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Acceptance of a payment for ecosystem services scheme: The decisive influence of collective action.

ABSTRACT

As scholars have shown, acceptance is key to the success of Payment for Ecosystem Services (PES) scheme. While many studies adopt a static cost-benefit perspective, few address the social process leading to acceptance. Drawing on Suchman (1995), this article examines the legitimacy process underlying the acceptance of a PES in agriculture. In particular, the role of collective action in the legitimisation process is analysed, using a combination of qualitative and quantitative methods of discourse analysis. Data from an agroenvironmental PES scheme in France on water quality shows that acceptance depends on the normative and cognitive legitimacy that actors confer upon a public policy.

KEYWORDS

Payment for ecosystem services, EU Water Framework Directive, collective action, legitimacy, discourse analysis

1. INTRODUCTION

The Millennium Ecosystem Assessment (MEA, 2005) marked an important step in the international community's recognition that the environment provides multiple ecosystem services, such as water quality and climate regulation, which are indispensable for human life.

Since agriculture both uses and produces ecosystem services, it plays a major role in ensuring these services (OECD, 2012). Thus, a large number of environmental policies currently being elaborated concern farming. For example, in the European Union, Agro-environmental Measures (AEMs) have become an essential way to integrate environmental concerns into the Common Agricultural Policy (CAP). These AEMs are a form of Payment for Ecosystem Services (PES) (Wunder, 2005). AEMs are voluntary contracts for a five-year period in which farmers commit to changing practices in exchange for compensation. The goal is to preserve ecosystem services such as water quality and biodiversity (European Parliament, 2012).

The challenge for public authorities, however, is to convince a sufficient number of farmers to participate in the programme. In fact, as several studies highlight (Falconer, 2000; Wilson and Hart, 2000; Prager and Nagel, 2008; Murphy et al., 2014), since taking part in an AEM is voluntary, its environmental effectiveness depends inevitably on farmers' participation. These studies have shown that farmers' individual characteristics, such as age, absence of a successor, education and size of the farm, influence their participation (Hodge and Reader, 2010), as well as additional factors such as the level of concertation among actors, the exchange of various forms of knowledge that result (Prager et al., 2012; Morris and Reader, 2006; Sattler and Nagel, 2010), and whether the programme is flexible and adapted to the local context (Ruto and Garrod, 2009). (1)

Certain studies show that although participation is necessary, it is not sufficient (Morris and Potter, 1995; Fish et al., 2003) because it does not always indicate farmers' motivation. For example, Webster and Felton (1993) found that farmers compensated for the loss of yield they expected on their AEM plots by intensifying production on their non-AEM lands. Therefore, the negative impact of their practices on natural resources remained unchanged or worsened. Thus, other researchers (Schenk et al., 2007; Prager and Freese, 2009; Sattler and Nagel, 2010) consider that the success of an AEM depends on acceptance, meaning that farmers interiorise the scheme's goals and are convinced that the farming practices benefit the environment (Prager and Freese, 2009). However, as Schenk et al. (2007) point out, few studies have addressed the process of gaining acceptance, and thus our understanding remains very incomplete (Prager and Freese, 2009). Yet understanding acceptance is crucial for improving the effectiveness of a voluntary public programme and for rethinking the management of ecosystem services. In addition, the literature on PES schemes calls for more research on the social mechanisms that lead to inclusive stakeholder participation, collaboration and learning in ecosystem services management (Mann et al., 2015).

This study addresses this gap and examines acceptance in greater detail. Suchman's (1995) conception of legitimacy is used here to identify the characteristics that a policy measure must have for it to be fully accepted by the actors involved; in other words, for it to become interiorised as a habit of thought (Hodgson, 2006). Drawing on Hodgson, we posit that for actors to accept a public policy, it must make sense for them and be considered grounded in reason. (2) To achieve that, the policy would first need to acquire both normative and cognitive legitimacy from the actors' perspective. Thus, we believe that the cognitive and normative legitimation of a PES scheme is indispensable for achieving its goal of creating new sustainable habits within a community of actors (Hiedanpaa and Bromley, 2014).

Then, following Bromley (2008), we examine the process that leads actors to change their reasons for acting and the power of collective action to trigger that process. Bromley considers reason, and the act of legitimation on which it is based, to be a process of explanation and justification based on group interactions and collective action. Thus, we hypothesise that the acceptance of a public policy measure results from a social process through which local actors work through the cognitive and normative meanings that the PES scheme entails in order to adapt them to the characteristics of the local socio-ecological context. For some authors (Van Hecken et al., 2015), this collective action of reformulating meanings is primordial for ensuring the effectiveness of a PES scheme.

Therefore, drawing principally on the contributions of Suchman (1995), Hodgson (2006) and Bromley (2008), the present study analyses both the result of local collective action (acceptance of a public policy) and the collective process leading to that acceptance (its normative and cognitive legitimation). Privileging an interdisciplinary approach, this article falls within the school of ecological economics open to other disciplines, defended by Spash (1999). In its use of discourse analysis, this study builds on other research adopting this method to understand the choices of environmental public policies and their implementation (see for example Rydin, 1999 and Plumecocq, 2014 in this journal).

We then apply this approach to an empirical case of a regional AEM on water pollution managed by a farmers' cooperative in France. As seen below, this AEM is unusual both because of the high rate of adoption and because of the collective action that is part of the scheme. These two characteristics guided our choice to study this unique case in order to complement the majority of AEM studies that have focused on the factors hindering adoption (for example, Rousset and Louis, 2012; Kuhfuss et al., 2012), to instead examine the reasons why an AEM is adopted and the process leading to that outcome.

