Marianne Lefebvre

Yann Raineau

Cécile Aubert

Niklas Möhring

Pauline Pedehour

Marc Raynal

Green Insurance for Pesticide Reduction: Acceptability and Impact for French Viticulture

Keywords: Q18, Q14, D81, C99 Choice experiment, Pesticides, Viticulture, Insurance, Decision Support System, Integrated Pest Management, Index, Mutual fund

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Pest management is central for ensuring crop yield and quality [START_REF] Savary | The global burden of pathogens and pests on major food crops[END_REF]) and its importance is expected to further increase under climate change [START_REF] Chaloner | Plant pathogen infection risk tracks global crop yields under climate change[END_REF]. Current pest management strategies are mainly based on pesticide use, with increasing evidence of pesticides' adverse effects on the environment and human health (IPBES 2019;[START_REF] Geiger | Persistent negative effects of pesticides on biodiversity and biological control potential on European farmland[END_REF]Edlinger et al. 2022;[START_REF] Willett | Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems[END_REF][START_REF] Snelders | Triazole Fungicides Can Induce Cross-Resistance to Medical Triazoles in Aspergillus fumigatus[END_REF]). As a consequence, reducing pesticide use and risks has become an important public policy goal on regional and global levels [START_REF] Möhring | Pathways for advancing pesticide policies[END_REF][START_REF] Möhring | Successful implementation of global targets to reduce nutrient and pesticide pollution requires suitable indicators[END_REF].

Decision support systems (DSS) for farmers to optimally time applications according to actual local disease pressure have the potential to reduce pesticide use while maintaining yield levels [START_REF] Pertot | A critical review of plant protection tools for reducing pesticide use on grapevine and new perspectives for the implementation of IPM in viticulture[END_REF][START_REF] Chen | Delaying the first grapevine fungicide application reduces exposure on operators by half[END_REF][START_REF] Anastasiou | Precision farming technologies for crop protection: A metaanalysis[END_REF], and fungicide in particular [START_REF] Lázaro | Decision support systems halve fungicide use compared to calendar-based strategies without increasing disease risk[END_REF]. However, their uptake is often low. One important reason is that expected risks of yield losses are perceived as higher when adapting management strategies [START_REF] Gent | Perceptions of Risk, Risk Aversion, and Barriers to Adoption of Decision Support Systems and Integrated Pest Management: An Introduction[END_REF]Pethybridge 2011, Shtienberg 2013;[START_REF] Möhring | Why farmers deviate from recommended pesticide timing: the role of uncertainty and information[END_REF].

Green insurance, which insures potential yield losses when switching practices, is not currently included in policy toolboxes, despite its potential to increase farmers' uptake of DSS-based crop protection strategies. With green insurance, the insured producer receives financial compensation in case of yield losses caused by the failure of best management practices (here the inability of the DSS to contain diseases).2 If producers have biased perceptions regarding the effects of new practices on the level and variability of yields or profits (Feather and Amacher 1994), green insurance could help them revise these perceptions by allowing them to try these practices risk-free [START_REF] Mitchell | Factors Determining Best Management Practice Adoption Incentives and the Impact of Green Insurance[END_REF]C. Aubert et al. 2020). In other risk areas, it has been shown that sub-optimal insurance levels are observed when agents face an explicit or implicit cost to discovering the true probability of losses, but public subsidy can trigger optimal insurance decisions [START_REF] Kunreuther | Neglecting Disaster: Why Don't People Insure Against Large Losses?[END_REF]. It suggests that a subsidy for green insurance could be needed. Compared to agri-environmental schemes (AES), subsidizing green insurance can be more cost-effective since public support is triggered only for actual losses [START_REF] Baerenklau | Some Simulation Results for a Green Insurance Mechanism[END_REF], and the level of support required to induce participation by risk-averse producers does not need to include a risk premium.

A few green insurance contracts have been experimented with in the US and in Europe. 3But these experiments have only been conducted on a small scale, with no proper measure of cost-efficiency nor evaluation of the levers to increase acceptability. Some authors have modeled producers' decision to contract green insurance [START_REF] Baerenklau | Some Simulation Results for a Green Insurance Mechanism[END_REF][START_REF] Harris | Using BMP insurance to improve farm management[END_REF], but more empirical research is needed to evaluate its potential uptake and impact. A fundamental challenge is to design insurance products that will be adopted by a large range of farmers, will actually lead to best management practices' adoption and are more cost-efficient than other instruments [START_REF] Hazell | Best practices for subsidizing agricultural insurance[END_REF]. Ex-ante evaluation is thus important for industry and policy to develop products and support programs that are attractive to producers. Such (subsidized) risk management tools for pesticide use reduction may have a high global relevance -in the EU as well as beyond [START_REF] Möhring | Successful implementation of global targets to reduce nutrient and pesticide pollution requires suitable indicators[END_REF]).

Here we assess the effect of different insurance designs on acceptability, as well as the potential impact of a subsidized green insurance, targeting fungicide use in French viticulture. Grapevine production is globally among the most pesticide-intensive and economically relevant crops, and therefore represents a key entry point to reduce pesticide use in agriculture. In France, the iconic wine production covers only 3.3% of the agricultural area but is responsible for 14.4% of total agricultural pesticide use [START_REF] Butault | Écophyto R&D: quelles voies pour réduire l'usage des pesticides? Synthèse du rapport de l'étude[END_REF]. Fungicides represent more than 80% of pesticides used on vines in France (French agricultural ministry 2022). Due to their high technical efficacy and low cost, they are sometimes referred to as a very attractive "insurance" (although they are more adequately termed a prevention mechanism than an insurance properly speaking). 4 In our analysis, we begin with developing a theoretical model to explain decisions to subscribe to green insurance and to comply with DSS recommendations (therefore reducing the protection offered by chemical pesticides). We then conduct a large discrete choice experiment with 412 French grapevine growers on the uptake and design of the insurance and combine it with field experimental data on the pesticide use reduction potential. We evaluate the acceptability of both loss-based and index-based insurance, since the latter is perceived as having a large potential, also in developed countries, to contribute to better farm-level risk management and more efficient use of natural resources [START_REF] Dalhaus | Phenology Information Contributes to Reduce Temporal Basis Risk in Agricultural Weather Index Insurance[END_REF]. Doing so, we contribute to the very narrow literature on insurance covering pest attacks and diseases, which accounts for only 0.9% of the agricultural insurance literature [START_REF] Vyas | Mapping global research on agricultural insurance[END_REF].

Adjusting for sampling bias, we find that between 48% and 60% of the vine growers are likely to subscribe to the green insurance, depending on contract design and prices. Producers transitioning to organic certification are more interested in the contract. This result suggests that green insurance could -in addition to intensive margin effects on pesticide intensity -also have extensive-margin effects: it could help reduce pesticide use by supporting transitions to organic farming. Clear preferences emerge for contract design: all producers exhibit less interest in group and index-based contracts. Using data from field experiments on the DSS impact on fungicide use, we estimate that adopters could reduce their fungicide treatments by 45% on average. The chosen set-up would entail higher potential subsidy costs, compared to existing policy tools in France, but also a higher pesticide reduction potential. Our results remain valid throughout a series of robustness checks. This paper is structured as follows. In Section 2, we present the insurance scheme and model vine growers' decisions to subscribe to green insurance. In Section 3, we explain the DCE approach and describe the experimental setting and methods used for data analysis. Results are presented in Section 4 and further discussed in Section 5.

Modelling vine growers' decisions

We analyze the conditions under which a risk-neutral producer will choose to subscribe to a green insurance scheme, that partly covers yield losses due to fungal diseases in grapevines. The scheme entails free access to a DSS that provides treatment recommendations based on climatic and fungus pressure information. If producers follow the DSS recommendations, a public subsidy is added to the indemnity paid by the private insurer (which is independent from compliance with DSS recommendations). By subsidizing indemnities rather than the premium, public money is not disbursed for each policy-holder, but only for those suffering a loss. 5 Directing public support to increasing the indemnity is more efficient than reducing the premium (or paying a fixed subsidy as in an AES) when vine growers are risk-averse or pessimistic about losses associated with greener practices since it induces participation at a lower cost. Vine growers tend to apply fungicides too early in the season and repeat applications as soon as the previous application has been washed away by rain, whatever the actual disease pressure -thus they overtreat [START_REF] Davy | Decitrait ®: a DSS for vineyard protection[END_REF]. The subsidy provides an incentive to producers to follow the DSS, even when it recommends treating less than they would have done, thus reducing the externalities of pesticide use.

Pesticide use is measured by the Treatment Frequency Index (TFI), i.e. the applied quantities over the course of an agricultural season normalized by their standard treatment dosage [START_REF] Lechenet | Reducing pesticide use while preserving crop productivity and profitability on arable farms[END_REF]. We focus on changes in pesticide use per hectare, i.e. intensive-margin effects of the insurance scheme. 6We first describe the green insurance contract, and then derive theoretical expectations on the impact of pesticide reduction, profits and the producer's decision to subscribe to the insurance.

The features of a green insurance contract

A green insurance contract is characterized by its price P (per unit of insured capital), and the way the indemnity I is computed and granted. 7 The indemnity typically does not fully cover losses: contracts entail a deductible, to induce farming efforts. 8 . To incentivize producers to reduce fungicide use, the insurance scheme entails a bonus in the form of increased indemnity, financed by public authorities, for vine growers respecting the treatment protocol (timing, doses) provided by the DSS, and not carrying out any treatment other than those recommended by this protocol. This is verifiable since it is mandatory for producers to record data on treatments (date, product, quantity) and legal audits take place. While the delivery system of agricultural insurance varies widely among different countries [START_REF] Smith | Agricultural Insurance in Developed Countries: Where Have We Been and Where Are We Going?[END_REF], we restrict our attention to two characteristics: loss-based vs. indexbased trigger; individual vs. collective contract.

(changes in land use decisions), resulting in effects on producers input use [START_REF] Möhring | Crop insurance and pesticide use in European agriculture[END_REF]Wu 1999;[START_REF] Goodwin | An Empirical Analysis of Acreage Effects of Participation in the Federal Crop Insurance Program[END_REF]. These studies mostly focus on arable farming. Significant extensive margin effects are unlikely to occur for grapevine production in France, at least in the short-to midterm. Grapevine production in France is historically very established and follows a plethora of local, regional and national customs, rules and regulations, as well as established consumer preferences, which strongly limit both extending production areas and switching to other varieties. In addition, most adequate soils for grapevine production are already under production. Such rules, regulations, and customs may however evolve, especially in the long run, and risk management tools should then carefully be evaluated with regard to their effects, for example for the decision to plant resistant or climate change adapted varieties -which is out of the scope of this study. However, note that extensive margin effects comprising switches to different production conditions, such as organic production, might be possible. 7 In the insurance literature, insurance premia are customarily expressed as a proportion m of the indemnity, where m is the "loading factor" (as in, e.g., Clarke 2016). This factor is 1 for "actuarially fair" insurance (meaning that a risk-neutral insurer makes no profit, as is customarily assumed under perfect competition). This approach assumes that one can compute the expected indemnity (paid ex-post) to assess the corresponding premium (paid ex-ante). In our context however, the probability of losses is endogenous to the contract, since it depends on the incentives of the producer to reduce fungicide use and to follow the DSS. One can therefore not express the price P as a function of I. 8 Here, the green insurance is intended to induce a shift towards greener, IPM practices, which can increase yield losses, so the deductible runs contrary to the green objective. However, it remains needed to avoid negligent care.

Loss-based vs. index-based trigger.

Under loss-based insurance, the indemnity is paid when there are actual losses, as assessed by an expert mandated by the insurer. Under indexbased insurance, the indemnity is triggered when the realization of an index reaches a defined threshold, rather than based on verifiable losses. 9 Index-based insurance has the advantages of being less costly for the insurer (who saves on audit costs) and less contestable for the producer (who may resent the expert's assessment). It has been gaining much interest, first in developing countries where audit costs were prohibitive and more recently in richer countries [START_REF] Ahmed | The Impact of Commercial Rainfall Index Insurance: Experimental Evidence from Ethiopia[END_REF]. Index-based insurance can help reduce moral hazard, adverse selection and administrative costs, but may also create new issues [START_REF] Jørgensen | Natural insurance as condition for market insurance: Climate change adaptation in agriculture[END_REF]. Its risk-reducing effectiveness depends on how well the actual yield correlates with the index [START_REF] Glauber | Crop Insurance Reconsidered[END_REF]. While for climatic insurance, an index correlated with actual losses can be devised using meteorological data, at this date, no adequate index is available for pest attacks on vines. Although hypothetical at this date, a suitable index is the object of much research and might become available in future years. Producers' interest in such index-based contracts therefore deserves to be evaluated.

Individual vs. collective contract.

Most frequently, commercial insurance companies offer individual contracts, sometimes with public support. Alternatively, a mutual fund relies on group contracts, where targeted producers compulsorily adhere to the fund. Mutual funds establish financial reserves, built up through participants' contributions, which can be withdrawn by the members when losses occur, according to predefined rules. 10 The preference for individual vs. group contracts is likely to depend on beliefs and norms that are difficult to capture in the theoretical model and are therefore only assessed in the Discrete Choice Experiment.

9 We follow the widely-used convention of referring to this type of contract as "index insurance" although it is technically an index security, and is not an insurance in the US Generally Accepted Accounting Principles [START_REF] Clarke | A Theory of Rational Demand for Index Insurance[END_REF].

10 Mutual funds are one of the instruments subsidized by the European Common Agricultural Policy in its risk management toolbox and currently operate in Italy, the Netherlands and France.

Modelling the impact of pesticide reduction on profits

The producer's financial yield without pesticide reduction is y. Reducing treatment intensity entails the risk of production losses or deterioration of grape quality, at rate l ∈]0, 1], that reduces financial yield to (1-l)y. This happens with a probability that decreases in treatment intensity.11 Each producer's conditions vary with regard to fungal disease. These conditions translate into an initial fungicide treatment frequency index T F I, that the producer chooses if they do not contract the green insurance. This level is (weakly) higher than the environmentally optimal one, T F I * , which is the lowest achievable treatment level for the targeted yield (T F I * ≤ T F I).

We assume that this optimal level is achieved when one follows DSS recommendations.

