803*** -0.972*

 (0.168) (0.2985) (0.587)

Share of rented land to total land -0.0001***

 (0.00003)

Sells forest products (=1 if 

sells forest products, else =0)1 1.452* 0.987*** 1.058** 0.0001**

(0.820) (0.430) (0.362) (0.00005)

Full owner (=1 if farmer owns all land 

in operation, else =0) -0.413*

(0.223)

Sole proprietor (=1 if sole proprietor, else 0) 0.460 -0.8035***

(0.353 (0.2676)

Access (=1 if free public access to farm, else =0) 2.1782***

 (0.2879)

Conserve (=1 if farm participates in 0.4895*

 conservation programs, else =0) (0.2745)

Farm operator’s age -0.017 -0.004 -0.012** 0.0248** -0.013

(0.015) (0.010) (0.005) (0.0119) (0.011)

Operator education (=1 if operator has 0.403 0.444*** 0.423*** 0.7882*** 0.673***

 at least some college education, else =0) (0.458) (0.164) (0.103) (0.2601) (0.251)

Sources of information for farmers:

Internet (=1 if farmer has access to 

Internet, else =0) 1.070*** 0.702*** 0.4272* 0.350

(0.403) (0.170) (0.2166) (0.285)

MngtAdvice (=1 if farmer purchased farm 

 management advice, else =0) 0.773*** 0.5477*

(0.275) (0.2843)

Other Advice (=1, if farmer received free farm 

 management advice from source other than 

 Natural Resources Conservation Service, else =0) 1.091*** 0.596*** 1.463***

(0.246) (0.215) (0.400)

Farms according to farm typology:

RuResidence (=1 if rural residence farm, else =0) 0.397***

 (0.155)

Intermed (=1 if intermediate farm, else =0) 0.928*** 0.374*** 0.914** 0.9706***

 (0.296) (0.180) (0.129) (0.2366)

Farm location in rural area:

Noncore (=1 if farm is in Nonmetro-noncore -0.455***

county, else =0) (0.138)

—continued
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Appendix table 1

Logit model estimates of participation in different types of farm activities, 2007—Continued

Farm activity

Organic Value- Direct Agritourism Energy/

Farm/farmer characteristics production added marketing electricity

Estimates of coefficients

Geographic region2 where farm is located:

NEAST (=1 if farm is in Northeast, else =0) 3.217*** 0.807***

(0.573) (0.186)

LAKESTATES (=1 if farm is in Lake States, 

 else =0) 1.378** 0.847*** -1.240***

(0.636) (0.206) (0.373)

CORNBELT (=1 if farm is in Corn Belt,

 else =0) 1.105*

(0.676)

SPLAINS (=1 if farm is in Southern Plains, 

 else=0) -0.936*** 0.8938*** -1.318**

 (0.255) (0.2813) (0.572)

MOUNTAIN (=1 if farm in Mountain region, 

 else=0) 1.806*** 1.092*** 0.5165 1.287***

 (0.697) (0.426) (0.3503) (0.433)

PACIFIC (=1 if farm is in Pacific region,

 else =0) 3.368*** 0.778*** 0.843***

(0.464) (0.274) (0.204)

F(13, 2) F(11,4) F(13, 2) F(14, 1) F(11, 4)

F-value 21.96 7.97 22.86 60.75 15.58

Prob > F 0.044 0.023 0.043 0.100 0.009

-2 Log likelihood (restricted) 243,294 442,665 954,253 354,380 295,569

-2 Log likelihood 189,472 413,574 895,227 289,887 265,300

McFadden R2 0.221 0.066 0.062 0.182 0.102

Chi-squared 54,822 29,091 59,026 64,494 30,269 

Correct predictions (percent) 84.6 62.8 68.8 71.3 63.0

Sample 429 555 1,115 362 333

Weighted sample 21,669 45,474 124,092 34,417 27,498

Notes: Numbers in the parentheses are jackknife standard errors. ***, **, and * indicate that the coefficient is significantly different from zero at 

the 1-percent, 5-percent, and 10-percent level, respectively. Blanks in each model show that these variables were included in the early stages of 

analysis, but because their t-values were less than 1.0, in the final estimation, these variables were left out of the estimated equations.