This article is organised into five sections. The first section explains the concepts of legitimacy and collective action as used here. The second part presents the case study and the qualitative and quantitative discourse analysis methods used. The third section presents the results while the final sections discuss those results and present conclusions.

2. LEGITIMACY AND COLLECTIVE ACTION

2.1 Legitimacy

For Suchman (1995, p. 574), 'Legitimacy is a generalized perception or assumption that the actions of an entity are desirable, proper, or appropriate within some socially constructed system of norms, values, beliefs, and definitions'. Suchman's definition leads us to distinguish between several bases for legitimacy: regulatory, normative, and cognitive (Scott, 2001).

* The regulatory basis is the system of sanctions and incentives implemented by public authorities. By defining what is allowed or prohibited, this system legitimates certain behaviours.

* The normative basis refers to moral conventions, i.e., what a person should do or not do in certain circumstances. For Hodgson (2006), the legitimacy of laws depends primarily on the moral force that members of a society attribute to them.

* The cognitive basis refers to the system of meaning conveyed by an institution. Following Berger and Luckmann, (1966, p. 87), 'to be legitimate a rule must be endowed with a certain cognitive value: it must be socially recognised as 'a 'permanent' solution to a 'permanent' problem of the given collectivity'. Thus, the social recognition of the rule's pragmatic content explains why people follow it. The rule is then viewed as providing the scripts (3) for action.

From this, we observe that: (i) legitimacy is a multi-dimensional concept and (ii) the regulatory basis of legitimacy is not always a sufficient foundation for the acceptance of a public policy. In France, a recent study (CGAAER and CGEDD, 2011) on the European Water Framework Directive (WFD) (4) clearly showed that the farmers and their representatives were not sufficiently involved in the decision-making process. Therefore, they had not interiorised the goals of improving water quality sought by the Directive and thus were not committed to adopting new farming practices.

Thus, it seems clear that the acceptance of an AEM cannot be based solely on its regulatory legitimacy (even more so because participation is voluntary); acceptance by farmers would then require two additional conditions:

* First, that farmers recognise the logical soundness of the new moral values promoted by the AEMs (normative basis for legitimacy); i.e., that they take into account society's new expectations about preserving public environmental goods. Consequently, acceptance would require that the action be considered 'the right thing to do' (Suchman, 1995, p. 579), that is, as morally legitimate.

* Second, farmers would need to be convinced of the agronomic and economic effectiveness of alternative AEM practices (cognitive basis of legitimacy). They would have to consider the new practices as effective as the conventional techniques they had previously used.

In sum, a programme such as an AEM would only truly work once actors fully appropriate it, which first requires that they consider it legitimate cognitively and normatively and not simply a new rule to be followed. Considered as fair and effective, both the moral expectations and the practices recommended by the AEM would therefore be integrated as habits of thought, and the AEM would thus be grounded in reason. In Piagetian terms, (Piaget, 1932), this means that the AEM has to change status from a 'coercive rule' to a 'rational rule'. For Piaget, the 'coercive rule', which remains exterior to a person, is opposed to the 'rational rule', which is the result of a person's free decision to follow it. It is no longer perceived as obligatory and by becoming 'rational', the rule is then viewed as a progressive and autonomous construct that precedes collective cooperation. Certainly, the AEM is a voluntary programme and joining should theoretically result only from the farmer's free decision. But in practice, the farmers here viewed the AEM as a temporary incentive measure that would inevitably give way to more stringent water management regulations (CGAAER, 2009). For this reason, they may have perceived signing up for the AEM as a way to prepare for new legal obligations. (5)

Thus, building on Piagetian theories, we hypothesise that it is only by becoming a rational rule that an AEM will be interiorised and in the end, accepted. Our first working hypothesis is that this change in the status of the rule presupposes that social actors attribute cognitive and social legitimacy to the AEM and that to do so, they update their beliefs by breaking with their former practices. Some authors (Hiedanpaa and Bromley, 2014) believe that this is in fact the main challenge for a PES scheme. Thus, we will now turn to examine in greater detail the collective process of updating beliefs.

2.2. Changing reasons for acting through collective action

We consider that the legitimation of public water management policies requires a change in actors' beliefs both in terms of farming practices and in moral terms. Drawing on the work of Bromley (2008), we focus on the process that leads actors to re-examine the validity of the reasons underlying their choice of action.

Bromley (2008) considers that social interaction is the main mechanism triggering such a change. When a person encounters others' empirical claims, he/she is lead to test his/her beliefs about what is reasonable. The plausibility of the hypotheses underlying beliefs are thus re-examined and the initial reasons for action may be updated. Thus, collective action and group deliberation, as the primary drivers of learning, are key to legitimating new habits of thinking and ways of doing.

In placing collective action at the heart of the process of belief transformation, Bromley draws conclusions about 'rules to live by' (2008, p. 8). In particular, he finds that the success of public environmental policies is conditioned by the actors' capacity to socially construct a shared and objectified vision of nature (justified beliefs) and, on this basis, to define shared rules of action to protect that environment. The main challenge of collective action, in his view, is then to generate interactive dialogue on the truth or falsehood of different proposals.

In the present case study, implementing the AEM gave rise to a number of controversies on moral and technical issues: Will reducing the quantity of herbicides jeopardise effective weed control? Will mechanical weeding aggravate the problem of erosion on sloping plots? Should we irrigate without taking into account the scarcity of water resources? When we clean the sprayer, should we dump the dirty water into the field? Following Bromley (2008), we posit that collective action is crucial for settling debates and for establishing new 'truths' about what a farmer can and cannot do (in terms of practices) and should or should not do (in moral terms). These truths become 'settled beliefs', in other words, beliefs that are sufficiently decided upon and shared so that they dissipate doubts and influence actors' preferences.

