# Experience and trust to handle uncertainty of biodiversity responses to innovative agricultural practices: Lessons from citizen science on farms

Olivier Billaud, Emmanuelle Porcher, Etienne Maclouf

► To cite this version:

Olivier Billaud, Emmanuelle Porcher, Etienne Maclouf. Experience and trust to handle uncertainty of biodiversity responses to innovative agricultural practices: Lessons from citizen science on farms. People and Nature, 2025, 7 (3), pp.611-630. 10.1002/pan3.10778. mnhn-04991419

HAL Id: mnhn-04991419

https://mnhn.hal.science/mnhn-04991419v1

Submitted on 22 May 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
1 **Experience and trust to handle uncertainty of biodiversity responses to innovative agricultural practices: lessons from citizen science on farms.**

2

3
4 **Authors:** Olivier Billaud¹*, Emmanuelle Porcher¹, Etienne Maclouf¹,²

5

6 **Author details:**

7 ¹Centre d'Ecologie et des Sciences de la Conservation (CESCO), Muséum national d'Histoire naturelle, Centre National de la Recherche Scientifique, Sorbonne Université, Paris, France

8

9 ²Laboratoire de recherche en sciences de gestion Panthéon-Assas (LARGEPA EA3386), Université Paris Panthéon-Assas, 1, rue Guy de La Brosse, 75005 Paris

10

11

12 **Correspondence to :**

13 *Olivier Billaud, Centre d'Ecologie et des Sciences de la Conservation (CESCO), Muséum national d'Histoire naturelle, 43 rue Buffon CP 61 - 75005 Paris, +33-(0)677788707, olivier.billaud@ecomail.fr

13

14

15
16

**Abstract**

17

18

19

20

21

22

23

24

25

26

27

1. As for any innovation, integrating farmland biodiversity into agricultural practices is a complex process, leading to decisions made under uncertainty, due to the difficulty of foreseeing the consequences of decisions. This uncertainty comes under many different forms, depending among others on the type of environmental innovation in agriculture, in a continuum from Efficiency/Substitution-Based Agriculture (ESBA) to Biodiversity-Based Agriculture (BBA). To handle this uncertainty, new knowledge production tools, some of which rely on citizen science on farms (CSOF), are developed. They aim to support decision-making by collecting large databases but also by enabling knowledge production "on the field", directly by farmers. Yet, the ability of CSOF to actually meet these challenges has little been studied. It is now necessary to understand how new biodiversity knowledge production tools are designed and used.

28

29

30

31

32

33

34

35

2. Using a user-centred approach, we analysed a CSOF monitoring scheme, the Farmland Biodiversity Observatory (FBO), wherein farmers monitored biodiversity in their fields (3558 fields, from 2011 to 2023, still ongoing). As insiders in the research group, and using an exploratory approach based on 32 interviews, we observed an operational limitation of the scientific knowledge produced, which fails at producing technical recommendations to protect biodiversity locally. While this led to a difficulty to offer prescriptive practices, some participants shifted to non-prescriptive knowledge, i.e. they accepted high uncertainty and used FBO to gain knowledge and navigate through this uncertainty.

36

37

38

39

40

3. As a theoretical result, we witnessed that shifting to high uncertainty due to the complexity of ecological processes consists in accepting a leap into the unknown. Farmers must trust the action of biodiversity, making themselves "vulnerable" with respect to the possible responses of the ecosystems to a change in farming practices. We identified several factors likely to favour this trust towards biodiversity.

41

42

43

44

45

4. As a practical outcome, our results show that, to work with biodiversity, farmers must move from reduction and control of uncertainty to acceptance and reliance on trust towards biodiversity. The ensuing management recommendation is to favour a clinical and contextual production of knowledge, emerging from individual experience, over a controlled, standardised and statistical production, inherited from industrialization.

46

47

**Keywords**

48
49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

Citizen science on farms, Uncertainty, Trust towards biodiversity, Environmental transition of agriculture, Experiential knowledge, Environmental innovation, AKIS players, Learning tool.

# 1 | Introduction

In the face of complex challenges, numerous innovative technical decisions<fnref n="1" /> must be made under conditions of uncertainty, i.e. without the benefit of reliable knowledge about the potential outcomes. This is particularly true in agriculture, where farmers and advisers must develop innovative ways of farming when faced with the social and environmental damages (Merino et al., 2019) of industrial agriculture<fnref n="2" />. Decisions under uncertainty, i.e. the way farmers select techniques from several options in the context of environmental innovation, often involve farmland biodiversity, a key but complex element that new farming practices struggle to incorporate. Although technical choices are expected to be driven by the ability to foresee consequences, decisions regarding biodiversity can generally not count on reliable forecasts of future states. Horlings & Marsden (2011) thus distinguished two forms of environmental innovation in agriculture. On the one hand, "Efficiency/substitution-based agriculture" (ESBA, Duru et al., 2015), in line with the dominant agro-industrial regime, is based on sophisticated machines, biotechnologies and large scale engineered processes, and aims to increase resource use efficiency in order to decrease the financial costs and environmental impacts of agriculture (Horlings & Marsden, 2011). ESBA relies on so-called best practices, recycling, genetic improvement, substitution by biological inputs and "precision-agriculture" technologies. The corresponding changes in practices are usually justified as solutions to comply with environmental regulations (Duru & Therond, 2015). Innovators are mainly entrepreneurs and investors (startups, farmtechs) along with researchers and agricultural advisers providing top-down recommendations. On the other hand, "biodiversity-based agriculture" (BBA, Duru et al., 2015), also known as "ecologically intensive agriculture" (Kremen & Miles, 2012) or "eco-functional intensification" (Levidow et al., 2012), aims to create a disruption with the existing industrial model, by enhancing the ecosystem services provided by agrobiodiversity, from farm to landscape level. BBA seeks to

<footnote n="1">¹ We refer here to Savage's general definition of decision and its consequences (Savage, 1972): "To say that a decision is to be made is to say that one of two or more acts is to be chosen, or decided on. In deciding on an act, an account must be taken of the possible states of the world, and also of the consequences implicit in each act for each possible state of the world. A consequence is anything that may happen to the person." (p.31).</footnote>

<footnote n="2">² We adapt the definition of the main characteristics of industrial organisation by Aron (1967): significant capitalization is required to acquire modern farming techniques (chemicals, machines, biotechs...), financialization (investments need to be repaid, and need to generate sufficient profitability to acquire new plants when previous ones are obsolete), justify rationalisation and scales economies; standardisation reduces large-scale unit costs and also leads to new labour divisions with new players: scientists, experts and engineers, farmers being seen as technique applicators, and therefore not entirely free to interact with biodiversity.</footnote>
76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

diversify farming systems to increase resilience and provision of ecosystem services, with the aim to
reduce anthropogenic inputs (see Duru, Therond, & Fares (2015) for a review around BBA and the
challenges to implement it). In this article we will refer broadly to the integration of biodiversity into
farming practices as any kind of practices that intentionally involve the action of biodiversity into the
decision making.

Beyond the issue of technical choices in agriculture, uncertainty broadly refers to a “situation in which
there is not a unique and complete understanding of the system to be managed” (Brugnach et al.,
2008). Three kind of uncertainties are identified in the literature: epistemic uncertainty refers to the
imperfection or a lack of knowledge about the system; ontological (or radical) is related to the
unpredictability of a complex system; and ambiguity (or ethical uncertainty) results from the
simultaneous presence of multiple reference frameworks (different worldviews, values,
interpretations, etc.) for the system (Barnaud et al., 2023; Brugnach et al., 2008; Walker et al., 2003).
However, Brugnach et al. (2008) suggested that uncertainty derives from the relationship between an
individual and the system of interest. Hence uncertainty must not be understood in isolation as a
property of a system, but must include human experience in its definition. Any characterization of
uncertainty should acknowledge the possibility of different ways to give meaning to an uncertain
situation and to act.

Starting from this definition of uncertainty it is important to realise that the two extremes of
environmental innovation of agriculture have distinct relationships with, and face different forms of,
uncertainty. Born out of modern agronomy, which sought to control variation in living organisms
through tillage, genetics and chemical inputs (Cohen, 2017), ESBA is associated with fields that are
highly artificialized and standardised. ESBA generally also relies on evidence-based innovations that
farmers just need to adopt. These innovations often follow the large-scale standardisation of industrial
agriculture, hence restricting biodiversity to a limited number of factors that can be modelled and
whose reactions (i.e. the *consequences of decisions*) can be controlled. Hence, ESBA lowers the
uncertainty linked to the responses of biodiversity and to the supply of ecosystem services and faces
more uncertainty associated with the development of new technological solutions. Moreover, while
controlling variation at local scale, ESBA leads to uncertainties that go beyond the farm or regional
scale, such as increasing climate change, reliance on input supply and agro-industrial regime
complexity, etc. In contrast to ESBA, BBA uses ecosystem services that rely on ecological processes
involving complex networks. These complex adaptive systems are composed of multiple
interconnected components, associated with emergent properties and non-linear processes that are
incompletely understood (Anand et al., 2010). Moreover, some patterns may be detectable only at specific functional levels, spatial or temporal scales (Giampetro, 2002). For instance, Haan et al. (2020) showed that landscape configuration effects on agricultural pest suppression depended on the interaction between the grain of landscape structure and arthropod dispersal range (forcing predictions to partition species by traits), but also on indirect effects revealed while examining multitrophic interactions. Furthermore, long-term effects of agricultural practices on biodiversity may also exhibit threshold patterns that are challenging to assess precisely. Finally, uncertainty is also associated with transition from industrial agriculture to BBA, which implies drastic changes in several features of agroecosystems. Ecological processes must recover before supporting e.g. soil fertility and biological regulations. During the transition, ecosystem services may vary significantly, threatening the farm's economic stability. Hence uncertainties (both epistemic and ontological) faced by BBA and coming from biodiversity and its supplies of ecosystem services are stronger. Finally, as most farmers are positioned on a gradient of perception of environmental innovation of agriculture, they are likely to have different worldviews and frameworks for interpreting a given situation, creating more ambiguity around it.

While the need for environmental innovation in agriculture requires intense knowledge production, the associated uncertainty challenges the actual knowledge production system, which therefore needs to be revisited. First, the type of knowledge produced, which is prescriptive and production-oriented, is contested (Girard, 2014). While this knowledge supports a rationalisation and optimisation project made possible by the simplification of complex agroecosystems systems into monodisciplinary objects (Chevassus-au-Louis, 2006), the diversity, variety and unpredictability of the agroecosystems imply accepting a certain amount of risk and suboptimal functioning (Girard, 2014). At the same time, a body of research brings forward the local and informal knowledge of farmers, complementary to the formal scientific knowledge (see for instance (IAASTD, 2008; Ingram, 2008; Šūmane et al., 2018). Local and informal knowledge can be defined as “know-how, practices and skills, developed and sustained over time on the basis of local people’s experiences in their environmental and socio-economic realities” (Beckford & Barker, 2007). Informal farmers’ knowledge is linked to practical experiences on the farm, tends to be adaptive, dynamic and holistic, considering local systems as a whole and seeking locally specific solutions (Šūmane et al., 2018). Secondly, the top-down knowledge production process, where researchers produce knowledge that farmers can apply after transmission by advisers, is challenged. Thus the European Union’s Standing Committee on Agricultural Research stated a “gradual contestation of linear approaches to knowledge transfer and towards a more complex and network-like vision of knowledge, learning and innovation” (p24, EU
SCAR, 2012). Thus knowledge networks and the players of the Agricultural Knowledge Information Systems (AKIS) are also challenged. Moreddu & Poppe (2013) observed that AKIS are evolving from traditional, linear models to more interactive and flexible systems that aim to enhance the adoption of innovation and strengthen the competitiveness of the agri-food sector. Coquil et al. (2018) showed that the increasing expressions of local particularities and farm uniqueness challenge the prevalence of scientific expertise over action. A second difficulty comes from the need to make choices that cannot be based completely on facts, due to the complexity to anticipate consequences of reality and the emergency associated with many farming-related decisions. Thus, AKIS players should no longer be mere prescribers of solutions but instead potential contributors to the development of farmer experience, assisting farmers in their own discovery of their agroecosystems.

