Use of No-Till Practices As a Gateway to Carbon Credit Adoption

Home , Soil conservation

USE OF NO-TILL PRACTICES AS A GATEWAY TO CARBON CREDIT ADOPTION

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy

in the Graduate School of The Ohio State University

Melanie Joy Miller, B.S., M.S.

Graduate Program in Rural Sociology

The Ohio State University

2009

Dissertation Committee:

Dr. David Hansen, Advisor

Dr. William Flinn

Dr. Rattan Lal

Dr. Randall Reeder

Melanie Miller

2009

ABSTRACT

Climate change is a worldwide environmental problem that will affect every citizen of the planet. Societies can respond to climate change by reducing greenhouse gas emissions and reducing the rate and the magnitude of effects caused by climate change. Agriculture is a major contributor to the problem of climate change, but also has the capacity to be a part of the solution. No-till practices are considered dual purpose in that it has potential as a climate change mitigation strategy as well as an adaptation strategy.

Economists have proposed using a market system to aid in the mitigation of climate change because it creates financial incentives to innovate and conserve. Carbon markets have presented farmers with the opportunity to be paid for practices that decrease emissions and sequester carbon, such as no-till farming. No-till farming practices have been in use in the United States for decades by some farmers in order to reduce soil erosion. However, many farmers continue to use traditional tillage methods that release carbon into the atmosphere. In order to exploit the carbon sink capacity of agricultural soils and adapt to the effects of climate change, more farmers need to utilize carbon sequestering practices such as continuous no-till farming.

Most research on the introduction of new practices focuses on the initial adoption decision, with little research focusing on the continued use of the practices. The present

ii study focuses on the adoption of no-till practices and carbon credits, and considers continuous no-till farming as a gateway to the adoption of carbon credits.

A survey of 228 farmers at the Conservation Tillage and Technology Conference in Ada, Ohio, provided data used in this study. Fifty-nine percent of the respondents practice continuous no-till on some or all of their land, which indicates eligibility for carbon credits. Yet only four survey respondents currently participate in carbon credit programs. Results indicate that the majority, 88 percent, of no-till farmers surveyed are aware of carbon credit programs, which signals that lack of awareness of the program is not the main reason for non-participation.

Findings indicate that there is a substantial relationship between the use of no-till practices and satisfaction with them. Additionally, there is a relationship between the use of no-till practices and participation in other resource conservation programs. The study also finds that belief in anthropogenic climate change is strongly associated with liberal political beliefs, yet neither belief is associated with the use of continuous no-till practices. Additional findings suggest that older farmers tend to be more likely to adopt no-till practices and use of no-till practices is associated with smaller farm operations.

The importance attributed to human practices as a cause for climate change, degree of familiarity with carbon-related topics, education, and dedication to farm activities were not found to be statistically related to the use of no-till practices.

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ACKNOWLEDGMENTS

Successful completion of my doctoral program in rural sociology would not have been possible without all the contributions of support and encouragement which I received from many individuals throughout my education and career.

I want to thank my adviser, Dr. David Hansen for all of his support throughout my graduate program. In addition, I wish to thank my committee members Dr. William

Flinn, Dr. Rattan Lal and Dr. Randall Reeder for their support.

Innumerable appreciation is owed to Daniel Foster, who demonstrated patience,

understanding and encouragement throughout the writing of this dissertation.

Thanks to my family and friends in supporting me throughout all my endeavors.

Special thanks to Matthew Mariola and Ryan Foor who lent fresh perspective at the times

it was needed most. Thank you to Uncle Tim, whose stubborn refusal of carbon credits

for his conservation practices inspired this dissertation.

I would like to express my gratitude to my funding sources: the National Science

Foundation and The Ohio State University Graduate School. Without their financial

support this project would not have been possible.

VITA

January 9, 1980 ...... Born Rugby, North Dakota

December, 2002 ...... B.S in Agriculture from North Dakota State University

2005...... Research Assistant, Costa Rica

2005 – 2007 ...... Research Associate, The Ohio State University

2007 – 2008...... Teaching Assistant, The Ohio State University

2008 – 2009...... National Science Foundation GK-12 Fellow

PUBLICATIONS

Miller, Melanie, Matthew J. Mariola and David O. Hansen. 2008. EARTH to farmers: Extension and the diffusion of environmental technologies in the humid tropics of Costa Rica. Ecological Engineering 34(4): 349-357.

Miller, Melanie and Mathew J. Mariola. Forthcoming. The Discontinuance of Environmental Technologies in the Humid Tropics of Costa Rica: Results from a Qualitative Survey. Journal of International Agricultural and Extension Education 15(3): 31-42.

FIELDS OF STUDY

Major Field: Rural Sociology

Fields of Specialization:

1. Sociology of Agriculture

2. Sociology of Environmental

3. Development Sociology

TABLE OF CONTENTS

ABSTRACT...... ii

ACKNOWLEDGMENTS ...... iv

VITA...... v

LIST OF TABLES...... x

LIST OF FIGURES ...... xii

CHAPTER 1: INTRODUCTION...... 1

Climate Change and Agriculture ...... 2

Carbon Credits ...... 4

Statement and Significance of Problem...... 5

Organization of Study...... 6

Summary...... 7

CHAPTER 2: THE INNOVATIONS...... 8

Conservation Tillage...... 8 Conventional Tillage...... 10 History of Conservation Tillage...... 12 Benefits of No-Till...... 13 Use of Conservation Tillage and Conventional Tillage...... 15

Market-Based Conservation Programs and Carbon Credits ...... 16 Capitalism and the environment ...... 17 The market approach...... 20 Opposition to environmental service markets...... 21 Agriculture’s involvement in environmental service markets...... 22

CHAPTER 3: REVIEW OF LITERATURE...... 24

Theoretical Foundation ...... 24 Neoclassical Economic Theory...... 24 Diffusion of Innovations...... 25 Bourdieu’s Theory of Capital ...... 26 Forms of Cultural Capital ...... 28

Related Empirical Literature...... 30 Economic Factors...... 30 Knowledge ...... 32 Perception of Environmental Problems ...... 34 Perception and Adoption...... 35 Perception of Climate Change ...... 37 Social Factors...... 39 Good Farmer Status ...... 39

Conclusion ...... 42

CHAPTER 4: METHODOLOGY ...... 45

General Model and Study Objectives ...... 45

Research Hypotheses ...... 48 No-till practices...... 48 Carbon credits ...... 49

Sampling Procedure...... 50 Pre-Survey Preparation ...... 50 Data Collection ...... 51 Survey Instrument...... 52 Response Rate...... 53 Sample Bias ...... 53 Validity and Reliability of Measurement...... 54

Statistical Test of Model ...... 55 Descriptive Statistics...... 55 Correlation Analysis ...... 55 Multiple Regression...... 56

Measurement of Variables ...... 58 Age...... 58 Education ...... 58 Farm size...... 58 Dedication to farming ...... 59 vii

Conservation participation...... 59 Liberalness of political orientation ...... 59 Use of no-till practices...... 60 Awareness of carbon credit programs...... 60 Epistemic distance ...... 60 Carbon credit payments ...... 61 Alteration of practices...... 61 Satisfaction with no-till...... 61 Familiarity with carbon topics ...... 63 Belief in Anthropogenic Climate Change...... 64

Description of Survey Respondents...... 66

Qualitative Methodology ...... 70 Data Collection ...... 70 Data Analysis...... 71

Summary...... 72

CHAPTER 5: PRESENTATION OF DATA ON ADOPTION OF NO-TILL PRACTICES...... 73

Frequency Distribution of Variables in the Model ...... 74 Use of No-Till Practices...... 74 Satisfaction with no-till...... 75 Visual recognition of benefits from no-till agriculture...... 77 Familiarity with carbon topics ...... 78 Anthropogenic Causes of Climate Change...... 79

Statistical Test of Hypotheses in Model ...... 81 Relative Importance of Variables in the Model...... 82

Discussion of No-Till Adoption Model ...... 85

CHAPTER 6: PRESENTATION OF DATA ON ADOPTION OF CARBON CREDITS...... 86 Carbon credit eligibility...... 87 Characteristics of carbon credit adopters...... 88 Recruitment meetings ...... 90 The carbon credit contract ...... 92 Profiles ...... 94

Discussion...... 104

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CHAPTER 7: CONCLUSIONS ...... 107

Summary...... 107

Conclusions...... 110 Conclusion 1 ...... 110 Conclusion 2 ...... 111 Conclusion 3 ...... 113 Conclusion 4 ...... 114

Recommendations...... 115 Reduce emphasis on climate change...... 115 Rename the program...... 116 Specific price guarantees ...... 117 Summary of recommendations ...... 117

Recommended Additional Research...... 118

Final observation...... 119

REFERENCES ...... 121

LIST OF TABLES

Table 4.1 Davis (1971) conventions to describe correlations...... 56

Table 4.2 Inter-item correlation matrix of variables pertaining to satisfaction with no-till ...... 62

Table 4.3 Inter-item correlation matrix of variables pertaining to familiarity with carbon topics ...... 63

Table 4.4 Inter-item correlation matrix of variables for belief in anthropogenic climate change ...... 65

Table 4.5 Characteristics of respondents and their farms ...... 66

Table 4.6 Education of respondents ...... 67

Table 4.7 Political orientation of respondents ...... 68

Table 4.8 Dedication to farming ...... 69

Table 5.1 Response frequency – use of no-till practices ...... 74

Table 5.2 Response frequency –level of satisfaction with no-till practices ...... 76

Table 5.3 Response frequency –visual recognition of benefits from no-till ...... 77

Table 5.4 Response frequency – familiarity with carbon topics ...... 78

Table 5.5 Response frequency – anthropogenic causes of climate change ...... 80

Table 5.6 Correlation of individual variables with use of no-till practices ...... 81

Table 5.7 OLS regression of the use of no-till practices on age, farm size, conservation participation, epistemic distance variables and the satisfaction with no-till index ...... 84

Table 6.1 No-till practices and carbon credit eligibility ...... 87

Table 6.2 Eligible respondents awareness and participation in carbon credit programs ...... 88

Table 6.3 Description of respondents receiving carbon credits ...... 89

LIST OF FIGURES

Figure 4.1: General model ...... 47

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CHAPTER 1

INTRODUCTION

Climate change is a worldwide phenomenon that affects all inhabitants of the planet. Rising terrestrial temperatures, acidifying ocean waters and rising sea levels may have devastating effects on humanity through the deepening of poverty, loss of life, and destruction of livelihoods. Many scientists are convinced that societies need to undergo radical changes in all facets of life in order to reduce carbon emissions quickly enough to respond to the threat of climate change.

To think about environmental preservation means to reexamine how humans live within and relate to the environment. The environmental movement has been gaining momentum since the 1960s and has made a difference. An example is the passage of environmental legislation that protects water, air and many species of animals from human induced degradation and destruction. This movement is also credited with changing the behavior of millions by promoting recycling, the use of public transportation, and certain “green” products. Social movements take time to build momentum but climate change forces humanity to act quickly.

Climate change is a unique environmental problem because it is a large scale, diffuse problem. It is not as easily “seen” like other environmental disasters such as oil spills or deforestation. The abstract nature of the problem contributes to controversy on

how it is caused and who is to blame. Scientists generally agree that climate change is

caused by anthropogenic activities but public figures and the media have caused many,

particularly in the United States, to doubt that climate change is caused by humans

(Boykoff and Boykoff, 2007). It is difficult for a problem to be quickly and effectively

resolved, when its causes are in dispute.

The issue of climate change is further complicated because the location of

greenhouse gas (GHG) emissions is irrelevant to where the corresponding impacts of

climate change will take place. The geography of carbon emissions is highly uneven,

with most of emissions coming from developed countries (IPCC, 2007). All humans

enjoy the common protection of the atmosphere but lack of regulation in the pollution of

this resource has led to a classic formulation of Hardin’s “tragedy of the commons”

(1968). There is intense demand from many countries to put climate change on the

international agenda, but there is limited domestic pressure to take action because of the

global nature of the problem (Gupta and Gagnon-Lebrun 2002). Meanwhile, global GHG

emissions continue to increase. In fact annual emissions due to human activities have

increased 70 percent between 1970 and 2004 (IPCC, 2007).

Climate Change and Agriculture

The Intergovernmental Panel on Climate Change (IPCC) 2007 summary report identified agriculture as a key cause of global increases of GHGs. Annually, the agriculture sector accounts for about 10-12 percent of total global anthropogenic emissions of GHGs (IPCC, 2007). Greenhouse gas emissions from agriculture are expected to continue to increase due to rising demands for food, shifts in diet and more land being converted to agricultural use (ibid).

Agriculture emits greenhouse gases through many channels. The primary sources of emissions are from tillage, irrigation, application of fertilizers and pesticides, and land degradation (Lal, 2004a). Of particular interest is the land degradation, which encompasses a decline in the quality of soil, water, or vegetation. Agriculture involves many processes that degrade land including land clearing and replacing perennial vegetation with annual crops (Lal et al., 1998).

Plowing and other tillage operations degrade the land because they accelerate soil erosion. Carbon is emitted when soil is disturbed and eroded. Most carbon in soil is near the surface which means it is easily removed when exposed to water and wind (Lal,

2004a). In addition to releasing carbon, soil erosion and other degradation processes reduce the carbon sink capacity of soils because they represent a decrease in biomass productivity (Lal, Kimble, Follett and Cole, 1998).

Agriculture may be a major contributor to the problem of climate change, but it also has the capacity to be a part of the solution. New farming practices that limit the amount of carbon emission can be adopted. Other practices that facilitate the development of carbon sinks that capture carbon in the atmosphere and restore it to the soil can also be adopted.

Carbon sequestration, sometimes referred to as “carbon parking”, refers to the capture and storage of CO2 that would otherwise be emitted or remain in the atmosphere

(IPCC, 2007). Agriculture has the capacity to sequester carbon through land management practices that facilitate carbon being captured and stored in agricultural soils. Biotic and soil carbon sinks can be significantly improved but their potential may be limited because of current land use practices and other social and environmental factors (IPCC, 2007).

Already known agricultural management techniques, such as no-till farming, improve soil quality and sequester carbon. Farmers can receive compensation for carbon sequestration activities by selling carbon credits.

Carbon Credits

Carbon dioxide is just one of the gasses driving climate change. However, because it is the most abundant it has given rise to a new world currency, namely, carbon credits. A carbon credit is the equivalent of one metric ton of carbon dioxide.

Carbon credits can be obtained by farmers through the adoption of several carbon sequestration practices including rotational grazing, no-till cropping, seeding grass and planting trees. In order to receive credits for these practices, farmers must sign five to six year contracts with carbon credit aggregators such as the National Farmers Union or the

Iowa Farm Bureau’s AgraGate. Aggregators sell the carbon on the Chicago Climate

Exchange (CCX) and then make annual payments to participating farmers. Depending on the program, some portion of the annual payment is retained and then paid out at the end of the contract term in order to encourage farmers to complete the term of the contract.

In the United States carbon credits are traded through the CCX. It is a self- regulatory exchange that administers a voluntary, legally binding program for reducing and trading greenhouse gas emissions. The CCX is unique because the United States has not yet agreed to sign the Kyoto Protocol and does not have a domestic regulation of carbon emissions in place. Thus, the exchange of carbon credits in the U.S. is completely voluntary. In other countries that have signed on to the Kyoto Protocol agreement, the exchange of carbon credits is not optional. Differences between the

voluntary and non-voluntary programs exist, such as differences in carbon prices between

the European Carbon Exchange (ECX) and the CCX. Prices are much lower on the CCX

because of the voluntary nature of the program in the United States. Another major

difference between the ECX and the CCX is that the ECX does not currently trade

agricultural offsets.

Statement and Significance of Problem

Agriculture can have a significant impact on mitigation of climate change. One mitigation practice that can be adopted is no-till farming, which sequesters carbon in agricultural soils. No-till farming practices have been in use in the United States for decades by some farmers in order to reduce soil erosion. However, many farmers continue to use traditional tillage methods that release carbon into the atmosphere. In order to exploit the carbon sink capacity of agricultural soils, more farmers need to utilize carbon sequestering practices, such as continuous no-till farming.

Most research on the introduction of new practices focuses on the initial adoption decision, with little research focusing on the continued use of the practices. This study focuses on continuous no-till which is a major carbon sequestering practice. Are farmers engaged in no-till practices because they wish to conserve soil quality? Do they wish to reduce costs by practicing no-till agriculture? Do farmers engage in continuous no-till because they are concerned about climate change? To what extent do they practice no-till in order to benefit from carbon credits?

Evidence suggests that many farmers are already engaged in no-till practices and are eligible to receive carbon credits. The idea that farmers would enroll in a program to receive financial benefit for a practice that they are already doing seems intuitive.

However, anecdotal evidence suggests that many eligible farmers are not enrolling in

carbon credit programs. If indeed true, the question is “why” this may occur. Why don’t

farmers using no-till enroll in carbon credit programs? A goal of this study is to

understand the reasons why farmer decide whether or not to participate in carbon credit

programs.

Organization of Study

The present study will focus on the adoption of no-till practices and carbon

credits. Organizationally, it will be divided into two parts in order to address each topic

adequately.

The first half of the study will focus on farmer use of and satisfaction with no-till

practices. Research on the concept and practice of no-till farming is quite extensive.

However much of has been conducted in the context of a specific environmental problem namely, soil erosion, and in the context of a specific economic problem, namely, the farm crisis of that 1980s. Not much is known about the continued use of no-till practices beyond the initial adoption stage and the satisfaction of farmers with no-till practices.

Additionally, little is known about the use of no-till practices as a response to new environmental problems, in particular climate change.

