# How diverse are farmers’ preferences for large-scale grassland ecological restoration? Evidence from a discrete choice experiment

Marie Asma Ben-Othmen, Mariia Ostapchuk

► To cite this version:

Marie Asma Ben-Othmen, Mariia Ostapchuk. How diverse are farmers' preferences for large-scale grassland ecological restoration? Evidence from a discrete choice experiment. Review of Agricultural, Food and Environmental Studies, 2023, 10.1007/s41130-023-00200-x. hal-04348251

HAL Id: hal-04348251

https://hal.science/hal-04348251v1

Submitted on 16 Dec 2023

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>
1

2 How diverse are farmers' preferences for large-scale grassland ecological

3 restoration? Evidence from a discrete choice experiment.

4

5

6 Dr. Marie Asma Ben-Othmen*

7 INTERACT Research Unit–Innovation, Land Management, Agriculture, Agro-Industries,

8 Knowledge, and Technology, UniLaSalle (Rouen Campus), France

9 marie-asma.benothmen@unilasalle.fr

10

11 Dr. Mariia Ostapchuk

12 INTERACT Research Unit–Innovation, Land Management, Agriculture, Agro-Industries,

13 Knowledge, and Technology, UniLaSalle (Rouen Campus), France

14 Mariia.ostapchuk@unilasalle.fr

15 *Corresponding author Dr. Marie Asma Ben-Othmen

16 Conflict of Interest declaration

17 The authors declare no conflict of interest.

18 Acknowledgments

19 We extend our gratitude to the Chambre Regionale d'Agriculture de Normandie (CRAN)
20 stakeholders who assisted us in identifying and contacting farmers. Special thanks also go to
21 Romane Gabriel, Héva-Rati Sénégal, and Anne-Claude Aza for their invaluable contributions
22 during their internships, including building the questionnaire, collecting data, and conducting the
23 initial econometric analysis. Additionally, we appreciate the reviewers for their constructive
24 feedback, which greatly improved the manuscript.

25

26

27

28

29

30

31

32 Authors' contribution

33 Marie Asma Ben-Othmen : conceptualization, methodology, writing (review and editing),
34 resources, data collection supervision, data analysis and investigation, funding acquisition, and
35 project management.
1 Mariia Ostapchuk: conceptualization, methodology, data collection supervision, data analysis

2 and investigation, writing (review and editing).

3 **Funding**

4 This research has received financial support through a grant by Normandy Regional Council

5 through a State-Region contract – Grands Réseaux de Recherche – Territory, Environment,

6 Risks, and Agronomy (Terra) and the research network Vegetal, Agronomy, Soil and Innovation

7 (VASI). It also received support from the Normandy Chamber of Agriculture - Chambre

8 Régionale Agriculture Normandie (CRAN).

9

10 **Declarations**

11 *Conflict of interest*: The authors declare no conflict of interest.

12 *Competing interest*: The authors declare they have no relevant or nonfinancial interests to disclose.

13 *Ethics approval/declarations*: Does not apply

14

15 *Consent to participate* (include appropriate statements): *Farmers and stakeholders involved in this study have all consented.*

16

17 *Consent for publication*: The authors declare that this is an original work that has not previously been considered for publication.

18

19 *Availability of data and material/ Data availability*: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request

20

21 *Code availability* (software application or custom code): The codes generated during and/or analyzed during the current study are available from the corresponding author on reasonable request

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45
# How diverse are farmers' preferences for large-scale grassland ecological restoration
## Evidence from a discrete choice experiment

### Abstract

Despite their multiple ecological benefits, semi-natural grasslands are threatened by intensification and conversion to cropland farming practices. Previous agri-environmental schemes have yet to prove successful in giving adequate incentives to farmers to engage in their restoration on a large scale. Through a discrete choice experiment conducted with 110 farmers in northeastern France, we show that participation in grassland restoration can be enhanced by introducing a conditional monetary premium contingent upon compliance with the enrolled farmland's scope and spatial distribution conditions. We also found that although farmers clearly prefer to avoid signing a grassland restoration contract, technical support is a significant contract attribute. Besides, our findings underline that small dairy farmers who seem knowledgeable about ecosystem services provided by grasslands prefer signing a collective contract that engages them with their neighbors toward achieving significant ecological outcomes of grassland restoration. We conclude with policy implications within the post-2023 CAP as well as a research perspective to improve the implementation of large-scale grassland ecological restoration measures.

JEL Classification: Q15, Q24, Q57

Keywords: grassland restoration, discrete choice experiment, land management, agri-environmental scheme, ecosystem services.
1

## Introduction

2

3
4
Semi-natural grassland is an integral part of the Western and Central European landscape
5
(Cousins et al., 2008) and is known to be among the most species-rich European ecosystems,
6
with many services that contribute to maintaining biodiversity. These include carbon
7
sequestration (Ricard & Viglizzo, 2020), chemical and physical flux regulation (Eekeren et al.,
8
2010), and pollution mitigation (Balezentiene and Kusta, 2012). Semi-natural grassland is also
9
the most threatened ecosystem in Europe due to the increased agricultural practices
10
intensification since the late 19th century (Kahmen & Poschlod, 2008). Indeed, their low
11
productivity made them prone to conversion into more profitable high-input agricultural land:
12
increased fertilization levels, regular reseeding, frequent mowing and intensive grazing,
13
deforestation, conversion to cropland, or even complete abandonment have led to a dramatic
14
decline in semi-natural grasslands' ecological functions including biodiversity, soil, water, and
15
overall climate protection (Kahmen and Poschlod 2008; WallisDeVries et al., 2000). Reversing
16
this damage is challenging as grassland may recover very slowly after being altered (Bommarco
17
et al., 2013).

18

19
Grassland restoration and sustainable management are critical to the European Union's endeavor
20
to meet biodiversity and climate challenges. Following the reform of the Common Agricultural
21
Policy (CAP) in 2003, EU member states have been required to establish and enforce grassland
22
conservation and maintenance standards at the national level. One of these standards is
23
maintaining the ratio of land designated as permanent pasture in relation to the country's total
24
agricultural area.<fnref n="1" /> On the other hand, to receive direct payment -qualify for the cross-compliance
25
requirements- farmers must have complied with conditionality within standards that aim at
26
grassland preservation. Furthermore, since 1992, the CAP – within its second pillar – has been
27
promoting Agri-Environmental Measures (AEMs), renamed Agri-Environmental and Climatic
28
Measures (AECMs) to incentivize farmers to adopt environmentally-friendly practices, support
29
rural development, and mitigate environmental damages (Tyllianakis & Martin-Ortega, 2021).
30
More recently, the 2023 CAP reform prescribed the AECMs implementation within a three-layer
31
system, including cross-compliance and eco-schemes in Pillar one. While the former makes it
32
compulsory for farmers to respect good agri-environmental practices to be eligible for financial
33
aid, the latter allocates 25% of the first Pillar aid to farmers who agree to respect environmental
34
specifications. The adoption of the new AECMs is limited because they are more demanding for
35
farmers compared to eco-schemes. Thoyer & Lécole (2023) point out that even though eco-
36
schemes are expected to cover between 60 and 80% of the European utilized agricultural area in
37
the next decade, the coverage rate of AECMs was only 13% in 2019. Despite successive
38
reinforcements, particularly since 2014 with the greening of the first pillar aid, the stack of
39
measures combining regulatory obligations and financial incentives is still struggling to
40
convince farmers.

41

42
43
44
45
46
47
Agri-environmental measures targeting grassland restoration and conservation are particularly
relevant in France, where agriculture is the dominant land use as it covers more than half of the
country's total land area (Corine Land Cover 2012). France is also a leading producer of
agricultural products in the EU, producing approximately a quarter of the EU cereals and cattle,
and has been the largest whole fresh cow milk producer in the last decade (Agreste, 2020).
Grassland areas have been, however, alarmingly shrinking over time. While they represented

<footnote n="1">¹ land used under grasses or other herbaceous that are not included in the crop rotation of the farm for five years or longer</footnote>
43% of the agricultural area in 1970, this ratio dropped to 36% in 1988 and only 27% in 2010

(Puydarrieux & Devaux, 2013).

3

Within the "French Grassland Conservation Program," two agri-environmental schemes have specifically targeted grassland conservation: the "Premium for the maintenance of extensive livestock systems" established in 1993 and replaced in 2003 by the "Agri-environmental Grass Premium." These measures are five-year contracts that compensate farmers for extensive grassland management on their farms (box 1). In 2000, France launched a new Payments for Ecosystem Services Program as part of the National Plan for Rural Development, which included two subsidies for grassland conservation (measures 19 and 20) within the "Sustainable Agriculture Scheme." The voluntary five-year contract between a farm and the government aims to encourage farmers to take action to preserve the environment and improve production quality.

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

These measures include subsidizing the colonization of grasslands by shrubs and trees and their extensive management through mowing. There is a major difference between this contract and previous programs aimed at preserving grasslands. Unlike previous schemes, this contract does not mandate any specialization rate for the farms. With this sustainable agriculture program, farmers who previously couldn't receive "premiums for the maintenance of extensive livestock systems" and "agri-environmental premiums" due to their small amount of grassland can now enroll. The eligibility requirements of this measure centered, however, on limiting the use of nitrogenous fertilization, prohibiting plowing or carrying out heavy works on grassland, banning chemical weed management (except for localized treatment), and maintaining all the biodiversity elements in the enrolled areas.<fnref n="2" />

Insert box 1 here

24

25

## Box 1 – Agri-environmental schemes targeting grassland conservation in France

26

27

28

### The Premium for the maintenance of extensive livestock systems

29

30

31

Implemented in 1993, this scheme was a five-year contract during which farmers committed to dedicating some of their farm plots to grassland. The payment they receive in this contract was between €35/hectare/year to €46/hectare/year and conditional on three aspects:

32

33

34

1. The specialization rate (share of permanent and temporary grassland in the total utilized agricultural area) must be higher than 75%

35

36

37

2. Livestock units' density per hectare must be maintained below 1.4 per livestock units

38

39

3. Nitrogen use is not authorized to exceed 70 kilograms per hectare of grassland (introduced in 1998)

40

41

### The Agri-environmental Grass Premium

42

43

Implemented in 2003, the eligibility criteria for the agri-environmental grass premium are similar to the extensive livestock systems maintenance premium, with three main exemptions, depending on the department where the scheme takes place:

44

45

46

47

48

1. The specialization rate can be set between 50% and 75%

2. Livestock units' density per hectare can be set between 1.4 and 1.8

<footnote n="2">² See Appendix for a list of additional products and fertilizers on the plots dedicated to grassland conservation</footnote>
Initially, grassland conservation schemes were designed to address two main objectives. The first was to stabilize the amount of grassland areas within the agricultural landscape and prevent the conversion of grasslands into cropland (Xiao et al., 2015). The second was to support farmers in developing sustainable grassland management practices that could contribute to farmes economic and environmental performance, particularly in areas where agriculture faces decline threats (Princé & Jiguet, 2013). Although the financial compensation for agricultural yield loss resulting from complying with management conditions is offered within grassland AESs, these schemes have not successfully encouraged massive enrollment by French farmers. This picture contrasts with the overall European farmers' participation in similar schemes. Zimmermann and Britz (2016) historically document this trend in their study on European farmers' participation in AES between 2000 and 2009. The findings put forth that farmers were most likely to participate in AES aimed at managing grassland and semi-natural forage areas in the eastern part of the E.U., where payments were higher (above €300 per hectare) than in the western region, including France (where payments were under €100 per hectare). Other studies have also shown that the AES design at the farm scale, solely focusing on financial incentives to ensure farms' economic performance, has not been successful (Allaire et al., 2009; Benoit & Patsias, 2017) and is still considered to be far from being cost-effective (Chabé-Ferret & Voia, 2021). This may explain French farmers limited adoption of grassland AESs and why the degradation of these areas is still an ongoing problem.

Growing evidence indicates that grassland AESs designed at the farm level fail to prevent biodiversity loss on agricultural land (Nitsch et al., 2012). While some studies suggest that AESs emphasizing a reduction in local management intensity can contribute to biodiversity conservation (Attwood et al., 2008), others have attempted to link the implementation costs of such schemes with the biodiversity outcomes in grassland settings but failed to support this claim. For example, Zechmeister et al. (2003) have tried to correlate the subsidy amount received by Austrian farmers through AESs for grassland management with the plant species richness of those areas but found no positive relationship between subsidy amounts and botanical diversity. However, the authors have demonstrated that if plant species richness is to be preserved in these grasslands, farmers must benefit from AESs that are clearly defined at a larger scale to achieve the appropriate management objectives. Hasler et al. (2022) endorse this assertion and describe what is known to be an on-farm leakage. According to the authors, reducing environmental and climate impacts in a particular farm field might co-occur with increased environmental and climate impacts elsewhere on the farm. Thus, while a field-level analysis could be misleading, landscape-scale evaluations will likely measure the practical impacts.

