Ropars-Collet, Carole. 2022. "Evaluation Economique de L'Offre et de la Demande de Services Environnementaux Sur le Lac au Duc." Paper Presented at the 16èmes Journées de Recherches En Sciences Sociales INRAE-SFER-CIRAD, Clermont-Ferrand, France, December 15-16.

Russi, Daniela, Hélène Margue, Rainer Oppermann, and Clunie Keenleyside. 2016. "Result-Based Agri-Environment Measures: Market-Based Instruments, Incentives or Rewards? The Case of Baden-Württemberg." *Land Use Policy* 54: 69-77. doi:10.1016/j.landusepol.2016.01.012.

Ruto, Eric, and Guy Garrod. 2009. "Investigating Farmers' Preferences for the Design of Agri-Environment Schemes: A Choice Experiment Approach." *Journal of Environmental Planning and Management* 52 (5): 631-647. doi:10.1080/09640560902958172.

Salazar-Ordóñez, Melania, Macario Rodríguez-Entrena, and Anastasio J. Villanueva. 2021. "Exploring the Commodification of Biodiversity Using Olive Oil Producers' Willingness to Accept." *Land Use Policy* 107: 104348. doi:10.1016/j.landusepol.2019.104348.

Souchère, Véronique, Christine King, Nicolas Dubreuil, Véronique Lecomte-Morel, Yves Le Bissonnais, and Martine Chalat. 2003. "Grassland and Crop Trends: Role of the European Union Common Agricultural Policy and Consequences for Runoff and Soil Erosion." *Environmental Science and Policy* 6 (1): 7-16. doi:10.1016/S1462-9011(02)00121-1.

Tanaka, Katsuya, Nicholas Hanley, and Laure Kuhfuss. 2022. "Farmers' Preferences toward an Outcome-Based Payment for Ecosystem Service Scheme in Japan." *Journal of Agricultural Economics* 73 (3): 720-738. doi:10.1111/1477-9552.12478.

Thareau, Bertille, Abdoul Nasser Seyni, Thomas Coisnon, and Pierre Dupraz. 2023. "Designing Carbon Markets Connecting Farmers and Companies: Stakeholders Claiming Territorial - Based Devices to Promote Synergies between Diverse Environmental Challenges." *Review of Agricultural, Food and Environmental Studies* 104 (2): 167-191. doi:10.1007/s41130-023-00194-6.

Uetake, Tetsuya. 2014. "Agri-Environmental Management through Collective Action." *EuroChoices* 13 (3): 29-34. doi:10.1111/1746-692X.12072.

Uthes, Sandra, and Bettina Matzdorf. 2013. "Studies on Agri-Environmental Measures: A Survey of the Literature." *Environmental Management* 51 (1): 251-266. doi:10.1007/s00267-012-9959-6.

Vaissière, Anne Charlotte, Léa Tardieu, Fabien Quétier, and Sébastien Roussel. 2018. "Preferences for Biodiversity Offset Contracts on Arable Land: A Choice Experiment Study with Farmers." *European Review of Agricultural Economics* 45 (4): 553-582. doi:10.1093/erae/jby006.

Villamayor-Tomas, Sergio, Julian Sagebiel, and Roland Olschewski. 2019. "Bringing the Neighbors in: A Choice Experiment on the Influence of Coordination and Social Norms on Farmers' Willingness to Accept Agro-Environmental Schemes across Europe." *Land Use Policy* 84: 200-215. doi:10.1016/j.landusepol.2019.03.006.

Villanueva, A. J., M. Rodríguez-Entrena, M. Arriaza, and J. A. Gómez-Limón. 2017. "Heterogeneity of Farmers' Preferences towards Agri-Environmental Schemes across Different Agricultural Subsystems." *Journal of Environmental Planning and Management* 60 (4): 684-707. doi:10.1080/09640568.2016.1168289.

Villanueva, Anastasio J., José A. Gómez-Limón, Manuel Arriaza, and Macario Rodríguez-Entrena. 2015. "The Design of Agri-Environmental Schemes: Farmers' Preferences in Southern Spain." *Land Use Policy* 46: 142-154. doi:10.1016/j.landusepol.2015.02.009.

Villanueva, Anastasio J., Klaus Glenk, and Macario Rodríguez-Entrena. 2017. "Protest Responses and Willingness to Accept: Ecosystem Services Providers' Preferences towards Incentive-Based Schemes." *Journal of Agricultural Economics* 68 (3): 801-821. doi:10.1111/1477-9552.12211.