The second half of the study will focus on the adoption of carbon credits and its relationship to use of no-till practices. Research concerning farmer participation in carbon credit markets is limited. It is unclear if farmers are familiar with carbon credit programs in agriculture, if they know about the carbon sequestration benefits related to use of no-till practices, and if they understand the basic characteristics of carbon credit markets. Additionally, attitudes towards and beliefs about related topics, such as

anthropogenic causes of climate change, have not been extensively studies. This is

particularly true in relation to farming practices that result in the mitigation of climate

change. Farmers’ attitudes towards the topic of anthropogenic climate change will be

assessed in relationship to the adoption of use of no-till practices and the purchase of

carbon credits.

Research related to the diffusion of innovations model and conservation farming technologies has been a robust area of study in the disciple of rural sociology for decades.

Previous studies of conservation technologies will be reviewed in Chapters 2 and 3, along with the theories utilized in this study. Hypotheses will be formulated from this literature and stated in Chapter 4 along with the general model.

Summary

Climate change is a global phenomenon that affects every citizen of the planet.

Effective mitigation of its effects will require immediate attention to it. Agriculture is a major contributor to it, but also has the capacity to be a part of the solution. Carbon markets provide farmers with the opportunity to be paid for practices that reduce emissions and sequester carbon, such as no-till farming. However, little is known about farmer participation in carbon markets, their beliefs about to anthropogenic climate change, and their satisfaction with the use of carbon sequestering practices, such as no-till farming. Relationships between no-till practices and the purchase of carbon credits will be explored as will their relationships factors that can help explain their adoption such as farmer characteristics, beliefs about anthropogenic climate change, familiarity with carbon-related topics, and satisfaction with no-till practices.

CHAPTER 2

THE INNOVATIONS

Conservation Tillage

Soils are often overlooked or taken for granted as a natural resource. However, they are the most basic of all natural resources and a vital medium in the production of food. Well functioning soils contribute to high quality drinking water, biological diversity, and the economic wealth of human societies (Richter and Markewitz, 2001).

Economists often conceptualize soils as “natural capital,” or goods derived directly from natural that have the potential to contribute to economic productivity and welfare of a society (Barbier, 1998). Beyond a productive capacity, they are also responsible for the decomposition of waste, and can serve as a reservoir for pollutants (Richter and

Markewitz, 2001).

This study focuses on one particular agricultural method of handling soils, a form of conservation tillage called no-till. This type of tillage is supported as a conservation measure through government conservation programs and is a farming practice for which

8 farmers are eligible to receive carbon credits. This section outlines and compares conventional tillage methods and conservation tillage methods.

Conventional Tillage

Conventional tillage methods enabled explosive growth in population and

agriculture around the world (Montgomery, 2007). This type of tillage system has

persisted for thousands of years because it allows farmers to quickly and easily prepare

the ground for planting.

Conventional tillage has not been recognized as the definitive soil preparation

method. Researchers, who have documented the decline and collapse of many

civilizations, have pointed to misuse of soil resources as one of the main causes for them.

Montgomery (2007, pp 23) concluded that “A civilization can persist only as long as it retains enough productive soil to feed its people.” Jared Diamond (2005) cited various environmental factors associated with the collapse of the Mayan, Easter Island, Anasazi, and many other societies. One of the key variables in his analysis is soil degradation, related to soil erosion, soil fertility losses and salinization. These researchers have observed that conventional tillage is one of the principal causes of soil degradation.

Damage caused by soil degradation in the U.S. has been well documented. In the last 200 years of U.S. agriculture, it is estimated that 30 percent of farmland has been rendered un-farmable and abandoned due to erosion, salinization and waterlogging

(Pimentel et. al, 1995). It is also estimated that half of Iowa’s topsoil has been lost during the last 150 years of farming (ibid).

These appalling figures have captured the attention of farmers, researchers and the public, especially as the off-site damages of soil erosion have become evident. Soil, a valuable farm asset, becomes a pollutant once it is displaced. Most eroded soil goes into streams and rivers, which disrupts stream ecology, causes flooding, and causes a decrease

in wildlife habitat (Pimentel et. al, 1995). Eroded soil also causes many expensive

problems for communities through increased water treatment costs, gullying of roads, and

the silting of roadways, sewers and basements (ibid).

Many scientific studies confirm that soil erosion caused by conventional tillage

outpaces new soil production, and is therefore unsustainable (e.g. Wilkinson and

McElroy, 2007; Montgomery 2007). The real issue is that soil erosion under

conventional tillage occurs slowly enough that farmers can disregard it because it will not

have much of a negative impact over an individual lifetime (Osterman and Hicks, 1988).

However, soil erosion occurs fast enough to affect crop yields in just a few generations

(Montgomery, 2007).

Although the first problem identified with conventional tillage methods was soil

erosion, this tillage practice also results in many other problems. Conventional tillage

damages soil structure in other ways, such as the physical degradation of soil aggregates

through compaction and pulverization (FAO, 2008). These processes result in a

reduction of a soil’s capacity to absorb and hold water and air which are essential for

plant growth.

In addition, conventional tillage results in a decline in soil organic matter. This represents a decrease in the availability of essential plant nutrients and also impact negatively on the ability of soils to retain water, both of which affect crop yields.

Declines in the productive capacity of the land can be masked by increasing fertilizer applications for a time. However, this is only a temporary solution. As organic matter levels continue to drop, the capacity of soils to facilitate the uptake of fertilizer ingredients declines (FAO, 2008). Soils that have been worked with conventional tillage

methods typically are composed of only one to three percent organic matter (Hoorman,

2008).

Conventional tillage methods have also caused large amounts of carbon to be

emitted into the atmosphere, where it contributes to climate change. Carbon in soils is

often located near the soil surface, where it is easily released when the land is plowed.

Carbon is lost from the soil when it is exposed to water and wind (Lal, 2004a).

History of Conservation Tillage

Conservation tillage bloomed in the wake of the dust bowl of the 1930s. Edward

Faulkner (1934) leveled harsh criticism of traditional plow agriculture in his book

Plowman’s Folly . The U.S. government responded to soil erosion problems by encouraging farmers to adopt conservation practices such as terracing, planting of cover crops and contour planting (Rice, 1983). In the late 1940s and early 1950s scientists began to experiment with alternatives to the traditional plowing practices that had dominated agriculture in the U.S. since the nineteenth century. For the next several decades farmers and scientists worked together to develop new systems that came to be known as conservation tillage (Coughenour and Chamala 2000).

There are many kinds of conservation tillage, including ridge till, strip till and no till. Of particular interest for this study is no-till). Rice (1983, p 20) defines no-till as “a procedure whereby a crop is planted directly into a seedbed not tilled since harvest of the previous crop, and no tillage occurs during the growing and maturing season”. This procedure allows for seeds to be planted directly into the ground with only the immediate area around the seed disturbed (Rice 1983).

Through the latter half of the twentieth century no-till grew in popularity and surpassed other kinds of solutions that addressed soil erosion. Hall (1998) argued that the popularity of no-till was in part due to the fact that it contributed to the environmental goal of reducing soil erosion while simultaneously lowering capital costs and improving productivity. Conservation tillage became a popular cost saving strategy in the 1980s during a time of declining commodity prices, increased farm loan interest rates and increased global competition (ibid). As the profitability crisis worsened, the discourse around no-till became centered on its economic advantages rather than only on its ability to decrease soil erosion (ibid). Decreases in labor and decreased input costs – in particular fuel, machinery and chemicals – are frequently cited economic advantages of no-till (Rice 1983).

Whatever the reason for utilizing no-till, the fact is that it requires major changes in the way that farmers produce crops. No-till agriculture implies much more than simply stopping to plow fields. Farmers must also learn new techniques and adjust their cropping regimens in order to be successful with no-till farming. Converting to no-till farming can also involve large capital investments in new machinery.

Benefits of No-Till

As previously mentioned, conservation tillage was originally introduced to combat soil erosion. However, over time farmers and researchers began to notice many other soil-related benefits of no-till including increases in soil organic matter, enhanced soil biology and better water filtration (FAO, 2008). Farmers may initially experience a decline in yields after adopting no-till. However, after several years of utilizing conservation tillage their yields rise and stabilize (ibid). Farmers also began to experience

13 many real economic benefits associated with the technology as the ratio of outputs to inputs increased.

Researchers are also beginning to realize that conservation tillage has the potential to provide a holistic solution to many problems ranging from food security to social resilience to the improvement of ecosystems. Scientists and major international organizations such as the United Nations and the IPCC have focused on the climate change adaptation benefits that a no-till system offers (IPCC, 2007). In short, these organizations are promoting adaptation strategies in order to moderate anticipated negative effects of climate change on agriculture. No-till is promoted as an adaptation technology because it provides water retention benefits and improved nutrient cycling

(FAO, 2008).

A properly implemented no-till system boasts a lowered carbon budget compared to conventional tillage systems. Decreased levels of carbon emissions are achieved through carbon capturing processes and decreases in levels of direct emissions from fertilizer and diesel (McKinsey, 2009). Of particular interest to this study is the service that no-till provides in terms of carbon sequestration. Carbon sequestration is the capture and storage of CO 2 that would otherwise be emitted or remain in the atmosphere (IPCC,

2007). With respect to soils, there are three main ways that carbon can be sequestered.

The first is through the conversion of organic material into humus. Carbon can be stored for hundreds of years in humus (Lal, Kimble, Follett, and Cole, 1998). Humus is the black, rich part of the soil which contributes to soil quality. It also facilitates carbon sequestration. Carbon can also bond to clay particles and metallic elements in soils to form aggregates (Lal et. al, 1998). Finally, carbon can be sequestered through

translocation. As water passes through the soil, carbon can be swept up in the flow and

transported to a lower depth. Carbon remains fairly stable at lower depths because it is

less disturbed by biotic processes and is not exposed to climatic events (Lal et. al, 1998).

There is a strong relationship between soil quality and carbon sequestration, and many benefits can result from adopting best management practices, such as no-till, that sequester carbon in soil. Continuous no-till practices can improve the carbon pool in the soil, contribute to soil quality, increase biomass production, improve crop yields, and foster soil restoration (Lal et. al, 1998).

Improving soil quality through best management practices can make soils a sink for carbon and can decrease emissions of other greenhouse gases such as nitrous oxide and methane (Lal et. al, 1998). This is an extremely vital aspect of soil quality with respect to GHG emissions. Methane is 21 times more potent than CO 2 as a cause for climate change (Lal, personal communication). However, healthy soils can actually serve as a sink for methane. Nitrous oxide is 310 times more potent than CO 2 as a cause of climate change (ibid). While most soils emit some nitrous oxide, healthy soils emit less

(ibid). Simply improving soil quality through best management practices results in carbon sequestration. It also turns soils into a sink for more noxious GHGs, in addition benefiting farmers through increased soil fertility.

Use of Conservation Tillage and Conventional Tillage

Conservation tillage systems have increased in popularity for both environmental and economic reasons. However, traditional plowing methods remain the standard in agricultural systems around the world (FAO, 2008). Lal (2007) estimates that only about six percent of the farmland on the globe is cultivated using no-till. Most no-till farming

takes place in developed countries including the United States, Brazil, Canada, and

Australia (ibid). Fifty percent of the Ohio’s cropland is estimated to be no-tilled (Ohio

Department of Natural Resources, 2008).

Most farmers around the world continue to plow the soil in order to dry, loosen and aerate it, to bury weeds and crop residues, to mix in manure and other fertilizers, and to create a suitable seed bed. Many of the farmers that continue to use conventional plowing methods are small land holders in Africa, Asia, Central America, the Caribbean and the Pacific Islands, despite the fact that these are the regions where the potential benefits of no-till farming are the highest (Lal, 2007). Farmers in these areas continue to utilize conventional tillage techniques not only because of a widespread perception that conventional tillage methods are an essential and “modern” part of crop production, but also because many resource-poor farmers face constraints that prevent them from switching to conservation tillage methods (FAO, 2008). Additionally, education about conservation tillage has not been integrated into extension programs or other agricultural development programs in many countries (ibid).

Market-Based Conservation Programs and Carbon Credits

The role of farmers as stewards of the land has long been a prominent aspect of agrarian culture. Many farmers are financially motivated to maintain the quality and productivity of their land. Maintenance of soil quality results in high levels of productivity. Additionally, many farmers live on or near their farms so there is incentive to reduce on-site pollution that detracts from their own quality of life. But there is little incentive for farmers to spend time and valuable resources to reduce off-site pollution.

Consequently, farmers are a significant source of non-point source pollution in the United

States. Research suggests that agriculture contributes to a variety of environmental problems, including declining levels of ground water, contamination of groundwater with pesticides and nitrogen, and decreased air quality around confined animal feeding operations (Lambert, Sullivan, Claassen and Foreman, 2006).

Governments around the world have created incentives to reduce both the on-site and off-site environmental impacts of farming. For example, the United States government has created cost sharing programs that help farmers pay for conservation practices. Most of these programs also represent an important source of technical support. In the European Union, the creation of voluntary agri-environmental schemes has become a prominent feature of agricultural policy, with 24 percent of agricultural land enrolled in such programs in 2002 (Burton, Kuczera and Schwarz 2008).

More recently, new market-based conservation programs have further diversified the types of programs and funding available for farmers to use for conservation purposes.

Some of these market-based schemes are localized, such as water quality trading within watersheds. Others, such as carbon credit markets, are international in scope and farmers and landholders all over the world can participate in them. These market-based schemes are seen as an economically efficient way to pay individual farmers and landholders for the ecosystem services that they provide. How they are embedded into capitalism and world markets is further discussed below.

Capitalism and the environment

Capitalism is premised on a constant need for growth. It is often cited as the chief reason for the explosion of carbon emissions. Charts found in the 2007 IPCC report clearly

17 show the dramatic increase in carbon emissions since the advent and spread of capitalism and industrialization. This is because industrial capitalism is based on cheap energy, namely, fossil fuels. The rapid rate of growth in the use of fossil fuel energy is mirrored by the rate of growth of global GDP (IPCC, 2007). Economic growth is intertwined with the use of fossil fuels, and thus the growth in the rate of carbon emissions.

Humans have always mined nature resulting in deforestation, habitat destruction, decreased soil fertility, and nearly every other type of environmental degradation imaginable. The same is true for the atmosphere. While all humans enjoy the common protection of the atmosphere, the inability of governments to regulate the pollution of this resource has led to a classic formulation of Hardin’s “tragedy of the commons” (1968).

With no regulation of such activity, the gas composition of the atmosphere is changing rapidly, resulting in decreased climatic stability for all, not just the polluter.

Sandor (2006) defined the emission of pollutants into the atmosphere as a simple case of negative externality, that is, “harmful spillover from an economic activity” (pp

390). Attempts to regulate industrial contributions to environmental degradation problems have meant that previously “free” natural commodities have become embedded in the capitalist market.

A notion fundamental to neoliberal ideology is that anything of social worth must be tradable on the global market. This tenet has led to increasing commoditization of nature and the advent of markets that trade natural commodities such as wetlands, carbon dioxide emissions and biodiversity. Before these markets, capitalism was not disciplined enough to internalize all marginal costs, including those related to environmental degradation.

The market approach treats the environment as a scarce resource by applying monetary values to otherwise non-priced assets. Money is a universal means of measuring value. Marx (1970) postulated that “labor is not the source of all wealth.

Nature is just as much the source of use values.” The critical aspect of environmental markets is that they translate environmental worth into money, the basic expression of social power in society. Thus, environmental values are able to be understood in terms of effective demand, rather than imaginative demand. In other words, when environmental assets are priced in monetary terms, the importance of the environment is translated into the language of capitalism, not stuck in the language of conservationist altruism.

The market approach

Sandor (2006) argued that many obstacles must be overcome in order to create a new market. These include regulatory uncertainty, inadequate definition of the new commodity to be traded, lack of standards for monitoring, verifying and documenting the commodity, and a lack of clear market prices for the new commodity. Special challenges exist when trading a commodity that the buyer will never “see”. Scientific measures must be relied on in order to make accurate estimates of the commodity that is being exchanged.

Although many doubt that a market for emissions trading will by itself be an effective solution to the problem of climate change, these markets have been trialed on a limited basis. Supporters of emissions trading often cite the sulfur dioxide trading program that was created to curb acid rain which was a major public concern during the

1970s. In 1990 Congress passed the Clean Air Act. It required power plants to reduce their sulfur dioxide levels to below levels emitted in 1980. The legislation was innovative because it established a cap and trade system for sulfur dioxide emissions.

This reduced the costs of complying with the measure while ensuring that environmental goals were still met. The program is considered to be a success because the targeted emissions reductions have been exceeded and the abatement costs have been less than originally predicted. They were less than what they would have been had the program not existed (Stavins, 2005). Several features of the sulfur dioxide market allow it to function successfully. Among them are accurate measurement of emissions, clear rules and clear penalties for non-compliance (Stowell, 2005).

Opposition to environmental service markets

Opposition to emissions trading is widespread among many researchers and

environmental activists. A market strategy makes the assumption that the environment

can be valued in the same way that other commodities can, which opponents to carbon

markets find problematic. “It is possible to substitute artificial paper money for natural

gold, but it is not possible to substitute certificates and bonds, traded on a special stock

exchange, for an increase of CO 2 particles in the atmosphere, or for a rise in average temperatures” (Alvater, 2006: pp 48). In other words, it is not possible to give monetary value to abstract environmental services such as the protection that the earth’s atmosphere provides for humanity. It is on for this reason that opponents to the market strategy find it impossible to express environmental values in monetary terms.

Lohmann, Kill, Erion and Dorsey (forthcoming) argue that the sulfur dioxide market cannot be compared to current carbon markets. The goals of the sulfur dioxide program are narrow and well defined, making it very different from current carbon market schemes. Lohmann, Kill, Erion and Dorsey (forthcoming) argue that the sulfur dioxide program was successful because it targeted a very specific set of polluters and only certain technologies to lower emissions were allowed. In addition, instead of trying to monitor the actual reduction in emissions for individual polluters, officials were allowed to visit each facility to ensure that the correct technology was installed and in use. The monitoring of carbon markets and the technologies to reduce emissions are not as well defined, nor are emission abatements easy to measure. For these reasons opponents to carbon markets argue that carbon markets, modeled after the sulfur dioxide

21 market, employ faulty reasoning. They argue that conditions vary considerably from one market to another.