Thus, we come to our second working hypothesis: social cooperation and group learning are decisive in changing beliefs, which in turn legitimates new ways of acting. This hypothesis leads us to examine how the AEM's system of governance enabled a rationality context to be founded, which Vatn (2005; 2009) terms social rationality, that is to say, founded on cooperation.

3. METHOD AND DATA COLLECTION

3.1. Updating beliefs seen through discourse analysis

In light of our two hypotheses, this study sought to understand how beliefs are changed and how a public policy is legitimated through collective action. We thus followed the entire implementation of a PES policy to observe whether the farmers internalised the new policy, and if so, the role that collective action may have played.

Belief transformation is examined through the discourse of the actors involved. This method is useful for mapping the normative and cognitive aspects of beliefs. Discourse analysis is commonly used in sociology and political science and identifies the occurrence of words and the semiotic relationships between those words, thereby enabling us to construct mental maps (Gee, 2013). Each of these maps informs us about a person's normative and cognitive presuppositions and thus his/her 'rationality' (the reasonings on which his/her choices are based). Furthermore, textual analysis is even more relevant here because it brings to light the connections or oppositions that may exist among different discourses and therefore reveals the processes through which collective action fosters learning and updates established beliefs (Carley, 1997; Fairclough, 2003).

3.2 Case study

The data comes from a larger study on the implementation of an AEM-WFD programme in the Adour-Garonne River Basin in south-west France, which is severely threatened by diffuse agricultural pollution. According to the Adour-Garonne Water Agency (2013), nitrates are responsible for seventy per cent of surface water contamination and 38 per cent of ground water is polluted by the excessive use of pesticides. For this reason, in 2009 the French Ministries of Sustainable Development, Health, and Agriculture named this area an environmental priority zone (6) and it was subject to a Plan d'Action Territorial [Regional Action Plan]. (7)

This AEM-WFD, in the sub-catchment of the Gimone River, was selected because it takes a new approach to gaining farmers' acceptance. In particular, this case is unusual in that the farmers' cooperative, a business whose main revenue comes from selling chemicals (herbicides, fungicides, insecticides and fertilisers) (8) to farmers, was in fact in charge of locally implementing the scheme to use fewer chemicals. Moreover, the cooperative includes farmers who had converted to organic farming and who have several years of experience using alternatives to chemicals. Thus, this case study offers an interesting context for examining whether farmers interiorise an environmental policy and for analysing the possible influence of collective action in the learning processes leading to that interiorisation.

The main goal of this AEM-WFD is to promote a progressive reduction in the chemicals used in the river catchment area. By the end of the contract, farmers must have reduced the number of standard doses of herbicides by forty to fifty per cent. (9) This is verified by the Treatment Frequency Index (TFI), (10) which counts the number of standard doses applied over one hectare during a crop period. Moreover, the farmers who sign up for the AEM receive technical support for the duration of the contract to encourage them to adopt alternative farming practices.(11) This support, provided by the farmers' cooperative managing the project, takes various forms: individual visits, demonstrations in the field, creating decisional tools, and sharing information through newsletters. This technical support is billed at [euro]25 / ha / year to the farmers who sign up for the AEM through the cooperative. In return for their commitment, the farmers receive public subsidies ranging from [euro]147 to [euro]188 / ha / year for the duration of the contract. These funds are intended to support the farming business while changing practices to compensate for any reduction in yields and to enable farmers to pay for the cooperative's advising services.

Thus the cooperative, in charge of managing the AEM, plays an important role as intermediary between its farmer-members and government agencies. At first glance, it seems that the cooperative's traditional business, selling chemicals, would conflict with its participation in an agro-ecological programme. However, at a time of increasing environmental regulations and intensified competition, the cooperative sought to differentiate itself from its competitors by promoting its 'green' approach. Its managers wanted to address the challenge of more stringent environmental restrictions and the associated reduction in chemicals by developing a new paid advising service for farmers, and the AEM enabled them to test the feasibility of this service. Its initial goal was to have ninety contracts signed before 2011, out of the 180 farmers eligible for the AEM scheme. Yet by early 2010, nearly 122 farmers had joined the AEM, thus 68 per cent. (12) It should also be noted that none of the farmers abandoned the AEM during the programme and over ninety per cent of them reached the targets for reducing herbicides set by the AEM. This success differs in many ways from the results of similar schemes (Chabe-Ferret and Subervie, 2013). When this was observed during the case study, we tried to understand the reasons for this success and sought to identify the lessons that could be transposed to other areas.

3.3. Data collection and analysis methods

To better document changes over time in beliefs and practices, we observed the AEM's implementation over a five-year period. First, we identified the normative and cognitive legitimisation of the AEM using a discourse analysis method based on Grounded Theory (Glaser, 2001; Charmaz, 2006). Next, we sought to understand the results of this process, i.e., whether the contracting farmers had accepted the AEM, using the ALCESTE method of lexicometric analysis (Reinert, 2007). These discourse analysis methods are explained below. Finally, to verify the results of this lexicometric method, a questionnaire was sent by mail to a random sample of 328 farmers located in the same Regional Action Plan zone (larger than the Gimone sub-catchment area eligible for the AEM).

