M Alarcon

P Marty

A.-C Prévot

Caring for vineyards: transforming farmer-vine relations and practices in viticulture French farms

Keywords: care, France, farming, viticulture, farmer networks, biodiversity conservation

Caring for vineyards: transforming farmer-vine relations and practices in viticulture French farms Highlights:  Winegrowers care for vines but relate with other nonhumans within vineyards  Relations with nonhumans transform with time through evolving farming practices  The ways farmers take care of nonhumans evolve towards more attentive modes of care  Those transformations involve specific care spatialities (plots and edges)  Environmental programs based on observations of nonhumans contribute to those transformations

Introduction

The relationship between people and nature has attracted a rising interest among scientists [START_REF] Ives | Human-nature connection: a multidisciplinary review[END_REF]., first based on the evidence of health and well-being benefits [START_REF] Keniger | What are the benefits of interacting with nature?[END_REF]; and secondly, on the need to address sustainability challenges, including biodiversity conservation or fundamental society changes (e.g., [START_REF] Krasny | Civic ecology practices: Participatory approaches to generating and measuring ecosystem services in cities[END_REF]. In this context, values attributed to nature are of growing importance [START_REF] Díaz | The IPBES Conceptual Framework -connecting nature and people[END_REF]. Besides human interest (instrumental values) and interest for nature itself (intrinsic values), [START_REF] Chan | Opinion: Why protect nature? Rethinking values and the environment[END_REF] recently highlighted that people also consider "what is right" according to how they personally relate with nature and with others toward nature. They called this interest "relational values", which include a diversity of components, such as preferences, principles, and virtues, which also concern interactions with nature. Indeed, for the authors, some components of individual identities are rooted in long-term care with nature and stewardship (this is particular true for rural farmers). Relational values are prominent within different philosophies, including care ethics.

Care ethics are a growing school of ethics, arising out of feminist theoretical thought and further developed in philosophy as a part of a non-theoretical movement in applied ethics, concerning utility of moral principles in order to solve real life problems (Gilligan, 1993). From this perspective, care ethics start from concrete moral issues and observe how humans solve them. Care ethics highlight the value of specific and particular situations. Fundamental are to care ethics the fact that human beings are primarily relational, and the wish to refocus moral thought on relations between human entities instead of on moral action itself (Held, 2006). Thus, several concepts are central to visions of care, such as vulnerability, interdependence and attentiveness [START_REF] Tronto | Moral Boundaries: A Political Argument for an Ethic of Care[END_REF][START_REF] Wood | Who Cares? Women, Care, and Culture[END_REF]. Attentiveness is especially described in care studies as an ethical commitment and a concrete practice [START_REF] Krzywoszynska | Caring for soil life in the Anthropocene: The role of attentiveness in more-than-human ethics[END_REF]. The vast majority of work in care theory has focused on relations among humans and on the influence of societal structures on those relations. However, Fisher and [START_REF] Tronto | Toward a Feminist Theory of Caring[END_REF] enlarged the concept and included "everything we do to maintain and repair our 'world' in order to live in it as well as possible. That world includes our bodies, ourselves, and our environment" (40). [START_REF] Tronto | Moral Boundaries: A Political Argument for an Ethic of Care[END_REF] further extended care to relationships between humans and nonhumans (i.e. plants and animals), and explicitly defined care as both a disposal (care about) and a practice (take care of). Based on these thoughts, care theory highlights (i) interdependence and existing relations between humans, animals, plants and the environment; and (ii) the fact that human vulnerability is shared with other ecosystem elements, including nonhumans. Finally, since it draws the attention from wilderness to ordinary nature (i.e. areas where humans and nonhumans interact), care theory contributes to transforming the study of environmental concerns [START_REF] Laugier | Tous vulnérables ? : l'éthique du care, les animaux et l'environnement[END_REF]. Based on these ethics, co-living and building a common world is a crucial issue in different contexts nowadays; this is the case for landscapes that are dominated by industrial agriculture.

Industrial agriculture has become widespread in Western countries in the second part of the XX century, with the specific aim to increase food production. In this modern paradigm, farming practices became increasingly intensive, by promoting progressive control and management of nonhumans [START_REF] Stuart | The illusion of control: industrialized agriculture, nature, and food safety[END_REF] with mechanization and use of agrochemicals (especially inorganic fertilizer and pesticides) [START_REF] Woodhouse | Beyond Industrial Agriculture? Some Questions about Farm Size, Productivity and Sustainability: Questions about Farm Size, Productivity and Sustainability[END_REF]. In that perspective, farmers are often presented as making choices only for economic reasons and in order to optimize and raise quantities of food production [START_REF] Burton | Seeing Through the 'Good Farmer's' Eyes: Towards Developing an Understanding of the Social Symbolic Value of 'Productivist' Behaviour[END_REF][START_REF] Burton | Injecting social psychology theory into conceptualisations of agricultural agency: Towards a post-productivist farmer self-identity?[END_REF]). Yet, agricultural intensification has been recognized as one of the main driving forces of the global environmental crisis, especially biodiversity loss [START_REF] Newbold | Global effects of land use on local terrestrial biodiversity[END_REF]. Combined with food security issues, environmental issues recently became increasingly present in the public debates about agriculture, moving from farmers' discretion to that of public policy and market choices [START_REF] Meijboom | Farming ethics in practice: from freedom to professional moral autonomy for farmers[END_REF]. In this context, individual farmers are faced with an apparently contradictory injunction: food production and protecting biodiversity [START_REF] Barbier | Moins de technique, plus de nature: pour une heuristique des pratiques d'écologisation de l'agriculture[END_REF][START_REF] Mcguire | Reconstructing the good farmer identity: shifts in farmer identities and farm management practices to improve water quality[END_REF].

In this paper, we explore how farmers deal with this contradiction in French farming landscapes that have been transformed with agricultural modernization. We focus on the relationships farmers build with plants (both cultivated and spontaneous) and animals (both wild and domesticated) that are present in their farms, and explore whether, even in areas that seem structured by modern agriculture, some practices and relations towards those nonhumans can refer to caring attitudes and practices [START_REF] Tronto | Moral Boundaries: A Political Argument for an Ethic of Care[END_REF]. We use the term 'nonhumans', more general that "nature" when it comes to qualify all types of plants and animals farmers interact with in their farms, as the term is also used in several care studies (e.g., [START_REF] Krzywoszynska | What Farmers Know: Experiential Knowledge and Care in Vine Growing: Experiential knowledge and care in vine growing[END_REF][START_REF] Pitt | Questioning care cultivated through connecting with more-than-human communities[END_REF]. Based on the care theory, we make the hypothesis that farmers are not only driven by economic choices [START_REF] Rooij | Entrapped in Group Solidarity? Animal Welfare, the Ethical Positions of Farmers and the Difficult Search for Alternatives[END_REF][START_REF] Driessen | Farmers Engaged in Deliberative Practices; An Ethnographic Exploration of the Mosaic of Concerns in Livestock Agriculture[END_REF], and may be driven by attention towards nonhumans vulnerability too. We also hypothesize that attention towards nonhumans is being developed during formally observation sessions that are conducted in farmer groups (Deschamps and Demeleneare, 2013). Such collective field observations may contribute to build relationships between farmers and nonhumans that go beyond production and economic issues.

We focus on viticulture, for the two following reasons: first, ecological issues are particularly complex in this agricultural sector where plant-growing practices changed during the second half of the XX century, following mechanization and increased use of pesticides [START_REF] Clingeleffer | Mechanization in australian vineyards[END_REF][START_REF] Pertot | A critical review of plant protection tools for reducing pesticide use on grapevine and new perspectives for the implementation of IPM in viticulture[END_REF]. Secondly, global environmental issues such as climate change or water scarcity impact vine production [START_REF] Van Leeuwen | The Impact of Climate Change on Viticulture and Wine Quality[END_REF][START_REF] Costa | Modern viticulture in southern Europe: Vulnerabilities and strategies for adaptation to water scarcity[END_REF]. More particularly, we focus on winegrowers involved in local professional networks in the South of France (Languedoc region, Hérault department, see Fig. 1). We answer the three following questions: How do farmers relate and care for nonhumans and the environment? Can those relationships and the ways of taking care of nonhumans change in time? How do farmer networks contribute to develop farmers' attention towards nonhumans and environmental issues? This paper is organized as follows: The section 2 introduces relevant literature on care for nature in farming context and explores how care theory may contribute to broader conceptualizations of sustainable practices in agriculture. Care theory appears as a pertinent tool in order to study how farmers relate to nonhumans, beyond humans' (instrumental values, i.e. production) or nonhumans' (intrinsic values, i.e. protection) sakes. The sections 3 describes the ethnographic study we conducted in Hérault (France) among 20 winegrowers who mostly took part in two local programs of nonhumans' observations on farms (SVG and Biodiv'eau). The main findings of the study are presented in section 4: first, we highlight the complex relations that winegrowers have with a diversity of nonhumans on their farms, including caring for vine and production issues; second, we describe that farmers develop new ways of taking care of vine and biodiversity with field observations, at plots' and edges' scale; finally, we analyze how two programs contribute to foster different ways to take care of nonhumans (vine protection or biodiversity conservation).

In section 5, we discuss the broader possibility of new types of farmers and nonhumans communities based on attention and care, and the idea that different types of humans and nonhumans communities may exist and be developed, depending on the types of care for nonhumans collective actions may foster. We conclude by highlighting the need for more studies about the role of farmer networks for biodiversity conservation (section 6).

