The Reflexive Producer: The Influence of Farmer Knowledge Upon the Use of Bt Corn*

Transcription

1 Rural Sociology 73(1), 2008, pp Copyright E 2008 by the Rural Sociological Society The Reflexive Producer: The Influence of Farmer Knowledge Upon the Use of Bt Corn* Brent Z. Kaup University of Wisconsin-Madison ABSTRACT This paper examines the influence of farmer knowledge upon decision making processes. Drawing upon the sociological debates around the ideas of reflexive modernity and biotechnology as well as from classic adoption and diffusion studies, I explore the influences upon farmers useof Bacillus thuringiensis (Bt) corn. Utilizing survey data gathered from corn farmers in Minnesota and Wisconsin, I argue that farmers are reflexive actors who actively negotiate between expert and local knowledges when deciding to plant Bt corn. Furthermore, I hypothesize that farmers are more likely to be influenced by their first-hand or local experiences than by state or expert observations. Most people have never heard of a European corn borer (ECB) or a corn rootworm (CRW). Yet, in some places in the Midwest, one, two, or maybe even more of these bugs can be found on each stalk of corn. The average commercial farmer plants between 30 and 35 thousand stalks of corn per acre and around 80 million acres of corn are planted in the United States each year. Of these, it is estimated that 28 million acres of corn are infested with ECB and/or CRW. Farmer losses resulting from ECB and CRW control costs and damages exceed $1 billion each year (Ostlie, Hutchinson and Hellmich 2002, USDA ERS 2005). In 1996, Monsanto introduced the purported cure to farmers ECB problems with its YieldgardH Insect-Protected Bt Corn. In 2003, Monsanto released a second strand of YieldgardH insect resistant corn to control for CRW. These two strands of corn contain Bacillus thuringiensis (Bt), a naturally occurring soil bacterium used for insect control. But over the past ten years, genetically modified crops such as Bt corn have come under attack by activists and academics alike for their potential environmental and health-associated harms. In this context, as farmers make decisions about how to deal with these pests in their fields, they must evaluate the * Acknowledgments: This research was made possible by funds provided by the USDA Initiative for Future Agricultural and Food Systems (IFAFS). The author wishes to thank Frederick H. Buttel and Jessica Goldberger for the opportunity to work on the project, Anthony Ives for additional support, and Gary P. Green, Jane Collins, Amy Quark, and all the anonymous reviewers for their recommendations on previous drafts of this manuscript. The author takes sole responsibility for any errors. Direct correspondence to: Brent Z. Kaup, bkaup@ssc.wisc.edu, Address: University of Wisconsin-Madison, Department of Rural Sociology, 350 Agricultural Hall, 1450 Linden Drive, Madison WI 53706

2 Farmer Knowledge and Bt Corn Kaup 63 contradictory discourses put forth by Monsanto, activists, and academics alongside the real problems insects pose to their crops. The purpose of this paper is twofold. First, it seeks to explore the influence of local sources of knowledge on the use of Bt corn seed. Second, it illustrates the need to account for the intended use of such technology and to examine the influence of geographic locational differences upon this use. In doing so, I argue that farmers are reflexive actors who actively negotiate between expert and local knowledges when deciding to plant Bt corn. In an era of conflicting claims to truth, I hypothesize that farmers are more likely to be influenced by their first-hand or local experiences than by state or expert observations. To these ends, I employ data from surveys of corn producers in Minnesota and Wisconsin and examine the use of Bt corn in the 2003 growing season. I examine the influence of different sources of knowledge and locational specificities in the use of Bt corn. I also look at the influence of farmer and farm characteristics to explore how traditionally studied influences upon agricultural innovation adoption affect the use of Bt corn. To explain the influence of these variables upon the use of Bt corn, I draw from the sociological debates around the ideas of reflexive modernity and biotechnology as well as from classic adoption and diffusion studies. The Reflexive Producer in an Era of Reflexive Modernity In Melanie DuPuis (2002) work examining the use of biotechnology in the milk industry, she evokes the notion of reflexive consumption to account for the consumer who rationally contemplates the purchase of genetically enhanced foods. While DuPuis notion of reflexive consumption has its critics (see Buttel 2000), it offers several interesting entry points into the discussion of knowledge and human agency surrounding the use of biotechnology. In this section, I critically examine DuPuis idea of the reflexive consumer and introduce the potentially complementary concept of the reflexive producer. I suggest that the reflexive producer is a conscious actor whose decision- making processes represent sites of contestation in which they negotiate between expert and local knowledges. Bringing in the social theories and critiques of reflexive modernity (Beck 1994; Giddens 1994; Lash 1994; Wynne 1996; Bell 2004), I show how these theories can be used to further elaborate the concept of a reflexive producer. DuPuis (2002) notion of reflexive consumption refers to the consumer who listens to and evaluates the claims made by social