Data was collected through two types of interviews and a postal survey:

(i) In 2009, individual interviews were conducted with representatives from all institutions involved in setting up the AEM in the Adour-Garonne River basin: the Adour-Garonne Water Agency (AEAG), (13) two regional Chambers of Agriculture, (14) the Regional Agriculture, Food, and Forestry Department (DRAAF),(15) the Regional Department of Health and Social Services (DDASS), (16) and the farmers' cooperative. The representatives were selected based on their level of responsibility for the AEM. Participants were asked about the perceived goals of the project, their view of project organisation and governance, and their understanding of the rules that had emerged from negotiations.

(ii) Semi-structured interviews were conducted with the AEM farmers in 2010 and 2011 (each interview lasted two hours). The size of the farms varied from thirty to 200 ha. Out of the 122 farmers who signed up for the AEM, 38 were randomly selected to be interviewed. We ensured that the sample was representative of the total group of AEM farmers in terms of: the surface area of the farm; age group; and the level of TFI at the beginning of the scheme. The sample included both farmers who had signed up for the AEM in 2008 (21) and in 2010 (17). As will be explained below, comparing the discourse of these two groups of farmers revealed the changes in practices that had occurred over time and therefore the learning dynamics. We asked about their points of view, their activities in the AEM, and their perceptions of how these activities unfolded. Questions also focused on the individual characteristics of the farmers, their farming practices, their relationship to the advising services, what they thought of the AEM, and their opinion on the alternative farming practices.

(iii) A questionnaire sent by mail in 2011, at the half-way point of the AEM programme, completed the data set. The main goal of this survey was to compare the practices of AEM farmers with farmers who were not in the AEM (either by choice or because they were located outside the eligible zone). The questions addressed the farming practices they actually adopted, the kinds of advising services they had used, and the type of technical advice they had consulted. The response rate was 28 per cent and included an equal number of AEM and non-AEM farmers.

The interview data was then analysed using two different methodologies: Grounded theory and the lexicometric software ALCESTE, while descriptive statistical analyses were done on the questionnaire data.

Grounded theory: the social process of legitimating an AEM

Grounded theory is an inductive qualitative method that enables us to conceptualise complex social, cultural and psychological phenomenon. Data analysis consists of identifying and progressively sorting the information from interviews into large categories and finding connections between these categories in order to retrace the process of legitimation at work. One the analyst's main tasks is to identify broad themes in the corpus that explain the phenomenon observed and to find the connections between those themes to reconstruct meaning (Paille, 1994). Grounded theory is useful for analysing the process in which individuals construct a shared meaning of their environment and change their behaviour to collectively address a problem (Hutchinson, 1993). The social interactions underlying this process foster the sharing of experiences and make behaviours converge. We applied this method to both sets of interviews:

(i) Institutional Actors: The coding process consisted of extracting the characteristic utterances relating to the AEM, the procedures for its implementation and the interactions among actors. The meaning of these utterances' content was summarised. This initial coding enabled us to identify large categories of interrelated variables in the discourses that explained changes in beliefs and the social process of legitimating the AEM.

(ii) Farmers: We first coded the interview transcripts to identify possible changes in beliefs (i.e., 'reasonable beliefs' in the sense of Bromley, 2008). The grounded theory method was then used to identify the relevant and explanatory categories of the process leading farmers to change their practices by investing the new practices with meaning and granting them cognitive legitimacy. We identified the main factors influencing the farmers' choices of action (such as using chemical weed control rather than mechanical).

ALCESTE: the legitimation process

ALCESTE (Reinert, 2007) is a method and software for discourse analysis. Using semantic analysis, this method identifies what Reinert (2007) terms 'lexical worlds'. Defined as the 'trace of objects' in their discourse that individuals construct to establish their points of view (Reinert, 2007), in practice these worlds correspond to different profiles of speakers that share a certain discourse and representations. These profiles express the types of reasoning present. The ALCESTE method was used on the interviews with the 38 AEM farmers and is useful for grasping learning dynamics because we can compare the discourse of the farmers who had joined the AEM at different times: 2008 and 2010. Comparing the discourse profiles of these two groups then informs us about their level of acceptance of the AEM and whether collaboration among actors promoted their progressive interiorisation of the AEM over time.

Moreover, this method of lexicometric analysis enables us to counter certain problems inherent to manual textual analysis (sampling of words, data coding, and processing) (Schonhardt-Bailey, 2005). ALCESTE provides a descending hierarchical classification coupled with a similarity analysis. This method focuses not only on the statistical distribution of words in different corpora, but also on their organisational patterns (distribution and co-occurrence). First, following principles of lexical and distributional analyses, ALCESTE divides the corpus into small text segments called 'elementary context units'. Then, the software progressively divides the initial group of textual units into classes of elementary context units according to their degree of statistical dissimilarity.

Postal survey of farmers

The 92 responses to the survey were analysed using descriptive statistics (bivariate frequency table and Chi(2) tests) to compare the belief systems of AEM and non-AEM farmers, and from that comparison to draw conclusions about the change in beliefs triggered by collective action. Only the most relevant findings from this postal survey will be presented below; further information can be found in Del Corso et al. (2014).

4. RESULTS

4.1. Grounded theory: the importance of deliberation among actors

The grounded theory method revealed two significant results:

* For institutional actors, collective action and group deliberation facilitated an agreement on the targets of the AEM and the way to attain them, which led to an institutional compromise that formed the basis for the AEM's normative legitimacy.

* For the farmers, the results showed the importance of collaboration in constructing the 'truth' of the alternative agricultural practices. Together, these interactions forged the cognitive legitimacy of the programme.