Care for nature in farming context: a synthesis

Intensive farming systems aim to increase productivity at plot scale [START_REF] Wilson | From Productivism to Post-Productivism... and Back again? Exploring the (Un)changed Natural and Mental Landscapes of European Agriculture[END_REF][START_REF] Burton | Seeing Through the 'Good Farmer's' Eyes: Towards Developing an Understanding of the Social Symbolic Value of 'Productivist' Behaviour[END_REF], without any concern on biodiversity conservation. In order to address both objectives of farm productivity and biodiversity conservation, much research in ecology and agronomy has focused on farming practices, first within plots, and secondly, in natural areas around the plots [START_REF] Franin | The role of ecological infrastructure on beneficial arthropods in vineyards[END_REF].

Much studies focused on two sets of farming practices : 1) economically-driven technological developments and closed loops of energy, organic matter and minerals, aiming at increasing efficiency of input use and decreasing both production costs and environmental impacts [START_REF] Horlings | Towards the real green revolution? Exploring the conceptual dimensions of a new ecological modernisation of agriculture that could 'feed the world[END_REF]; 2) intensification of ecological interactions between the components of each specific farm, in order to promote fertility, productivity, and resilience to external perturbations [START_REF] Horlings | Towards the real green revolution? Exploring the conceptual dimensions of a new ecological modernisation of agriculture that could 'feed the world[END_REF][START_REF] Kremen | Reframing the land-sparing / land-sharing debate for biodiversity conservation 1355[END_REF], including increasing semi-natural areas around plots [START_REF] Franin | The role of ecological infrastructure on beneficial arthropods in vineyards[END_REF].

Concrete transformations in farmers' practices are sometimes driven by an increased attention to environmental issues [START_REF] Lamine | La fabrique sociale de l'écologisation de l'agriculture[END_REF]. This process combines individual trajectories and collective actions, which provide the necessary resources, including ecological knowledge and social bonds (see [START_REF] Hill | Conceptual Framework for the Transition from Conventional to Sustainable Agriculture[END_REF]. Thus, in the context of environmental crises, new co-operations between farmers and non-farmers develop, together with a greater inclusion of environmental issues in farming practices [START_REF] Hillis | Sustainability partnerships and viticulture management in California[END_REF]. Many studies highlight the key role of farmer networks for agroecological transition [START_REF] Compagnone | Farmers' Professional Dialogue Networks and Dynamics of Change: The Case of ICP and No-Tillage Adoption in Burgundy (France)[END_REF][START_REF] Šūmane | Local and Farmers' Knowledge Matters! How Integrating Informal and Formal Knowledge Enhances Sustainable and Resilient Agriculture[END_REF]. In France, those bottom-up and place-based farmer networks develop alongside historical professional structures, i.e. the farming federations and the Chambers of Agriculture [START_REF] Cordellier | Organisations professionnelles agricoles : histoire et pouvoirs[END_REF].

Research following these initiatives mainly focus on agronomic techniques and solutions, and not on how farmers relate to plants or animals, notably trough increased attention; neither on how these relations may change with transforming farming practices. Yet, linking attitudes towards nonhumans and farming practices is possible by applying care theory to agriculture. Indeed, care studies are mostly based on narratives and descriptions of individuals' practices (ethnographies), that highlight what people are experiencing, and what matters for them [START_REF] Beau | Ethique de la nature ordinaire: Recherches philosophiques dans les champs, les friches et les jardins[END_REF]. Recent studies have explored care in agricultural contexts [START_REF] Curry | Care Theory and "caring" systems of agriculture[END_REF][START_REF] Herman | Enchanting Resilience: Relations of Care and People-Place Connections in Agriculture[END_REF][START_REF] Bellacasa | Matters of Care: Speculative Ethics in More than Human Worlds[END_REF] especially breeding (Mol et al., 2010;[START_REF] Linn | Making Milk with Conscious Care: Raw Milk Ontologies and the Practices of 'Bath Milk' Producers in Victoria, Australia[END_REF][START_REF] Donati | Herding Is His Favourite Thing in the World': Convivial World-Making on a Multispecies Farm[END_REF], as well as specific forms of agriculture such as urban agriculture and agriculture in the global south [START_REF] Pitt | Questioning care cultivated through connecting with more-than-human communities[END_REF]Guétat-Bernard, 2017). In these studies, care states that practical engagements can only be delivered by situated and experiential understanding of situations by practitioners [START_REF] Mol | The Logic of Care: Health and the Problem of Patient Choice[END_REF][START_REF] Krzywoszynska | What Farmers Know: Experiential Knowledge and Care in Vine Growing: Experiential knowledge and care in vine growing[END_REF]. Focusing on the relationships of vine farmers to plants, [START_REF] Krzywoszynska | What Farmers Know: Experiential Knowledge and Care in Vine Growing: Experiential knowledge and care in vine growing[END_REF] showed how numerous interacting cares inherent to grape growing are enacted during vine pruning. More precisely, the author described how winegrowers focus on vine and winery needs, and act in order to satisfy these needs. She named these cares "local cares" and defined them as a caring patterning of actions "practiced with its typical flexible adaptation and an acceptance of uncertainty" (303). She calls for recognition of farmer expertise as a way to adapt and be attentive to the direct environment, through observations. Her study shows that care theory can contribute to explore relations between farmers and plants based on attention, that lead to adopt more flexible farming practices, depending of the existing needs at different scales of a farm (plots, or the entire farm management).

Other care studies started to explore farmers' relations to nonhumans within industrial agriculture paradigm and its transformations. [START_REF] Bellacasa | Matters of Care: Speculative Ethics in More than Human Worlds[END_REF] highlighted that, in modern agriculture, soils are viewed primarily as ingredients for yield productivity, i.e. ultimately as objects. Soil care exists, but as an utilitarian care vision (i.e., putting back to work worn out soils and increasing the soil's efficiency to produce). But other kinds of soil conceptualizations and human-soil relationships are possible, in relation to more attention-based farming production. In that matter, soils can be a matter of concern and care "not just for what they provide for humans but for ensuring the subsistence for soil communities more broadly" (Bellacasa 2017: 190). In this new conception of soils, humans are a part of the soil community, where they care for and are cared for. Bellacasa calls it "immersed ecological care". Thus, the development of affective involvement with soils ("haptic engagements in care", for example sensorial engagements, Bellacasa 2017: 197) is tied to the modification of existing relations of caretaking and especially immersed ecological care. Even if the author does not describe precisely the processes of learning how to be more attentive to soils, her work shows that care theory may contribute to consider different modes of farming production (e.g. utilitarian vs. attention-based), and further new types of communities where farmers work and cooperate with nonhumans, on the basis of the key concept of attention.

So, care theory provides a conceptualization of sustainable practices in agriculture. Farmers' narratives show how they become attentive to nonhumans and transform their practices accordingly. It contributes to a better understanding of how these changes in farmers' attitudes and practices operate at different scales (farm, plots, edges). Care theory may be accurate to imagine new farming communities of humans and nonhumans based on care and attention [START_REF] Latour | Moderniser ou écologiser. A la recherche de la Septième Cité[END_REF], beyond farmers' (instrumental values, i.e. utilitarian production) or nonhumans' (intrinsic values, i.e. protection) sakes [START_REF] Chan | Opinion: Why protect nature? Rethinking values and the environment[END_REF]. Given the existing contributions of care theory to the understanding of how farmers take into account environment and biodiversity in their attitudes and practices, we designed a study with the following goals: to analyze how farmers develop their affecting involvement with soils or other non-human entities; to study the potential of collective action and initiatives in transforming the relations between humans and nonhumans; to explore potential links between local cares (i.e., interactions with specific and direct non-human entities) and global cares (broader environmental issues). This qualitative research was conducted in Languedocian vineyard in south of France, particularly in the Hérault area (Fig. 1). Languedocian vineyard, historically dominated by co-operatives since the beginning of the XX century has changed since the second half of XX century [START_REF] Touzard | Les caves coopératives dans la transformation du vignoble languedocien[END_REF]. First, the vine area strongly decreased in size since 1980 due to several uprooting campaigns, supported by European Economic Community grants [START_REF] Clement | Les mutations paysagères engendrées par l'arrachage viticole, un vecteur de mobilisation des acteurs territoriaux dans l'Hérault[END_REF] in order to fight overproduction and decreasing prices by locally increasing the quality of the vine and of the local labels. Moreover, since 1985, cooperatives are less numerous and tend to amalgamate in order to be more competitive in wine market [START_REF] Touzard | Les caves coopératives dans la transformation du vignoble languedocien[END_REF]. In Hérault, most of the vine production is still dependent on co-operation (Département Hérault, 2010). However, a new generation of independent wine producers has recently appeared [START_REF] Johnson | The World Atlas of Wine[END_REF]. The Hérault area is the second French department with the largest vine surface area (Département Hérault, 2010). That department is one of the top three areas in France with the highest use intensity of insecticide spray [START_REF] Mailly | Quantifying the impact of crop protection practices on pesticide use in wine-growing systems[END_REF]. However, an increasing part of the production comes from organic and biodynamic farming [START_REF] Occitanie | Bilan des dynamiques d'engagements et arrêts de notification bio en[END_REF][START_REF] Preston | Viticulture and Winemaking in Contemporary Rural Change: Experience from Southern France and Eastern Australia[END_REF][START_REF] Foyer | Syncrétisme des savoirs dans la viticulture biodynamique[END_REF].