Conservation actions designed at a large-scale or landscape scale have proven effective for many ecosystems (Myles et al., 2013). Several studies have emphasized the importance of managing geographically proximate or adjacent plots to create contiguous habitats of a critical minimum size and establish connections between patches to promote species mobility and benefit biodiversity conservation (Margules & Pressey, 2000). In this context, collective action AESs implemented to ensure the proximity of farms could not only result in more significant environmental benefits (Sutherland et al., 2012), but also bridge social capital (de Krom, 2017). Therefore, understanding the conditions under which farmers would be willing to preserve grassland at a larger scale is essential in addressing one of the critical challenges policymakers face: implementing measures that protect and enhance the ecological quality of grasslands.
This study aims to fill the gap in informative research on two key questions: (1) what conditions would prompt farmers to participate in an AES aimed at grassland restoration at a large scale, and (2) how do farms and farmers' characteristics influence their preferences for participation? Overall, the research seeks to understand better the heterogeneity of farmers' preferences and how this information can inform the design of an improved and differentiated grassland restoration AES. Additionally, the study aims to uncover why some farmers choose to maintain the status quo and to estimate farmers' willingness to accept (WTA in euro/ha/year) for different features of the grassland restoration scheme.

Our approach sets itself apart from prior studies as we consider both collective action contracts and a farm-scale agglomeration bonus for the land involved in the scheme. This approach could lead to more significant ecological benefits at the landscape scale resulting from grassland conservation efforts. Our study utilized a Discrete Choice Experiment (DCE.) to evaluate farmers' preferences regarding adopting grassland agri-environmental schemes in Normandy, a region in northwestern France. The contracts for grassland conservation in our experiment are based on management plans prescribed by previous agri-environmental measures within the French grassland conservation program. We aim to gain insight into the varying acceptance levels of grassland conservation contracts based on their attributes. These include but are not limited to the annual payment, the percentage of farmland dedicated to grassland restoration, the availability of free technical support and advisory services, the option to sign a collective contract, and the potential for an additional conditional bonus. This bonus is offered to farmers only if they agree to enroll in a minimum contiguous area of the farm.

The paper is structured as follows: It begins by providing a background on agri-environmental schemes adoptions by farmers, followed by a description of the materials and methods employed, including the Normandy case study and survey design. Results and conclusions are presented in later sections.

26

27

# 1. Background to agri-environmental schemes adoption by farmers

28

A significant body of research indicates that farmers' participation in agri-environmental schemes is closely linked to their behavioral responses and attitudes (Cullen et al., 2020; Defrancesco et al., 2008; Wilson & Hart, 2001). Additionally, the suitability of the schemes to farmers' specific farming systems plays a crucial role in determining their participation (Coyne et al., 2021; Falconer, 2000; Lastra-Bravo et al., 2015). However, a consensus has yet to be found in the literature on this dependency's exact nature and extent (Defrancesco et al., 2008).

Potter & Gasson (1988) and Brotherton (1989) developed one of the earliest methodological frameworks for predicting participation in voluntary land diversion schemes aimed at converting arable land into grassland in environmentally sensitive areas in the United Kingdom. Their findings highlight two primary components that affect the participation: farmers' factors, such as their willingness and situational readiness to participate in the scheme, and schemes' factors, such as the incentives or benefits that the scheme offers and make it attractive for farmers to convert some of their lands.

More recently, Dessart et al. (2019) proposed an extended classification of behavioral factors affecting AESs adoption, including dispositional (e.g., environmental awareness, risk perception), social (e.g., interpersonal relationships), and cognitive (e.g., knowledge and competencies) factors. Mills et al. (2017) expanded on this idea and suggested that farmers' attitudes and motivations towards AESs should be differentiated in such a way as to facilitate the understanding of correlations between the willingness to adopt (attitudes towards both profit and environment), and the ability to adopt AESs (alignment with the economic characteristics of the farm). These considerations have triggered an interest in identifying (1) the heterogeneity of farmers' characteristics, including their attitudes, motivations, and ability to engage with AESs beyond the sole profitability rationale (Cullen et al., 2020), and (2) farmers' preferences for
1 AESs through identifying parameters that enhance participation in the offered schemes

2 (Vaissière et al., 2018).

3

4

5

6

7 Understanding farmers and farms' characteristics

8 The literature analyzing farmers' responses to AESs has traditionally divided the factors

9 explaining participation into two categories: internal and external (Coyne et al., 2021; Wilson,

10 1996; Wilson & Hart, 2001). Internal factors include farmers' attitudes, beliefs, and values, as

11 well as their socio-demographic characteristics. External factors, on the other hand, refer to the

12 farm's geographical characteristics, size, and whether the proposed AES fits the farm

13 management plan (see Coyne et al. (2021) for a review adapted to the context of dairy farming).

14 Following this line of thought, Busck and Kristensen (2014) suggest distinguishing between

15 factors and structures. According to the authors, factors such as a farm's geographical location

16 and size should be acknowledged as fixed structures that are difficult to alter. Conversely,

17 internal factors such as attitudes, motivations, and values are more conducive to active decision-

18 making and are, therefore, more susceptible to being influenced.

19 The literature recognizes that attitudes are not a direct precursor to behavior but simply one

20 factor heavily influenced by contextual factors, such as education, social norms, and farm

21 history (Ahnström et al., 2009). While Hayden et al. (2021) refer to the relationships between

22 farmers' attitudes and behavior as "farmer's mentality," Willock et al. (1999) argue that farmers'

23 attitudes are linked to a set of goals, including job satisfaction, status, quality of life and

24 management goals. Moreover, intrinsic motivations such as the need for achievement and

25 altruistic reasons have been identified as specific objectives for farmers (Hansen & Greve, 2014;

26 Öhlmér et al., 1998). In particular, the intrinsic value placed on the environment has been

27 recognized as a crucial driver of farmers' engagement in agri-environmental measures (Bottazzi

28 et al., 2018; Cullen et al., 2020). This indicates that environmental management has increasingly

29 become essential to farming practices aligned with farmers’ personal and business goals

30 (Wheeler et al., 2018).

31 Research has shown that farmers' environmental attitudes are multi-dimensional in nature. For

32 example, Reimer et al. (2012) found in their study of farmers' conservation behavior in Indiana

33 (USA) that farmers motivated by off-farm ecological benefits and those who identified with

34 stewardship responsibilities were likelier to adopt conservation practices. In a more recent study,

35 Despotović et al. (2021) attempted to evaluate this multi-dimensional property by measuring a

36 broad set of environmental attitudes and knowledge to assess an environmental awareness

37 construct. Their findings indicated that environmental knowledge, which refers to the extent of

38 farmers' understanding of environmental problems at various levels and their potential solutions

39 (Bamberg & Möser, 2007), is the most significant predictor of farmers' environmental awareness

40 construct.

41 Relationships between farmers' attitudes and behaviors have been explored within studies based

42 on the conceptual microeconomic models grounded in the theory of planned behavior (Ajzen,

43 1991) and derived from the theory of reasoned action (Ajzen, 1988; Ajzen & Fishbein, 1980).

44 While the former state that behavioral performance is directly linked to the intention an

45 individual tends to perform, the latter suggests that attitudes, social norms, and perceived

46 behavioral control influence intentions and behaviors. Both theories have helped develop social-

47 psychology models to explore farmers' behavior change and adoption of AESs.
For example, Borges & Oude Lansink (2016) provided an example of how farmers' intentions to use improved natural grassland in Brazil are mainly influenced by their perceptions of the social pressure to engage in this practice (subjective norm), followed by their perceptions about their own capability (perceived behavioral control), and finally their evaluation of the improved natural grassland use (attitude). These findings are consistent with other studies highlighting the importance of social factors, such as neighboring farm influence (Defrancesco et al., 2008) and perceived resources (Daxini et al., 2018; Senger et al., 2017) as critical drivers of participation in AESs. Siebert et al. (2006) support these observations in their review of 160 publications on farmers' attitudes toward AESs adoption in six European countries. The authors conclude that, although economic factors are essential for farmers, they are not the only determinants influencing their decision-making since cooperation within farmers' communities is also a significant factor in determining their behaviors. Interestingly, a systemic approach that includes farmers' socioeconomic characteristics and psychological factors to explain their behavior has received scarce attention in the literature. This claim is supported by Foguesatto et al. (2020) review of 63 papers on farmers' adoption of sustainable agricultural practices worldwide. The authors found that most of these studies should have considered psychological factors in modeling farmers' decision-making processes.

## Understanding farmers' preferences for AES and their attributes

Apart from farmers' personal traits, participation in AESs depends on the design features of the schemes themselves. Because farms are economic entities, AES implies trade-offs between profits and the different measures proposed by the scheme, which vary in their levels of profitability. Farmers' perception of whether the financial compensation offered by the scheme allows sustaining profit maximization, risk minimization, and long-term farm viability has long been recognized as a crucial economic driver of their participation (Ducos et al., 2009; Espinosa-Goded et al., 2013). Certainly, farmers require financial compensation to cover not only their productivity losses but also transaction cost and, if possible, a risk premium (Christensen et al., 2011). This indicates that the required financial compensation also depends on other features of the AES.

Several empirical studies have examined farmers' willingness to participate in AESs by assessing their monetary valuation of the different contract attributes using the economic concepts of willingness to accept (Espinosa-Goded et al., 2010; Villanueva et al., 2015). These studies are grounded in the theoretical framework of Lancaster Consumer Theory and the Random Utility Theory (Lancaster, 1966; McFadden, 1973), which suggest that farmers' decisions about the contracts hinge on the relative level of utility they derive from the contract's attributes.

Mainly, the Discrete Choice Experiment (DCE), a stated preference valuation technique using survey methods to evaluate individual preferences in hypothetical scenarios (Louvière et al., 2000), has been extensively used to model farmers' decisions regarding participation in environmentally friendly practices within a contract with public authorities (Chèze et al., 2020; Lienhoop & Brouwer, 2015; Ridier et al., 2021; Vaissière et al., 2018; Villanueva et al., 2015). By modeling farmers' choices, these studies estimate how they balance different levels of AES attributes against payment per hectare while identifying ways to enhance farmers' participation by testing different contract attributes. For example, Emery & Franks (2012) have shown that offering support to farmers as part of the contract may reduce the financial compensation required for enrolling in the contract. Other attributes tested include the area of farmland to be enrolled in the scheme (Espinosa-Goded et al., 2010), the opportunity to return to agriculture after the end of the contract (Lienhoop & Brouwer, 2015), the administrative burden (Christensen et al., 2011), and the opportunity offered by the contract to benefit from free
advisory services ( Espinosa-Goded et al., 2010). The DCE has also been used to explore innovative designs of AESs, which includes offering incentives for spatial coordination of conservation practices across farms by testing subsidies paid for conserving land units bordering each other (i.e., agglomeration bonus)

(Parkhurst & Shogren, 2007). These studies have yielded, however, mixed results. According to Kuhfuss et al. (2016) a collective bonus that is conditional in nature and offered in conjunction with the standard AES payment can not only enhance participation but also shift farmers' pro-environmental social norms, increase land enrollment (nudging effect), and lower overall budgetary costs. On the other hand, Villanueva et al. (2015) found that collective participation in AESs could reduce public transaction costs but may also make farmers reluctant due to increased control by local authorities and other farmers.

In light of the above, in this study, we aim to analyze farmers' preferences for grassland conservation contracts in Normandy, France.

## 2. Materials and Methods

### 2.1. Methodology

#### 2.1.1. Modelling farmers' decision to enroll in a grassland conservation AES

Farmers' decision to enroll in a grassland ecological restoration contract will result from comparing the utility they derive from each proposed alternative. According to Lancaster's theory (Lancaster, 1966) and the Random Utility Theory (McFadden, 1973), if we assume that the utility depends on choices made from a G set of all alternatives, the random utility function can be specified as follows:

$$U_{itn} = V_{itn} + \epsilon_{itn} \quad [1]$$

Where $U_{ixn}$ is the utility for farmers $n \in \{1, ..., N\}$ derived from choosing alternative $i \in \{1, ..., I\}$ within the choice question t.