3 64 Rural Sociology, Vol. 73, No. 1, March 2008 movements organized around food issues. To DuPuis, the reflexive consumer is able to evaluate and change his or her own purchasing actions based on what he or she feels is the legitimacy of such claims (2002:228). With regard to food biotechnology, DuPuis claims that reflexive consumption is an individual act and that a reflexive food consumer is able to weigh the potential consequences of putting genetically modified foods into his or her own body. While DuPuis tries to distance her idea of reflexive consumption from the sociological debate surrounding reflexive modernization, an array of parallels can be drawn between the two. Ulrich Beck (1994), Anthony Giddens (1994), and Scott Lash (1994) have all utilized the notion of reflexive modernity, albeit in somewhat different fashions, to explain the interaction between societal influence and human action. The driving force behind this reflexive modernity is the idea of one model of modernization, that of Western industrial society. Beck, Giddens, and Lash see the industrial era of modernization as having come to an end, giving birth to an era of reflexive modernization that is characterized by its push for individualization. Such individualization is captured in DuPuis (2002) notion of the reflexive consumer who holds voluntary risk. While DuPuis uses the reflexive consumer to allude to the power of consumer movements, the idea that such consumers and movements are built around the individual action of consumption echoes the ideas of Beck (1994), Giddens (1994), and Lash (1994) surrounding increasing individualization in the era of reflexive modernity. However, neither DuPuis (2002), Beck (1994), Giddens (1994), nor Lash (1994) see individual actions unfolding outside of societal influence. DuPuis (2002) sees the reflexive consumer acting in response to conflicting truths. These truths come from businesses, government experts, activists, and scientists who put forth both scientific and cultural discourses about what is good and what is bad. To DuPuis, the act of purchase is a site of contestation in which the consumer negotiates between conflicting notions of what is good and bad. Similar to DuPuis, Beck (1994), Giddens (1994) and Lash (1994) note that while the era of reflexive modernization has pushed for individualization, it is not devoid of societal constraints. Beck calls the point at which individuals negotiate between goods and bads a point of self-confrontation (Beck 1994:5). He notes that in contemporary society, individuals are exposed to multiple goods and bads that are beyond their control (for example, universal healthcare or global warming). This allows individuals to question government and scientific

4 Farmer Knowledge and Bt Corn Kaup 65 monopolies on truth, thus forcing them to individually confront these goods and bads within their own lives (1994:25). Building upon Beck, Giddens (1994) suggests that individual decisions are constrained by expert systems or societal institutions. Looking at the past, Giddens points to guardians, or those with privileged access to truth (1994:79), as the creators of tradition. This tradition grants the guardians and their representative institutions with the power to disseminate societal values. Today, in what Giddens (1994) classifies as the post-traditional society, science has attempted to replace tradition as the new expert system and source of knowledge. But instead of creating a new truth, science has created multiple authorities that cast doubt upon its very foundations. In the age of globalization, Giddens argues that the destruction of the traditional local community has led to a reformulation of truth and tradition. He believes that tradition has become merely routinized habits based in individualism with no connection to a formulaic truth. As a result, Giddens sees an individualization of society but still believes that that society is regulated by an/multiple expert system(s). In contrast, Lash (1994) suggests that the system of modernity has created its own reflexivity that allows for greater levels of human agency. With modernization demanding a more educated work force with greater abilities to process information, it has inherently created a more knowledgeable society. With greater information processing skills, social actors are able to engage in a rational critique of the system itself. This engagement frees people from some of the constraints of the system, and allows people to evaluate some of the risks such a system puts forth. As a result, Lash (1994) sees reflexive modernization as a process that promotes human agency due to its inherent demands for increased levels of knowledge and critical thought. Building upon Lash s ideas, Brian Wynne (1996) further problematizes Giddens idea of expert systems by accounting for what he sees as the reflexive processes of everyday people. Wynne believes that Giddens notion of the expert system is based upon an objective view of science in which he fails to recognize the validity of lay or informal knowledges in reflexive processes. Wynne extends this critique by noting, that far from emptying indigenous lives of meaning, the expert knowledges are typically importing dense but inadequate meanings (1996:60). Thus, everyday people are able to question expert systems because they make-up a crucial part of these systems. As a result, reflexivity is a continuous negotiation between locally produced lay knowledges and the imported knowledges of expert systems.