Materials and methods

Study site

In Hérault, we focused our study on two different areas and wine designations: Pic Saint Loup (north of the area) and Côtes the Tongue (southwest of the area). The Protected Designation of Origin Pic Saint Loup relates to 17 towns and the majority of the production is made by independent wineries. [START_REF] Johnson | The World Atlas of Wine[END_REF] present it as one of the most distinctive terroirs in Languedoc wines. On the other hand, the Protected Geographical Indication Côtes de Thongue relies in a stronger co-operative tradition; it suffered an overproduction crisis in the 1970s, which made that co-operatives and winegrowers establish quality processes in order to increase vine and wine qualities. At that time, a part of the winegrowers transformed their winery to become independent from co-operatives.

In these two areas, diverse environmental and agroecological programs were implemented in different periods (Fig. 2). In this study, we focused on two local programs:

(1) The program "Sustainable viticulture groups" (SVG) gathers voluntary groups of winegrowers. It was initiated in the early 1990's due to the pre-existence of informal groups made of local winegrowers. In Hérault the 25 currently active official SVG groups include between 8 and 25 winegrowers each and are animated by an expert from the Chamber of Agriculture. They meet every two weeks for four-hour sessions of collective surveys of different pre-defined acarian species and diseases in references plots.

Sessions also include collective discussions about potential treatments or preventions against vine diseases. The main objective of this program is to decrease the use of chemicals and to potentially test alternatives to chemicals. The collective diagnoses are associated with a Chamber of Agriculture meteorological software called Performance Vigne.

(2) The program Biodiv'eau was created between 2010 and 2012, following a demand from local winegrowers to know more about the biodiversity present in their vineyards. This voluntary program now gathers many local structures (Chamber of Agriculture, water supply agency, Hérault department for financial support), and is animated by the local environmental NGO named Conservatory of natural areas of Languedoc-Roussillon (CNALR). Biodiv'eau consists of a flora inventory made by winegrowers directly on the edges of their vineyards. Ecologists of CNALR worked closely with winegrowers to build the inventory grid. Each session of the program includes several steps (Fig. 3).

We carried out ethnographic qualitative work, including participant observations and semi structured interviews [START_REF] Beaud | Guide de l'enquête de terrain[END_REF]. In April 2018, author #1 spent two days conducting participant observations: one with a Biodiv'eau training with 15 winegrowers, and one with a sustainable viticulture group with 15 winegrowers.

Semi-structured interviews

20 semi-structured interviews with winegrowers were conducted by author #1 from November 2017 to May 2018 (Table 2). Winegrowers were found and contacted by the snowball technique, starting from contacts of participants to Biodiv'eau. All these interviews were individual, completed by phone (7) or face to face (13). The mean duration of the interviews was 1 hour (from 35 minutes to 4 hours). During four of them, the interview included a visit of the vineyard with the winegrower. All the interviews were recorded, transcribed, and anonymized. Notes were also taken during the interviews.

Qualitative data analysis

Following the principles of thematic coding, we coded the interviews [START_REF] Paillé | L'analyse qualitative en sciences humaines et sociales[END_REF].

Through several cycles of coding, we filtered, focused, and highlighted the salient features in the data, focusing first on caring disposals and practices and second on the part of farmer networks and environmental programs in learning and developing environmental preoccupations and new farming practices. All authors thoroughly discussed results throughout the process to reach consensus and ensure reliable assessment validity.

RESULTS

Winegrowers care primarily about vines

Winegrowers primarily care for their vine, since they have to produce grapes. However, if vine is the main care-receiver within vineyards, winegrowers also form relationships with a diversity of other nonhumans and they care for environmental issues too.

Vine as the main worry

Winegrowers mainly relate to vine as their main concern, especially on vine health and on its vulnerability. Almost all the winegrowers mentioned one or several existing vine diseases as a serious concern, because they threaten vine production. Words such as "diseases", "vulnerability", "sensitive', "risk", or "pressure" were consistently used relating to vine, and were mostly associated with stress and anxiety. Besides diseases, climate events were also qualified as a threat for vine:

My nephew [associate] is very scared of leafhopper and flavescence dorée that we can't perfectly control with ecological products, and it devastates the vine. (Respondent 7)

We are not enough here, and it's been two years that there has been hail, then there was drought, so we lost 75% of our crop over two years. (Respondent 9)

According to winegrowers, pests or animals such as wild boars threaten crops. But only a few winegrowers mention wild boars as a problem for vine crops and enclose their farm or hunt them. We detected different types of relations with animals that interact with the vines: some animals, especially the ones that are considered as a threat for the crop, are less accepted than birds or animals considered as harmless and thus much more appreciated:

The only hunted animals in my farm are wild boars, because they come here, they are everywhere.

It is hard to enclose the farm. Wild boars attack crops. Moreover, they attack young partridges, rabbits, and hares. So, we have to eliminate them. This year they ate 10% of the crop.

(Respondent 19)

For me, seeing a young Bonelli's eagle in my vineyard is amazing; and the vine is full of young partridges, we see them running after their moms and dads, it is wonderful. (Respondent 3)

The worry for vine is translated into concrete manners to take care of it: 1) Protection of the vine, which is the priority for the majority of the winegrowers. The protection aims at avoiding or decreasing the risk of diseases, pests or weather events (such as drought or frost) that could decrease vine or wood health.

It comes before the current diseases, pest or weather event; 2) Cure of the vine, after diseases, pests, or extreme weather events aims at healing vine or woods; 3) Reinforcement of the vine, in order to get it stronger in front of possible diseases or extreme weather events. Reinforcement differs from protecting, it consists of a more general improvement of vine health. This approach is characteristic of biodynamy:

When you do biodynamic farming, you are asking yourself: why are my vines sick? What should I do to make that they are less sick? And if they are sick anyway, I'll try not to treat only with natural chemicals but with plants and specific preparations too. What we aim is to revive microbial life of soils. We work with this microbial life in order to make plant able to digest the organic stuff that are in the soil, oligo-elements and so on. (…) All the vegetation becomes stronger: leaves are thicker, woods are thicker and much stronger. Plant grows better. Then it resists much more to cryptogamic diseases that are related to humidity. So, the most the leaves are thick, the most it resists. Then, there are consequences on grapes quality. (Respondent 9)

The majority of the people we met used synthesis chemicals to prevent or cure diseases. However, some preferred to use organic certified products, and a minority (only 2) used preparations recommended in biodynamic farming.

Broader environmental preoccupations: the case of biodiversity

Besides production issues and care for vine, winegrowers are concerned for non-production issues too.

More specifically, they mentioned quite often such environmental issues, such as biodiversity conservation, soil composition, or water quality. Those concerns were relevant for them and important to care, but were not considered as an immediate threat (compared with vine diseases). Most of the time, when they mentioned biodiversity, winegrowers never explicitly defined it. However, after using this term, they mostly talk about i) birds, ii) plants, and iii) useful biodiversity for the vine. Only a few winegrowers, the ones engaged in organic agriculture especially, referred to biodiversity loss as a real problem:

Three weeks ago, we knew that a study showed we lost 30% of birds in ten years. This is catastrophic. This is a disaster. There are no bird anymore. We see it today, you raise your eyes and there are no swallows, no swifts, and no goldfinches. (Respondent 19) So, winegrowers are not only focused on production and economic issues. They feel concerned by biodiversity loss too. However, they regret the few support they get in order to address such issue.

Indeed, a few winegrowers asked for support in order to understand and accurately address environmental issues, because they assumed not having enough time to deal with them alone or because they did not know how to.

Winegrowers change how they take care of their vineyard

The ways farmers relate to nonhumans can be transformed with increased attentiveness and experimental practices. Indeed, at plot scale, winegrowers mainly transform their farming practices by adapting their interventions with the practice of plots observation; those changes are most of the time progressive and come with experimentations. At edges' scale, winegrowers let vegetation coming back more naturally, and implement agroecological infrastructures in order to improve biodiversity.

Winegrowers change their ways to take care of vines through transforming farming practices

First, all interviewed winegrowers mentioned adaptations of their practices to specific contexts and singular situations. For instance, they described how they adapted their farming actions to vine diseases or weeds through adapting quantities and types of products to the contexts of uses (weather, grape variety and plot location). Observing their plots was described as a key-issue for them, in order to adapt the quantities and the type of interventions on vines. Instead of doing systematic treatments every two weeks, as they used to do, conventional winegrowers declared spending time now on their plots and adjusting their interventions to the threat they see, making limits between conventional and organic farming not as clear as one may imagine:

My philosophy is closed to organic farming. Chemistry is of course as powerful and amazing as a medicine. It is quick. However, if we overdo it, we decay everything. Here, if we can work only with natural products, we do it. It happened that we only worked with organic products during a full year. In 2017, we did not have that much water in the soil but air was wet, so we did not have that many weeds. We only used a few herbicides. But in June we got mildew and using copper was not enough, so we used chemicals. (Respondent 15)

Most conventional winegrowers declared that they tend to reduce quantities of chemical use:

I am using an optimal method. It means that instead of putting a full dose of chemicals, I use 40% of it. If there are very difficult conditions, I put 60% of it. (Respondent 16) Some of them also declared that they tend to suppress specific categories of chemicals while staying into conventional farming:

In this area, I am supposed to do three treatments against flavescence dorée. Do not repeat it, but I do none of them. I don't want to use insecticides. (Respondent 10) However, some of the interviewed farmers proceeded to more radical and systemic change than chemical reduction at plot scale, and these transformations affected the whole production and distribution system of the farm. This is the case of change from a conventional system to an organic or biodynamic one for example. A few winegrowers chose to transform a former conventional vineyard into an organic one as self-fulfillment, sometimes while changing their way of life and becoming a winegrowers after having first worked in a completely different sector: Another motivation for such a radical change from conventional intensive to an alternative system is the project to diversify the production and to re-localize the distribution, which are both politically and socially grounded. For instance, one independent winegrower explained that he rents a part of the land in his farm to a bread-maker to cultivate cereals, and that he built a local production shop on the farm.