The moral argument against carbon markets is that they perpetuate environmental injustice. Opponents argue that carbon markets would widen existing inequalities between nations because the geography of carbon emissions and carbon mitigation efforts are highly uneven. A market strategy allows for the global south to be engaged in many climate mitigation activities due to the low costs of mitigation efforts in those countries.

These developing countries are not responsible for most of the world’s carbon emissions, but could bear the burden of climate mitigation. Bachram (2004) names this phenomenon “carbon colonialism”. She finds that the carbon credit market enables those in the developed world to not alter their lifestyles. Rather, they believe that this situation results in the “responsibility for over-consumptive lifestyles of those in richer nations being pushed onto the poor, as the South becomes a carbon dump for the industrialized world” (Bachram, 2004: pp 7). Carbon market proponents counter this argument with claims that carbon markets could facilitate the transfer of large amounts of capital to the developing world. An estimated 16.8 billion dollars of revenue would flow into tropical and subtropical countries of the developing world (Niles, Brown, Pretty, Ball and Fay, 2002).

Agriculture’s involvement in environmental service markets

The creation of market-based programs to combat environmental problems has been relatively recent. According to a historical timeline created by Stavins (2007), the theory behind market-based environmental schemes was developed in the 1960s. The first on-the-ground schemes were created in the 1970s. The idea caught hold during the

1980s. Examples of early environmental service markets, such as the sulfur dioxide market and wetlands mitigation banking, are viewed as both successes and failures.

Proponents claim that they have been successful because they appear to mitigate the environmental problem on a long-term and practical scale. Opponents consider them unsuccessful because they fail to adequately value the environmental service, spur real innovation and allow alternate solutions.

In any case, it is safe to conclude that success stories related to sulfur dioxide mitigation and wetlands banking have caused market-based environmental programs to expand to an increasing number of environmental services. New markets encompass many different environmental services, many of which affect agriculture. Water quality credits, carbon credits, and biodiversity credits are just a few of the trading programs in which farmers and other landholders can participate in addition to government-sponsored programs. These programs are very different than the traditional conservation programs that have been offered to farmers. They are market-based. Farmers are not guaranteed a price for the environmental service that they offer and they compete against other sectors as providers of environmental services.

CHAPTER 3

REVIEW OF LITERATURE

The following review of literature pertains to the topics discussed in the previous chapter, namely, no-till farming practices and carbon credits. Much of the literature reviewed is relevant to both topics. In fact both can be considered innovations which share many characteristics. For example, each innovation has benefits which accrue to the individual and to the environment. On the other hand, each topic has its own unique set of background literature which will also be reviewed in this chapter.

Theoretical Foundation

Neoclassical Economic Theory

Economists typically assume that the decision to adopt a certain practice, including farming practices, is usually based on profit-maximizing behavior. Variables to be considered include the time and management effort required to adopt an innovation, type of farming operation, and the specific skills of the farmer. In many cases, profit- maximizing behavior has been shown to be an excellent predictor of the adoption of innovations, including conservation innovations. For example, Hopkins and Johansson

(2004) found that, over a fifteen year time span, corn producers reduced soil erosion by seventy-five percent by adopting conservation tillage for economic reasons. But is profit maximization the only explanatory motivation for good stewardship?

Research has also shown that not all conservation practices save time or increase profits. Nonetheless, they have also been adopted by many farmers. By the same token, even practices that enhance profits many not be adopted because they are incompatible with goals of farmer, or because the practice may not be appropriate for their particular farm operation or because the practice may be perceived as too risky. Decades of research on this topic have shown that profit-maximization is not the only factor that motivates farmers to adopt a practice.

Diffusion of Innovations

Rural sociologists have traditionally given priority to the adoption of new technologies and ideas by the farming population. They have worked side by side with other agricultural disciplines to understand farmer decision making processes and to facilitate the diffusion of new technologies, practices and ideas. Rural sociologists pick up where the economists leave off, attempting to explain factors affecting adoption that stretch beyond the notion of economic rationality.

Rural sociologists have consistently worked on developing a diffusion of innovations model since the late 1940s as they have attempted to understand the adoption of technologies and practices by farmers. Typical diffusion studies cover topics such as how technical innovations are spread in a social system, the characteristics of adopters, and the role of interpersonal networks and other communication channels in determining rates of adoption (Rogers, 2003).

Uncertainties about the model began to appear in the 1970s, including its ability to explain the diffusion of conservation practices and technologies. Rural sociologists responded by adapting the model to address new aspects of diffusion. They began to take

into account widely recognized faults of the model and addressed issues that had not been

widely researched previously. These issues included a pro-innovation bias, the

individual blame logic, the consequences surrounded the diffusion of a new technology,

and the role of infrastructure and other barriers to adoption (Fliegel and Korsching,

2001).

As diffusion researchers worked on alterations to the classical model, they

struggled to include social factors and context of adoption. The classical model, like

functionalist theory that underlies it, focuses on characteristics of the adopter and

individual decision making rather than structural variables. Other models, such as that

proposed by Brown (1981) have given more emphasis to social factors and context.

Although social variables are acknowledged to play an important role in the decision

making process, they remain largely understudied in the diffusion literature.

Leaving aside the specific limitations of Rogers’ model, there is a wealth of

information to be gained by using such an approach to understand the adoption of

innovations. On the other hand, new approaches, such as Bourdieu’s theory of capital,

may provide more robust interpretations of the role that social and cultural variables play

in the adoption process.

Bourdieu’s Theory of Capital

The theories of Pierre Bourdieu have translated into a progressive research agenda which has proved to be more than just a passing fad (Sallaz and Zavisca, 2007). Among other major theoretical contributions, Bourdieu (1986) proposed the existence three fundamental forms of capital, namely, economic capital (material property), social capital

(networks of social connections) and cultural capital (prestige).

Bourdieu’s work on capital is a reformulation of Weber’s conceptualization of class and status groups. Weber differentiated between class and status, finding that class describes an individual’s situation in the economy, while status groups are social groups that are based on common lifestyles (DiMaggio and Mohr, 1985). He theorized that class and status may overlap, but not always. Researchers have struggled to find a measure of status and many studies use a proxy variable such as occupational status. The intricacies of these concepts can be further explored, thanks in large measure to their reconceptualization as different forms of capital by Bordieu.

What is unique about Bourdieu’s theory is his conceptualization of culture and social networks as forms of capital. Social capital has been embraced by American sociologists, evident by a flurry of studies published in a number of journals. Robert

Putnam’s book Bowling Alone: The Collapse and Revival of American Community brought the concept of social capital into the world of popular sociology. Likewise,

American researchers have embraced the concept of cultural capital and made it a major part of their research agendas. Cultural capital has become a prominent feature in the sociology of culture, but also figures in other dimensions of the sociological literature, such as that related to education and social mobility. Early studies in this area conceptualized cultural capital as familiarity with so-called “high culture,” but have since acknowledged that cultural capital is shaped not only by the powerful social classes, but also by the social and political contentions of other fields (Stampnitzky 2006).

Like social capital, cultural capital has been plagued with a host of measurement quandaries. Empirical measures of cultural capital indices appear in a number of studies, but vary widely in their operationalization of the concept. For example, Lewicka (2005)

created an index of cultural capital based on father’s and mother’s educational levels and

the number of books in the home, while Khawaja and Mowafi (2006) measured it as

equivalent to the degree of participation in recreational activities and in the arts. These

operational measures do not acknowledge the Weberian roots of cultural capital and are

poor indicators of how membership in a status group affects the actions of individuals.

Better measurement of the concept is needed in order for research in this area to

effectively move forward., namely, Rural sociologists in the United States have not

embraced the concept of cultural capital to the extent of European rural sociologists have.

One of the outcomes of this study will be to have brought this concept forward in the

American rural sociological literature in order to address and help interpret a

longstanding and elusive issue in rural sociology, namely, the adoption of conservation

innovations.

Forms of Cultural Capital

Bourdieu (1986) described cultural capital as having three distinct forms: 1) the embodied state, which is connected to the individual’s character and guides his or her actions and tastes, 2) objectified cultural capital, which is the means of cultural expression that symbolically transmits messages to others, and 3) institutionalized cultural capital, which consists of items such as the academic qualifications an individual holds.

Cultural capital is distinct from economic capital because it cannot be transmitted instantaneously as, for example, money can. However, it is often found to function as symbolic capital. The rural sociological literature does address many of the visible material objects - objectified cultural capital - that are found to be important in farming

28 culture. These are mainly found to be symbols related to intensive production agriculture such as weed-free and pest-free fields, modern machinery, and high quality livestock

(Burton 2004).

Previous research by sociologists and psychologists shows that cultural capital plays an important role in group membership. Group membership is relevant to this discussion because of the powerful role that group membership has on individual motivation and action (Baumeister and Leary 1995). Group membership is developed and maintained through the display of a visible commitment to the same symbols and their attached meanings. This may be through a financial investment in symbolic meaning or through the display of approved behaviors. From this viewpoint, behavior is not only functional, but is a way in which individuals symbolically express group membership. Identifying with a particular social group provides a sense of security in addition to “a stable framework with which to understand the world through offering shared meanings, interpretations and understanding of events and objects” (Burton,

Kuczera and Schwarz 2008, p 20).

Through cultural capital farmers construct, relate to and make sense of their environment and identities. Sociologists have historically taken some social variables into account to help explain such behavior. However, recent research has also begun to look at the cultural value of farming activities in order to help explain such behavior.

This analysis will include an analysis of cultural variables, in conjunction with the diffusion of innovations model and neoclassical economic theory, in order to more fully understand the role of culture in decisions to participate in voluntary carbon markets.

Related Empirical Literature

Since the mid-1980s a number of studies have addressed the relationship between agriculture and the environment. This research attempts to identify factors that affect farm behavior and decision making such as socio-economic constraints, farmer values and attitudes, government policies and family structure.

Economic Factors

Economic considerations are undoubtedly very important to understand technology adoption, mainly because most resource conservation options are viewed as nonproductive expenditures by farmers (van Es, 1983). Even if long-term benefits exist, the short term farm budget still needs to be taken into consideration (van Es, 1983). This is because individual farmers cannot pass on additional costs of environmental protection to the consumers. Thus, additional costs of a conservation innovation must be absorbed by the farm business (van Es, 1983).

Much diffusion research has been based on scientific, reductionist values oriented to profit (Frank, 1992a). When evaluating a new agricultural technology or program it is a vital issue to be considered. According to the diffusion model, farmers will be reluctant to enter into an environmental program contract unless they view it as profitable to their farming operation. Profitability is not an objective fact, but a perception on the part of the farmer that may vary with the farmer’s scale, style of farming, and ecological context.

What is important is not the “true” economic profitability of an innovation. Rather, it is a farmer’s perception of its profitability (Saltiel, Bauder, and Palakovich, 1994). Therefore, economic issues related to participation in an environmental program are very complex and are subject to many different factors.

Much literature deals with research about the degree to which farmers attempt to maximize their profit in light of environmental protection. The degree of profit maximization is difficult to determine because the environment is mostly an unpriced asset with an imaginative demand. Bowers and Cheshire (1983) argue that farmers behave as true profit maximizers because they do not attach economic value to the destruction of non-productive natural assets or the pollution emitted from their farm. Hill and Gasson (1985) find that farm tenure is also an important profit maximizing variable because farmers on rented land behave more like profit maximizers than do their farmer counterparts who own their land.

Financial flexibility is another important issue to consider. Some farmers have the financial flexibility to introduce changes in their arm operations that may cost money and carry with them a certain amount of financial risk. Considerable capital outlay is often required when adopting a new technology such as no-till, and farmers may have to forego income until the new innovation produces revenues (Vanclay and Lawrence,

1994). Even when economic reasons to participate are apparent, many farmers simply do not have the financial flexibility to adopt new technologies (Geertz, 1963).

Much literature has investigated the relationship between socio-economic status and the flexibility of farmers to respond to agro-environmental problems. Marsden et al.

(1986) found that those with severely constrained finances were restricted in their ability to change their farming practices to respond to environmental pressures, while those with more financial flexibility could more easily change their practices. Munton et al. (1989) and Robson et al. (1987) found a relationship between farm firm economic flexibility and the work status of farmers. Farmers working part-time off of the farm were found to

have less flexibility to respond to environmental concerns. Economic pressures also

affect farmer attitudes and willingness to change farming practices. Gasson et. al (1988)

found a relationship between the economic status of a farm and attitudinal disposition for

entering an agro-environmental program.

Knowledge

Previous studies have concentrated on the set-up and context of conservation

programs, but there is evidence that communication channels – specifically how people

learn about a new subject - are also important. The fact that access to knowledge about

conservation innovations affects adoption, in addition to knowledge about the

environmental problem itself, is well documented in the literature (Saltiel et al, 1994).

Both Saltiel et al. (1994) and Nowak (1983) surmised that the role of information is to

help reduce perceived risk on the part of farmers.

Brown (1981) argued that structural factors often inhibit communication about a new innovation. These might be physical barriers, such as distance to an extension resource or a lack of communication devices such as telephones or internet. Quality of information is also a vital factor. In fact, low acceptance levels are often associated with poor information strategies (Schenk, Hunziker and Kienast, 2006).

In a pivotal study on voluntary conservation programs in Switzerland Schenk,

Hunziker and Kienast (2006) found that the way information about conservation programs is transferred is vitally important. Different channels can be used to convey information, but not every channel is equally successful. For example, mass media channels, such as newspaper and radio, often do not reach farmers to the extent that is generally expected. Furthermore, these impersonal channels are often less effective

because even if farmers read or hear the information, they often do not feel affected by

these messages because they are not directly addressed. Likewise, Schenk, Hunziker and

Kienast (2006) found that public meetings are not as successful as many expect. They

found that farmers fail to retain all the presented information, which presents a risk that

the information will be misunderstood and misinterpreted. Public meetings can be a

source of rumors, and the researchers found that rumors normally exaggerate negative

aspects of the presented information. These rumors are difficult to combat even with

extensive corrective media campaigns. The researchers found that personal meetings

about conservation programs are more successful in transferring information to farmers.

They allow the farmer and the program representative to talk not only about the program

in a theoretical manner, but also about the individual’s farm context and personal

situation.

Schenk, Hunziker and Kienast (2006) also found that farmers are less interested in

theoretical and academic information. Rather they prefer to receive information which

they can associate with their own lives and their actual farming situation. This

phenomenon has also been documented in the context of the United States. Vanclay

(1992) found that farmers are suspicious of information from academic and government

sources because they often feel that “city” ideas are being forced on them. Additionally,

farmers receive information from numerous sources and those sources often contradict

each other (ibid). In an environment of uncertainty, many farmers feel that non-adoption

is the appropriate management decision (Vanclay and Lawrence 1994).

Timing of communication has also proven to be p in the adoption process.

Schenk, Hunziker and Kienast (2006) found that being informed late in the process leads

33 those affected to initially adopt a critical attitude and can ultimately lead to rejection of the ideas presented. Some respondents in their study were suspicious of the program administrators and thought that they intentionally did not inform them properly in order to undermine their participation in the program. Late and insufficient information caused farmers to feel ignored and undervalued.

Previous research has demonstrated the importance of awareness and knowledge, but these factors do not always lend themselves to eventual adoption. Particularly with conservation innovations, farmers may be aware and knowledgeable about environmental problems and their solutions, but fail to adopt because of other factors affecting the decision making process (Vanclay and Lawrence, 1994). The diffusion model assumes that farmers seek new innovation to solve their problems, when in fact this may not be the case. In addition, farmers may have knowledge about a new innovation, but adoption may not occur because the innovation may not be appropriate for their needs or for other reasons.

Perception of Environmental Problems

Soil health, non-point source pollution, and global warming are environmental problems that are difficult, if not impossible, to be observed by humans through their senses. A budding literature on epistemic barriers and environmental problems associated with production agriculture has addressed this issue in an attempt to move the rural sociological literature beyond merely measuring perceptions of environmental problems and the adoption of conservation technologies to alleviate them. The literature on epistemic barriers explores why people “see” different things when they look at various agricultural practices.

Epistemic distance is described by Carolan (2006) as “socio-biophysical objects, effects, and relationships that are beyond direct perception” (p 243). Epistemologically distant objects cannot be perceived through an individual’s senses - taste, touch, hearing, sight, or smell – making it necessary to rely on other technologies such as detectors or tests to do the “seeing” (Carolan 2006). Many aspects of agriculture can be said to be epistemologically distant such as soil nutrient loss, genetically engineered crops, or bovine growth hormone.

Epistemological distance is not simply an effect of scale, involving only objects that are too small or too large for humans to perceive directly (Carolan 2006). Epistemic distance can also be said to exist when causal relationships are extended over time or when relationships are unclear due to their complexity. For example, farmers can quickly and easily perceive a difference in milk production after injecting their cows with recombinant bovine growth hormone, but may find it difficult to perceive a causal relationship between tillage methods, nutrient loss and decreased crop yields.

The concept of epistemic distance has been used in order to understand how farmers perceive sustainable and conventional agriculture. Many aspects of sustainable agriculture are largely “invisible” such as increases in soil fertility and beneficial soil microorganisms (Carolan 2006). Conversely, many aspects of conventional agriculture are readily apparent such as weed-free fields and pest-free crops (ibid).

Perception and Adoption

Rogers (2003) noted that the adoption process often begins with the recognition of a problem or need. Previous research has shown that if farmers do not define water quality, soil erosion and other environmental concerns as urgent problems by farmers,

35 they will not attempt to fix them (Nowak and Korsching, 1983). The most successfully diffused technologies are those whose benefits are clear and certain from the perspective of the farmer (Rogers 2003). Conversely, technologies with unclear benefits have been found to have lower adoption rates among growers (Kremer et al. 2001). In fact, farmers may define environmentally friendly practices as solutions to non-existent problems

(Nowak and Korsching, 1983), which would lead to low adoption rates for conservation innovations.