The percentage of reduction in fungicide use reachable thanks to the DSS is k ≡ (T F I -T F I * )/T F I. Reducing fungicides by k% reduces the financial costs of applying fungicides, denoted C k . To simplify, we assume that a producer not following DSS recommendations will not reduce its pesticide use at all (k = 0). In that case, the cost of applying fungicide is

C 0 > C k .
The probability of facing losses l is ρ k . We assume ρ k is increasing in the reduction of treatment intensity k, since for a given T F I * , producers with higher initial TFI are less experienced with low-fungicide management practices. While overestimation of probabilities cannot be systematic in an expected utility model, producers may overestimate the risks from a lower TFI, compared to the true probability of losses as assessed by experts (e.g., on research plots).

We focus here on the analysis for a risk-neutral producer. The case of a risk-averse producer is detailed in Appendix A. Under risk neutrality, the expected profit of a producer reducing her pesticides use by k > 0, thanks to the DSS but in the absence of insurance, would be

EU N o k = (1 -ρ k )y + ρ k (1 -l)y -C k = y(1 -ρ k l) -C k .
Most French vine growers currently do not use such a DSS despite its low cost. 12 We therefore assume that the following inequality holds:

EU N o 0 > EU N o k , that is: (ρ k -ρ 0 )ly > C 0 -C k .
The cost savings on fungicides are not enough to compensate for the higher risk of losses when reducing one's TFI, even with the help of the DSS. This is why additional incentives are needed, such as those provided by a conditional green insurance.

Modelling the subscription to green insurance

We assume that y is both the maximum yield and the one insured under the contract, at price P . The indemnity I is paid depending on i) whether there are actual losses (l > 0%) in the case of loss-based insurance, or ii) whether the realization of an index reaches a threshold, in the case of index-based insurance. The insurance coverage is α in [0,1[ (due to the positive deductible). If the producer follows DSS recommendations and thus reaches T F I * , her indemnity is increased by a bonus b funded by public authorities, so her indemnity is equal to α + b % of losses. We assume that α + b < 1 so that losses are never fully covered by the contract. We define the participation and incentive constraints for a risk-neutral producer for both types of insurance:

loss-based and index-based. Appendix A. details the more general case of a risk-averse producer and contains proofs and illustrative simulations.

Loss-based insurance

With loss-based insurance, the indemnity (α + b)ly is received in case of yield losses l, as assessed by an expert, which happens with probability ρ k for a reduction in TFI of k. Under this insurance, a producer complying with the DSS obtains an expected profit

of EU LB k ≡ (1 -ρ k )y + ρ k [(1 -l)y + (α + b)ly] -C k -P ,
which can be rewritten as:

EU LB k = y[1 -ρ k l(1 -(α + b))] -C k -P
A producer who does not reduce pesticide use and does not subscribe to green insurance faces a probability of loss of ρ 0 and obtains an expected profit of [1 -ρ 0 l]y -C 0 .

The producer's participation constraint (P C) LB to the insurance contract is therefore met if the expected profit without insurance EU N o 0 is lower than the expected profit with insurance, that is if:

(P C) LB : [ρ 0 -ρ k (1 -α -b)]ly ≥ P -(C 0 -C k )
or equivalently

(P C) LB : (C 0 -C k ) + ρ k (α + b)ly ≥ (ρ k -ρ 0 )ly + P.
Participation is ensured if the cost savings on fungicides plus the expected indemnity compensate for the higher risk of losses plus the insurance premium.

If the producer was to subscribe to the insurance contract without following the DSS, the expected profit (without bonus), under risk neutrality, would be [1 -ρ 0 l(1 -α)]y -C 0 -P . An incentive constraint (IC) LB must be satisfied for the insurance contract to induce effective TFI reduction. Complying with the DSS is more attractive than not reducing fungicide use, while insured, if and only if

(IC) LB : (C 0 -C k ) + ρ k bly > (ρ k -ρ 0 )(1 -α)ly.
The green insurance will indeed induce a reduction in TFI if the savings on fungicides and the bonus compensate for the higher risk of losses. Recall that we assume the cost savings are not sufficiently attractive in the absence of insurance. With green insurance, the foregone revenues in case of losses are reduced, and even more with the bonus triggered by compliance with DSS recommendations. The larger the bonus b, the smaller the residual losses borne by the producer, and the more likely it is that the producer prefers to follow the DSS and reduce her TFI. The bonus is therefore an effective complement to the green insurance in inducing fewer treatments.

The bonus thus causes an increase in both participation and compliance with the DSS recommendations. It helps make the insurance contract more attractive and more effective.

The intuitions are similar under risk-aversion (cf. Appendix A.).

If individual experience with fungicide reduction is limited or the DSS does not manage to reduce loss frequency (ρ k is high), producers will be both less likely to participate and less likely to comply with the DSS due to the increase in residual losses. A countervailing effect arises from the savings on treatments, which favors both participation and compliance. In this respect, a tax on fungicides, by increasing these cost savings, would be complementary to the insurance bonus. Such a tax would have an impact not only from direct incentives to reduce use, but also by facilitating the adoption of the DSS supported by insurance.

The constraints also yield ambiguous results regarding the relative participation of high-use producers vs. low-use ones. For producers who initially had a high TFI, the DSS will likely induce a large reduction k, with the direct negative impacts on participation and compliance noted above. However, for such high-use producers, who tend to face adverse conditions or lack the expertise needed to reduce fungicide use, the initial cost of treatment C 0 will be higher, which tends to make compliance more beneficial.

Index-based insurance

The indemnity is triggered by the value of an index, built in order to correlate with the realization of yields, but correlation is imperfect. The premium P equals to βP , with β ≤ 1, since index-based insurance is assumed to be less costly to implement.

We define four scenarios corresponding to discrete states of nature (as in [START_REF] Clarke | A Theory of Rational Demand for Index Insurance[END_REF][START_REF] Lichtenberg | Index insurance and basis risk: A reconsideration[END_REF][START_REF] Lichtenberg | Index insurance and basis risk: A reconsideration[END_REF]. Associated probabilities are denoted ρ k A+B where A ∈ {l, nl} takes the value l in the event of a loss and nl otherwise, and B ∈ {i, ni} takes the value i in the event of the index triggering the indemnity, and ni otherwise (cf table A.1). The total probability of a loss is ρ k ≡ ρ k l+i + ρ k l+ni and the total probability of receiving an indemnity is

ρ i ≡ ρ k l+i + ρ k nl+i .
The probabilities with which losses are suffered but no indemnity is paid, and vice-versa, are an essential determinant of the insurance properties of the index-based contract.

Under index-based insurance, expected profits are equal to the actual yields in the two states of nature, plus the indemnity when triggered by the index, minus the costs of pesticides and insurance. The expected profit for the risk-neutral producer is

EU IB k = (1 -ρ k )y + ρ k (1 -l)y + ρ i (α + b)ly -C k -βP , or equivalently EU IB k = y[1 -(ρ k -ρ i (α + b))l] -C k -βP
The participation constraint is

(P C) IB : (C 0 -C k ) + ρ i (α + b)ly ≥ (ρ 0 -ρ k )ly + βP
that is: as in the case of loss-based insurance, the expected indemnity plus the cost savings on fungicides must compensate for the higher risk of losses plus the insurance premium.

The incentive constraint for a risk-neutral producer is

(IC) IB : (C 0 -C k ) + ρ i bly ≥ (ρ k -ρ 0 )ly
where the only insurance term playing a role is b. The savings on treatments and the bonus in case of indemnification should exceed the increase in losses. Contrary to the case of loss-based insurance, the probability with which the indemnity is paid is independent from compliance with the DSS. 13 But receiving the bonus depends on this compliance. A subsidy to reduce the premium, granted to all subscribers, would not improve compliance, contrary to the bonus. Our results therefore show that a green index insurance, in order to maximize chances of inducing the adoption of greener practices, should be clearly conditional on these practices. Because the bonus is paid ex-post, it is a more efficient form of subsidy than a reduction in the premium, even if this reduction was also made conditional on compliance (because it would have to be adjusted months after the premium has been paid, which would create implementation difficulties).

Comparative statics are similar to loss-based insurance, but low DSS performance in terms of reducing the frequency of losses (high ρ k ) makes both participation and compliance less attractive and does not change the probability of receiving the compensation. 13 The probability of obtaining the indemnity and the indemnity paid by the insurer both depend only on the index and not on treatments. So the expected value of the indemnity cancels out in the incentive constraint: it is paid exactly in the same way whether treatments have been reduced or not. This is different for loss-based insurance as the probability of actually suffering losses, and therefore obtaining an indemnity, does depend on whether the farmer has reduced treatment intensity.

Loss-based vs Index-based insurance The index-based insurance contract is more attractive to a risk-neutral producer than the loss-based one if and only if

EU IB k ≥ EU LB k ⇔ (ρ i -ρ k )(α + b)ly + (1 -β)P ≥ 0.
Index-based insurance is thus always more attractive than loss-based insurance to a riskneutral producer if the probability of indemnity ρ i is larger than the probability of actual losses ρ k , or if the index-based contract is sufficiently cheaper.

If producers overestimate the probability of losses ρ k , they will tend to find loss-based insurance more attractive. Moreover, our model predicts that the growers further away from T F I * will be less likely than the other growers who depend on the same index (same ρ i ) to prefer index-based insurance since they face a higher probability of losses ρ k .

Under risk aversion, the analysis is more complex since the index increases the variability in the producer's return: it creates a very positive state where indemnity is received in the absence of losses and a very negative state in which no indemnity is received despite losses.

Although an increase in variability is costly to a risk-averse producer, the index-based insurance can still be more attractive if the probability of receiving a bonus even when not suffering losses (ρ nl+i ), is sufficiently large. Knowing the producer's risk-aversion is not sufficient to determine the overall effect, since it depends on both the shape of the utility function and the beliefs about the probabilities of the four states. The more pessimistic the producer is about ρ i , the less attractive the index-based insurance, as the variance effect will tend to dominate.

The need for an empirical test

The model allows us to explore how the decision to subscribe to a green insurance is impacted by the probability and value of losses (ρ k ly), the increase in risk due to pesticide use reduction (ρ k -ρ 0 ), as well as by the cost savings from pesticide use reduction (C 0 -C k ). We presented the impact of some insurance characteristics such as how damage is evaluated, the coverage α and bonus b rate, and the premium P . Comparative statics are presented in Appendix A.

The model focuses on one source of heterogeneity across producers, based on production context and expertise: the initial fungicide treatment frequency index (T F I), which impacts the probability of losses when less fungicide is used (ρ k ). Other producers' characteristics such as risk-aversion or attitudes towards digital farming may also impact the decision to subscribe to the green insurance scheme under study. To account for this larger set of factors, we assessed the impact of insurance and producers' characteristics on subscription decisions through a choice experiment. The design is detailed in the next section.

Method

To analyze preferences for a green insurance which is not available at the time of the study, we rely on a Discrete Choice Experiment (DCE). DCEs are particularly valuable for investigating individuals' preferences in hypothetical decision-making situations, when purchase or adoption data are not available [START_REF] Louviere | Stated Choice Methods: Analysis and Applications[END_REF]. The method has been previously used to evaluate ex-ante programs targeting pesticide use reduction. 2016) include an attribute on the insurance provider (private insurer vs mutual fund). In the next sections, we present our experimental design, data collection process and methods for data analysis.

Choice experiment design

Four attributes presented in Table 1 characterize the different types of green insurance systems tested in the choice experiment: individual or group contracts, loss-based or index-based damage evaluation, level of coverage and premium. The first two correspond to binary qualitative dimensions and the other two to financial attributes, with four levels for each. Attributes and levels have been chosen to be relevant and realistic, taking into account literature and experience of pioneering contracts in the South West of France (VitiREV). We have then verified the experimental design with experts, including insurers, vine growers, and agronomic advisers.

Finally, the attributes have been tested in two pilots in November 2022 and May 2023, with respectively 24 and 43 producers. The design has been pre-registered.14

The financial parameters of the contract are twofold: First, subscription to the insurance contract requires paying a premium, between 3% and 8% of the insured capital. This insured capital is equal to the insured yield multiplied by the price at which the vine grower values their production and by total surface. Note that, to reduce moral hazards, grapevine growers have to insure their entire vineyard. The insurance premium is presented both in %, and in e/ha according to the insured yield and production value provided by the respondent in questions preceding the choice cards. Second, the coverage level defines the guaranteed fraction of the losses (between 40% and 65%). There is a positive deductible but, to simplify, no supplementary triggering threshold is applied.

Grapevine growers following DSS recommendations receive an additional 30% compensation, financed by public authorities. This amount was determined with policymakers and insurers and we decided not to vary it in order to reduce design complexity and to be able to analyze the impact of the total coverage (including the bonus) on the willingness to subscribe to insurance.

Producers can choose to stop following DSS recommendations during the year if they turn out to be incompatible with their farm objectives and aversion to risk, which is an interesting flexibility for growers and possibly a factor of attractiveness. We also do not vary the level of best practice requirements, contrarily to Jorgensen et al.( 2020) and [START_REF] Heikkilä | Anything left for animal disease insurance? A choice experiment approach[END_REF], since partial adoption of DSS recommendations is unlikely to reach the environmentally optimal TFI and will lead to more subjective and complex control by insurers.

Behavioral interventions can be effective in increasing the adoption rate of programs aimed at influencing producer practices (Ferraro et al. 2022;[START_REF] Serfilippi | Insurance contracts when individuals "greatly value" certainty: Results from a field experiment in Burkina Faso[END_REF].

In particular, we know that equivalent descriptions of outcomes using different framing can result in different choices [START_REF] Tversky | The Framing of Decisions and the Psychology of Choice[END_REF]. There is mixed empirical evidence on the effectiveness of positively-framed versus negatively-framed information in influencing proenvironmental choices [START_REF] Lopes | Nudges and compensation: Evaluating experimental evidence on controlling rice straw burning[END_REF]. We contribute to this field of research, by assessing whether a different framing of the bonus incentive has an impact. To do so, we rely on a split sample approach, comparing a bonus and penalty framing [START_REF] Tonsor | Producer Decision Making under Uncertainty: Role of Past Experiences and Question Framing[END_REF]). In the bonus framing, the insurance coverage rate is presented as a base indemnity plus a 30% bonus in case of full compliance with DSS recommendations. In the penalty framing, the coverage is presented as the full indemnity minus a 30% penalty in case of non-compliance with DSS recommendations. From the behavioral literature, we know that the penalty framing is likely to reduce willingness to participate in the scheme (due to loss aversion since non-compliance with DSS recommendations reduces compensation). On the other hand, loss aversion can reinforce the incentive to comply with DSS recommendations. As a result, we expect vine growers to be more willing to pay for green insurance in the bonus framing, but less likely to comply with DSS recommendations, compared to the penalty framing.