At the same time, he aims for energy autonomy:

I bought a small combine harvester that goes between grape lines and it will be the only motorized tool in the vineyard. Since it is a diesel motor, it will work with grape or sunflower oil.

Actually, I am looking for autonomy, not for autarky. I am open to other people, but I want to be self-sufficient. It is more than ecological issues… it is political and social. (Respondent 17)

New ways to take care of vines come with experimentation in time and in different areas

Based on the interviewed winegrowers, the adjustments and transformations of farming practices come with plot experimentation. They mentioned curiosity or desire to experiment new practices. Most of them mentioned some mistakes they did in their practices, and the changes they made in the way to plant and grow vines, use chemicals, and remove weeds. Most of the time, a new practice leads to other new practices, in a long time process:

Our practices changed, and they will change again. They changed gradually with knowledge and techniques. (…) We started here to evolve on re-planting vines, around 1986. We started by tillage, we stopped using herbicides. We stopped all the herbicides. Gradually we replaced our vineyard.

We put cultivation for animals such as lucerne. (Respondent 2)

Note that the processes of change were not all linear. One winegrower for instance declared having decided to stop a new practice after a while:

I stopped doing biodynamic farming. I did it for three years, but it was too esoteric, too crazy. I prefer peasant common sense. (Respondent 17)

The winegrowers we met generally took many years to experiment and adjust their farming practices.

No change was hasty. On the contrary, interviewed conventional winegrowers mostly started by reducing quantities of chemicals first on one plot and then on the whole vineyards progressively in a few years process. Thus, experimentations were commonly conducted progressively within the farm:

When we had the proof that changing practices could lead to a better water quality, we said 'ok let's do it'. And then we started to use less herbicides, then we completely stopped using them on our plots affected by this catchment area, and then we stopped using it on our entire vineyard.

( Thus, the interest on biodiversity issues comes progressively for the majority of the farmers we met.

Farmer networks contribute to transform farmers' relation to vine and broader biodiversity

The changes of farming practices described above are often linked with collective initiatives. These initiatives lead to different types of knowledge and of observations of plots or edges, that transform the way winegrowers care for nonhumans. Thanks to the observations conducted in SVG, winegrowers learn to protect and cure the vine with less systematic control of pests and diseases. Biodiv'eau program contributes to interest winegrowers into biodiversity in order to conserve it both for itself and for the vine; moreover, the program helps winegrowers to take care of biodiversity properly, while improving the agroecological infrastructures of their vineyards.

Farmer networks are part of the farming practices' transformation process

Most winegrowers we met were part of several networks and initiatives (see Table 2). A small part of our sample (one biodynamic and one organic) belongs to farmer networks located in other French vine regions, or in a virtual network.

All interviewed winegrowers mentioned the relevance of social and technical interactions with other winegrowers in the process of transforming their farming practices. Sharing ideas and experiments allows them to discuss possibilities of innovations and to know how others deal with the issues they share. The need to talk with other farmers was considered as crucial for those who were conducting self-questioned processes about their own farming practices and their willingness to conduct radical agricultural changes:

Lots of my colleagues don't convert to organic farming but they are almost free from herbicides.

They use them just once a year, as late as possible. (...) We talk about what we do with friends, with colleagues, so sometimes one of them say he got something and asked if we had the same.

(...) I am thinking about changing glyphosate for tillage and covering with grass, and then cutting it.

We have one colleague who is on the cutting-edge of these kinds of practices. (Respondent 10)

These social professional networks contribute to building different farming and environmental programs. This is the case for SVG and Biodiv'eau. Most of the winegrowers we interviewed took part in one or two of those programs. For conventional farmers, it was quite common to start with SVG and then take part in Biodiv'eau program. However, we observed two different kinds of relationships between participants in the programs. In SVG, during the observed group sessions, the main questions asked to the participants were related to the presence of mildew in plots. The decision to treat against mildew came partly from collective discussions during observation sessions. Because the sessions were animated by an extension officer from the Chamber of Agriculture, and because meteorological and technical information on diseases are regularly provided to winegrowers by the Chamber of Agriculture, the final decision of winegrowers regarding treatments is probably influenced by the social norms coming from this institution. However, some interviewed winegrowers that belong to SVGs declared that they do not always respect the collective decisions or the weekly advice delivered by the Chamber of Agriculture. In Biodiv'eau, the process is less hierarchical. During the first collective training day, when winegrowers discover the program and learn collectively how to use the inventory grid, ecologist and farmers share knowledge about biodiversity loss, farming responsibility on it, and possibilities for farmers to improve it.

This day is the occasion for the group to ask questions to the ecologist who runs the program. During our participatory observation, winegrowers asked many questions about the existence of protected species or the ecological validity of some green infrastructures they already established.

Thus, social relations differ between two programs: even though SVG encourages winegrowers to communicate and take decision together, the program still promotes hierarchical relations between farmers and extension officers that possess the knowledge; on the contrary, Biodiv'eau promotes participatory and co-construction of the ecological knowledge between farmers and ecologists.

Winegrowers are likely to transform their farming practices with SVG

Participation in sustainable viticulture groups (SVG) contributes to transforming farming practices on plot scale, especially for conventional farmers who compose the majority of those groups. Indeed, SVGs mainly focus on vine diseases and chemical use, they deal with the following main issues: when winegrowers should treat the vine, and against what type of problems; what type of chemicals they should use and how they can reduce the quantities of chemicals used. The focus of these groups is, as a result, more technical than properly environmental, even if chemical alternatives are presented and discussed.

Because it asks for regular and collective observation practices, SVG trains winegrowers to develop a habit of going on their plots and observe. This way, winegrowers progressively learn to judge and decide by themselves what kind of actions they want to implement on their plots, notably regarding treatments against pests. They progressively can stop applying theoretical recommendations. Thus, they learn to adapt the use of chemicals to contextual and located problems, which is linked to a decrease and local adaptation of chemical use:

Those groups were for us the possibility to meet up with other winegrowers, to speak, and see that it was possible to change our farming system (…) With those groups, we were not alone, and we had technical help. When you want to reason acarian problems, you must recognize them first.

You have the harmful ones, the neutral and the useful. You need to train if you want to recognize them. We realized that those useful acarian were disappearing because we did not have good techniques concerning chemical use, and we badly maintained soils and edges. All this causes us to ask ourselves new questions and we ask 'what should I do for this to work better?'. (...) After that, we could have entire years without putting mildew chemicals because the mushrooms didn't develop, whereas traditionally people here protect against mildew even if there is no mildew.

(Respondent 1) SVG sessions also provide opportunities to learn more about alternative vine caring. For instance, during the group session we studied by participant observation, a winegrower presented a plot that he recently converted to biodynamic farming. Before we came to his plot, some conventional winegrowers were joking and laughing at him. However, when we observed his biodynamic plot, all the winegrowers were asking questions about his practices, such as the type of treatments he was using on his vine to protect it. The owner of the plot answered all the questions, talking about the specific biodynamic preparations and presenting the aims of his conversion.

So, while getting the habit of observing their plots with SVG, winegrowers learn progressively to take care of their vines differently as they used to do it without observations. They still aim at protecting the vine and at curing it against diseases or nonhumans considered as threats (acarians, pests for example), but they learn to do it less systematically as they used to. Finally, Biodiv'eau contributes to transform how winegrowers concretely take care of biodiversity at edges' scale, on their farms. Indeed, Biodiv'eau participants receive technical support, with CNALR advises on how to improve biodiversity on edges, especially by implanting agro-ecological infrastructures. They receive also financial support, with departmental funding for semi-natural infrastructure implementation. This way, Biodiv'eau program gives the possibility to match environmental sensitivity to actions. Some winegrowers that belong to this program declared that after their participation to the program, they knew more how to contribute to biodiversity conservation at their vineyard scale and became aware of the benefits for their vine to do it:

Winegrowers broaden their conceptions of biodiversity with Biodiv'eau

We could see the interest of having hedges or low walls to get birdhouses for insects, and what this could give us to get insects and birds. We learned those things, and we saw all this differently.

(...) We could see how we could conserve better. (Respondent 11)

Discussion

Complexity of environmental care

We showed that winegrowers have diverse relations with nonhumans entities, and that care for vines is only one of them (see also [START_REF] Meijboom | Care and Responsibility as key concepts of agricultural ethics[END_REF][START_REF] Krzywoszynska | What Farmers Know: Experiential Knowledge and Care in Vine Growing: Experiential knowledge and care in vine growing[END_REF]. Besides vines, winegrowers have various environmental preoccupations (water, soils, broader biodiversity) and diverse manners to care about them (e.g., seriously worrying about vine diseases, being interested in environmental issues).