Much research has established that when farmers perceive an environmental problem, they are more likely to adopt innovations that aid in the management of it

(Vanclay and Lawrence, 1994). As Nowak (1983) points out, perception of an environmental problem is complex because producers tend to evaluate the environment themselves. They often underestimate their own environmental problems, even if they acknowledge that there are serious environmental problems at the national, state, and community levels. Farmers may even think that their neighbors may have a serious environmental problem, but the same problem is not present on their own farm.

Education efforts have often been counterproductive as a means to help farmers identify their environmental problems (Nowak, 1983). In the case of soil erosion, extension efforts often utilize dramatic pictures of the Dust Bowl, exposed tree roots, and other extreme examples of erosion, that farmers do not see taking place on their own farms (Vanclay and Lawrence, 1994). They believe that they do not have a problem because they do not see the same type of extreme degradation taking place on their own farm (Nowak, 1983). When farmers do acknowledge the problem on their own farm,

36 they often develop a fatalistic attitude, rather than changing their management practices

(Vanclay and Lawrence, 1994).

Perception of Climate Change

Climate change is an abstract and diffuse problem. Most individuals fail to identify any direct effects from it at this time. Behavior research conducted over the past

30 years has explored why the general public and public officials show less concern about climate change than most climate scientists. Weber (2006) finds that climate change does not evoke strong reactions because of the time-delayed and abstract nature of the problem.

Research findings produced by thousands of scientists from across the world is analyzed and compiled by the Intergovernmental Panel on Climate Change (IPCC). The

IPCC purports that climate change is caused almost exclusively by anthropogenic activities, and that the probability of climate change being caused by natural climatic processes alone is less than five percent (IPCC, 2007). However, this important message appears to be lost amid conflicting politics and media, particularly in the United States

(Boykoff and Boykoff, 2007).

Controversy over climate change is widespread, with the discussion centered on questions of if it is happening and what is causing it. Controversy over environmental problems is exacerbated by epistemic distance, which masks already unclear relationships and effects (Carolan 2006). The problem of global warming and agriculture’s contribution to this abstract problem is a complex issue which forces farmers to rely on highly politicized information sources.

Media portrayals of scientific discourse on the subject of climate change have

done little to reduce confusion found in general public opinion. Boykoff and Boykoff

(2007) found that coverage by the U.S. media between 1988 and 2002 caused a

divergence of popular discourse from scientific discourse. Journalistic norms call for

balanced reporting, with each side given equal weight. The consensus that the majority

of the scientific community had reached on climate change was negated by the equal

weight that was given to the few dissenters in the majority of media coverage on the

subject. Boykoff and Boykoff note that biased media coverage has led to confusion in

public understanding of climate change. It has also undermined efforts to mitigate the

human-induced effects of climate change.

Alignment of political parties with opposing views of climate change causes

additional confusion in public opinion. McCright and Dunlap (2003) find that the

conservative movement forms a key segment of the anti-environmental counter-

movement that has capitalized on the abstract nature of climate change and attempted to

frame the issue as non-problematic. Conservatives reject claims that climate change is

caused by anthropogenic activities and maintain that rising global temperatures are part

of the natural cycle of the Earth. Most importantly, the conservative movement continues

to frame the issue as controversial (McCright and Dunlap 2003) despite the fact that

thousands of scientists worldwide agree that climate change is happening and state with

95 percent confidence that it is caused by anthropogenic activities (IPCC 2007).

As mentioned above, climate change is a diffuse, epistemologically distant environmental problem that is not directly perceived by individuals. Research data, on the other hand, can be used to demonstrate that climate change is happening and as a

38 result of human activity. However, this message has also been lost and misrepresented in the media. Characteristics of climate change make it difficult to enact meaningful mitigation endeavors. Efforts to diffuse a technology or convince individuals to participate in a program that combats climate change may be difficult if they do not see climate change as a pressing environmental problem that requires immediate action to solve.

Social Factors

Personal and farm operation objectives held by farmers hold are very important considerations when discussing the appropriateness of any given technology. Vanclay and Lawrence (1994) note that farmers are more likely to adopt innovations that are compatible with other farm and personal objectives. They also point out that new and often complex innovations represent major changes in the management of the farm, and may not be compatible with other farm operations. Because of the nature of the family farm, farmers take into consideration their personal capital needs, such as household expenditures and education of their children, as well as their farm-firm needs when making decisions related to the farm operation. Environmental innovations often require major changes to current agricultural practices. Therefore, most environmental innovations are deemed to be not compatible with current farm management practices or farm and personal goals (Vanclay and Lawrence, 1994).

Good Farmer Status

The socio-behavioral context of the adoption environment should be taken into consideration, but there is little room for this in the classical diffusion model because it focuses on the traits of individuals and does not provide an easy way to include

contextual variables. Much early research that was based on the classical diffusion model

failed to consider contextual variables.

Most farmers give great importance to local norms that defines a “good farmer”

(Heffernan, 1984). Social acceptance of an innovation by neighbors has been categorized

as an additional benefit to the adoption of an innovation (Frank 1995b), but the lack of

acceptance can just as easily serve as a deterrent until openness to a given technology

achieves a critical mass among a local group of farmers. “Except for a small number of

maverick innovators, most farmers would not want to be the only ones to undertake a

new practice or grow a new crop” (Vanclay and Lawrence, 1994).

Utility of an innovation has long been assumed to drive adoption. Frank (1995b) surveyed the social acceptance and perceived utility of ten different innovations within the beef industry. For each of the ten innovations, a different “dimension” of utility, such as personal satisfaction, profitability, and efficiency, was weakly or inconsistently correlated with adoption. Additionally, for all ten innovations studied by him, the social acceptance of the practice by neighbors was perceived to be a reward by farmers. In other words, there is a highly socio-cultural dimension to adoption that has proved more lasting and consistent as a causal factor than the “utility” of the practice, which has long been assumed to drive adoption.

A growing literature among European rural sociologists assumes that farmers construct cultural capital and “good farmer status” through the performance of farm management activities. The idea that farmers monitor each other’s activities through

“roadside farming” is not entirely new (Seabrook and Higgins 1988). The application of

Bourdieu’s theory to this practice has revealed it to be an important means of obtaining

40 and maintaining social status in a community and is an important factor in the decision making process of individual farmers.

Burton et al. (2008) contend that three conditions are required for a farming activity to be able to display embodied cultural capital to other farmers. First, the activity must include some outward demonstration of a skill which allows others to distinguish between good and poor performances. Second, the outcome of the activity must exhibit the level of skill required to execute it. Third, these outward signs of skill must be visible or otherwise accessible to other members of the farming community. Burton et al. (2008) identified three categories of skills related to mechanized agricultural production: motoric, mechanical and managerial.

The aforementioned skills are outwardly demonstrated to other farmers in a variety of ways. For example, farmers have been found to place their best cattle in fields readily visible to others (Burton et al. 2008). Several studies, as reviewed by Burton

(2004), noted the importance of maintaining weed-free fields as a sign of good farm management.

Motoric ability is displayed through straight and parallel lines of crop rows and neatly turned corners with little overlap. Farmers in a variety of contexts have consistently shown a preference for “tidy” landscapes and farmsteads. All of these symbols of farming originate from the notion that farm management should involve minimizing waste and maximizing production. In other word, they are all related to economic efficiency (Burton et al. 2008). This is consistent with the strong preference that farmers have shown for features in the landscape that are characteristic of intensive agriculture (Brush et al. 2000).

The adoption of conservation innovations may not results in the demonstration of

a good farmer status, if other farmers cannot see or recognize signs of environmental

improvement (Burton, 2004). In this case, farmers are unable to demonstrate and

reinforce their status as good farmers. For example, increases in soil fertility or

beneficial soil microorganisms are difficult for others to observe. One no-till farmer

profiled in an industry magazine, remarked that he wished that other people could see the

changes that he could see in his fields after four years of no-tilling (No-Till Farmer

2003). Likewise, environmental programs, such as voluntary carbon credit programs,

have “invisible” benefits and do not have the traits necessary to contribute positively to a

farmer’s status.

The failure of farmer to participate in agri-environmental programs can be at least

partially explained by their failure to generate symbolic cultural capital. The net result of

participation within such programs could be that farmers may actually lose significant

amounts of cultural capital despite apparently generous financial compensation (Burton et

al., 2008). Low participation in such programs may be partially explained by the fact that

the farm extension educators and farm input representatives continues to direct their

actions toward the perpetuation of the “productivist” farming culture common in many

farming communities.

Conclusion

When considering an issue of farmer participation in conservation programs, it is essential to keep in mind that such programs are not universally applicable. Each farm, based on its environmental situation, will need to employ appropriate environmental management strategies (Vanclay and Lawrence, 1994). The farming system and relate

42 characteristic, such as soil type, topographic conditions, and climate, are just a few of the factors that need to be taken into consideration (Nowak and Korsching, 1983; Heffernan,

1984). In addition, farm characteristic, such as type of farm enterprise, farm size, land tenure, and planning horizon, have been shown to influence adoption (see Buttel, Larson and Gillespie, 1990). A farmer takes a diverse array of factors related to his farm environment into consideration when evaluating a new technology.

The pro-innovation bias is a serious shortcoming in diffusion research, because it implies that an innovation should be diffused and adopted by all members of a social system (Rogers, 2003). The general idea of innovation is a positive notion associated with improvement (Down and Mohr, 1976; Rogers, 2003). These values infer that technological innovations are desirable, suitable for adoption, and directly applicable to the personal environment of the target audience (Frank, 1995a). While the pro- innovation bias is latent and largely unintentional, it is still a limiting factor in much diffusion research (Rogers, 2003). In the case of farmer participation in carbon credit programs, it may seem intuitive from an economic standpoint, that farmers will be eager to be paid for practices that they have already implemented. However, this pro- innovation bias does not take into account the diverse array of factors that farmers may actually consider in deciding to participate in carbon markets.

The relevance of the foregoing discussion for researchers is that contextual factors need to be considered, particularly when studying issues related to farmer participation in voluntary conservation programs. Previous research has found that traditional, quantitative social science approach to the adoption of conservation innovations will not reveal all intricacies of adoption decisions (see Miller, 2006 for a full discussion).

Attempts to discover a set of independent variables that represent individual attributes to

“explain” adoption are ineffective because they exclude other unmeasured social and cultural variables that also play a critical role in the adoption process.

Consistent with the earlier discussion of cultural and social variables, it can be concluded that other important factors, such as the role of knowledge and perception in the adoption process, need to be considered. Previous research that has attempted to link attitudinal variables with adoption decisions has met with mixed success. Examples are attempts to link environmental concerns with the adoption of conservation technologies

(Miller 2006). What may be more fundamental explanatory factors are knowledge and perception factors that are driving forces behind these attitudes. These factors will be considered in this study along with social and cultural factors in order to understand their role in the adoption of no till practices and participation in carbon markets.

CHAPTER 4

METHODOLOGY

Methodological procedures are found in this chapter. They relate to the collection

and analysis of data used to carry out formal tests of hypotheses and to interpret these

results. The chapter begins with the presentation of a general model for adoption of

climate change related agricultural practices. The model represents a series of

hypotheses used to guide the statistical analyses. This is followed by a discussion of the

data that were used in the study, including the sampling procedure, process of data

collection, an explanation of how the variables in the model were measured, the statistical

methods used to test hypotheses, and a description of study respondents and a discussion

of qualitative data collection procedures.

General Model and Study Objectives

The model found in Figure 4.1 guides the data analysis. The first part of the

model addresses the use of no-till practices. The adoption of no-till practices by farmers has been extensively studied in the past. This study attempts break new ground by reaching beyond the classical diffusion model which focuses on the relationship between demographic characteristics of farmers and the use of conservation practices. This study assumes that the decision to adopt this practice is complex and related to a number of factors and perceptions about the practice. The proposed model accounts for many

45 additional factors considered by farmers when making the decision to participate in carbon credit programs. Specifically the model explore relationship between the use of no-till practices and satisfaction with no-till practices, anthropogenic climate change beliefs, familiarity with carbon topics, in addition to farm and farmer demographic characteristics.

The second portion of the model addresses the adoption of carbon credits by no- till farmers. The analysis begins with a review of the types of no-till practices being used in order to determine present eligibility of these farmers to receive carbon credits. This is followed by an analysis of the relationship between the adoption of carbon credits and satisfaction with no-till practices, anthropogenic climate change beliefs, familiarity with carbon topics and farm and farmer demographic characteristics.

Carbon Credits Carbon Practices Use of No-Till No-Till of Use No-Till No-Till Distance Epistemic in Belief Practices Farm and and Farm Variables Demographic Demographic Anthropogenic Anthropogenic Satisfaction with with Satisfaction Topics Carbon Climate Change Climate Familiarity With With Familiarity Figure 4.1 General Model 4.1 General Figure

The model contains several hypotheses that represent the relationship between

perceptions of respondents and their adoption of no-till practices and carbon credits, in

addition to their relationships with demographic characteristics of farmers and their farm

operations. These hypotheses are listed below.

Research Hypotheses

No-till practices

1. There is a positive relationship between the use of no-till practices and

satisfaction with those practices.

2. There is a positive relationship between the use of no-till practices and belief in

anthropogenic climate change.

3. There is a positive relationship between the use of no-till practices and familiarity

with carbon-related topics.

4. There is a positive relationship between the use of no-till practices and farm size.

5. There is a positive relationship between the use of no-till practices and education

level.

6. There is a positive relationship between the use of no-till practices and dedication

to farming.

7. There is a positive relationship between the use of no-till practices and liberalness

of political orientation.

8. There is a positive relationship between the use of no-till practices and level of

active conservation behavior.

9. There is a negative relationship between the use of no-till practices and

respondent’s age.

Carbon credits

1. There is a positive relationship between the use of no-till practices and the

adoption of carbon credits.

2. There is a positive relationship between the adoption of carbon credits and

satisfaction with no-till practices.

3. There is a positive relationship between the adoption of carbon credits and belief

in anthropogenic climate change.

4. There is a positive relationship between the adoption of carbon credits and

familiarity with carbon-related topics.

5. There is a positive relationship between the adoption of carbon credits and

education level.

6. There is a positive relationship between the adoption of carbon credits and

dedication to farming.

7. There is a positive relationship between the adoption of carbon credits and

liberalness of political orientation.

8. There is a positive relationship between the adoption of carbon credits and level

of active conservation behavior.

9. There is a negative relationship between the adoption of carbon credits and

respondent’s age.

Sampling Procedure

The target population for the study is individuals who support no-till enthusiasts and are currently engaged in the agriculture industry. The sample frame consists of members of the Ohio No-Till Council. The sample includes farmer and agricultural industry professional members of this Council who attended the Conservation Tillage and

Technology Conference in Ada, Ohio on February 26 and 27, 2009.

Pre-Survey Preparation

In order to understand better how farmers might respond to the questionnaire a pilot test was conducted at one of the biannual meetings of the Ohio No-Till Council.

The meeting took place on December 9, 2008 in Plain City, Ohio. Questionnaires were distributed to meeting attendees and collected on-site. The pilot survey consisted of 102 respondents currently engaged in no-till practices.

The pilot questionnaire included space for comments pertaining to the questionnaire itself. Question responses and non-responses were analyzed and comments reviewed. Questions were modified or re-worded to ensure clarity. Graphic and layout changes were introduced to make it easier for respondents to navigate the survey instrument. Questions deemed unnecessary were omitted in order to reduce its length.

Several questions contained in the pilot questionnaire were qualitative in nature with space to write in answers. The answers from these questions utilized to identify items included in the final draft of the survey instrument which were in turn used to construct indices to measure attitudinal and perception variables.

Data Collection

In order to obtain the data needed to answer the research questions a self- administered survey was conducted at the Conservation Tillage and Technology

Conference. A total design method, as described by Dillman (2000) was followed to develop and implement the survey. A copy of the questionnaire can be found in

Appendix A.

Dillman (2000) suggests printing questionnaires in booklet form because they are more easily handled and understood by respondents if presented in the way. Two distinct questionnaires were placed in the survey packet, with one geared toward farmers and the other targeted toward agriculture industry non-farm professionals. Dillman (2000) suggests using high quality paper of a neutral color in order to print the survey. In this case, two different colors of paper were chosen in order to aid participants in identifying the survey in the booklet that was most appropriate for their situation. A cover letter printed on departmental letterhead was stapled to the front of the booklet. The cover letter explained the importance of responding, described the rights of the respondent, and included directions for completing the questionnaire. The questionnaire ended with a thank you and extra space to leave additional comments, as suggested by Dillman (2000).

The back cover of the booklet was left blank except for an identification number, which was used for keeping responses confidential.

Dillman (2000) suggests that survey responses are greatest in the months of

January through March, a time which coincides with the slowest part of the year for Ohio farmers. The survey was conducted in February 2009 which is a time of year when many

51 producers take time to attend meetings and conferences in order to expand and enhance their knowledge about certain agricultural practices.

The survey was implemented using Dillman’s total design method for group administration of self-administered questionnaires. Five steps guided the survey implementation from introduction through debriefing. First, the emcee of the conference welcome session introduced the task by briefly describing the study and explaining its purpose. He also expressed appreciation for their participation in the study. He then reviewed the steps of the survey process and told reminded them that they could find a copy of the questionnaire on their seats as they entered the room. Completed questionnaires were collected from the respondents as they left the room. Additionally, drop boxes were provided in several locations for those who did not have sufficient time in the welcome session to complete the questionnaire. Conference participants were reminded to turn in their surveys at the end of the session, as well as at the group luncheon and other conference sessions.

Survey Instrument

Dillman (2000) suggests beginning the questionnaire with a question that is easy, interesting and applies to everyone. The first question of the farmer questionnaire inquired about farming practices, while the first question of the industry questionnaire asked respondents to categorize their job into one of several given categories.