We created a Bayesian D-efficient design using NGENE on the basis of prior results from the pilot survey. To make the presented alternatives as realistic as possible, restrictions were included in the design: we rejected choice cards where the index insurance is more expensive than the loss-based insurance, unless it provides better coverage. This resulted in a design with 12 choice cards, divided into 3 blocks of 4 cards. Moreover, we control for order effects due to learning and lassitude by having 3 other blocks with the same choice cards, but presented in a different order. To measure framing effects, the 3x2 blocks are duplicated (6 with the penalty version of the indemnity attribute, and 6 with the bonus version). Thus, subjects were randomly assigned to one of the 12 blocks. Figure 1 shows an example of a choice card, with two insurance contracts labeled "A" and "B" and an opt-out option. Individual: Each vine grower decides individually whether or not to subscribe to the insurance (as for a classic insurance).

Collective: The vine growers subscribe to a group contract, for example within the framework of a mutual fund between vine growers of the same cooperative, appellation or wine basin. In this case, membership is compulsory for all the vine growers in the group concerned. Damage evaluation Loss-based: Real losses are assessed by an expert, who comes to observe in each grower's plots the consequences of fungal diseases and then the harvest. The amount of the compensation depends on the expert's evaluation of real losses. The expertise allows an evaluation of the losses specific to each grower farm, but is subject to the subjectivity of the expert and is more expensive than index evaluation.

Index-based: Losses are estimated based on a local fungal pressure index measured, for example, in control vineyards near each grower's home. The amount of compensation depends on the value of this index. Real losses will be sometimes higher and sometimes lower than in the control vineyards, but the index can help to reduce insurance costs and make the assessment of losses more objective than expert evaluation.

Coverage

Bonus framing

Penalty framing Base indemnity + Bonus

Total indemnity -Penalty 40+30%,50+30%,55+30%,65+30% 70-30%,80-30%,85-30%,95-30%

The coverage is a percentage of assessed losses. No triggering threshold is applied.

The coverage is higher for growers respecting the treatment protocol (dates, doses) and not carrying out any treatment other than those recommended by this protocol, thanks to a bonus financed by public authorities.

The coverage is a percentage of assessed losses. No triggering threshold is applied. A part of this coverage is funded by public authorities. A penalty of 30% (the part funded by public authorities) will lower the total coverage for growers preferring not to follow the treatment protocol (dates, doses) and carrying out other treatments than those recommended by this protocol.

Coverage levels have been chosen such that they correspond to values growers are used to: The 70% level corresponds to 30% deductible imposed by the EU to subsidize insurance schemes (at the time of the experiment). The 80% level corresponds to the base contract for climatic risks in the new French law on climatic insurance (20% deductible). The 95% level for total coverage corresponds to the contract currently under test in two cooperatives in the Southwest of France (VitiREV project); This pilot entails very advantageous conditions as it is highly subsidized (5% deductible).

We added an intermediary level of 85%. Premium 3%, 5%, 6%, 8% of insured capital Subscribing to insurance is costly and the price is defined in % of insured capital. Insured capital equals insured yield multiplied by the price value of production.

Price levels have been chosen such that the expected net gain from insurance is positive for producers in an average situation for France (average yield of 50 hl/ha with a value of e100/hl (e5000/ha of insured capital), and expected losses of 10% per year). In most of these scenarios, insurers can also make money if the 30% coverage bonus is subsidized by public authorities.

Data collection

In autumn and winter 2022, we first collected forecasts from experts on vine growers' preferences for the different attributes, and on impacts of producers' characteristics on these preferences (Appendix B). They helped us elaborate hypotheses to be tested (Appendix C).

The survey was administered online to around 20,000 vine growers (on some 59,000 vinegrowing farms in France), via a company selling inputs and material for vineyards and several other channels (the list of all these channels is available in Appendix G). The growers received no financial incentive to participate, but were told that their answers were important to design future pesticide policies. We received 412 complete answers (participation rate of approximately 2%).

The survey was structured as follows: The questionnaire started with a filter question to ensure that only vine growers taking financial and vineyard sanitary decisions were participating.

Then, producers were asked to provide general data about their vineyard characteristics and crop protection strategy. Third, the green insurance scheme was presented. In the fourth part, the DCE was conducted with 4 choice cards, followed by questions to understand choice heuristics.

The English version of the questionnaire is available in Appendix H.

Data analysis

Based on DCE results and field data on DSS environmental performance, we provide estimates on insurance adoption rate according to contract features, impact on fungicide use and efficiency of public support to the insurance scheme.

Adoption rate

The econometric estimation is in line with the behavioral framework of the random expected profit approach developed by McFadden (1974) . Grapevine growers are assumed to choose their preferred insurance scheme such that the net expected profit from that contract is greater than either the other contract or opting out. For each vine grower i the expected profit obtained from alternative s in choice set t can be written as:

U ist = V ist + ϵ ist = X ′ ist β i + ϵ ist
The expected profit is a function of observable attributes V ist plus an unobserved random component ϵ ist (the stochastic error term). X ist refers to the vector of levels of the attributes, i.e.

the insurance scheme characteristics. We also include in X ist an Alternative Specific Constant (ASC) equal to one for the status quo alternative of not entering into any of the proposed contracts. The price and coverage for each alternative are specified as a continuous variable. For the other attributes, we include one dummy variable for each level of the attribute described in Table 1 except one. This excluded level per attribute represents the reference level for each attribute.

To estimate preferences, we rely on the Random Parameter Logit (RPL) model which allows parameters to vary randomly across respondents such that it gives a continuous distribution of preferences [START_REF] Boxall | Understanding Heterogeneous Preferences in Random Utility Models: A Latent Class Approach[END_REF]. We assume all respondents prefer contracts with a lower premium, and therefore consider log-normal distributions of individual price coefficients.

All other coefficients are assumed to be normally distributed. To further understand heterogeneity, we also estimate a latent class model. Latent class models work similarly to mixed logit models, except that the distribution of β coefficients is assumed to follow a discrete rather than normal mixing distribution [START_REF] Pacifico | Lclogit: A Stata Command for Fitting Latent-Class Conditional Logit Models via the Expectation-Maximization Algorithm[END_REF].

From RPL estimates, we obtain individual parameters for each attribute, and can calculate the utility of each respondent for different insurance types. In our analysis we focus on the three insurance schemes the most likely to be offered on the market. Scheme 1 (S1) is an individual and loss-based insurance: this disease insurance could be offered with similar features as the current multiple-peril crop insurance available to vine growers in France. Scheme 2 (S2) is an individual index-based insurance. Scheme 3 (S3) is a group index-based insurance. 15We assume that a grower subscribes to insurance if their utility from a given insurance scheme S defined by its X characteristics x S is higher than their utility from the status-quo (as captured by the coefficient associated to the alternative specific constant ASC). For each respondent, we know whether they would subscribe or not to the insurance for different prices ranging from 3% to 8% (while we keep a fixed 50% coverage), and we can calculate an adoption rate at sample level.

Impact on fungicide use

We then assess the potential of green insurance to reduce fungicide use. We assume that a grower willing to contract the insurance and to follow DSS recommendations can reach a T F I * equal to those of the growers constituting the lower part of the TFI distribution in the same area, for the same type of practices (conventional or organic). T F I * is thus considered regionand practice (organic vs. conventional)-specific, as agronomic intuition and data on different TFI levels across French regions suggest.16

In the field experiment conducted in the VitiREV project on around a hundred hectares in the South West of France over the 2019-2022 period, growers have used the DSS under study here (C. [START_REF] Aubert | Le soutien à la prise de risque comme levier pour une transition environnementale[END_REF]. Organic vine participating growers reduced their fungicide use less than their conventional peers. This can be measured with reference to the survey on practices in vine growing conducted by the French Ministry of Agriculture for the same year as the VitiREV experiment (French agricultural ministry 2022): T F I * is equal to the TFI of the second percentile in the same region for non-organic producers, and to the TFI of the thirty-fifth percentile for organic producers (Q conventional = 2; Q organic = 35) .17 . This difference between hazard since losses evaluation cannot be manipulated by a single producer [START_REF] Baerenklau | Some Simulation Results for a Green Insurance Mechanism[END_REF]. If appropriately designed, these contracts can also incentivize change in aggregate behavior by motivating producers to improve production practices relative to neighbors.

organic and conventional viticulture is due to the different models used by the DSS. As organic winegrowers can only rely on preventive products (as opposed to curative), the DSS advises them to start chemical protection earlier. Following the assumptions on T F I * , for each respondent, we can calculate the TFI reduction potential k in % from their current TFI (average over the last 3 years).

To adjust for sampling bias and extrapolate our results to the population of French vine growers, we simulate the insurance uptake and impact at the national scale by approximately correcting for the sampling bias (over-representation of organic farmers). We define eight cells characterized by three variables: whether the farm follows organic practices or not, whether it is located in a high or low fungicide use region, and whether the farm has a high or low TFI compared with the other producers in the same region with the same type of practices.

To calculate the weights, we allocate the 57,878 French vine growers to the 8 cells (Table 2).

The 22 French wine regions are ranked according to the average fungicide TFI in the region.

We name "high-use regions" the eleven regions with the highest average TFI.The number of organic producers in each region is obtained from a national database covering organic producers (Agence Bio 2020). This number is then divided into two groups: high-users are those with a higher average TFI than the region median in the last 3 years for the same type of practices. We then transpose the cell-specific adoption rate and average impact of green insurance on a meta-analysis conducted by Lazaro et al. ( 2021) that shows that DSS can reduce fungicide treatments by at least 50% without compromising disease control.

fungicide use to the population, accounting for the weight of each cell. Finally, we conduct a range of sensitivity analyses on the underlying assumptions on the efficiency of the decision support system to reduce producers' pesticide use.

Cost-efficiency

We investigate whether public authorities would find it beneficial to finance the green insurance bonus, rather than other programs targeting pesticide use reduction. The cost-efficiency ratio CE is measured as the public cost in euro to reduce TFI by 1% :

CE = n i=1 bρ k ly i k i
where k i is the pesticide use reduction of grower i and y i their insured capital. The cost for public authorities is the bonus b equal to 30% of the expected losses, which are assumed to equal 10% of insured capital annually (ρ k l = 0.1), in line with DSS predictions. We assume that the incentive constraint is satisfied for all growers, who are thus all eligible for the bonus.

Results

We test our pre-registered hypotheses (listed in Appendix C), and run complementary exploratory analyses. We first investigate the profile of vine growers more likely to adopt the green insurance. We then analyze contract features more likely to trigger adoption. In particular, we compare preferences for individual vs group contracts, loss-based vs index-based insurance, and a bonus vs penalty framing. We also provide estimates of the reduction in fungicide use likely to be obtained with green insurance, and the cost-efficiency of the instrument.

Descriptive statistics

The final sample included in the analysis consisted of 412 responses. The sample is compared to the population of 57,878 producers growing vine for socio-demographic and farm characteristics in Table 3. 75.24% of respondents are farm owners. The average farm size in the sample is twice as large as the French viticulture average (many owners of small vine areas use service providers for technical management of the vineyards, and therefore do not correspond to the profile sought in our survey), but heterogeneity is very high, both in the population and the sample (including an outlier of 6,000 ha). 42.2% are members of cooperatives. 26.9% of vine growers are certified organic farming (17% in the population), 6.1% transitioning to organic certification and 51.7% have obtained an alternative certification such as High Environmental

Value or Terra Vitis, two environmental certifications, dissociated from but compatible with organic certification, and giving access similarly to an official label on the marketed products.

These numbers suggest that the sample is more experienced with fungicide reduction than the population.

Average survey completion time was about 22 minutes. We checked reading time of attributes and interview time to be sure to avoid questionnaire-surfing in our responses. Note 1: Respondents can sell their production under several categories. The total therefore sums to more than 100%. The population statistics represent the share of total production under each indication, which cannot be directly compared to the share of respondents selling partly under each category. Note 2: The collective score, between 1 and 5, is the sum of the grower's participation (yes=1; no=0) to a wine cooperative, a cooperative to share material (CUMA), a collective sale point, a group to share best environmental practices (GIEE) and institutions as representative of the vine producers. Note 3: When a farmer has both the organic certification and another one, we identify it as organic. The total therefore sums to 100%.

Adopters' profile

While we have a split-sample approach to compare the bonus and penalty framing, our analysis is performed on the full sample. Indeed, we found that framing has no significant effect on the number of insurance subscribers, nor on the TFI profile of the subscribers. Producers are not significantly less likely to opt out in the bonus framing (the coefficient ASC x Bonus is not significant, cf. Figure 3). Separate estimations for the bonus and penalty framing sub-samples confirm this result (Appendix D). Moreover, the framing has no selection impact: producers subscribing to insurance in the bonus framing group are not significantly different from those in the penalty framing group in terms of TFI reduction potentials

(Figure A.3 in Appendix D).
All the remaining analysis is therefore run on the full sample.

With the RPL model, we find that vine growers overall prefer the green insurance to the status-quo (the coefficient associated to the alternative-specific constant is negative), suggesting a significant interest for the scheme (Figure 3).

Two classes of adopters. Concerning the pest management strategy, we find that transitioning towards organic certification has the strongest influence on the probability of belonging to class 1. Organic and other certifications also increase the probability of belonging to class 1 compared to having no certification, but to a lower extent. Stated willingness to reduce fungicide use in the near future (variable sanitary strategy) also increases the probability of belonging to class 1.

Concerning the influence of the risk profile on willingness to subscribe to green insurance, we test the impact of the following variables: self-evaluated risk tolerance, being insured against climatic risk, having diversified crops or having larger vineyards. None has an influence on the probability of belonging to class 1.

In Appendix E (Table A.6), we present estimates on adoption rates by French region, to highlight heterogeneity.

Preferences for contract features

Better coverage increases the probability that the vine grower will adopt the insurance scheme and higher contract price decreases this probability (Figure 3), confirming one of our preregistered hypotheses. While both characteristics have a significant impact, their effect sizes are very different: the contract price has a much bigger effect than the percentage of the base coverage.

With regard to the contract design features, we find that on average, producers prefer individual contracts to group contracts. We also find a preference for loss-based compared to index-based insurance, but the absolute value of this coefficient is low.

Figure 3: RPL model: Probability to subscribe to green insurance Note: The choice of contracting to green insurance is explained by contract attributes (Bonus, Group, Index, Coverage, and log-normal Price) and some interaction variables -selected according to pre-registered hypotheses.

We focus on two sources of heterogeneity with regard to preferences for the Group and Index attributes. We find that the growers engaged in groups of producers (such as cooperatives where wine-making or vineyard materials are mutualized, or collective sale points) are less reluctant to adopt group contracts (variable CollectiveScore). However, we find no evidence that knowledge or prior experience with index insurance has an influence on preferences for the index-based contract (variable IndexExperience).

