AHUM-A367, revised version
Converting or not converting to organic farming in Austria: Farmer types and their rationale
Ika Darnhofer and Walter Schneeberger Institute of Agricultural Economics, University of Natural Resources and Applied Life Sciences Vienna, Austria
Bernhard Freyer Institute for Organic Farming, University of Natural Resources and Applied Life Sciences Vienna, Austria Address for correspondence: Ika Darnhofer, Institute of Agricultural Economics, Peter Jordan Str. 82, A-1190 Vienna, Austria. Phone: +43-1-47654-3587, Fax: +43-1-47654-3592; E-mail:
[email protected]
Abstract. Reasons for converting to organic farming have been studied in a number of instances. However, the underlying rationale that motivates behaviour is not always made clear. This study aims to provide a detailed picture of farmers’ decision making and illustrate the choice between organic and conventional farm management. Based on 21 interviews with farmers a decision tree highlighting the reasons and constraints involved in the decision of farmers to use, or not to use, organic production techniques was formulated. The accuracy of the decision tree was tested through a written survey of 65 randomly sampled farmers. The decision tree allows to identify decision criteria and examines the decision making process of farmers in choosing their farming method. It also allows to characterize farmer strategies and values, identifying five types of farmers: the ‘committed conventional’, the ‘pragmatic conventional’, the ‘environment-conscious but not organic’, the ‘pragmatic organic’ and the ‘committed organic’. The importance of taking into account heterogeneity in farmers’ attitudes, preferences and goals and their impact on the choice of farming method is emphasized.
Key words: Austria, decision tree, farmer decision-making, motivation, organic farming
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INTRODUCTION
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A goal of the Common Agricultural Policy of the European Union (EU) is to integrate
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environmental considerations in agricultural policy. As a major tool to achieve this, the EU has
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created the framework for national agri-environmental program in Regulation 2078/92. The
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national implementations are based on voluntary schemes, inviting individual farmers to
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contract with government agencies to produce environmental goods in return for compensatory
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payments. As organic farming is generally seen as an environmentally sound farming practice,
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Austria is actively seeking to increase the number of organic farms by including an organic
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farming scheme in the ÖPUL, the Austrian agri-environmental program.
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The ability to reach this policy goal depends on the willingness of farmers to participate in the
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program and implement changes on their farm (Morris and Potter, 1995; Wilson, 1997; Beedell
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and Rehman, 2000; McGregor et al., 2001). Various studies have investigated the factors
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influencing farmers’ motivations for participation in agri-environmental schemes in general,
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and adoption of organic farming in particular. Most of these studies take a normative approach
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through surveys, i.e. formal questionnaires including farm and farmer characteristics and
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allowing farmers to select important barriers to conversion from a predefined list (Schulze Pals,
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1994; Häfliger and Maurer, 1996; Freyer, 1998; Burton et al., 1999; Drake et al., 1999;
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Hollenberg et al., 1999; Kirner and Schneeberger, 2000; Rämisch, 2001; Schneeberger and
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Kirner, 2001; Schneider, 2001; Schneeberger et al., 2002). A strength of this approach is that it
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allows for the inclusion of a large number of participants. Thus, the data can be analyzed
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statistically, models built, and the importance of individual barriers quantified. However, These
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studies offer only limited insight into the structure and the relationship between various barriers
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to conversion. This can impair a thorough understanding of farmers’ motivation, as usually
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several interrelated factors are at play, rather than one single barrier being decisive (Kirner,
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2001). The separate analysis of individual factors can thus be misleading (Wilson, 1997).
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This is where qualitatively oriented studies of farmers’ decision making can offer additional
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insights and help interpret the findings of normative surveys. This was the goal of studies which
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have explicitly tried to understand farmers’ choices regarding their farming method by
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appreciating the role of different motives and convictions as well as their relationship to a 2
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personal frame of reference. In-depth interviews on reasons for conversion have been reported
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by several authors (Vogel, 1995; Buck et al., 1997; Lockeretz, 1997 and 1999; Duram, 1999
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and 2000; Fairweather, 1999; Kaltoft, 1999; Guthmann, 2000; Lund et al., 2002). Although
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usually encompassing a smaller sample size (usually less than 80 farmers), these type of studies
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provide a rich picture of the decision-making process and the various factors affecting farmers’
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choices, both concerning the actors themselves as well as the structural factors in which they
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are embedded.
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The actor-oriented approach starts with the assumption that different farmers define and
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operationalize their objectives and farm management practices on the basis of different criteria,
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interests, experiences and perspectives. However, inevitably, behavior will be linked to
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structural features of the economy in which any given farmer operates, especially agricultural
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policy, market configurations and technology design. Indeed, there is a growing recognition of
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the complex interplay between external and internal factors (Falconer, 2000). Farmers should
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thus not be seen as the passive recipients of government programs, nor as so routinized that
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they simply follow laid-down rules or conventions (Long and van der Ploeg, 1994).
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This study, which is essentially exploratory and descriptive, presents a qualitative model
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depicting the logical structure of the beliefs and preferences that motivate behavior. It should
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not be understood as either within a methodological-individual framework which
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conceptualizes farmers as independent decision-makers, or within a structuralist framework that
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gives primacy to how broader economic and political forces shape farming practices. It aims at
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an integration of both approaches. Indeed, the farmers’ objective world is molded by how they
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perceive and interpret structures. Some structural elements may affect one farmer’s decisions,
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but be viewed as unimportant by another (Long and van der Ploeg, 1994; Duram, 2000).
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Structural factors should then be understood as mediated by an actors’ perception: they enter
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the decision process only if perceived to be decisive.
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The aim of the study is to provide a detailed picture of farmers’ decision making and illustrate
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the choice between organic and conventional farm management. First, the decision tree method,
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the study area and the data collection process will be introduced. Then, the decision tree and
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each decision criterion will be presented, illustrated through examples taken from the 3
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interviews with farmers. In the discussion, the identified farmer types will be characterized and
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compared with published research results, shedding light on the strategies and rationales of the
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decision makers. Before drawing conclusions from the study, the strengths and weaknesses of
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the decision tree method will be discussed.
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METHOD
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The decision tree approach
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The approach selected to analyze farmers’ choice between organic and conventional farming is
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Ethnographic Decision Tree Modeling. The method was developed by Gladwin (1976, 1989a)
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to model the way people make real-life decisions. Its distinctive features are a reliance on
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ethnographic fieldwork techniques to elicit decision criteria. When eliciting the criteria, special
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care is taken to ensure the decision criteria are derived from farmers’ experiences and not the
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views of the researcher. The criteria are then combined in the form of an inverted tree which is
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read like a flow-chart, i.e. a set of “if-then” rules. The decision tree reflects the beliefs and
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motives for selecting the farming method. This descriptive and predictive approach allows to
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identify the influence of a wide array of factors (McGregor et al., 2001).
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Hierarchical theory of choice assumes that decisions are decomposed so that the various
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alternatives are compared sequentially using several characteristics or aspects (Gladwin, 1976).
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The decision criteria can be elicited and formulated as a series of questions which form a
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decision tree. The decision process is hypothesized to encompass two stages: first there is a pre-
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attentive process, where a problem is rapidly simplified through eliminating all alternatives that
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have some aspect that the decision maker does not want. He or she is then left with two or three
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alternatives that he or she can decide about in a more detailed way in the second stage, the
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“hard core” phase of the decision process. In this second stage the alternatives are passed
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through a set of decision criteria (Gladwin, 1976). The position of a criterion in the decision
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tree does not necessarily reflect its relative importance in the decision (Murray-Prior, 1994).