Wunder, Sven. 2015. "Revisiting the Concept of Payments for Environmental Services." *Ecological Economics* 117: 234-243. doi:10.1016/j.ecolecon.2014.08.016.

Zabel, Astrid, and Brian Roe. 2009. "Optimal Design of Pro-Conservation Incentives." *Ecological Economics* 69 (1): 126-134. doi:10.1016/j.ecolecon.2009.08.001.

Zavalloni, Matteo, Meri Raggi, and Davide Viaggi. 2019. "Agri-Environmental Policies and Public Goods: An Assessment of Coalition Incentives and Minimum Participation Rules." *Environmental and Resource Economics* 72 (4): 1023-1040. doi:10.1007/s10640-018-0237-9.
Appendix

<table><caption>Table A1. Conditional logit estimations.</caption><thead><tr><th>Specification</th><th>CL1</th><th>CL2</th><th>CL3</th></tr></thead><tbody><tr><td><strong>Attributes</strong></td><td></td><td></td><td></td></tr><tr><td><em>PAYMENT</em></td><td>0.004*** (0.000)</td><td>0.004*** (0.000)</td><td>0.004*** (0.000)</td></tr><tr><td><em>COVER</em></td><td>-0.024 (0.023)</td><td>0.020<sup>+</sup> (0.012)</td><td></td></tr><tr><td><em>COVER</em> – 90%</td><td></td><td></td><td>0.064 (0.064)</td></tr><tr><td><em>COVER</em> – 95%</td><td></td><td></td><td>0.072 (0.070)</td></tr><tr><td><em>HEDGEROWS</em></td><td>-0.004 (0.003)</td><td>-0.004<sup>+</sup> (0.002)</td><td></td></tr><tr><td><em>HEDGEROWS</em> – 60 m/ha</td><td></td><td></td><td>0.127<sup>+</sup> (0.065)</td></tr><tr><td><em>HEDGEROWS</em> – 100 m/ha</td><td></td><td></td><td>-0.230* (0.104)</td></tr><tr><td><em>BONUS sponsorship</em></td><td>0.402** (0.124)</td><td>0.381*** (0.113)</td><td>0.393*** (0.117)</td></tr><tr><td><em>BONUS sponsorship/</em><br><em>collective result</em></td><td>0.266<sup>+</sup> (0.134)</td><td>0.248* (0.123)</td><td>0.265* (0.124)</td></tr><tr><td><strong>Alternative-specific constant</strong></td><td></td><td></td><td></td></tr><tr><td><em>ASC<sub>sq</sub></em></td><td>0.941*** (0.254)</td><td>2.286* (1.139)</td><td>0.720*** (0.189)</td></tr><tr><td><strong>Goodness-of-fit</strong></td><td></td><td></td><td></td></tr><tr><td>Log Likelihood</td><td>-990.63</td><td>-1078.90</td><td>-1076.53</td></tr><tr><td>Adjusted-R<sup>2</sup></td><td>0.16</td><td>0.15</td><td>0.15</td></tr><tr><td>AIC</td><td>1993.27</td><td>2169.79</td><td>2169.06</td></tr><tr><td>BIC</td><td>2023.15</td><td>2200.15</td><td>2209.55</td></tr><tr><td>Observations</td><td>1075</td><td>1165</td><td>1165</td></tr><tr><td>Number of farms</td><td>120</td><td>130</td><td>130</td></tr></tbody></table>

Note. Significance levels: ***robust *p* value <0.001, **robust *p* value <0.01, *robust *p* value < 0.05, +robust *p* value < 0.1. Robust standard errors in parentheses. ASCsq; alternative-specific constant associated with the status quo alternative. AIC: Akaike information criterion. BIC: Bayesian information criterion.

Source: own elaboration.

<table><caption>Table A2. Selection of Latent class model.</caption><thead><tr><th>Number of classes</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><td>Log Likelihood</td><td>-842.25</td><td>-746.27</td><td>-692.42</td><td>-709.94</td></tr><tr><td>AIC</td><td>1710.51</td><td>1532.53</td><td>1503.18</td><td>1487.87</td></tr><tr><td>BIC</td><td>1775.25</td><td>1632.14</td><td>1637.64</td><td>1657.19</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.2758</td><td>0.3512</td><td>0.3636</td><td>0.3701</td></tr><tr><td>Average probability to belong to the attributed class</td><td>0.9753</td><td>0.9634</td><td>0.9560</td><td>0.9516</td></tr></tbody></table>

Note. AIC: Akaike information criterion. BIC: Bayesian information criterion.