We also calculate the adoption rate for different types of green insurance contracts (S1, S2, and S3 defined in section 3.3.1) (Table 5). In the sample, between 45% and 58% of the vine growers are likely to subscribe to green insurance depending on contract design and prices.

Adjusting for sampling bias, based on adoption rates such as the one presented in Table 4 for contract S1, the adoption rates vary between 48% and 60%.

As expected from previous results on preferences for individual and loss-based attributes, the adoption rate is higher for scheme S1. Since differences in adoption rates across contract types and prices are limited, in the remaining analysis we focus on one contract: the preferred contract (S1), in the case of fair insurance (5% price and 50% coverage). This contract is considered fair since the loading factor is equal to one when one assumes an expected annual loss of 10%, as expected under production with the here selected DSS.18 51) 45 ( 48) Note: The first number in each cell corresponds to the percentage of adopters in the sample. The number in parenthesis corresponds to the percentage extrapolated at population level, after correcting for sampling bias.

Impact on fungicide use

To estimate the potential impact of green insurance on fungicide use in French vineyards, we rely on the pesticide use reduction (k) that could be obtained thanks to the DSS recommendations for those willing to subscribe to insurance19 . We assume that the incentive constraint is satisfied and all subscribing vine growers fully comply with DSS recommendations.20

Overall, we estimate that adopters in the sample would reduce their fungicide treatment by 35% on average and a quarter of them can reduce their fungicide treatments by more than 55%. But the average TFI reduction potential hides important heterogeneity: the expected TFI reduction potential of those willing to subscribe to green insurance ranges from 0 to 84.9%. We highlight in Table 6 the differences across practices (organic or not), regions and users' relative position in the region with regard to their TFI. The highest impact is expected for non-organic producers treating more than the median TFI in low-use regions (-74.70% compared to their initial TFI on average). The impact is higher in low-use regions since other growers in these areas have managed to reduce their TFI, which indicates favorable environmental conditions and lower pest pressure. Extrapolating results to the population, given that 56% of the population would adopt S1-type green insurance (at a 5% price and 50% coverage), fungicide use could be reduced by 45% on average at national level (compared to 35% in our sample due to the over-representation of organic producers, who have on average a lower reduction potential). We run a sensitivity analysis on the impact of the assumptions on the efficiency of the DSS if they do not suit you". Those who have selected this intermediary option are also considered as following DSS recommendations). We can distinguish "adopters" of green insurance and "full adopters": adopters are those with a utility from an insurance contract higher than their utility from the status-quo, so that their participation constraint (P C) is satisfied; full adopters are willing to follow DSS recommendations, so that their compliance incentive constraint (IC) is also satisfied. In the pre-registration, we indicated that we would measure the impact in terms of TFI reduction potential only for the full adopters. However, only 231 out of the 412 respondents answered the question on DSS adoption. And only 10% indicated they would not follow DSS recommendations and therefore only count on the base indemnity. We therefore decided to assume that those willing to subscribe to green insurance would also most likely follow DSS recommendations. Estimates of average TFI reduction are robust to dropping respondents who declared they won't follow DSS recommendations.

to reduce pesticide use on our results (Figure A.4 in Appendix F). To do so, we vary the target percentile one can reach with the DSS for both types of practices (Q conventional ;Q organic ). We find that fungicide reduction will be substantial (more than 40%) only if conventional producers using the DSS can reach TFI levels that are lower than the 5% vine growers treating least intensively. This is in line with field experimental observations for the DSS, but underlines the importance of this parameter for results. The overall impact is less dependent on assumptions regarding the DSS's efficiency for organic producers since the TFI distribution for organic producers is narrower.

We find limited evidence of windfall effects. 23% of the adopters in the sample have a null expected reduction since their TFI corresponds to TFI* (63% of them being organic growers).

They would benefit from more secured revenues, partly thanks to the public support of the bonus, without achieving additional fungicide reduction. Given the For conventional producers, the proportion of producers with null reduction potential is not significantly different at the 5% level in the adopters and non-adopters groups. For organic producers, the proportion of producers with null reduction potential is significantly higher in the non-adopters groups. Both results suggest that the scheme does not particularly attract producers likely to benefit from windfall effects.

Cost-efficiency of public support

Adjusting for sampling biais, for an average annual loss of 10%, the program would cost on average e1457 per hectare and per year. In terms of cost efficiency, the average cost of public bonus subsidy is e104 per hectare for a 1% reduction in TFI. But we find significant differences in the cost and cost-efficiency of green insurance subsidies across the eight cells in Table 7.

This heterogeneity is driven by different TFI reduction potential k, but also differences in the financial value of insured yields (since the cost of the bonus is proportional to y). Indeed, the financial yield is significantly lower in low-use regions (which is driven either by lower yields in regions such as Côtes du Rhône or lower wine value in regions such as Val de Loire or Gaillac).

As a result, the public spending to compensate losses is lower in the low-use regions, compared to high-use regions.

Robustness checks

We apply several tests to check the robustness of the results to different specifications (Appendix F). We find no significant difference with the estimate for the full sample presented in Figure 3 and the estimates for different sub-groups. As a further robustness check, to account for inattention bias, we estimated a 3-classes latent class model with all parameters in one class restricted to zero (Table A.7 in Appendix F). We find that 40.5% of the respondents were classified as making random choices, which is similar to what was found by [START_REF] Malone | A simple diagnostic measure of inattention bias in discrete choice models[END_REF] . Estimates for the two other classes remain robust to this specification, but the proportion of respondents sensitive to the green insurance attributes (belonging to class 1) is reduced to 25% (instead of 40% in Table 4) and we find no significant determinants of class 1 membership.

We also tested for overshooting, as defined by Glenck et al. (2023). The criterion compares the highest level of the price attribute with the willingness to pay (WTP) for the bundle of attributes yielding the highest utility (loss-based individual contract with a 65% coverage rate), without including utility captured via alternative specific constant. We find evidence of reasonable overshooting: the average WTP for the bundle of attributes yielding the highest utility is 13.46 (median 2.89). With the highest price level equal to 8, this corresponds to a 68% over-shooting magnitude. As a comparison, over the 305 DCE studies considered by Glenck et al. (2023), the mean relative magnitude of overshooting is 337%, with a median of 102%.

Discussion

A high pesticide use reduction potential Our results indicate that green insurance is an innovative tool that has the potential to induce a shift in practices towards less pesticide use. More than half of the surveyed French vine growers are interested in green insurance, in particular if insurance contracts are similar to those offered for current multiple-peril crop insurance (i.e. individual and loss-based).

Producers transitioning to organic grapevine growing are more interested by green insurance and have a lower price elasticity. A first reason could be that they are already more likely to have lower yields than their targets (higher ρ 0 ), and are therefore interested in the financial security offered by the scheme, suggesting some form of adverse selection. They may also be interested in the technical assistance offered by the DSS to comply with the regulation on maximum copper use (European Union 2018), reduced from 6 to 4kg/ha in 2018, technically constraining in some years. However, being already committed and experienced in constrained technical itineraries, they may also simply have more confidence in their ability to safely decrease fungicide use and obtain the 30% bonus by following DSS recommendations. In other words, the increase in the probability of losses due to lower pesticide use (ρ k -ρ 0 ) appears to be lower for them compared to other growers, not only because of higher ρ 0 but also because of lower ρ k . This could explain why organic growers with the highest fungicide use in their region (compared to other organic growers) have the highest adoption rates: observing their neighbours, they can be confident that they can safely reduce their fungicide use with limited risk on yields. In any case, these results suggest that green insurance is perceived by growers as a useful tool to support the transition to organic farming. Green insurance could thus be an interesting complementary tool to support the political objectives on the up-scaling of organic or pesticide-free production systems, [START_REF] Schebesta | Game-changing potential of the EU's Farm to Fork Strategy[END_REF][START_REF] Möhring | Pesticide-free but not organic: Adoption of a largescale wheat production standard in Switzerland[END_REF], and secure transitions at a time when market signals are currently negative. Beyond the substitution of chemical pesticides with other crop protection strategies, support for organic farming can also contribute to other environmental benefits, such as better pest control based on biodiversity [START_REF] Muneret | Evidence that organic farming promotes pest control[END_REF].

The fact that the scheme is particularly attractive to producers transitioning to organic practices has two consequences: First, it could increase the overall risk to be covered by the insurer, as organic growers already face a higher risk. Second, it reduces the overall environmental performance since the DSS reaches higher TFI reduction for conventional producers. Yet, we found that producers subscribing to the green insurance scheme can reduce by 45% on average their treatment frequency index.

We observe regional heterogeneity in adoption rates and impact in terms of TFI reduction, probably driven by the local fungal pressure and wine value. Such results corroborate findings from the theoretical model on the determinants of the participation constraint. To tailor insurance contracts to different circumstances and increase their attractiveness, insurers will have to gather sufficient data to correctly price the contracts. This data is not yet available because not enough growers are making the decision to use very low pesticide levels, and the ones who do probably have specific characteristics. By inducing a switch to greener practices by a sufficient share of farmers, subsidized green insurance can generate data on the actual losses incurred over a few years when using less pesticide. In addition, one may expect the quality of the DSS to improve thanks to a wider pool of farmers providing feedback on the impact of its recommendations. The development of green insurance can improve the information available to farmers, insurers, and public authorities.

A gap between producers' preferences and insurers' opinions Despite high acceptability as assessed in the DCE, green insurance is a new product and both the performance of the DSS and the ability of all insurance subscribers to effectively follow it are still little known.

Providers of green insurance will therefore face contract development costs. Low subscription rates would further increase transaction costs and delay learning. Our results shed light on producers' preferences for specific green insurance designs. While the insurers we interviewed believe green insurance should be developed in priority under group and index-based contracts, we highlight a disinterest in such contracts by most producers.

Follow-up questions show that only 2% declared to have already subscribed to an index insurance and only 27% have already heard about this system before. We could not detect a significant effect on knowledge and experience with index-insurance on preferences towards such products to cover fungal disease risk, but the direction of the effect is consistent with previous work. Farmers' limited understanding of a product is an important factor in their reduced demand for index insurance (e.g. [START_REF] Cole | Barriers to Household Risk Management: Evidence from India[END_REF] and [START_REF] Sibiko | Small farmers' preferences for weather index insurance: insights from Kenya[END_REF] among others). Extension services and insurers will have to put effort into explaining to farmers how index insurance works, including the basis risk, often mentioned as the major obstacle [START_REF] Jensen | Index Insurance Quality and Basis Risk: Evidence from Northern Kenya[END_REF][START_REF] Jensen | How basis risk and spatiotemporal adverse selection influence demand for index insurance: Evidence from northern Kenya[END_REF]. In the DCE, the basis risk may have been perceived by respondents as high since we were not very explicit about the specification of the index. Last but not least, respondents may have not perceived the potential cost-saving associated with index insurance. It was underlined in the presentation of attributes that index contracts might reduce transaction costs, and therefore allow insurers to offer significantly cheaper contracts. But, in the choice cards, it was less explicit since some schemes had both an index-based evaluation of damages and a higher price (for a higher coverage).

Moreover, while French farmers frequently complain about private insurance companies, they

show no interest in group-based contracts, that could be implemented in the form of a mutual fund, a typically low-cost non-profit organization [START_REF] Meuwissen | Supporting Insurance in European Agriculture: Experience of Mutuals in the Netherlands[END_REF].

Beyond farming, group-based contracts are widely used for risk-sharing. They reduce problems of asymmetric information and serve as a catalyst for risk prevention and knowledge sharing [START_REF] Meuwissen | The role of group-based contracts for risk-sharing; what are the opportunities to cover catastrophic risk?[END_REF]. French vine growers' reluctance could be explained by their willingness not to engage in contracts with collective commitments, but previous work with

French vine growers has shown that they value collective commitment when it can benefit the environment [START_REF] Kuhfuss | Nudging farmers to enrol land into agri-environmental schemes: the role of a collective bonus[END_REF]). In the experiment, we defined the likely perimeter for group contracts as the cooperative, the perimeter of the indication of origin or the wine region. But at such a scale, fungal disease risk can be systemic and growers may lack trust in the mutual fund's financial robustness [START_REF] Giampietri | The role of trust and perceived barriers on farmer's intention to adopt risk management tools[END_REF]. Insurance experts generally agree that pools consisting of less than one-third of a sector cannot sufficiently spread the risks and are too small to survive [START_REF] Meuwissen | Supporting Insurance in European Agriculture: Experience of Mutuals in the Netherlands[END_REF]. Offering index insurance to groups is foreseen as a promising opportunity to offer insurance products that are both attractive and easy to implement [START_REF] Traerup | Informal networks and resilience to climate change impacts: A collective approach to index insurance[END_REF][START_REF] Santos | Dynamics of informal risk sharing in collective index insurance[END_REF][START_REF] Meuwissen | Prospects for agricultural insurance in Europe[END_REF].

Indeed, group index-based insurance can be more attractive if a common risk-sharing pool is set up to transfer excessive payouts, as a remedy to the imperfect correlation between the index and losses. This system could for example be implemented through cooperatives as follows: an index-based contract is signed by a cooperative. If the index triggers the indemnity, payment is made to the cooperative, which then distributes it according to their local understanding of growers' performance for the year. Loss evaluation is then in the hands of the cooperative, possibly reducing the cost of insurance and the basis risk perceived by growers.

Another option considered by insurers to reduce costs is to integrate climatic and green insurance contracts [START_REF] Meuwissen | Prospects for agricultural insurance in Europe[END_REF]. But crop insurance can have unintended effects and lead to pesticide use increases [START_REF] Möhring | Crop insurance and pesticide use in European agriculture[END_REF]Wu 1999; Comparison with other pesticide programs While green insurance may potentially induce sizable pesticide use reductions, it should be compared with other tools. Based on sample estimates, the program would cost less than e480 per hectare and per year for half of the adopters (e1580 on average). This is much more costly than other programs designed to reduce pesticides. In 2018, the DEPHY network, a system of farm advisors implemented on a large sample of farms in France, cost e150 per hectare and per year [START_REF] Lapierre | Providing technical assistance to peer networks to reduce pesticide use in Europe: Evidence from the French Ecophyto plan[END_REF]. The Agri-environmental scheme (AES) payment to reduce herbicides in French vineyards from 2007 to 2014 ranged from e141 to e350 per hectare and per year. Another AES available to vine growers close to water catchments from 2014 to 2020 provided incentives to reduce TFI up to 80% of the local average TFI for a compensation of e301 per hectare and per year (EAFRD 2014).