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The major methodological concern is to find the specific aspects and constraints decision
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makers are using. The researcher must determine what information is actually used in making 4
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decisions, as opposed to information unused in decision making that may nevertheless be given
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in response to interview questions (Gladwin, 1976).
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To incorporate an explanation of the selection of criteria, Murray-Prior (1994) proposes to use
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personal construct psychology (Kelly, 1955, quoted in Murray-Prior, 1994). Personal construct
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psychology likens people to scientists, which attempt to make sense of the world by developing
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hypotheses, or constructs, about how they anticipate the world to behave and continually test
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these constructs against what they construe has occurred. They are trying to make sense, discern
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patterns and establish order in the complexity of the world in which they find themselves. This
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approach puts the emphasis for the explanation of behavior with the person. Although the
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environment influences behavior, its effects are determined by the construction system of the
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individual (Murray-Prior, 1994).
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Decision tree models have been used to study farmers’ decision making in a variety of contexts,
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e.g. the adoption of agricultural recommendations in Mexico (Galdwin, 1976), the adoption of
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improved maize varieties in Kenya (Franzel, 1984), the use of chemical and/or organic fertilizer
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in Malawi (Gladwin, 1989b), the adoption of improved feed crops in Ethiopia (Darnhofer et al.,
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1997), strategic decisions by wool producers in Australia (Murray-Prior, 1998) or the choice
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between organic and conventional production in New Zealand (Fairweather, 1999). The method
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has been found useful for assembling information on farmers’ opinions, perceptions and values
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in a systematic manner to show the logic behind farmers’ decisions (Franzel, 1984; Murray-
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Prior and Wright, 1994).
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Some authors (Franzel, 1984; Fairweather, 1999) have used decision tree models as descriptive,
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not predictive models. However, when used to provide predictions about farmers’ decisions, the
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trees were accurate, allowing for the conclusion that a hierarchical decision process is at least as
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plausible as a linear-additive decision process, since most linear-additive econometric models
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do not give accurate predictions of individual decisions without the use of dummy variables
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(Murray-Prior and Wright, 1994). Indeed, when compared with other decision-modeling
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methods, decision trees performed well: Murray-Prior (1994) reports that it allowed identifying
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the important reasons for particular decisions where other models, e.g. expected utility and
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regression models, were not successful. When compared to the models generated by a 5
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classification and regression software (CART), the manually drawn models were more
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accurate, if more detailed, than the concise trees generated by CART, which also did not
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necessarily have a ‘logical’ succession of criteria (Darnhofer et al., 1997). Another strength of
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the decision tree method is that it allows the use of both qualitative and quantitative decision
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criteria and that the theoretical bias is reduced as no a-priori assumptions are made about which
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factors should be included in the models (Murray-Prior and Wright 1994).
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Structural influences on farmers in the Weinviertel
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The area selected for the study is the Weinviertel, a region in the North-East of Austria (Figure
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1). The area was selected for its low percentage of organic farms (1%, compared to the national
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average of 9.8%) and the prevalence of diversified farming, with annual cash-crops, perennial
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crops (vineyards) and some animal husbandry. Due to the agro-climatic situation, the general
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yield level of the area is comparatively low, and the expected yield decrease after conversion to
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organic farming is moderate.
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Figure 1. The study area
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In the study area more than 90% of farms participate in one or several schemes of the Austrian
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agri-environment program and thus receive compensatory payments. To be eligible for
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compensatory payments for organic farming, farmers must convert their whole farm, be 6
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certified and commit themselves to remain organic for at least 5 years. From an economic point
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of view, the preconditions for conversion to organic farming in the study area are favorable: on
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the one hand the moderate yield decrease combined with the higher prices for organic produce
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means that the income from product sales should remain at a level comparable to conventional
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farming. On the other hand, the income from compensatory payments increase significantly,
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resulting in a higher farm gross margin (Darnhofer et al., 2003). Overall, the agri-environment
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program can be seen as supporting conversion through mitigating risk. Still, as the low
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percentage of organic farms in the study area testifies, the compensatory payments are not
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sufficient to motivate a large number of farmers to convert .
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Data collection
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Past research on motivations for organic farming has focused on organic farmers and the
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diversity of their motivations. However, to some extend, this leads to a one-sided perspective,
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as it assumes that those farmers who did not convert simply did not share the reasons put forth
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by the organic farmers. It does not allow for conventional farmers to have a reasoning that is
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not defined relative to organic farming, e.g. have strong reasons favoring conventional farming.
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As Fairweather (1999) shows, including conventional farmers allows to shed light on
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constraints affecting conventional farmers who have considered organic farming. Assuming
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that both organic and conventional farmers are diverse rather than a uniform group, and to
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highlight the reasons that lie behind farmers’ choices of either organic or conventional
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production, both organic and conventional farmers were included in the study. Organic farmers
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were asked why they chose to convert, i.e. which factors they considered at the time they made
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the decision. Given that some of the farmers had converted several years ago, it cannot be
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entirely excluded that their experiences as organic farmers influence the narrative of their
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conversion decision. However, during the interviews careful attention was made to focus on the
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decision criteria at the time of conversion. Conventional farmers were asked whether they had
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ever considered converting to organic farming and which factors influenced their decision, i.e.
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what aspects of conventional farming were important to them, and/or, if they had considered
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converting, which factors they perceived as obstacles.
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The data were collected early 2001 in a two-stage process. First, 21 on-farm interviews with a
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non-random sample of farmers were made to elicit decision criteria, 9 with organic and 12 with
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conventional farmers. These farmers were suggested by the local office of the Chamber of
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Agriculture and selected for diversity, i.e. to include farms with various enterprises and size, as
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well as, for the organic farms, time since conversion. This procedure was felt to be the most
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efficient way to achieve a large variety of interview partners despite the possibility of a
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selection bias, i.e. that only farmers on good terms, or those sharing certain views with the
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Chamber of Agriculture, might be included. However, the interviews revealed a wide variety of
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motivations and no significant decision factor advanced in the literature was found missing.
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Also, as the decision tree would be tested using a random sample, a high error rate would reveal
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a bias in the sample used to build the tree.
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The on-farm interviews provided detailed background information as well as the factors that
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were taken into consideration when the decision to convert to organic farming was made, or
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those that explain why a farmer prefers to remain conventional. Farmers’ reasons for and
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constraints against action were then formalized as decision criteria that are posed as discrete
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questions, which can be either accepted or rejected. These criteria are then combined to a
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decision tree. The different decision criteria for each farmer in the model-building sample are
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combined in a logical fashion while preserving the ethnographic validity of each individual
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decision model (Gladwin, 1989). The tree must allow each farmer to move downwards through
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a series of criteria to an end-point which reflects the farmer’s choice. In addition, the tree must
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combine criteria for all farmers in a logical way. The tree tells why a particular outcome is
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achieved for each farmer because the outcome is preceded by a subset of criteria relevant to
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particular farmers (Fairweather, 1999).
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As recommended by Gladwin (1989a) this decision tree was tested for its predictive accuracy
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through a mail survey of a second, random, farmer sample, using a structured questionnaire
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where each decision criterion was represented through one question. These questions were no
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longer open-ended but designed to elicit informant’s yes/no responses. The wording in the
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questionnaire is adhered to in the decision tree, as closely as translation allows. The
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questionnaire was mailed to a random sample of 70 farmers, 35 conventional and 35 organic.