Each attribute is linked with a utility level. The utility encompasses a deterministic component which is the indirect utility level: $V_{itn}$ and a stochastic component $\epsilon_{itn}$.

The indirect utility levels $V_{ixt}$ is derived from the -K- observable contract's attributes denoted by $X_i = (x_{i1}, ..., x_{ik})$ in addition to a set A including the farmer' and farms' characteristics (e.g., attitudes, age, income, farm size, cropping systems, etc.), which are denoted by $Y_n = (Y_{n1}, ..., Y_{nA})$.

Hence, alternative $i$ is chosen over alternative $j$ if $U_{itn} > U_{jtn}$.

The deterministic component of the utility function can be expressed by introducing a vector column following the linear specification as $\beta_n = (\beta_{n1}, ..., \beta_{k1})'$. The coefficients quantify the linear effect of the -K- observable attributes on utility and can be specified for each farmer ($n$).

We also introduce an alternative specific constant (A.S.C.) corresponding to the status quo to capture any potential unobserved effect (omitted variables) on the utility function. Thus, we define the dummy variable $ASC^{S.O.}$, which takes the value 1 if the status quo alternative is selected and 0 otherwise.
Thus, the model is specified so that the probability of selecting alternative -i- depends on the alternative's attributes $X_i$, the alternative specific constant $ASC^{SQ}$ of the status quo alternative, and farmers' and farms' characteristics $Y_n$. Since the indirect utility function $V_{itn}$ is considered to be additive, equation [1] becomes:

$$U_{itn} = (\mu + Y_n ASC^{SQ})SQ + X_i(ASC_i^{SQ}, \dots, ASC_A^{SQ})' + \epsilon_{itn} [2]$$

The column vector of the coefficient $ASC^{SQ} = (ASC_i^{SQ}, \dots, ASC_A^{SQ})'$ captures the effects of farmers' and farms' characteristics on the status quo utility.

## 2.1.2. Estimating the willingness to accept grassland conservation AES

Farmers' willingness to accept compensation for the contract's different attributes is explored through implicit prices. These prices capture welfare measures that may translate into individuals' willingness to accept (WTA) or willingness to pay (WTP) estimates (Louvière J et al., (2002). We estimate farmers' Willingness to Accept attribute K in grassland restoration contracts.

$$WTA_k = -\frac{\beta_k}{\beta_m} \quad [3]$$

Where $\beta_k$ and $\beta_m$ are the parameters representing the attribute k and the monetary attribute (payment), respectively.

Individual WTA of farmers (i) for each attribute k is estimated as follows:

$$WTA_{ki} = -\frac{\beta_{ki}}{\beta_m} \quad [4]$$

## 2.2. The Normandy case study and survey design

Normandy is considered the top agricultural region in France, as 70% of its land is used for farming (Puydarrieux & Devaux, 2013). The region has a long history of incorporating grassland into its agricultural landscape, which has resulted in the production of unique cheeses and high-quality milk from cows that are fed on grass (known as terroir). Additionally, most farms in Normandy have a Utilized Agricultural Area (UAA) of over 100 hectares, with the average being 73.6 hectares.

Agriculture in Normandy derives 60% of its wealth from dairy, cereals (mainly in Eure), and livestock (mainly in Orne). In brief, agricultural practices in Lower Normandy are dominated by dairy farming, with up to 75% of agricultural activities involving dairy. This region, particularly La Manche, has the largest dairy herd in France. On the other hand, Higher-Normandy is specialized in cereals and livestock (Figure 1).

Insert Figure 1 here
<figure><img src="image_2.png" /><figcaption>Figure 1. Normandy's geographical localization</figcaption></figure>

Even though permanent grassland represented 47% of the UAA in 2016, this share decreased to reach 38% in 2020 in favor of arable lands that have progressed, particularly for the cultivation of cereals and oilseeds (Agreste, 2020). At the same time, the average size of groups of contiguous plots used for permanent grassland, which was less than 6 hectares in 2016, has been continuously decreasing, resulting in a growing gap between land use and livestock production. This trend poses a significant threat to local biodiversity, as Huyghe (2005) noted. Moreover, in the last decade, French authorities have enforced more stringent regulations on farms in Normandy, restricting the number of permits to convert grassland to cropland (Ben-Othmen & Ostapchuk, 2019). These regulations follow the collective ratio of permanent grassland areas specified in the Pillar 1 mandatory green payment. However, this rigid authorization system has proved to be challenging for both farmers and authorities, as it further restricts the already limited bureaucratic process and complicates policy implementation while imposing strict regulations on farmers.

Within this study, we worked with three Normandy local authorities (Orne, Calvados, and Seine-Maritime) to design and test new incentives to improve farmers' adoption of grassland ecological restoration. A shared interest emerged between participants in designing new contracts supporting the already existing farm-based management approach to grassland restoration.<fnref n="3" /> The overreaching goal was to maintain a voluntary approach to adopting grassland restoration while overcoming the challenges associated with conversion authorization regimes and advocating for the various ecological benefits that large-scale grassland restoration can provide. As a result, we were tasked with providing insights into the diverse range of preferences among farmers and recommending contract attributes that would best suit Normandy's farmers and their farming practices.

In addition to the contract attributes, the questionnaire aimed to elicit farmers' environmental awareness and knowledge about the ecosystem services of semi-natural grassland. Hence, farmers were asked to state, on a rating scale, their agreement or disagreement with several statements. We used items and scales that have demonstrated success in previous empirical studies of environmental attitudes in agriculture, such as those conducted by Siebert et al. (2010) and Vogel (1996).

<footnote n="3">³ A description of the farm-based management plan presented to farmers during the survey in available in annex 1</footnote>
Three focus groups with farmers (between 8 and 10) were conducted between February and
September 2018 and helped determine the attributes and levels of grassland conservation
contracts and prepare follow-up questions for the survey. The questionnaire was then pre-tested
with ten farmers from Orne, Calvados, and Seine-Maritime departments and adjusted
consequently.<fnref n="4" /> Factor analysis was also used within the pre-test of the attitudinal questions to
prepare them for the final questionnaire.<fnref n="5" />

To ensure that farmers answered as truthfully as possible, we have had to deal with potential biases, such as the social desirability bias – resulting from respondents conforming to the social norm -. Hence, respondents were informed that the survey was entirely anonymous (Hoffman et al., 1996; Larson, 2019). Furthermore, we followed Cummings and Taylor (1999) recommendations to reduce the potential hypothetical bias by including a cheap talk script in the questionnaire.

The final version of the questionnaire included five sets of questions. (1) structural characteristics of the farm (used agricultural area, economic and technical orientation), (2) farmers' characteristics (gender, age, off-farm income, education), (3) information about the agricultural practices (soil management, stoking rates, the yield obtained) (4) choice set and follow up questions (as described in sections 2.2.1, 2.2.2. and 2.2.3), and (5) farmers' attitudes towards the environment and their knowledge about grasslands multiple ecosystem services. The questionnaire required 20 minutes to be completed. Thanks to a list of contacts provided by local farmer union bodies (Chambre d'Agriculture), 1310 farmers were contacted, informed about the study's goals, and asked if they would accept participating in the survey by arranging face-to-face meetings on their farms' sites from February through September 2019.

## 2.2.1. Grassland restoration contracts attributes description

As previously stated, within this study, we deploy the discrete choice experiment framework to create a survey setting where farmers are requested to choose their preferred grassland conservation contract from two hypothetical contracts. Still, if none of the contracts fits their expectations, they can keep their current practices by selecting the status quo option. Each time, three alternative contracts, described in choice cards, were successively submitted to farmers who were requested to state their preferred contract.

The terms and conditions of the contract were first presented to the farmer. These are as follows
1. The grassland conservation contract is additional to regulatory obligations; it could not
replace existing cross-compliance requirements within the CAP, 2. Farmers must agree to allow
access to their farmland for ecological monitoring and compliance control by local authorities, 3.
For five years, there is no right to withdraw from the contract.

Grassland conservation contracts have five attributes as described in Table 1. The first attribute is the Land Enrollment Ratio. Ecologists suggest that permanent grassland promotes habitats with rich biological values and high species richness depending on a specific minimum area covered by the grass on the farmland and how it is managed by the farmer to ensure viability

<footnote n="4">⁴ In summer 2018, we attended and shared information about the goal of our study during the annual forum *Prairiales Normandie 2018*, the largest annual gathering of Normandy and France's great west practitioners and professionals sharing a typical interest in grassland and fodder production. This gathering has also offered opportunities to start the conversation with farmers about their opinion regarding new grassland ecological restoration contracts and collect contacts http://www.prairiales-normandie.fr/</footnote>

<footnote n="5">⁵ Final questions are available in annex 2</footnote>
(Waldén & Lindborg, 2018). Hence, three levels of land enrollment were presented to farmers: (1) the farmer chooses to enroll at least 5% - which is like other "greening" contracts requirements that he (or she) would experience in a "real-life" context - (2) 10% or (3) 15% of his (or her) farmland in grassland ecological restoration.

The second attribute is the availability of *Free Technical Support and Advisory Services*. This attribute has emerged from the focus group consultation as a requirement that farmers could consider necessary as they were interested in getting additional knowledge about environmentally friendly techniques to enhance ecosystem services from extensive grassland management. Hence, the two levels of this attribute are "available" or "not available" in the scenario.

The third attribute is a *Premium*, a conditional monetary bonus associated with the farmer's commitment to achieving additional ecologically sound conditions suggested in the scenarios he (or she) can accept or not. Accordingly, the farmer is requested to enroll at least 4ha of his farmland in grassland ecological restoration that must be included on the same parcel (e.g., in one piece); hence, accepting the *Premium* implies the farmer getting €250/ha/year payment in addition to the baseline payment. In the scenario, the two levels of this attribute are "available" or "not available."

The fourth attribute, *Collective participation*, sought to investigate farmers' willingness to participate in a landscape-scale grassland conservation effort by ensuring in the contract that the farms' proximity and farmers' collaboration contribute to significant ecological outcomes derived from the grassland conservation (Sutherland et al., 2012). Hence, the attribute included two levels. 1) *Collective Action Contract* requires that the farmer work closely with at least three of his neighbors in cross-farm boundary and perform collective actions to ensure better outcomes from grassland restoration. 2) *The Individual Contract* requires the farmer to enroll in a contract without any obligation of collective action with the neighboring farmers.

The fifth attribute is the *Financial Compensation* of €130/ha/year, €230/ha/year, or €330/ha/year. The current payments for agri-environmental schemes in Normandy have guided the choice of €130/ha/year as a lower limit<fnref n="6" />. The scope of these levels has been discussed and approved during the focus group meetings.

*Insert Table 1 here*

<table><caption>**Table 1.** Attributes' description, levels, and their coding to characterize grassland ecological restoration contracts submitted to farmers</caption><thead><tr><th>Contract's attributes</th><th>Description</th><th>Levels</th><th>Coding</th></tr></thead><tbody><tr><td>Land Enrollment</td><td>The percentage of farmland to be enrolled by the farmer in grassland restoration</td><td>5%,<br>10%,<br>15%,<br>Opt-out</td><td>1<br>2<br>3<br>0</td></tr><tr><td>Technical support</td><td>Technical support and advisory services for farmers about best management practices of farming systems using permanent grassland.</td><td>Available<br>Not available<br>Opt-out</td><td>1<br>-1<br>0</td></tr><tr><td>Premium</td><td>Farmers are requested to enroll at</td><td>Available</td><td>1</td></tr></tbody></table>

<footnote n="6">⁶ The two other levels are like the current AESs compensations for temporary pasture and fields vegetable crops, respectively.</footnote>
<table><tr><td></td><td>least 4 ha of their farmland in grassland conservation. These must be located on the same parcel (e.g., in one piece) to benefit from €250/ha/year in addition to the yearly (baseline) payment.</td><td>Not available in the contract<br/>No premium because this is the opt-out response</td><td>-1<br/>0</td></tr><tr><td rowspan="3">Collective participation</td><td rowspan="3">Farmers' collective participation is the participation of at least three farmers in a contract involving collaboration between them</td><td>Collective contract</td><td>1</td></tr><tr><td>Individual contract</td><td>-1</td></tr><tr><td>Opt-out</td><td>0</td></tr><tr><td rowspan="4">Financial compensation</td><td rowspan="4">Payment received annually by the farmer per enrolled hectare</td><td>€130/ha/year</td><td>130</td></tr><tr><td>€230/ha/year</td><td>230</td></tr><tr><td>€330/ha/year</td><td>330</td></tr><tr><td>Opt-out</td><td>0</td></tr><tr><td rowspan="2">Opt-out (status quo)</td><td rowspan="2">The farmer prefers to keep his/her current practices</td><td>Opt-out</td><td>1</td></tr><tr><td>Grassland contract 1 or 2</td><td>0</td></tr></table>

### 2.2.2. The discrete choice experiment

To assess and understand the relative importance of each attribute of the contract for farmers, we combined their levels into scenarios intended to describe different grassland conservation contracts. Since we have five attributes with 2 to 3 levels each and two choice cards, the complete factorial design generates 5112⁷ different choice cards. Following the questionnaire pre-test, the OPTEX procedure in S.A.S. was used to search for optimal experimental design (Street et al., 2005) and led to ten combinations.