The following sections of the questionnaire collected information about their participation in conservation programs, their perception of benefits provided by no-till farming, their familiarity with topics relating to climate change, and their perceptions of climate change. Responses to these questions were used as indicators for items found in

52 measures of perception and attitudinal variables. Data were analyzed to assess internal consistency of measurement and to enhance the reliability of measurement.

At this point respondents were asked to respond to specific sections of the rest of the questionnaire, depending on whether or not respondents received carbon credit payments. Those who received carbon credits were guided to a bank of questions pertaining to their participation in these programs, while other respondents were directed toward a bank of questions that address possible barriers to participation in carbon credit programs.

The final section of the questionnaire was designed to collect information pertaining to demographic and associated farm characteristics of respondents. The order of the questions was designed to prevent biases and to discourage non-responses

(Dillman (2000).

Response Rate

A total of 409 questionnaires were returned. Of these, 6 were eliminated in using list-wise deletion due to missing data. Of the remaining questionnaires, 228 were completed by farmers and 175 by agriculture industry professionals. An estimated 895 individuals attended the conference which suggests a survey response rate of 45 percent.

Sample Bias

Self-selection bias can occur whenever the individual respondents have control over their choice to participate (Dillman 2000). Some individuals at the conference may have decided not to fill out this self-administered questionnaire out and return it, despite repeated requests to do so. Dillman (2000) states that timing and details associated with questionnaire delivery are vital in order to reduce conditions that lead to self-selection

bias. In this particular case, the survey was distributed when most conference

participants were at the welcome session. Additional copies of the questionnaire were to

late arrival participants when they registered to be filled out during other sections of the

conference. Dillman (2000) supports group administration of self-administered surveys

because it reduces non-response and is usually not associated with the content of the

questionnaire.

Validity and Reliability of Measurement

Data from the pilot survey were used to determine how to measure several of the

study variables. The construct validity (Trochim and Donnelly, 2007 ) and inter-item reliability of several key indices was addressed using the same data. Reliability and validity of data measurement was further assured by the use of measurements found in previous studies. The face validity (Trochim and Donnelly, 2007 ) of the survey instrument was assessed by a panel consisting of a no-till expert, a carbon credit aggregator, an extension agent and a survey methodology expert.

Data from the pilot survey were used to assess inter-item reliability for measures of several of the attitudinal and perception variables using the Chronbach alpha coefficient of reliability (Trochim and Donnelly, 2007). Questions used to measure items in these indices were posed in positive and negative ways in order to enhance reliability.

Statistical Test of Model

The raw data from the survey was coded, entered into Microsoft Excel, and then transferred to Statistical Package for the Social Sciences 17.0 for statistical analysis. Data used to conduct the statistical test of the model and study objectives are found in the following section.

Descriptive Statistics

Descriptive statistics are used to describe the characteristics of farmers.

Descriptive statistical procedures used in this study include frequencies, percentages, means and standard deviations.

Correlation Analysis

Relationships of independent variables were assessed through correlations.

Correlations indicate the significance of linear relationships between variables. Pearson product-moment correlation coefficients are measures of the linear relationships between two variables and the coefficient is one of the most widely used measures of relationships

(Fraenkel and Wallen, 1996). It is calculated by summing the products of the deviations of the scores from the mean. It is symbolized in the equation below by the letter r.

The size of the correlation coefficient indicates the strength of the relationship

between the two variables. A perfect correlation has an absolute value of 1, and no

relationship has a value of 0. Correlation coefficients in this study are interpreted

according to the conventions outlined in Davis (1971).

Description Coefficient

Very strong .70 or greater

Substantial .50 to .69

Moderate .30 to .49

Low .10 to .29

Negligible .01 to .09

Table 4.1 Davis (1971) conventions to describe correlations

The sign of the correlation coefficient indicates the direction of the relationship.

A positive correlation coefficient means that as the value of one variable increases the other variable increases, and a negative correlation coefficient means that the value of one variable increases the other decreases.

Multiple Regression

Multiple regression is a statistical technique that measures the relative relationship of two or more independent variables with a dependent variable (Fraenkel and Wallen,

1996). This technique lowers the error of prediction and accounts for a greater proportion of the variance of the dependent variable than do simple correlation coefficients (Kachigan, 1986). This statistic is widely used in the social and natural sciences. The general multiple regression equation is as follows:

y’= a + b1x1 + b2x2 + …bkxk

56 where Y’ is the predicted value of the dependent variable and the independent variables are x 1, x 2…x k. The constant ( a) and the slope ( b) are determined from the sample data.

The coefficient of determination is symbolized by r2. It indicates the percentage of the variance in scores for the dependent variable scores that can be attributed to differences in the scores of the independent variables (Fraenkel and Wallen, 1996).

As with many other statistical tests, multiple regression relies on a certain set of assumptions about the variables used in the analysis. The assumptions must be met in order to achieve statistically robust results. The four assumptions of multiple regression are as follows (Hair, Black, Babin, Anderson and Thatham, 2006):

1) Linear Relationships – The relationship between the dependent and independent variables must be linear in order for multiple regression to accurately estimate the relationship between them.

2) Normal Distribution – Non-normally distributed variables can distort relationships and significance tests

3) Absence of Error Measurement – Unreliable measurement causes relationships

to be overestimated if covariates are not reliably measured. The effect of the addition of

less reliable variables to the regression equation causes each succeeding variable to claim

part of the error variance not explained by the unreliable variable.

4) Homoscedasticity – The variance of errors is assumed to be the same across all

levels of the independent variable. Osborne and Waters (2002) indicate slight

heteroscedasticity has little effect on significance tests, but when it is substantial it can

lead to a distortion of the findings.

If the above four assumptions are met, the risk of Type I and Type II errors can be minimized (Osborne and Waters, 2002). If the assumptions are not met, the validity of the results is called into question (ibid).

Measurement of Variables

Most variables found in the model are measured using standard indicators. The indicators have been used by other researchers in the conduct of previous field research.

Age

The age of the respondent is measured in years.

Education

Although many farmers receive informal training and work experience by working on the family farm, the measure used reflects the respondents’ level of formal education. This measure was selected because many farmers and related industry representatives are exposed to new ideas and innovations through their interaction with institutions of higher learning.

Respondents select the category that best describes their highest formal educational attainment: some high school (1), high school diploma (2), some college (3), undergraduate college degree (4), or graduate degree or coursework (5).

Farm size

Farm size is an indicator of the scale of the respondent’s farming operation, size of machinery used and impact on the environment. Farm size is measured by the combination of the number of acres owned and the number of acres rented.

Dedication to farming

Because of the nature of family farming, those that classify themselves as

“farmers” devote varying levels of participation of their time to farm work. Adoption of conservation technologies can be impacted by the nature of their work status on the farm, the time that they devote to their farming operation and how they learn about new farming innovations.

Dedication to farming is measured in this study using a categorical variable that indicates level of farm work status: retired (1), hobby (2), part time (3), and full time (4).

Conservation participation

Conservation behavior can be measured in many different ways such as the attendance at conservation-oriented meetings, attitudes toward conservation, or actual conservation strategies implemented on the farm. For purposes of this analysis, conservation behavior is operationalized as participation in conservation programs. This measurement was selected because it reflects the willingness of farmers to enroll in formal conservation programs and to commit to specific conservation strategies. Level of conservation participation is measured by the total number of private or government conservation programs in which respondents participate.

Liberalness of political orientation

Political orientation is operationalized in this study using an ordinal measure that indicates the degree of liberal political orientation of respondents. A standard indicator of political orientation is used to measure this variable. It avoids reference to specific political parties and includes the following categories: very conservative (1), conservative

(2), moderate (3), liberal (4), and very liberal (5). A higher number indicates a stronger orientation toward a liberal political viewpoint.

Use of no-till practices

The manner in which no-till practices are implemented on individual farms varies widely depending on the type of crops grown, the specific agro-ecological context and the management strategy chosen by the individual farmer. A farmer is classified a practicing continuous no-till if he has land that is no-tilled in consecutive cropping seasons independent of the type of crop being raised. The occasional practice of no-till implies that no-till practices are not used in consecutive years on specific land areas, but that they vary depending on the crop grown and other agro-ecological variables.

The use of no-till practices of respondents is operationalized in the following manner: does no currently use no-till practices (1), uses no-till practices occasionally (2), continuously no-tills some cropland (3), and continuously no-tills all cropland (4).

Awareness of carbon credit programs

Awareness of carbon credit programs is measured by a dummy variable. No awareness of carbon credit programs was coded as 0 and awareness as 1.

Epistemic distance

Farmers often are forced to rely on academic and outside information about conservation technologies in order to ensure that the practices that they implement are helping to remedy environmental problems. In the case of no-till farming, the concept of epistemic distance indicates the ease with which farmers can notice by themselves the specific benefits of their no-till practices. Because farmers often feel that they know their

60 land best, a second measure of epistemic distance was created in order to address how they perceive epistemological distance for other observers.

Two items on the questionnaire address the topic of epistemic distance, namely,

“easy for farmers to see benefits” and “easy for others to see benefits”. Five point Likert scales are used for each item. Possible answers for each item ranged from 1 (difficult to notice) to 5 (easy to notice).

Carbon credit payments

The receipt of carbon credit payments is operationalized by a dummy variable

with no coded as 0 for non-receipt of payments and as 1 for receipt of payments.

Alteration of practices

Continuous no-till is required in order to receive carbon credits. Some farmers

may need to alter their practices in order to meet the requirements to participate in carbon

credit programs. Alteration of farming practices is measured by a dummy variable with

no alteration of practices coded as 0 and alteration of practices as 1.

Satisfaction with no-till

Five questionnaire items refer to possible benefits received from use of no-till

practices. They are found in Table 3.1 and reflect the five most commonly noted benefits

of no-till farming that farmers free listed on the pilot survey. The items address both

economic and environmental benefits of no-till including: yields, inputs, correcting

problems, soil health, and labor. Each item offers five alternative responses on a Likert

scale which vary from 1 (lowest benefit) to 5 (highest benefit). The mean and standard

deviation of each item are also found on Table 4.2.

1 2 3 4 5 Index

1. Yields ---

2. Inputs 0.517 ---

3. Corrects Problems 0.496 0.493 ---

4. Soil Health 0.544 0.487 0.648 ---

5. Labor 0.199 0.265 0.247 0.190 ---

Index 0.754 0.719 0.792 0.736 0.528 --

Mean 3.680 4.090 3.740 4.080 4.020 19.633 Std. Deviation 1.044 0.890 1.014 0.871 1.092 3.452

n=215

Table 4.2 Inter-item correlation matrix of variables pertaining to satisfaction with no-till

Table 4.2 displays the correlations between the five variables that address the benefits of no-till practices. The inter-item correlations range from low to substantial

(Davis, 1971). The degree of correlation among these variables suggests that they are appropriate to combine into an index of satisfaction with no-till practices (Trochim and

Donnelly, 2007). In this case, an index is more meaningful and interpretable than each of the five variables on an individual basis (ibid).

Individual index scores represent the sum of the scores from each item, thus ranging from 5 (lowest) to 25 (highest). The Cronbach’s alpha coefficient for this index is .741 1.

Familiarity with carbon topics

Five items on the questionnaire address the degree to which respondents are familiar with five different carbon-related topics. These five topics include carbon sequestration, the Kyoto Protocol, the Chicago Climate Exchange, cap and trade legislation and carbon credit programs for farmers. Respondents were asked to self-rate their level of familiarity with each of the five topics listed. Each item offered five alternative responses which varied from 1 (lowest familiarity) to 5 (highest familiarity).

The mean and standard deviation for each item are presented in Table 4.3.

1 2 3 4 5 Index

1. Carbon Sequestration ---

2. Kyoto Protocol 0.398 ---

3. Chicago Climate Exchange 0.565 0.525 ---

4. Cap and Trade Legislation 0.416 0.616 0.623 ---

5. Carbon credit programs for farmers 0.707 0.407 0.692 0.519 ---

Index 0.779 0.734 0.854 0.801 0.823 --

Mean 3.180 2.110 2.330 2.070 2.960 12.650 Std. Deviation 1.163 1.230 1.244 1.183 1.113 4.738

Table 4.3 Inter-item correlation matrix of variables pertaining to familiarity with carbon topics

1 Cronbach’s alpha coefficient of reliability assesses how well a set of items measures a latent unidimensional construct. It is an average of the split-half correlations for all possible ways of dividing a test into two parts (Trochim and Donnelly, 2007). 63

Table 4.3 displays the correlations among the five variables that address the familiarity with five specific carbon-related topics. The inter-item correlations range from moderate to very strong (Davis, 1971). The degree of correlation among these variables suggests that they are appropriate to combine into an index of familiarity with carbon topics (Trochim and Donnelly, 2007). In this case, an index is more meaningful and interpretable than each of the five variables on an individual basis (ibid).

Individual index scores represent the sum of the scores from each item, thus ranging from 5 (lowest familiarity) to 25 (highest familiarity). Cronbach’s alpha for this index is .857.

Belief in Anthropogenic Climate Change

Four items relating to belief in anthropogenic climate change were selected for use from a survey commissioned by the National Wildlife Federation and conducted among Ohio voters in 2008 by Midwest Communications and Media.

The items address the occurrence of climate change, the anthropogenic cause of climate change, if the U.S. should be a world leader in addressing climate change, and if climate change is a problem that we should take responsibility for now and not pass on to the next generation. These items were selected from the National Wildlife Federation survey because they represent different aspects of anthropogenic climate change ranging from the cause of climate change to the immediacy of addressing the problem. Each item offered five alternative responses on a Likert scale which varied from 1 (strongly disagree) to 5 (strongly agree). Table 4.4 also displays the mean and standard deviation for each of the four items.

1 2 3 4 Index

1. Anthropogenic cause --

2. Is occuring 0.371 --

3. U.S. should be a world leader 0.540 0.423 --

4. Address problem now 0.613 0.428 0.798 --

Index 0.774 0.696 0.862 0.884 --

Mean 2.200 3.140 2.890 2.890 12.650 Std. Deviation 1.101 1.201 1.212 1.189 4.738

n=228

Table 4.4 Inter-item correlation matrix for belief in anthropogenic climate change

Table 4.4 displays the correlations among the five indicators used to measure belief in anthropogenic climate change. The inter-item correlations range from moderate to very strong (Davis, 1971). The degree of correlation among these variables suggests that they are appropriate to combine into an index of climate change views (Trochim and

Donnelly, 2007). In this case, an index is more meaningful and interpretable than each of the five variables on an individual basis (ibid).

Individual index scores represent the sum of the scores from each item, thus ranging from 4 (least belief) to 20 (highest belief). Cronbach’s alpha for this index is

.818.

Description of Survey Respondents

A total of 228 producers were surveyed at the Conservation Tillage and

Technology Conference (CTTC). The demographic characteristics of the respondents

and their farms are displayed in Table 4.5.

Minimum Maximum Mean Std. Deviation

Age 18 77 49.00 12.46

Education 1 5 3.35 1.06

Political orientation 1 5 2.10 0.75

Dedication to farming 1 4 3.48 0.69

Acres own 0 3200 389.79 474.75

Acres rent 0 3400 726.02 717.88

Farm size 36 5100 1138.26 1030.46

n=228

Table 4.5 Characteristics of respondents and their farms

Respondents range in age from 18 to 77 years. The average age of the farmer sub-sample is 49 years, which is younger than the Ohio average farmer age of 53.8 years

(USDA 2007). This distribution suggests that those interested in no-till farming tend to be middle aged with ore of them ranging in age from 36 to 62 years (one standard deviation from the mean).

The amount of land owned by farmers ranged from zero to 3,200 acres, with a

mean of 390 acres. Values for rented acreage ranged from zero to 3,400. The mean

rented acreage was 726 acres. In terms of total acreage, the smallest farm is 36 acres and

the largest 5,100 acres. The overall mean farm size is 1,138 acres.

Level of Education Frequency Percent

Graduate degree or coursework 31 14 Undergraduate degree 89 39 Some college 45 20 High school diploma 63 27 Some high school 2 1

Total 228 100 n=228

Table 4.6 Education of respondents

Table 4.6 displays the frequencies of the level of education of respondents included in the survey. Those with an undergraduate degree compose the largest portion of the sample, 39 percent. Those with some high school compose the smallest portion of the sample, 1 percent.

Frequency Percent

Very conservative 45 19 Conservative 125 53 Moderate 51 22 Liberal 6 3 Very liberal 1 0

Total 228 100 n=228

Table 4.7 Political orientation of respondents

Table 4.7 displays frequencies pertaining to the political orientation of respondents. Respondents that self-categorize their political orientation as conservative compose the majority of the sample at 53 percent. The moderate category is also substantial, composing 22 percent of the total sample. Respondents that categorize themselves as liberal or very liberal make up about three percent of the sample.

Frequency Percent

Full time 127 56 Part time 82 36 Hobby 10 4 Retired 9 4

Total 228 100 n=228

Table 4.8 Dedication to farming

Data on the variable of farm work status is presented in Table 4.8. A majority of farmers (56 percent) work full time on their farms. This is comparable to the Ohio average of 55.9 percent of farmers working full time on their farm (USDA 2007). Part time farmers compose 36 percent of the sample, hobby farmers 4 percent, and retired 4 percent.

Qualitative Methodology

Consistent with the emphasis being given to social, cultural and contextual factors related to the adoption environment, qualitative data were also collected for analysis.

Quantitative data are very useful in providing an overall picture of farmer and industry professionals’ perceptions, but little insight is gained on individual farmers, their specific agro-ecological context, and the social and cultural norms in their communities. The primary goal of the qualitative data analysis is to add depth to interpretation of the data gathered through the survey. Qualitative data are valuable to the study because they have the potential to lend key insights into the role of culture on decisions related to no-till farming, and related to this, to highlight issues related to adoption of carbon credits not previously considered.