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The sample was drawn randomly from the official list of farmers receiving direct payments 8
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within the Common Agricultural Policy of the EU. Farmers were classified as organic if they
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participate in the agri-environmental program and receive compensatory payments for organic
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farming. To ensure a high return rate and comply with personal data protection requirements,
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the list of randomly sampled farmer’s names was sent to the local Chamber of Agriculture who
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contacted the farmers and inquired whether the farmer was willing to participate in the study. If
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a farmer was not willing, the next farmer of the list was contacted. Although possible, a bias
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due to the involvement of the Chamber of Agriculture is expected to be very small, since the
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Chamber was provided with a predetermined list of farmers. Of the 70 mailed questionnaires,
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65 were returned, 33 from organic and 32 from conventional farms.
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RESULTS
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Description of decision criteria
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The structure of the decision tree (see Figure 2) shows that organic farming must be considered
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a ‘feasible’ production method before a farmer will consider organic farming in any detail. Just
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as some farmers oppose organic farming on fundamental grounds, i.e. exclude organic farming
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preattentively, others opt for organic farming based on a deeply held conviction. Farmers who
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neither feel strongly that conventional farming is the ‘only way’, nor hold deep convictions
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regarding organic farming go on to consider the potential impact that a conversion would have
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for their farm organization, the second stage of the decision process. It includes the perceived
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constraints on action and pertain to the requirements of sugar beet production, vineyards and
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animal husbandry under organic farming. If these farm activities are not present or if the
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requirements are not perceived as a constraint, then reasons for producing organically are
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considered. Farmers for whom those reasons are not attractive remain conventional. In Figure 2
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the number in parentheses next to each of the possible ‘yes’ or ‘no’ answers indicate the
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number of farmers from the test sample selecting this particular answer. To reflect arguments
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what were mentioned together by farmers as well as to allow for a more concise tree, several
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criteria in the figure are a combination of two sub-criteria. These were stated as individual
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questions in the questionnaire, and discussed separately below, but combined in the decision
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tree as a negative answer to either question leads to the same next question or end-point. 9
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Figure 2. Decision tree depicting the criteria considered in the choice between organic and
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conventional farm management in the Weinviertel
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Criterion 1 combines two questions assessing two fundamental beliefs of the farmers regarding
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organic farming: is it more environmentally friendly than conventional farming and/or are
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organic foods healthier? Although these two aspects are very different, only those respondents
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who answer either or both question with a ‘yes’ move on to the second criterion. Those who
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cannot agree with either statement are classified as conventional. These farmers do not perceive
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their methods as being harmful to the environment, since most participate in one of the schemes
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of the agri-environmental program (e.g. “reduction of yield increasing farm inputs”) and,
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therefore, use significantly less nitrogen fertilizer than ten years ago. They feel that the
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environmental issues are exaggerated by the media and that they are not linked to their farming
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practices. Indeed, inputs (fertilizer, biocides) are expensive and as good managers they only use
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the minimum required amount and apply it only when needed. These conventional farmers also
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do not perceive organic farming as being environmentally friendly. For example they note that
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since some organic pesticides are more efficient than conventional products, they cannot be
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more environmentally benign. Also, the fields of most organic farms are surrounded by those of
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conventional farms, and therefore organic produce is bound to contain synthetic pesticides from
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their neighbor’s spray drift. Lastly, they point out that the repeated application of copper-
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containing preparations in the vineyards leads to the accumulation of trace metals in the soil.
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These farmers not only see organic farming as falling short of its environmental claim, they also
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doubt the statements made regarding the ‘healthiness’ of organic produce: as synthetic
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pesticides are not used, plants have to ‘defend’ themselves and this may result in increased
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concentrations of secondary plant metabolites. Also, since synthetic fungicides are not used,
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there is a higher level of fungi incidence and therefore higher mycotoxin contamination levels
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in grain. Some point out that the quality of processed organic food is not higher than that of
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conventional food, since it contains just as many additives.
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The second criterion of the decision tree addresses the perceived feasibility of organic farming,
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i.e. does it ‘work’? It has two aspects, depending on whether a farmer defines ‘successful
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cropping’ through the yield level (i.e. quantity of produce) or profit per hectare. The first aspect
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is related to criterion 1, and identifies farmers to whom high yields are important. They are
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convinced that this is only possible through using synthetic fertilizers, pesticides and fungicides
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as needed. Regarding wheat quality, they point out that without nitrogen fertilizers a high
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protein content cannot be achieved, as the nitrogen release from organic (green) manures cannot
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be synchronized with plant requirement. They are also convinced that under the climatic
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conditions in the Weinviertel, it is not possible to achieve high quality produce without
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fungicides. The second aspect of this criterion is the profitability of organic farming: if yields
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are lower, prices must be significantly higher. However, many farmers do not think that the
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majority of consumers are willing to pay a higher price for organic produce. They see the
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organic market as a niche and expect the supply to rise faster than the demand, leading to a drop
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in prices, thus affecting profit per hectare.
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These two elimination criteria identify 20 conventional farmers who do not think that organic
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farming is an environmentally desirable and/or technically or economically viable alternative
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and, therefore, do not deem it worth of serious consideration. Most of these farmers do not seek
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further information on organic farming as they do not feel that it is relevant to their farm. For
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the remaining 45 farmers, who did not answer any of these questions with a ‘yes’, organic
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farming is at least an option they are willing to contemplate as they consider it a viable farming
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method. The next criteria seeks to identify those farmers who have a strong environmental
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motivation for their choice of farming method.
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Criterion 3 seeks to identify farmers to whom net income maximization is not a primary goal
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and to whom other values such as the environmental impact of their production method, health,
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ethical and/or lifestyle issues are key. Their willingness to risk some income to farm in an
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environmentally friendly way does not imply that, in the long run, they expect a lower net
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income as organic farmers. These 31 farmers are willing to adapt their farm organization and
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select new farm enterprises so that the requirements of organic farming will match their
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resources.
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Of the farmers who are willing to forgo some of their income, eight were faced with a perceived
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constraint preventing them from participating in the organic farming scheme of the agri-
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environmental program up to now. Criterion 4 addresses the issue of higher labor requirements
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in organic farming. Indeed, organic farming is generally perceived to be more labor-intensive
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than conventional farming. This is an issue for a number of interviewed farmers, who pointed
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out that modern living standards are defined by leisure time and that those who are not engaged
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in farming have fewer working hours and have the possibility to take extended vacations. A
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farmer also pointed out that although he and his wife do not mind, his son finds it difficult to
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work late, particularly in summer when his friends roam around with their motorcycles after 5
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pm. The farmers also noted that due to the limited supply of family labor they might have to
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hire labor, which many are not willing to do. This is partly linked to the fact that few Austrians
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are willing to perform manual farm work and that hiring foreign nationals (primarily Czechs as
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the border is quite close) involves burdensome and often expensive administrative
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requirements. Also, language barriers can result in miscommunications.
328 329
The 14 farmers who are not willing to risk foregoing some of their net income for the benefit of
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environmentally friendly farming go on to assess the technical requirements and organizational
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changes a conversion to organic farming would imply (criteria 5 to 13). Since environmental,
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health and/or lifestyle considerations are less important for most of the farmers on this branch
333
of the decision tree, the current prices of organic products and the level of compensatory
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payments were triggers for organic farming to be perceived as an option worth considering.
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However, as these farmers do not have a strong emotional motive for organic farming, they
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tend not to be willing to change the organization of their whole farm to fulfill organic farming
337
requirements.