The combinations were then organized in pairs, generating five choice cards. To each choice card, an opt-out answer declaring "I prefer to keep my current agricultural practices" and referring to the status quo was added (Figure 2).

More specifically, performing a two-step procedure led to the five-choice cards. Firstly, an orthogonal efficient design with all the initial parameters set to zero was performed using the dedicated software (Ngene) to determine the specific previous parameters of the sample and to choose the pre-test study choice cards. The obtained D-error was 0.021019 was judged appropriate. Then, a Multinomial Logit Model was applied to analyze the pre-test study data to understand better each attribute's prior parameters' values. Secondly, a Bayesian efficient design using the pre-test study parameters was performed and helped make the final study cards choice (using Ngene software) (Chaloner & Verdinelli, 1995).

In order to avoid any confusion with the opt-out (status quo) answer and following the literature (Bech & Gyrd-Hansen, 2005; Vaissière et al., 2018), we opted for effects coding rather than dummy coding for the *Premium*, *Technical Support*, and *Collective Participation* variables. Their presence in the grassland conservation contract is coded 1. In contrast, the opt-out alternative has, by definition, no premium, no technical support, and no collective participation

<footnote n="7">⁷ The full factorial design generates (3x2x2x2x3) x ((3x2x2x2x3)-1)=5112 choice cards.</footnote>
1 and thus coded zero. The absence of *Premium, Collective Participation, and Technical Support* in a contract is coded -1⁸.

2

3

4 Insert Figure 2 here

5

<figure><img src="image_3.png" /></figure>

6

7

<caption>**Figure 2** – Example of the choice sets</caption>

8

9

### 2.2.3. Follow-up questions

10 Follow-up questions asked at the end of the presentation of the scenarios to farmers were drawn with the help of the focus group participants. These questions helped confirm the quality of the responses to the choice experiment and improved the interpretation of the results.

11 12 Hence protest answers were screened and eliminated from the analyzed sample. This has 13 concerned farmers who answered no to the question: "*Did the survey's questions and the choice cards seem clear to you?*" and those farmers who systematically refused to choose any contract 14 without trying to understand their arrangements. According to Barrio & Loureiro (2013), such 15 answers do not add useful information on contract attribute preferences. We have also removed 16 all the responses from farmers who answered that the primary reason for their opt-out was that 17 (a) she or he thinks that biodiversity conservation is inconsistent with agricultural activities, and 18 (b) she or he was against agri-environmental schemes in general or those who answered that (c) 19 non-withdrawal from the contract is intolerable. These reasons were presented to farmers who 20 have chosen the opt-out answer systematically or at least once <fnref n="9" />. However, farmers who gave 21 attention to the scenario description compared the five choice sets during the survey and 22 systematically selected the opt-out option remained in the final sample. 23 24

25

## 3. Results

27

<footnote n="8">⁸ As suggested by (Haaijer et al., 2001) in their paper: "*the no-choice alternative in the conjoint choice experiment,*" an additional variable was employed for the opt-out option. This takes the value of 1 if the farmer chooses to keep his current farming practices, with the five attributes of the grassland conservation contract taking the value of 0.</footnote>

<footnote n="9">⁹ The other suggested answers were: "I prefer my current land use plan," "I did not like any of the contracts," "the contract is not feasible on my farm," "the payment is not enough," and "no answer."</footnote>
## 3.1. Sample characteristics

From the final 120 completed questionnaires, protest answers were removed from the final sample (ten questionnaires), resulting in a sample size of 110 farmers.

Table 2 presents the descriptive statistics of the final sample composed of 110 usable responses.

The respondents' ages range from 36 to 64 years, with an average of 49 years. The average used

agricultural area is 98 hectares of agricultural utilized area<fnref n="10" />. Figure 3 visually represents the

sample farms' size distribution compared to the regional level. The representativeness of mid-

sized and large farms is good; however, we observed an over-representation of small farms.

<figure><img src="image_4.png" /><figcaption>**Figure 3 - Farms sample versus Normandy's farms' size**</figcaption></figure>

In our sample, on average, farmers dedicate 72 hectares to crops for sale purposes. The average area dedicated to permanent grassland is about 22 hectares (which represents 23% of agricultural land). There is, however, a significant variability among farms revealed by the standard deviation (32.1), which can be explained by the discrepancies between farmers' agricultural practices that have historically characterized the grassland-cropland conversion in Normandy.

31% of respondents are dairy farmers, which is substantially above the national average (14%) and consistent with the regional ratio (29%), as shown in Figure 4. The representativeness of livestock mixed-crops farms is also good as it is consistent with the regional ratio.

Insert Table 2 here

<footnote n="10">¹⁰ The mean age is below the average for French farmers which is 52-year-old.</footnote>
<table><caption>Table 2. Sample descriptive statistics</caption><thead><tr><td>Variables</td><td>Mean</td><td>Standard Deviation</td><td>Min</td><td>Max</td><td>Explanation</td></tr></thead><tbody><tr><td>Age</td><td>45.41</td><td>25.25</td><td>32</td><td>69</td><td>Farmer's age in years</td></tr><tr><td>U.A.A.</td><td>98</td><td>83.445</td><td>27</td><td>307</td><td>Utilized Agricultural Area in hectares</td></tr><tr><td>P.G. share (%)</td><td>0.23</td><td>0.321</td><td>0.03</td><td>1</td><td>Share of permanent grassland in U.A.A.</td></tr><tr><td>Maincrop share (%)</td><td>0.65</td><td>0.2925</td><td>0.17</td><td>1</td><td>Share of the dominant crop in arable rotation</td></tr><tr><td>Livestock units</td><td>1.05</td><td>0.632</td><td>0.5</td><td>1.9</td><td>Livestock Units per hectare (1 L.U. = 500 live weight)</td></tr><tr><td>Dairy*</td><td>0.31</td><td>0.522</td><td>0</td><td>1</td><td>Farmers with on-farm dairy production</td></tr><tr><td>Dairy stoking rate</td><td>0.80</td><td>0.5225</td><td>0.42</td><td>1.67</td><td>Number of dairy cows in L.U. per hectare of U.A.A.</td></tr><tr><td>Awareness bout the environmental impact of intensive agricultural practices **</td><td>0.58</td><td>0.222</td><td>0</td><td>1</td><td>Agriculture is responsible for several negative impacts on the environment that are unacceptable (5 = I fully agree)</td></tr><tr><td>Knowledge about grassland ecosystem services**</td><td>0.32</td><td>0.252</td><td>0</td><td>1</td><td>Grasslands are a rich ecosystem that provides ecosystem services (5 = I fully agree)</td></tr><tr><td>External source of income*</td><td>0.19</td><td>0.242</td><td>0</td><td>1</td><td>Farmers who have an external source of income</td></tr></tbody></table>

Note:*Dummy variable, **Measured on a rating scale 1-5
1

2

3

4

5

6

7

8

9

Insert Figure 4 here

<figure><img src="image_5.png" /><figcaption>**Figure 4** – Economic and technical orientations of the sample's farms *versus* Normandy's farmers</figcaption></figure>

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

76 % of respondents accepted at least one of the contracts. We may have expected that farmers who are strong advocates for grassland as they use it in their farming practices have responded disproportionally, allowing for potential self-selection into the sample. This, however, was not the case as we observed that none of the farmers accepted grassland conservation contracts in all choice cards as everyone had chosen the opt-out option at least once. 24% of respondents systematically chose the status quo option without being considered protest answers. This is because (a) they prefer to keep their current farming practices and are reluctant to change even if the contract offers a higher payment, (b) they were unsatisfied with the previous grassland AES, or (c) the grassland conservation contract is technically impossible on their farmland.

The answer to the question asked to elicit farmer's environmental concerns, "Intensive farming practices are harmful to the environment; farmers have to adapt their practices towards sustainability," reaches an average score of 3.3 on a ranking scale from (1 - I totally disagree to 5 - I totally agree). When asked about the value they attach to ecosystem services provided by grassland in Normandy, farmers' responses reflect that, for most of them, all the ecosystem services appear to be important. Soil quality, fertility, and grassland's contribution to creating a sense of place (cultural heritage) were considered to be very important by farmers. Similarly, the forage quantity and quality are recognized as very important by 39% and 36% of respondents, respectively (Fig 5).

Insert Figure 5 here
<figure><img src="image_6.png" /><figcaption>Figure 5. Farmers’ perception of grassland ecosystem services benefits</figcaption></figure>

1

2

3

4

5

6

7

8

## 3.2. Econometric estimation

### 3.2.1. Farmers' decision to enroll in a grassland ecological restoration contract.

At first, farmers' decisions to enroll in a grassland conservation contract were analyzed using a conditional logit (CL) model. The comparison between the full conditional logit model and partial conditional logit model through dropping alternatives successively one by one highlights the rejection of the independence from irrelevant alternative (I.I.A.) assumption and leads to the invalidity of the CL estimations for our sample (with $Prob>Chi^2 = 0.3127$, 0.3501, 0.3712, and 0.4366).

10

11

12

13

14

15

The I.I.A. assumption was then relaxed. Farmers' decision to enroll in grassland conservation contracts was modeled with a random parameter logit model (R.P.L.), also called the mixed logit model, to analyze the data. The analysis involved two stages. First, farmers' participation was modeled according to the grassland conservation contract attributes. Second, the model was extended by including farmers' and farms' characteristics to explain the choice of the status quo (opt-out option). Since these characteristics cannot be included alone in the model (they are unvarying across one individual), they are captured through interaction variables. The models have reasonable goodness of fit with an estimated McFadden Pseudo R² of 0.25 and 0.27, respectively.

16

17

18

19

20

21

22

23

24

25

#### 3.2.1.1. RPL. *model without interaction*

26

27

The estimated mean and standard deviation of the normally distributed coefficients provides information on the proportion of respondents that positively value a specific attribute and those that place a negative value. The standard deviation of most coefficients is statistically significant, confirming the accuracy of using the R.P.L. model to analyze the data.

28

29

30

31

Given the small sample size and following Chèze et al. (2020), we chose to retain the conservative value of 5% rather than 10% as a significance level to reject the null hypothesis of the student test. In this way, we attempted to reduce the probability of making a Type-I error.

32

33

34

35

36

37

All attributes and levels except *Collective Participation* and *Land Enrollment* are statistically significant at this level. This is not surprising given the constraint imposed by these attributes compared to the other contract's attributes. The fact that farmers are less likely to participate in
contracts that require a high ratio of land enrollment compared to other contract attributes is not unexpected. This reflects the prevailing trend of farm specialization in Normandy, where there is a significant opportunity cost associated with enrolling land in grassland restoration contracts.

In our understanding, farmers are most likely to perceive collective contracts that involve collaboration with neighboring farmers as a burden rather than a support measure. Nonetheless, there is a persistent heterogeneity in preferences that we could not identify, as shown by the significant standard deviations of both attributes *Collective Participation* and *Land Enrollment*. Furthermore, the likelihood of farmers' participation increases with the availability of *Free Technical Support and Advisory* and the conditional *Premium*. It also slightly increases with the financial compensation.