Source: own elaboration.
Appendix

<table><caption>Table A1. Conditional logit estimations.</caption><thead><tr><th>Specification</th><th>CL1</th><th>CL2</th><th>CL3</th></tr></thead><tbody><tr><td><strong>Attributes</strong></td><td></td><td></td><td></td></tr><tr><td><em>PAYMENT</em></td><td>0.004*** (0.000)</td><td>0.004*** (0.000)</td><td>0.004*** (0.000)</td></tr><tr><td><em>COVER</em></td><td>-0.024 (0.023)</td><td>0.020<sup>+</sup> (0.012)</td><td></td></tr><tr><td><em>COVER</em> – 90%</td><td></td><td></td><td>0.064 (0.064)</td></tr><tr><td><em>COVER</em> – 95%</td><td></td><td></td><td>0.072 (0.070)</td></tr><tr><td><em>HEDGEROWS</em></td><td>-0.004 (0.003)</td><td>-0.004<sup>+</sup> (0.002)</td><td></td></tr><tr><td><em>HEDGEROWS</em> – 60 m/ha</td><td></td><td></td><td>0.127<sup>+</sup> (0.065)</td></tr><tr><td><em>HEDGEROWS</em> – 100 m/ha</td><td></td><td></td><td>-0.230<sup>*</sup> (0.104)</td></tr><tr><td><em>BONUS sponsorship</em></td><td>0.402** (0.124)</td><td>0.381*** (0.113)</td><td>0.393*** (0.117)</td></tr><tr><td><em>BONUS sponsorship/</em><br><em>collective result</em></td><td>0.266<sup>+</sup> (0.134)</td><td>0.248<sup>*</sup> (0.123)</td><td>0.265<sup>*</sup> (0.124)</td></tr><tr><td><strong>Alternative-specific constant</strong></td><td></td><td></td><td></td></tr><tr><td><em>ASC<sub>sq</sub></em></td><td>0.941*** (0.254)</td><td>2.286<sup>*</sup> (1.139)</td><td>0.720*** (0.189)</td></tr><tr><td><strong>Goodness-of-fit</strong></td><td></td><td></td><td></td></tr><tr><td>Log Likelihood</td><td>-990.63</td><td>-1078.90</td><td>-1076.53</td></tr><tr><td>Adjusted-R<sup>2</sup></td><td>0.16</td><td>0.15</td><td>0.15</td></tr><tr><td>AIC</td><td>1993.27</td><td>2169.79</td><td>2169.06</td></tr><tr><td>BIC</td><td>2023.15</td><td>2200.15</td><td>2209.55</td></tr><tr><td>Observations</td><td>1075</td><td>1165</td><td>1165</td></tr><tr><td>Number of farms</td><td>120</td><td>130</td><td>130</td></tr></tbody></table>

Note. Significance levels: ***robust *p* value <0.001, **robust *p* value <0.01, *robust *p* value < 0.05, +robust *p* value < 0.1. Robust standard errors in parentheses. ASCsq; alternative-specific constant associated with the status quo alternative. AIC: Akaike information criterion. BIC: Bayesian information criterion.

Source: own elaboration.

<table><caption>Table A2. Selection of Latent class model.</caption><thead><tr><th>Number of classes</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><td>Log Likelihood</td><td>-842.25</td><td>-746.27</td><td>-692.42</td><td>-709.94</td></tr><tr><td>AIC</td><td>1710.51</td><td>1532.53</td><td>1503.18</td><td>1487.87</td></tr><tr><td>BIC</td><td>1775.25</td><td>1632.14</td><td>1637.64</td><td>1657.19</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.2758</td><td>0.3512</td><td>0.3636</td><td>0.3701</td></tr><tr><td>Average probability to belong to the attributed class</td><td>0.9753</td><td>0.9634</td><td>0.9560</td><td>0.9516</td></tr></tbody></table>

Note. AIC: Akaike information criterion. BIC: Bayesian information criterion.

Source: own elaboration.