338 339
Criterion 5 investigates the importance of sugar beet for the income from farming. For
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conventional farms in the area sugar beet is one of the most profitable crop. Criterion 6
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addresses the fact that in organic farming, where herbicides cannot be applied, weeding must be
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done manually and requires substantial labor for extended periods of time. Also, since there is
343
no market for organic sugar beet in Austria, no price premium can be expected. The high labor
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requirement thus results in a lower return per hour of labor compared to conventional
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production methods. Farmers with a sugar beet quota who are interested in converting to 12
346
organic farming and for whom the production of sugar beet is not attractive could consider
347
selling their sugar beet quota (criterion 7), which is possible since 1998. Although none of the
348
farmers included in the test sample indicated that sugar beet is a major constraint, this path was
349
left in the decision tree as it was mentioned in the interviews.
350 351
Criterion 8 selects farmers who have vineyards and for whom they are an important source of
352
income. For farmers who have only small vineyards, these issues do not carry much weight in
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their choice of farming method. The primary arguments against organic management of
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vineyards is that the production risk is too high and/or the fear that the quality requirements
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cannot be met using organic methods (criterion 9). The risk involved in organic vineyard
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cultivation is mainly the fear of a year of very high pest incidence or unfavorable climatic
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conditions leading to high fungi incidence, which could lead to a total crop failure. Since
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organic methods mainly aim at preventive measures, the effectiveness of organic methods are
359
limited once there is a high pest incidence. Also, one interviewee thinks that many fining agents
360
used in the wine making process are prohibited in organic processing and, therefore, may
361
endanger continuous wine quality. She expects a high marketing risk if she were to convert. On
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the one hand she does not feel that her customers are willing to pay a price premium for organic
363
wine. On the other hand, she was concerned that if she had a bad year (regarding quantity or
364
quality), she might lose customers, who would be difficult to regain the following year.
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However, for farmers who have both field crops and vineyards and who are interested to
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convert only their fields to organic production, can, under narrowly defined conditions, split
367
their farm in a crop farm which they convert to organic farming and a vineyard farm which
368
remains conventional (criterion 10).
369 370
Criterion 11 addresses issues related to animal husbandry. Although 26% of farmers in the
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study region are engaged in animal husbandry, most keep only a few animals to cover their
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family needs. If animal husbandry is an important source of income, two issues are crucial: feed
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supply and animal housing. Producing certain feed crops under organic management can be
374
perceived as a problem (criterion 12). For instance, one farmer raises steers and knows from
375
another farmer in the village that weeds cause a serious problem in organic maize production,
376
as it is a fairly dry area. He does not perceive conversion to organic farming as an option, as he
377
would have to purchase organic feeds, which are expensive. As far as animal housing (criterion 13
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13) is concerned, strict regulations have to be implemented, requiring significantly more space
379
per animal in the barn as well as access to outdoor areas. It is therefore often necessary to build
380
a new barn or adapt the current one resulting in a major long-term investment. The fact that
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none of the farmers included in the test sample stated that this was a decision criterion to them
382
might be due to the subordinate role of animal husbandry in the region.
383 384
The nine farmers (6 organic and 3 conventional) not identifying obstacles in their farm structure
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and organization, go on to consider the contractual aspect of participating in the agri-
386
environmental program. Criterion 14 addresses the regulations imposed by the organic farming
387
scheme of the agri-environmental program as well as those by the organic farmer associations.
388
Criticism is mainly voiced regarding the sometimes vague definitions, leaving room for
389
interpretation by the controllers and thereby making the farmer dependent on his/her
390
subjectivity. Also, these farmers feel uneasy at the prospect of signing a five-year contract
391
within the agri-environmental program, aware that the government may change the regulations
392
within the contract duration. These on-going updates and changes result in ever more complex
393
regulations, making it increasingly difficult for a farmer to keep up and be certain to comply
394
with all rules and regulations. This is not made easier by fact that farmers perceive the
395
information provided as insufficient and do not feel adequately informed of upcoming changes.
396 397
Finally, the seven farmers who do not perceive either the technical or farm structural aspects
398
nor the regulations as obstacles to conversion, assess whether the perceived ‘side-effects’ of
399
converting to organic farming are attractive to them. Criterion 15 differentiates between those
400
farmers who are looking for the challenge to do things ‘differently’, i.e. who are not interested
401
in following the conventional path of specialization and increasing mechanization. For example
402
one of the interviewed farmers worked part-time when he took over the farm, minimized on-
403
farm labor requirements and focused on small grains. This management concept was simple,
404
but it was not rewarding. He decided to become a full-time farmer and to try his ideas for new
405
crops and activities. For him, the compensatory payments guarantee some income, offering the
406
room for experimentation and time to learn from experiences. This farmer also got engaged in
407
processing and direct marketing, enjoying the contact with customers and the feedback on his
408
products. This search for a ‘different’ way also includes farmers who perceive organic farming
14
409
as a challenging and promising option for financial reasons, and who want to take advantage of
410
the current high level of demand and the attractive price premiums.
411 412 413
Accuracy of the model
414 415
Since one of the aims of a decision model is to predict farmers’ choices, the proper test of the
416
model is its ability to explain the decision of a random sample of decision makers. The
417
accuracy of the decision tree can thus be calculated as the ratio of correctly predicted choices
418
divided by the total number of farmers included in the test sample (Gladwin, 1976). A
419
misclassification occurs if the decision tree predicts that farmers reaching a specific end-point
420
would be organic, whereas a particular farmer following this path is actually conventional or
421
vice-versa (the number of farmer with a farming method that is different from the one expected
422
at each end point, is listed separately in Figure 2). Of the 65 farmers included in the decision
423
tree, 11 were misclassified, leading to an accuracy of 83%. This is within the range of the
424
models presented by Gladwin (1976) and those reviewed by McGregor et al. (2001), who report
425
that models developed using this approach have predicted better than 80% of decisions by
426
farmers not included in the sample used to develop the model.
427 428
In the model, the main criterion leading to misclassifications is criterion 4, addressing the issue
429
of labor requirements. Indeed, criterion 4 does not lead to a clean split between organic and
430
conventional farmers: out of 31 farmers following this path, two conventional farmers are
431
predicted to be organic, and six organic farmers are predicted to be conventional. If criterion 4
432
would be deleted, and all 31 respondents answering ‘yes’ to criterion 3 would be labeled
433
organic, the model would improve its accuracy from 83% to 92%. The mislabeling of six
434
organic farmers caused by criterion 4 is due to the fact that these farmers stated that the labor
435
requirements in organic farming are higher than the available family labor and that they are not
436
willing to hire non-family labor. Yet, despite this perceived labor bottleneck, they are still
437
organic. McGregor et al. (2001) suggest that when a decision is inconsistent with the answer to
438
a criteria, it indicates that the criterion reflects a belief rather than a decision rule. The
439
ambivalence regarding labor availability may be due to short-term issues which will be solved
440
through reorganization of the farm. It may also reflect how different people on the farm 15
441
perceive and value the type of labor they carry out and their specific control over the labor
442
process and benefits derived (Jansen, 2000). As Franzel et al. (2001) point out, the acceptability
443
of a technology depends on its feasibility from the farmers’ point of view, and its value to them.
444
Therefore, apparent constraints such as labor difficulties that farmers mention when attaching a
445
low value to an activity, may disappear when the farmers’ perception of the value increases. It
446
then seems that subjectivity plays an important role when labor is cited as an key issue in
447
relation to organic farming. This is also reflected in the inconclusive reports in the literature
448
regarding the generalized character of higher labor requirements in organic farming, and the
449
lack of understanding of the precise factors that cause an increase in labor demand (Jansen,
450
2000).