1For energy/or electricity farms, “sells forest products” represents actual value of sales from forest products not a dummy variable. For agritourism, when “sells forest products” (dummy variable) was included in the this model, the “sells forest products” coefficient was positive but not 

significant even at the 10-percent level. We dropped this variable because its inclusion raised the number of explanatory variables to 15 and, since 

the 2007 ARMS data supplies only 15 replicate weights, there are not enough degrees of freedom available to calculate the F-statistic via the 

delete-a-group jackknife method.

2There are 10 geographic regions in the 48 contiguous United States—Northeast, Lake States, Corn Belt, Northern Plains, Appalachia, 

Southeast, Delta, Southern Plains, Mountain, and Pacific. See the footnote 1 to table 2, p. 11, for a list of States in each region.

Source: USDA, Economic Research Service and National Agricultural Statistics Service, Agricultural Resource Management Survey, 2007.
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Appendix table 2

Logit model estimates of odds ratios on the participation in five farm activities, 2007

Farm activity

Organic Value- Direct Energy/

Farm/farmer characteristics production added marketing Agritourism electricity

Odds ratios1

ACRES: Farm size in acres (100) 1.0002 0.998 1.0012 1.007** 1.0004

(0.05) (-0.42) (0.69) (2.67) (0.22)

Farm family net worth ($10,000) 0.999* 1.0003* 1.0003***

 (-1.88) (1.86) (2.45)

Share of cropland to total land 0.643** 0.3395*** 0.378

 (-2.62) (-3.62) (-1.65)

Share of rented land to total land 0.9999***

 (-3.24)

Sells forest products2 (=1 if farm sells

 forest products, else =0) 4.273* 2.682** 2.88*** 1.0001**

(1.77) (2.30) (2.92) (2.19)

Full owner (=1 if farmer owns all land 

 in operation, else =0) 0.662*

(-1.85)

Sole proprietor (=1 if sole proprietor, else=0) 1.584 0.448***

(1.30) (-3.00)

Access (=1 if free public access to farm, else =0) 8.83***

 (7.57)

Conserve (=1 if farm participates in conservation 1.632*

 programs, else =0) (1.78)

Farm operator’s age 0.983 0.996 0.988** 1.025** 0.987

(-1.16) (-0.38) (-2.44) (2.08) (-1.21)

Operator education (=1 if operator has 1.496 1.559** 1.527*** 2.2*** 1.961***

 at least some college education, else =0) (0.88) (2.70) (4.10) (3.03) (2.68) 

Sources of information for farmers:

Internet (=1 if farmer has access 

 to Internet, else =0) 2.916** 2.018*** 1.533* 1.419

(2.66) (4.13) (1.97) (1.23)

MngtAdvice (=1 if farmer purchased 

 farm management advice, else =0) 2.167** 1.729*

(2.81) (1.93)

Other Advice (=1 if farmer obtained unpaid farm 

 management advice from source other than

 Natural Resources Conservation Service, else =0) 2.976*** 1.814** 4.318***

(4.43) (2.77) (3.66)

Farms according to farm typology:

RurResidence (=1 if rural residence farm,

 else =0) 1.488**

 (2.56)

Intermed (=1 if Intermediate farm, else =0) 2.531*** 1.453** 2.495*** 2.64***

(3.14) (2.08) (7.08) (4.10)

Farm location in rural area:

NONCORE (=1 if farm in Nonmetro-noncore

 county, else =0) 0.634***

(-3.30)

—continued
41

Farm Activities Associated With Rural Development Initiatives / ERR-134 

Economic Research Service/USDA

Appendix table 2

Logit model estimates of odds ratios on the participation in five farm activities, 2007—Continued

Farm activity

Organic Value- Direct Energy/

Farm/farmer characteristics production added marketing Agritourism electricity

Odds ratios1

Geographic region3 where farm is located:

NEAST (=1 if farm is in Northeast, else =0) 24.96*** 2.240***

(5.61) (4.34)

LAKESTATES (=1 if farm is in Lake States, 

 else =0) 3.967** 2.333*** 0.289***

(2.17) (4.12) (-3.32)

CORNBELT (=1 if farm is in Corn Belt, 

 else =0) 3.02

(1.64)

SPLAINS (=1 if farm is in Southern Plains,

 else=0) 0.392*** 2.444*** 0.268**

 (-3.67) (3.18) (-2.30)

MOUNTAIN (=1 if farm is in Mountain region,

 else=0) 6.083** 2.979** 1.676 3.622***

(2.59) (2.56) (1.47) (2.97)