Accompanying these changes in the knowledge production process, new knowledge production tools are created. An appreciable fraction of them originate from citizen science (see Ebitu et al., 2021; Mourad et al., 2020; Van de Gevel et al., 2020 for literature reviews about citizen science for sustainable agriculture), such as citizen science on farms (CSOF)). There is no universal definition of citizen science embedding the full plurality of practices. Its specificity could be defined as “the claim that amateurs (general public) can contribute to the production of scientific knowledge, with education as an associated goal or a by-product” (Strasser et al., 2019). Regarding agriculture research, CSOF involves new elements, based on new digital technologies, that were absent in former participatory research in agriculture (Van de Gevel et al., 2020). They aim to help handling uncertainty by collecting large databases supporting a more efficient and effective scientific knowledge (see Ruck et al. (2024) for a review of approaches) but also by enabling knowledge production “on the field”, directly by farmers, thus supporting learning from participants (Couvet & Prevot, 2015; Minet et al., 2017; Ryan et al., 2018). Hence, this growing methodology claims to reconcile formal research with informal knowledge, thanks to studies based on farmer's observations and to knowledge production directly on the farms. However, as any decision-support tools, CSOF will face the uncertainty inherent in the new knowledge-action relationship and in the difficulty to foresee consequences of decisions about biodiversity. Furthermore, CSOF relies both on farmers and on AKIS players for data collection, analysis and interpretation. It therefore also faces the transformation of both of their roles in the knowledge production process. However, in general the processes of these new tools to produce knowledge and make it manageable for farmers and AKIS players are still poorly understood. For instance managerial and political implications of these new knowledge production tools, their rationalisation and performance criteria (i.e. the relationships between knowledge and action induced) are seldom analysed in depth (Girard, 2014).
To understand the ability of CSOF to actually meet the challenges listed above, we analysed a nationwide CSOF scheme, the French Farmland Biodiversity Observatory<fnref n="3" /> (FBO), wherein farmers monitored biodiversity in their fields in order to better understand relationships between farmland biodiversity and their practices. The originality of FBO lies in its main subject, farmland biodiversity, and its participants, farmers and AKIS players. FBO aims to amplify the key role of farmers in preserving and restoring farmland biodiversity and is designed to encourage and support voluntary change on farms involved in the scheme. Furthermore, FBO has been supported by the French ministry of Agriculture since its launch in 2011; the ministry helped to expand the scheme nationwide and to reach a diversity of organisations working in agriculture, leading to a diversity of farmers involved. This longevity, together with the diversity of participants, offers a wide variety of contrasting situations to explore. Last but not least, FBO offers two different scales of knowledge production: nationwide at the request of the Ministry, and local for farmers and advisers. Hence, we specifically investigated (1) the types of knowledge produced by FBO, formal and prescriptive vs. informal and experiential knowledge, and their uses (2) the changes in perception of the agronomic role of biodiversity and the associated ecosystem services, and the ensuing uncertainties, fueled by FBO. To this end, our methodology focused on FBO users, mainly farmers and local advisors. We aimed (1) to grasp the potential and effective benefits of CSOF, through uses, expectations and knowledge and (2) to capture the full diversity of uses of FBO, both intended and unintended.

<footnote n="3">³In French: Observatoire Agricole de la Biodiversité</footnote>
197 2 | Materials and methods

198

199 2.1 | Citizen science on farms to monitor farmland biodiversity

200

201 FBO was launched in 2011 with two objectives: to produce massive data, enabling the study of long-
202 term temporal trends of farmland biodiversity and of their relationships with farming practices (see
203 Billaud et al., 2020; Bimonte et al., 2021; Van der Meersch et al., 2021) ; to raise awareness of local
204 stakeholders and bring new knowledge for farmers willing to lower the impacts of their practices on
205 biodiversity. FBO focuses on five taxonomic groups chosen for their interactions with agriculture :
206 earthworms (soil fertility, Lemtiri et al., 2014) ; aboveground invertebrates (pests and predators,
207 Kromp (1999) ; Symondson et al. (2002)) including a simple identification of beetles and mollusks
208 and a counting of centipedes, ants, woodlice and spiders ; solitary bees (pollination services, Potts et
209 al., 2016), bats (pest predators, Kunz et al., 2011) and butterflies (sensitive to changes in land use at
210 the landscape scale, Dover & Settele, 2009). Monitoring protocols are simple, yet standardised. Two
211 yearly training sessions are organised in different regions of France. They are mostly attended by
212 local advisers who would like to start a local network. A website<fnref n="4" /> gives access to various materials
213 including keys to identify individuals to either functional groups or taxonomic rank (genus or
214 sometimes species level), protocol information sheets, yearly reports, scientific results and
215 communication tools. Participants can upload their data on this website or more recently through a
216 mobile phone application. Various pieces of information on farming practices are requested upon data
217 uploading, even though only a few are mandatory. Since 2021, regular webinars on farmland
218 biodiversity have been organised (see FBO website).

219

220 While initiated by the French ministry of Agriculture and animated by the National Museum of
221 Natural History, FBO relies on local agricultural development organisations to recruit farmers, to
222 assist monitoring and to return feedback about observations to participants locally. Local
223 organisations are for instance the “chambers of agriculture” (public structures representing all
224 strategic players in agriculture), departmental hunting federations, environmental associations,
225 association for agricultural development, etc., depending on the local context. This diversity in local
226 animation produces diversity in the profiles of farmers involved in the scheme. Participation is
227 voluntary and is not rewarded financially. Each farmer chooses which taxonomic group(s) will be
228 monitored on their farm, even though the person collecting the data is not always the farmer :

<footnote n="4">⁴ https://www.observatoire-agricole-biodiversite.fr/</footnote>
sometimes a trainee or an employee of the local organisation does the field work. Thus, FBO
participants are diverse, in terms of profession or organisation. We benefited from this diversity by
choosing an exploratory approach, which is comprehensive and non-representative, prior to future
studies to quantify or identify causal explanations. Hence, we sought to meet and understand
contrasting situations and uses of FBO in order to explore various aspects of FBO and of the link
between uncertainty and knowledge.

235

236 **2.2** | **Data collection**

237

238 2.2.1 | Research position and data collection

239

240 Both the 1st and 2nd authors had a particular relationship with FBO, being part of the scientific team
241 dedicated to data analysis. The 3rd author was also familiar with the context, as a member of the
242 research team and a specialist in organisational studies, but was primarily an observer, external to the
243 operational management of FBO. The three authors think that modern agriculture needs deep changes
244 to address ecological challenges and that more space should be given to local and informal
245 knowledge. The authors recognised the uncertainty around the potential new model(s) of agriculture
246 and aimed to find insights to better understand what the latter could be. Social data and analysis came
247 from two complementary methods : i) a "participatory-research", in which the 1st and 2nd authors
248 were knowledge producers inside FBO (Billaud et al., 2020; Van der Meersch et al., 2021), seeking
249 self-reflection and reflexivity on their data analysis methodology (i.e. find patterns and relationships
250 in the FBO database), allowing them to understand the potentials or limitations of the data ; ii) with
251 theoretical and methodological support from the 3rd author in the field of organisational studies
252 applied to the relationship between agricultural organisation and ecosystems, the 1st author conducted
253 an exploratory qualitative study inside the FBO network to grasp the ways participants appropriate
254 and enact knowledge. During this period, monthly meetings made it possible to share and challenge
255 the approach until the results were analysed.
256

256

257 For the qualitative study, we conducted two sets of interviews between March 2020 and February
258 2021, for a total 32 semi-structured interviews with 12 farmers, 14 local advisers and 6 advisers from
259 national institutions (see map on Figure S1). We sought to explore a wide range of agricultural
260 production systems and types of organisations. Hence farmers have different commercial outlets,
261 although standard for their systems (Table 1). All farmers owned their farm. Only two farmers (17%)
262 were women, which is less than the 25 % in France (Table 1). We conducted ten interviews with
farmers on their farm and two by phone due to the Covid lockdown in France. We met farmers from two FBO well-developed and long-standing networks. We aimed to meet participants committed to FBO and involved in group dynamics, i.e. “ideal” situations in terms of FBO potential effects. We interviewed farmers that have been involved in FBO monitoring for several years. The names of the first farmer(s) interviewed in each network were suggested by a prior interview with the local network adviser. Then we asked each farmer if they could suggest someone else to meet. Most adviser interviews were by phone or videoconferencing. Local advisers were mostly biodiversity or agronomy consultants working with farmers, providing knowledge on biodiversity and advice to conduct the FBO monitoring. More broadly they provide help on all farm-related biodiversity subjects. We aimed to interview advisers from a diversity of organisations in order to study a diversity of situations.

274

Interviews were semi-structured, relying on a predetermined thematic framework (Notes S1, S2, S3). The order of the themes and the form of the questions were adjusted during each interview. The questions to farmers included their general vision of farming, management practices, farm future development, FBO monitoring (motivations, results and barriers) and their representation about the links between farmland biodiversity and farming activities. Adviser interviews addressed local agriculture, initiatives for biodiversity, FBO context and uses. Interviews with national animation focused on FBO history, its evolution, the management strategy, links with other organisations, etc. Finally, in all non-phone interviews, we showed graphs (Figure S2) displaying temporal trends in abundance of farmland biodiversity, produced by statistical analysis of the data collected in FBO (Billaud et al., 2020), for the interviewees to comment on them. Before recording all interviewees gave their oral consent for the use of interviews in our research, all quotes being anonymised.

286

<table><caption>Table 1: Land tenure, production and farm management of interviewed farmers</caption><thead><tr><th>Production</th><th>Farm size (ha)</th><th>Years of farming</th><th>Farm managing</th><th>Commercial outlet</th></tr></thead><tbody><tr><td>Field crops and seed multiplication</td><td>150</td><td>34</td><td>Individual</td><td>A huge diversity of crops that link to various cooperatives as customers. Can change every year.</td></tr><tr><td>Maize monocropping + walnut205 + 4 production</td><td>300</td><td>30</td><td>Individual</td><td>Cooperative</td></tr><tr><td>Maize monocropping</td><td>300</td><td>40</td><td>Family (with brother)</td><td>Various cooperatives depending on maize type: edible, animal feed and seeds. Can change every year.</td></tr></tbody></table>
<table><caption>Table 2: National and local advisers interviewed</caption><tr><td>Field crops</td><td>223</td><td>36</td><td>Family (with son)</td><td>Cooperative</td></tr><tr><td>Field crops + vineyards</td><td>280 + 40</td><td>8</td><td>Family (with father and uncle)</td><td>Cooperative and local wine cellar.</td></tr><tr><td>Crop-livestock (dairy) farming</td><td>290</td><td>20</td><td>Couple</td><td>Cooperative</td></tr><tr><td>Suckling farm</td><td>118</td><td>30</td><td>Individual</td><td>Local cooperative (organic)</td></tr><tr><td>Suckling farm</td><td>85</td><td>16</td><td>Individual</td><td>Direct sale (organic)</td></tr><tr><td>Vineyards</td><td>4,5</td><td>20</td><td>Individual</td><td>Wine cellar and direct sale.</td></tr><tr><td>Field crops</td><td>80</td><td>12</td><td>Individual</td><td>Cooperative</td></tr><tr><td>Crop-livestock (dairy) farming</td><td>90</td><td>35</td><td>Family (with brother)</td><td>Cooperative</td></tr><tr><td>Apple production</td><td>4</td><td>26</td><td>Couple</td><td>Transformation on farm. Outlet at local shops and markets.</td></tr></table>

<table><thead><tr><td>Scale</td><td>Organisation</td><td>Total</td></tr></thead><tbody><tr><td>National</td><td>Ministry of Agriculture</td><td>2</td></tr><tr><td>National</td><td>National Museum of Natural History</td><td>3</td></tr><tr><td>National</td><td>National Assembly of Chambers of Agriculture</td><td>1</td></tr><tr><td>Local</td><td>Chamber of agriculture</td><td>4</td></tr><tr><td>Local</td><td>Naturalist association</td><td>3</td></tr><tr><td>Local</td><td>Hunting federation</td><td>2</td></tr><tr><td>Local</td><td>Agricultural cooperative</td><td>3</td></tr><tr><td>Local</td><td>Wine-growers union</td><td>1</td></tr><tr><td>Local</td><td>Territorial community</td><td>1</td></tr></tbody></table>

### 2.2.1 | Meeting and documentary

We supplemented our data by taking part in various workshops and meetings related to FBO: steering committee, working groups, national network days and presentation of results (Table S1). We kept a notebook with associated records of the discussions and issues raised. These data were supplemented further by additional documents on the FBO: minutes of the steering committees, national and regional reports, newsletters, and several tools created to help advisers and farmers carrying out protocols and developing their local FBO network.