451 452 453
DISCUSSION
454 455
Farmer types and their rationale
456 457
The decision tree allows insights into the values and beliefs underlying farmers’ choices. Based
458
on these, farmer types were identified, each with a distinctive rationale motivating their
459
behavior. The labels used to characterize the farmer types partly reflect those proposed by
460
Fairweather (1999), who identified two types of organic farmers, the “committed” and the
461
“pragmatic” as well as three types of conventional farmers. These are labeled according to their
462
position towards organic farming: the “hopeful organic” and “frustrated organic” who are
463
interested in a conversion, and those who are not, and thus “do not grow organic”. However, if
464
the “pragmatic” and “committed” labels were taken from Fairweather (1999), the symmetry in
465
the labeling of organic and conventional farmers reflects the results of Schoon and Te
466
Grothenhuis (2000), who differentiate between “idealistically motivated farmers”, i.e.
467
conventional and “ecological” farmers whose choice of farming practice is rooted in strong
468
convictions and ideals; and “pragmatically motivated farmers”, which can switch more or less
469
easily between conventional and “ecological” farming. This symmetry is also identified in the
470
current study, which shows that of the 32 conventional farmers, over 60% reject organic
471
farming for fundamental reasons without assessing the implication of conversion for their own
472
farm while of the 33 organic farmers, nearly 85% are organic by conviction. 16
473 474
The decision tree (Figure 2) identifies five types of farmers, three being conventional and two
475
organic: the ‘committed conventional’ (20 farmers), the ‘pragmatic conventional’ (8 farmers),
476
the ‘environment-conscious but not organic’ (2 farmers), the ‘pragmatic organic’ (3 farmers)
477
and the ‘committed organic’ (21 farmers). In Figure 2, farmers whose farming method is not the
478
one predicted by the decision tree are not included in the total number of farmers for each
479
farmer type. For example only two farmers are labeled ‘environment-conscious but not organic’
480
as of the eight farmers reaching this end-point, two are correctly predicted conventional farmers
481
whereas six are actually organic farmers.
482 483
‘Committed conventional’ farmers are characterized by not even considering a conversion to
484
organic farming. They do not view organic farming as more environmentally friendly than
485
conventional production methods, they do not believe the health claim made of organic foods,
486
nor do they perceive that organic production is technically and/or economically feasible. Their
487
arguments echo those found in publications discussing potential shortcomings of organic
488
farming (Kirchmann and Thorvaldsson, 2000; Edwards-Jones and Howells, 2001; Rigby and
489
Cáceres, 2001; Trewavas, 2001). Their attitudes and values reflect several elements of the
490
conventional paradigm as described by Beus and Dunlap (1990). They structure their farm
491
along the lines designed by science and agribusiness and articulate clearly that this conventional
492
approach to agriculture is the only sensible one. This reflects the characterization of the
493
“vanguards” or “optimal farmers”, who are guided by the modernization paradigm in its focus
494
on external inputs, new technology, scale-increase and specialization (Long and van der Ploeg,
495
1994). These “optimal” farmers focus on minimizing production costs and maximizing output
496
per hectare while producing primarily for bulk-markets (Roep and de Bruin, 1994).
497 498
‘Pragmatic conventional’ farmers do not have a fundamental stance opposing organic
499
farming. However, they point out that a conversion can entail a substantial amount of risk: they
500
focus on the technical challenges of conversion, the uncertainty of price and market
501
development and the regulatory constraints. Overall, they perceive a conversion as entailing
502
profound changes in their farm organization and are not eager to implement them unless
503
expecting a tangible benefit. They are likely to be more open to conversion once technological
504
uncertainties have been cleared by the experiences of organic farmers in the area, and once the 17
505
market for organic products are established. However, if a lower economic risk of conversion
506
can help to increase the number of farmers who consider organic farming, it is unlikely that by
507
itself it will be a sufficient motive for conversion. Indeed, reports in the literature (Burton et al.,
508
1999; Lohr and Salomonsson, 2000; Pietola and Lansink, 2001), as well as the decision model
509
presented here, show that for most farmers perceived economic viability may be a necessary
510
condition for conversion, but it is not a sufficient one. In their search for a way out of the
511
“squeeze on agriculture” resulting from the modernization paradigm (van der Ploeg et al.,
512
2000), the ‘pragmatic conventional’ farmers will include organic farming as well as options that
513
do not require a conversion (e.g. farm diversification, off-farm income). However, as they do
514
not rule out organic farming, they can be seen as a pool of potential converters.
515 516
‘Environment-conscious but not organic’ farmers are committed to environmentally friendly
517
farming practices, but are currently not receiving payments within the organic farming scheme
518
of the agri-environmental program. This farmer type can include a variety of subtypes, e.g.
519
conventional environmentally friendly or self-declared organic. As they do not subject
520
themselves organic regulations and controls, these farmers keep a measure of flexibility e.g. to
521
manage only some farm enterprises following organic guidelines and/or to use some synthetic
522
inputs on crops in case of need, thereby reducing their risk. This farmer type also includes
523
farmers who have converted their whole farm, but are reluctant to sign the agri-environmental
524
contract binding them for five years. Others might follow the organic standards very closely,
525
but be wary of the bureaucracy and paperwork involved in certification and/or participation in
526
the agri-environmental program. This has been reported by Burton et al. (1999) who describe
527
self-declared organic producers who are not registered, who tend to have strong views
528
regarding the perceived disadvantages of certification and/or who want to remain independent
529
of the regulations. To some extend, the cost of participating in the organic aid scheme might
530
also play a role. As Falconer (2000) points out transaction costs (e.g. certification, record-
531
keeping) can cost farmers up to 10% of typical compensation payments for organic farming.
532
Thus, for some environmentally committed farmers, the administrative aspects discourage them
533
from participation in the agri-environmental program. Marketing aspects also play a role, as
534
some interviewed farmers feel that alternative farming methods ensure a product of high
535
quality. However, since they have an established clientele willing to pay a premium without
18
536
requiring the organic label, they do not see the need to convert to organic farming, as they have
537
forged a relation based on trust and product quality.
538 539
To ‘pragmatic organic’ farmers, health, ethical or sustainability aspects are not dominant
540
motivations for conversion. This type of farmer tends to perceive organic farming as offering a
541
good prospect for securing their income. Especially the compensatory payments within the agri-
542
environment program are then an important incentive for conversion. Of the sample included in
543
the study, most of the farmers in this type have converted since 1995, the year where the agri-
544
environmental program was introduced in Austria. The decision tree thus supports evidence that
545
many of the farmers who have converted since the late 1990s may have been driven more by
546
financial motives rather than by non-economic considerations (Jansen, 2000; Padel, 2001;
547
Rigby et al., 2001). However, even if financial motives play an important role, it is not
548
necessarily related to an income-maximization attitude (Padel, 2001). Mostly, this type of
549
farmer perceives the compensatory payment as enabling a process of “learning by doing”, of
550
experimenting with new ventures, supporting him/her in the search for a more satisfying work.
551
He/she appreciates the skill requirements, the craftsmanship and the diversity of tasks. This
552
echoes several reports on how individual realization of professional and personal potentials
553
have gained importance as a motivation for conversion (Noe, 1999; Hall and Mogyorody, 2001;
554
Michelsen, 2001). It also supports Morgan and Murdoch (2000) in their assessment of organic
555
farming as offering more autonomy and control to farmers, who can once again become
556
“knowing agents”, as organic farming revalues local knowledge and local identities.
557 558
This type of farmer follows a strategy that is close to the one of “farming economically”
559
described by van der Ploeg (2000), i.e. a strategy based on low level of dependency on external
560
inputs, flexible and multiple use of resources, local innovativeness, and low expenses. They are
561
primarily looking for an alternative to the conventional farming system, either because they
562
resent agri-business or because they do not believe in the modernization paradigm and thus seek
563
a way out of the “treadmill” (Ward, 1993). In this strategy, they can be understood as
564
‘pragmatic conventional’ farmers who have selected the organic option.