Institutional compromise at the heart of the normative legitimation process When implementing the AEM, a number of coordination problems among institutional actors came to light. In fact, each institution had distinct goals that reflected their various conceptions of the AEM. The main concern of the Regional Department of Health and Social Services [DDASS] was public health: improving drinking water contaminated by diffuse agricultural pollution. Therefore, they wanted the AEM to raise farmers' awareness about water quality and for farmers to take greater responsibility. The Adour-Garonne Water Agency considered the AEM as part of an integrated approach to land planning and regional water management. Thus, they thought that the AEM should be integrated more broadly into a Plan d'Action Territorial [Regional Action Plan]. The view of the DRAAF and the farmers' cooperative was more geographical. The DRAAF sought to reach the targets of the French government's Ecophyto 2018 programme to reduce pesticide use by half by 2018. The cooperative and its members wanted the AEM to help them assess the use of more ecological farming practices over a large territory. Their goal was to test the economic and agronomic feasibility of alternative practices to chemicals.

These various visions were different enough to cause problems for the intelligibility of the AEM programme and to jeopardise its effectiveness in practice. Analysis of the institutional partners' discourse indicates that this obstacle was overcome by the establishment of common rules (17) among the actors. This category was at the very core of the process of investing the AEM with meaning, as illustrated by an institutional actor:

Creating this document [the AEM project] certainly required expertise,
of really knowing the area, and ... common rules.


These common rules were an institutional compromise through which the actors harmonised their preferences and agreed on the normative expectations of the AEM. More specifically, through this compromise the AEM was adapted to the specificities of the region. In so doing, the societal goal of improving water quality was reconciled with the farmers' possibilities for action in practice, (18) making this an important moment in the policy's normative legitimation (i.e. considered 'the right thing to do'). Two additional key categories closely related to these common rules (or institutional compromise) emerged from the actors' discourse and explain the process of legitimation at work: delimiting the zone for the AEM and the targets to be met.

(i) Choosing the geographical boundaries for the AEM was not self-evident. For financial reasons, the DRAAF had modest goals: limit the AEM to a test zone of 200-300 hectares. For different reasons, the DDASS, the AEAG, and the farmers' cooperative wanted to extend it to the entire basin. The AEAG wanted 'to define a zone for the programme needed for acting and obtaining results'. This agency's position was similar to that of the DDASS, which saw the AEM as a means to 'improve water quality' by significantly reducing the chemicals used. The cooperative felt that if the AEM zone were too small, they would not be able to truly assess the medium- and long-term effects on the farms' economic viability. For the cooperative, this was an important issue, considering the reservations, even distrust, expressed by other local professional farming organisations such as the Chamber of Agriculture. (19) In the end, these different concerns were taken into account by the organisations in charge of project management, and it was decided that the AEM would cover the entire basin of 10,000 hectares.

(ii) The targets of the AEM were also a source of divergence. One of the major hurdles was to make improving water quality compatible with the farmers' agronomic and economic concerns. Farmers signing up for the AEM needed to know that its targets were attainable. In particular, agreement was reached on the method for calculating the Treatment Frequency Index (TFI) to check whether AEM farmers had fulfilled their annual obligations on contracted plots. After negotiating with the other institutional actors, the cooperative obtained changes to the initial calculation method, so that the risks for farmers of not attaining the targets would be spread out over the entire farm and over the entire duration of the contract.

The diagram below summarises the normative legitimation process at work, based on categories identified in the actors' discourses.

Grounded theory enabled us to identify how the people involved in implementing the AEM were able to arrive at a common definition of the programme's targets. In particular, it was the interactions among actors that enabled them to adapt the AEM to the specifics of the region. These adaptations to local context gave the programme credibility and thus legitimised new norms for preserving water quality. As we will see, this stage of normative legitimation of the AEM paved the way for its cognitive legitimation.

The decisive influence of trust in the process of cognitive legitimation

The AEM's normative legitimation enabled institutional actors to reach an agreement on reasonable and desirable targets for improving water quality in the Adour-Garonne basin. Cognitive legitimation, however, involves what can actually be done in the field by the cooperative managing the project and the AEM farmers. One of the main challenges for the cooperative's advisors was to convince the farmers that using alternative practices would not jeopardise the economic viability of their farms. Coding the farmers' discourse using Grounded theory showed that trust between farmers and the cooperative's advisors was a decisive and explanatory category in the cognitive legitimation of the AEM. This trust had developed during prior projects by the cooperative. As the farmers considered those projects successful, they then trusted the cooperative's expertise. These projects had also encouraged collaboration among actors. Over time, the cooperative was thus able to create a context of social rationality (see 2.2) that fostered the sharing of knowledge and experience among the farmers. During the AEM, the cooperative was able to draw on the experience of the more innovative farmers, in particular those who had joined the organic farming initiative set up by the cooperative in 2002. The power of collective action to update beliefs was thus fundamentally based on trust.

This category trust was also closely linked with the category collective learning. In fact, the high level of farmers' trust toward the cooperative accelerated the change in practices. This trust encouraged emulation among the farmers and fostered the spread of new practices among the community of farmers. Learning costs were thereby reduced as farmers, particularly those who had less experience with alternative practices, did not have to validate the information about those practices, as that task was taken on by the cooperative. In this way, updating beliefs about the effectiveness of a given farming technique was facilitated, sometimes even surpassing the expectations of the cooperative's managers, as one of them told us:

Some of them [the farmers] have even bought a weeder harrow. We never
expected them to do that. They used to say to us, 'I don't even want to
hear about that'. And today, they're the ones ... who are all for it.