### 2.3 | Coding
300

301

302

303

304

305

306

307

308

309

310

311

Interviews were transcribed and coded by the 1st author using Nvivo, with frequent informal checks within the research team. Coding followed a comprehensive approach, i.e. a hybrid between deductive and inductive research (Dana & Dumez, 2015) involving a combination of pre-defined and emergent codes. Pre-defined codes drew on theory or on personal hypotheses. These encompassed codes as general farming, agricultural practices, biodiversity observation approach or use of naturalist knowledge for instance (see Table S2 for more details about code background - theory or observations - and code description). Emergent codes allowed enriching analyses with themes coming from the data such as agronomic issues or innovation testing and adoption for instance. Codes were defined by the author that led the interviews. Further discussion with other authors led to questioning and reframing codes. Then the second set of interviews and presentations of pre-results helped to confirm the coding process.
312 **3 | Results**

313
314 We found two main results, one related to the types of knowledge produced by FBO and their uses
to cope with uncertainties, and the other to the changes in relationships towards biodiversity,
315
316 characterised by an increasing trust towards its action, despite the associated uncertainty. First, our
317 experience and interviews showed that participants often failed in producing normative, evidence-
318 based and operational knowledge from data gathered in FBO, that would depict clear biodiversity
319 responses to their practices and help them to make decisions. However, we noted that some
320 participants shifted to another kind of knowledge, experiential, which was non-prescriptive and
321 recognised high uncertainty. We also witnessed that the implementation of the specific FBO-
322 knowledge production practices required the development of new social and technical relations. As a
323 result, FBO forms an ensemble of interconnected local social systems, each of them corresponding
324 to specific relationships with biodiversity, as well as with uncertainty associated with the
325 reintroduction of biodiversity in crop production. Secondly, farmers had two different kinds of
326 relationships towards biodiversity. For some farmers, protection of biodiversity was important, but
327 mostly for heritage reasons and much less to integrate it into their agronomic thinking. Conversely,
328 some farmers developed a feeling of trust towards the action of biodiversity, despite high uncertainty,
329 and wanted to protect it as an agronomic ally. In the following we detail these two main results.
330

330

331 **3.1 | Shifting from a prescriptive to an experiential knowledge**

332
333 3.1.1 | At all levels, FBO fails to produce normative, evidence-based and operational knowledge from
334 the data

335
336 FBO was designed to be a decision support tool. Locally, participants tried to apply their new
337 knowledge to improve their practices. Once biodiversity had been measured, they wanted to take a
338 step towards action thanks to the inference of direct links with farming practices, and to select
339 favourable farming techniques. To do so, farmers expected to foresee the effects of their technical
340 decisions on biodiversity. In addition, beyond observations, several networks developed a
341 prescriptive approach of FBO to locally "demonstrate" best practices: management indicators for
342 local authorities, production of "how-to guides", etc. These expectations emanated from farmers
343 seeking for general solutions [1] or more broadly from institutions, for instance funding organisations
344 [2].
345

345
346 « (...) I would like to see how my observations compare with those of my colleagues. At the regional,
347 but also national level. Because I want to find explanations. Last year we had an explanation with
348 the drought, which was very particular. But I'm not satisfied with that. This year it should have been
349 better. We need, that's the impression, we need to understand, to gauge ourselves and to know what
350 we can improve. » (Farmer 1) [1]<sup>5</sup>

351
352 « the final aim set to us by the regional council, which funds us, is to produce a small guide... To
353 produce something that outlines what is favourable for biodiversity on a farm. » (Adviser chamber of
354 agriculture 1) [2]

355
356 Nevertheless, the use of data for revealing best practices to promote farmland biodiversity while not
357 decreasing crop yields proved to be difficult. Many advisers did not feel comfortable interpreting the
358 data. At local level, observations suffered from limitations inherent to citizen science: i) coarse
359 taxonomic identification of individuals; ii) heterogeneous compliance with protocols and with
360 regularity of observations; iii) uncoordinated sampling, at the "whim" of the farmers, which did not
361 allow a strict control of environmental conditions; iv) often too few participants per network to obtain
362 proper statistical power, given the heterogeneous sampling (even if some networks managed to enrol
363 a large cohort of farmers, with up to 50 participants or more).

364
365 « Unfortunately, I had a lot of trouble because I felt that the data was not reliable enough. I sent all
366 the farmers some overall figures, but I didn't dig into it. I would have felt like I was usurping the data
367 by doing that. I had time gaps at one point, I couldn't make graphs because there was a month where
368 I didn't have information. There were too many gaps to come up with anything serious » (Adviser
369 agricultural cooperative 1) [3]

370
371 Finally, the multifactorial nature of biodiversity observation made it difficult to produce general
372 "how-to guides" from the FBO data.

373

374

375

<footnote n="5">⁵ The original French version of the verbatims can be found in the supplementary materials (Note S4)</footnote>
376 « I find this complicated because it doesn't only depend on the farmer and where the nesting boxes
377 are placed. Studies of butterflies and bees are really on a landscape scale » (Adviser agricultural
378 cooperative 2). [4]

379
380 Trying to give a sense to this difficulty in interpreting data, some networks put forward their lack of
381 data and hope to find more results once they have gathered more data through the years.

382
383 « Interpretation is not always easy in fact. Even if the farmers give us the information about their
384 plot. [...] I think that over the years, with the number of years, we'll come up with something
385 interesting but with the first few years it's a bit difficult to interpret. » (Adviser territorial community)
386 [5]

387
388 These limitations were partly overcome at national level by the research team, working on synthetic
389 indicators, with the assumption that data accumulation smoothed out high local variability (see
390 Billaud et al., 2020 for details). Nevertheless, these indicators were based on simplifications that limit
391 knowledge transfer to local situations. By bringing together many variables to summarise them (for
392 instance a "field crop" category pooling crops as different as wheat, barley and maize), this approach
393 excluded the possibility of tracing causal chains. Its use was aiming to verify the statistical relevance
394 of data and methodologies. As observed in the reception of the results by farmers, the interactions
395 documented by FBO between biodiversity and agriculture, although validated by scientific peers and
396 published, were not applicable. Farmers stated that these results were interesting but that they would
397 not be able to decide on a change of practice on their basis. They could not “enact” this knowledge
398 (Daft & Weick, 1984), that is to make sense from it in a way that would help to build a representation
399 of their environment (here responses of farmland biodiversity to their practices).

400
401 « It depends to whom it is addressed. For us farmers, we need to go deeper into the analysis. We need
402 to know which factors matter, in one way or another. This being said, for the general public, I'm
403 tempted to say that it's a bit boring when you go into the details. But for the farmer, it would probably
404 be more interesting to have more accurate data. » (Farmer 2) [6]

405
406 Thus, the promise of finding solutions based on data gathered locally or nationally was not kept.
407 Participants and researchers faced an operational limitation: the complexity, variability and
408 uncertainty of ecosystems prevented farmers and scientists from producing prescriptive knowledge
409 to maintain and enhance biodiversity on the farm (Figure 1). However, as explained below, we also
410 observed another use of FBO bypassing this difficulty and shedding a different light on FBO benefits.

411
412 3.1.2 | Beyond the scientific evidence: FBO as vector of social and technical relationships about
413 biodiversity

414
415 Instead of trying to demonstrate links between farming practices and biodiversity, some local
416 networks put FBO forward for the promotion of biodiversity and to illustrate ecological and naturalist
417 knowledge such as links between habitats and species.

418
419 « The idea behind these observations is to make these links more tangible. You see, we observe such
420 and such species and when we look at its ecology it's linked to such and such type of habitat. It's not
421 a matter of formal study or scientific demonstration. It's more a matter of demonstration and summary
422 characterisation of the links between habitats and species. » (Adviser chamber of agriculture 2) [7]

423
424 Other networks did not seek to find and prove a truth, but rather to encourage innovation through
425 collective learning processes. For example, an adviser from a cooperative explained that she chose
426 where farmers conducted their monitoring in order to emphasise differences in the biodiversity
427 observed and encourage exchanges between farmers about their practices and their link with the
428 biodiversity observed through the monitoring.

429
430 « We deliberately surveyed sites in areas where we knew that fields were in the middle of hectares of
431 maize, where we knew there was only maize. And other sites in more varied places, where high
432 biodiversity was almost known. But this made it possible to strengthen differences. » (Adviser
433 agricultural cooperative 3) [8]

434
435 Generally, these advisers accepted the contingency of knowledge; they warned farmers that they
436 could not guarantee results and there was no ready-made recipe to improve farmland biodiversity and
437 see benefits on their crops thanks to ecosystem services.

438
439 « If you create a habitat you're going to favour the species richness of some taxonomic groups. On
440 the other hand, I am unable to give the number of beneficial organisms that will come and what
441 services they will provide. Today, we don't have access to the service provided and its qualification
442 and quantification. So, on the whole, the farmers we work with understand, we are able to make them
understand by teaching. So on the habitat-species link and what the FBO says on an individual scale,
I think they can hear and understand that it is not easy to interpret. » (Adviser chamber of agriculture
2) [9]

Thus, we observed that some farmers and advisers expected high uncertainty related to biodiversity
and produced an experiential knowledge without a will to increase in generalisation. This experiential
knowledge is often tacit, valuing personal experience and is built in action. It is thus related to field
reality and is context-dependent by nature (Krzywoszynska, 2016). Each observation equally reduced
some uncertainties while revealing others. Participants were aware of this and used FBO as a tool that
allowed them to navigate through this uncertainty.

In addition, we observed an important production of social and technical relations. For many
participants, FBO was used to enhance the value of their work on farms and to show their commitment
to biodiversity to other stakeholders.

« For us, for the association, working with the FBO is an image of quality. Because it's national, it's
not just anyone. It's a method, a tool that we use that allows us to value ourselves » (Farmer 3) [10]

The national coordinator also used indicators, even if they were not perfect, to legitimate biodiversity
observation and environmental efforts.

« I see the benefit of [FBO] first of all to convince. We're in an ultra-cartesian world, so it's very
difficult to get away from it. A lot of us are looking for hard evidence, except that when it comes to
biodiversity it's difficult to put a figure on it. So the advantage of having a biodiversity monitoring
indicator, such as the number of carabid beetles under the wooden board, is that it can be convincing.
» (National adviser 1) [11]

Moreover, it turned out that FBO belonged to two different cultural worlds, agriculture and natural
history. By promoting monitoring of biodiversity by agricultural professionals, FBO led to exchanges
between these two worlds, which are often in conflict. At the local level, farmers and naturalist
associations regularly worked together to carry out surveys (12 interviewee over 26 mentioned it).
These led sometimes to partnerships in other projects (for instance farmers being part of an
experiment aiming to promote birds of prey with perches in fields) or to the integration of FBO into
local naturalist surveys (for instance a biodiversity survey of a group of villages). At institutional
level, a national adviser told us how observations enabled him to be invited (and accepted) in meetings related to the national conservation plans on butterflies and bats, despite negative preconceived ideas from naturalists and staff of the Ministry of the Environment towards officials of the Ministry of Agriculture (in charge of the FBO). This partnership finally led to a joint work on conservation actions in agricultural ecosystems.