Those preoccupations are associated with specific practices, in order to protect, cure or reinforce their vines, in a continuum of practices from conventional, organic to biodynamic ones. In a theoretical perspective, these findings are consistent with care literature that analyzes both preoccupations (care about) and actions (take care of) [START_REF] Tronto | Moral Boundaries: A Political Argument for an Ethic of Care[END_REF]. The farming care diversity we found is also consistent with the key concepts of adaptation to singular situations and concrete experience in care theory [START_REF] Laugier | Tous vulnérables ? : l'éthique du care, les animaux et l'environnement[END_REF], as part of experiential knowledge [START_REF] Krzywoszynska | What Farmers Know: Experiential Knowledge and Care in Vine Growing: Experiential knowledge and care in vine growing[END_REF].

Moreover, our results add several highlights for ecological care literature: first, we found spatial logics in farming care. Indeed, different areas in the vineyard deserve different environmental attentions: for instance, plots are important for vine, while edges are particularly important for biodiversity (as confirmed by scientific ecological surveys, see [START_REF] Franin | The role of ecological infrastructure on beneficial arthropods in vineyards[END_REF][START_REF] Franin | The role of ecological infrastructure on beneficial arthropods in vineyards[END_REF][START_REF] Froidevaux | Does organic farming enhance biodiversity in Mediterranean vineyards? A case study with bats and arachnids[END_REF].

Secondly, our work highlights the complexity of the specific biodiversity care for farmers, in a context of increasing double injunction of producing food for human consumption and protecting biodiversity. As we showed, biodiversity is generally not at the core of farming priorities. Still, it is present despite other environmental types of preoccupations, and might be something positive for vine (see Biodiv'eau). We therefore propose here biodiversity conservation to be of interest for winegrowers, for the two following reasons: because useful biodiversity may help the vine production, but also because winegrowers feel concerned about biodiversity loss. As such, we confirmed, as proposed more widely by [START_REF] Chan | Opinion: Why protect nature? Rethinking values and the environment[END_REF], that the relationship of farmers with nonhumans is not only binary (intrinsic vs.

instrumental value). We also propose to rely these results to the notion of "interest" defined by the philosopher E. Hache (2011). For this author, interest is not strictly utilitarianism but refers to a relation established both for oneself (utilitarianism, instrumental value) and for others (altruism, intrinsic value).

Being interested in nonhumans highlights existing complex relations between humans and nonhumans.

We therefore propose here biodiversity conservation to be of interest for winegrowers, because useful biodiversity may help their vine production, and because winegrowers feel also concerned about biodiversity loss. As such, we also confirmed, as proposed by [START_REF] Chan | Opinion: Why protect nature? Rethinking values and the environment[END_REF], that the relationship of farmers with nonhumans is not only binary (intrinsic vs. instrumental value). More studies are needed to explore more thoroughly the specificities of relational values in people working with nature and biodiversity. More studies are needed to explore more thoroughly the specificities of relational values and interests in people working with nature and biodiversity.

Finally, our work insists on the choices that farmers make regarding care. Winegrowers give priority to certain types of preoccupations, and their vines are clearly their first priority. Moreover, the relations they have with nonhumans are not all attentive modes of care relations established in order to keep them alive; farmers choose what and how to care for, or not. This is close to the results of [START_REF] Pitt | Questioning care cultivated through connecting with more-than-human communities[END_REF] who found different types of human to non-human relationships in urban gardens: some were cooperation or care, whereas some nonhumans are considered enemies or strangers [START_REF] Pitt | Questioning care cultivated through connecting with more-than-human communities[END_REF]. Within this perspective, despite responsibility [START_REF] Jonas | Le Principe responsabilité: une éthique pour la civilisation technologique[END_REF] towards nonhumans and environmental issues, our findings suggest that different types of communities (sensu [START_REF] Latour | Moderniser ou écologiser. A la recherche de la Septième Cité[END_REF] may exist in farms. In this theoretical framework, so-called 'ecological communities' are characterized by cooperative relationships with nonhumans and responsibility facing environmental issues, while separation between humans and nonhumans or selection within nonhumans characterize so-called 'modern communities' (Hache, 2011;[START_REF] Latour | Moderniser ou écologiser. A la recherche de la Septième Cité[END_REF]. Our work confirmed the existence of a continuum of human-non human communities in farming systems, and that people might transform their relationships to nonhumans and biodiversity issues, and thus the community they build with non-humans, with time. Cooperation with nonhumans and responsibility facing environmental issues characterize ecological communities, while separation between humans and nonhumans or selection within nonhumans characterize modern communities (Hache, 2011;[START_REF] Latour | Moderniser ou écologiser. A la recherche de la Septième Cité[END_REF].

Caring for vine and for the environment is being transformed with time

We showed that winegrowers can change the way they care for their vines with time, generally after getting used to observing their plots. This changing care comes generally through a decreasing systematic control of pests and diseases, making the winegrowers free of practice standardization that is at the core of the modern agriculture paradigm. In other words, farmers are used to caring for their vines (by trying to protect it), but they can transform how they care for them, especially with less systematic control [START_REF] Stuart | The illusion of control: industrialized agriculture, nature, and food safety[END_REF].

More specifically, we showed that farmers can change their relations with nonhumans. First, observing and experimenting new practices make farmers developing new knowledge, made of attentiveness, responsiveness and adaptation, which characterizes good care [START_REF] Tronto | Moral Boundaries: A Political Argument for an Ethic of Care[END_REF][START_REF] Mol | The Logic of Care: Health and the Problem of Patient Choice[END_REF]. Attentiveness is key to expanding ethical relations with nonhumans [START_REF] Krzywoszynska | Caring for soil life in the Anthropocene: The role of attentiveness in more-than-human ethics[END_REF]. It is learned with the training of self-judgment capacities (see also [START_REF] Deschamps | L'observatoire agricole de la biodiversité[END_REF]. This new type of care, i.e. attentive mode of care, confirms that new types of care come with situated and experiential expertise [START_REF] Enticott | The local universality of veterinary expertise and the geography of animal disease: The local universality of veterinary expertise[END_REF][START_REF] Krzywoszynska | What Farmers Know: Experiential Knowledge and Care in Vine Growing: Experiential knowledge and care in vine growing[END_REF]. Secondly, and consequently, winegrowers can develop new relations to animals and plants that extend from the vineyard, based on observation and contemplation, and not only on economic perspectives. In such dynamics, the number of natural entities that winegrowers care for -in an attentive mode of care-progressively increases and the relations between farmers and nonhumans increase thus in complexity. These results confirm that more attentive ways to take care of environment are tied to sensorial involvements with it [START_REF] Bellacasa | Matters of Care: Speculative Ethics in More than Human Worlds[END_REF]. Here, the consequence of increased complexity within farmers and nonhumans relations is that farmers do not focus on plots only, but focus on edges too. Thus, care is spread from plots to entire vineyard. This allows us to complete our result regarding care spatial logics. We propose the idea of specific and transforming care spatialities, linked to this increase of attentive mode of care. We define care spatialities as the places where farmers take care of nonhumans.

The farming transformations that we highlighted are gradually situated between the modern agriculture frame and the ecologization of practices, i.e. processes of farming transformation toward more and more environmental consideration (see [START_REF] Lamine | La fabrique sociale de l'écologisation de l'agriculture[END_REF]. On one hand, some winegrowers still struggle and control weed and pests with a main focus on useful biodiversity; as a practice change, they mainly reduce or stop use of a part of the chemicals they use (Goulet and Vinck,2012 define it as "removal innovations"), but they keep conventional farming systems (see also the weak ecologization of agriculture, Horling and Marsden, 2011). On the other hand, some winegrowers develop local economies and social links and choose to prevent and care with natural preparations or to co-operate with nonhumans within the socio-ecosystems (see also the strong ecologization of agriculture, Horling and Marsden, 2011).

Caring for nonhumans and for the global environment as part of a collective experience

The fact that being affiliated to several networks shape preoccupations and farming practices was our last main result (see also [START_REF] Mcguire | Reconstructing the good farmer identity: shifts in farmer identities and farm management practices to improve water quality[END_REF][START_REF] Compagnone | Farmers' Professional Dialogue Networks and Dynamics of Change: The Case of ICP and No-Tillage Adoption in Burgundy (France)[END_REF][START_REF] Hillis | Sustainability partnerships and viticulture management in California[END_REF]. Diverse networks and programs carry out diverse visions of farming and of humans and nonhumans interactions.

They tend to develop different affective involvements with nonhumans (as proposed by [START_REF] Bellacasa | Matters of Care: Speculative Ethics in More than Human Worlds[END_REF].

In our research, we highlighted two specific trends in these socially shared visions: fear and protection against some nonhumans qualified as pests (see SVG), or interest for and cooperation with other nonhumans (see Biodiv'eau). We also highlighted two different kinds of relationships between people: hierarchical relations between farmers and so-called experts (technicians or agronomists) that possess the knowledge (SVG); participatory and co-construction of the ecological knowledge between farmers and ecologists (Biodiv'eau).

Thus, collective actions may foster different types of ecological care: care for a selection of non-human entities or environmental issues with agronomic and technical solutions mostly, where agronomists or institutions decide, versus care for broader communities composed of humans and nonhumans facing complex environmental issues with combination of practices not only at the plot scale, where farmers are involved in the choices.