The cooperative's role in fostering the farmers' acceptance of the programme was even more important since implementing the AEM-WFD lead to a sharing of risks among actors, which functioned in an interrelated way with trust, as explained by one of the cooperative's leaders:

It was a shared risk. In the end, we're all the same. When we get a
farmer to reduce the chemicals used, we run a risk: that of not
collecting a quality product. Furthermore, we do not sell any chemicals
which would enable us to pay advisors in the field.


This feeling of shared risks reinforced trust and stimulated a dynamic of experimentation, innovation, and forecasting. This category innovation and forecasting identified in the farmers' discourses also proceeded from the trust among actors. Thus, the AEM-WFD studied here cannot be reduced to a simple adoption of practices that use fewer chemicals; rather it involved an overall agronomic management approach at the scale of the entire farm. For each successive farming campaign, the AEM was collectively used to plan for certain practices such as stubble ploughing, false seedbeds, etc. It is therefore understandable that in the end, farmers did not limit their changes to the lands covered by the AEM but on the contrary, extended those practices to their entire farm, as stated by one of the cooperative's managers:

When we signed them up, we though they'd say 'OK, I'm going to only do
it on the plot that covered by the contract'. But that's really not it.
From the first year, they told us 'Anyway, there are changes to be made
in the crop rotation, so, we're going to do it for the whole farm and
we'll see what happens'.


These categories in the actors' discourse summarise the process of cognitively legitimating the alternative practices:

In the end, extending the change in practices to the entire farm was the most tangible sign that the AEM had been gradually invested with cognitive legitimacy by the farmers. Moreover, contrary to what has been observed in other regions, these farmers did not adopt opportunistic behaviours of intensifying production on non-AEM lands in order keep the net production volume unchanged (Webster and Felton, 1993). Thus, they did not try to circumvent the AEM requirements. As we have seen, the actors collectively appropriated this programme to reinforce the effectiveness of their actions. The rules of the AEM thus led to cognitive gains and opened up new opportunities for action.

4.2. ALCESTE: Retracing learning over time

The textual analysis method ALCESTE was also used on the farmers' discourse. First, it revealed profiles of beliefs according to the date that the farmers signed the AEM contract, thus bringing to light the change in beliefs over time. Second, the descending hierarchical classification and similarity analysis revealed the main subjects raised by the farmers, their relative importance, and their interrelationships.

The ALCESTE textual analysis distinguished three 'lexical worlds' or classes of textual units in the farmers' responses (Figures 3 and 4). Analysing these classes enabled us to delineate the profiles of people with certain beliefs about the AEM and its practices.

Class 1 is composed of technical discourse. This class defines a lexical world that primarily refers to stages in farming (seeds, tilling), soil characteristics (soil, clay), and farming practices (harrow, stubble ploughing, rolled). The practices mentioned essentially concern sowing and working the land, which are essential in preventing weeds and fungal diseases and help reduce chemical use and were recommended by the cooperative. The discourse of this class focuses on the technical character of the AEM. The farmers employing this discourse seem to perceive the AEM as a group of practices to adopt and a list of specifications to be followed, as can be seen in an excerpt from this class:

I left the stubble until September 15-20 so that everything could grow
back again. I rolled and ploughed and in the spring, the ploughed land
was perfectly neat, because the grass grew before it could break the
seed, so a bit of rolling and ploughing just after, it works.


Class 2 discourse focused on farmers' questions regarding the AEM itself. This class is distinguished by words referring to weeds (wild oats, grasses, wild radish), diseases (fusarium) and chemicals (weeding, fungicide, insecticide, clean). Several words with high occurrence, such as dose and TFI, directly refer to the AEM goals to reduce chemicals and to the TFI as the indicator. Yet the words that best define this class are those like worry, dead end, fear, bad, dirty and disease, which express farmers' uncertainties about the impact of reducing chemicals on weeds, harmful insects, and fungal diseases. This can be seen in the following representative excerpt of class 2:

In two or three years, will the product work? We use only a half-dose,
is that going to be enough? There's a lot of pressure ... all the weeds
that are going to come up, I'm worried about what's going to show up
this winter ... I' m not going to go out and cut the weeds with
scissors every morning because I didn't use chemicals this year ... I
don't know where I'm headed.


Class 3 is dominated by discourse expressing the farmers' trust in the cooperative and its management of the AEM. Certain words, such as cooperative, and leader referred to the cooperative itself, and others qualified the nature of the relationship between the farmer and the cooperative, such as pay, sell, say, follow, monitor, and advise. Among the words found to be significant, many of them contribute complementary information on the quality of these relationships: good, cooperation, understand, trust, me, and him. As confirmed by certain discourse excerpts selected by the software, class 3 reveals the mutual understanding between the cooperative and the farmer. It corroborated our prior analysis about the role of factors such as trust for collaborative learning (see Section 4.2):

Well, already, we trust the cooperative's technical team, especially
Luc [a cooperative manager]. And so, each time he gets us to do
something new that goes in that direction, I mean, it has worked out
well--both financially and technically. So, we've always followed his
advice about it.


ALCESTE also took into account the correlations between classes and between individuals who spoke these utterances, who were identified by the software according to a number of predetermined characteristics such as age or by the interview transcribed (Figure 2). Thus, class 1 corresponds to a discourse profile shared by ten out of 38 farmers. This class, however, does not provide information about the possible difference in beliefs depending on the date of signing up for the AEM, as it includes farmers who had signed up in 2008 (six farmers) and those in 2010 (four farmers).