« We went to present the OAB [...] to a steering committee for the butterfly national action plan. It was a totally magnificent session in terms of interculture. We were up against around fifty people, all of them naturalists. [...] And we arrived [...] to present a scheme run by the Ministry of Agriculture. [...] They were astonished that representatives of what they saw from afar as the big bad wolves were talking about a scheme that allowed them to take an interest in biodiversity. » (National adviser 2) [12]

489
<figure><img src="image_2.png" /><figcaption>2) What FBO produced</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Figure 1. Synthesis of FBO production, compared to what it was designed for. 1) FBO theoretical functioning and results. While data were supposed to reveal best practices to combine the promotion of farmland biodiversity with sufficient crop yield, participation would have raised awareness of participants and motivated change of practices. Knowledge of best practices and motivation to change would lead to implementation of practices working with farmland biodiversity 2) Observations of FBO results. Failure to prescribe evidence-based practices combining the promotion of farmland biodiversity with sufficient crop yield. While the knowledge produced is not applicable,</figcaption></figure>
the participation in biodiversity monitoring promotes an experiential knowledge linked to
observations and increases awareness about the high contingency around it. The new biodiversity
knowledge reveals new uncertainties and the vulnerability (as a quality of being easily influenced or
hurt) of a system when farmers work with farmland biodiversity. Their system depends at least in part
on a factor, farmland biodiversity action, they cannot fully control or anticipate.

Through the farmers’ participation, FBO hence allowed us not only to study knowledge production
under uncertainty, but also to better understand how farmers interact with biodiversity and what kind
of relationships they form with it and its action, in particular to handle uncertainty.

## 3.2 | Relationships towards biodiversity: from heritage protection to trusting a mysterious ally

Our observations highlighted two kinds of working relationships between farmers and farmland
biodiversity. For some farmers, the protection of biodiversity was mainly a question of not depleting
it for heritage reasons, but without integrating it into their agronomic thinking. Conversely, some
farmers developed a feeling of trust towards biodiversity's action and wanted to protect an agronomic
ally. Both relationships can be observed to varying degrees in the same farmers, depending on the
elements of biodiversity and their experience.

### 3.2.1 | Biodiversity as a heritage to be protected

For several farmers participating in the FBO, biodiversity issues were mainly centred on the limitation
of their impacts for heritage reasons. They therefore wanted to understand what was in their fields in
order to "do their job well".

« When I arrived in the village I was not the son of a winegrower, nothing, I arrived in a place that was rather nice, so in a way, I benefit from what others have done before, so it's up to me to give back and to make sure that we don't destroy the environment in which we are, and on the contrary, if we can improve it, let's improve it.» (Farmer 5) [13]

Moreover, environmental issues challenge their farmer ethos and FBO provided them a means to act.
For some farmers, their participation in FBO allows them to challenge and even to experience
narratives about farmland biodiversity loss, by being an actor of the monitoring.
532 « We already had this approach of observing things, around crop biodiversity and so on. So when I
533 was told that, I said to myself, here we are, today I'm told that there are no more insects, I'm not
534 convinced. But I really want to see what is happening in my field with the FBO. (Farmer 6) [14]

535 For other farmers, contributing to FBO was less a way to challenge a narrative than a means to
536 understand their impact, by measuring it.

538 « And we realised, well we knew it before, but at least now we measured it, to say to ourselves, is
539 everything we have done so far, is it rather positive or rather negative? On principle, I couldn't be a
540 farmer if it meant doing something negative. » (Farmer 4) [15]

542 These farmers expressed their environmental sensitivity and their desire to incorporate possible
543 impacts on biodiversity into their thinking. Their main concern was to not undermine biodiversity
544 and the environment, to meet a current demand in society. Accordingly, we felt that their vision of
545 the link between biodiversity and agriculture remained fairly coarse. They knew, or had the intuition,
546 that biodiversity loss would also be detrimental to the functioning of their fields, as well as to their
547 economic activity, but without going into the details of this link.

549 Even though we noticed elements of discourse from scientific ecology, revealing the acquisition of
550 new skills, this knowledge was mostly about biodiversity outside of the fields. For example, a farmer
551 explained how he learned to leave room for biodiversity around his fields by working on habitats:

553 « Today I leave grass under the pylon and the habitat, sometimes we have - not anymore because in
554 conservation agriculture we no longer have this problem - but at the time when we ploughed, we used
555 to bring out stones regularly, we piled them up, under the pylons for example, a pile of stones. From
556 time to time in winter, I would have had someone come and remove the stones, to clean under the
557 pylon, because a pile of stones would have been considered a nest for vermin, and conversely this
558 pile of stones can be a reservoir of biodiversity. So this will be a conservation of a habitat. A dead
559 tree at one time would have been cut down to keep it clean, but today I'm going to leave it and I'm
560 going to see it as a reservoir of biodiversity. A bramble is the same thing, I'm going to control it
561 anyway because I don't want to be overtaken by it and have it get in the way of my field, but I'm going
562 to contain it in certain places where it doesn't bother me, but this bramble can be a reservoir of
563 biodiversity. » (Farmer 8) [16]

565
Thus, although these farmers were very interested in biodiversity and wanted to change their practices (generally around the field) to promote it, we did not get the impression that they were working with biodiversity. Biodiversity remained a heritage element to be protected, and they recognised that it certainly plays an important role, but this role still appeared to be poorly integrated into agronomic considerations.

3.2.2 | Biodiversity as an uncertain support, but a support nonetheless, and a mysterious ally that must be trusted

Other farmers described a slightly different relationship with biodiversity that was no longer just a heritage to be preserved but an ally to work with. They mobilised naturalist knowledge to observe and identify elements of biodiversity and recognised their important role in their agronomic practices. Naturalistic observations then played an important role in the decision to spray chemical inputs. :

« Yes, like that, when you start to see ladybirds in the field, you say to yourself 'they're here'. You can't necessarily see the larvae but if there are ladybirds, there are certainly larvae. That's how I see it. I could see three or four ladybirds on a faba bean plant, or when I see them in my canopies, sometimes on sunny days in spring, before I have planted a new crop, I'd say "look, there are ladybirds already". It's reassuring, you don't have to worry too much. » (Farmer 9) [17]

This involved a feeling of confidence towards the action of biodiversity they observed:

"Above all, you have to let them do their things. Because if you reset everything, you will have to start all over again." (Farmer 9) [18]

We presented in introduction and showed in the first part of the results that working with biodiversity services implied to manage an inherent uncertainty. Biodiversity is thus a "mysterious ally" for farmers, in the sense that its actions can not always be anticipated and therefore there is no other option but to trust it. We identified several factors favouring this trust (Figure 2). First, this confidence seemed to go hand in hand with the acquisition of naturalist knowledge, the ability to identify species [19], to know their ecological traits [20], dynamics, etc.

« I didn't know much about carabid beetles before, I had heard of them, and even less about rove beetles. There are rove beetles, there are many species. Some even destroy a few wireworms.
600 Everything will take care of itself, and that's what's interesting about it. It's a balance, it must not be
601 broken. »(Farmer 9) [19]

602
603 « I think the trigger was this : the discovery of conservation agriculture. At the beginning I was
604 fascinated by earthworms. I think they're pretty ugly animals, but the work they do is enormous. Then
605 there are insects. And also birds, the ability to eat aphids: two pairs of chickadees can wipe out all
606 the aphids on a hectare, it's crazy! You think you can do without insecticides. I have never used any. »
607 (Farmer 10) [20]

608
609 Secondly, this scientific knowledge interacted with experiential knowledge, such as observations on
610 the field or agronomic changes and experiments. This latter type of knowledge is essential to test
611 farmers' trust towards biodiversity services. Farmers thus have their own ways to rationalise
612 biodiversity observations and integrate them into their decisions. For instance, a farmer explained
613 how he rationalised his reduction of slug pellets by measuring the slug pressure and linking it to each
614 crop sensitivity:

615
616 « Slugs are managed either blindly, we see a slug or we don't see one, but we see a trace and we
617 apply chemicals. I'd say that maybe 80 or 90% of people do this. I put little squares of wet felt and I
618 count my slugs. I have the slug pressure per square metre. If the crop is more or less sensitive, on a
619 rapeseed, if you find a slug per square metre, you have to be very careful. On a wheat crop, if I have
620 30 slugs per square metre, it doesn't bother me that much. I've even seen as many as 50 slugs per
621 square metre and I didn't necessarily have any problems. So I do my counts and then I look at my
622 crop and how it's developing. If the weather is good and the crop develops quickly, there will be a
623 little damage but it's not a problem. If the weather is colder and the crop grows more slowly, then I
624 have to keep an eye on it. It's monitoring but it's exciting. You don't use the products when they are
625 not needed. That's what it's all about, because I'd say that 50% of the spraying, I say it a bit like that,
626 but there are largely 50% of the spraying that could be avoided. » (Farmer 9) [21]

627
628 This trust is dynamic, always challenged by new experiences, either consistent with ecological
629 (scientific and experiential) knowledge or raising new uncertainties. In the same slug management
630 example, the farmer explained their current questioning regarding biodiversity action.

631
632 « There are slugs but we no longer need to use slug pellets, all we need are carabid beetles and rove
633 beetles that manage the populations. And we have a lot of ladybirds in spring to deal with aphid
problems. But in autumn we always have aphids on the cereals and the ladybirds are not always there. [...] With a little training, I realised that there are fewer crop auxiliaries on the soil surface in the autumn. For example, if we sow on October 15, the aphids will be treated in the first ten days of November, and there are already far fewer active auxiliaries. » (Farmer 9) [22]

Moreover this trust also relied on a general belief that agriculture should learn how to work with biodiversity and promote its services. This belief is not isolated but rather fueled by scientific and experiential knowledge. Reciprocally, it fueled a motivation and an urge to learn more about biodiversity.

« I wish I had discovered this 30 years earlier, because in fact, this kind of agriculture is brilliant. It's really great and we can see we are improving a lot of things and that biodiversity is coming back, despite our negative effects due to production. [...] It's exciting actually. I've regained a taste for it all » (Farmer 8) [23]

Finally, this new knowledge and trust about biodiversity led to reframing interpretation of agronomic situations, often giving a greater prominence to biodiversity action and reducing the role of chemical inputs.

« Why do we have such a plant problem? We realised that these plants have an indicator character and that in reality it's a problem of soil compaction, a problem of hydromorphy, a problem of this and that, and that ultimately we have to work on rotations, on soil life, we need to work on dose modulation of certain fertilisers, the elimination of certain molecules or dose modulation. There are many parameters to take into account, but first and foremost we need to deal with the cause and not the consequence. » (Farmer 7) [24]

Thus acquisition of ecological knowledge and its fine-tuned integration into activity and experience appears to be a way of replacing (or reducing) the place of chemical inputs. The biodiversity-trusting farmers generally explained that crop yield is not increased by their new farming practices, but neither does it drop significantly when certain practices are stopped, which increases the economic margin. Finally, this replacement of practices of agricultural modernisation led to a reversal of the farmers' risk aversion. Whereas risk aversion was traditionally used to explain a fear of losing yield by stopping some practices associated with modern farming, such as mineral fertilisation or pesticide
667 use, biodiversity-trusting farmers were afraid of weakening their ally, of destroying the biological
668 communities by using useless chemistry, even if it meant some yield loss.