However, the relatively low cost of these other programs does not imply a high effectiveness.

First, because they attract a very limited number of producers. For example, only around 4,000 hectares out of the 220,000 hectares of vine in the Nouvelle-Aquitaine region (including Bordeaux, Cognac, and Bergerac amongst others) have been engaged in an AES targeting herbicide reduction over the period 2015-2022. Second, participation in the AES does not necessarily influence management practices. High windfall effects have been reported for some AES schemes (Chabé-Ferret and Subervie 2013; Ait [START_REF] Sidhoum | Effects of agri-environment schemes on farm-level eco-efficiency measures: Empirical evidence from EU countries[END_REF]. A public subsidy to green insurance bonus is more costly per hectare, but it has the potential to attract more growers and generate more impact in terms of pesticide use reduction. Unfortunately, ex-post evaluations of adoption rates and impact in terms of TFI reduction of other pesticide programs are scarce, limiting the possibility of comparing them with the ex-ante evaluation we conducted.

In any case, and whatever the program, the environmental and health benefits associated with pesticide reduction should also be taken into account in these cost-benefit analyses, such as the reduction in exposure risk offered by the use of the DSS system [START_REF] Tago | Pesticides and Health: A Review of Evidence on Health Effects, Valuation of Risks, and Benefit-Cost Analysis[END_REF].

We also discussed in the theoretical model that a tax on fungicides, by increasing cost savings from using fewer pesticides, would be complementary to the insurance bonus to foster green insurance adoption. With a lower bonus and a pesticide tax, one could therefore reach similar adoption levels, and increase the cost-efficiency of the scheme.

Other risk management tools than green insurance exist and may better meet some produc-ers' needs. As mentioned by several respondents, the financial risk can be managed by stocking part of the wine of very productive years, to be included in the blend of the following year, as authorized by European law within the framework of inter-branch organizations (European Union 2013). Inter-annual wine transfers between producers (rather than money transfers through insurance indemnity) are preferable when producers fear to loose markets if they have no wine to sell in a given year. Regarding sanitary risks, an alternative to chemical protection is the adoption of resistant varieties [START_REF] Finger | Short supply chains and the adoption of fungus-resistant grapevine varieties[END_REF]. This alternative instrument has not been mentioned by the interviewed growers but is developing rapidly. Further research is needed to be able to compare the effectiveness and cost-efficiency of short-term solutions such as reducing pesticide use with green insurance and longer-term ones such as the adoption of resistant varieties.

Robustness The robustness of our results relies on the complementary between the theoretical predictions and the empirical test with the DCE, particularly relevant when predictions are ambiguous or behavioral factors are coming into play. In particular, we found that high-use producers, who tend to face adverse conditions or lack expertise needed to reduce fungicide use, are less likely to subscribe to green insurance, suggesting that their higher cost savings due to less fungicide use (compared to low-use growers) are not sufficient to counteract the high perceived risk of losses. The empirical evidence on growers' low interest for index-based contracts is consistent with the model, if producers overestimate the probability of losses ρ k or are pessimistic with regard to the probability of being indemnified ρ i . This is likely in the absence of detailed information in the DCE on the index specification. Furthermore, we predicted that risk aversion had an ambiguous impact on insurance subscription, especially for index-based insurance. This is consistent with the absence of influence of self-evaluated risk aversion on green insurance adoption in the DCE. This confirms the relevance of the risk neutrality assumption in our model, in particular given that agriculture professionals are known to be less risk-averse than the general population [START_REF] Rommel | Farmers' risk preferences in 11 European farming systems: A multicountry replication of Bocquého et al[END_REF][START_REF] Vieider | Common components of risk and uncertainty attitudes across contexts and domains: evidence from 30 countries[END_REF]).

High adoption rates should be taken with a grain of salt given the hypothetical nature of choices in DCEs. Respondents may indeed underestimate the costs of participating in such a new green insurance scheme, including the transaction costs to enroll in the program, but also the opportunity cost of time to understand and enact DSS recommendations. Moreover, one may wonder whether there have been some attributes non attendance. In particular, the small effect size of the coverage attribute suggests that respondents have focused more on the certain financial cost of the contract (the premium), and less on the uncertain financial benefit (function of the coverage level). This may also be due to an anchoring effect to the multi-peril insurance coverage levels (70%, which corresponds to the lower level of the green insurance). This level may have been perceived as standard and producers' choices were impacted to a limited extent by higher coverage rates. Note that given that the framing treatment concerned the coverage attribute, this may also explain why we found no significant impact of framing. This null result is nevertheless in line with previous evidence on the limited impact of nudges on farmers [START_REF] Davidson | Nudge to insure: Can informational nudges change enrollment decisions in pasture, rangeland, and forage rainfall index insurance?[END_REF]Goodrich 2023, Chabé-Ferret, Le Coent, et al. 2019).

Our study focused on grapevine production in France, where both incentives to use pesticides, and uncertainties about the impact of reducing TFIs on financial yields, are especially high, suggesting that potential adoption of these tools would be even more important for other crops.

Future research on green insurance should test results for other regions and crops.

Conclusion

Green insurance could be an important tool for supporting farmers' transition to more environmentally friendly production practices, by managing potential economic risks. We provide evidence of French grapevine growers' interest in a green insurance scheme, coupled with free access to a Decision Support System, to secure yields while reducing fungicide use. By providing a secure environment to test reduced pesticide use practices, green insurance coupled with a DSS can help producers revise their perceptions on the number of treatments needed to control fungal pressure. Among the 412 surveyed French grapevine growers, 54% are willing to subscribe to a loss-based individual insurance for an annual price of 5% of the insured capital and an 80% coverage of their losses (including the 30% bonus obtained by complying with DSS recommendations). Farmers transitioning to organic farming and organic farmers are more likely to subscribe to green insurance and are less sensitive to insurance price than other producers.

More innovative schemes such as group and index-based contracts are less attractive, despite having the potential to reduce the costs of green insurance.

We also find that publicly subsidized green insurance could provide an innovative policy tool to reach ambitious pesticide reduction targets. Extrapolating the results to the population level, we estimate that insured producer would reduce their fungicide treatment by 45% on average, thanks to DSS recommendations. Having in mind the EU Green Deal target of reducing pesticide use by 50% by 2030, green insurance could significantly contribute to the objective for the wine sector.

Our results are of direct relevance for policymakers attempting to reform agricultural policy to achieve an up-to-now elusive reduction in pesticide use, but also to insurers designing new insurance products to cover disease risks. We believe that they are of relevance beyond the 'tough' context of wine-growing, and beyond France. The producer's decisions. The decision to subscribe or not to the insurance (participation) is taken at the start of the grapevine growing season and the decision to comply with the DSS is made afterwards, during the season. We abstract from timing aspects here, but consider the two decisions, that translate into constraints for the insurance scheme to be effective:

• Participation constraint (P C): The producer prefers to take up the insurance if it provides a higher profit than not reducing their TFI and not benefiting from the contract.

• Incentive constraint (IC): The producer prefers to take up the insurance and comply with the DSS if it provides a higher profit than taking up the insurance but not reducing her TFI.

The status-quo strategy, with no pesticide use reduction, yields expected profits of [(1 -

ρ 0 ) + ρ 0 (1 -l)]y -C 0 = (1 -ρ 0 l)y -C 0 .
The participation constraint (P C) will be met if the profits obtained when taking up an insurance contract and reducing one's TFI are higher than this expression.

Loss-based insurance.

With loss-based insurance, the indemnity is perceived if and only if y = (1 -l)y, which happens with probability ρ k . Indemnity is I = (α + b)ly.

which can be writen as

(IC) IB : (C 0 -C k ) + [ρ k nl+i u (1 + (α + b)l)y -ρ 0 nl+i u (1 + αl)y ] +[ρ k l+i u 1 -l(1 -α -b)y -ρ 0 l+i u 1 -l(1 -α)y ] ≥ (ρ 0 nl+ni -ρ k nl+ni ) u y + (ρ 0 l+ni -ρ k l+ni ) u (1 -l)y
Clearly, beliefs as to the probabilities of the four states, respectively when following the DSS and when not reducing one's TFI, are crucial determinants of the decision to reduce fungicide use within the insurance contract, together with the bonus b.

Under risk neutrality, the problem becomes much simpler as one can group profit terms along the probabilities of the two events, suffering losses and receiving an indemnity. The value of the index is unrelated to the fungicide use of the producer, so that

ρ 0 l+i + ρ 0 nl+i = ρ k l+i + ρ k nl+i = ρ i .
Under risk neutrality, the incentive constraint becomes

(IC) IB : (C 0 -C k ) + [ρ k nl+i (1 + (α + b)l)y -ρ 0 nl+i (1 + αl)y +[ρ k l+i (1 -l(1 -α -b)y -ρ 0 l+i (1 -l(1 -α)y] ≥ (ρ 0 nl+ni -ρ k nl+ni ) y + (ρ 0 l+ni -ρ k l+ni ) (1 -l)y ⇔ (C 0 -C k ) + (ρ k nl+i + ρ k l+i )bly ≥ (ρ k nl+ni + ρ k l+i + ρ k l+ni + ρ k nl+i -ρ 0 nl+ni -ρ 0 l+i -ρ 0 l+ni -ρ 0 nl+i )y +[ρ k l+i -ρ 0 l+i + ρ k l+ni -ρ 0 l+ni ]ly -[ρ k l+i -ρ 0 l+i ]αly ⇔ (C 0 -C k ) + ρ i bly ≥ (1 -1)y + (ρ k -ρ 0 )ly -(ρ i -ρ i )αly ⇔ (C 0 -C k ) + ρ i bly ≥ (ρ k -ρ 0 )ly
Since the conditions for the payment of the base indemnity are independent from fungicide use, the incentive to reduce one's TFI only depends on the associated cost savings and probabilities of yield losses (as without an insurance contract) and the bonus (triggered only in case of compliance with DSS recommendations). The higher b and the probability that the index triggers the indemnity, ρ i , the more likely it is that the producer will indeed follow the DSS.

Participation. The participation constraint states that taking up the insurance contract and following the DSS, is more profitable than foregoing insurance:

(P C) IB : ρ k nl+ni u y + ρ k nl+i u y + (α + b)ly +ρ k l+i u 1 -l(1 -α -b)y + ρ k l+ni u (1 -l)y -C k -βP + ρ k nl+ni u y + ρ k nl+i u y + (α + b)ly ≥ (1 -ρ 0 )u(y) + ρ 0 u(1 -ly) -C 0 ⇔ ρ k nl+i u y + (α + b)ly + ρ k l+i u 1 -l(1 -α -b)y ≥ (1 -ρ 0 -ρ k nl+ni )u y + (ρ 0 -ρ k l+ni )u (1 -l)y + βP -(C 0 -C k )
Participation is obtained if the utility gained in the two states of nature where there is indemnification is enough to compensate for the the situations with no indemnity, and for the premium reduced by cost savings.

For a risk-neutral producer, the participation constraints simplifies into With lower capital value (y < 5000) or more expensive pesticides (C 0 > 400), producers could be better off reducing their pesticide without insurance than not reducing their pesticide.

(P C) IB : y[1 -(ρ k -ρ i (α + b))l] -C k -βP ≥ y[1 -ρ 0 l] -C 0 -βP ⇔ [ρ 0 -ρ k + ρ i (α + b)]ly ≥ βP -(C 0 -C k ) Public support
Other motivations (risk-aversion, health, environmental concerns,...) may also tilt the balance in favor of reducing treatments. Characteristics of the production context, beyond the fungal disease pressure as captured by ρ 0 , may also impact the decision to subscribe to the green insurance scheme under study. These aspects are better captured in the choice experiment where the characteristics of the respondents capture a large diversity of individual situations.

To access to the scheme, vine growers pay a price which is a percentage of their insured capital.

The insured capital is equal to the insured yield multiplied by the price at which the production is valued.

The subscription is necessarily made for the whole of a wine-producing exploitation.

The coverage is a percentage of the evaluated losses, without any threshold.

All losses are compensated, according to a basic compensation percentage defined by the contract.

Grapevine growers who respect the treatment protocol (dates, doses) receive a higher coverage compared to those who carry out more treatments. This bonus is funded by the public authorities.

B1: In your opinion, what proportion of vine growers would never be interested in such a guarantee (whatever its characteristics and price)? % of vine growers.

B2: For which of the two aspects do you think that the vine growers would be interested in the device?

❍ Coverage for losses due to fungal diseases.

❍ Treatment protocol to reduce fungicides while maintaining yields.

❍ Other:

Comment on your choice here:

Your answers will be compared to the choices made by vine growers in another survey.

B5: Phyto user profile

Choose the appropriate answer for each row: We would like to know your opinion on the parameters that could make the system attractive to vine growers.

IFT

INTRODUCTION

Expected answers

We are expecting answers from people in charge of financial decisions in a vineyard, but also in charge of decisions related to phytosanitary treatments. Do not hesitate to answer as a duo with the financial manager and the technical manager.

You will need to provide your Fungicide Treatment Frequency Index (TFI) for the last three years (it will not be disclosed). You will then be able to compare it with regional averages.

Please have this information ready before you start. If you do not already have access to TFI, you can calculate it here. To get your Fungicide TFI from this calculator, enter only your fungicide treatments. The first value in the table (1 st line "TFI") is your fungicide TFI.

Data management

This study is anonymous. Neither your name nor your company's name will be asked.

In accordance with the principles of open science, and in compliance with the General Data Protection Regulation, data will be anonymized before archiving and made available for scientific use.

Duration

The survey takes about 12 minutes. You can stop at any time and return to the questionnaire later, by clicking on the same link. To return to the questions on the previous page, click on "previous" at the bottom left of each page but do not go back on the navigator, otherwise your answers will not be saved. ❍ You think that your use is much lower than that of others.

Contact

❍ You think your use is slightly lower than others.

❍ You think your use is about average.

❍ You think your usage is slightly higher than others.

❍ You think your usage is much higher than others. ❍ For the moment, reducing my use of fungicides is not my priority.

❍ I have already reduced my use of fungicides, it is difficult to go further.

❍ I am trying to reduce my use of fungicides, but I find it complicated.

[PENALTY framing] The insurance scheme provides:

1. Financial coverage co-funded by public authorities for annual losses due to diseases.

The diseases concerned are downy mildew, powdery mildew and black rot.

2. The provision of a treatment protocol formulated by the IFV (French Institute of Vine and Wine) to reduce fungicide treatments as safely as possible.