5 66 Rural Sociology, Vol. 73, No. 1, March 2008 In a similar fashion to Wynne (1996), Michael Bell also pushes to recognize the role of local knowledge in processes of reflexivity. In what Bell calls dialogue, he attempts to recognize how the modern, which he equates to industrial agriculture, interacts with the post-modern, which he equates to local knowledge (2004:24 25). Bell also challenges Beck (1994), Giddens (1994), and Lash s (1994) notions of reflexive modernity. In doing so, he questions their idea that people within Western societies now actively engage in debates around science, technology, and the economy in an open and self-critical way. He sees such a view as overly optimistic and believes that critical awareness is not part of the everyday. Instead, Bell argues that when critical awareness occurs it represents a major struggle against the continuing power of the modernist monologue (2004:26) Adding Beck s (1994), Giddens (1994), and Lash s (1994), as well as Wynne s (1996) and Bell s (2004) ideas on reflexivity and reflexive modernization to DuPuis (2002) concept of the reflexive consumer, individual human agency is acknowledged but tempered by societal constraint. As such, reflexivity becomes an act of negotiation between expert and local knowledge systems. Drawing from this synthesis, I evoke the concept of the reflexive producer. Like DuPuis reflexive consumer, the reflexive producer is a conscious actor able to make decisions based on available information. While the reflexive producer s decision is ultimately his or her own choice, this choice is a negotiation between conflicting knowledges and truths. Building upon Wynne (1996) and Bell (2004), I wish to acknowledge the influence of both local and expert knowledge within agricultural production. In the following section I suggest how we might use the concept of the reflexive producer to advance our understanding of farmer decisions about biotechnology and their potential to influence the politics around this issue. While all reflexive producers are also reflexive consumers, the extension of reflexivity to the realm of agricultural production extends the influence of farmer knowledge and experience into the biotechnology debate. By recognizing the reflexivity of the producer, I hope to bring the biotechnology discussion back inside the farm gate. The Reflexive Producer in the Adoption and Use of Biotechnology Agricultural researchers have historically tied farmers decisions to utilize new forms of agricultural technology to the expected profitability of innovation use (Griliches 1957; Rogers 1995; Alexander, Fernandez-Cornejo, and Goodhue 2002). While decisions to use

6 Farmer Knowledge and Bt Corn Kaup 67 agricultural technology are ultimately determined by individual producers, researchers have linked traits of both the farmer and the farm to agricultural technology use and adoption. These farmer and farm characteristics include, but are not limited to, age, education, farm size, and gross farm income. Many technology use studies have placed these adopter characteristics within the category of socioeconomic status. Rogers (1995) explores the relationship between socioeconomic characteristics and innovativeness, or the likelihood to be an early adopter of an innovation. He generalizes that early adopters usually have more years of formal education, higher levels of income, and larger farms, while the relationship between adoption and age is relatively inconsistent. Many of the farmer and farm characteristics put forth by Rogers (1995) can be reframed as examples of a farmer s reflexivity. Farm income is one example. Farmers are forced to act within their economic means and thus make decisions based upon these means. Rogers (1995) acknowledges that the influence of income upon adoption partially reflects economic circumstance. He points out that early-users usually have increased access to economic resources, are able to invest in new, more capital intensive technologies, and can more easily absorb the negative risks of failed technologies. The influence of a farmer s education and a farmer s knowledge about a particular innovation also exemplifies how knowledge informs farmer decision-making processes. The reflexive producer makes decisions about biotechnology based on what he or she knows. As a result, a farmer will use the technology, or in this case use the seed, that he or she perceives will work the best with the least amount of risk. According to Lash (1994), the nature of modern society demands that people have greater information processing skills. New market pressures require farmers to have these skills in order to engage in a rational critique of the system itself. This engagement frees farmers from some of the constraints of the system, and allows them to evaluate the benefits and downfalls of innovation use and adoption (Lash 1994). Recognizing how individual knowledge bases influence decisions around innovation use and adoption also allows for Wynne s (1996) lay knowledges to be evaluated. Farmer s familiarity with or demand for a product could be the result of first hand experiences either with the product itself or with the problems the product is intended to control. While Rogers (1995) hypothesized that early adopters usually have different socioeconomic characteristics than late or non-adopters, recent studies have shown that several of these characteristics

7 68 Rural Sociology, Vol. 73, No. 1, March 2008 significantly affect the use and late adoption of genetically engineered crops. Alexander et al. (2003) show that farm size and income have a positive significant effect upon the use of genetically engineered corn. Furthermore, Fernandez-Cornejo and McBride (2002) suggest that the use of transgenic seed is positively correlated with both farm size and education. Speaking more specifically about Bt corn, they note that a causal account of adoption should take into account a wider variety of independent variables than earlier research and point to the importance of including a locational variable to control for differences in levels of ECB infestation. Because ECB Bt corn is designed to target a spatially specific pest, its rate of adoption will more than likely be higher in areas with larger infestations. However, while higher insect populations may motivate a farmer to adopt Bt corn, the insect population itself does not lead to adoption. Instead, the farmer must evaluate the different reports of insects in their area, whether from government agencies, an agricultural extension agent, or their own observations. But traditional agricultural technology use and adoption studies have often failed to recognize how different sources of information influence farmer decision-making processes. However, researchers have explored where farmers generally receive information from, which sources farmer s perceive as valuable, and the frequency with which farmers use these different sources. Ford and Baab (1989) show that farmers rely most often on information from family and friends, other farmers, commercial newsletters, and county extension agents when deciding which crops to plant. Ortman et al. (1993) state that farmers use their own farm records and budgets as the number one source of information influencing their production decisions, followed by soil fertility consultations, other people working on the farm, university specialists, and field day conferences. In Ostrom, Jackson-Smith, and Moon s (2000) examination of extension use amongst Wisconsin dairy farmers, they report that 76 percent get information from an extension publication at least twice year, and over 33 percent spoke with an extension agent or visited their county extension office. As Ortman et al. (1993:395) note, the diversity of sources reported as frequently used by farmers and the sources they rely most upon could reflect the overarching complexities of production decisions. In this way, such studies suggest that farmers reflexively negotiate an array of local and expert knowledge sources. In my case study, I build upon past and explore additional influences on transgenic seed use to recognize how different sources of information influence farmer decision-making processes. I draw upon