669
670 "The idea is to avoid spraying. We'll keep an eye on things and we'll realise that the auxiliaries are
671 there and will regulate the situation. And I'd like to say that we're almost prepared to lose a bit of
672 yield but we want to keep this biodiversity. As soon as we apply an insecticide, we'll start all over
673 again, and that's what we don't want." (Farmer 8) [25]

674
675 This new relationship represented a qualitative leap forward in the way biodiversity was integrated
676 into agronomic decision-making. It also showed how some farmers cope with ecological uncertainty,
677 developing a specific trust towards biodiversity services. Finally, FBO participation seemed to be
678 part of this process of working with uncertainty, offering space for naturalistic observations and for
679 building ecological knowledge.
4 | Discussion

In this study we investigated how a CSOF scheme helped to understand biodiversity responses to agricultural practices and to integrate this knowledge into new practices, despite the uncertainties associated with biodiversity and its services. We observed an operational limitation of the scientific knowledge produced, creating as many new uncertainties and questions as it solved. This led to a difficulty to offer prescriptive pro-biodiversity practices guiding decision-making. However, we also observed how some farmers developed a trust towards biodiversity action, accepting a leap into the unknown, because no solution could be based on a reliable assessment of the consequences. This trust helped them to cope with a high uncertainty and was fueled by “trials and errors” and relied on an eclectic mix of scientific knowledge on the ecology of species, measurement techniques and observations in the field, as well as personal experience. In the following we first discuss the lessons learnt from this study on farming under uncertainty and how a CSOF program can help. We discuss the operational limitation of large scale statistical ecological knowledge for local management. We then explain how FBO shifted from a prescriptive to a learning tool thanks to the peculiar nature of farmland biodiversity observation, seen as a boundary object accepting flexible interpretations. Thereafter we discuss how the trust towards biodiversity action allows farmers to act under uncertainty and end by promoting knowledge built from clinical evidence. Secondly we examine how relevant FBO (as an example of citizen science scheme) is as a knowledge tool to understand biodiversity responses to practices. We specifically examine FBO through the lenses of (1) appropriation by participants and (2) knowledge production.

4.1 | How to work under uncertainty: operational limitation of formal statistical knowledge, biodiversity observation as a boundary object, need for trust to act and suggestion to shift to clinical evidence

4.1.1 Operational limitation of CSOF to deduce general formal knowledge that would lead to new practices

Our observations show the limitations of knowledge from CSOF to deduce normative technical recommendations that would set standard practices to develop farming practices working with farmland biodiversity. Both from the research and from the local advisers perspective, the knowledge produced from in-field observations could not come up with “one size-fits-all” best practices for farmland biodiversity, despite the willingness of most FBO participants and coordinators to change
farming practices. On the one hand, researchers succeeded in producing formal scientific knowledge based on statistics and considering large categories of crops (Billaud et al., 2020). These results led to interest and discussion from other scientists and institutional actors, such as national advisers or officials from the Ministry of Agriculture. However, when presented to farmers and advisers this knowledge based on synthetic indicators appeared too broad to be applied and trigger new practices. On the other hand, local farmers and advisers did not have enough accurate data to face the multifactorial nature of biodiversity observation and to produce formal knowledge based on their regional particularities.

These results are broadly consistent with known limitations associated with the use of statistical models to inform agroecosystem management. As pointed out by (Duru, Therond, Martin, et al., 2015), statistical models often allow the design of conceptual models or the definition of proxies that are useful to learn about ecological groups of species. However, they are generally not conceived to transfer such knowledge into practice; besides, the amount of data available is often not large enough to incorporate the full complexity of local contexts into the analysis. This does not imply that statistical studies are useless, but it does point out the limitations for action associated with this mode of knowledge production, which is nonetheless often used with citizen science schemes, which provide large amounts of data. We think such statistical studies require more contextualisation, but also more data to cope with local specificities, to provide knowledge that can be put into practice.

#### 4.1.2 Farmland biodiversity observation, a boundary object allowing CSOF participants to shift to a learning tool supporting exchanges between farmers and AKIS players and the exploration of each farming system informal and situated knowledge

Recognising these difficulties, some farmers and advisers therefore accepted uncertainty and merely tried to illustrate the links between biodiversity, practices and habitats, without any claim to find a "one best way", but rather to support understanding of each farming system separately. This shift from a prescriptive tool to a learning tool supporting the exploration of the farm environment is based on the particular status given to biodiversity observation.

As a CSOF, FBO was meant to be easily used by participants, thereby facilitating interactions with farmland biodiversity via simple monitoring protocols adaptable to local situations. This helped farmers and advisers to take a first step in observing, and learning about, farmland biodiversity. Moreover, FBO's strong focus on monitoring placed biodiversity observation at the centre of
748 agronomic thinking. Farmland biodiversity observation therefore became a boundary object (Star & Griesemer, 1989), adaptable to different viewpoints, but with the different interpretations still structured enough to be recognised by other players and to be a source of discussion and negotiation.
749
750 Bearing an 'interpretative flexibility' (Star & Griesemer, 1989), biodiversity observation takes on different qualities depending on the context and actors, showing its 'multiplicity' and its evolution
751
752 according to the appropriations. For example, from local to national levels, farmland biodiversity
753 observation can be: an irreducible experience for the farmer, providing information on their field and
754 a source of naturalist learning; data that are more or less contextualised depending on the networks,
755 a support for comparisons, a source of questions and frustrations; a vector of legitimacy for
756 agricultural players, also generating discussions, negotiations and cooperation with the naturalist
757 sector; or, finally, one piece of data among many, simplified to conform to the database that will be
758 used for statistics.
759
760

761 Thus biodiversity observation contributes to knowledge management, at the boundary between
762 groups, through two activities of translation and transformation (Carlile, 2004). Translation on the
763 one hand, involves exchanges of views in indeterminate situations, possibly leading to compromises
764 and visions acceptable to all. Examples of translation occur during the interpretation of observations,
765 whether locally in the discussions between farmers and advisers to understand the observations, or
766 nationally to discuss the statistical analyses. The seminar organised to present the national trends was
767 an opportunity for some of the participants, farmers and local advisers, to grasp the inherent
768 imprecision of data. This recognition contributed to their subsequent switch from a use of FBO to
769 find best practices, to a consideration of FBO as a useful tool for local animation on biodiversity
770 issues. On the other hand, biodiversity observation also involves transformative activity, playing the
771 role of "social mediator", a source of negotiation of interests and discussion of identities, as observed
772 in the links between the agricultural and naturalist sectors. Naturalistic observation became a source
773 of joint work and exchanges. At national level, the compilation of these observations into a database
774 shows the efforts of the agricultural sector in the eyes of naturalists and enables acceptance and
775 collaboration on national public conservation actions.
776

776
777 4.1.3 Developing a trust towards biodiversity action to work under uncertainty

778
Following the definition of trust<fnref n="6" /> by Mayer et al. (1995), we chose this concept to describe the relationship of some farmers towards biodiversity because the latter remains a *mysterious ally*. Any social change is accompanied by a questioning of standards and norms resulting in a loss of references (Durkheim, 1897). Farmers face two related sources of such anomie: organisational (Hodson, 1999) — the failure of industrial approach to prescribe solutions — and environmental (Brown, 2022) — the impending environmental disasters that will impact agriculture, experienced here as a leap into the unknown. The farmers we met admit that they still did not understand everything, and that grasping the dynamics of biodiversity in their fields required neverending investigation. Moreover, results from FBO showed the impossibility to monitor the action of biodiversity entirely. Thus, shifting to “farming with biodiversity” consists in accepting a leap into the unknown, thus making oneself “vulnerable” with respect to the possible “responses” of the ecosystems to a change in farming practices. Despite this situation of change and the uncertainty inherent in working with biodiversity, the trust developed by these farmers gave them a way to comprehend a situation, reducing the uncertainty linked to the ambiguity of interpretation. From our observations, combined with earlier studies, we can introduce a conceptual framework to improve our understanding of how trust towards farmland biodiversity, or in other words “biodiversity trustworthiness”, is assessed by farmers, depending on four identified factors (Fig.2): i) formal scientific knowledge about the ecology and natural history of species, ii) experiential knowledge leading to new practices, iii) belief that agriculture can rely mostly on farmland biodiversity services iv) and gradual changes that allow a limited exposure to uncertainty and a gradual build-up of the trust.

We mainly observed how these factors interact in strategic explanations of this trust, with farmers evaluating the trustworthiness of farmland biodiversity, i.e “its capacity and commitment to fulfil the goals and desire of the farmer” (adapted from Robbins, 2016). As suggested by Duru et al. (2015), farmers used their formal knowledge on the functional ecology of species (for instance large carabids as mostly slug predators) to integrate some mechanisms and hypotheses about farmland biodiversity actions and explain why it can be trusted. However this trust is not theoretical only: farmers put it to the test with trial and error and observations. Farmers also developed their own “situated” rationality to give sense to their biodiversity observations, despite inherent uncertainty. For instance a farmer explained how he counted slug pressure using squares of wet felt to decide if he needs to apply chemicals [19]. By trial and error he gradually set up his own way to manage slugs, combining a trust

<footnote n="6">⁶ “Trust refers to ‘the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party”</footnote>
towards their predators, which he can identify, and a rational approach based on monitoring to decide whether chemicals are needed. This combined contribution of both scientific and experiential knowledge on farmland biodiversity to changing behaviours is consistent with the results of Toffolini et al. (2017), who showed how farmers mobilised and used basic ecological knowledge 'in situation', to build interpretations of in-field observations, producing their own rationality. Furthermore these farmers believed that farmland biodiversity can really provide beneficial services to their crop and help them produce high yields while reducing their chemical inputs. This belief is fueled by knowledge on wild species (for instance how many pests a given predator can eat per day) and enhances the biodiversity's trustworthiness. Indeed in a way, this belief allows farmers to trust the "commitment" of biodiversity to help.

These results remain preliminary but the notion of trust towards biodiversity, which is barely explored to our knowledge, could open up new perspectives for studying agroecological transitions and deserves further attention. Further work could dwell on existing literature in several fields. First, trust is a widely used notion to explain changing practices in innovation and adoption of new technologies (Bahmanziari et al., 2003; Li et al., 2008). Second, the literature related to experiences of nature (Clayton et al., 2017; Miller, 2005) or to farmers' mental models (Bardenhagen et al., 2020; Teixeira et al., 2018; Vuillot et al., 2016) may also be enlightening to understand how trust could be linked to farmers's representation of biodiversity. Finally, as shown by Gardezi & Stock (2021) in precision agriculture, trust has an important cultural component, driven by the moral values of a given culture and social context. For instance, while the farmers we met were FBO participants and already at different degrees in the process of improving their practices towards biodiversity, a farmer mentioned how a cooperative adviser could influence the decision to apply chemicals; he felt his fear that applying chemicals could damage biodiversity in his fields was not shared by most advisers. Hence it seems important to investigate the social construction of trust towards farmland biodiversity, for instance through the expansion of agroecology from individual farms to a broad socio-political movement (Dumont et al., 2021; Wezel et al., 2009).
<figure><img src="image_4.png" /><figcaption>Figure 2: Conceptual framework for how farmers assess the “trustworthiness” of farmland biodiversity, i.e “its capacity and commitment to fulfil the goals and desire of the farmer” (Robbins, 2016). A) Belief that biodiversity action will be beneficial for agriculture. This belief is fueled by knowledge about biodiversity and motivates farmers to learn more about it. B) Knowledge and tests to assess the capacity of biodiversity to help agriculture. B1 Scientific and experiential knowledge interact to provide situated knowledge. B2 The situated knowledge is tested through gradual changes that help reduce uncertainty. Green arrows show how FBO helps to develop trust towards biodiversity. Purple arrows show limitations observed during the study. Arrows are either directly supported by our observations (*) or come from other published studies (°).</figcaption></figure>

#### 4.1.4 Advocating clinical judgement of the farmers to work in an uncertain environment.

In the face of current ecological issues and the need to develop practical knowledge adapted to the contingency of the living world, it is important to work on the operational capacities of farmers, as close to the fields as possible. Modern agronomic sciences seek to be prescriptive, and are therefore based mainly on experiments, statistics and modelling, which often require simplification in order to be able to observe co-variations between a limited numbers of factors. However, such simplification considerably undermines the capacity to understand real situations and thus to provide relevant, locally-adapted prescriptions.
859

860

861

862

863

864

865

866

867

868

869

870

871

872

873

874

875

876

877

878

879

880

881

882

883

884

885

886

887

888

889

890

891

Given the unknowns of biodiversity in agricultural settings, our results argue in favour of approaches based on clinical observations and the accumulation of specific cases. These approaches would be similar to clinical judgement in medical practice, which stands back from evidence-based practices (Tonelli, 2006) to give more space to professional experience and judgement. Clinical thinking accepts that, given the high uncertainty related to human health, there is no single appropriate "method", in the sense of a clear series of steps or exact protocols to follow, to identify a relevant treatment. Instead, clinical thinking seeks more for a deeper understanding of species contexts, than for a general and conceptual explanation. To this end, clinical thinking relies on exploration to integrate different perspectives (see Mugerauer (2021) for a deep epistemic explanation), one of which can be formal scientific knowledge that supplements, but does not replace, professional judgement (Pandey & Garg, 2022).