3. If vine growers prefer not to follow the DSS recommendations, and use more fungicides, a penalty applies such that the coverage is lowered (no co-funding by public authorities in this case).

Access to the insurance scheme is subject to the payment of a fee. The subscription is necessarily made for the whole of a vineyard. The access to the decision support system communicating the treatment protocol is included in the subscription fee.

You can view the testimony of a vine grower who has experienced this insurance scheme as part of the VitiREV project in New Aquitaine (Right click on the image).

The video is also available here Some details:

-The insurance scheme adoption is open to all, with or without certification. A specific version of the treatment protocol exists for vineyards in organic agriculture.

-The practices of the vine growers can be controlled (treatment notebooks, visits).

Even if these schemes do not exist for now, please choose between the two schemes the one you would prefer for YOUR vineyard, as you would do in reality if these schemes were offered to If you had the opportunity to actually subscribe to this tool, how would you use the DSS recommendations?

[BONUS]

❍ Not following the DSS recommendations at the beginning of the season, and therefore only receiving basic coverage for losses.

❍ Follow the DSS recommendations at the beginning of the season and stop if they do not suit you.

❍ Follow the DSS recommendations until the end of the campaign to be sure to benefit from the bonus, in addition to the basic coverage.

[PENALTY]

❍ Not following the DSS recommendations at the beginning of the season, and therefore receiving the coverage reduced by the penalty of 30% in case of losses.

❍ Follow the DSS recommendations at the beginning of the season and stop if they do not suit you.

❍ Follow the DSS recommendations until the end of the campaign to be sure to benefit from the maximal coverage.

M5: In the schemes we have proposed to you, the assessment of losses was sometimes based on an index. Were you aware of this type of insurance before you answer this survey?

With index insurance, the compensation depends on an index and not on your own perfor- ❍ That the scheme we have presented to you remains independent of the MRC.

❍ That this system and the MRC are associated in a global coverage contract

  For example, in grapevine production,Kufhuss et al.(2016) and[START_REF] Lapierre | Designing agri-environmental schemes to cope with uncertainty[END_REF] rely on DCE to analyze the attractiveness of innovations in AES design, to better account for uncertainty on the costs and benefits associated with the adoption of new practices for herbicide reduction. The method has also been used to analyze preferences for insurance schemes conditional on compliance with specific farm practices. Jorgensen et al.(2020) study the willingness to pay (WTP) of Danish crop producers for an insurance contingent on investing in sustainable soil management; and[START_REF] Heikkilä | Anything left for animal disease insurance? A choice experiment approach[END_REF], that of Finnish producers for animal-disease insurance, conditional on fulfilling bio-security requirements to reduce sanitary risks. Both include the contract price as monetary attribute and estimate the impact of best management practice requirements on WTP. Jorgensen et al.(2020) also estimate the impact of the insurance type (yield vs rainfall), whileHeikkilä et al.(

Figure

  Figure 1: Example of choice card

  Figure 2 displays interesting results regarding the distribution of the individual coefficients in the RPL model. The distribution of individual coefficients forthe ASC and, to a lower extent, price and coverage is bimodal. This suggests that a share of our sample is more interested by green insurance (negative coefficient for the alternativespecific constant ASC) and another share is more sensitive to the financial parameters (with high coefficients for the price and coverage attributes). Latent class estimates in Table4confirm that our sample can be divided into two classes. The number of classes has been chosen to ensure interpretability, together with maximizing class membership prediction accuracy (TableA.6 in Appendix F). Class 1 comprises of about 59.5% of the respondents. For class 1, the coefficient estimates are close to those estimated by the RPL model. Given the negative sign associated with ASC, growers belonging to class 1 are on average interested by the green insurance. Moreover, they have a lower price elasticity than class-2 respondents. On the contrary, respondents in class 2 are not significantly more interested by the insurance than the status-quo, and have no significant preference for the specific insurance attributes. However, they have a high-price elasticity.

Figure 2 :

 2 Figure 2: Random Parameter Logit: Individual coefficients. Kernel density estimates

  Enjolras and M.[START_REF] Aubert | Le soutien à la prise de risque comme levier pour une transition environnementale[END_REF]. The coverage of disease risk through green insurance could thus further raise complex cross-effects. In a follow-up question (answered by 241 respondents), 58% declared they would prefer green insurance remaining independent of climatic insurance. But whether contracts should clearly delineate between losses due to direct weather and climate impacts and losses due to indirect consequences of different climatic conditions (such as fungal diseases, which are more likely to develop in humid and warm weather conditions) remains an open question.

  doi: 10.1162/rest_a_01215. one insured under the contract. The indemnity I depends on losses (in the case of loss-based insurance) or the realization of an index (in the case of index-based insurance). The insurance coverage is α in [0,1[ (meaning there is a deductible of 1 -α). If the producer follows DSS recommendations he receives an additional bonus indemnity b > 0 in case of yield losses, funded by public authorities. Otherwise, there is no bonus. b > 0 if k = k * (DSS recommendations are followed) b = 0 if k ̸ = k * (DSS recommendations are not followed)

  Figure A.2 shows that producer should stay in the status quo situation without reducing their pesticide use when the frequency of loosing all the harvest with pesticide is lower than one year out of 40 (0.025). When pesticides do not offer full protection and this frequency ρ 0 is higher, reducing pesticide use by half covered by green insurance is more profitable. In this setup, index-based insurance is always more profitable since it is cheaper for the same probability of receiving an indemnity. If producers perceive the probability of being indemnified to be lower with index-based insurance than loss-based insurance, the opposite result would hold.

  scheme Prefer expertise to index Prefer voluntary membership to membership in a mutual fund

  Sensitivity analysis: Impact of green insurance on TFI reduction at the population level, according to DSS environmental performance Note: the target TFI percentile (Q) is the TFI of the Q less-intensive producers in terms of fungicide use, by type of practices (conventional and organic), according to French agricultural practices survey (2019). It is considered as a metric for DSS environmental performance: the DSS performs better (in environmental terms) if one can reach the TFI of a lower percentile of the population.

For❍□❍

  any question : [contact email provided] B1: Consent ❍ I confirm that I have read and understood the above information. I am at least 18 years old and I give my consent to participate in this study. ❍ I do not give my consent. (The survey ends here if the answer is "I do not give my consent.") Filters C1: Are you responsible for the financial management decisions of a vineyard ? ❍ Yes ❍ No (The survey ends here if the answer is "No") C2: Are you in charge of decisions regarding vineyard management, including treatments? ❍ Yes, I am responsible for vineyard treatments. ❍ Yes, I participate in the reflections on the treatments of the vineyard. ❍ No, someone else is in charge and I am not consulted. (The survey ends here if the answer is "No, someone else is in charge and I am not consulted.") C3: What is your role in the vineyard ? long have you been working in viticulture ? If you have been working in viticulture for less than one year, put 0. Years Profile of the vineyard D1: In which grapevine-growing area is your vineyard located (or most of it if it is located in several regions)? Côtes-du-Rhône Nord (northern part of the departments 07 and 26) ❍ Côtes-du-Rhône Sud (southern part of the departments 07 and 26) the products from your vineyards commercialized under one or more of these designations? Protected denomination of origin (AOP/AOC) □ Protected geographic indication (IGP) □ Without indication (e.g. Vin de France or Vin de la Communauté Européenne) D3: What is the size of your vineyard ? Mixed (specify in the comment area) D5: Are you in one or more of these situations ? □ Member of a cooperative □ Member of a CUMA □ Member of a GIEE □ Member of a collective sales outlet □ Elected official/vine grower representative Phytosanitary strategy E1: Concerning the management of vine diseases, for example to test new protection practices, how would you position yourself between very careful and risk-taking? your vineyard or wine production follow one or more of these specifications? □ Certified High Environmental Value (Level 3) □ Certified Organic Agriculture □ Certified Demeter or Biodyvin □ Certified Terra Vitis □ My vineyard was certified OA but is no longer certified □ In conversion towards Organic Agriculture □ None □ Other : E3: Do you know your Fungicide TFI for 2020, 2021 and 2022? Reminder: If you do not already have access to it, you can calculate it here [link provided]. To get the fungicide TFI from this calculator, please fill in only your fungicide treatments. The 1 st value of the table obtained (1 st line "TFI") will then be your fungicide TFI. ❍ Yes ❍ No Over the last 3 years, what has been the fungicide TFI (including copper) of your vineyard?Please also count the fungicides authorized in Organic Agriculture such as copper. you at least tell us the number of fungicide applications (including copper)the specificities of your farm, how would you rate your use of fungicides compared to other vine growers in your area?

E9:

  Was your farm engaged in a AECM contract (Agri-Environmental and Climatic Measure of the CAP) with a commitment to reduce the Treatment Frequency Index over at least part of the period 2015-2022? is your current state of mind regarding your plant protection strategy?

  you. It is like choosing between two menus in a restaurant: Drink/Entry/Main Course/Dessert, without being able to choose each menu item separately. If you are not interested in any of the proposed features, you can choose "No Guarantee". There is no right or wrong answer. Keep in mind the situation of YOUR vineyard. This is what will allow us to more reliably assess what is best for each individual situation. You can review what each pictogram means by clicking on (i). This opens a new information tab that you must close to return to the questionnaire. You have to make a choice again. You do not have to worry about your previous choice to choose on this page because the scheme presented are different. You can review what each pictogram means by clicking on (iYou have to make a choice again. You may find this a bit long but we need your choice on several possible configurations of the scheme. Thank you. for respondents who always choose no guarantee] You may choose up to 3 reasons and rank them in order of importance. □ I have few fungal diseases in my vineyards □ I am not interested in reducing my use of fungicides □ I manage my phytosanitary risk myself without needing anyone else □ I have organized my activity to support a financial loss one year (Individual Complementary Volume, diversification, provision...) □ I do not trust that I will be correctly compensated in case of losses □ I do not see the interest because I already have a multi-risk climate insurance □ I imagine very heavy administrative procedures □ The price is too high □ Other (please specify in the next question) M3: Please specify for what other reason you have chosen not to purchase any of the guarantees presented. M4: The insurance scheme has been designed to give you flexibility in following the DSS recommendations. [BONUS] Reminder: If you do not follow the treatment protocol, you will receive the basic coverage. If you follow the treatment protocol (dates, doses) and do not perform any treatments other than those recommended by the protocol, you would receive the basic coverage + bonus. 107 [PENALTY] Reminder: If you follow the DSS recommendations (dates, doses) and do not perform any treatments other than those recommended by the protocol, you would receive the maximal coverage. However, if you do not follow the DSS recommendations, a penalty would lower the coverage and you would receive 30% less than the maximal coverage.

❍

  No, I did not know about it ❍ Yes, I have heard of it but I have never bought it ❍ Yes, I have already taken out index insurance (or parametric insurance) in a professional or private context. M6: What was the main reason for your interest in the insurance scheme we presented? ❍ Compensation for fungal disease losses ❍ The DSS recommendations to reduce my fungicides while maintaining yields ❍ Both are equally important for me ❍ Other: M7: If you were insured against climatic risks (with multiple risks, hail or frost guarantee) at least once in the last 5 years, what was your level of satisfaction? ❍ I have not been insured ❍ Very satisfied ❍ Quite satisfied ❍ Quite dissatisfied ❍ Very dissatisfied M8: It is possible that this fungal disease insurance system could be systematically associated with an MRC contract. What would you prefer?

Table 1 :

 1 Attributes and levels

	Attributes	Levels
	Type of contract

Table 2 :

 2 Share of producers in each cell, used as weights to adjust for sampling biais and extrapolate results at population level (n=57878)

	Non-organic(n=48034) High users Low users
	High-use regions	23.6%	23.6%
	Low-use regions	17.9%	17.9%
	Organic (n=9844)	High users Low users
	High-use regions	2.8%	2.8%
	Low-use regions	5.7%	5.7%

Source: Agence Bio (2020) for number of organic producers and French agricultural practices survey 2019 (French agricultural ministry 2022) for average TFI by region.

Table 3 :

 3 Sample characteristics

	Variable	Description	Sample mean Population
	Vine grower's characteristics
	Age	Years old	49.46	46.50
	Gender	1 = male, 0 = female	0.876	0.781
	Seniority	Years of work in vines	23.3
	Education in viticulture	1 = Yes, 0 = No	0.767	0.92
	Vineyard's characteristics
	Land size (vineyard)	Hectares	45.8	22.1
	Ownership of the vineyard	1= Yes, 0 = No	0.752
	Protected indication (see Note 1)
	Protected denomination of origin (AOP/AOC) 1 = Yes, 0 = No	0.90	0.46
	Protected geographic indication (IGP)	1 = Yes, 0 = No	0.37	0.28
	Other	1 = Yes, 0 = No	0.20	0.08
	Collective implication
	Member of a cooperative	1 = Yes, 0 = No	0.422	0.58
	Collective score (see Note 2)	between 1 and 5	1.11
	Certification (see Note 3)
	Organic farming	1 = Yes, 0 = No	0.269	0.17

Table 4 :

 4 Latent class: Probability to subscribe to green insurance

		Class 1	Class 2
		(60%)	(40%)
	ASC	-1.587***	0.631
		(-5.64)	(0.62)
	Group	-0.430***	-0.151
		(-5.69)	(-0.34)
	Index	-0.294***	-0.430
		(-4.17)	(-1.03)
	Coverage	0.0192*** 0.00203
		(3.79)	(0.10)
	Price	-0.0982*** -0.535***
		(-3.96)	(-3.83)
	Probability to belong to class 1
	Organic	0.929**
		(2.74)
	Organic transition 2.293***
		(3.38)
	Other certification 0.911**
		(2.95)
	Sanitary strategy	0.348**
		(2.77)
	Constant	-1.354**
		(-3.03)
	N	4944
	t statistics in parentheses
	* p<0.05, ** p<0.01, *** p<0.001

Table 5 :

 5 Adoption rate by contract type and price (50% coverage)

	Contract type	S1	S2	S3
	Price	Individual loss-based Individual index-based Group index-based
	3%	58 (60)	56 (58)	53 (54)
	5%	54 (56)	51 (54)	48 (51)
	6%	51 (54)	49 (53)	47 (50)
	8%	49 (51)	48 (

Table 6 :

 6 Average percentage fungicide reduction (with population weight in parenthesis)

	Non-organic	High users	Low users
	High-use regions 56.63 (23.6%) 27.65 (23.6%)
	Low-use regions 74.70 (17.9%) 39.78(17.9%)
	Organic	High users	Low users
	High-use regions 37.34 (2.8%)	1.56 (2.8%)
	Low-use regions 49.21(5.7%)	3.19(5.7%)
	Kruskal-Wallis test for difference across cells χ 2 (7) = 395.088; Prob = 0.0001

Table 7 :

 7 Average public cost in e/ha for a 1% reduction in TFI (with population weight in parenthesis)

	Non-organic	High users Low users
	High-use regions 13 (23.6%) 221 (23.6%)
	Low-use regions 16 (17.9%) 34 (17.9%)
	Organic	High users Low users
	High-use regions 50 (2.8%)	570 (2.8%)
	Low-use regions 15 (5.7%)	145 (5.7%)
	Kruskal-Wallis test for differences across cells: χ 2 (7) = 75.468; Prob = 0.0001

  Descrozaille, Frédéric (2022). Report on behalf of the Commission for Economic Affairs in the French parliament on the draft law reforming the tools for managing climate risks in agriculture (in French). FR. Tech. rep. Rapport n°4874 -15e législature. The Review of Economics and Statistics, pp. 1-40.