565 566
The ‘committed organic’ farmers are deeply rooted in the founding philosophy of organic
567
farming, which is based on the rejection of synthetic fertilizers and pesticides, while seeking 19
568
closed nutrient cycles and improved soil health. Economic considerations are secondary and
569
these farmers are willing to risk forgoing some of their income. They will adapt their crop and
570
animal management practices as is necessary to overcome a variety of challenges, as their
571
primary aim is to remain true to their philosophical ideal. Mostly, they are “pioneers”, as 76%
572
of the farmers included in the study who converted before 1995 are ‘committed organic’. They
573
have selected organic farming for reasons of producer and/or consumer health, as well as ethical
574
and lifestyle considerations (Tovey, 1997; Michelsen, 2001). To them organic farming cannot
575
be reduced to a set of techniques and practices, it is also a social movement and a political
576
statement.
577 578 579
Strengths and weaknesses of the decision tree method
580 581
Analyzing farmers’ decisions using the decision tree method not only allowed to identify
582
decision criteria on which the choice to remain conventional or to convert their farm to organic
583
methods was based. It also allowed to identify different farmer types which differ in their
584
rationale and approach to both conventional and organic farming. The decision tree shows that
585
not all farmers deliberately evaluate the specific technical aspects of conventional vs. organic
586
farming methods. Doing so would imply a willingness to reconsider current farming practices
587
and the values on which they are based, which the ‘committed’ farmers are not inclined to do.
588
They consider their current farming method as the only one right for them. For the ‘pragmatic’
589
farmers however, the decision tree shows that not one single criteria is decisive, but a number
590
of aspects are considered, i.e. that a conjunction of factors must be perceived as ‘right’ for a
591
farmer to convert his/her farm.
592 593
Moreover, the decision criteria as well as the tree structure suggests that it is not so much farm
594
structural characteristics in and of themselves that are decisive, but that they are mediated by
595
farmers’ perceptions and values. Indeed, the loops in the tree show that it is not decisive
596
whether or not a farmer keeps animals or has a vineyard, what is decisive is how he/she
597
perceives the implications of a conversion and what alternative solutions to perceived obstacles
598
he/she is willing to consider. The decision tree thus counters a determinist view of farmer
599
decision making which assumes that structural factors determine farmers’ behavior. As Long 20
600
and van der Ploeg (1994) point out, even though farmers may experience the same overall
601
structural conditions, it is possible for each farmer to choose a very different strategy depending
602
on his/her individual values and priorities.
603 604
Regarding the identified decision criteria, it is necessary to ensure that the reported reasons for
605
conversion are as close as possible to those that were decisive at the time when the decision was
606
taken, and not significantly influenced by experiences with organic farming after conversion
607
took place. This is difficult to ascertain, particularly for those farmers who have converted years
608
ago. Indeed, farmers’ attitudes are likely to change and evolve once organic production is
609
attempted (Wilson and Hart, 2001; Padel, 2002), and the relation between motivation and
610
conduct can be seen as one of mutual influence (Schoon and Te Grotenhuis, 2000). This
611
dynamic nature is supported by Rigby et al. (2001), who point out that explanatory variables
612
may change not only from one farmer to another, but also over time. Even if the risk of bias can
613
be minimized through careful interviewing, some influence is likely. However, if farmers give
614
strongly biased answers regarding the criteria used in their decision to convert, the decision tree
615
would yield a high error rate when tested. Thus, testing the decision tree with an independent
616
sample allows to ensure the validity of the decision criteria, and thereby supports the
617
effectiveness of the method.
618 619
Despite the strength in revealing a number of aspects of the decision to convert or not to
620
convert to organic agriculture, as well as the structure of the decision process, the method also
621
has some limitations.
622 623
First, although the interviewing at the first stage of data collection allows for a rich picture of
624
the decision process, the criteria must then be reduced to dichotomous questions, thus
625
eliminating the complexities that are often present in individual cases. The structure of the
626
decision tree thereby severely limits the nuances that can be included. With its unidirectional
627
arrows, the decision tree also gives the impression of a deterministic, static and linear decision
628
process, which is not implied. The decision tree only reflects a snapshot, i.e. the status at one
629
point in time. Change and learning processes, while certainly present, cannot be appropriately
630
captured in decision trees.
631 21
632
Second, as with the strengths, the limits inherent in case studies also apply. The specificity of
633
the decision criteria, which leads to the high accuracy of the predicted behavior of the test
634
sample, also means that the applicability of the decision tree is site- and time-specific and
635
findings cannot be generalized readily. Indeed, given the implicit or explicit influence of
636
external factors (e.g. agro-climatic situation, market and policy environment), spatial and
637
temporal comparisons are difficult.
638 639
Third, decision trees cannot appropriately capture the influence of personality traits, although
640
the literature indicates that individual psychological differences can influence behavior
641
(McGregor et al., 1996; Willock et al., 1999; Austin et al., 2001; Nuthall, 2001). Indeed,
642
Regouin (2002) argues that conversion to organic agriculture depends on traits like curiosity,
643
flexibility, and the willingness to take risks, as well as persistence and creativity in exploring
644
innovative marketing approaches. However, since personality traits are not a decision criteria
645
for individual farmers, this type of factors cannot be included in the decision tree. The influence
646
of personality traits such as risk aversion can thus only be inferred from farmers’ assessment of
647
various decision criteria.
648 649 650
CONCLUSION
651 652
The study supports research showing that farmers in general and potential converters in
653
particular are not one homogeneous group. While farming methods are, to some extent,
654
influenced by issues related to technical aspects of agricultural production and farm structure,
655
personal values play an important role in decision-making. The identified farmer types should
656
not conceal the complexity of circumstances by which growers enter into organic production.
657
However, they can help to better understand the attitudes and motives on which distinct farmer
658
types base their decision, i.e. recognize the relationship between farmers’ values and the
659
heterogeneity of farming practices.
660 661
This has implications on the strategy to promote organic farming and achieve policy goals.
662
Understanding how organic farming fits into the diverse strategies of farmers could allow to
663
modulate policy measures to fit these strategies. Market segmentation, e.g. based on farming 22
664
styles, could also allow to target extension programs and communications to particular farmer
665
audiences (Thomson, 2001). These could be promising approaches, particularly once the rate of
666
adoption slows as is currently the case in Austria. To support the development of organic
667
farming, further research should allow for diversity, instead of attempting to identify one or
668
several most important criteria for conversion, assuming these apply to all farmers.
669
Acknowledging farmers’ multiple realities and strategies would thus allow to shift the attention
670
on how organic farming is mediated by farmers, how they transform and reconstruct its
671
meaning to fit their farming rationale.
672 673 674
Acknowledgements
675
The authors are would like to thank the farmers for their time and openness and the reviewers
676
for their incisive and constructive comments. We thank M. Eder for supplying the map of
677
Austria. The research presented in this article was part of the project „Full conversion to
678
organic farming in two Austrian regions – Development of scenarios” which was lead by B.
679
Freyer. It was funded by the Austrian Federal Ministry of Education, Science and Culture
680
within the framework of the “Austrian Landscape Research” program.
681 682 683
Literature
684 685 686 687 688 689 690 691 692
Austin, E., I. Deary and J. Willock (2001). Personality and intelligence as predictors of economic behaviour in Scottish farmers. European Journal of Personality 15: 123-137. Beedell, J. and T. Rehman (2000). Using social-psychology models to understand farmers' conservation behaviour. Journal of Rural Studies 16 (1): 117–127. Beus, C. and R. Dunlap (1990). Conventional versus alternative agriculture: The paradigmatic roots of the debate. Rural Sociology 55 (4): 590–616. Buck, D., C. Getz and J. Guthman (1997). From Farm to Table: The Organic Vegetable Commodity Chain of Northern California. Sociologia Ruralis 37 (1): 3-20.