Conclusion and perspectives

In the context of the ecological crisis, the public debate about agriculture often present farmers' choices as dominated by economic and production-centered dimensions [START_REF] Burton | Seeing Through the 'Good Farmer's' Eyes: Towards Developing an Understanding of the Social Symbolic Value of 'Productivist' Behaviour[END_REF][START_REF] Burton | Injecting social psychology theory into conceptualisations of agricultural agency: Towards a post-productivist farmer self-identity?[END_REF]. By studying the transformations in winegrowers' practices and the ways in which their care for nonhumans develops, we shaded the idea that farmers are only driven by economic choices (see also De [START_REF] Rooij | Entrapped in Group Solidarity? Animal Welfare, the Ethical Positions of Farmers and the Difficult Search for Alternatives[END_REF][START_REF] Driessen | Farmers Engaged in Deliberative Practices; An Ethnographic Exploration of the Mosaic of Concerns in Livestock Agriculture[END_REF]. We found that farmers can be preoccupied both by production (vine) and environmental issues (biodiversity conservation). The ways farmers relate to nonhumans go beyond the theoretical opposition between intrinsic and instrumental values towards nature [START_REF] Chan | Opinion: Why protect nature? Rethinking values and the environment[END_REF]. Farmers can simultaneously produce food and be attentive to nonhumans. [START_REF] Bellacasa | Matters of Care: Speculative Ethics in More than Human Worlds[END_REF].

On the other hand, our study highlights the role of networks for biodiversity conservation. Indeed, farming transformations ask for necessary resources. When it comes to knowledge, social resources are crucial. This is why farmer networks, among other elements, are decisive for agroecological transitions, since they contribute to developing farmers' means in order to transform their farming systems. Still, we found that farmer networks (and related environmental programs) promote different visions of farming ecologization (Horling and Marsden, 2011). More research is needed regarding the role of such networks for agroecology transitions at different scales, with a focus on the types of relationships with nonhumans they promote, the type of knowledge they foster and how they contribute to biodiversity conservation.

Fig. 1 .

 1 Fig. 1. Map of Hérault vineyard (France) including the two research areas: Pic Saint Loup and

Fig. 2 .

 2 Fig. 2. Main collective environmental initiatives in study area for viticulture.

Fig. 3 .

 3 Fig. 3. The five steps of Biodiv'eau program.

I

  didn't find myself anymore in what I was doing. I was 45 years old and it took me 5 years more to know, to be sure in myself about what I wanted to do. Therefore, for me, to grow vines is meaningful. My brother-in-law was a co-operator, so he did not make the wine himself. He had taken the family vineyard over in the 1990's, and he was in a receding spiral. He was almost never working, the farm had very bad results. So, in 2011, when I felt ready, I told myself 'this is what I want to do'. So, I proposed him to be his associate, to work hard together and to save what could be saved.(Respondent 6)

  3)4.2.3. Winegrowers develop ways of taking care of biodiversity with more natural vegetation within edgesInterviewed winegrowers used to change first farming practices at plot scale, and then brought their attention to the areas around plots and changed the way they treat edges. Even the farmers who started directly with organic farming took time to improve and evolve (towards biodynamy for example) and to focus on edges. The concerned winegrowers changed the way they treat vineyard edges by (i) letting vegetation coming back more naturally, and (ii) implementing agroecological infrastructures such as hedges or ponds to improve biodiversity outside of the plots:We realized with CNALR that we should mostly do nothing. It has been six or seven years I changed. Before and for a long time I tried to manage just a few, but nature comes back very quickly. Last year I used shredders in order to focus on bushes, stuff that might cause problems.Therefore, we need to open environments in order for biodiversity to appear. But generally, we take just a few actions; we try to keep what grows naturally. (Respondent 4) Each year we try to improve what we do. We first planted hedges, and then trees for auxiliary insects. After this, we created a pound, and we put hedges in again. This year we opened some landscapes.(Respondent 14)

  Biodiv'eau program contributes to transform how winegrowers relate to biodiversity. The program is specifically dedicated to non-productive areas in the vineyards, the edges. Many interviewed winegrowers who had taken part in the program referred to the role of edges' observation practice in changing their way to look at natural areas at the farm scale. Both the self-made inventory of biodiversity, and the final ecological diagnosis proposed by the CNALR, encourage farmers to consider edges as biodiversity areas. Biodiv'eau program offers farmers the possibility to look at vineyards differently than in a strict economic way. It gives them the possibility to interact with plants and other wild species on a contemplative perspective and not on economic perspective only: I became aware of edges and I evolved towards letting more things develop by themselves.Before, I was looking at some areas as wasteland, now I look at them as wildlife habitat.(Respondent 20) Yes, my way to look at edges changed. Now, I know the endemic species from our area, which I totally ignored before. My dad never taught me, maybe someone had taught it to him... There was much more knowledge before, and now I re-appropriate a knowledge. With Biodiv'eau program, I discovered knowledge about nature. Before, I was not able to go into ecstasies in front of a pistachio tree. Now I do. My look is changing. Even for a tree. You understand it, you respect it. (Respondent 4) Moreover, the program contributes to the development of participants' ecological knowledge about biodiversity loss and how farmers can contribute to biodiversity conservation on their farms, in close relation with the benefits in their vines. Thus, the program invites winegrowers to conserve biodiversity in order to cooperate with it.

Table 2

 2 Interviewed winegrowers.

	Respondent	Sex	Type of farming	Type of	Local initiatives	Type of
	number			winery		interview
	1	Male	Conventional	Independent	BE, SVG, TV	Face to face
	2	Male	Conventional	Co-operative	BE, SVG	Phone
	3	Female	Conventional	Independent	CA, TV, BE	Face to face
	4	Male	Organic	Independent	BE	Face to face
	5	Male	Conventional	Independent	CA, BE	Phone
	6	Female	Organic	Independent	BE	Phone
	7	Male	Conventional	Co-operative	BE	Phone
	8	Male	Conversion to	Co-operative	BE	Face to face
			organic
	9	Female	Biodynamic	Independent	BE	Face to face
	10	Male	Conventional	Co-operative	BE	Face to face
	11	Male	Conventional	Co-operative	BE, SVG, TV	Phone
	12	Male	Organic	Independent	BE	Face to face
	13	Male	Biodynamic	Independent	BE	Face to face
	14	Male	Conversion to	Independent	BE, TV, HEV	Face to face
			organic
	15	Male	Conventional	Co-operative	BE	Face to face
	16	Male	Conventional	Co-operative	BE, SVG, DEPHY	Face to face
	17	Male	Organic	Independent	/	Face to face
	18	Male	Conventional	Independent	BE	Phone

Acknowledgments

We thank the members of the Conservatory of Natural Areas of Languedoc-Roussillon who helped us in this work by sharing ideas and offering us the possibility of being in contact with viticulturists in Hérault.

Author statement : Margaux Alarcon : conceptualization, methodology, investigation, formal analysis, writing (original draft).

Anne-Caroline Prévot and Pascal Marty : conceptualization and supervision.

All authors contributed substantially to writing (review and editing).


Youssef Hmamouche

Laurent Prévot

Magalie Ochs

Thierry Chaminade

BrainPredict: a Tool for Predicting and Visualising Local Brain Activity

Keywords: conversation, human-robot interactions, brain activity, predictions

In this paper, we present a tool allowing dynamic prediction and visualization of an individual's local brain activity during a conversation. The prediction module of this tool is based on classifiers trained using a corpus of human-human and human-robot conversations including fMRI recordings. More precisely, the module takes as input behavioral features computed from raw data, mainly the participant and the interlocutor speech but also the participant's visual input and eye movements. The visualisation module shows in real-time the dynamics of brain active areas synchronised with the behavioral raw data. In addition, it shows which integrated behavioral features are used to predict the activity in individual brain areas.

Introduction

Studying human social interactions using functional Magnetic Resonance Imaging (fMRI) has become increasingly popular in recent years. Recently, we recorded a corpus of human-human and human-robot conversations while participants brain activity was recorded with fMRI [START_REF] Rauchbauer | Brain activity during reciprocal social interaction investigated using conversational robots as control condition[END_REF]. This corpus has the potential to bring new insights about how the human brain behaves during natural conversations, and allows comparisons between the cases where the interlocutor is a human or a robot. Unsurprisingly, visualization of brain activity is necessary when dealing with this type of tasks.

In the literature, several projects have been developed to visualize brain activity, and many works have been proposed to predict the fMRI signals based on behavioral features [START_REF] Mitchell | Predicting Human Brain Activity Associated with the Meanings of Nouns[END_REF][START_REF] Huth | Natural speech reveals the semantic maps that tile human cerebral cortex[END_REF][START_REF] Knops | Recruitment of an Area Involved in Eye Movements During Mental Arithmetic[END_REF]. However, we did not find works providing tools for displaying together brain activity prediction of non-controlled conversations, the raw material used in this prediction as well as the features used for these predictions. In this paper, we tackle this problem, and we propose a new tool for predicting and visualising the local brain activity during conversations. It consists of two modules, the first one is for generating predictions based on the conversation's raw behavioral data. It includes the audios of the participant and the interlocutor, the video of the interlocutor and the eye movement recordings of the participant. And the second part is for visualising the activation of the brain areas during the conversation based on the obtained predictions.