	EAFRD (2014). French National Framework Program, The European Agricultural Fund for Rural
	Development 2014FR06RDNF001.
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	10.1038/s41559-022-01799-8.
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	markets in agricultural products and repealing Council Regulations (EEC) No 922/72, (EEC)
	No 234/79, (EC) No 1037/2001 and (EC) No 1234/2007.
	-(2018). R ÈGLEMENT D'EX ÉCUTION (UE) 2018/1981 DE LA COMMISSION du 13
	décembre 2018 renouvelant l'approbation des substances actives ≪composés de cuivre≫ comme
	substances dont on envisage la substitution, conformément au règlement (CE) no 1107/2009
	du Parlement européen et du Conseil concernant la mise sur le marché des produits phy-
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	Anchoring in Procurement Auctions". In:

  Would you like to participate to a random draw for the first 50 people who completed the entire survey? To be won: Six bottles of Crémant de Loire made for the 50th anniversary of the University.

	Region Bouches-du-Rhône Dordogne Côtes-du-Rhône Nord E1: ❍ Yes Charentes	A.3: Respondents and adopters by region Low use/High use+ Adopter* Sample Population** Low-use 0,00% 0,24% 1,12% High-use 0,00% 0,97% 0,92% Low-use 25,00% 0,97% 2,69% High-use 38,23% 8,25% 7,51%
	Champagne		High-use	44,00%	12,14%	21,44%
	❍ No Languedoc hors PO	Low-use	46,43%	13,59%	23.20%
	Bourgogne		High use	46,66%	7,28%	3.72%
	Provence (Var-Vaucluse) Low-use E2: Please enter your email, the winner will receive an e-mail asking her to indicate her postal 48,15% 6,55% 9,19% Alsace Low-use 50,00% 2,43% 4,92%
	address to receive the bottles at home. Corse High-use	50,00%	0,97%	0,58%
	Côtes-du-Rhône Sud	Low-use	50,00%	1,94%
	Bordelais		High-use	55,36%	13,59%	10,41%
	E3: Would you like to receive the results of the study by email in 2023? Pyrénées-Orientales Low-use 60,00% 1,21%	2,09%
	Val de Loire		Low-use	63,88%	17,48%	2,55%
	Beaujolais ❍ Yes Lot-et-Garonne		High-use High-use	66,00% 66,66%	2,18% 0,73%	5,81% 0,71%
	Cher ❍ No Bugey Savoie		Low-use High-use	66,67% 71,43%	1,46% 1,70%	0,52% 0,57%
	Jura		Low-use	77,77%	2,18%	0,45%
	E4: Please enter your email address to receive the results Gaillac Low-use 85,71%	1,70%	0,29%
	Gers		High-use	85,71%	1,70%	0,85%
	Cahors		High-use	100,00%	0,73%	0,44%
	+ "Low-use regions" are the regions where regional average TFI are below the national median
		(according to French agricultural practices survey 2019)
	F. Comments	* Adopter of the S1 contract (5% price and 50% coverage)
	F1: Thank you for your answers. They will be compared to the choices made by vine growers ** Grapevine growers population according to French agricultural census 2020.
	in another survey. You can indicate here any reaction or suggestion related to this A.4: Adoption rates of the S1 contract (5% price and 50% coverage)(with population weight in
	C1: According to you, what minimum coverage and maximum price should be offered to vine questionnaire. parenthesis)
	growers to make the insurance scheme attractive? Non-organic High users		Low users
	Coverage (in % of assessed loss) High-use regions 55.17 (23.6%) 53.3 (23.6%)
		Low-use regions 67.5 (17.9%)	51.72(17.9%)
	Thank you for your time in answering this questionnaire. Organic High users	Low users
		High-use regions 66.7 (2.8%)		39.13 (2.8%)
	Premium (in % of the insured capital) If you have any comments, please do not hesitate to contact us: [email address provided] Low-use regions 56.52(5.7%) 51.02(5.7%)
	Thank you for completing this questionnaire. Kruskal-Wallis test for difference across cells χ 2

To limit moral hazard, contracts include requirements to provide full documentation on the adoption of best practices, as well as the right to deny claims when evidence of lack of due diligence is apparent. The objective is to facilitate expert evaluation of losses, in particular, to distinguish losses ensuing from the adoption of best practices from losses related to other factors.

For example, in the US, producers who implement voluntary practices to minimize nutrient contamination of surface and groundwater, can be indemnified for yield losses[START_REF] Harris | Using BMP insurance to improve farm management[END_REF]. The same existed in the early 2000s for US corn producers following Integrated Pest Management (IPM) recommendations not to treat with insecticides against corn root-worm, with a similar contract (by IGF Insurance) for potato growers using IPM for potato blight[START_REF] Mitchell | Factors Determining Best Management Practice Adoption Incentives and the Impact of Green Insurance[END_REF]. In Veneto (Italy), corn producers following IPM recommendations can benefit from yield loss coverage in case of recommendations failure, paid by a mutual fund. Within the VitiREV project, in France, two wine cooperatives have tested such an insurance conditional on the reduction of fungicide use, in very specific conditions including a premium initially entirely covered by public subsidies (C.[START_REF] Aubert | Le soutien à la prise de risque comme levier pour une transition environnementale[END_REF]. A new experiment has just been launched between a different insurer and a large private wine group, a proof of viticulture actors' interest in such contracts.

An insurance improves outcomes in bad states of the world, whereas a prevention tool reduces the probability of these bad states occurring. In the following, we model fungicides as reducing the probability of suffering very low yields because of diseases, in line with the definition of a prevention mechanism.

In the EU, multi-peril crop insurance premia are subsidized, but compensation rates are considered too low to foster adoption (Descrozaille 2022).

Previous studies have found that risk management tools can also have important extensive margin effects

We assume that the probability of a loss depends on treatments but not the extent of the loss. Because we consider risk-neutral farmers in the main text, this has no impact on our description of the choice made by the grower.

The DSS used in the green insurance experimentation run since 2019 in the Bordeaux area, Decitrait, is available at a fixed price of about e250, which is quite small compared to expected cost savings of about e200 per hectare for a 50% reduction in TFI.

https://osf.io/mbvpa

One example of such contract S3 is described by[START_REF] Harris | Using BMP insurance to improve farm management[END_REF]: producers receive indemnities based on the annual deviation from long-run production averages among a cohort of nearby producers. This approach aims to lower monitoring costs by using county-wide information to inform claims. It also reduces moral

Note that in the theoretical model, T F I * was defined as the lowest achievable treatment level for the targeted yield. We therefore assume that this level can be empirically approximated by the treatment levels achieved by the group of most efficient producers of the respective population.

In the choice experiment, to facilitate respondents' understanding, and as supported by the IFV Technical Institute, we communicated that following DSS recommendations allows reducing fungicide use by 40 to 70%, depending on the year and fungal pressure. With our sample, we verify ex-post that the reduction potential calculated as explained above is between 0 and 90 %, with an average and median of 31%. This is in line with

In practice, there may be only a limited number of agricultural insurers willing to offer a green insurance, so that perfect competition may not apply. However public authorities may be able to impose actuarial fairness as a requirement associated with the public bonus. Insurers may find this attractive if they expect their customers to subscribe to other products they may offer.

Recall that the following estimates focus on the adoption rate of the preferred contract (S1), in the case of fair insurance (5% price and 50% coverage)

After the choice experiment, we asked growers whether they would most likely follow the recommendations to be sure to benefit from the bonus, or would rather focus on the base indemnity and not reduce their pesticide use (an intermediary choice was also offered: "Follow the recommendations at the beginning of the season and stop

American Journal of Agricultural Economics 81.2, pp.305-320. doi: 10.2307/1244583. 1043

a workshop organized by the European Commission on "Alternative business models for pesticide reduction" (November 2023). We acknowledge funding by Région Pays de Loire (project BEHAVE) and the support of the Grand Plan d'Investissements d'Avenir through the program Territoires d'Innovation (PIA VitiREV) as well as support of the French National Research Agency (ANR) under the grant 20-PCPA-0010 (VITAE). Access to some confidential data, on which is based this work, has been made possible within a secure environment offered by Centre d'accès sécurisé aux données (Ref. 10.34724/CASD , French agricultural ministry 2022).

check before you collect" webinar, as well as participants to the FRIES webinar organized by ETH. The results have been discussed with insurers, crop protection advisers and policy makers during

Appendix Appendix A: Modelling vine growers' decisions

This subsection presents general results for a weakly risk-averse producer (A.1). The case of strict risk-aversion is treated here, whereas the main text focuses on the case of risk neutrality. This appendix also presents simulations for different values of the bonus b and the probability of loss in case of pesticide use reduction ρ k (A.2.).

A.1. The model

Yields and treatments.

We consider a producer with a maximum financial yield y (in e/ha). The actual financial yield may be lower due to pest attacks. "Losses" are the difference between the maximum yield and the low yield, and are ly where l ∈ [0, 1]. They occur with probabilities that depend on climatic and geologic conditions but also on treatment intensity.

Each producer has an 'initial' fungicide treatment frequency index T F I. This is the TFI he would choose in the absence of an insurance scheme. We denote T F I * the lowest achievable treatment level for the targeted yield. We assume that following DSS recommendations, thanks to the extensive expertise and information analysis embedded in the tool, allows to reach T F I * .

The corresponding percentage reduction in fungicide is denoted k. In regions with more fungi pressure T F I * will remain higher than in regions with lower pressure. To simplify, we assume that a producer not following DSS recommendations will not reduce its pesticide use at all.

Thus, k = T F I-T F I * T F I if DSS recommendations are followed = 0 if DSS recommendations are not followed

The cost of treatments C k increases in the TFI and is therefore lower under DSS recommen-dations.

C = C k if DSS recommendations are followed = C 0 if DSS recommendations are not followed, with C 0 > C k .

We denote by ρ k the probability of a low yield when the producer commits to reduce fungicide by k. ρ k is increasing in k: producers less experienced with less intensive practices will face more losses more frequently.

For a risk-averse producer with a strictly concave utility function u(.), expected welfare when following the DSS without insurance, is

Under risk neutrality, the expression is

In practice in French vineyards, the TFI chosen by producers is widely recognized as too high (as discussed in the introduction). Moreover, producers do not choose to use a DSS despite its low cost. For instance, the DSS used in the green insurance experimentation run since 2019 is available at a fixed price of about e250 , which is quite small compared to expected cost savings of about e200 per hectare for a 50% reduction in TFI. We can therefore assume that the following inequality holds, assuming that the cost of the DSS is incorporated in C k :

The equivalent inequality for risk neutrality is (ρ k -ρ 0 )ly > C 0 -C k .

The cost savings on fungicides are not enough to compensate for the higher risk of losses when reducing one's TFI, even with the help of the DSS.

The insurance contract.

We denote P the price of the contract per unit of insured capital and I the indemnity in case of losses. We assume that y is both the maximum yield and the

The expected utility for the producer is

Under risk neutrality, expected profits are

taking up the contract and following the DSS is profitable if and only if the following participation constraint (P C) is satisfied:

Participation is ensured if the cost savings on pesticides plus the benefit of receiving the indemnity (instead of bearing the full loss) at least compensate for the higher probability of a loss (and therefore lower probability of obtaining u(y)) and the premium.

The loss-based insurance contract ensures that the producer is perfectly insured when the deductible (1 -α) is null and the insurance actuarially fair. They also benefit from a reduction in cost of crop protection. Moreover, taking up the insurance contract and complying with the DSS recommendations is more attractive than taking up the contract without following the DSS if and only if the following incentive constraint (IC) LB is satisfied:

Index-based insurance.

The indemnity is triggered by the value of an index, imperfectly correlated with realized yields. Such insurance is cheaper than loss-based, and the premium is βP , with β ≤ 1. The producers receive an indemnity with probability ρ i (even where the index triggers an indemnity). Note that ρ i is the same for all producers whose contract depends on the same index. The distinct events "suffering losses" and "receiving an indemnity" lead to a partition in four states of nature, whose probabilities are given in Table A.1. For instance, with probability ρ k l+ni , the grapevine grower will suffer losses but no indemnity will be paid.

A.1: Probabilities associated to states of nature for index-based insurance

For a risk-averse producer, the four states of nature need to be distinguished:

And for a risk-neutral producer:

Incentives to reduce TFI. The incentive constraint is satisfied if the profits above are higher than the ones obtained when taking up the insurance contract but not reducing one's TFI. This strategy provides profits of

The incentive constraint that ensures compliance with the DSS thus writes as

tract is more attractive to a risk-neutral producer than the loss-based one if and only if

A.2. Simulation

We simulate here the impact on expected profits of (A) one characteristic of the insurance scheme not varied in the DCE: the bonus b; (B) pesticide efficiency in reducing the risk of catastrophic loss ρ 0 , capturing heterogeneity in the fungal disease pressure faced by producers.

We focus on a set-up where producers are always better-off reducing their pesticide use by half with insurance than without. We focus on whether they are better off relying on chemical protection (k = 0) or reducing their pesticide use by half, covered by the green insurance (k = 0.5).