Although formal scientific knowledge is only one of many bricks in the decision process, clinical thinking is not a step backwards. According to our data and literature, clinical observations by farmers are in fact extremely technical and rely on their professional experience (Šūmane et al., 2018; Toffolini et al., 2017). Moreover, each case study also provides robust scientific evidence and is promoted by some ecologists as part of a framework to improve application of ecology (Driscoll & Lindenmayer, 2012; Shrader-Frechette & McCoy, 1993). We think that clinical thinking can provide an epistemic background to initiatives supporting farmers' informal knowledge, as well as their capacity to explore their environment (Coquil et al., 2018) and to be directly involved in solving their local issues (see for instance the new vision of EU's agri-environment schemes, which seeks to get farmers more involved, Cullen et al., 2018; Moran et al., 2021).

Regarding FBO, switching to clinical evidence would mean to promote a better exploration of their local environment by farmers, to allow them to find local solutions that could inspire other groups afterwards. While FBO simple protocols allowed to engage a lot of farmers, the programme could rely on the local networks and the relationship created with naturalist associations and researchers to develop local experiments on practices integrating farmland biodiversity. The results of these experiments could then be shared thanks to the national scale of FBO.

## 4.2 | Prospects and challenges for a citizen science producing knowledge on farmland biodiversity
892 4.2.1 CSOF: a knowledge producing tool halfway between a rigid data-driven structure and an easy
893 appropriation

894
895 Our results showed that elicitation of experiential knowledge, as well as integration of scientific and
896 local knowledge, depend on whether FBO is locally appropriated (DeSanctis & Poole, 1994).
897 Appropriation, i.e. the way users "take possession" of a technology (Carroll, 2004), is a reciprocal
898 process: users' practices are shaped by technology but simultaneously they reshape its features and
899 find unanticipated purposes and uses.

900
901 FBO was steered by the will to produce statistical trends at the national scale and evidence-based
902 knowledge to guide the decision of farmers and control uncertainty. Therefore, most participants
903 initially focused their use of FBO on producing a formal knowledge, which was top-down and based
904 on statistics that cannot be used for a given management situation because they were based on
905 synthetic indicators. Because FBO has been conceived to produce a national database about farmland
906 biodiversity, its spatial resolution is not fully relevant to enhance local knowledge and help adaptive
907 management of the farm system. The scale at which FBO should be applied was not clearly defined
908 (as advised by Martin (2015) to allow research approaches to contribute to the development of
909 farmers' adaptive capacity and the exploration of their environment), leading to frustration of local
910 participants hoping to demonstrate benefits of new farming practices on their territory with data that
911 were in fact more suitable for national scale analysis. Moreover FBO aims at gathering standardised
912 data, as is often the case in structured citizen science schemes (Bonney et al., 2009; Couvet et al.,
913 2008; Legrand & Chlous, 2016), which may deter participants from including their own experiential
914 knowledge and from adapting protocols for local situations. Hence, parts of FBO features (rules and
915 resources offered) and spirit (general intent, goals and underlying values) (DeSanctis & Poole, 1994)
916 are designed for a statistical scientific knowledge that would control uncertainty with evidence-based
917 practices. We saw in the results that these features influenced some participants that tried to have the
918 same statistical approach to be able to recommend best farming practices for biodiversity. We detailed
919 how most of the local networks fail to produce this recommendation because of statistical limitations.
920 Thus, designing FBO to gather standardised data in order to produce large scale knowledge challenges
921 the development of an experiential knowledge, leading more participants to look for statistical
922 knowledge difficult to produce.

923
924 Conversely, some advisers and farmers focused their observation on understanding local situations.
925 They used FBO protocols as tools to collect new information (on biodiversity) in their systems and
to develop their own experiential knowledge about farmland biodiversity. They showed that FBO was user-friendly and adaptable to local situations, with participation on a voluntary basis and modifiable protocols thus also offering flexibility to participants. Hence, influenced in its development by different general goals (large-scale data gathering and development of farmers' experience), FBO has a hybrid participatory nature, between crowdsourcing and the community-based approaches of citizen science (Legrand & Chlous, 2016). While this hybrid nature entails a great recruitment capacity, we also saw that it could lead to ambiguities in its appropriations, impacting the relevance of the knowledge produced.

### 4.2.2 Potential highjacking: different relationships to participation embedded into different ways to envision knowledge production.

From a multi-level perspective (Geels, 2002), agronomic knowledge production may be seen at the centre of interactions between a current dominant regime, characterised by a top-down framework from industrial approaches, scientific expertise and institutes, to farmers. This dominant regime leads to ESBA, and the development of niches in which agronomic alternatives such as BBA are being built mainly from local and experiential knowledge (Barbier & Elzen, 2012; Ingram, 2015, 2018). The FBO participatory nature is subject to the same interactions. The position of FBO is intermediate between these two modes of knowledge production: the initial objective of accumulating data for scientific research and conducting national monitoring of farmland biodiversity is part of a top-down vision of knowledge production, with the idea that better generic knowledge and national assessments driven by public action will lead to changes in practices. The participatory approach has been incorporated afterwards, to overcome the lack of data on farmland biodiversity. In a previous work (Billaud et al., 2020), we followed this rationalist approach, seeking to draw general lessons from the database collected. In this study, we confronted this general knowledge with practitioners, both local advisers and farmers, and showed their operational limitations.

Our results revealed two dynamics inside FBO, which have contrasting relationships with uncertainty, but also which encapsulate two different approaches to participation. The use of FBO standardised protocols by local networks, agri-food sector and national institutions to produce farmland biodiversity indicators and evidence-based decision prioritises the requirements of organisations (Ministry of agriculture, cooperative, etc.), considering farmers as mere "applicators" of their advice. Conversely, networks focusing on FBO as a tool for farmers to explore little-known elements of their farms are much more oriented towards the participants' feelings and requirements. They prioritise
data collection and exploration of agroecosystems to produce local and experiential knowledge. These
observations are consistent with the literature on the dynamics of innovations, which highlights the
possibility of deviating from innovations. Faced with a new tool such as CSOF, one should keep a
critical eye on discourses and practices (Figure 3) and start questioning the real impacts of each
approach.

965

<figure><img src="image_5.png" /></figure>

Figure 3: The two dynamics of knowledge production observed in FBO can be associated with a social level of innovation (regime and niche) linked to particular approaches of citizen participation and uncertainty reduction. 1) Top-down knowledge production is typical of our actual industrial regime leading to ESBA and a participation geared towards the production of large-scale data for studies based on statistics. ESBA, "efficiency/substitution-based agriculture" (Duru et al., 2015), is a form of environmental innovation of agriculture, in line with the dominant agro-industrial regime and based on sophisticated machines, biotechnologies, engineered processes, and a top-down knowledge production. 2) Bottom-up knowledge production developed in BBA is a niche of the industrial regime. It looks for developing farmers' own experience about farmland biodiversity. BBA,

966
"biodiversity-based agriculture" (Duru et al., 2015), is a form of environmental innovation of agriculture that aims to create a disruption with the existing industrial model, by enhancing the ecosystem services provided by agrobiodiversity. BBA seeks to diversify farming systems to increase resilience and provision of ecosystem services, with the aim to reduce anthropogenic inputs.

980

## Conclusion

982

In this paper we produced a reflexive analysis of a new knowledge production tool in agriculture, citizen science on farm, exploring how the resulting knowledge helps to cope with uncertainty due to environmental innovation in agriculture. We advocate that such reflexive approaches are necessary in citizen science to go beyond its claims of renewing knowledge production and to provide a deeper understanding of its impacts. Our study showed all the complexity inside a citizen science programme from its ambiguities leading to potential frustrations to the various appropriations by participants leading to a production of different kinds of knowledge.

990

Regarding agronomic knowledge production we revealed the operational limitation of the search for generic solutions that will be prescribed to farmers and the limited applicability of formal statistical knowledge based on a large database and synthetic indicators. Thus accepting uncertainty seems essential for farmers to work with biodiversity. This acceptance has a cost for farmers however: it implies their constant exploration of agroecosystems, which fuels experience and trust towards biodiversity action. Hence, shifting from uncertainty reduction to uncertainty acceptance is a true leap for farmers that must be supported by society. Moreover, bottom-up and local knowledge to accept the uncertainty of biodiversity responses involves an emancipation from industrial approaches. New knowledge production tools such as CSOF must therefore focus on supporting farmers in their exploration of new interactions with biodiversity in their landscape, including lower production risks, promoting an experiential knowledge far from generalisation but close to the ground. We also showed in this study how these tools are influenced by institutional contexts, mostly leading to top-down knowledge production. These findings clearly call for more in-depth analysis of these new tools and their rationalisation (Girard, 2014).

1005

## Author contributions

1007

OB led the writing of the interview protocol, conducted the interviews, coded the interviews. EP, EM and OB conceived the ideas and designed methodology, contributed to situating the work in relevant
1010 literature and led the writing of the manuscript. All authors contributed critically to the drafts and
1011 gave final approval for publication.

1012

1013 Acknowledgements

1014

1015 We thank all farmers and advisers who welcomed us for the interviews. We thank the FBO
1016 management team (Elodie Chauvet, Marion Demade, Marine Gérardin, Romain Julliard, Christophe
1017 Pinard, Nora Rouillier) and the French Ministry of Agriculture, which supported the program. This
1018 research project was funded by the Crédit Agricole through sponsoring OB.

1019 Conflict of Interest

1020

1021 The other authors declare that the research was conducted in the absence of any commercial or
1022 financial relationships that could be construed as a potential conflict of interest.