8 Farmer Knowledge and Bt Corn Kaup 69 innovation adoption research and the aforementioned studies that explore where farmers get their information to examine how farmers are reflexive producers. Utilizing observations that illustrate that farmers draw upon and value knowledge originating from both local sources of knowledge, such as family and friends, personal farm records, and other farmers, and expert sources of knowledge, such as university specialists and extension, I look at the influence of both expert and local perceptions of pest levels on the farms under investigation. I also build upon Fernandez-Cornejo and McBride s (2002) suggestion that adoption is linked to the spatially variable severity of pest infestation. In doing so, I hypothesize that farmers local perceptions of pests have a greater influence upon their use of Bt corn. To further examine the influence of local knowledge, I also examine how a farmer s local knowledge, derived from a familiarity with the technology in question, can influence the use of similar kinds of innovation. I hypothesize that farmers with longer histories of transgenic corn use are more likely to adopt and use other varieties of transgenic corn due to their familiarity with the technology. Accounting for the influence of contested knowledge sources in classic adoption studies, theories of reflexive modernization become consistent with the variables of more traditional innovation-diffusion models. A farmer s transgenic crop planting history displays experience and familiarity with the technological innovation of genetically modified crops. While the traits of these crops can differ, the science and technology behind such crops is similar. Accounting for crop history, therefore, could also be seen as an example of merely routinized habit as a substitute for the expert system (Giddens 1994). It could also illustrate how local or first hand knowledge comes into play when farmers make decisions about innovation adoption. Furthermore, when farmers evaluate different knowledge sources about pests, they can look to both expert and local observations. In these situations, Wynne (1996) and Lash (1994) suggest that farmers are not passive actors who merely accept expert knowledge as the objective truth. Instead, they are able to question existing expert systems because they are a part of such systems. As a result, on-the-ground observations often play a crucial role in the negotiation of decision-making processes. In the following sections, I put forth my case study examining the use of Bt corn. Following an explanation of the methods used, I use theories of the use and adoption of agricultural technology, as well as the ideas of reflexive modernization, to discuss and explain the findings from my case study of the use of Bt corn.

9 70 Rural Sociology, Vol. 73, No. 1, March 2008 Data and Methods Sample The sampling population for this study was derived with the help of the Minnesota and Wisconsin Agricultural Statistics Services (MASS and WASS, respectively) divisions of the U.S. Department of Agriculture (USDA). In each state 2,000 farmers listed in the MASS and WASS farmer databases were randomly and confidentially selected to receive surveys. The samples were limited to farmers who had reported planting corn during the 2003 growing season. In the spring of 2004, a questionnaire, cover letter, and business reply envelope were sent to each person in the two samples. One thousand five hundred and twenty-seven corn producers returned their questionnaires, giving a response rate of 38 percent. In order to insure the statistical validity of respondents, I compared the variables examining age, education, gross income, and farm size to USDA National Agricultural Statistics Service (NASS) and Economic Research Service (ERS) data. These variables were similar with the NASS and ERS data in both states. The sample of farmers was slightly more educated, farmed slightly more acres, and reported slightly higher gross incomes. In the survey used for this study, farmers were asked about their past and projected use of transgenic (HT and Bt) corn, why they did or did not plant Bt and HT corn, their experiences with corn insect problems, and a number of background questions about the farmer and farm operation such as operator s age, education, total cropland acres, total corn acres, farm location (i.e., county), sources of farm household income, and gross farm income. Dependent Variable: Planting Bt corn varieties In the survey, farmers were asked the following question: In 2003, did you plant any Bt corn varieties on your farm? I coded farmers answers into two categories: 1 for farmers who planted Bt corn in 2003 and 0 for farmers who did not plant Bt corn in Independent Variables Following the literatures on reflexive modernization and agricultural technology use and adoption, this study tests the influence of expert and local knowledge as well as farm and farmer characteristics on the use of Bt corn seed. The first set of variables examines the influence of farmer and farm characteristics upon the use of Bt corn seed. The second set of variables examines the influence of local and expert