To do so, we rely on the following assumptions: (1) Producers are risk neutral;

(2) The probability of loss lρ k when pesticides are reduced by half, and DSS recommendations are followed, is calibrated at 10% per year, which corresponds to a catastrophic loss of 100% one year out of ten, while the full yield is reached the nine other years ;

(3) the probability to be indemnified is equal in loss-based and index-based contracts, and producers correctly perceive these probabilities (ρ k = ρ i ). Indeed, while no suitable index exists as yet in the context of French grapevine growing, insurers would not offer a contract based on an index whose frequency was much higher than that of actual losses. And conversely, vine growers would not accept an index whose frequency was much lower than that of actual losses; (4) The insured capital y is equal to e5000/ha (yield of 50 hl/ha and economic value of e100/hl, which corresponds to French averages); (5) the cost of treatments C 0 is set at e400/ha, reduced to C k =e200/ha when pesticides are reduced by half; (6) the price P of the loss-based contract is set at 5% of the insured capital, while the index-based contracts is 10% cheaper (4.5%), for a base coverage of 50% of losses. In the first simulation (Figure A.1), the bonus is varied from 0 to 50%, such as the total coverage does not exceed 100% of losses. In simulation B, the bonus is set at 30%, as in the DCE. In the second simulation (

Appendix B: Experts forecasts

We conducted a prediction survey in autumn and winter 2022. We present here the questionnaire sent to forecasters and their forecasts. Experts' predictions have been used to formulate hypotheses, as adviced by [START_REF] Dellavigna | Predict science to improve science[END_REF].

B.1. Questionnaire sent to forecasters

We would like your opinion as a professional of the wine sector on the interest that vine growers could have for a new insurance scheme. This scheme would allow to mitigate the risks linked to fungal diseases in a context of limited use of fungicides. Currently, only the climatic risk (frost, hail) is concerned by a subsidized insurance scheme. Your answers are useful for us to guide the decisions of public authorities regarding new tools better adapted to the needs of vine growers.

Our team only includes researchers working for public institutions Our study is totally anonymous and is conducted without any commercial or political purpose.

Thank you for your time. We are talking about a risk management system for fungal diseases. This insurance scheme is not available for vine growers. Your opinion will help us to think about the interest and the best way to conceive this device.

The scheme provides for:

1. Financial coverage for losses due to diseases. The diseases covered are downy mildew, powdery mildew, and block rot.

2. A decision support system providing treatment recommendations to reduce fungicide treatments, formulated by the French Institute of Vine and Wine.

3. A bonus, funded by the public authorities, if vine growers follow the protocol's recommendations.

Some details:

• The IFV tool models the development of fungal diseases based on field observations and weather data. It advises vine growers on how to carry out fungicide treatments. These decision rules have been thought out beforehand with vine growers.

• According to the tests over 4 campaigns, following this protocol allows to save, depending on the year, between 40 and 70% of fungicides, while still achieving at least 90% of the yield target.

• The scheme is open to all, with or without certification. A specific version of the treatment protocol exists for organic farming.

• Subscription to this compensation scheme for losses due to disease is independent of the multi-risk climate insurance (MRC) which guarantees climatic risks (frost, hail...).

• The practices of the vine growers can be controlled (treatment booklet, visit).

B3: The evaluation of the losses can be done:

• by an expert, who comes to observe the consequences of the fungal diseases in the vineyard plots and then the harvest

• by an index of fungal pressure, based on observations of control vines close to of the vine growers

In your opinion, what proportion of vine growers would prefer a loss-based evaluation rather than an index-based evaluation of losses?

% of vine growers.

B4: Grapevine growers can join the scheme. . .

• Voluntary (as for a classic insurance)

• Compulsory (as for a mutual fund between vine growers of the same cooperative, appellation or wine area)

According to you, what proportion of vine growers would prefer a voluntary membership rather than a mandatory one as for a mutual fund?

% of vine growers.

Use the criteria below to draw the profile of the winemaker who:

• In your opinion, is most likely to be interested in this type of tool;

• In your opinion, prefers expertise to the index;

• According to you, prefers voluntary membership to compulsory membership (as for a mutual fund between vine growers of the same cooperative, appellation or wine area). We summarize here experts' forecasts on vine growers' preferences collected with the prediction survey. While the prediction survey included more questions, we focus here on the forecasts related to the pre-registered hypotheses.

Experts anticipate more interest for green insurance from vine growers treating less than the average. → Pre-registered hypothesis H3

Concerning the impact of producers and farms' characteristics, most experts forecast that vine growers already committed to and satisfied with a climatic insurance, would be interested by green insurance. → Pre-registered hypothesis H2

According to the expert's estimates, 71% of vine growers would prefer a voluntary individual contract rather than a mandatory group one. → Pre-registered hypothesis H5

On average the experts believe that 64% of vine growers would rather prefer an expert-based insurance than an index-based insurance. → Pre-registered hypothesis H6

Most experts predict that vine growers would be more interested by the green insurance with the bonus framing, but have ambiguous forecasts concerning the impact of framing on compliance with DSS recommendations. → Pre-registered hypothesis H7

Appendix C: Pre-registered hypotheses

The following hypotheses are based on the literature on pesticide use reduction and Agri-Environmental Scheme and insurance contract choices. They are also in-line with results from a prediction survey conducted in autumn and winter 2022 to collect experts' forecasts on vine growers' preferences for the different attributes, and impacts of farm and producers' characteristics on these preferences.

H1: Grapevine growers have a positive willingness to pay for such a green insurance covering yield losses due to fungal diseases.

H2: Risk and producers risk preferences will both influence producers willingness to adopt green insurance and to follow DSS recommendations to reduce their fungicide use.

H3: Self-selection into the scheme: H3a: Grapevine growers who are treating intensively will be (despite characteristics that make them treat more) attracted by the technical and financial assistance provided by the scheme, because this group likely includes the most risk-averse vine growers. H3b: Grapevine growers who are treating less intensively will be the most attracted by the scheme, as they are more likely to receive the bonus and get windfall benefits.

H4: Grapevine growers are more willing to pay for a green insurance contract with better coverage (lower deductible) and lower premium.

H5: Vne growers are less willing to pay for a collective contract, but this effect is reduced for vine growers who are already working together, especially those members of a cooperative.

H6: Grapevine growers are less willing to pay for an index insurance, but this effect is reduced for those who have already experienced such insurance and those more innovationoriented (proxied by producers' characteristics).

H7: Grapevine growers are more willing to pay for green insurance in the bonus framing, but are less likely to comply with DSS recommendations, compared to the penalty framing.

H8: Green insurance is an effective instrument to achieve fungicide reductions in line with targets of the French National Action Plan (Ecophyto) and the EU green deal.

Appendix D: Framing effect -Results

To test for the impact of framing on preferences towards green insurance, we estimate the RPL model for the bonus and penalty framing sub-samples separately. The hypothesis that we have significant effects in presenting the indemnity as a bonus or penalty is verified by the means of a likelihood ratio (LR) test [START_REF] Höhler | Unfair milk prices? Lessons from a split-sample choice experiment[END_REF][START_REF] Contini | Price vector issue in a choice experiment: A methodological proposal[END_REF]. The value of the LR (-2(-1276.68-(-724.825-516.79)) test is lower than the critical value for χ 2 statistic at 5% with 10 degree of freedom (18.307). We can therefore not reject the null hypothesis. This indicates that presenting the indemnity as a bonus or penalty does not significantly change respondents' preferences, even when the scale differences between treatments are controlled for.

Moreover, we verified that the framing has no selection impact: producers subscribing to

Appendix E: Adoption rate by regions

We observe in Table A.3 that there are less adopters in regions where wine value is higher (Côtedu-Rhône Nord, Charentes, Champagne, Bourgogne) or disease pressure is lower (Languedoc, Provence). When the production value is high, the uninsured part of losses (1-α-b)ly dampens the incentive to get the green insurance. Where the risk of losses is lower, the premium P appears too costly and the cost savings from following the DSS are limited (low C 0 -C k ). In both cases, the participation constraint (P C) is less likely to be satisfied. Nevertheless, due to the interplay of different regional characteristics, there is no significant difference in the adoption rate between the regions where regional average TFI is below the national median (Low-use regions) compared to the other regions (High-use regions). Organic growers (including those in transition) are particularly likely to subscribe to green insurance if their are high users in high-use regions (Table A. 4).

Appendix F: Robustness checks

We run the same RPL model than the one presented in the main text (Figure 2) to different subgroups. In column (1), we first exclude responses to the first choice card seen by respondents, to account for learning effects. In column (2), we exclude responses to the last choice card in order to check for a lassitude effect. In column (3), we exclude respondents who indicated no interest in the scheme before answering choice cards, since they are more likely to have provided random answers. In column (4), we exclude those who have always opted-out. Finally, in column ( 5), we exclude respondents who read the description of attributes, including the video presenting the DSS, in less than thirty seconds, which is considered as too short for a comprehensive overview of the green insurance scheme. In column ( 6), we exclude the 36 respondents who were interviewed face to face in order to keep only self-completed responses.

A

Appendix H: English version of the vine grower survey

We are looking for your point of view on new tools, designed to help you compensate the risks associated with fungal diseases.

Currently, only the climatic risk (frost, hail) can benefit from a subsidized system. However, the risks of losses associated with diseases also prevail for the wine sector, even more in a context of reduction of phytosanitary products use.

By responding to this study today, you are helping to guide the decisions of public authorities to design and implement new tools better adapted to the needs of vine growers.

Our team includes only researchers working for public institutions. Our study, totally anonymous and validated by an ethics committee, is conducted without commercial or political purposes.

Thank you for your time.

This project is funded by: [institutional logos included] ❍ I am in the process of reducing my fungicide use.

E11: Do you currently use a decision support system (DSS) to adjust your phytosanitary treatments ?

By the term DSS, we are talking about the different digital tools offered to vine growers today, from various organizations (agriculture chambers, technical institutes, private companies), to advise you in the adjustment of phytosanitary treatments.

❍ Yes and I am satisfied ❍ Yes and it is not very satisfactory ❍ No, not for now but I am thinking about it ❍ No, I don't use it and I'm not interested in it

The insurance scheme

We would like to know your opinion on an insurance scheme, still in reflection, for managing the risks associated with fungal diseases.

[(]The participants were assigned randomly to questions on the scheme in the bonus or the penalty framing. Hereafter will be displayed the presentation of both bonus and penalty framing but the participants only read the parts they were assigned to.]

[BONUS framing] The insurance scheme provides:

1. Financial coverage for annual losses due to diseases. The diseases concerned are downy mildew, powdery mildew and black rot.

2. The provision of a treatment protocol formulated by the IFV (French Institute of Vine and Wine) to reduce fungicide treatments as safely as possible.

3. A financial bonus, financed by the public authorities, if the vine growers follow the recommendations of the protocol.

-Subscription to this insurance scheme for losses due to diseases is independent of the multi-risk climate insurance (MRC). You may wish to subscribe to one or the other, or both but with no obligation.

Insurance scheme versions

From the general insurance scheme presented above, several versions can be considered.

Your opinion will allow us to think about the interest and the best way to conceive this insurance scheme if it is one day proposed to the vine growers.

Here are the possibilities considered:

TYPE OF CONTRACT which can be:

Each vine grower decides individually whether or not to subscribe to the insurance (as for a classic insurance).

The vine growers subscribe to a group contract, for example within the framework of a mutual fund between vine growers of the same cooperative, appellation or wine basin. In this case, membership is compulsory for all the vine growers in the group concerned.

DAMAGE EVALUATION:

Your real losses are assessed by an expert, who comes to observe in your plots the consequences of fungal diseases and then the harvest. The amount of the compensation depends on the expert's evaluation of your real losses. The expertise allows an evaluation of the losses specific to your farm but is subject to the subjectivity of the expert and is more expensive than the index evaluation.

Your losses are estimated on a local fungal pressure index measured, for example, in control vineyards near your home. The amount of compensation depends on the value of this index. Your real losses will sometimes be higher and sometimes lower than in the control vineyards, but the index can help to reduce insurance costs and make the assessment of losses more objective than an expert evaluation. (between 40 and 65% of losses). No triggering threshold is applied. The coverage will be higher for vine growers respecting the treatment protocol (dates, doses) and not carrying out any treatment other than those recommended by this protocol, thanks to an additional 30% bonus financed by the public authorities. With the bonus, the total coverage is between 70% (40%+30%) and 95% (65%+30%) of the losses.

[PENALTY FRAMING] The coverage is a percentage of assessed losses (between 70 and 95% of losses). No triggering threshold is applied. A part of this coverage is funded by public authorities. A penalty of 30%

(the part normally funded by public authorities) will lower the total coverage for vine growers preferring not to follow the treatment protocol (dates, doses) and carrying out other treatments than those recommended by this protocol. With the penalty, when not following the treatment protocol, the coverage is between 40% (70%-30%) and 65%

(95%-30%) of the losses.

PREMIUM:

Subscribing to insurance is costly and the price is defined in % of the insured capital. The insured capital is equal to the insured yield multiplied by the price value of the production. To help your choice, in addition to the mention of the % of the insured capital (between 3 and 8%), a corresponding amount (in eper hectare) will be provided, depending on the price at which you declare to value your production and the yield you have declared to insure.

Simulation

Let's simulate the case of your vineyard.

Imagine that you want to sign up for this insurance scheme, please indicate below the parameters you would choose to view the compensation amounts at the bottom of the page.

If you have several values in mind, please give us the one for your main production.

H1: On average over the last 5 years, at what price do you value your production (in eper hectoliter of wine)?

We need this information to provide you with compensation amounts that are appropriate for your situation. If you do not know or do not wish to give an exact amount, please give an estimate.

(e/hL) H2: What level of return do you want to ensure ?

You can set it to your potential return. The only constraint is that this level has been reached at least once in the last 5 years.

(e/hL) H3: The following table shows you the level of compensation you would receive under this insurance scheme for an amount of losses of 10% due to fungal disease.

As a reminder, following this DSS treatment protocol associated with the device allows to save, depending on the year, between 40 and 70% of fungicides, while allowing to reach at least 90% of the yield objective. Losses will therefore rarely exceed 10%.

[BONUS FRAMING] Estimation of the compensation for YOUR vineyard:

Coverage rate

Basis coverage in e/ha e/ha (40%) e/ha (65%)

Coverage with the bonus of 30% in e/ha e/ha (70%) e/ha (95%)

[PENALTY FRAMING] Estimation of the compensation for YOUR vineyard:

Coverage rate

Basis coverage in e/ha e/ha (70%) e/ha (95%)

Coverage with the penalty of 30% in e/ha e/ha (40%) e/ha (65%)

H4: What do you think?

❍ I might be interested in this device, depending on how it is set up and its price.

❍ A priori, I am not interested in such a device to manage the risk of losses due to fungal diseases, whatever its price and the way it is set up.

[(]For the 4 following choices, the cards are displayed here in the bonus framing only but participants to the survey were assigned to bonus or penalty and saw the corresponding choice cards]

Choice 1/4

On each of the following 4 pages, you will see two insurance schemes with different characteristics (Scheme A and Scheme B, shown below in columns).