Insert Table 3 here

<table><caption>Table 3. RPL. model estimates</caption><thead><tr><th rowspan="3">Attributes</th><th colspan="2">R.P.L. Model without interaction</th><th colspan="2">R.P.L. model with interaction</th></tr><tr><th>Coefficient</th><th>Stand. Dev.</th><th>Coefficient</th><th>Stand. Dev.</th></tr><tr><th>(SE)</th><th>(SE)</th><th>(SE)</th><th>(SE)</th></tr></thead><tbody><tr><td>ASC_SQ</td><td>-0.766**<br>(0.283)</td><td>2.381***<br>(0.653)</td><td>-0.518***<br>(0.522)</td><td>2.715***<br>(0.699)</td></tr><tr><td>Land Enrollment</td><td></td><td></td><td></td><td></td></tr><tr><td>Land Enrollment (10%)</td><td>-0.6182*<br>(0.273)</td><td>0.925**<br>(0.337)</td><td>-0.971*<br>(0.201)</td><td>0.870*<br>(0.578)</td></tr><tr><td>Land Enrollment (15%)</td><td>-1.564*<br>(0.499)</td><td>1.259**<br>(0.368)</td><td>-1.608*<br>(0.778)</td><td>1.144*<br>(0.614)</td></tr><tr><td>Free Technical Assistance and advisory<br>(Yes = 1)</td><td>0.457***<br>(0.409)</td><td>0.128**<br>(0.604)</td><td>0.299***<br>(0.378)</td><td>0.121**<br>(0.562)</td></tr><tr><td>Collective Participation</td><td>-0.689*<br>(0.136)</td><td>1.456*<br>(0.778)</td><td>-1.148*<br>(0.478)</td><td>1.983*<br>(0.341)</td></tr><tr><td>Premium</td><td>0.219***<br>(0.067)</td><td>0.121**<br>(0.651)</td><td>0.378***<br>(0.098)</td><td>0.242*<br>(0.871)</td></tr><tr><td>Compensation Level (€/hectare/year)</td><td>0.002***<br>(0.0009)</td><td></td><td>0.008***<br>(0.0005)</td><td></td></tr><tr><td colspan="5"><strong>• Crossed with Land Collective Participation</strong></td></tr><tr><td>Grassland ecosystem services knowledge</td><td></td><td></td><td>0.168***<br>(0.0061)</td><td></td></tr><tr><td>U.A.A.</td><td></td><td></td><td>-0.065**<br>(0.0245)</td><td></td></tr><tr><td colspan="5"><strong>• Farms and Farmers' characteristics crossed with the ASC_SQ</strong></td></tr><tr><td>Age (in years)</td><td></td><td></td><td>2.122***<br>(0.367)</td><td></td></tr><tr><td>Environmental attitudes</td><td></td><td></td><td>-0.325***<br>(0.076)</td><td>---</td></tr><tr><td>Outside income (Yes = 1)</td><td></td><td></td><td>-0.165***<br>(0.514)</td><td>---</td></tr></tbody></table>
<table><tr><td>Dairy Stoking rate</td><td>1.737***<br>(0.531)</td></tr><tr><td>Livestock units</td><td>1.537***<br>(0.768)</td></tr><tr><td></td><td>2.965**<br>(0.511)</td></tr><tr><td>Dairy</td><td>-3.121***<br>(0.154)</td></tr><tr><td>Number of respondents</td><td>110</td><td>107</td></tr><tr><td>Number of observations</td><td>2830</td><td>2580</td></tr><tr><td>Chi<sup>2</sup></td><td>443.66123</td><td>398.34543</td></tr><tr><td>AIC</td><td>1483.0987</td><td>1278.2098</td></tr><tr><td>BIC</td><td>1510.3450</td><td>1310.4491</td></tr></table>

Standard errors in parenthesis

Significant levels: ***p<0.01, **p<0.05, *p<0.1

It is worth noting that the availability of the *Premium* significantly increases the probability of farmers signing a contract for 62% of the choices. They, however, could not benefit from the *Premium* if their choice of the enrolled area corresponding to 5%, 10%, or 15% is lower than 4ha<fnref n="11" />. This was the case for 15% of respondents. While conducting the survey, farmers were informed that they would not receive the *Premium* if the size of the land enrolled in the contract was less than 4 ha. Nonetheless, 3% of the respondents refused to accept the *Premium* although they agreed to enroll more than 4 ha of their land. This result suggests that they disagreed with the ecological constraint imposed by this particular contract attribute.

The *Alternative Specific Constant*, corresponding to the status quo variable (ASC<sup>SQ</sup>), is statistically significant and negative. This indicates that in *ceteris paribus*, farmers negatively value maintaining the status quo and choosing not to enroll in one of the contracts presented to them. Overall, the standard deviation reveals that preferences for all the attributes are heterogenous in our sample. It is also highly significant for the ASC<sup>SQ</sup> and has a positive sign, meaning that some farmers value the status quo.

### 3.2.1.2. Interactions with farmers and farms' characteristics

Heterogeneity among farmers was analyzed extensively by considering the interaction effect with additional variables, namely farmers' and farms' characteristics. We have mainly used demographic variables (age, income, availability of an outside income, education level), the structural characteristics of the farm (size, main crop share, dairy, and livestock production), and the variables reflecting farmers' environmental concerns in general, and their knowledge about the ecosystem services provided by grassland. A backward elimination procedure was applied to circumcise the variables with the most substantial interactions with each attribute.

The most significant variables were then selected to estimate the interactions with the *status quo* variable (ASC<sup>SQ</sup>). The obtained results are noteworthy as they provide insights into the farmers' and farm characteristics that increase the likelihood of moving away from the status quo. The extended model's results are presented in the two right-hand columns of Table 3.

The interaction term coefficient between the *Collective Participation* attribute and the dummy variable reflecting farmers' knowledge about the ecosystem services grassland provides is statistically significant and positive at the 1% level. In other words, this result demonstrates that

<footnote n="11">¹¹ A choice card with “premium available” and “land enrollment =0” does not exist among the choice cards.</footnote>
farmers who are aware of the multiple ecosystem services offered by grassland are more likely to accept collective contracts. On the other hand, in our sample, small-scale farmers are more likely to accept the collective participation option proposed in the contract. This result points to current concerns in the French agricultural landscape about those small farms increasingly facing significant constraints linked to scale, fragmentation, and resource access which are challenging to overcome (Agarwal & Dorin, 2019). We might presume that these farmers need to engage in another farming model wherein they can pool their land, labor, and capital, resulting in shared costs and benefits.

Turning to the interaction results with the ASC<sup>SQ</sup> variable, the interaction coefficient of Age is significant and positive at the 1% level, meaning that older farmers are more likely to select the *status quo* option. This finding may be explained as a reluctance effect or a production loss risk aversion due to the considerable change the contract could imply for these farmers. Besides, the outside revenues coefficient is significant and negative at the 1% level, meaning farmers with additional income sources are more willing to enroll in a grassland restoration contract.

Although farmers' perception of whether the financial compensation fully covers the extra costs positively affects participation, farmers concerned about the adverse effects of agricultural practices on the environment are more likely to change the *status quo* option. Finally, farmers value negatively maintaining their current farming practices and not participating in grassland ecological restoration, as reflected by the statistically significant and negative ASC<sup>SQ</sup> coefficient.

### 3.2.1.3. WTA estimation

The delta method at 95% interval was used to estimate standard deviation and the confidence intervals around the mean WTA values. Table 4 shows the WTA results calculated using the R.P.L. model and the extended version accounting for additional interaction effects with attitudinal variables (environmental awareness and knowledge about grasslands ecosystem services) that would help understand farmers' choices relative to their goals. Both models yielded similar estimates, showing that they are relatively robust.

*Insert Table 4 here*

<table><caption>**Table 4.** WTA and confidence intervals (euro/ha/year)</caption><thead><tr><th rowspan="2"></th><th colspan="2">R.P.L. Model without interaction</th><th colspan="2">R.P.L. model with interaction</th></tr><tr><th>WTA (€/ha/year)</th><th>Confidence Interval [95% CI]</th><th>WTA (€/ha/year)</th><th>Confidence Interval [95% CI]</th></tr></thead><tbody><tr><td>Land Enrollment</td><td>219</td><td>[150.88;290.98]</td><td>201</td><td>[150.36;281.36]</td></tr><tr><td>Technical Assistance</td><td>-42.36</td><td>[-28.31; -91.61]</td><td>-47.60</td><td>[-26.31; -92.31]</td></tr><tr><td>Collective Participation</td><td>80.98</td><td>[45.78; 147.31]</td><td>78.25</td><td>[36.78; 122.7]</td></tr><tr><td>Premium</td><td>-123.48</td><td>[-191.41; -89.87]</td><td>-128.47</td><td>[-189.10; -80.56]</td></tr><tr><td>Opt-out</td><td>-510.01</td><td>[-1987;530.21]</td><td>-503.98</td><td>[-2018.7;547.01]</td></tr><tr><td>Environmental awareness</td><td></td><td></td><td>-6.28</td><td>[-4.18; 17.36]</td></tr><tr><td>Environmental knowledge</td><td></td><td></td><td>-12.91</td><td>[5.12; 18.13]</td></tr></tbody></table>
All else being equal, farmers tend to accept higher financial compensation for contracts with collective participation that involves collaboration since they require €80/ha/year in subsidies.

Moreover, farmers are prepared to forgo €123/ha/year to receive the *Premium*, although its actual value is €250/ha/year, meaning they will still derive a net benefit from it. Implementing a contract with ecological constraints - imposing to enroll at least 4ha of his farmland in grassland ecological restoration that must be included on the same parcel - will cost the regulator €127 ha/year. On the other hand, farmers are willing to forgo 42€/ha for contracts that provide technical assistance and €510/ha/year to maintain their current farming practices, which means they would only participate in contracts that provide a payment higher than €510/year/ha.

Compared to income obtained from other crops ranging between 750 and 2200€/ha in Normandy, the payment offered by the grassland restoration contract only amounts to €330/ha/year, and we might presume that some farmers require the contract's compensation to be as lucrative as their current land use. Finally, even if environmental awareness significantly influences farmers' WTA, those who believe that the ecosystem services provided by grassland are important to very important have a much higher expressed WTA and are willing to pay almost twice the amount compared to those who are merely concerned about environmental issues.

## 4. Discussion and conclusion

Grasslands provide numerous environmental benefits, such as improved product quality and enhanced animal and human health and serve as a ruminant feeding source. However, these areas face significant threats from intensification and conversion to other land uses. Despite the existence of grassland protection measures under the Grassland Conservation Program in France, these schemes have attracted only a few farms that meet the required stocking rate thresholds and have opted for extensive grazing systems. In contrast, farmers' participation is limited in areas that offer opportunities for cultivation and intensification. Additionally, these schemes only focus on farm-scale management approaches and have failed to achieve large-scale ecological benefits from grassland restoration.

Despite their potential for ecological gains through ecological restoration, farmers' preferences for large-scale approaches to grassland restoration have received little attention in the literature. These approaches rely on collaborative action and collective contracts between neighboring farmers who must formally work together to achieve landscape-oriented ecological outcomes (Prager, 2015). One may also wonder if grassland restoration contracts with restrictive conditions justified by achieving ecological performance while offering additional financial incentives (e.g., Premium) and technical support can match farmers' preferences and constraints.

We conducted a Discrete Choice Experiment study in Normandy, northwestern France, targeting over 1310 farmers. We received 110 usable responses. The response rate, as well as the sample size, are relatively low<fnref n="12" />. Five attributes describe different grassland restoration contracts: the land enrolment rate, the annual payment, the availability of free technical support and advisory services, the possibility of collective enrolment, and the option of receiving a financial premium in return for complying with the scope and spatial distribution conditions of the enrolled land. We used collective enrolment to refer to the collaborative effort between farmers toward grassland restoration. We have provided insights into two main categories of factors that can

<footnote n="12">¹² Normandy has 26500 farms.</footnote>
1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

influence participation as well as non-participation (choosing the status quo option), (i) the contract characteristics and (ii) farmers' and farms' characteristics.

Overall, farmers prefer flexible contracts and wish to keep the decision power over the percentage of land to be enrolled in the contract. Because we have an overrepresentation of small farms in our sample, it is possible that we under-evaluate the effect of higher percentages of land enrolment on farmers' decisions. This observation is a common conclusion in the literature, as stringent management prescriptions and less flexibility in implementing conservation practices reduce farmers' willingness to participate (Ruto & Garrod, 2009). Farmers prefer flexible contracts that allow them to react to ever-changing market and weather conditions (Hasler et al., 2022; Viaggi et al., 2021).