693
Burton, M., D. Rigby, and T. Young (1999). Analysis of the determinants of adoption of
694
organic horticultural techniques in the UK. Journal of Agricultural Economics 50 (1): 47–
695
63. 23
696
Darnhofer, I., R. Gretzmacher, and W. Schneeberger (1997). Modelling farmers’ decisions: a
697
comparison between HDM and CART for oats-vetch adoption in the Ethiopian Highlands.
698
Die Bodenkultur 48 (4): 271–280.
699
Darnhofer, I., M. Eder and W. Schneeberger (2003). Modellrechnungen zur Umstellung einer
700
Ackerbauregion auf Biolandbau (Estimating the economic impact of the conversion of a
701
cash-crop region to organic farming). Berichte über Landwirtschaft 81 (1): 57-73.
702
Drake, L.; P. Bergström, and H. Svedsäter (1999). Farmers’ attitudes and uptake. In: G. van
703
Huylenbroeck and M. Whitby (Eds). Countryside stewardship: Farmers, Policies and
704
Markets (pp. 89–111). Elsevier Science Ltd.
705 706 707 708 709 710
Duram, L. (1999). Factors in organic farmers' decisionmaking: Diversity, challenge and obstacles. American Journal of Alternative Agriculture 14 (1): 2–10. Duram, L. (2000). Agent’s perceptions of structure: How Illinois organic farmers view political, economic, social, and ecological factors. Agriculture and Human Values 17 (1): 35-48. Edwards-Jones, G. and O. Howells (2001). The origin and hazard of inputs to crop protection in organic farming systems: are they sustainable? Agricultural Systems 67 (1): 31–47.
711
Fairweather, J. (1999). Understanding how farmers choose between organic and conventional
712
production: Results from New Zealand and policy implications. Agriculture and Human
713
Values 16 (1): 51–63.
714 715
Falconer, K. (2000). Farm-level constraints on agri-environmental scheme participation: a transactional perspective. Journal of Rural Studies 16: 379-394.
716
Franzel, S. (1984). Modeling farmers’ decisions in a farming systems research exercise: The
717
adoption of an improved maize variety in Kirinyaga District, Kenya. Human Organization
718
43 (3): 199–207.
719
Franzel, S., R. Coe, P. Cooper, F. Place, and S.J. Scherr (2001). Assessing the adoption
720
potential of agroforestry practices in sub-Saharan Africa. Agricultural Systems 69 (1-2):
721
37–62.
722 723 724 725 726 727
Freyer, B. (1998). Umfrage zum umweltgerechten Landbau in den Kantonen BL und BS. Agrarforschung 5 (7): 329–332. Gladwin, C. (1976). A view of the Plan Puebla: An application of hierarchical decision models. American Journal of Agricultural Economics 58 (5): 881–887. Gladwin, C. (1989a). Ethnographic decision tree modelling. Sage University Paper Series on Qualitative Research Methods, Vol. 19. Beverly Hills, CA: Sage. 24
728 729 730 731 732 733 734 735 736
Gladwin, C. (1989b). Indigenous knowledge systems, the cognitive revolution and agricultural decision making. Agriculture and Human Values 6 (3): 32–41. Guthmann, J. (2000). Agrarian dreams? The paradox of organic farming in California. PhD Thesis, University of California, Berkeley. 469 pp. Häfliger, M. and J. Maurer (1996). Umstellung auf Biolandbau – Motivation und Hemmnisse. Agrarforschung 3 (11-12): 531–534. Hall, A. and V. Mogyorody (2001). Organic farmers in Ontario: An examination of the conventionalization argument. Sociologia Ruralis 41 (4): 399–322. Hollenberg,
K.,
R.
Siebert,
and
H.
Kächele
die
Brandenburg
auf
738
Ökologischen Landbau. (Determinants of the conversion-willingness of farm managers)
739
In: H. Hoffmann and S. Müller (Eds.). Proceedings of the 5. Wissenschaftstagung zum
740
Ökologischen Landbau „Vom Rand zur Mitte“, 23.-25. February 1999 (pp. 333–336).
741
Berlin: Verlag Dr. Köster,.
744 745
in
für
Umstellungsbereitschaft
743
Betriebsleiter
Determinanten
737
742
landwirtschaftlicher
(1999).
Jansen, K. (2000). Labour, livelihoods and the quality of life in organic agriculture in Europe. Biological Agriculture and Horticulture 17 (3): 247–278. Kaltoft. P. (1999). Values about nature in organic farming practice and knowledge. Sociologia Ruralis 39 (1): 39–53.
746
Kelly, G. (1955). The psychology of personal constructs. New York: Norton.
747
Kirchmann, H. and G. Thorvaldsson (2000). Challenging targets for future agriculture.
748
European Journal of Agronomy 12 (3-4): 145–161.
749
Kirner, L. (2001). Die Umstellung auf biologischen Landbau in Österreich – Potential,
750
Hemmnisse, Mehrkosten in der biologischen Milchproduktion (Conversion to organic
751
farming in Austria – Potentials, obstacles and additional costs in organic dairying).
752
Schriftenreihe 92, Bundesanstalt für Agrarwirtschaft, Wien, 179 pp.
753
Kirner, L. and W. Schneeberger (2000). Bereitschaft der Marktfruchtbetriebe zur Umstellung
754
auf biologische Wirtschaftsweise in Österreich. Analyse einer schriftlichen Befragung.
755
(Willingness of cash crop farms to convert to organic farming in Austria. Analysis of a
756
written survey). Die Bodenkultur 51 (2): 135–142.
757
Lockeretz, W. (1997). Diversity of personal and enterprise characteristics among organic
758
growers in the Northeastern United States. Biological Agriculture and Horticulture 14 (1):
759
13–24. 25
760 761
Lockeretz, W. (1999). Farm profile: Sörtorp, an organic farm in Sweden. American Journal of Alternative Agriculture 14 (1): 37–41.
762
Lohr, L. and L. Salomonsson (2000). Conversion subsidies for organic production: results from
763
Sweden and lessons for the United States. Agricultural Economics 22 (2): 133–146.
764
Long, N. and J. van der Ploeg (1994). Heterogeneity, actor and structure: towards a
765
reconstitution of the concept of structure. In: D. Booth (ed.). Rethinking social
766
development. Theory, research & practice. Addison, Weley, Longman, Harlow, Essex.
767
Pp: 62-89.
768 769
Lund, V. S. Hemlin, and W. Lockeretz (2002). Organic livestock production as viewed by Swedish farmers and organic initiators. Agriculture and Human Values 19 (3): 255–268.
770
McGregor, M., J. Willock, B. Dent, I. Deary, a. Sutherland. G. Gibson, O. Morgan and B.
771
Grieve (1996). Links between psychological factors and farmer decision making. Farm
772
Management 9(5): 228-239.
773 774 775 776 777 778 779 780
McGregor, M.J., M.F. Rola-Rubzen, and R. Murray-Prior (2001). Micro and macro-level approaches to modelling decision making. Agricultural Systems 69 (1-2): 63–83. Michelsen, J. (2001). Organic farming in a regulatory perspective. The Danish case. Sociologia Ruralis 41 (1): 62–84. Morgan, K. and J. Murdoch (2000). Organic vs. conventional agriculture: knowledge, power and innovation in the food chain. Geoforum 31: 159-173. Morris, C. and C. Potter (1995) Recruiting the New Conservationists: Farmers' Adoption of Agri-environmental Schemes in the U.K. Journal of Rural Studies 11 (1): 51–63.