10 Farmer Knowledge and Bt Corn Kaup 71 knowledges upon the use of Bt corn seed. I conducted a collinearity diagnostics test examining the Tolerance and Variance Inflation Factor (VIF) for each variable to determine the presence of multicollinearity. All of the variables fell within acceptable levels of each test, with none of the variables reporting tolerance levels below.5 and/or VIF levels above 2. Table 1 reports the data for each independent variable for the surveyed population. Farm and farmer characteristics. The five variables examining the influence of farm and farmer characteristics upon Bt corn use include the state where the farm is located and the farmer s age, formal years of education, gross income, and farm size. To control for state-variances, I include a two category dummy variable. Respondents from Minnesota were coded 1. Respondents from Wisconsin were coded 0. The age variable is coded as a continuous variable ranging from 21 to 87 years of age. The education variable is coded on a four-point scale from 1 ( completed less than high school ) to 4 ( completed a 4 year college degree program ). The gross farm income variable was obtained from a survey question that asked: Which of the following categories best represents the total farm receipts your farm business operated in 2003? Responses are coded on a ten-point scale from 1 ( under $20,000 ) to 10 ( $500,000 or more ). The farm size variable is coded as a continuous variable that illustrates a farmer s total cropland acres in Expert and local knowledge sources. To examine the influence of expert and local knowledge upon the use of Bt corn seed, I first examined why farmers did or did not plant Bt corn. Farmers were provided with a list of twelve possible reasons and asked to check all that applied. The possible reasons provided to farmers choosing to plant Bt corn were different than the possible reasons provided to farmers not choosing to plant Bt corn. Farmers planting Bt corn were allowed to mark reasons such as to allow better insect control, to increase corn yields, fits well with my existing corn production practices, and anticipated having corn borer and/or corn rootworm problems. Farmers not planting Bt corn were allowed to mark reasons such as concerned I will have trouble selling my Bt corn, concerned about possible environment or safety issues, not satisfied with net return of Bt corn, and did not anticipate having corn borer and/or corn rootworm problems. A complete listing of farmer responses is found in Table 2. Across these two sets, I identified overlapping reasons influencing Bt corn planting choices. From these reasons, I developed and examined four variables that illustrate the influence of different knowledge

11 72 Rural Sociology, Vol. 73, No. 1, March 2008 Table 1. Descriptive Statistics for Variables in Analysis (N=1527) Coding Percent Mean Dependent Variable: Did you plant Bt corn in no or undecided 15 yes 43 Independent Variables: Farmer and Farm Characteristics State: 15Minnesota, 05Wisconsin Minnesota 44 Wisconsin 56 Age of farmer In years 52 Education 1 5 less than high school, year college degree Less than high school 9 Completed high school 40 Completed some college, tradeschool, 37 or two year degree Completed a four-year degree 14 program or more Gross Income 1 5 under $20,000, 10 5 $500,000 or more Under $20, $20,000 to $39, $40,000 to $49,999 6 $50,000 to 74,999 8 $75,000 to $99,000 8 $100,000 to $149, $150,000 to $199,999 8 $200,000 to $249,999 7 $250,000 to $499,000 9 $500,000 or more 5 Farm Size in acres In acres 363 Expert Knowledge State-reported insect level 0 5 less than one bug per plant, 1 5 one or more bugs per plant Less than one bug per plant 15 One or more bugs per plant 85 Local Knowledges Farmer reported ECB or CRW problem in the last five years 0 5 no 1 5 yes No problem 17 Noticeable problem 83 Years planted Bt corn since never planted, 6 5 adopted 6 times Never planted Bt-corn 52 Planted Bt-corn once 10 Planted Bt-corn twice 8 Planted Bt corn three times 7 Planted Bt corn four times 6 Planted Bt corn five times 7 Planted Bt corn six times 11 Years planted HT corn since never planted, 6 5 adopted 6 times Never planted HT-corn 59

12 Farmer Knowledge and Bt Corn Kaup 73 Table 1. Continued Coding Percent Mean Planted HT-corn once 15 Planted HT-corn twice 8 Planted HT corn three times 7 Planted HT corn four times 4 Planted HT corn five times 3 Planted HT corn six times 5 sources upon Bt corn use. 1 Since both farmers choosing to plant and not plant Bt corn indicated that anticipated pest problems affected their decision, two of these variables examine expert and local perceptions of pest levels. Since both sets of farmers also indicated that a personal knowledge of the technology or a similar technology affected their decision, two of these variables represent farmers first hand experience and history with transgenic corn seed. The two variables representing diversity in reported bug levels include one accounting for state-reported ECB and CRW levels and one accounting for farmer-reported problems of ECB and CRW. The first variable is based on state-recorded ECB and CRW concentrations obtained from farm field samples in Minnesota and Wisconsin by the Agricultural Development Division of the Minnesota Department of Agriculture and the Wisconsin Department of Agriculture, Trade and Consumer Protection (Abrahamson 2002, WI DATCP 2002). The bug concentration levels recorded by the Minnesota and Wisconsin Departments of Agriculture reflect similar findings to those of agricultural extension agents and other university specialists (see, for example, Bt Evaluation Tool 2007). Since farmers are likely to make their decisions about seed based upon their last year s experience, I chose to use data from Both states rate the presence of ECB and CRW in categories of intensity and by the damage done to corn plants. Comparing bug concentrations to levels of bug damage, places with 1 or more bugs per plant most frequently correspond to places with greater plant damage. Since the scales used by each state to measure bug levels is slightly different, I created a dummy variable in which 0 represents intensity levels of less than one bug per plant while 1 represents intensity levels of 1 or more bugs per plant. Because survey respondents were asked to provide the names of the counties in which 1 Since causal influence using binary logistic regression analysis cannot be determined utilizing questions only answered by subset of the total population under investigation, it was necessary to use variables applicable to all respondents to illustrate the influence of expert versus local knowledge.