Technical support increased farmers' likelihood of enrolling in grassland restoration contracts by 5%. It is useful to mention that there is a widespread perception among French livestock farmers of grassland being an outdated and unsuitable technical model. The fodder deficit associated with the drought and heat wave episodes over recent years further reinforces this assessment (Michaud et al., 2020). Thus, farmers need to receive technical advice on best practices for managing grassland, their fields, and fodder systems with diverse environmental conditions and agronomic and ecological potential (Michaud et al., 2020). Fleury et al. (2015) underscore the positive relationship between farmers' positive value of biodiversity and their requirement for training, education, and advice within a scheme aimed at grassland restoration. Moreover, Schroeder et al. (2013) found that although English dairy farmers were uncertain if they would achieve the ecological target from grassland restoration, they considered joining the proposed AES to benefit species richness in grasslands if professional advice was available. Finally, while Matzdorf & Lorenz (2010) suggest that the availability of professional advice would increase the willingness of risk-averse farmers to participate in AES, Del Rossi et al. (2021) argue that farmers can expect costs to be lowered as a result of these services.

Our findings highlight that higher payment levels and the *Premium* offered for the increased extent of the interconnectedness of contracted land improve the likelihood of farmers signing a grassland restoration contract. In addition, farmers' mean WTA to change their farming practices is even smaller than the range of payments offered during the survey. There are, however, significant payment expectations at the top of the suggested levels. Farmers require compensation to cover additional costs and even to be as lucrative as their current land use<fnref n="13" />. This is consistent with Lakner et al. (2020) study on farmers' preferences for targeted grassland conservation in eastern Germany, concluding that those additional costs constitute a significant barrier to farmers' adoption, particularly for farms focusing their farm management on productivity and profitability optimization.

Farmers in our sample seemed reluctant to join a collective contract involving collaboration, and the range of WTA is relatively significant. This is in line with previous findings. Villanueva et al. (2015) explain farmers' reluctance through their apprehension of being controlled by the other group members. We may expect further dedicated studies to help understand how attaching more significant sign-up payments to collective participation could impact farmers' decision to enroll adjacent land parcels within collective grassland restoration contracts and maximize ecological benefits.

<footnote n="13">¹³ Compared to income obtained from other crops ranging between 750 and 2200€/ha depending on crop type in Normandy, the compensation offered by the grassland restoration contract only amounts to €330/ha.</footnote>
Our findings offer compelling evidence that knowledgeable farmers about grassland ecosystem services understand that successful restoration schemes need to involve large-scale actions that require them to work closely with their neighbors in a cross-farm boundary. They, therefore, tend to accept to join a contract collectively. This evidence is of interest because studies have demonstrated that farmers' views of the potential success of AES are a significant determinant of their participation (Siebert et al. 2006). Sutherland et al. (2012) suggest that when prescribed in a contract, collective participation can help share experiences among neighbors and, consequently, lowers both the uncertainty of AES implementation on farm management and the pressure to comply with social norms. These observations are also consistent with studies on environmental psychology that identify how social pressure can influence farmers' perception of their ability to undertake a particular measure and assess their resources. Michel-Guillou and Moser (2006) state that social pressure can even trigger environmental awareness. It also leads to win-win outcomes in terms of both productivity and reducing environmental impact (Daxini et al., 2018; Rezaei et al., 2019)

Two other findings worth mentioning. First, while farms' characteristics (limited dominant crops, high livestock unit per hectare, and high dairy stoking rate) explain limited farmers' enrolment in the contract, farmers' characteristics (age, availability of external income, and environmental attitudes) clarify their choice of moving away from the status quo. Second, farmers' environmental knowledge about ecosystem services provided by grassland plays a role in their decision (about 61% of the sample). These farmers tend to combine environmental management with their farming goals, including conservation stewardship, production, and profitability, which is consistent with the literature (Wheeler et al., 2018).

There are some policy implications for our results. According to experts, one of the critical priorities in determining the extent to which the new Common Agricultural Policy green architecture tackles the biodiversity crisis will heavily depend on massive effort put into grassland restoration by farmers (Pe'er Guy et al. 2021). In addition to achieving more comprehensive ecological outcomes at a landscape scale, collective, collaborative participation is a valuable tool for reducing the number of contracts signed by groups of farmers involved in grassland ecological restoration. For collective contracts to be effective, national, and regional grassland restoration agendas must be harmonized with site-level goals. For example, the local authority could define an inclusive and comprehensive distribution of grassland areas that should be restored, given their potential ecological advantages. In this case, the policy would have to specify a share between neighboring farmers of the ecological restoration requirement within a contract that involves collective participation and promotes a "neighborhood effect" supporting farmers' collaborative efforts (Defrancesco et al., 2018). Our results show, however, that farmers prefer flexible contracts. This measure might impact the participation rate. Therefore, setting a cost-effective monetary incentive is critical to promoting collective participation in the scheme. Including collective participation in the grassland restoration scheme could also reduce implementation costs for the regulator (Banerjee et al., 2014). Although previous CAP. regulations included a 30% bonus to promote such participation, the uptake of these schemes is low, and there is little known about farmers' (non)resistance to such schemes (Riley et al., 2018). Further research is needed to understand and manage farmers' expectations and perceptions about collaborative contracts and fill the knowledge gap about costs and practical gains from farmers' collective participation in grassland ecological restoration schemes.

Our results show that increasing the enrolled acreage per farmer by setting a conditional monetary premium could positively impact farmers' participation. Even if it will translate into an increased cost per signed contract for the developer (Vaissière et al., 2018), designing schemes
1

that include conditional monetary premium would maximize the odds of reaching acres thresholds for successful ecological restoration of grasslands.

3

The heterogeneity of preferences observed among farmers in our study favors differentiated payments when possible. Besides, it uncovers an appealing scope for further exploration of contracts targeting small, extensive dairy farms with low stocking densities. These small-scale farming systems are essential as they create and maintain species-rich semi-natural grasslands (Babai et al., 2015). Even if the trend is widespread in Europe, the number of small dairy grazing farms in France has decreased by a third since 2010 (Ben-Othmen et al., 2020). Policymakers must deal with the compounded economic pressure on these farms, including milk price instability and uncertainty that continuously challenged their business operations (Clay et al., 2020) and caused many farms to expand their herds and change management practices to sustain their visibility.

14

The post-2023 CAP offers ground to expand the scope and possibilities for grassland restoration beyond their sole prescription as good agricultural and environmental conditions. Our results contribute to the debate on the opportunities this reform offers to transition towards sustainable agriculture. We also identify two potential interventions to reinforce grassland restoration in the new CAP green architecture. First, because outside revenue may encourage participation, increasing area-based payments funded under Pillar 1 (article 31-CAP. 2021-2027) that reserve part of its funding for environmental performance payment might be required. In this context, eco-scheme can be valuable in supporting the maintenance of farmers' income, mainly during phases of transition that involve restructuring farming systems and investments in new practices. Interestingly, eco-schemes can potentially target specific environmental issues across entire regions (Lampkin et al., 2020). However, they differ from agri-environmental and climatic schemes as they can offer farmers payments beyond the income loss and the direct costs of implementing environmental measures. Therefore, payment under an eco-scheme, regardless of production levels, could help secure income for dairy farmers who rely on extensive grazing systems and improve their profitability. Second, our findings suggest that promoting large-scale conservation via collective action schemes under Pillar 2 can strengthen other measures that have shown promise in supporting grassland preservation and improving the overall biodiversity of farmland. Such measures encompass result-based schemes (Chaplin et al., 2019), support for mixed-species grassland (Dumont et al., 2022), and investment for silvo-pastoral ecosystems establishment (Helena Guimarães et al., 2023).

35

There are many limitations to our study. Although our sample can represent several key figures of the Normandy farmers' population, it is relatively small; therefore, our results must be considered with vigilance and are, by no means, representative of the entire population of farmers in France. Additional research is needed to unveil preferences for collective participation in the contract and how nudges can be used to start group dynamics toward improving grassland ecological benefits at the landscape level. Another shortcoming of our study is that it does not specify what collective participation would mean in terms of collaborative efforts to achieve effective ecological outcomes. In fact, grassland restoration programs need to be designed to achieve semi-natural permanent cover from ryegrass and include measures to improve their biological diversity. Further research is needed to examine what is precisely required by farmers within the collective contract to deliver these ecological benefits while using fewer farm resources than in an individualized contract. In addition, we did not include details about how this collective participation could be implemented financially and the benefits for farmers from enrolling in such a contract. These aspects need to be considered in further studies.

51
This paper addresses the need for effective instruments to complement France's current Grassland Conservation Program. It is the first attempt to measure and help understand the relative importance farmers attach to the contract's attributes targeting grassland ecological restoration and how farms' and farmers' characteristics determine the heterogeneity of preferences. Additional studies of this type would help substantiate or upgrade the obtained results using data from other samples of farmers.

## Append 1. - Grassland restoration scheme: on-farm management requirements presented surveyed farmers.

<figure><img src="image_7.png" /></figure>

## Appendix 2 - Environmental attitudes as a Farmer – adapted from (Vogel, 1996)
<table><thead><tr><th colspan="6">Indicate your agreement or otherwise with the statements below</th></tr><tr><th></th><th>Strongly disagree</th><th>Desagree</th><th>Agree</th><th>Neither Desagree Nor Agree</th><th>Agree</th><th>Strongly agree</th></tr></thead><tbody><tr><td>Agricultural activities today lead to the destruction of natural biotopes and a reduction in wildlife and wild plants.</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Grassland conversion into cropland reduces the continuing biological productivity of the land and the product quality.</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>The use of chemical substances in agriculture works against nature.</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>The media exaggerate environmental problems resulting from agricultural activities.</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>The groundwater burden resulting from washing out of fertilizers is worst than people could imagine.</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Farmers are the best protectors of the natural environment, even if mistakes are made occasionally.</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Commercial fertilizers and pesticides have no harmful effects; they promote high-quality production.</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Using chemicals in agriculture makes sense as long as it brings greater returns than Maintaining a proper balance in nature requires a more complex form of operational organization.</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

1

2

3

4

5

6

7

8

9

10

11

12

## References

14

15 Agarwal, B., & Dorin, B. (2019). Group farming in France: Why do some regions have more cooperative ventures than others? *Environment and Planning A*, 51(3), 781–804.

16 https://doi.org/10.1177/0308518X18802311

17 Agreste. (2020). *En 2019, l'excédent agroalimentaire progresse mais les échanges avec l'Union européenne sont désormais déficitaires*.

18 www.agreste.agriculture.gouv.fr%0AMinistère

19 Ahnström, J., Höckert, J., Bergeå, H. L., Francis, C. A., Skelton, P., & Hallgren, L. (2009). Farmers and nature conservation: What is known about attitudes, context factors and actions affecting conservation? *Renewable Agriculture and Food Systems*, 24(1), 38–47.

20 https://doi.org/DOI: 10.1017/S1742170508002391

21 Ajzen, I. (1988). *Attitudes, Personality and Behaviour* (D. Press (ed.)).

22 Ajzen, I. (1991). The Theory of Planned Behavior Organizational Behavior and Human Decision Processes. *Organizational Behavior and Human Decision Processes*, 50(2), 179–211.

23 Ajzen, I., & Fishbein, M. (1980). *Understanding attitudes and predicting social behaviour*.

24 Allaire, G., Cahuzac, E., Simioni, M., Allaire, G., Cahuzac, E., & Simioni, M. (2009). Contractualisation et diffusion spatiale des mesures agro-environnementales herbagères.

25 Revue d'Etudes En Agriculture et Environnement - Review of Agricultural and Environmental Studies, INRA Editions, pp.5-28.

26 Babai, D., Tóth, A., Szentirmai, I., Biró, M., Máté, A., Demeter, L., Szépligeti, M., Varga, A., Molnár, Á., Kun, R., & Molnár, Z. (2015). Do conservation and agri-environmental regulations effectively support traditional small-scale farming in East-Central European cultural landscapes? *Biodiversity and Conservation*, 24(13), 3305–3327.

27 https://doi.org/10.1007/s10531-015-0971-z
1 Bamberg, S., & Möser, G. (2007). Twenty years after Hines, Hungerford, and Tomera: A new meta-analysis of psycho-social determinants of pro-environmental behaviour. *Journal of Environmental Psychology*, 27(1), 14–25.
https://doi.org/https://doi.org/10.1016/j.jenvp.2006.12.002

5 Banerjee, DeVries, Soest, Van, & Hanley. (2014). The Impact of Information Provision on Agglomeration Bonus Performance: An Experimental Study on Local Networks. *American Journal of Agricultural Economics*, 96(4), 1009–1029.