However, in the discourse permeated with doubts of class 2, we observed a difference in the belief profiles depending on the date of contracting the AEM. This discourse was only pronounced by farmers having signed up for the AEM more recently in 2010 (thirteen of seventeen farmers interviewed). At the time, they had hardly begun to benefit from the new advisory service and had only a few years of perspective on the new practices, thus they were concerned about the risks they had taken. They questioned the effectiveness of the recommended practices and were not yet completely convinced by the cooperative's advisors.

The discourse of fifteen of the farmers who had signed up earlier (out of 21 interviewed) is contained in the third semantic class focused on trust toward the cooperative. These fifteen participating farmers had been members of the cooperative since they had begun farming. Two of these fifteen farmers had joined the local network of organic wheat farmers and four others had participated in the cooperative's earlier environmental programs. The difference between the discourses of these farmers in class 3 who had signed up in 2008 and those above who had joined the AEM later in 2010 suggests that the former group's beliefs had changed over time. The relationship of trust established with the cooperative and the sharing of experiences seems to have played an important role (as the discourse of class 3 attests) in the process that enabled these farmers to overcome their doubts and to establish new beliefs.

The results from the postal survey enabled us to verify the ALCESTE results on the change in beliefs and the decisive influence in this process of the collective action initiated by the cooperative. Without going into too much detail here (see Authors, 2014), after controlling among farmers with similar profiles, the data showed significant differences in the ways of thinking and acting between the AEM and non-AEM farmers. For example, AEM farmers were more convinced of the agronomic benefits over the long term (61 per cent compared to 41 per cent for non-AEM farmers) and of the long-term economic benefits (57 per cent compared to 27 per cent) of alternative farming techniques. In addition, the AEM farmers more easily established a positive connection between changing practices and better health for the farmer (82 per cent compared to 62 per cent) and between changing practices and improving water quality (74 per cent compared to 44 per cent). In other words, the farmers' acceptance of the new techniques promoted by the AEM seems to have increased their awareness of growing environmental concerns. Finally, this survey revealed that the two groups of farmers did not use the same kinds of information in their decision-making: 73 per cent of the non-AEM farmers consulted technical farming magazines and granted them considerable importance, whereas only thirteen per cent of the AEM farmers said they did. However, this latter group all preferred the cooperative's newsletters on techniques and the meetings it organised. We consider that this result confirms the preponderant role of collective action in updating the beliefs of AEM farmers.

5. DISCUSSION

Discourse analysis showed that the two dimensions of legitimacy were interdependent: normative legitimacy prepared the way for cognitive legitimacy. In fact, it was by first coming to an understanding of what was collectively desirable (normative legitimacy) that actors were able to define what could actually be done in practice (cognitive legitimacy).

Focused on preserving water quality, the agreement on shared norms for action also took into account the farmers' possibilities for action. In other words, the concern to preserve the economic viability of the farms guided the choice of norms. This seems to have been a necessary condition for getting them to join the AEM. The high rate of participation attests to the fact that farmers agreed to rethink their practices in regards to new societal norms about 'the right thing to do'. Accepting this new normative framework about how farmers are supposed to work then made possible the cognitive legitimacy of alternative farming practices.

In fact, reassured that the normative changes expected by the AEM were possible, farmers were even more receptive to changing their practices. This case study shows, moreover, that the intelligibility of the alternative practices at the core of the AEM's cognitive legitimacy resulted not from an individual process but from collective action managed by the cooperative. These results are consistent with Mettepenningen (2013), who found that farmers' acceptance is facilitated when they are involved with a programme's implementation and when the AEM is supported by a collaborative approach based on sharing knowledge and mutual learning.

While the present study has demonstrated that normative legitimacy is a prior condition for cognitive legitimacy, it also revealed that cognitive legitimacy reinforces normative legitimacy. We observed that the destabilising of former beliefs (such as the need to systematically control weeds) gradually led the farmers to re-think their professional expertise and to integrate new moral obligations. For example, the way they conceived of a 'good farmer' was no longer necessarily associated with keeping plots free of weeds. Similarly, some farmers justified buying equipment to treat contaminated sprayer water not by citing legal reasons, but by their obligations towards other water users. Significantly, the interiorisation of moral obligations occurred when farmers acquired the conviction that the alternative practices suggested were actually feasible both technically as well as environmentally. This conviction then contributed to the actors' objectification of new environmental demands. It was precisely this dialectic between normative and cognitive legitimacy that, in the end, provided the foundations for acceptance of the AEM.

6. CONCLUSION

The main contribution of this study is to provide a better understanding of how an environmental public policy measure becomes interiorised. We found that to be accepted, a programme must be invested with meaning by the actors both normatively and cognitively. This supposes that (i) these actors, or their representatives, are directly involved with defining the environmental objectives (normative understanding on the desirable and reasonable goals to be reached); and that (ii) the effective implementation of the programme is supported by collective action, which convinces participants that the recommended practices are feasible, reasonable and, in the end, socially acceptable (cognitive legitimation). With these findings, this study opens up new avenues for research in comparative institutional analysis. It is evident, however, that the sample size of our study, 38 farmers and fifteen institutional actors, all members of the same cooperative and the same AEM, means that the study should be replicated on a larger scale and with a longitudinal approach. The present study provides evidence of correlation more than direct causality. In the same way, to confirm these results about deliberation, future research should include focus groups and observation of stakeholder meetings in order to directly observe whether communicative action in the sense of Habermas (1984), or deliberative democracy following Sen (2009), take place.

This study found that the acceptance of public policy occurs through its normative and cognitive legitimation. We understand legitimation as a process of explanation and justification based on collective action and discussion. For the AEM studied here, we identified two main levels of collective action: one among institutional actors, and the second among farmers.