13 74 Rural Sociology, Vol. 73, No. 1, March 2008 Table 2. Major Reasons Farmers Chose to Plant or Not Bt corn in 2003 Percent of farmers marking response* Reasons farmers chose to plant Bt corn: To allow better insect control 74.5 To reduce overall insecticide use 36.0 To reduce overall corn production costs 15.5 To increase corn yields 75.9 To reduce the labor required to grow corn 11.4 Fits well with my existing corn production practices 21.2 Seed dealers/consultants recommended Bt corn to me 40.2 Neighbors recommended Bt corn to me 7.9 University or extension agents recommended Bt corn to me 3.4 Anticipated having corn borer problems 52.1 Anticipated having corn rootworm problems 9.7 Other 5.0 Reasons farmers chose not to plant Bt corn Concerned that I will have trouble selling my Bt corn 17.3 Concerned that I will get a lower price for my Bt corn 7.4 Concerned about possible environmental or safety issues 20.4 Concerned about having to segregate Bt corn from non-bt corn 14.9 Concerned that insect resistance management requirements 8.2 would be too much trouble or too complicated Concerned about insect resistance 7.7 Not satisfied with the net return of Bt corn 10.6 Not satisfied with Bt corn yields 7.0 Price of Bt corn is too high 57.4 Did not anticipate having corn borer problems 38.3 Did not anticipate having corn rootworm problems 33.5 Other 14.9 * Farmers were instructed to check all that apply. their farms are located, I was able to assign each respondent to one of the two categories. The second variable representing bug concentration levels is derived from respondents own evaluations of ECB and CRW problems on their farms. A dummy variable is coded 1 if a respondent felt that ECB or CRW had caused noticeable crop damage, resulted in higher production costs, or made production more difficult, and 0 if not. Using data from direct questions that asked farmers to report whether or not they planted Bt and HT corn in each year from 1997 to 2003, I also created two transgenic corn planting history variables (i.e., one for Bt corn and one for HT corn). These variables represent the number of times a farmer planted Bt and HT corn between 1997 and Each variable is coded on an eight-point scale from 1 ( never adopted from 1997 to 2003 ) to 8 ( adopted 7 times between 1997 and 2003 ).

14 Farmer Knowledge and Bt Corn Kaup 75 Analytical Approach The primary question in this research is why do farmers decide to use Bt corn seed. Within this context, I am attempting to explore how farmers negotiate between local and expert knowledges. To answer this question, I utilize binary logistic regression to examine how the aforementioned independent variables influence the use of Bt corn. Binary logistic regression analysis allows for the assessment of the relationship between each independent variable and a binary dependent variable while simultaneously controlling for the effects of all other independent variables. To determine the influence of classic agricultural technology use and adoption variables as well as what I classify as local and expert knowledge upon the use of Bt corn seed, I put forth three models. In Model 1, I examine the influence of the classic agricultural technology use and adoption variables accounting for farm and farmer characteristics. In Model 2, I add in the variable that takes into account statereported insect levels by county. This variable represents what I classify as an expert knowledge source. While the state is not directly telling farmers to plant Bt corn to control for pests, they are making generalizations based upon county level pest populations that are meant to inform farmers of pest concentrations in order to help them determine the best pest management strategy. This information is released to farmers both through the mail and internet postings. In Model 3, I add in the variables accounting for farmer-reported insect problems and transgenic seed planting history. These variables represent what I classify as local knowledge sources. Farmer-reported ECB and CRW insect problems illustrate farmers first hand knowledge of the insect problems they are having on their individual farms. Farmers transgenic seed planting history illustrates a first hand familiarity with an enhanced seed. If a farmer has planted a seed or similar seed once or more, he or she is likely to have an idea about how that seed performs in comparison to other seeds he or she has used. Since I am interested in exploring the influence of local and expert knowledges upon the adoption of Bt corn, Model 2 and Model 3 are my primary models of focus for the purpose of this study. As noted above, I hypothesize that the variables representing local sources of knowledge (i.e. farmer reported insect problems, Bt seed planting history, and HT corn planting history) have the greatest influence upon Bt corn adoption. Table 3 summarizes the findings from the intended Bt corn use binary logistic regression, illustrating the strengths of the relationships, in terms of standardized logistic regression coefficients or betas, between the independent variables and the use Bt corn in 2003.