781
Murray-Prior, R. (1994). Farmers as scientists: A personal construct theory interpretation of
782
hierarchical decision models. Paper presented to the 38th Annual Conference of the
783
Australian Agricultural Economics Society, Victoria University, Wellington, Feb. 7-11,
784
1994. Available on-line: http://www.curtin.edu.au/curtin/muresk/publications
785
Murray-Prior, R. (1998). Modelling farmer behaviour: A personal construct theory
786
interpretation of hierarchical decision models. Agricultural Systems 57 (4): 541–556.
787
Murray-Prior, R. and Wright, V. (1994). Farmer decisions and aggregate supply: An
788
explanation of the impact of major price changes. Paper presented to the 38th Annual
789
Conference of the Australian Agricultural Economics Society, Victoria University,
790
Wellington,
791
http://www.curtin.edu.au/curtin/muresk/publications
Feb.
7-11,
1994.
Available
on-line:
26
792 793 794 795 796 797
Noe, E. (1999). Who are the organic farmers in Denmark of today? Paper presented at the XVII Congress of the ESRS, 24-28 August 1999 in Lund, Sweden. Nuthall, P.L. (2001). Managerial ability a review of its basis and potential improvement using psychological concepts. Agricultural Economics 24: 247–262. Padel, S. (2001). Conversion to organic farming: A typical example of the diffusion of an innovation? Sociologia Ruralis 41 (1): 40–61.
798
Padel, S. (2002). Studying conversion as a human activity system. J. Powell (ed.). UK Organic
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research 2002. Proceedings of the COR conference, 26-28 March 2002, Aberystwyth, pp.
800
101-104.
801 802
Pietola, K. and A. Lansink (2001). Farmer response to policies promoting organic farming technologies in Finland. European Review of Agricultural Economics 28 (1): 1–15.
803
Rämisch, G. (2001). Regionale Marktchancen für Produkte des ökologischen Landbaus -
804
dargestellt am Fallbeispiel der Region um Scheyern. (Regional marketing opportunities
805
for organic products – A case study in the region around Scheyern). Berichte über
806
Landwirtschaft 79 (2): 212–233.
807 808 809 810 811 812
Regouin, E. (2002). To convert or not to convert to organic farming. Paper presented at the OECD Workshop on Organic Agriculture, 23-26 September 2002, in Washington D.C. Rigby, D. and Cáceres, D. (2001). Organic farming and the sustainability of agricultural systems. Agricultural Systems 68 (1): 21–40. Rigby, D., R. Young, and M. Burton (2001). The development of and prospects for organic farming in the UK. Food Policy 26 (6): 599–613.
813
Roep, D. and R. de Bruin (1994). Regional marginalisation, styles of farming and technology
814
development. In: J. van der Ploeg and A. Long (eds). Born from within. Practice and
815
perspectives of endogenous rural development. Van Grocum, Assen, The Netherlands.
816
Pp.217-227.
817 818
Schoon, B. and R. Te Grotenhuis (2000). Values of farmers, sustainability and agricultural policy. Journal of Agricultural and Environmental Ethics 12 (1): 17–27.
819
Schneeberger, W. and L. Kirner (2001). Umstellung auf biologischen Landbau in Österreich.
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(Conversion to organic farming in Austria). Berichte über Landwirtschaft 79 (3): 348–
821
360.
27
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Schneeberger, W., I. Darnhofer and M. Eder (2002). Barriers to the adoption of organic farming
823
by cash crop producers in Austria. American Journal of Alternative Agriculture 17 (1):
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24–31.
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Schneider, R. (2001). Umstellung von Marktfruchtbetrieben im Marchfeld und Weinviertel auf
826
die biologische Wirtschaftsweise - Umstellungshemmnisse, Umstellungsprobleme und
827
Wirtschaftlichkeit (Conversion to organic farming of cash crop farms in the Marchfeld
828
and
829
considerations). PhD Dissertation, University of Natural Resources and Applied Life
830
Sciences Vienna.
Weinviertel
–
Expected
obstacles, encountered problems and economic
831
Schulze Pals, L. (1994). Ökonomische Analyse der Umstellung auf ökologischen Landbau.
832
(Economic analysis of the conversion to organic farming). Reihe A, Heft 436,
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Schriftenreihe des Bundesministeriums für Ernährung, Landwirtschaft und Forsten.
834
Münster: Landwirtschaftsverlag, 312 pp.
835
Thomson, D. (2001). Different pebbles, same pond: “Farming styles” in the Loddon catchment
836
of Victoria. Paper presented at “Exploring beyond the boundaries of extension”,
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Australasia-Pacific Extension Network, University of Southern Queensland, Toowoomba,
838
Queensland, 3-5 October 2001. Available at: http://www.regional.org.au/au/apen/2001/p
839 840 841 842
Tovey, H. (1997). Food, environmentalism and rural sociology: On the organic farming movement in Ireland. Sociologia Ruralis 37(1): 21-37. Trewavas, A. (2001). Urban myths of organic farming. Organic farming began as an ideology, but can it meet today’s needs? Nature 410: 409–410.
843
van der Ploeg, J. (1994). Styles of farming: An introductory note on concepts and methodology.
844
In: J. van der Ploeg and A. Long (eds). Born from within. Practice and perspectives of
845
endogenous rural development. Van Grocum, Assen, The Netherlands. Pp.7-30.
846 847
van der Ploeg, J. (2000). Revitalizing agriculture: Farming economically as starting ground for rural development. Sociologia Ruralis 40(4): 497-511.
848
van der Ploeg J., H. Renting, G. Brunori, K. Knickel, J. Mannion, T. Marsden, K. de Roest, E.
849
Devilla-Guzmán and F. Ventura (2000). Rural Development: From practices and policies
850
towards theory. Sociologia Ruralis 40(4): 391-408.
851
Vogel, S. (1995). Die Umstellung auf Biologischen Landbau – Bäuerinnen und Bauern
852
bewerten ihre Ziele und Erfahrungen im Umstellungsprozeß. (Conversion to organic
853
farming – Farmers assess their goals and experiences during the conversion process). B. 28
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Freyer, B. Lehmann , W. Schneeberger and U. Zerger (Eds). Betriebswirtschaft im
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biologischen Landbau. Proceedings of a conference held in Zurich, 3-5 April 1995. SÖL
856
Sonderausgabe 57, Bad Dürkheim: SÖL. Pp.199-203.
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Ward, N. (1993). The agricultural treadmill and the rural environment in the post-productivist era. Sociologia Ruralis 33(3/4): 348-364.
859
Willock, J., I. Deary, G. Edwards-Jones, G. Gibson, M. McGregor, A. Sutherland, J. B. Dent,
860
O. Morgan, and R. Grieve (1999). The role of attitudes and objectives in farmer decision
861
making: business and environmentally-oriented behaviour in Scotland. Journal of
862
Agricultural Economics 50 (2): 286–303.
863 864 865 866
Wilson, G. (1997). Factors influencing farmer participation in the environmentally sensitive areas scheme. Journal of Environmental Management 50 (1): 67–93. Wilson, G.A. and K. Hart 2001: Farmer participation in agri-environmental schemes: towards conservation-oriented thinking? Sociologia Ruralis 41 (2): 254-274.
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Figure 2. Decision tree depicting the criteria considered in the choice between organic and
869
conventional farm management in the Weinviertel 30