At the first level, the institutional actors had to adapt the AEM to the specificities of the local context. The institutional compromise they forged certainly promoted greater legibility of the policy and, therefore, a greater credibility of that policy in the eyes of the farmers. As a result, the AEM gained normative legitimacy for the farmers. It is clear, however, that dialogue among institutional partners would benefit from being enhanced, since it was not able to resolve all their differences. It is also worth noting that in France, institutional actors' margin for manoeuvring at a regional level remains limited.

At the second level, the dialogue between the cooperative's managers, advisors and farmers played an important role in establishing the cognitive legitimacy of the public policy and the alternative practices recommended. By facilitating the sharing of experiences, collaboration among actors helped reduce learning costs. Each farmer did not need to verify the information on the effectiveness of alternative practices; he/she could rely on the experience of other members. To a certain extent, managing the risks of agronomic innovation became the collective's responsibility. This was only possible, however, because the cooperative established a context of social rationality based on reciprocity and trust. Without that trust, the farmers would have perceived the agronomic and economic risks of changing practices as too high and their acceptance of change would have been less. One of the most tangible proofs of the significant motivation and acceptance triggered by the collective dynamic examined here is that the farmers did not limit their changes to only the plots under the AEM contract--they voluntarily adopted these new practices for their entire farms, without the incentive of added compensation. The economic and agronomic rationales reinforced the effectiveness of this public policy in terms of its societal cost/benefit ratio. These findings fully justify Kenneth Arrow's view that trust is an 'invisible institution' (1974).

ACKNOWLEDGEMENT

We would like to thank the referees for their insightful comments that helped to improve the paper. We are grateful to Cynthia J. Johnson who translated this text from French into English.

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JEAN-PIERRE DEL CORSO

Universite Federale Toulouse Midi-Pyrenees, LEREPS/IEP, UT1-Capitole, UT2J, ENSFEA, 2 route de Narbonne--Auzeville, BP 22687, 31326 Castanet-Tolosan Cedex, France Email: jean-pierre.del-corso@educagri.fr

THI DIEU PHUONG GENEVIEVE NGUYEN

Universite Federale Toulouse Midi-Pyrenees, UMR AGIR, INRA, INPT-ENSAT, Avenue de l'Agrobiopole--Auzeville, BP 32607, 31326 Castanet-Tolosan Cedex, France

CHARILAOS KEPHALIACOS

Universite Federale Toulouse Midi-Pyrenees, LEREPS/IEP, UT1-Capitole, UT2J, ENSFEA, 2 route de Narbonne--Auzeville, BP 22687, 31326 Castanet-Tolosan Cedex, France

(1.) Mettepenningen et al. (2013) and Uthes and Matzdorf (2013) offer more information on the factors affecting AEM participation.

(2.) This refers to the actors' conscious reasoning about a rule and decision to follow that rule.

(3.) Understood by Cole (1996, p. 62) as 'the effective units of culture vis-a-vis mind'.

(4.) Adopted in 2000, the WFD sets targets for preserving and restoring ground and surface water throughout the European Union.

(5.) This initial reason for signing up from the programme was often mentioned during interviews with farmers. Nonetheless, as we will see below (section 4), the farmers' motivations changed over the AEM period.

(6.) Termed a 'captage prioritaire "Grenelle"'--500 such zones were identified in France in 2009.

(7.) The Plans d'Actions Territoriaux (PAT) or Regional Action Plans seek to mobilise the actors in a given region to restore water quality that has been contaminated by diffuse pollution.

(8.) In France, this is one of the traditional activities of farmers' cooperatives. As part of their role as suppliers, the cooperatives provide free advising to farmers on the use of the chemicals they sell. Thus a considerable part of the cooperatives' revenue comes from these sales, since they do not bill for their advising services.

(9.) The standard dose is defined by the registered guide for the given chemical and is the dose that should be used to effectively control a parasite or a disease.

(10.) The TFI was developed in Denmark in the 1980s to statistically count the growing use of chemicals.

(11.) For example: extending cropping systems by planting crops such as barley, reputed to limit the risk of disease and growth of weeds; replacing chemical weeding by using a weeder harrow; or using pesticides (herbicides, fungicides, insecticides) at the optimum moment according to the weather forecast.

(12.) In France overall, the percentage of farmers signing up for AEM-WFDs is very low, and only three per cent of the eligible surface area is contracted under AEMs according to Villien and Claquin (2012).

(13.) A public body that manages the watershed.

(14.) Chambers of Agriculture represent various agriculture businesses.

(15.) These councils were set up by the Ministry of Agriculture to implement national directives regionally.

(16.) The DASS is a local government agency that implements national public health policies locally.

(17.) This category was identified during the coding of institutional actors' discourse (see 3.3.1). This and future categories are indicated in italics.

(18.) Moreover, this result confirms the findings of Van Hecken et al. (2015). Borrowing the idea of 'institutional bricolage' from Cleaver (2012), they found that PES schemes are often deconstructed and reconstructed by actors in practice.

(19.) Several official reports from French agencies (such as CGAAER, 2009) also underline the reticence expressed by professional farming organisations toward the AEM.

Table 1. Statistics from the ALCESTE Textual Analysis

Total number of distinct words analysed           88,225
Per cent of the total ECUs classified                 84
Number of classes identified                           3
                                         Class 1      23
Per cent of ECU per class                Class 2      25
                                         Class 3      52
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Author:Del Corso, Jean-Pierre; Nguyen, Thi Dieu Phuong Genevieve; Kephaliacos, Charilaos
Publication:Environmental Values
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Date:Apr 1, 2017
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