1023

1024 References

1025

1026 Anand, M., Gonzalez, A., Guichard, F., Kolasa, J., & Parrott, L. (2010). Ecological Systems as
1027 Complex Systems: Challenges for an Emerging Science. *Diversity, 2*(3), 3.
1028 https://doi.org/10.3390/d2030395

1029 Aron, R. (1967). *Eighteen lectures on Industrial Society* (M. K. Bottomore, Trans.). Weidenfeld &
1030 Nicolson.

1031 Bahmanziari, T., Pearson, J. M., & Crosby, L. (2003). Is Trust Important in Technology Adoption?
1032 A Policy Capturing Approach. *Journal of Computer Information Systems, 43*(4), 46–54.
1033 https://doi.org/10.1080/08874417.2003.11647533

1034 Barbier, M., & Elzen, B. (2012). *System Innovations, Knowledge Regimes, and Design Practices
1035 towards Transitions for Sustainable Agriculture*. INRA-Département Sciences pour l’Action
1036 et le Développement (SAD). https://hal.archives-ouvertes.fr/hal-01191290

1037 Bardenhagen, C. J., Howard, P. H., & Gray, S. A. (2020). Farmer Mental Models of Biological Pest
1038 Control: Associations With Adoption of Conservation Practices in Blueberry and Cherry

1039 Orchards. *Frontiers in Sustainable Food Systems, 4*, 54.

1040 https://doi.org/10.3389/fsufs.2020.00054

1041 Barnaud, C., De Longueville, F., Gonella, G., Antona, M., Dendoncker, N., & Waylen, K. A.

1042 (2023). Participatory research on ecosystem services in the face of disputed values and other uncertainties: A review. *Ecosystem Services*, 63, 101551.

1043 https://doi.org/10.1016/j.ecoser.2023.101551

1044 https://doi.org/10.1016/j.ecoser.2023.101551

1045 Beckford, C., & Barker, D. (2007). The role and value of local knowledge in Jamaican agriculture:

1046 Adaptation and change in small-scale farming. *The Geographical Journal*, 173(2), 118–128.

1047 https://doi.org/10.1111/j.1475-4959.2007.00238.x

1048 Billaud, O., Vermeersch, R.-L., & Porcher, E. (2020). Citizen science involving farmers as a means to document temporal trends in farmland biodiversity and relate them to agricultural practices. *Journal of Applied Ecology*, 58(2), 261–273. https://doi.org/10.1111/1365-2664.13746

1049 to document temporal trends in farmland biodiversity and relate them to agricultural practices. *Journal of Applied Ecology*, 58(2), 261–273. https://doi.org/10.1111/1365-2664.13746

1050 practices. *Journal of Applied Ecology*, 58(2), 261–273. https://doi.org/10.1111/1365-2664.13746

1051 2664.13746

1052 Bimonte, S., Billaud, O., Fontaine, B., Martin, T., Flouvat, F., Hassan, A., Rouillier, N., & Sautot, L. (2021). Collect and analysis of agro-biodiversity data in a participative context: A business intelligence framework. *Ecological Informatics*, 61, 101231.

1053 L. (2021). Collect and analysis of agro-biodiversity data in a participative context: A business intelligence framework. *Ecological Informatics*, 61, 101231.

1054 https://doi.org/10.1016/j.ecoinf.2021.101231

1055 https://doi.org/10.1016/j.ecoinf.2021.101231

1056 Bonney, R., Cooper, C. B., Dickinson, J., Kelling, S., Phillips, T., Rosenberg, K. V., & Shirk, J.

1057 (2009). Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy. *BioScience*, 59(11), 977–984. https://doi.org/10.1525/bio.2009.59.11.9

1058 Scientific Literacy. *BioScience*, 59(11), 977–984. https://doi.org/10.1525/bio.2009.59.11.9

1059 Brown, A. R. (2022). Environmental anomie and the disruption of physical norms during disaster.

1060 *Current Sociology*, 0(0). https://doi.org/10.1177/00113921221129316

1061 Brugnach, M., Dewulf, A., Pahl-Wostl, C., & Taillieu, T. (2008). Toward a Relational Concept of Uncertainty: About Knowing Too Little, Knowing Too Differently, and Accepting Not to

1062 Uncertainty: About Knowing Too Little, Knowing Too Differently, and Accepting Not to
1063 Know. *Ecology and Society*, 13(2). http://www.jstor.org/stable/26267972.

1064 Carlile, P. R. (2004). Transferring, Translating, and Transforming: An Integrative Framework for

1065 Managing Knowledge Across Boundaries. *Organization Science*, 15(5), 555–568.

1066 https://doi.org/10.1287/orsc.1040.0094

1067 Carroll, J. (2004). Completing Design in Use: Closing the Appropriation Cycle. *ECIS 2004*

1068 *Proceedings*, 44. https://aisel.aisnet.org/ecis2004/44

1069 Chevassus-au-Louis, B. (2006). Refonder la recherche agronomique. In B. Chevassus-au-Louis

1070 (Ed.), *Les défis de l'agriculture mondiale au XXIe siècle: Les leçons inaugurales du Groupe*

1071 ESA (Groupe ESA, pp. 193–226).

1072 Clayton, S., Colléony, A., Conversy, P., Maclouf, É., Martin, L., Torres, A.-C., Truong, M.-X., &

1073 Prévot, A.-C. (2017). Transformation of Experience: Toward a New Relationship with

1074 Nature: New experiences of nature. *Conservation Letters*, 10(5), 645–651.

1075 https://doi.org/10.1111/conl.12337

1076 Cohen, A. G. (2017). Des lois agronomiques à l'enquête agroécologique. Esquisse d'une

1077 épistémologie de la variation dans les agroécosystèmes. *Tracés. Revue de Sciences*

1078 *humaines*, 33, 33. https://doi.org/10.4000/traces.6989

1079 Coquil, X., Cerf, M., Auricoste, C., Joannon, A., Barcellini, F., Cayre, P., Chizallet, M., Dedieu, B.,

1080 Hostiou, N., Hellec, F., Lusson, J.-M., Olry, P., Omon, B., & Prost, L. (2018). Questioning

1081 the work of farmers, advisors, teachers and researchers in agro-ecological transition. A

1082 review. *Agronomy for Sustainable Development*, 38(5), 47. https://doi.org/10.1007/s13593-

1083 018-0524-4

1084 Couvet, D., Jiguet, F., Julliard, R., Levrel, H., & Teyssedre, A. (2008). Enhancing citizen

1085 contributions to biodiversity science and public policy. *Interdisciplinary Science Reviews*,

1086 33(1), 95–103. https://doi.org/10.1179/030801808X260031

1087 Couvet, D., & Prevot, A.-C. (2015). Citizen-science programs: Towards transformative biodiversity
1088 governance. *Environmental Development, 13*, 39–45.

1089 https://doi.org/10.1016/j.envdev.2014.11.003

1090 Cullen, P., Dupraz, P., Moran, J., Murphy, P., O'Flaherty, R., O'Donoghue, C., O'Shea, R., &

1091 Ryan, M. (2018). Agri-Environment Scheme Design: Past Lessons and Future Suggestions.

1092 *EuroChoices, 17*(3), 26–30. https://doi.org/10.1111/1746-692X.12187

1093 Daft, R. L., & Weick, K. E. (1984). Toward a Model of Organizations as Interpretation Systems.

1094 *The Academy of Management Review, 9*(2), 284–295. https://doi.org/10.2307/258441

1095 Dana, L.-P., & Dumez, H. (2015). Qualitative research revisited: Epistemology of a comprehensive

1096 approach. *International Journal of Entrepreneurship and Small Business, 26*(2), 154–170.

1097 https://doi.org/10.1504/IJESB.2015.071822

1098 DeSanctis, G., & Poole, M. S. (1994). Capturing the Complexity in Advanced Technology Use:

1099 Adaptive Structuration Theory. *Organization Science, 5*(2), 121–147.

1100 https://doi.org/10.1287/orsc.5.2.121

1101 Dover, J., & Settele, J. (2009). The influences of landscape structure on butterfly distribution and

1102 movement: A review. *Journal of Insect Conservation, 13*(1), 3–27.

1103 https://doi.org/10.1007/s10841-008-9135-8

1104 Driscoll, D. A., & Lindenmayer, D. B. (2012). Framework to improve the application of theory in

1105 ecology and conservation. *Ecological Monographs, 82*(1), 129–147.

1106 https://doi.org/10.1890/11-0916.1

1107 Dumont, A. M., Wartenberg, A. C., & Baret, P. V. (2021). Bridging the gap between the

1108 agroecological ideal and its implementation into practice. A review. *Agronomy for

1109 *Sustainable Development, 41*(3), 32. https://doi.org/10.1007/s13593-021-00666-3

1110 Durkheim, E. (1897). *Suicide* (G. Simpson, Ed.; J. A. Spaulding & G. Simpson, Trans.; Free Press).

1111 https://www.simonandschuster.com/books/Suicide/John-A-Spaulding/9780684836324

1112 Duru, M., & Therond, O. (2015). Livestock system sustainability and resilience in intensive
1113 production zones: Which form of ecological modernization? *Regional Environmental Change*, 15(8), 1651–1665. https://doi.org/10.1007/s10113-014-0722-9

1115 Duru, M., Therond, O., & Fares, M. (2015). Designing agroecological transitions: A review.

1116 *Agronomy for Sustainable Development*, 35(4), 1237–1257. https://doi.org/10.1007/s13593-015-0318-x

1118 Duru, M., Therond, O., Martin, G., Martin-Clouaire, R., Magne, M.-A., Justes, E., Journet, E.-P., Aubertot, J.-N., Savary, S., Bergez, J.-E., & Sarthou, J. P. (2015). How to implement biodiversity-based agriculture to enhance ecosystem services: A review. *Agronomy for Sustainable Development*, 35(4), 1259–1281. https://doi.org/10.1007/s13593-015-0306-1

1120
1121
Ebitu, L., Avery, H., Mourad, K. A., & Enyetu, J. (2021). Citizen science for sustainable agriculture
– A systematic literature review. *Land Use Policy*, 103.

1122
1123
Ebitu, L., Avery, H., Mourad, K. A., & Enyetu, J. (2021). Citizen science for sustainable agriculture
– A systematic literature review. *Land Use Policy*, 103.

1124 https://doi.org/10.1016/j.landusepol.2021.105326.

1125 EU SCAR. (2012). *Agricultural knowledge and innovation systems in transition – a reflection paper*.

1127 Gardezi, M., & Stock, R. (2021). Growing algorithmic governmentality: Interrogating the social construction of trust in precision agriculture. *Journal of Rural Studies*, 84, 1–11.

1129 https://doi.org/10.1016/j.jrurstud.2021.03.004

1130 Geels, F. W. (2002). Technological transitions as evolutionary reconfiguration processes: A multi-level perspective and a case-study. *Research Policy*, 31(8–9), 1257–1274.

1132 https://doi.org/10.1016/S0048-7333(02)00062-8

1133 Giampetro, M. (2002). Complexity and Scales: The Challenge for Integrated Assessment.
*Integrated Assessment*, 3(2–3), 2–3. https://doi.org/10.1076/iaij.3.2.247.13568

1135 Girard, N. (2014). Quels sont les nouveaux enjeux de gestion des connaissances? L’exemple de la transition agroécologique de la transition agricole. *Revue Internationale de Psychosociologie et de Gestion Des Comportements Organisationnels*, 19(49), 51–78.

1137
1138 Haan, N. L., Zhang, Y., & Landis, D. A. (2020). Predicting Landscape Configuration Effects on

1139 Agricultural Pest Suppression. *Trends in Ecology & Evolution*, 35(2), 175–186.

1140 https://doi.org/10.1016/j.tree.2019.10.003

1141 Hodson, R. (1999). Organizational Anomie and Worker Consent. *Work and Occupations*, 26(3),

1142 292–323. https://doi.org/10.1177/0730888499026003002

1143 Horlings, L. G., & Marsden, T. K. (2011). Towards the real green revolution? Exploring the

1144 conceptual dimensions of a new ecological modernisation of agriculture that could ‘feed the

1145 world.’ *Global Environmental Change*, 21(2), 441–452.

1146 https://doi.org/10.1016/j.gloenvcha.2011.01.004

1147 IAASTD. (2008). *Agriculture at a crossroads. International Assessment of Agricultural*

1148 *Knowledge, Science and Technology for Development*.