8 Barrio, M., & Loureiro, M. (2013). The impact of protest responses in choice experiments: an application to a Biosphere Reserve Management Program. *Forest Systems*, 22(1 SE-Research Articles), 94–105. https://doi.org/10.5424/fs/2013221-03103

11 Bech, M., & Gyrd-Hansen, D. (2005). Effects coding in discrete choice experiments. *Health Economics*, 14(10), 1079–1083. https://doi.org/https://doi.org/10.1002/hec.984

13 Ben-Othmen, M. A., Dubois, M. J., & Sauvée, L. (2020). *Evolution Agrotechnique Contemporaine III Animal & Techniques* (Collection). Université de technologie de Belfort-Montbéliard.

16 Ben-Othmen, M. A., & Ostapchuk, M. (2019). Farmers’ preferences for grassland restoration: Evidence from France. *172nd EAAE Seminar ‘Agricultural Policy for the Environment or Environmental Policy for Agriculture?’ May 28-29, 2019. Brussels*.

19 Benoit, M., & Patsias, C. (2017). Greening the agri-environmental policy by territorial and participative implementation processes? Evidence from two French regions. *Journal of Rural Studies*, 55, 1–11. https://doi.org/10.1016/j.jrurstud.2017.07.016

22 Bommarco, R., Kleijn, D., & Potts, S. G. (2013). Ecological intensification: Harnessing ecosystem services for food security. *Trends in Ecology and Evolution*, 28(4), 230–238. https://doi.org/10.1016/j.tree.2012.10.012

25 Borges, J. A. R., & Oude Lansink, A. G. J. M. (2016). Identifying psychological factors that determine cattle farmers’ intention to use improved natural grassland. *Journal of Environmental Psychology*, 45, 89–96. https://doi.org/10.1016/j.jenvp.2015.12.001

28 Bottazzi, P., Wiik, E., Crespo, D., & Jones, J. P. G. (2018). Payment for Environmental “Self-Service”: Exploring the Links Between Farmers’ Motivation and Additionality in a Conservation Incentive Programme in the Bolivian Andes. *Ecological Economics*, 150(April), 11–23. https://doi.org/10.1016/j.ecolecon.2018.03.032

32 Brotherton, I. (1989). Farmer participation in voluntary land diversion schemes: Some observations from theory. *Journal of Rural Studies*, 5(3), 299–304. https://doi.org/https://doi.org/10.1016/0743-0167(89)90008-9

35 Chabé-Ferret, S., & Voia, A. (2021). “Are Grassland Conservation Programs a Cost-Effective Way to Fight Climate Change? Evidence from France” Are Grassland Conservation Programs a Cost-Effective Way to Fight Climate Change? Evidence from France *. *Working Papers, September*.

39 Chaloner, K., & Verdinelli, I. (1995). Bayesian Experimental Design: A Review. *Statistical Science*, 10(3), 273–304. http://www.jstor.org/stable/2246015

41 Chaplin, S., Robinson, V., LePage, A., Keep, H., Le Cocq, J., Ward, D., Hicks, D., & Scholz, E. (2019). *Pilot Results-Based Payment Approaches for Agri-environment schemes in arable and upland grassland systems in England. FinChaplin, S., Robinson, V.*, (2019). *Pilot Results-Based Payment Approaches for Agri-environment schemes in arable and upland grassland*.

46 Chèze, B., David, M., & Martinet, V. (2020). Understanding farmers’ reluctance to reduce pesticide use: A choice experiment. *Ecological Economics*, 167(January 2019), 106349. https://doi.org/10.1016/j.ecolecon.2019.06.004

49 Christensen, T., Branth, A., Oersted, H., Raun, M., Hasler, B., & Denver, S. (2011). Determinants of farmers ' willingness to participate in subsidy schemes for pesticide-free buffer zones — A choice experiment study. *Ecological Economics*, 70(8), 1558–1564.
1 https://doi.org/10.1016/j.ecolecon.2011.03.021

2 Clay, N., Garnett, T., & Lorimer, J. (2020). Dairy intensification: Drivers, impacts and alternatives. *Ambio*, 49(1), 35–48. https://doi.org/10.1007/s13280-019-01177-y

4 Cousins, S. A. O., Lindborg, R., Bengtsson, J., Eriksson, O., Gustafsson, T., Per, K., & Lenoir, L. (2008). *A landscape perspective on conservation of semi-natural grasslands*. 125, 213–222. https://doi.org/10.1016/j.agee.2008.01.006

7 Coyne, L., Kendall, H., Hansda, R., Reed, M. S., & Williams, D. J. L. (2021). Identifying economic and societal drivers of engagement in agri-environmental schemes for English dairy producers. *Land Use Policy*, 101 (April 2020), 105174. https://doi.org/10.1016/j.landusepol.2020.105174

11 Cullen, P., Ryan, M., O'Donoghue, C., Hynes, S., HUallacháin, D., & Sheridan, H. (2020). Impact of farmer self-identity and attitudes on participation in agri-environment schemes. *Land Use Policy*, 95 (April), 1–8. https://doi.org/10.1016/j.landusepol.2020.104660

14 Daxini, A., O'Donoghue, C., Ryan, M., Buckley, C., Barnes, A. P., & Daly, K. (2018). Which factors influence farmers' intentions to adopt nutrient management planning? *Journal of Environmental Management*, 224 (July), 350–360. https://doi.org/10.1016/j.jenvman.2018.07.059

18 de Krom, M. P. M. M. (2017). Farmer participation in agri-environmental schemes: Regionalisation and the role of bridging social capital. *Land Use Policy*, 60, 352–361. https://doi.org/10.1016/j.landusepol.2016.10.026

21 Defrancesco, E., Gatto, P., & Mozzato, D. (2018). To leave or not to leave? Understanding determinants of farmers' choices to remain in or abandon agri-environmental schemes. *Land Use Policy*, 76 (February), 460–470. https://doi.org/10.1016/j.landusepol.2018.02.026

24 Defrancesco, E., Gatto, P., Runge, F., & Trestini, S. (2008). Factors affecting farmers' participation in agri-environmental measures: A northern Italian perspective. *Journal of Agricultural Economics*, 59(1), 114–131. https://doi.org/10.1111/j.1477-9552.2007.00134.x

28 Del Rossi, G., Hecht, J. S., & Zia, A. (2021). A mixed-methods analysis for improving farmer participation in agri-environmental payments for ecosystem services in Vermont, USA. *Ecosystem Services*, 47 (May 2019). https://doi.org/10.1016/j.ecoser.2020.101223

31 Despotović, J., Rodić, V., & Caracciolo, F. (2021). Farmers’ environmental awareness: Construct development, measurement, and use. *Journal of Cleaner Production*, 295. https://doi.org/10.1016/j.jclepro.2021.126378

34 Dessart, F. J., Barreiro-Hurlé, J., & Van Bavel, R. (2019). Behavioural factors affecting the adoption of sustainable farming practices: A policy-oriented review. *European Review of Agricultural Economics*, 46(3), 417–471. https://doi.org/10.1093/erae/jbz019

37 Ducos, G., Dupraz, P., & Bonnieux, F. (2009). Agri-environment contract adoption under fixed and variable compliance costs. *Journal of Environmental Planning and Management*, 52(5), 669–687. https://doi.org/10.1080/09640560902958248

40 Dumont, B., Franca, A., López-i-Gelats, F., Mosnier, C., & Pauler, C. M. (2022). Diversification increases the resilience of European grassland-based systems but is not a one-size-fits-all strategy. *Grass and Forage Science*, 77(4), 247–256. https://doi.org/https://doi.org/10.1111/gfs.12587

44 Eekeren, N. Van, Boer, H. De, Hanegraaf, M., Bokhorst, J., Nierop, D., Bloem, J., Schouten, T., Goede, R. De, & Brussaard, L. (2010). Soil Biology & Biochemistry Ecosystem services in grassland associated with biotic and abiotic soil parameters. *Soil Biology and Biochemistry*, 42(9), 1491–1504. https://doi.org/10.1016/j.soilbio.2010.05.016

48 Emery, S. B., & Franks, J. R. (2012). *The potential for collaborative agri-environment schemes in England: Can a well-designed collaborative approach address farmers’ concerns with current schemes?* 28, 218–231. https://doi.org/10.1016/j.jrurstud.2012.02.004

51 Espinosa-Goded, M., Barreiro-Hurlé, J., & Dupraz, P. (2013). Identifying additional barriers in
1 the adoption of agri-environmental schemes: The role of fixed costs. *Land Use Policy*, 31,
2 526-535. https://doi.org/10.1016/j.landusepol.2012.08.016

3 Espinosa-Goded, Maria, Barreiro-Hurlé, J., & Ruto, E. (2010). What do farmers want from agri-environmental scheme design? A choice experiment approach. *Journal of Agricultural Economics*, 61(2), 259-273. https://doi.org/10.1111/j.1477-9552.2010.00244.x

6 Falconer, K. (2000). Farm-level constraints on agri-environmental scheme participation: A transactional perspective. *Journal of Rural Studies*, 16(3), 379-394.
8 https://doi.org/10.1016/S0743-0167(99)00066-2

9 Fleury, P., Seres, C., Dobremez, L., Nettier, B., & Pauthenet, Y. (2015). "Flowering Meadows", a result-oriented agri-environmental measure: Technical and value changes in favour of biodiversity. *Land Use Policy*, 46, 103-114.
12 https://doi.org/10.1016/j.landusepol.2015.02.007

13 Foguesatto, C. R., Borges, J. A. R., & Machado, J. A. D. (2020). A review and some reflections on farmers’ adoption of sustainable agricultural practices worldwide. *Science of The Total Environment*, 729, 138831. https://doi.org/https://doi.org/10.1016/j.scitotenv.2020.138831

16 Haaijer, R., Kamakura, W., & Wedel, M. (2001). The “no-choice” alternative in conjoint choice experiments. *International Journal of Market Research*, 43(1), 93-106.
18 https://doi.org/10.1177/147078530104300105

19 Hansen, B. G., & Greve, A. (2014). Dairy farmers’ values and how their values affect their decision. *Agricultural and Food Science*, 23(4), 278-290.
21 https://doi.org/10.23986/afsci.46423

22 Hasler, B., Termansen, M., & Ørsted, H. (2022). European Agri-environmental Policy: Evolution, Effectiveness, and Challenges. *Review of Environmental Economics and Policy*, 16(1).

25 Hayden, M. T., Mattimoe, R., & Jack, L. (2021). Sensemaking and the influencing factors on farmer decision-making. *Journal of Rural Studies*, 84(March), 31-44.
27 https://doi.org/10.1016/j.jrstud.2021.03.007

28 Helena Guimarães, M., Pinto-Correia, T., de Belém Costa Freitas, M., Ferraz-de-Oliveira, I., Sales-Baptista, E., da Veiga, J. F. F., Tiago Marques, J., Pinto-Cruz, C., Godinho, C., & Belo, A. D. F. (2023). Farming for nature in the Montado: the application of ecosystem services in a results-based model. *Ecosystem Services*, 61, 101524.
32 https://doi.org/https://doi.org/10.1016/j.ecoser.2023.101524

33 Hoffman, E., McCabe, K., & Smith, V. L. (1996). Social Distance and Other-Regarding Behavior in Dictator Games. *The American Economic Review*, 86(3), 653-660.
35 http://www.jstor.org/stable/2118218

36 Huyghe, C. (2005). *prairies et cultures fourragères entre logiques de production et enjeux territoriaux*.

38 Kahmen, S., & Poschlod, P. (2008). Effects of grassland management on plant functional trait composition. *Agriculture, Ecosystems and Environment*, 128(3), 137-145.
40 https://doi.org/10.1016/j.agee.2008.05.016

41 Kuhfuss, L., Préget, R., Thoyer, S., & Hanley, N. (2016). Nudging farmers to enrol land into agri-environmental schemes: the role of a collective bonus. *European Review of Agricultural Economics*, 43(4), 609-636. https://doi.org/10.1093/erae/jbv031

44 Lakner, S., Zinngrebe, Y., & Koemle, D. (2020). Combining management plans and payment schemes for targeted grassland conservation within the Habitats Directive in Saxony, Eastern Germany. *Land Use Policy*, 97(February).
47 https://doi.org/10.1016/j.landusepol.2020.104642

48 Lampkin, N., Stolze, M., Meredith, S., de Porras, M., L., H., & Mészáros, D. (2020). Using Eco-schemes in the new CAP: a guide for managing authorities. *IFOAM EU, FIBL and IEEP, Brussels*, 76. www.ifoam-eu.org

51 Lancaster, K. J. (1966). A New Approach to Consumer Theory. *Journal of Political Economy*,
1 74(2), 132–157.

2 Larson, R. B. (2019). Controlling social desirability bias. *International Journal of Market

3 Research, 61(5), 534–547. https://doi.org/10.1177/1470785318805305

4 Lastra-Bravo, X. B., Hubbard, C., Garrod, G., & Tolón-Becerra, A. (2015). What drives

5 farmers’ participation in EU agri-environmental schemes?: Results from a qualitative meta-

6 analysis. *Environmental Science and Policy*, 54, 1–9.