Data Collection

Qualitative data was collected using a variety of techniques including:

• Semi-structured interviews with farmers

• Semi-structured interviews with representatives of carbon credit aggregator firms

• Direct observation at meetings of no-till producers

• Direct observation at carbon credit recruitment meetings

Interviews involved direct interaction between the researcher and respondents.

Semi-structured interviews were conducted with the researcher asking guiding questions.

This format is uninhibited in comparison to the structured survey instrument format

(Trochim and Donnelly 2007). Semi-structured interviews are a common research technique and are particularly useful when exploring new contexts (ibid).

Direct observation consists of observing and sometimes participating in relevant events while being as unobtrusive as possible (Trochim and Donnelly 2007). It is distinguished from participant observation because a direct observation implies that the researcher does not attempt to become a participant in the context being observed.

Data Analysis

Unlike quantitative data, the interpretation of qualitative data does not use mathematical methods. One technique used to analyze qualitative data is grounded theory. This approach requires the researcher to develop specific ideas prior to starting research and then make new observations to clarify previously developed ideas. The use of grounded theory allows previously defined concepts to be better understood in a new context and in relation to a different population.

Data from interviews were recorded using a digital voice recorder. They were later transcribed into a Microsoft Excel spreadsheet on which individual interviews occupy rows, and new themes are represented by individual columns. This method allows for an easy assessment of major themes addressed in interviews.

The researcher recorded direct observations by hand during the event. These observations were similarly transcribed into a Microsoft Excel spreadsheet, allowing for common themes to be grouped together.

Summary

An overview of methodological tools and related considerations germane to this study were presented in the chapter. Initially the general model and associated hypotheses that guide this study were presented. This was followed by a discussion of the data collection process and the measurement of variables found in the model.

Measurement considerations included consideration of reliability and validity of the data used to represent variables in the model. Statistical tests used in the following chapters to assess the validity of the proposed models for carbon credit adoption were also described.

A description of the qualitative data collection process and analysis followed.

CHAPTER 5

PRESENTATION OF DATA ON ADOPTION OF NO-TILL PRACTICES

Statistical tests related to the model for adoption of no-till agriculture is presented in this chapter. No data are presented on proposed relationships between variables in the model and the purchase of carbon credits for no-till farming because practically none of them purchased these credits. A full discuss of this unexpected finding is presented in

Chapter 6. Initially, descriptive statistics for several of the variables found in the model are presented. These are use of no-till practices, satisfaction with no-till practices, familiarity with carbon-related topics, belief in anthropogenic climate change, and degree of epistemic distance from no-till impacts. This section is followed by a statistical test of the model beginning with a test of the hypotheses contained therein. Tests of these individual bivariate hypotheses are followed by a multiple regression analysis that estimates the overall level of predictability of the model.

Frequency Distribution of Variables in the Model

Use of No-Till Practices

The prevalence of different patterns of use of no-till practices is found in Table

5.1. A majority of the farmers either have all (37 percent) or some (23 percent) of their land in no-till. This represents a major change in tillage practices because the transition from tillage to no-tillage practices occurred during the last thirty years. It is particularly significant in view of the machinery costs involved in making the transition. These may be off set by reduced costs for other inputs. Farmers that use no-till practices occasionally make up 36 percent of the sample. These farmers practice traditional tillage methods in addition to no-till methods and are apt to move from one form of tillage to another, depending on the crops which they have under cultivation and other factors, such as soil quality and characteristics. Five percent of the farmers who participated in the survey do not use any no-till methods at this time. Several responded that they are in the process of learning more about no-till so that they can implement the practice on their own farms.

No-Till Practices Frequency Percent

All land in continuous no-till 85 36

Some land in continuous no-till 53 23

Use no-till practices occasionally 82 36

Do not use no-till practices at this time 11 5

Total 231 100 n=228

Table 5.1 Response Frequency - Use of No-Till Agriculture 74

Satisfaction with no-till

The distribution of scores for the index representing satisfaction with no-till agriculture is presented in Table 5.2. These data suggest that most of the farmers are quite satisfied with their use of no-till practices. As was explained in the previous chapter, scores range from a possible high of 25 to a low of 0. High scores are defined to be those in the top quintile of possible scores (20 – 25). Forty-three percent of the farmers who responded placed themselves in this category. Another 46 percent of the respondents rated their satisfaction with no-till agriculture such that they fell into the second tier of possible scores (16 -19). Only eleven percent of the respondents indicated that they had lower levels of satisfaction with no-till practice usage (scores = 0 – 15). Lower levels of satisfaction ranged from 8 – 15, so none of the respondents indicated that they were highly unsatisfied with no-till farming. The mean score for this index is which suggests an overall high level of satisfaction with the practice.

Score Frequency Percent Index Rating

25 14 7 24 15 7 High Satisfaction 23 18 8 22 24 11 43% 21 22 10 20 23 11 19 21 10 Moderate Satisfaction 18 26 12 17 15 7 46% 16 14 7 15 8 4 14 7 3 Low Satisfaction 13 2 1 12 1 1 9% 11 1 1 10 0 0 9 2 1 Very low satisfaction 8 2 1 7 0 0 2% 6 0 0 5 0 0

Total 215 100 n=215

Table 5.2 Response frequency - level of satisfaction with no-till practices

Visual recognition of benefits from no-till agriculture

Data in Table 5.3 are about the epistemic distance phenomenon discussed in previous chapters. Essentially, epistemic distance refers to the ease with which individuals are able to empirically observe the benefits that accrue to a behavior, such as the use of no-till agriculture. In the latter case, it refers to whether or not individuals can see a difference in crops and/or related soils, etc. due to use of no-till practices.

Data are presented about whether or not respondents believe that farmers can see the benefits from use of no-till methods and about whether or not they believe that others see the benefits from use of no-till methods. A majority of the respondents (59 percent) agree that farmers do in fact observe the benefits of no-till. On the other hand, only 33 percent of them agree that the benefits of no-till are easily observed by others. The

Easy for Farmers to See Benefits Easy for Others to See Benefits Frequency Percent Frequency Percent Strongly Agree 53 24 13 7 Agree 75 35 56 26 Neutral 66 31 107 49 Disagree 18 9 31 15 Strongly Disagree 3 2 8 4

Mean 3.71 3.17 Std. Deviation 0.990 0.901 n=215

Table 5.3 Response frequencies - visual recognition of benefits from no-till

difference is most marked in the strongly agree category with about one fourth of the respondents strongly agreeing that farmers can observe differences, but only seven

77 percent strongly agreeing that other can observe differences. About a third voiced a neutral response in reference to farmers’ ability to observe changes and about half indicated neutral responses for the ability of others to observe changes.

Familiarity with carbon topics

Data on the level of familiarity of respondents with carbon topics are presented in

Table 5.4. The maximum range of scores for this index are from 25 – 0. Assuming that

Score Frequency Percent Index Rating

25 5 2 24 2 1 Very Familiar 23 1 0 22 6 3 9% 21 3 1 20 3 1 19 6 3 18 6 3 Familiar 17 13 6 16 10 4 24% 15 19 8 14 15 7 13 15 7 Not Familiar 12 26 12 11 17 8 41% 10 21 9 9 16 7 8 13 6 Not Familiar At All 7 11 5 6 6 3 27% 5 14 6

Total 228 100 n=228

Table 5.4 Response frequencies - familiarity with carbon topics

the top quintile of possible scores represents much familiarity with carbon topics, it is

evident that only 9 percent of the respondents possess this level of knowledge. To the

contrary, assuming that the bottom quintile of possible scores represents minimal

knowledge of carbon issues, over a quarter of the respondents would fit into this

category. Furthermore, another 41% would be classified as having very limited

knowledge of these topics. In sum, approximately 7% of the sample has little or no

knowledge of carbon topics related to climate change.

Anthropogenic Causes of Climate Change

Data about respondents’ beliefs about the degree to which humans are responsible for

climate change are found in Table 5.5. These data have been used to categorize

respondents according to the degree to which they believe in human induced climate

change. Those with scores equivalent to the four highest possible outcomes on the index

were categorized as Believers and represent eleven percent of the sample. Those with the next four highest possible outcomes were categorized as Acceptors . They represent forty percent of the sample. When combined with Believers , they represent fifty-one percent,

or a slight majority of the sample. Those with scores equivalent to the following four

categories were categorized as Skeptics . They represent twenty-eight percent of the sample. And those with scores equivalent to the bottom four categories were categorized as Non-Believers . They represent approximately twenty-one percent of the respondents.

Score Frequency Percent Index Rating 20 5 2 19 3 1 Believer 17 6 3 11% 16 11 5 15 12 5 14 26 11 Acceptor 13 19 8 40% 12 35 15 11 25 11 10 16 7 Skeptic 9 11 5 28% 8 12 5 7 14 6 6 13 6 Non-believer 5 7 4 21% 4 13 6

Total 228 100 n=228

Table 5.5 Response frequencies – anthropogenic causes of climate change

Statistical Test of Hypotheses in Model

A statistical test of the hypotheses found in the general model underlying this

study is found in the following table:

______Use of ______Independent Variables ______No-Till Mean SD_

Background Age……………………………………………………. .13* 49.0 12.5 Farm Size……………………………………………… -.12* 1138.3 1030.5 Farmer Education……………………………………... .02 3.4 1.1 Farm Work Status…………………………………….. .01 3.5 0.7 Behaviors Participation in Conservation Programs…..………….. .18** 1.5 1.3 Attitudes/Beliefs Liberal/Conservative Political Orientation…………… .04 2.1 0.8 Belief in Antrhopogenic Cause of Climate Change….. .02 11.1 3.8 Familiarity with Carbon Topics……………………… .10* 12.7 4.7 Epistemic Distance – Farmers………………………… .51*** 3.7 1.0 Epistemic Distance – Others………………………….. .33*** 3.2 0.9 Satisfaction with No-Till Practices…………………… .55*** 19.6 3.5 ______* p < .05; ** p < .005; *** p < .001

Table 5.6: Correlations of Independent Variables with Use of No-Till Practices

Data found in Table 5.6 indicate that not all of the hypothesized relationships are statistically significant for this sample. In regard to demographic characteristics, the data indicate that age is positively correlated with use of no-till practices, while farm size is negatively correlated with their use. Neither level of education nor farm work status was correlated with use of no-till practices at a statistically significant level. Thus, they will be dropped from further analyses.

Data in the same table show that the number of conservation programs in which farmers participate is significantly correlated with use of no-till practices. This is as expected since use of no-till practices is also a conservation practice. As was true for farmer demographic characteristics, the significance of relationships between use of no- till practices and different attitudes and beliefs is also mixed. Farmer political orientation and belief in the importance of anthropogenic causes for climate change failed to correlate with use of no-till practices at a statistically significant level. On the other hand, variables reflecting farmer assessment of the visibility of effects of no-till farming on farm production systems were highly correlated with use of no-till practices, as was farmer satisfaction with the use of them. And familiarity with carbon topics related to climate change was also correlated with use of no-till practices, although at a lower level of statistical significance.

These findings will require some modifications to the conceptual model presented in Chapter IV which will be addressed in the discussion section of this chapter.

Those variables that are not related to use of no-till practices at a p < .05 should be deleted from the model. They will also be discarded from further analyses.

Relative Importance of Variables in the Model

In order to assess the relative ability of the model – absent those variables that did not have a statistically significant correlation with use of no-till practices – the remaining variables were entered into a regression equation. In this equation, use of no-till practices was regressed on the remaining variables. The results are found in Table 5.7.

They indicate that the unstandardized regression of no-till practices on each of the independent variables was at least twice the size of the standard error, except in the case

82 of the epistemic variable dealing with “others.” Thus, all of these variables would be expected to remain in the model. The fact that the “others” epistemic variable was not statistically significant probably reflects a high degree of colinearity between it and the

“farmer” epistemic variable.

The importance of the different independent variables in predicting use of no-till practices varies considerably as reflected in the “Beta” coefficients in the model. As might be expected, the strongest relationship is between satisfaction with no-till agriculture and use of no-till practices. The B for this relationship is .42. This is followed in level of importance by the farmer epistemic variable which has a B of .24.

Participation in other conservation programs has smaller sized B (.13) as do the two background variables. The B for the age – no-till relationship is .15 and that for the farm size – no-till relationship is -.05.

The adjusted R squared for the model is .37 which implies that the variables in the model explain 37 percent of the variation in us of no-till practices. Although not very robust statistically, it does indicate that the model has value in helping to explain the use of no-till practices.

b Independent Variables (s.e.) β

Age 0.010* 0.148 (0.005)

Farm size 0.042 -0.053 (0.002)

Conservation participation 0.091* 0.133 (0.045)

Easy for farmers to see 0.220* 0.244 0.097

Easy for others to see -0.104 -0.107 0.088

Satisfaction with no-till index 0.107** 0.419 0.024

Constant -0.210 Adjusted R 2 0.370 S.E.E. 0.703

* Significant at p<.05 in a one-tailed test n=215 ** Significant at p<.001 in a one-tailed test

Table 5.7 OLS regression of the use of no-till practices on age, farm size, conservation participation, epistemic distance variables and the satisfaction with no-till index

Discussion of No-Till Adoption Model

The no-till adoption model was tested in this section. Statistical analyses resulted in the need to modify the original model by deleting several of the variables found in the original version because of the lack of statistically significant relationships. The results suggest that several of the demographic variables remain as important predictors of the use of no-till practices. Perhaps the most significant variables are satisfaction with no-till agriculture and the belief that the effects of use of no-till agriculture on production and the physical environment are evident to farmers. This is an important addition to the classical diffusion model and suggests that Bordieu’s theory of cultural capital is relevant to the interpretation of the practice of no-till agriculture .

CHAPTER 6

PRESENTATION OF DATA ON ADOPTION OF CARBON CREDITS

Presented in this chapter are results pertaining to the adoption of carbon credits.

Because the adoption of carbon credits, in this case, directly relate to adoption of no-till practices, data pertaining to no-till practices and carbon credit eligibility are presented first. Following this are data pertaining to actual carbon credit adoption and characteristics of those adopters. The majority of the results presented in this chapter are qualitative in nature, simply because the data set that was used for quantitative analyses does not contain sufficient numbers of no-till farmers who participate in the carbon credit market. This factor was unanticipated, but probably reflects the need for a cap and trade policy in order to make participation in this market attractive to Ohio farmers.

Carbon credit eligibility

No-Till Practices Frequency Percent Eligibility

All land in continuous no-till 84 37 Eligible for carbon credits Some land in continuous no-till 52 23 59%

Use no-till practices occasionally 82 36 Not eligible for carbon credits Do not use no-till practices at this time 10 5 41% n=228

Table 6.1 No-till practices and carbon credit eligibility

Table 6.1 displays results pertaining to the use of no-till practices and carbon credit eligibility. Farmers with land in continuous no-till are eligible for carbon credits.

Fifty-nine percent of the sample has all or some of their land in continuous no-till and are eligible for carbon credits. Respondents making up 41 percent of the sample use no-till practices occasionally or not at all, making them ineligible to receive no-till carbon credits.

Participating in Program Aware of Program Frequency Percent Frequency Percent

No 132 97 17 12

Yes 4 3 119 88

Total 136 100 136 100 n=136

Table 6.2 Eligible respondents awareness and participation in carbon credit programs

Table 6.2 displays data on farmer participation and awareness of carbon credit programs. The majority of eligible respondents, 97 percent, indicate that they do not currently receive carbon credit payments. Only four respondents in the sample do currently receive carbon credits. This contrasts with a high percentage of eligible respondents, 88 percent, that have heard of carbon credit programs. Eligible respondents who have not heard of carbon credit programs compose 12 percent of the sample. This data indicates that most farmers engaged in continuous no-till farming are aware of carbon credit programs but do not currently participate.

Characteristics of carbon credit adopters

A description of the respondents receiving carbon credits is found in Table 6.3.

Ages of the four respondents vary from 29 to 59 years. The four respondents all have college degrees, and one has coursework toward a graduate degree. All of the farmers work full-time on their farms. Participation in conservation programs varies from one to four programs. All of the respondents receiving carbon credits are conservative, but vary

88 widely in their beliefs pertaining to climate change, with two respondents that are skeptical, one that is an acceptor and one that is a believer. Farm size varies among the

Farmer 1 Farmer 2 Farmer 3 Farmer 4

Respondent characteristics: Age 45 29 57 59 Education College degree College degree College degree Graduate coursework Work Status Full-time Full-time Full-time Full-time Conservation participation 4 1 2 2 Political views Conservative Conservative Conservative Conservative

Farm variables: Acres own 500 70 125 160 Acres rent 900 400 1300 2000 Total farm size 1400 470 1425 2160

Index scores: Satisfaction with no-till High High Moderate High Familiarity with carbon topics Very familiar Familiar Not familiar Very familiar Climate change beliefs Believer Skeptic Skeptic Acceptor

Carbon credits: Alter practices No No No No Tell friends? Yes No Yes No n=4

Table 6.3 Description of respondents receiving carbon credits

four respondents, but one characteristic that they do have in common is that they rent more land than they own. Familiarity with carbon topics varies widely among the four respondents, ranging from not familiar to very familiar. Satisfaction with no-till practices is moderate to high, and none of the respondents had to alter their farming practices in order to receive carbon credits. Two of the respondents tell friends and neighbors that they receive carbon credits, while two do not reveal this information.

Recruitment meetings

Several meetings designed to recruit farmers into a carbon credit program were attended by the researcher. A typical recruitment meeting will be described here, along with observations and comments from the attendees.

The recruitment meeting begins with a power point presentation that gives an overview and explanation of carbon credit markets and the CCX. This is accompanied by dollar figures that demonstrate the amount of money that has been dispersed to farmers through the carbon credit program. A description of the carbon credit aggregator business is included at this point.

One of the carbon credit aggregators observed likes to draw parallels between the new carbon markets and familiar agricultural terms. For example, he refers to carbon as a “new commodity”. Farmers are familiar with the term commodity and through the comparison, immediately have a sense of how carbon markets are like any other agricultural commodity markets. Additionally, he describes the carbon credit aggregator that he works for as the “country elevator of carbon credits”. By describing the aggregation business as an elevator, farmers have a sense of the role that carbon credit aggregators play - a middleman between the farmer and the commodity market.