The prediction module used is based on classifiers that we trained on a corpus of human-human and human-robot conversations recorded during an fMRI experiment performed on 24 participants in which the brain activity and the behavioral signals were recorded synchronously. This module consists of two processing steps. In the first step, the integrated behavioral time series from the raw signals of the conversation are computed. The idea here is to extract, from raw recordings of the conversation, high level multi-modal descriptive features that will be used as predictor variables of the prediction models. The second processing of the prediction module consists in applying classifiers to predict if a local brain area is active or not, i.e., a binary classification. Two types of conversations are taken into account, human-human and human-robot, thus, depending on the type of the interaction, the prediction module chooses the appropriate prediction models, and selects the relevant behavioral features.

The second part of our tool allows the visualization of the brain activity predictions during the conversation using two forms, the first one as a time series showing the evolution of the brain areas activation over time, and the second one showing the activation of the studied areas in the brain directly using the Visbrain Python library [START_REF] Combrisson | Visbrain: A multi-purpose gpu-accelerated open-source suite for multimodal brain data visualization[END_REF].

In this paper, we focus on the global functioning and the visualisation part of the tool since the prediction module is "plug and play" and can be updated depending on the task. We propose to illustrate the tool on a specific example of individual brain area prediction depending on behavioral cues during human-human or human-robot interactions.

The rest of the paper is organized as follows. The next section contains a description of the multi-modal behavioral signals used by the tool, and a description of the extracted time series used as predictive features of the prediction models. In Section 3, we describe the functioning, the inputs and the outputs of the proposed tool. We illustrate its use in Section 4. And Section 5 is for conclusions and future work.

Datasets

As mentioned in Section 1, the proposed tool extracts multimodal time series from the conversation signals before applying prediction models. These models are trained on conversational data and neurophysiological signals recorded during a fMRI experiment (see Figure 1). In this section, we describe this experiment, the recorded raw signals, and the extracted time series.

Processing fMRI signals

Standard functional MRI acquisition procedures were used, described in details in [START_REF] Rauchbauer | Brain activity during reciprocal social interaction investigated using conversational robots as control condition[END_REF]. Blood Oxygen-Level Dependent (BOLD) signal 3-dimensional images are recorded in the whole brain every 1.205 seconds. Standard SPM12 preprocessing procedures are used [START_REF] Penny | Statistical Parametric Map-ping: The Analysis of Functional Brain Images[END_REF], including correction for time delays in slice acquisition ("slice timing"), image realignment, magnetic field inhomogeneities correction, normalization to the standard MNI space using the DARTEL procedure for coregistration of individual participants' anatomy [START_REF] Ashburner | A fast diffeomorphic image registration algorithm[END_REF], and finally spatial smoothing with a 5-mm full-width half-maximum 3-dimensional Gaussian kernel. Extraction of the BOLD raw time series in regions of interest is performed using the conn toolbox (Whitfield-Gabrieli and Nieto-Castanon, 2012), and includes several denoising procedures, firstly a linear detrending using a high-pass filter with a threshold of 128 seconds, secondly using realignment parameters to calculate nuisance regressors related to participants' movement during scanning, thirdly taking heartbeat and breathing recordings to remove physiological artifacts with the PhysIO toolbox [START_REF] Kasper | The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data[END_REF], and finally extracting BOLD signal in the white matter and cerebrospinal fluid and using the 5 first eigenvariates of the time-series as nuisance representing signal fluctuations in non-cortical brain tissues. A parcellation based on functional and anatomical connectivity patterns [START_REF] Fan | The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture[END_REF] defines 275 regions of interest (ROIs) for the whole brain, and specific regions are chosen based on their anatomical locations and known functions. Continuous time-series (385 time points) are extracted for each ROI, and each session and participant, representing the mean activity after denoising.

For the current demonstration, we focus on 6 ROIs chosen in order to validate our approach using well-defined functional areas: the left and right Fusiform Gyrus ROIs corresponds to the Fusiform Face Area involved in face perception, the left and right Motor Cortex ROIs support speech production, and the left and right Superior Temporal Sulcus ROIs which are involved in speech perception, In the rest of this paper, we use the following abbreviations of the names of the ROIs:

• Motor Cortex: MC • Superior Temporal Sulcus: STS • Fusiform Gyrus: FG

Processing multimodal behavioral signals

We describe in this part how we analysed the multimodal behavioral signals in order to extract time series that can be used as input features for prediction models. The speeches of the conversations are first transcribed into text, then we annotated and segmented them word-by-word automatically using SPPAS (Bigi, 2015). From the obtained transcriptions, we extracted linguistic time series that consist of the speech activity (the presence of the speech), overlaps, laughters, filled pauses and the reaction time, which represents in our case the amount of time taken by an interlocutor to speak after the other interlocutor finishes his turn [START_REF] Rauchbauer | Multimodal corpus of bidirectional conversation of human-human and human-robot interaction during fmri scanning[END_REF]. We also focused on interpersonal particles items, i.e., words that may express the mood of the speaker (e.g., but, well, maybe), discourses markers, which are expressions used to make the discourse organized (e.g., I mean, so, therefore, okay) [START_REF] Schiffrin | Discourse markers[END_REF], and feedback lexical items, which are words representing reaction and perception (e.g., yes, no, okay, right) [START_REF] Gravano | Affirmative cue words in task-oriented dialogue[END_REF]. The time series describing these items are resampled according to the fMRI acquisition frequency by calculating the percentage of their existence in each 1.205s time bin. We also included lexical richness based on two metrics from [START_REF] Ochs | Toward an automatic prediction of the sense of presence in virtual reality environment[END_REF], that consider the number of the different words (type-token ratio) and the number of adjectives plus the number of adverbs resp., divided by the number of total words in the text of each speaking turn. Sentiment analysis is also considered by calculating the polarity and the subjectivity using the Pattern library [START_REF] Smedt | Pattern for python[END_REF]. The polarity score fluctuates between -1 (negative behavior) and 1 (positive behavior). The subjectivity is between 0 (objective) and 1 (personal). The method of their calculations is based first on a manual association of the polarity and the subjectivity scores to a set of adjectives among the most used. Second, another set is extracted with the most frequent nouns and the preceding adjectives as features. Finally, a kNN classifier is learned to determine the scores of neighbor adjectives to those manually annotated [START_REF] Smedt | Pattern for python[END_REF].

For facial features, in the experiment we recorded the videos of the interlocutors. The Openface [START_REF] Baltrusaitis | OpenFace 2.0[END_REF] software is used to detect facial landmark coordinates (based on the pixel coordinate system), head pose translations and rotations, and facial action units (Bartlett et al., 1996), which represent the existence and the intensity of movement of well-defined facial expressions based on the Facial Action Coding System [START_REF] Ekman | Measuring facial movement[END_REF]. The time series associated with these features are constructed by analyzing each image of the videos. Eyetracking data of the participants are recorded using the "EyeLink 1000 Plus" system (SR Research, 2019) during the fMRI experiment. The raw data are recorded in European Data Format files with a frequency of 1000HZ. They contain the coordinates of the gaze on the screen using the pixel coordinate system, and other information like the beginning and end of conversations, fixations, saccades, and blinks. From the obtained coordinates, we compute the speed of the gaze movements, and we extract ocular saccades and blinks as separate features. Then, we combine the gaze movements coordinates with Openface output, more precisely the landmarks, to detect where the sub- ject is looking in at each time step (face, eyes, mouth). Finally, the computed behavioral features are re-sampled and gathered together to build multivariate time series with the same number of observations for each subject.

Description of the tool

The tool is composed of two main separate modules: a machine learning part, dealing with computing features and prediction, and a visualisation part. It is designed in a modular way, allowing fast update in case new prediction models have to be added or new features have to be extracted. The interface of the tool is developed using Qt Creator with C++ (The Qt Company, 2019), while the prediction module is implemented in Python.1

Inputs

The inputs of the tool are raw recordings of a conversation between a participant and an interlocutor. The interlocutor can be a human or a robot. The recordings consist of the video of the interlocutor, a file containing the eye movement coordinates of the participant, and the audios of both the participant and the interlocutor. The video of the partic- ipant is not used, as the current experimental setup didn't allow its recording inside the MRI scanner.

Outputs

The outputs consist of a time series format of the predictions, showing the evolution of brain area activation during the conversation. Time is in abscissa, while the ordinate shows the binary 0 for inactive and 1 for active. A brain visualisation is also provided using the Visbrain library [START_REF] Combrisson | Visbrain: A multi-purpose gpu-accelerated open-source suite for multimodal brain data visualization[END_REF]) by constructing in real-time a video where each image contains a visualisation of the predicted areas at the corresponding time of the conversation. The color code matches the time-series predictions and the saturation of the color matches the activity (low for inactive, high for active). The tool also shows the names of the integrated behavioral time series used for the prediction of the selected brain area. They are used by the tool, but they are also recorded in an external file as well as the obtained predictions, which can be useful to the user for further investigations.

Functioning

The goal is to visualise the brain activity of specific brain areas based on previous values of the extracted behavioral features from the conversation. This tool is specific to predictions of the BOLD signal following the fMRI acquisition protocol used in the training step, where a whole brain image is recorded every 1.205 seconds. We detail in the rest of this part the prediction procedure used by the tool, including feature extraction, and the way the used classifiers are evaluated and trained.