7 https://doi.org/10.1016/j.envsci.2015.06.002

8 Lienhoop, N., & Brouwer, R. (2015). Agri-environmental policy valuation: Farmers’ contract

9 design preferences for afforestation schemes. *Land Use Policy*, 42, 568–577.

10 https://doi.org/10.1016/j.landusepol.2014.09.017

11 Louvière J., Hensher, D., Swait, J. (2000). *Stated Choice Methods: Analysis and Applications*.

12 ambridge University Press.

13 Margules, C. R., & Pressey, R. L. (2000). Systematic conservation planning. *Nature*, 405(6783),

14 243–253. https://doi.org/10.1038/35012251

15 Matzdorf, B., & Lorenz, J. (2010). How cost-effective are result-oriented agri-environmental

16 measures?-An empirical analysis in Germany. *Land Use Policy*, 27(2), 535–544.

17 https://doi.org/10.1016/j.landusepol.2009.07.011

18 McFadden. (1973). Conditional logit analysis of qualitative choice behavior, I. In *n: Frontiers in*

19 *Econometrics, Zarembka P*, (eds), Academic Press (pp. 105–142).

20 Michaud, A., Plantureux, S., Baumont, R., & Delaby, L. (2020). Grasslands, a source of richness

21 and support for innovation for more sustainable and acceptable ruminant livestock farming.

22 *Inra Productions Animales*, 33(3), 153–172. https://doi.org/10.20870/productions-

23 animales.2020.33.3.4543

24 Mills, J., Gaskell, P., Ingram, J., Dwyer, J., Reed, M., & Short, C. (2017). Engaging farmers in

25 environmental management through a better understanding of behaviour. *Agriculture and*

26 *Human Values*, 34(2), 283–299. https://doi.org/10.1007/s10460-016-9705-4

27 Myles, M., Kingsley, D., & Richard, H. (2013). Hurdles and Opportunities for Landscape-Scale

28 Restoration. *Science*, 339(6119), 526–527. https://doi.org/10.1126/science.1228334

29 Öhlmér, B., Olson, K., & Brehmer, B. (1998). Understanding farmers’ decision making

30 processes and improving managerial assistance. *Agricultural Economics*, 18(3), 273–290.

31 https://doi.org/10.1016/S0169-5150(97)00052-2

32 Parkhurst, G. M., & Shogren, J. F. (2007). Spatial incentives to coordinate contiguous habitat.

33 *Ecological Economics*, 64(2), 344–355. https://doi.org/10.1016/j.ecolecon.2007.07.009

34 workingpaper/ThuenenWorkingPaper_175_Vol1.pdf

35 Potter, C., & Gasson, R. (1988). Farmer participation in voluntary land diversion schemes: Some

36 predictions from a survey. *Journal of Rural Studies*, 4(4), 365–375.

37 https://doi.org/https://doi.org/10.1016/0743-0167(88)90005-8

38 Prager, K. (2015). Agri-environmental collaboratives for landscape management in Europe.

39 *Current Opinion in Environmental Sustainability*, 12, 59–66.

40 https://doi.org/10.1016/j.cosust.2014.10.009

41 Princé, K., & Jiguet, F. (2013). Ecological effectiveness of French grassland agri-environment

42 schemes for farmland bird communities. *Journal of Environmental Management*, 121, 110–

43 116. https://doi.org/10.1016/j.jenvman.2013.02.039

44 Puydarrieux, P., & Devaux, J. (2013). Quelle évaluation économique pour les services

45 écosystémiques rendus par les prairies en France métropolitaine ? *Notes et Études Socio-

46 *Économiques*, 37, 51–86. http://www.developpement-durable.gouv.fr/IMG/pdf/ED68EN-

47 2.pdf

48 Reimer, A. P., Thompson, A. W., & Prokopy, L. S. (2012). The multi-dimensional nature of

49 environmental attitudes among farmers in Indiana: implications for conservation adoption.

50 *Agriculture and Human Values*, 29(1), 29–40. https://doi.org/10.1007/s10460-011-9308-z

51 Rezaei, R., Safa, L., Damalas, C. A., & Ganjkhanloo, M. M. (2019). Drivers of farmers’
1 intention to use integrated pest management: Integrating theory of planned behavior and

2 norm activation model. *Journal of Environmental Management*, **236**, 328–339.

3 https://doi.org/https://doi.org/10.1016/j.jenvman.2019.01.097

4 Ricard, M. F., & Viglizzo, E. F. (2020). *MethodsX Improving carbon sequestration estimation*

5 through accounting carbon stored in grassland soil. **7**, 1–7.

6 Ridier, A., Roussy, C., & Chaib, K. (2021). Adoption of crop diversification by specialized grain

7 farmers in south-western France: evidence from a choice-modelling experiment. *Review of Agricultural, Food and Environmental Studies*, **102**(3), 265–283.

9 https://doi.org/10.1007/s41130-021-00144-0

10 Riley, M., Sangster, H., Smith, H., Chiverrell, R., & Boyle, J. (2018). Will farmers work

11 together for conservation? The potential limits of farmers’ cooperation in agri-environment

12 measures. *Land Use Policy*, **70**(October 2017), 635–646.

13 https://doi.org/10.1016/j.landusepol.2017.10.049

14 Ruto, E., & Garrod, G. (2009). Investigating farmers’ preferences for the design of agri-

15 environment schemes: a choice experiment approach. *Journal of Environmental Planning*

16 *and Management*, **52**(5), 631–647. https://doi.org/10.1080/09640560902958172

17 Schroeder, L. A., Isselstein, J., Chaplin, S., & Peel, S. (2013). Agri-environment schemes:

18 Farmers’ acceptance and perception of potential “Payment by Results” in grassland-A case

19 study in England. *Land Use Policy*, **32**, 134–144.

20 https://doi.org/10.1016/j.landusepol.2012.10.009

21 Senger, I., Borges, J. A. R., & Machado, J. A. D. (2017). Using the theory of planned behavior

22 to understand the intention of small farmers in diversifying their agricultural production.

23 *Journal of Rural Studies*, **49**, 32–40.

24 https://doi.org/https://doi.org/10.1016/j.jrurstud.2016.10.006

25 Siebert, R., Berger, G., Lorenz, J., & Pfeffer, H. (2010). Assessing German farmers’ attitudes

26 regarding nature conservation set-aside in regions dominated by arable farming. *Journal for*

27 *Nature Conservation*, **18**(4), 327–337.

28 https://doi.org/https://doi.org/10.1016/j.jnc.2010.01.006

29 Siebert, R., Toogood, M., & Knierim, A. (2006). *Factors Affecting European Farmers’*

30 *Participation in Biodiversity Policies*. **46**(4).

31 Street, D. J., Burgess, L., & Louviere, J. J. (2005). Quick and easy choice sets: Constructing

32 optimal and nearly optimal stated choice experiments. *International Journal of Research in*

33 *Marketing*, **22**(4), 459–470. https://doi.org/https://doi.org/10.1016/j.ijresmar.2005.09.003

34 Sutherland, L. A., Gabriel, D., Hathaway-Jenkins, L., Pascual, U., Schmutz, U., Rigby, D.,

35 Godwin, R., Sait, S. M., Sakrabani, R., Kunin, W. E., Benton, T. G., & Stagl, S. (2012).

36 The “Neighbourhood Effect”: A multidisciplinary assessment of the case for farmer co-

37 coordination in agri-environmental programmes. *Land Use Policy*, **29**(3), 502–512.

38 https://doi.org/10.1016/j.landusepol.2011.09.003

39 Thoyer, S., & Lécole, P. (2023). Quel bilan tirer de la Pac? *Cahiers Français, La*

40 *Documentation Française*, **431**, 64–71.

41 Tyllianakis, E., & Martin-Ortega, J. (2021). Agri-environmental schemes for biodiversity and

42 environmental protection: How were are not yet “hitting the right keys.” *Land Use Policy*,

43 *109*(June), 105620. https://doi.org/10.1016/j.landusepol.2021.105620

44 Vaissière, A.-C., Tardieu, L., Quétier, F., & Roussel, S. (2018). Preferences for biodiversity

45 offset contracts on arable land: a choice experiment study with farmers. *European Review*

46 *of Agricultural Economics*, **45**(4), 553–582. https://doi.org/10.1093/erae/jby006

47 Viaggi, D., Raggi, M., Villanueva, A. J., & Kantelhardt, J. (2021). Provision of public goods by

48 agriculture and forestry: Economics, policy and the way ahead. *Land Use Policy*, **107**.

49 https://doi.org/10.1016/j.landusepol.2020.105273

50 Villanueva, A. J., Gómez-Limón, J. A., Arriaza, M., & Rodríguez-Entrena, M. (2015). The

51 design of agri-environmental schemes: Farmers’ preferences in southern Spain. *Land Use*
1 *Policy*, **46**, 142–154. https://doi.org/10.1016/j.landusepol.2015.02.009

2 Vogel, S. (1996). Farmers' Environmental Attitudes and Behavior: A Case Study for Austria.

3 *Environment and Behavior*, **28**(5), 591–613. https://doi.org/10.1177/001391659602800502

4 Waldén, E., & Lindborg, R. (2018). Facing the future for grassland restoration – What about the farmers? *Journal of Environmental Management*, **227**(March), 305–312.

5 https://doi.org/10.1016/j.jenvman.2018.08.090

6 Wallis DeVries, M. F., Poschlod, P., & Willems, J. H. (2002). Challenges for the conservation of calcareous grasslands in northwestern Europe: integrating the requirements of flora and fauna. *Biological Conservation*, **104**(3), 265–273.

7 https://doi.org/10.1016/S0006-3207(01)00191-4

11 Wheeler, R., Lobley, M., Winter, M., & Morris, C. (2018). “The good guys are doing it anyway”: The accommodation of environmental concern among English and Welsh farmers. *Environment and Planning E: Nature and Space*, **1**(4), 664–687.

12 https://doi.org/10.1177/2514848618817487

15 Willock, J., Deary, I. J., McGregor, M. M., Sutherland, A., Edwards-Jones, G., Morgan, O., Dent, B., Grieve, R., Gibson, G., & Austin, E. (1999). Farmers’ attitudes, objectives, behaviors, and personality traits: The Edinburgh study of decision making on farms.

16 *Journal of Vocational Behavior*, **54**(1), 5–36. https://doi.org/10.1006/jvbe.1998.1642

19 Wilson, G. A. (1996). Farmer environmental attitudes and ESA participation. *Geoforum*, **27**(2), 115–131. https://doi.org/10.1016/0016-7185(96)00010-3

21 Wilson, G. A., & Hart, K. (2001). Farmer Participation in Agri-Environmental Schemes : Towards Conservation Oriented Thinking. *Sociologia Ruralis*, **41**(2).

23 Xiao, Y., Mignolet, C., Mari, J. F., & Benoît, M. (2015). Characterizing historical (1992-2010) transitions between grassland and cropland in mainland France through mining land-cover survey data. *Journal of Integrative Agriculture*, **14**(8), 1511–1523.

26 https://doi.org/10.1016/S2095-3119(15)61103-0

27 Zechmeister, H. G., Schmitzberger, I., Steurer, B., Peterseil, J., & Wrbka, T. (2003). The influence of land-use practices and economics on plant species richness in meadows.

29 *Biological Conservation*, **114**(2), 165–177. https://doi.org/10.1016/S0006-3207(03)00020-X

31 Zimmermann, A., & Britz, W. (2016). European farms’ participation in agri-environmental measures. *Land Use Policy*, **50**, 214–228. https://doi.org/10.1016/j.landusepol.2015.09.019

33

34

35

36

37