Inevitably, the discussion turns to the reason for the new markets’ formation and climate change. The problem that carbon causes in the atmosphere, how carbon is released from soil and how it is sequestered in soil are topics covered in this section. It should be noted here, that in all meetings attended, the presenter sympathized with non- believers in climate change. Presenters did not try to convince them that it was occurring

or that it has anthropogenic causes. Rather, they used vague and third-person terms such

as “ some believe that climate change is caused by the actions of humans.”

After discussing climate change, the presenter would turn the discussion to the benefits that the market can have on farmers, no matter what their belief. One aggregator put it this way: “Whether or not you believe global warming is happening, this is a market that can benefit you. We want agriculture to be a part of it.”

It should be noted that the explanation of carbon credit markets and climate change usually lasted from 30 to 45 minutes. At this point, the presenter begins to explain the more technical aspects of the program such as the conservation practices eligible for enrollment in the program. Details such as the allowable machinery, crop rotations and land disturbance are discussed at this point. The presenter also walks through a sample equation that shows farmers how to calculate how much they might receive per acre.

Farmers typically have many questions by the end of the presentation. Questions often touch on the voluntary nature of the market, wondering who buys the credits and if the program will remain voluntary. “You might not have any buyers if it is all voluntary?” asked one meeting participant. The nature of carbon offsets are often also a source of questions. “We’re selling something we’ve been making all along?” asked one meeting participant. “Why, all of a sudden, is there a demand for it?” Other participants want to know more about the technical aspects of the program, such as, “What would happen if your farming practices change and you want to till. Who has to pay back the credits?” Other common questions include the minimum acreage for enrollment, what

91 crop rotations and machinery can be used, zoning restrictions, and the enrollment of fallow acres.

Discussion after the meeting ends often focused on the material presented.

Farmers discussed the merits and drawbacks of the program amongst themselves and with the presenter. These comments vary widely, but one comment made by a participant sums up the general attitude, “Money for nothing, I like that part of it. Kind of feels like you are getting rewarded for doing the right thing. But I’m not willing to relinquish control for that kind of money.”

The carbon credit contract

It is vital to examine the contract required to be completed by farmers because difficult and excessive amounts of paperwork is a possible barrier to participation in a carbon credit program.

There are currently two carbon credit aggregators selling soil offset carbon credits on the Chicago Climate Exchange (CCX). The information required by the CCX is uniform across aggregators, thus the contracts from each business are very similar. The largest difference between the contracts is that one aggregator supports exclusively web- based contracts, while the other accepts paper contracts. As noted in one of the carbon credit aggregator profiles below, the nature of the web-based contracts are one of the biggest barriers for some farmers who are not comfortable on a computer. In this case, they need to find a computer literate individual with a computer and an internet connection that is willing to help them fill out the contract.

The contract itself is relatively short and simple. The first page requests the farmers’ contact information and signatures. The subsequent pages contain space to

92 enroll separate parcels of land. For each parcel the farmer is required to enter the irrigated and non-irrigated aces, the FSA tract number, the legal description of the land, and note any exempt areas that are not being enrolled. The farmer is also required to note if any crop residue removal occurs and the number of acres. The form for each parcel of land occupies one-third of a page.

Supporting documents that are required are 1) the contract (described above), 2) a copy of the FSA maps for the enrolled parcels of land, 3) a copy of the FSA 578 reports,

4) a voided check from a checking account (to allow for an electronic transfer of funds).

The carbon credit aggregators utilize the FSA documents because they are commonly held documents by farmers across the nation that are required in order to receive crop subsidies and most crop insurance. Additionally, if they do not have updated FSA maps of their land, the carbon credit aggregators accept documents from Google Earth or other computer-based satellite mapping programs.

Also contained in the carbon credit contract are all the terms and conditions for

CCX offset projects. In the printed contract, a map of the carbon sequestration rate is provided in order that the farmer may examine the CCX sanctioned carbon sequestration rate for their area. Specific information is provided for each zone including acceptable practices, machinery, and crop rotations.

Finally, it should be noted that the contact information of the carbon credit aggregators are featured in several prominent places on the contract. Information provided includes street address, toll-free phone number, e-mail address and web address.

Profiles

Individual profiles are composed of data from qualitative interviews conducted

throughout the duration of the study. Profiles of farmers are presented first and industry

professionals second. These profiles have been chosen because they serve as a

representative examples of the individuals interviewed.

Farmer 1

Farmer 1 runs a 953 acre farm features a mixture of produce, pigs, cattle and crops. His land has been continuously no-tilled since 1971 with a corn-soy-wheat-cover crop rotation. The cover crops were the “icing on the cake” feature that he added in

1978. The cover crops that he rotates are cereal rye, hairy vetch, Austrian winter peas and oilseed radishes. He is a strong believer in the notion that “soil should always be green”.

Farmer 1’s farm has been in his family for over two hundred years. He purchased the farm from his grandfather in 1976, but isn’t sure how long it will stay in the family because urban development is creeping closer and closer to his farm.

Farmer 1 has a deeply rooted conservation mentality. He says that he most respects farmers that plan for the long-term and are concerned about preserving and improving soil. He says that he can visibly judge the conservation efforts of other farmers by looking in their ditches after a hard rain. If the ditch is filled with soil, he can tell that the farmer isn’t doing a good job at soil conservation. He is also biased to older farmers on small farms, because he says that many “young fellows” try to manage thousands of acres and forget about maintaining the soil. They may be successful in the

94 short term, but experience has taught him that soil maintenance is essential to long-term productivity.

Farmer 1 often mentors farmers who want to try no-till and other conservation practices on their land. He says that this is important to him because he sees agricultural researchers and extension agents at a loss for giving farmers practical advice. As an experienced farmer and conservationist, he is glad to share his practical experience with neighbors. He also notes that researchers are often honed in on only one small area of production, and few, if any, research how the system can work as a whole. He is proud that many OSU researchers visit his farm to see how various aspects of his farming system works. Farmer 1 also actively promotes general agricultural literacy in his community by organizing farm field days through community organizations.

Farmer 1 is not currently enrolled in the carbon credit program, although he has a personal relationship with one of the carbon credit aggregators in the area. He says he isn’t getting carbon credits mainly because he rents 90 percent of the land he farms, with contracts ranging from one to five years. He is hesitant to enroll because of his uncertainty to fulfill the five-year carbon credit contract. However, he has considered putting just his home place, which he owns, in a carbon credit program.

Farmer 1 is aware that climate change legislation may change the opportunities for farmers to participate in carbon credit programs and may also influence the price of credits. Part of the reason why he hasn’t enrolled his land in carbon credits is because he is waiting to see what the new president is going to do about climate change and what new legislation is introduced.

Furthermore, Farmer 1 finds that the price is too low for carbon credits to be an

urgent concern at this time. He is interested in water quality credits because he sees it as

a much more lucrative program.

Farmer 1 notes that there are many different “cliques” of conservationists. There

are a group of farmers who are in favor of easements, another group which is against

easements, and yet another group of farmers who are in favor of an easement program

that decreases your taxes while you are enrolled. Yet another conservationist clique is

composed of farmers who define themselves as soil conservationists.

Farmer 2

Farmer 2 has been continuously no-tilling for about 12 years on his 1,000 acre farm. He uses a corn-soy rotation, and said he wasn’t completely comfortable with no-till until he achieved 200 bushels of corn per acre. He currently receives carbon credits for all of his no-till acreage.

Farmer 2 notes that comfort with continuous no-till is a must before enrolling in carbon credits. There are a limited number of farmers in continuous no-till in the state because many farmers use traditional tillage practices for their corn and only use no-till practices for soybeans. Farmer 2 says that it is hard to promote carbon credit to non- continuous no-tillers because it would require a change in their current management practices. “Continuous no-tilling,” he said, “requires a person to rethink their whole management system.”

Farmer 2 was first made aware of the notion of carbon sequestration through a participatory research project on his farm with the University of Akron. The student workers on the project taught him the science behind carbon sequestration and how

96 farming practices affect it. Due to exposure to this research project, Farmer 2 was already aware of the relationship between no-tilling and carbon sequestration by the time he found out about carbon credit programs for farmers.

Farmer 2 found that enrollment in Iowa’s AgraGate carbon credit program was easy. “I only had to buy a couple of postage stamps, make a couple of phone calls…I think I made about a ten dollar investment into finding out about carbon credit programs.” Likewise, he says that the paperwork is not overwhelming and that it is easy to fill it out with the help of the required FSA forms. If questions arise, he said, “The secretary in Iowa always answers the phone and is knowledgeable about the program. It is easy to get answers to your questions.”

Farmer 2 said that enrolling in carbon credits was a “no brainer” and that it is one of the least invasive programs that farmers have the opportunity to enroll in because “no one is breathing down your neck.” He understands the erratic tendency of the market- based program and says that the payments are like taking a “leap of faith.” He isn’t particularly bothered by the unreliable prices because he says he would be continuously no-tilling even if he wasn’t receiving carbon credits. Farmers are used to fluctuating prices because all agricultural commodities are sold on markets with variable prices, he notes. Not all of Farmer 2’s credits have been able to sell this year, due to a glut of credits on the market and low prices.

Despite the low rate of compensation and the risk of not selling the credits at all,

Farmer 2 feels that the program sends a positive message to farmers. “Before, I knew I was doing something good. Now I feel like I’m being rewarded for my conservation

97 efforts.” Participation in carbon credit programs is just one “spoke of the wagon wheel” in terms of the various rewards he receives for his conservation efforts.

Stewardship of the land is a frame of mind as well as a lifestyle, according to

Farmer 2. He credits his conservation mindset to his tech school education. Getting off the farm for a couple of years and learning about agriculture from a different perspective made him feel like he was “a part of something bigger.” He now appreciates how agriculture and his farm are a part of a bigger system and thanks his dad for forcing him to continue his education. Farmer 2 is focused on leaving his land better than he found it, noting that improvement efforts are “a way of life”.

When asked for his opinion on why other farmers aren’t interested in carbon credit programs, he cites two main reasons. First, many farmers do not continuously no- till because they prefer a “rotational” tillage depending on the crop. Second, he said,

“Folks don’t grasp carbon credits as a financially viable thing…so if you aren’t already conservation minded you aren’t going to want to be a part of that positive thing.” He took special care to note that Al Gore isn’t his hero or anything, but that he respects all good stewards of the land.

Among his friends and neighbors that meet the program requirements, Farmer 3 said that he thinks they don’t sign up because they don’t have “that conservation mentality”. In order to get more people to sign up he said that program administrators should try to “get it believable”, mainly by getting local people to promote the program and better educating farmers as to what it is all about.

Farmer 3 Farmer 3 has a 3,300 acre farm that he has maintained since 1980. He didn’t learn to farm with his dad, as many other farmers did. Rather, he developed his farming philosophy by going to farmer conferences, meetings and other extension events. He says that this gave him a leg up over other producers, who had to fight the we’ve-always- done-it-this-way mentality.

Farmer 3 has been no-tilling since he bought his farm in 1980. He says you can tell who is a good farmer by seeing if they use a plow or not. He maintains a firm belief that “tillage covers mistakes”. The disconnect between farming philosophies has caused

Farmer 4 to see a clear division in “farmer cliques”, with one clique composed of no-till farmers and the other made up of those still doing traditional farming practices. The lines of the cliques are not hard and fast, because he has friends from the “other side”.

However, his closest friends are no-tillers and they often talk about their no-till practices together.

Farmer 3 is interested in getting carbon credits. He first heard about carbon credits at the National No-Till Conference, from meetings of the Ohio No-Till Council, and through a personal relationship he has with a carbon credit aggregator. He says that he has been intending to get the paperwork done for months, but just hasn’t gotten around to it. He notes the current low price of carbon, and the fact that there have been wild fluctuations in the market over the last year. In particular, he is worried about some re- tiling that he would like to do in certain spots on his farm in the near future, and is worried that carbon credits would restrict the flexibility he would have to do this. He

99 doesn’t know enough details about the program to know if the tillage required with the re-tiling project would be restricted or not.

Farmer 3 doesn’t believe that climate change is caused by human activity, and cites the natural cycles that have altered the climate in the past. However, he is still interested in the economic incentive of carbon credits, especially because he is already doing those practices. He isn’t worried that by getting carbon credits his friends and neighbors would think that he believes in climate change, but rather that he is a good farmer for getting paid for a practice that he is already doing.

Farmer 4

Farmer 4 is an interesting case in the fact that he has obtained carbon credits specifically to gain knowledge about the process and also to give himself cultural capital in the form of credibility with his students.

Farmer 4 is a professor of economics at a small liberal arts school, where he has worked for the last twenty years. He teaches many undergraduate classes in economics, including environmental economics. It is for purposes of this class in particular that he has gotten carbon credits in order to lend credibility to himself as an instructor because he can explain the process first-hand to students. By doing so he demonstrates that his confidence in carbon credit markets extends beyond academic knowledge. He notes that for him, obtaining carbon credits is “not about the money, it is about the message.”

Farmer 4 is also involved in other contemporary agricultural movements. He describes himself as a “cheerleader” of both the local foods and the farmland preservation movements.

100

It is important to note that Farmer 4 was in the process of trying to enroll his acreage in another conservation program when he heard about carbon credits. He became frustrated with the structure of the government agency he was working with at the time.

The agency seemed to be structured in such a way that the personnel were knowledgeable about the particular programs that they offered, but there was no one to act as an advocate for farmers to advise them which program they should apply for. The lack of a holistic, farmer-based approach to conservation frustrated Farmer 4, who abandoned ideas to enroll in the Wetlands Reserve Program and pursue carbon credits instead.

Farmer 4 had access to high quality information about carbon credits. The carbon credit aggregator he was currently working with was an individual he had known for years. Thus, he had already had a relationship of trust with this individual and had no hesitation on relying on him for advice about carbon credits.

Carbon Credit Aggregator 1

Aggregator 1 worked in the beef cattle marketing division of his company for many years and has only recently been transferred to the carbon credit division. His job description includes diffusing information about carbon credit programs and motivating farmers to participate. At this time he mainly works with no-till farmers and farmers that convert cropland to grass. Another individual in the company works with forestry offsets.

Aggregator 1 believes that most farmers do not participate in carbon credit programs because they do not have enough information about the program to make an informed decision. The type and quality of information that they have may also be a factor. They may have heard of carbon credits in agriculture industry publications, at

101 meetings or on the news, but they do not have a personal connection with an individual who can help them enroll in the program.

The link between face-to-face personal relationships and participating in carbon credit programs is a vital component to the diffusion of carbon credit programs among farmers, according to Aggregator 1. He works in many different areas of the country, and indicates that the easiest way to get farmers to consider enrolling in carbon credits is to

“sell” a local change agent on the idea. He cites an example of a case of successful diffusion in Nebraska. Several employees of a local Soil and Water Conservation District participated in a training program on carbon credits. Deciding that the program had a lot of potential for the farmers in their area, the SWCD employees presented the program to farmers in Nebraska. The enthusiasm and interest in the program spread and many

Nebraska farmers signed up. Aggregator 1 contributes the successful diffusion of the program in Nebraska to the combination of the enthusiasm of the SWCD employees who were genuinely excited about the program’s potential and the long-term personal relationships that the SWCD employees have with the farmers in their area.

Because of the benefits of locally-based change agents, Aggregator 1 sees his role as mainly spreading the word about carbon credit programs. He usually associates himself with a local change agent in the area in order to lend himself credibility among farmers as he attempts to spread the word about carbon credits.

Another issue with the diffusion of carbon credits is the awkward conversion between the price of a ton of carbon dioxide and the carbon credit payment that the farmer receives per acre. Additionally, fluctuating market prices do not allow Aggregator

1 to say with any certainty what the price of the carbon credit payment will be for

102 prospective farmers. Emphasizing to farmers that prices are not locked in when they enroll, he encourages them to “not be discouraged by the low price you see right now.

We’ve seen big price jumps in a matter of months.”

Aggregator 1 notes, “We can’t tell a landowner what price we are going to sell their credits at. We can’t even tell them when we are going to write them a check.” He can only estimate what the payment will be and demonstrate for prospective farmers what the likely future market scenario will be.

Aggregator 1 finds that his presentations about carbon credits are usually met with skeptical attitudes related to the notion of climate change, the feasibility of the program and the current low price of carbon.

Carbon Credit Aggregator 2

Aggregator 2 has worked for the aggregator agency for 15 years, and only in the past three years has been reassigned to the carbon credit division. She has many years of experience working with farmers.

Aggregator 2 finds that the farmers she works with to obtain carbon credits fall into three categories: no-till farmers, ranchers (rotational grazing), and retired farmers converting cropland to grassland. She says that all of the no-till farmers that she works with have decided to go no-till for other reasons, such as conserving moisture, fuel and labor.

In order to obtain carbon credits through her aggregation company, farmers must complete an online form. She says that this is perhaps one of the biggest barriers that farmers face when obtaining carbon credits because many farmers are not comfortable using a computer. Many of the forms and phone calls that come through her office are

103 from the children or grandchildren of the farmer. They are more computer literate and can easily find and translate the worth of information found online.

Aggregator 2 finds that one of the biggest obstacles that carbon credits programs face is a bad reputation. Carbon credits seem to have a bad reputation both inside and outside of the agriculture industry because many farmers are getting credits for practices that they are already doing, reinforcing the image of farmers with their hands out.

Aggregator 2 answers questions about the relationship between carbon credits and climate change on a regular basis, although many farmers make the connection between climate change and carbon credits. The majority of individuals she talks to do not believe in anthropogenic climate change. One farmer went so far as to tell her that he “didn’t want to climb in bed with Al [Gore]”, but wanted to sign up for carbon credits anyway.