1149 Ingram, J. (2008). Agronomist–farmer knowledge encounters: An analysis of knowledge exchange

1150 in the context of best management practices in England. *Agriculture and Human Values*,

1151 25(3), 405–418. https://doi.org/10.1007/s10460-008-9134-0

1152 Ingram, J. (2015). Framing niche-regime linkage as adaptation: An analysis of learning and

1153 innovation networks for sustainable agriculture across Europe. *Journal of Rural Studies*, 40,

1154 59–75. https://doi.org/10.1016/j.jrurstud.2015.06.003

1155 Ingram, J. (2018). Agricultural transition: Niche and regime knowledge systems’ boundary

1156 dynamics. *Environmental Innovation and Societal Transitions*, 26, 117–135.

1157 https://doi.org/10.1016/j.eist.2017.05.001

1158 Kremen, C., & Miles, A. (2012). Ecosystem Services in Biologically Diversified versus

1159 Conventional Farming Systems: Benefits, Externalities, and Trade-Offs. *Ecology and*

1160 *Society*, 17(4). https://doi.org/10.5751/ES-05035-170440

1161 Kromp, B. (1999). Carabid beetles in sustainable agriculture: A review on pest control efficacy,

1162 cultivation impacts and enhancement. *Agriculture, Ecosystems & Environment*, 74(1–3),
1163 187-228. https://doi.org/10.1016/S0167-8809(99)00037-7

1164 Krzywoszynska, A. (2016). What Farmers Know: Experiential Knowledge and Care in Vine

1165 Growing. *Sociologia Ruralis*, 56(2), 289-310. https://doi.org/10.1111/soru.12084

1166 Kunz, T. H., Braun de Torrez, E., Bauer, D., Lobova, T., & Fleming, T. H. (2011). Ecosystem

1167 services provided by bats. *Annals of the New York Academy of Sciences*, 1223(1), 1-38.

1168 https://doi.org/10.1111/j.1749-6632.2011.06004.x

1169 Legrand, M., & Chlous, F. (2016). Citizen science, participatory research, and naturalistic

1170 knowledge production: Opening spaces for epistemic plurality (an interdisciplinary

1171 comparative workshop in France at the *Muséum national d'Histoire naturelle* [“National

1172 museum of natural History”]). *Environmental Development*, 20, 59-67.

1173 https://doi.org/10.1016/j.envdev.2016.10.002

1174 Lemtiri, A., Colinet, G., Alabi, T., Cluzeau, D., Zirbes, L., Haubruge, E., & Francis, F. (2014).

1175 Impacts of earthworms on soil components and dynamics. A review. *Biotechnologie*

1176 *Agronomie Societe Et Environnement*, 18(1), 121-133. https://popups.uliege.be/1780-

1177 4507/index.php?id=10881

1178 Levidow, L., Birch, K., & Papaioannou, T. (2012). Divergent Paradigms of European Agro-Food

1179 Innovation: The Knowledge-Based Bio-Economy (KBBE) as an R&D Agenda. *Science*,

1180 *Technology, & Human Values*, 38(1), 94-125. https://doi.org/10.1177/0162243912438143

1181 Li, X., Hess, T. J., & Valacich, J. S. (2008). Why do we trust new technology? A study of initial

1182 trust formation with organizational information systems. *The Journal of Strategic*

1183 *Information Systems*, 17(1), 39-71. https://doi.org/10.1016/j.jsis.2008.01.001

1184 Martin, G. (2015). A conceptual framework to support adaptation of farming systems –

1185 Development and application with Forage Rummy. *Agricultural Systems*, 132, 52-61.

1186 https://doi.org/10.1016/j.agsy.2014.08.013

1187 Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An Integrative Model of Organizational
1188 Trust. *The Academy of Management Review*, 20(3), 709–734.

1189 https://doi.org/10.2307/258792

1190 Merino, L., Minang, P. A., Sidorovich, A., Andrade, L., Aumeeruddy-Thomas, Y., Badola, R., Bai,

1191 X., & Benessaiah, K. (2019). *Chapter 2. Status and trends; indirect and direct drivers of change 2.1 Chapter 2.1. Status and Trends – Drivers of Change* (p. 245). IPBES.

1193 Miller, J. R. (2005). Biodiversity conservation and the extinction of experience. *Trends in Ecology & Evolution*, 20(8), 430–434. https://doi.org/10.1016/j.tree.2005.05.013

1195 Minet, J., Curnel, Y., Gobin, A., Goffart, J.-P., Mélard, F., Tychon, B., Wellens, J., & Defourny, P. (2017). Crowdsourcing for agricultural applications: A review of uses and opportunities for a farmsourcing approach. *Computers and Electronics in Agriculture*, 142, 126–138.
1198 https://doi.org/10.1016/j.compag.2017.08.026

1199 Moran, J., Byrne, D., Carlier, J., Dunford, B., Finn, J., Ó hUallacháin, D., & Sullivan, C. (2021). Management of high nature value farmland in the Republic of Ireland: 25 years evolving toward locally adapted results-orientated solutions and payments. *Ecology and Society*, 26(1). https://doi.org/10.5751/ES-12180-260120

1203 Moreddu, C., & Poppe, K. J. (2013). Agricultural Research and Innovation Systems in Transition. *EuroChoices*, 12(1), 15–20. https://doi.org/10.1111/1746-692X.12014

1205 Mourad, K. A., Hosseini, S. H., & Avery, H. (2020). The Role of Citizen Science in Sustainable Agriculture. *Sustainability*, 12(24), 10375. https://doi.org/10.3390/su122410375

1207 Mugerauer, R. (2021). Professional judgement in clinical practice (part 3): A better alternative to strong evidence-based medicine. *Journal of Evaluation in Clinical Practice*, 27(3), 612–623. https://doi.org/10.1111/jep.13512

1210 Pandey, D., & Garg, P. K. (2022). Do we need evidence for evidence-based medicine? *Journal of Evaluation in Clinical Practice*, 28(5), 731–732. https://doi.org/10.1111/jep.13577

1212 Potts, S. G., Imperatriz-Fonseca, V., Ngo, H. T., Aizen, M. A., Biesmeijer, J. C., Breeze, T. D.,
1213 Dicks, L. V., Garibaldi, L. A., Hill, R., Settele, J., & Vanbergen, A. J. (2016). Safeguarding pollinators and their values to human well-being. *Nature*, 540(7632), 220–229.
1214 https://doi.org/10.1038/nature20588
1215

1216 Robbins, B. G. (2016). What is Trust? A Multidisciplinary Review, Critique, and Synthesis.
1217 *Sociology Compass*, 10(10), 972–986. https://doi.org/10.1111/soc4.12391

1218 Ruck, A., van der Wal, R., S. C. Hood, A., L. Mauchline, A., G. Potts, S., F. Wallis DeVries, M., & Öckinger, E. (2024). Farmland biodiversity monitoring through citizen science: A review of existing approaches and future opportunities. *Ambio*, 53(2), 257–275.
1219 https://doi.org/10.1007/s13280-023-01929-x
1220

1221 Ryan, S. F., Adamson, N. L., Aktipis, A., Andersen, L. K., Austin, R., Barnes, L., Beasley, M. R., Bedell, K. D., Briggs, S., Chapman, B., Cooper, C. B., Corn, J. O., Creamer, N. G., 1222 Delborne, J. A., Domenico, P., Driscoll, E., Goodwin, J., Hjarding, A., Hulbert, J. M., ... 1223 Dunn, R. R. (2018). The role of citizen science in addressing grand challenges in food and 1224 agriculture research. *Proceedings of the Royal Society B: Biological Sciences*, 285(1891), 1225 1226 20181977. https://doi.org/10.1098/rspb.2018.1977
1227

1228 Savage, L. J. (1972). *The Foundations of Statistics* (Second Edition). Dover Publications.

1229 Shrader-Frechette, K. S., & McCoy, E. D. (1993). *Method in ecology: Strategies for conservation*.
1230 Cambridge University Press.

1231 Star, S. L., & Griesemer, J. R. (1989). Institutional Ecology, “Translations” and Boundary Objects: Amateurs and Professionals in Berkeley’s Museum of Vertebrate Zoology, 1907-39. *Social Studies of Science*, 19(3), 387–420. https://doi.org/10.1177/030631289019003001

1232 Strasser, B. J., Baudry, J., Mahr, D., Sanchez, G., & Tancoigne, E. (2019). “Citizen Science”?
1233 Rethinking Science and Public Participation. *Science & Technology Studies*, 32(2), 2.
1234 https://doi.org/10.23987/sts.60425
1235

1236 Šūmane, S., Kunda, I., Knickel, K., Strauss, A., Tisenkopfs, T., Rios, I. des I., Rivera, M., Chebach,
1238 T., & Ashkenazy, A. (2018). Local and farmers' knowledge matters! How integrating

1239 informal and formal knowledge enhances sustainable and resilient agriculture. *Journal of*

1240 *Rural Studies*, 59, 232–241. https://doi.org/10.1016/j.jrurstud.2017.01.020

1241 Symondson, W. O. C., Sunderland, K. D., & Greenstone, M. H. (2002). Can generalist predators be

1242 effective biocontrol agents? *Annual Review of Entomology*, 47, 561–594.

1243 https://doi.org/10.1146/annurev.ento.47.091201.145240

1244 Teixeira, H. M., Vermue, A. J., Cardoso, I. M., Peña Claros, M., & Bianchi, F. J. J. A. (2018).

1245 Farmers show complex and contrasting perceptions on ecosystem services and their

1246 management. *Ecosystem Services*, 33, 44–58. https://doi.org/10.1016/j.ecoser.2018.08.006

1247 Toffolini, Q., Jeuffroy, M.-H., Mischler, P., Pernel, J., & Prost, L. (2017). Farmers’ use of

1248 fundamental knowledge to re-design their cropping systems: Situated contextualisation

1249 processes. *NJAS - Wageningen Journal of Life Sciences*, 80, 37–47.

1250 https://doi.org/10.1016/j.njas.2016.11.004

1251 Tonelli, M. R. (2006). Integrating evidence into clinical practice: An alternative to evidence-based

1252 approaches. *Journal of Evaluation in Clinical Practice*, 12(3), 248–256.

1253 https://doi.org/10.1111/j.1365-2753.2004.00551.x

1254 Van de Gevel, J., van Etten, J., & Deterding, S. (2020). Citizen science breathes new life into

1255 participatory agricultural research. A review. *Agronomy for Sustainable Development*,

1256 40(5), 35. https://doi.org/10.1007/s13593-020-00636-1

1257 Van der Meersch, V., Billaud, O., San Cristobal, M., Vialatte, A., & Porcher, E. (2021). Landscape

1258 floral resources provided by rapeseed correlate with next-year reproduction of cavity-nesting

1259 pollinators in a national participatory monitoring program. *Landscape Ecology*.

1260 https://doi.org/10.1007/s10980-021-01353-0

1261 Vuillot, C., Coron, N., Calatayud, F., Sirami, C., Mathevet, R., & Gibon, A. (2016). Ways of

1262 farming and ways of thinking: Do farmers’ mental models of the landscape relate to their
1263 land management practices? ECOLOGY AND SOCIETY, 21. https://doi.org/10.5751/ES-

1264 08281-210135

1265 Walker, W. E., Harremoës, P., Rotmans, J., van der Sluijs, J. P., van Asselt, M. B. A., Janssen, P.,

1266 & Krayer von Krauss, M. P. (2003). Defining Uncertainty: A Conceptual Basis for

1267 Uncertainty Management in Model-Based Decision Support. Integrated Assessment, 4(1),

1268 5-17. https://doi.org/10.1076/iaij.4.1.5.16466

1269 Wezel, A., Bellon, S., Doré, T., Francis, C., Vallod, D., & David, C. (2009). Agroecology as a

1270 science, a movement and a practice. A review. Agronomy for Sustainable Development,

1271 29(4), 503-515. https://doi.org/10.1051/agro/2009004

1272

1273