Features extraction

Feature extraction is performed using the same approach applied in the data processing step. Subsection 2.2. describes this approach including the tools used to compute the features from raw signals. Knowing that the BOLD signal response to a behavioral event reaches its peak around 5 seconds after the event [START_REF] Gössl | Bayesian modeling of the hemodynamic response function in bold Figure 5: Illustration of the output of the BrainPredict tool[END_REF], we compute each prediction based on the values of behavioral features in the previous 5 seconds. This delay (5s) is not fix for all ROIs and participants. Therefore, our approach consists in considering more points near to this delay, i.e., we model the BOLD signal at time t based on sequences of lagged variables of each feature between t -7s and t -3s. This model has an auto-regressive form, and can be written as follows:

Y (t) = f (X 1 (t-τ 1 : t-τ 2 ), . . . , X k (t-τ 1 : t-τ 2 ))+U (t),
(1) where f is the function between the BOLD variable and the behavioral features that we aim to determine, Y (t) represents the discretized BOLD variable, {X 1 , . . . , X k } are k behavioral features, X i (t -τ 1 : t -τ 2 ) is a sequence of temporal variables of the feature X i where τ 1 = 7s and τ 2 = 3s, and U (t) represents the error of the model. This problem can be modeled using binary prediction. The classifiers used are from the Scikit-learn library [START_REF] Pedregosa | Scikit-learn: Machine Learning in Python[END_REF]: Support-Vector Machine (SVM), Random Forest (RF), and the Logistic Regression (LREG). We also used the Long Short Term Memory (LSTM) network from the Tensorflow library [START_REF] Abadi | Ten-sorFlow: A System for Large-Scale Machine Learning[END_REF], since the predictive variables are in form of sequences of original behavioral features. We also included a baseline classifier generating random predictions based on 3 strategies: a stratified way, by generating predictions regarding the distribution of the training data, a uniform way by generating random predictions uniformly, and a last strategy that consists in generating constant predictions based on the most frequent label.

The evaluation procedure: Two evaluations preformed independently, according to the type of the conversations where the interlocutor is a human or a robot. In total, for each condition, we have the data of 24 participants, which consists of 13248 observations, where the predictive variables represent the temporal variables associated to the behavioral features, and the target variables are the discretized BOLD signal of 6 ROIs: left and right Motor Cortex (MC), Left and right Superior Temporal Sulcus (STS), and left and right Fusiform Gyrus (FG).

The data of all subjects are concatenated in order to have generic prediction models independent from participants. One particular remark that we should mention is that when handling multi-subjects tasks, it is important not to use the data of a given subject in the training and test sets, even with different observations, because this may cause over-fitting and inflate predictions. Therefore, we kept the data of 4 participants as test set, which is almost 17% of all data. And data from the rest of participants (20) are used are as a training set. To evaluate the models, we considered 3 classification measures, the weighted recall, precision and F-score. We concentrate here on the weighted F-score since it considers the recall and the precision. Moreover, it is more preferable than the accuracy for imbalanced data, because it takes into account the frequencies of both classes. A 10-fold-cross-validation is applied on the training data to find the best parameters of each classifier with a feature selection step to find the most relevant predictive variables for each ROI, where each time, the data of 2 participants are used as a validation set. Then, the best parameters of each classifier are chosen based on the mean of the obtained 10 F-scores. The 10-fold-cross-validation is applied on all classifiers except the LSTM network because it takes a huge amount of time. For this specific model, we ap-plied a training-test pass directly with a fixed architecture composed of one LSTM hidden layer and a fully connected output layer containing one neuron to provide one prediction each time using the sigmoid activation function. The network is trained using the stochastic gradient descent algorithm and uses the binary cross-entropy as a loss function. Finally, the classifiers with the best parameters found in the 10-fold-cross-validation are trained on the training set, then evaluated on test data.

The results: Figure 3 provides results for the baseline and the classifiers for each brain area, for both conditions, i.e., human-human and human-machine conversations. The values represent the obtained scores of the test data set, while the error bars represent the standard deviations of the 10-fold-cross-validation results. The results show that globally the Random Forrest is almost the best or very close to the best classifier for all ROIs. The LSTM network does not provide good predictions for some ROIs. This is logical because generally artificial neural networks need many examples to learn, while in our case we still do not have a lot of observations considering the fMRI recording frequency. The baseline classifier provides F-scores between 0.5 and 0.55, while the best F-scores vary between 0.65 and 0.75. Logically, it is hard to get scores close to 1, because the ROIs activation's depends on other factors that are not recorded during the fMRI experiment, like the personality of each participant for example. But what is important is that we can detect the most relevant set of behavioral factors that allows predicting each brain area. We have also conducted a statistical test using the Student's T-test to test the equality (null hypothesis) of the means of the F-scores between the best and the baseline classifiers obtained by the 10-fold-cross-validation on the training data. This statistical test is investigated in [START_REF] Dietterich | Approximate statistical tests for comparing supervised classification learning algorithms[END_REF] and it is one of recommended methods to compare the performance of machine learning algorithms. For the hardest ROI for which we got the worst predictions, which is the right FG, the test provides a p-value (the probability of the null hypothesis) equal to 0.0005 for human-human, and 0.003 for human-robot conditions. Therefore, the obtained F-scores of the best algorithms are significantly better than the baseline classifier at threshold of statistical significance less that 0.01.

In the utilization part, based on the obtained F-scores, we select the best classifier and the best set of behavioral predictors for each brain area that will be used by the prediction module.

Example

Let's illustrate the outputs of the BrainPredict tool using the example provided in Figure 5. In this example, the inputs are the visual input of the interlocutor (the video of the robotic conversational agent in its feminine version in this case, shown in the "interlocutor" window) and the audio from both the scanned participant and the interlocutor. The regions of interest are selected for the current proofof-concept as they correspond to brain areas involved in speech production (left and right MC), speech perception (left and right STS), and face perception (left and right FG). The time series are displayed in the order given in the top left selection window, and the colour corresponds to the brain areas colors displayed on the left. As the sampling rate of the fMRI is one volume every 1.205 seconds, we have 50 observations for a conversation of 1 min. The illustration shows the predictions until approximately the time 45s of the conversation, when it is predicted that both the left and the right ventral primary motor cortex will not be activated, which is shown in grey in the first line in the window "Time series" and on the brain render (top, right). For the FG, only the left area that is activated and shown in green on the bottom view of the brain render. Similarly, the left STS is predicted to be activated but not the right STS (pink and cyan lines). The obtained predictor features of the selected brain areas are stored in a csv file, but can be viewed in a separate table via the button "Show Predictors". An illustration of this table is shown in Figure 4, where the features are organized by modalities: speech (speech features of the interlocutor), speech left (speech features of the participant), and facial features. For each brain region, each modality contains a subset from its behavioral features (see Table 1). For example, the IPU represents the speech activity, the AUi are the facial action units, and {pose Rx, pose Ry, pose Rz} are the head rotation coordinates.

Conclusion

In this paper, we have proposed a new tool for the prediction and visualisation of local brain activity. The raw data recorded during the fMRI experiment investigating natural human-human and human-robot conversations are the primary sources of the prediction module of this tool. Prediction entails calculating integrated behavioral time-series on the basis of these raw recordings. The tool also contains a visualisation module allowing to see the brain activity unfold during a given conversation. We have developed this project with the goal to provide a machine learning tool that can be used by social cognitive neuroscience researchers to analyse the dependencies between behavioral features and local brain areas, and to study the differences between human-human and humanrobot interactions during unconstrained interactions. In the future, we aim to include the possibility to visualize areas from the whole brain, as well as extracting more behavioral time series as predictors to improve the prediction models.

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Figure 1 :

 1 Figure 1: The fMRI experiment.

Figure 2 :

 2 Figure 2: Schema of the BrainPredict tool.

Figure 3 :

 3 Figure 3: A comparison between the performance of the evaluated prediction models based on the F-score for humanhuman (a) and human-robot (b) conditions.

Figure 4 :

 4 Figure 4: Example of the predictor features output

Table 1 :

 1 Concretely, Table1contains the names of all the extracted behavioral features. The extracted behavioral features.

	Raw signals	Extracted Features
	Speech	Speech activity, Particles items, Dis-
		course Markers, Overlap, Reaction
		Time, Filled-breaks, Feedbacks,
		Laughters, Lexical Richness, Polarity,
		Subjectivity.
	Video	Facial Action Units, Head pose coor-
		dinates, Gaze coordinates.
	Eyetracking data Coordinates of the gaze movement of
		the participant, and binary variables
		categorizing resp. the presence of sac-
		cades, and if the participant is looking
		at the face, the eyes of the mouth of
		the interlocutor.
	3.3.2. Prediction
	Since we focus on predicting if a brain area is active or
	not, we first discretize the BOLD signal in each ROI into a
	binary variable. The BOLD time series of each participant
	are normalized, then a threshold for each ROI is used for
	discretization. Different thresholds are tested, and the most
	appropriate discretization threshold in terms of prediction
	scores is used for each ROI .

  Fu si fo rm G yr us Le ft M ot or Le ft ST S Ri gh tF us ifo rm G yr us Ri gh tM ot or Ri gh tS TS

	F-score	0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00	baseline LREG	LSTM	RF	SVM
		Brain areas
		(a)
	F-score	0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00	baseline LREG	LSTM	RF	SVM
		Brain areas
		(b)

Le ft Fu si fo rm G yr us Le ft M ot or Le ft ST S Ri gh tF us ifo rm G yr us Ri gh tM ot or Ri gh tS TS

The BrainPredict tool is still undergoing development, but a first version is provided in https://github.com/ Hmamouche/BrainPredict, including the code source of the prediction module and the interface, the detailed prediction results, and a demo showing its utilization.