15 76 Rural Sociology, Vol. 73, No. 1, March 2008 Table 3. Logistic Regressions of the Influences upon Farmers Use of Bt corn in 2003 Model 1 Model 2 Model 3 b e b b e b b e b Farm and Farmer Characteristics: State 2.924*** *** Age of farmer * Education category.273*** ** Gross Farm Income.179*** *** Farm Size in operated acres.001*** *** Expert knowledge: State-reported insect level.435* Local knowledge: Farmer reported insect problem 1.248*** Years planted Bt corn 2.104*** Years planted HT corn Constant 2.979* ** ***.002 Chi-square *** *** *** 22 log likelihood *p,.05; **p,.01; ***p,.001. b 5 unstandardized coefficient estimate. e b 5 exponential of b or the odds ratio. Findings and Discussion Descriptive statistics for all variables are presented in Table 1. Of the total population, 43 percent of farmers used Bt corn in While equal numbers of surveys were sent out to farmers in each state, 56 percent of the respondents are from Wisconsin and 44 percent were from Minnesota. The mean age of the farmers is 52, and the majority of the sample had more than high school education. The mean farm size was 363 acres, and the majority of farmers had a gross farm income of over $75,000 a year. However, over a third of the samples gross farm income was under $40,000 a year. Table 2 displays the reasons why farmers chose to plant or not to plant Bt corn. Over 50 percent of farmers indicated that they planted Bt corn because they anticipated having corn borer problems (one of the major target pest of Bt corn), thought Bt corn allowed better insect control, and/or believed Bt corn would increase their yields. Over 20 percent of farmers indicated they planted Bt corn because it fit well with their existing farm practices, they thought it would reduce insecticide use, and/or their seed dealers recommended it to them. Of the farmers choosing not to plant Bt corn, over 50 percent indicated that the price of Bt corn seed was too high. Over 20 percent indicated that they did not anticipate having corn borer and/or corn rootworm

16 Farmer Knowledge and Bt Corn Kaup 77 problems, and/or they were concerned about possible environmental or safety issues that planting Bt corn could have. As noted above, I examined these reasons to find four variables that would reflect both farmers decisions to use and not use Bt corn, sources of farmer knowledge, and that I could support with other data in the survey or with data from outside sources. Since over 30 percent of farmers indicated their decision was based upon anticipated corn borer and/or corn rootworm problems, I utilized two variables to measure where farmers may have received the information about ECB and CRW pests. As shown in Table 1, state-reported insect levels, which I classify as an expert source of information or knowledge, illustrate that over 85 percent of counties average more than one bug per plant. Similarly, 83 percent of farmers indicated the ECB or CRW pests presented a noticeable problem in their fields. Since over 20 percent of farmers using Bt corn indicated they did so because it fit well with their existing practices and some farmers choosing not to plant Bt corn indicated they did so because they were not satisfied with their past Bt corn returns and or yields 2, I also utilized two variables that illustrate farmer s first hand knowledge of transgenic corn seed by measuring their past usage of Bt and HT corn seed. Of surveyed respondents, 52 percent never planted Bt corn, and 59 percent never planted HT corn. Of those planting transgenic corn, 10 percent planted Bt corn only once, and 15 percent planted HT corn only once. Of those planting Bt and HT corn multiple times, 18 percent of farmers planted Bt corn five or more times, while only 8 percent of farmers planted HT corn five or more times. The results of the binary logistic regressions are illustrated in Table 3. Model 1 portrays the relationship between the classic agricultural technology use and adoption variables accounting for farm and farmer characteristics and the use of Bt corn. Following classic agricultural technology use and adoption studies, the findings illustrate that increases in education and gross income have significant and positive influences upon Bt corn adoption (Rogers 1995). While farm size is also statistically significant, it has very little influence upon Bt corn use. Interestingly, living in Minnesota has a negative influence upon the adoption of Bt corn. Model 2 has similar results to Model 1 with the addition of a variable accounting for expert knowledge. As with Model 1, education and gross 2 As indicated in Table 1, 10.6 percent of farmers not using Bt corn stated they were not satisfied with the net return of Bt corn and 7 percent indicated they were not satisfied with Bt corn yields.

17 78 Rural Sociology, Vol. 73, No. 1, March 2008 income have significant and positive influences upon Bt corn use, farm size has a significant but weak influence upon use, and living in Minnesota has a negative influence upon use. To account for the influence of expert knowledge upon the use of Bt corn, I took into account the state-reported insect levels of ECB and CRW by county. This variable proved to have a positive significant influence upon the use of Bt corn. In Model 3, the variables accounting for local knowledges were added. These variables increased the fit of the model. Of the significant variables in Models 1 and 2, none remained significant in Model 3. While age is significant in Model 3, it has a relatively weak influence upon Bt corn use. Also, the expert knowledge variable represented by state recorded insect levels is not statistically significant when accounting for local knowledge. As hypothesized, both farmers who reported an insect problem and those who had longer histories using Bt corn seed were more likely to plant Bt corn seed. However, farmers past use of HT seed did not have a significant influence upon the use of Bt seed. The data presented in Model 3 illustrates the need to account for local knowledges when examining the use of Bt corn seed. Farmers reported insect problems and their Bt corn planting history had significant positive influences upon the use of Bt corn. In contrast, state-reported insect levels did not have a significant influence upon farmers decisions to use Bt corn. As a result, my data shows that farmers are making decisions based upon individual observations. Aligning with the notions of Lash (1994) and Wynne (1996), these findings illustrate that farmers are more likely to make decisions based upon local first hand experiences. While experts are producing knowledge meant to aid farmers decision making processes, farmers are more likely to plant Bt corn when they know first hand that they have had a ECB and/or CRW problem in the past. They are also more likely to plant Bt corn when they have planted it before and have first hand experience with the technology, and thus more than likely being satisfied with its past results. While farmers who have repeatedly planted Bt corn could merely be performing an act of what Giddens (1994) calls routinized habit due to the loss of a legitimate expert knowledge system, my findings suggest otherwise, as farmers choose to use Bt corn based upon their own perceptions of their farms insect levels. As a result, farmers as reflexive producers are critical human agents in an era of reflexive modernity and conflicting truths. Farmers use their own local knowledge to negotiate between the information they have derived themselves and that provided by the expert, in this case the state.