Aggregator 2 eloquently summarizes the single largest barrier that carbon credit programs for farmers face: “They think of themselves as good stewards of the land, not good stewards of the atmosphere .”

Discussion

Although no statistical analysis of the adoption of carbon credits can be carried out using the available data, the qualitative data presented allow for further discussion about each hypothesis.

From the above profiles and the available quantitative data on carbon credit adopters, it is possible to see a pattern between the use of no-till practices and the adoption of carbon credits. First, notice that none of the four carbon credit adopters altered their use of no-till practices in order to be eligible to participate in a carbon credit program. This suggests tentative support for the first hypothesis, that there is a positive

104 relationship between the use of no-till practices and the adoption of carbon credits.

However, the use of continuous no-till practices and therefore eligibility for the program, is clearly not enough to spur farmers to participate. The majority of the sample, 59 percent, is eligible for carbon credits, yet only a small fraction of those actually participate. The continuous use of no-till practices is essential in order to be eligible for carbon credits, yet not all those who engage in continuous no-till practices participate in carbon credit programs. Thus, there does not appear to be a strong relationship between the use of no-till practices and the adoption of carbon credits.

The second hypothesis purports that there is a positive relationship between the adoption of carbon credits and satisfaction with no-till practices. The four carbon credit adopters indicate moderate to high levels of satisfaction with no-till practices.

Qualitative data further reveals that many of carbon credit adopters have been no-tilling for years and have no plans to engage in conventional tillage in the future. However, non-adopters of carbon credits also indicate moderate to high levels of satisfaction with no-till practices. The level of satisfaction with no-till practices does not appear to be a significant predictor of adoption of carbon credits.

The third hypothesis is related to belief in anthropogenic climate change and the adoption of carbon credits. Of the four carbon credit adopters, only one is a strong believer in anthropogenic climate change. In fact, two are labeled as “skeptics” and the other as an “acceptor”. From this small sample, it appears that those that adopt carbon credits do not necessarily believe in the theory of anthropogenic climate change.

Additionally, many eligible farmers that are strong believers in anthropogenic climate change do not participate in carbon credit programs. Therefore, there appears to be very

105 little support for a relationship between belief in anthropogenic climate change and the adoption of carbon credits.

The fourth hypothesis foresees a positive relationship between the adoption of carbon credits and familiarity with carbon-related topics. Again, there is a wide variation among the four carbon credit adopters in the familiarity with carbon related topics. The majority of eligible respondents that are familiar with carbon related topics do not participate in carbon credit programs. Thus, there does not appear to be a relationship between the level of familiarity with carbon-related topics and the adoption of carbon credits.

The final hypotheses seek to find a relationship between various demographic characteristics and the adoption of carbon credits. While no conclusions can be drawn even with the help of qualitative data, it is interesting to note that all four of the adopting farmers have a college degree, a conservative political orientation and work full-time on their farms. These are relationships that should be investigated in future studies pertaining to the adoption of carbon credits.

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CHAPTER 7

CONCLUSIONS

This chapter contains the conclusions, implications and recommendations of the current study. Study limitations and topics of further research needed are also discussed.

Summary

The adoption of no-till practices and participation carbon credit markets were the principal topics addressed in this study. A general model, based on a review of the diffusion of innovations literature, was devised to lead the analyses found herein. The adoption of no-till agriculture and participation in carbon credit markets were discussed together in the review of literature chapter, but separately in the analysis section. The first half of the model addresses the use and satisfaction with no-till practices. The second half of the model addresses the adoption of carbon credits as a result of the adoption of no-till practices.

The study was guided by two general research questions having to do with the adoption of conservation-related agricultural practices.

• What factors are related to the use of no-till practices?

• What are factors related to participation in carbon credit programs?

The latter question assumed that many farmers who use no-till agriculture would be signed up for carbon credits. This was not a valid assumption. The study was

107 exploratory in nature analyzing the impacts of several potential key variables related to use of no-till practices, such as farmer satisfaction with them, belief in anthropogenic climate change, and knowledge and perception of carbon markets.

Analyses were based on two data sources. The first was a survey of 228 farmers while attending a conservation tillage conference in Ohio. The second was a series of qualitative interviews with farmers at the conference, some of whom participated in carbon credit markets and some of whom did no participate in them as well as with agriculture industry professionals related to carbon credit markets. The in-depth interviews provided a rich background that was especially useful in helping to interpret some of the quantitative results based on information provided in the survey questionnaires.

An unexpected methodological glitch was encountered in the study. It had been assumed that many farmers who practice no-till agriculture would sign up for carbon credits, because it would be at little additional cost to them and because farmers typically were interested in receiving government support for eco-system service activities. In fact, only four of the farmers interviewed were actually participating in carbon credit programs. While this non-participation in carbon credit markets is an interesting phenomenon in itself, it precluded the statistical test of hypotheses about participation in carbon credit markets found in the general model.

The results from the data analysis in Chapter 5 support two of the original hypotheses pertaining to the use of no-till practices. First, there is a positive relationship between the use of no-till practices and satisfaction with them. There is a positive

108 relationship between the use of no-till practices and participation in other resource conservation programs.

Younger farmers were expected to more likely to adopt no-till practices and farm size was expected to be positively correlated with their adoption. However, the data suggest the opposite occurs for this sample. Older farmers tend to be more likely to adopt no-till practices and use of no-till practices is associated with smaller farm operations. Both of these relationships were statistically significant. Observations from the qualitative interviews with farmers shed some light unto these unexpected finding.

Young farmers tend to be more likely to operate large tracts of land with the idea of maximizing yields and incomes. Much of this land may be rented and they are less concerned about sustaining the quality of the land. On the other hand, older farmers are more likely to farm their own land and less rented land. They are also more likely to have experimented with and committed themselves to no-till agriculture.

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Conclusions

Conclusion 1

There is a strong relationship between the use of no-till practices and satisfaction with those practices.

Implications

Data presented in Chapter 5 indicate that satisfaction with no-till practices is strongly related to their use. The implications of this finding are interesting, especially when considering the tillage trends believed to currently be happening in the state.

According to the Conservation Tillage Information Center, the use of no-till practices peaked at 50 percent of the state’s total cropland in 2000 and has since declined to about

50 percent of total cropland (Ohio Department of Natural Resources, 2005). The decrease in no-till practices indicates a shift towards “rotational tillage” in which farmers rotate conventional tillage with conservation tillage. Rotational tillage doesn’t offer the same benefits as continuous no-till, because conventional tillage often obliterates the positive effects of continuous conservation tillage (Ohio Department of Natural

Resources, 2005). The enhanced soil biology, increased soil organic matter and water filtration benefits of conservation tillage are reduced and sometimes erased by the reintroduction of conventional tillage methods.

Farmers that utilize a rotational tillage technique will not experience the benefits of no-till that to the same degree that continuous no-tillers experience. Each time that they employ conventional tillage methods they will be decreasing the positive benefits of the previous practice of no-till methods.

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Conclusion 2

Most farmers are aware of carbon credit programs but few currently participate in them. Eighty-eight percent of the eligible no-till farmers who were surveyed in this study were aware of carbon credit programs, but only 3 percent of them were currently participating in the programs.

Implications

Most farmers in the sample indicated that they have heard of carbon credit programs but were not interested in participating in them. This raises an interesting question about related to the nature of their non-adoption status. There is some discussion in the literature of the topic of non-adoption. The major conclusion surrounding this discussion can be summed up as follows: the non-adoption of any given technology is probably not a case of laggardness or antiquarianism as previously insinuated in the diffusion of innovations literature. Rather, it is a rational act on the part of farmers, based on the informational, socioeconomic, and environmental context in which they find themselves. Those who fail to seek out new innovations may simply not feel economic pressure or perceive immediate environmental problems (Frank, 1995).

They are satisfied with their situation and have good reasons for deciding to not adopt. In many cases, then, farmer decisions not to adopt are active and rational acts (Frank,

1995a).

In the case of carbon credits, many farmers in the sample indicated that they are aware of carbon credit programs for farmers. Their subsequent adoption (participation) decision is either passive, which occurs when they currently do not have sufficient

111 information to make a decision, or active, when they have enough information to make a decision not to participate.

Both types of non-adoption were present in the sample for this study, as evidenced by the qualitative data from interviews. Passive non-adoption is found among farmers without enough information to make a decision about the program. Many farmers did not know where to obtain carbon credits if indeed they were to become interested in these programs. Other farmers indicated that they thought that related carbon credit contracts might involve too much paperwork to be worth the small amount of money provided by the programs. This suggests that they probably had not seen an example of a carbon credit contract, because they are typically very short and easy to complete with the aid of Farm Service Agency (FSA) forms that most farmers already have to complete.

Qualitative interview data collected during the study suggest that active non- adoption is also prevalent among the farmers. Many of them have personal contact with change agents in the state from whom they can receive accurate and detailed information about carbon credit programs. However, they choose to not adopt for many reasons.

Perhaps the principal reason is their land tenure status. Many farmers rent land, with contracts on average ranging from one to three years. The risk of completing a five year carbon credit contract using rented land is high, because farmers cannot anticipate their land contracts being renewed.

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Conclusion 3

Belief in anthropogenic climate change is strongly associated with liberal political beliefs. 2 Most farmers in the sample identified themselves as being politically conservative which suggests that they do not believe in human induced causes of climate change.

Implications

In the context of the United States this finding is not unexpected. As discussed in the review of literature, the political parties have aligned themselves on opposite sides of the climate change debate. Additionally, the media have given equal weight to each side of the climate change debate. The association of climate change with carbon credits may challenge a farmer’s cultural and ideological system if the prevailing beliefs in farming communities are that climate change is a natural phenomenon. Sixty-two percent of the farmers found in this study identified themselves as politically conservative. Most of them do not believe in climate change. Their political and climate change beliefs probably reflect prevailing attitudes among farmers in Ohio communities.

On one hand, political beliefs seem to be independent of conservation behavior, as reflected in the participation in carbon credit markets, at least for this sample. All four of the farmers in the sample who signed up for carbon credit programs identified themselves as politically conservative. These data indicate that political beliefs and belief in climate change may not be major predictors of participation in carbon credit programs. The relationship between these two phenomena should be a topic of further study in order to better understand their relationship to participation in carbon credit programs.

2 The correlation of these two variables for the sample under consideration was .52. 113

Conclusion 4

There is some evidence that older farmers tend to be more likely to adopt no-till practices and the use of no-till practices is associated with smaller farm operations. Other respondent related demographic and farm characteristics do not seem to be strongly related to the adoption of no-till practices or to participation in carbon credit markets.

Implications

Demographic variables such as age, education, conservation behavior, farm work status and farm size were not strongly related to the adoption of no-till practices. Age and farm size were statistically related to adoption of no-till practices, but in the opposite direction than anticipated. Larger farms and younger farmers were less likely to adopt them. The data also suggest that the relationship between these variables and participation in carbon credit markets is minimal, although no definitive conclusions can be drawn in this regard because the sub-sample of farmers receiving carbon credits is so small.

The lack of statistically strong relationships of the demographic variables with the adoption variables contributes to an ongoing debate about the diffusions of innovations model. However, this finding needs to be placed in the context of an on-gong debate about the appropriateness of the traditional, quantitative social science approach to adoption of technologies. The diffusion of innovations model will not reveal all of the facets and intricacies of adoption, simply because of the nature of the model. Attempts to discover the individual-level characteristics that “explain” adoption are seen as ineffective because the geographic, socioeconomic, and communication contexts matter a great deal in any given innovation-adoption scenario.

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The implication of this finding is that targeting of carbon credit programs to farmers with specific demographic or farm characteristics will probably not increase overall participation in carbon credit programs.

Recommendations

This study has demonstrated the low level of participation of farmers in carbon

credit markets and provided several suggestions for why this may be the case. The

question remains: how can carbon credit programs for farmers be adapted in order to

encourage greater participation? Three recommendations are included below.

Reduce emphasis on climate change

One of the most palpable discoveries of this study is that the topic of climate

change elicits a negative reaction from many members of the agriculture community. Yet

the topic of climate change takes precedent at many of the carbon credit recruitment

meetings. Although carbon credit recruiters do not attempt to change the opinion of

farmers toward climate change, they go to great lengths to describe the climate change

debate, the development of carbon markets and how farmers can fit into those markets.

The focus on climate change, a politically charged topic, may be a turn off to farmers

from the very beginning.

Secondly, carbon sequestration is a largely invisible process. Farmers cannot

actually see or otherwise sensually verify that they are sequestering carbon with no-till

practices or that their neighbor, who uses conventional practices, is actually releasing it

into the air. In a sense, no-till farmers are producing an invisible crop of carbon. Unlike

other “crops,” sequestered carbon cannot be discussed and bragged about with farmers

down at the coffee shop in the same manner that 200 bushel/acre corn can. Instead,

115 farmers learn at recruitment meetings to associate their carbon crop with an environmental problem, climate change, which they may not believe is happening.

A recommendation for carbon credit recruiters would be to take the focus off the discussion of climate change and carbon. This would be consistent with the observation that farmers are more concerned with the technical aspects of the program rather than the abstract theoretical justification found in climate change. Recruiters may find a more receptive farmer audience if the focus on the technical and economic aspects of a carbon credit program instead of focusing on climate change.

Rename the program

Many farmers noted that they like the idea of getting paid for their no-till practices, however, not because they necessarily contribute to climate change mitigation.

Rather, they feel that they are “doing the right thing” for soil erosion and soil health by practicing no-till agriculture. They also feel that it is proper to receive payment for it. In this sense, farmers tend to feel positively about the program even if they do not agree with climate change. If done, “seeing” or “not seeing” their invisible crop of carbon becomes immaterial.

To take the idea outlined above a step further, carbon credit aggregators should consider reframing their programs in ways that remove any reference to the association of carbon credit programs with climate change. Instead, the name of the program could focus on the practice, no-till farming, which the farmers find very positive. For example, carbon credit programs could be framed to emphasize that they are “rewards programs for no-till farmers.” Disassociating the program from climate change may increase the

116 willingness of farmers to participate by relieving social concerns related to the perception of others about their participation.

Specific price guarantees

One of the perceived barriers to participation is the market-based nature of the program and the fluctuating price of carbon. The perceived risk of farmers could be diminished if carbon credit programs were designed in ways that would guarantee farmers a minimum price that would be locked in for the duration of the five year contract. The minimum price would have to be estimated so that the actual price of carbon would be likely to be above the price guaranteed to farmers. At the end of the five year contract, any profits generated beyond the minimum guaranteed price could be paid out to farmers as a bonus for completing the five year contract.

These programmatic changes would not represent a substantial deviation from the current program structure. Currently, carbon credit programs retain twenty percent of the carbon credit payments until the end of the contract period in order to act as a bonus to farmers that fulfill the entirety of the five year contract. The big difference would be to lower the perceived risk of farmers by guaranteeing a specific price and varying the bonus, rather subjecting farmers to annual fluctuating payments and reserving a flat twenty percent as the bonus at the end of the contract.

Summary of recommendations

The above recommendations were designated so that any individual recommendation could be implemented by carbon credit aggregators. In the long term, all three recommendations could be combined in order to address some of the barriers to adoption explored in this study.

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A risk associated with programmatic changes is that new legislation related to carbon emissions may impact carbon markets in the United States. The future of carbon credit markets and the possible participation of farmers in them are in the hands of legislators and related to proposed cap and trade legislation. Carbon markets in the U.S., as we know them now, may become mandatory. If this happens, the price of carbon is likely to change drastically, and the types of offsets that are allowed to be traded may be altered. Whether or not agriculture continues to be a part of the discussion remains to be seen.

Recommended Additional Research

Further research should address some of the methodological issues addressed

above. A larger sample size would have a greater likelihood of capturing more

participants in carbon credit programs would also allow for a robust statistical analysis of

factors related to participation in these programs. Additionally, the framing of related

climate change issues and related possible questionnaire items should be pretested with a

farming population before actually being used in a study.

Further research on farmer participation in carbon credit markets might also be

focused on other farming conservation practices such as new grass plantings, rotational

grazing and forestry. The factors that affect carbon credit adoption for these practices

may be very different than those discussed for no-till practices in this study. A study that

considers and compares all of the possible practices that farmers may adopt in order to

receive carbon credits may unearth a more nuanced set of factors that affect the decision

to participate in a carbon credit program.

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Further research would benefit from sampling of farming populations across the

United States. There are several justification for this. First, reasons for use of no-till practices vary widely across the country in different agro-climatic conditions and with differing crops and soil types. For example, exploratory interviews with Great Plains farmers indicate that one of the main reasons for no-tilling is that it conserves moisture.

The topic of moisture was never addressed by any Ohio farmer, neither in interviews or when asked to free list the reasons for no-tilling. Even taking into account technical differences in crop production, farming culture the United States varies from region to region. In fact, social and contextual variables that were addressed in this study may be very different between regions. This would argue for including farmers from different agro-climatic and cultural situations in future samples and related data analyses.

National data sets containing variables related to the use of no-till practices and participation in carbon credit programs are vital to the conduct of more meaningful and fruitful research on these topics. The Center for Conservation Tillage used to conduct a survey of tillage methods across several states, including Ohio. It is no longer done, so current tillage usage is based on estimates. While information on government conservation programs is publicly available, such a data source for private market-based programs, such as those related to carbon credit markets, does not exist. A national data source would minimize the impact of data limitations noted above.

Final observation

It came as a surprise that the content of the survey questionnaire used for this

study may have been viewed by respondents as politically charged. The items related to

climate change elicited an emotional response from many respondents at the

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Conservation Tillage and Technology Conference. A large number of them approached the researcher with complaints about the study and also wrote negative comments on the survey questionnaire. Many of these comments represented reflected detailed thinking about why they did not believe in climate change and cited evidence that supported their opinions. Many respondents wrote degrading comments about Al Gore, a person who is associated with the climate change movement and the carbon credit market, but whose name was never mentioned on the survey.

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APPENDIX A

QUESTIONNAIRE

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