PACIFIC (=1 if farm is in Pacific region,

 else =0) 29.02*** 2.176** 2.324***

(7.26) (2.84) (4.13)

Predicted probability by the model 0.0099 0.0207 0.0565 0.0157 0.0125

Sample 429 555 1,115 362 333

Weighted sample 21,669 45,474 124,092 34,417 27,498

Notes: Numbers in the parenthses are t-values derived from jackknife standard errors. ***, ** and * indicate that the odds ratio is significantly 

different from zero at the 1-percent, 5-percent, and 10-percent level, respectively. Blanks in each model show that these variables were included 

in the early stages of analysis, but because their t-values were less than 1.0, in the final estimation, these variables were left out of the estimated 

equations.

1For a dummy variable, odds ratio shows change when a dummy variable is equal to 1 relative to when that dummy variable is equal to 0.

2For energy/electricity farms, “sells forest products” represents actual dollar value of sales, not a (0,1) dummy variable.

3There are 10 geographic regions in the 48 contiguous United States—Northeast, Lake States, Corn Belt, Northern Plains, Appalachia, 

Southeast, Delta, Southern Plains, Mountain, and Pacific. See the footnote 1 to table 2, p. 11, for a list of States in each region.

Source: USDA, Economic Research Service and National Agricultural Statistics Service, Agricultural Resource Management Survey, 2007.
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Farm Activities Associated With Rural Development Initiatives / ERR-134 

Economic Research Service/USDA

Appendix table 3

Information sources, by farm activity

Farm activity Topic and source of information

Organic • The U.S. organic industry, perceived benefits and emerging issues (Greene et al., 2009)

• National standards for organic certification (Greene and Kremen, 2003)

• Factors influencing organic involvement in local sales, and effects on organic farm incomes

(Park and Lohr, 2010)

• Bottlenecks in organic markets (Martinez et al., 2010)

• Logit analysis of factors related to farmer involvement in organic activities (Oberholtzer et

al., 2008)

• 2007 Census of Agriculture (http://www.ers.usda.gov/Briefing/Organic/)

• Surveys of organic producers (Fernandez-Cornejo et al., 1998; Walz, 1999)

Value added • General information on value-added activities and their potential benefits to farmers and the

rural community (Lewis, 2002)

• Organic farmers involved in value-added activities (Waltz, 2004; Oberholtzer et al., 2008)

Direct marketing • Barriers to local food-market entry and expansion, and possible solutions to these problems

(Martinez et al., 2010)

• Characteristics of local food suppliers, including statistics and map using data from 2007

Census of Agriculture (Martinez et al., 2010)

• More on characteristics of local food suppliers (Hunt, 2007; Starr et al., 2003)

• Relationship between direct sales and other farm entrepreneurial activities (Martinez et al.,

2010)

• Importance of direct sales to intermediaries, and factors related to inter-county variations in

the level of direct sales activity (Low and Vogel, 2011) 

Agritourism • General information about agritourism (Barnardo et al., 2004; Brown and Reeder, 2007)

• Importance and effects on other farm activities (Veeck et al., 2006; Carter, 1998)

• Growing importance in Europe and worldwide (OECD, 2009)

• U.S. survey of farm visitation, part of the 2000 National Survey on Recreation and the

Environment http://www.srs.fs.usda.gov/; (Hellerstein, 2003) 

• Reasons why farmers are involved and successful in agritourism (Nickerson et al., 2001;

Mace, 2005; McGehee and Kim, 2004; Rilla, 2000)

• Statistical studies identifying factors related to agritourism (Barnardo et al., 2004; Brown

and Reeder, 2007; Barbieri and Mshenga, 2008)

• A U.S. county-level map and accompanying text using 2007 Census of Agriculture data (Bagi and

Reeder, 2011; http://www.ers.usda.gov/AmberWaves/March11/Indicators/OnTheMap.htm/)

Energy/electricity • Survey of renewable energy production on farms (USDA/NASS, 2011)

• USDA programs supporting renewable energy (USDA, 2010)

• Definition of renewable energy (Oregon Department of Energy, 2006)

• The need for research on costs relative to environmental gains (Islam, 2007)

• Methane digesters (The Minnesota Project; Lazarus and Rudstrom, 2004; Zinkand, 2006;

Islam, 2007)