18 Farmer Knowledge and Bt Corn Kaup 79 Unexpectedly, my hypothesis that familiarity with similar innovations would increase Bt corn use does not prove to be statistically significant. My findings show that a farmer s HT corn planting history did not have a significant influence upon his or her use of Bt corn. This could be attributed to the seed s target function: HT seed is used to control for weeds, while Bt seed is used to control for insects. Furthermore, HT corn crops still require herbicide spray, while Bt corn seed supposedly does not require pesticide spray. This is because HT corn is herbicide resistant, allowing farmers to spray their fields without killing the corn. In contrast, Bt corn is insect resistant, emitting a substance that repels target and some non-target pests. As a result, future studies examining the use of transgenic seed should be careful to control for differences in seeds target functions. Conclusion Utilizing the social theories of reflexive modernization to examine the intended use of Bt corn, I have attempted to acknowledge the complexities of farmer decision-making processes. In this paper, I examine how expert and local sources of knowledge influence such processes in the use of Bt corn. In doing so, I argue that a farmer s local knowledge derived from first hand experience is more likely to influence his or her use and adoption decisions than state or expert knowledge. To this end, I introduce the idea of the reflexive producer to show that farmers are reflexive actors who negotiate between expert and local knowledges when making decisions. My results illustrate that, when looking at knowledge only as originating from the state, farmers decisions to plant Bt corn are influenced by expert knowledge. However, when accounting for local knowledges, the influence of state expert knowledge is limited. Such results follow Wynne s (1996) critique of Giddens (1994), illustrating the necessity to recognize lay or informal knowledge. However, in an era of evolving knowledge and new technology, the influences affecting farmers decisions to use transgenic crops will likely continue to grow. While the farmers in my case study draw largely upon local knowledge when making their decisions, the environmental and political complexities surrounding transgenic seed extend beyond a local context. Environmental activists cite concerns surrounding possible gene flow from transgenic to non-transgenic crops. Scientists warn that improper use of transgenic corn could lead to mutant pests and weeds. Health advocates question the quality, safety, and nutritional aspects of transgenic foods. And some governments have

19 80 Rural Sociology, Vol. 73, No. 1, March 2008 banned or placed restrictions upon the use or import of transgenic crops. As a result, farmers decisions to use transgenic crops in the future may be influenced by increasingly complex and, at times, contradictory concerns. Building on the present study, future research could be pursued in several directions. While my study illustrates that farmers negotiate between local first hand and expert state knowledge, data limitations constrained my analysis from including more types of expert knowledge as well as other knowledge types. As a result, future studies could examine the influence of seed dealers, agricultural consolidation, neighbors, and/or community groups on the negotiation processes of the reflexive producer. Examining the influence of neighbors and/or community groups, Lash s (1994) idea of a reflexive community could be drawn from to examine local and community seed trends. Also, exploring the influence of seed dealers and agricultural consolidation, future research could look at how farmers decisions to choose from non-transgenic and transgenic seed could be becoming more limited due to the elimination of certain seed types as well as the increasing tendency to cross transgenic and non-transgenic seed. Furthermore, as new strands and varieties of transgenic seed continue to be developed, future studies should continue to examine how local and expert knowledge influences farmers decisions to plant transgenic seed in order to more fully understand the complexities underpinning farmer decisions in an era of contested knowledge sources. References Abrahamson, M Minnesota Pest Report. MPR Agricultural Development Division Minnesota Department of Agriculture Plant Pest Survey and Biological Control Program, Retrieved May 5, 2006 ( pestreports/2002aug23.pdf). Alexander, C., J. Fernandez-Cornejo, and R.E. Goodhue Determinants of GMO Use: A Survey of Iowa Maize-Soybean Farmers Acreage Allocation. Pp in Market Development for Genetically Modified Foods, edited by V. Santaniello, R.E. Evenson, and D. Zilberman. Wallingford, UK: CABI Publications Farmers Adoption of Genetically Modified Varieties with Input Traits. Giannini Foundation Research Report 347. Oakland, CA: Regents of the University of California Division of Natural Resources. Alston, J.M., J. Hyde, M.C. Marra, and P.D. Mitchell An Ex Ante Analysis of the Benefits from the Adoption of Corn Rootworm Resistant Transgenic Corn Technology. AgBioForum 5: Retrieved May 5, 2007 ( org/v5n3/v5n3a01-alston.htm) Bell, M.M Farming for Us All. University Park, PN: The Pennsylvania State University Press. Beck, U Risk Society: Towards a New Modernity. London, UK: Sage Press The Reivention of Politics: Towards a Theory of Reflexive Modernization. Pp in Reflexive Modernization: Politics, Tradition and Aesthetics in the Modern Social Order, editedbyu.beck,a.giddens,ands.lash.stanford,ca:stanford University Press.