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Contract attributes are strong motivators for eliciting farmers’ preferences for a particular agri-environmental scheme. Our study aims to conduct a systematic literature review to highlight the attributes used in choice experiment studies of agri-environmental schemes using the PRISMA framework. We obtained 34 studies for an in-depth review, through which we extracted 32 attributes that were classified into five typologies: ‘monetary’ (7 attributes), ‘general’ (4 attributes), ‘flexibility’ (6 attributes), ‘prescription’ (12 attributes), and ‘purpose’ (3 attributes). Though monetary attributes should theoretically define farmers’ choices; general design and flexibility attributes are more critical for farmers’ participation and willingness to accept. The study also discusses the lesser-used attributes that could be potentially explored in future studies. Thus, our review can be used as a reference by future AES studies to select their bundle of choice attributes and test with a broader range of attributes in their choice experiments.
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Bio-based and Applied Economics 10(2): 137-152, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9678
Bio-based and Applied Economics
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Copyright: © 2021 N. Raina, M. Zavalloni, S. Targetti, R. D’Alberto, M. Raggi.
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Citation: N. Raina, M. Zavalloni,
S. Targetti, R. D’Alberto, M. Raggi,
Davide Viaggi (2021). A systematic review
of attributes used in choice experi-
ments for agri-environmental contracts.
Bio-based and Applied Economics 10(2):
137-152. doi: 10.36253/bae-9678
Received: September 5, 2020
Accepted: December 4, 2020
Published: October 28, 2021
Data Availability Statement: All rel-
evant data are within the paper and its
Supporting Information les.
Competing Interests: The Author(s)
declare(s) no conict of interest.
Editor: Simone Cerroni.
ORCID
NR: 0000-0002-7617-4731
MZ: 0000-0002-6291-7653
ST: 0000-0002-2154-546X
RDA: 0000-0002-7227-7485
MR: 0000-0001-6960-1099
DV: 0000-0001-9503-2977
A systematic review of attributes used in choice
experiments for agri-environmental contracts
N R*, M Z, S T,
R D’A, M R, D V
University of Bologna, Italy
*Corresponding author. E-mail: nidhi.raina@unibo.it
Abstract. Contract attributes are strong motivators for eliciting farmers’ preferences
for a particular agri-environmental scheme. Our study aims to conduct a systematic
literature review to highlight the attributes used in choice experiment studies of agri-
environmental schemes using the PRISMA framework. We obtained 34 studies for an
in-depth review, through which we extracted 32 attributes that were classied into ve
typologies: ‘monetary’ (7 attributes), ‘general’ (4 attributes), ‘exibility’ (6 attributes),
‘prescription’ (12 attributes), and ‘purpose’ (3 attributes). ough monetary attributes
should theoretically dene farmers’ choices; general design and exibility attributes
are more critical for farmers’ participation and willingness to accept. e study also
discusses the lesser-used attributes that could be potentially explored in future stud-
ies. us, our review can be used as a reference by future AES studies to select their
bundle of choice attributes and test with a broader range of attributes in their choice
experiments.
Keywords: choice experiment, agri-environmental schemes, willingness to accept,
contract attributes, systematic literature review
JEL codes: Q15, Q20, Q57.
1. INTRODUCTION
Farmers’ decision to participate and their willingness to accept (WTA)
a particular agri-environmental scheme (AES) is aected by the contract’s
design. Studies have tried to investigate the choice behaviors of farmers
using various methodologies. e choice experiment (CE) methodology, a
type of stated preference method, is widely applied in valuation studies and
is useful for analyzing dierent policy scenarios (Kanchanaroek & Aslam,
2018). CEs are based on the theory of consumer choice, which states that
individuals’ choices depend on utility or value gained from the attributes
of the goods being consumed (Lancaster, 1966). Utility generally depends
on attributes of the choices and socio-economic characteristics of an indi-
vidual. So, CEs provide an attribute-based approach that can investigate
individual preferences (Chèze et al., 2020) as well as quantify the trade-os
between the alternatives (Hynes et al., 2011). us, the CE method is par-
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Nidhi Raina et al.
ticularly suited for evaluating choices among dierent
AESs and to elicit farmers’ or landowners’ preferences
for dierent attributes in a contract (Espinosa-Goded
et al., 2010; Horne, 2006; Ruto & Garrod, 2009, etc.).
Many studies have reported that even though socio-
economic, demographic, or cultural characteristics can
inuence farmers’ preferences, such ndings are usually
insucient to quantify these choices (Dachary-Bernard
& Rambonilaza, 2012; Dramstad et al., 2006; Swanwick,
2009, etc.). us, CEs can be a useful tool to understand
specific preferences by evaluating farmers’ behavior
towards contract attributes.
Studies generally use evidence from previous lit-
erature to select the contract attributes and their levels
for their CE. ere exists a plethora of literature on the
motivations and attitudes of farmers exhibiting conser-
vation behavior that the AES studies use while choos-
ing attributes (Greiner, 2016; Le Coent et al., 2017). Few
studies have conducted in-depth literature reviews to
understand why farmers join a particular AES (like,
Lastra-Bravo et al., 2015) and the attractive attributes
in a contract that motivate farmers’ participation (like,
Brandyberry, 2015). Lastra-Bravo et al. (2015) collected
160 variables through a review of AES studies which
they classied into ve dierent categories that depicted
the socio-economic and demographic conditions of the
farm and the farmers. However, they studied trade-os
between attributes within only one category: ‘farmers’
attitudes towards agri-environmental schemes.’ us,
there is still a substantial knowledge gap in the litera-
ture about attribute selection for contract design because
of the lack of a denitive catalog of management and
policy-based attributes used by previous studies. State
of the art has majorly focused on reviewing the meas-
ures included in the AESs (e.g., Lastra-Bravo et al., 2015
and Rakotonarivo et al., 2016), but no study has speci-
cally concentrated on reviewing the contract attributes
used for designing the CEs. is gap creates a divide
between contract attributes studied by researchers and
actual attributes preferred by the farmers, which may
lead to inecient contract designs. Also, studies shortlist
choice attributes using previous literature, but there is a
lack of studies that employ a systematic review approach.
Some studies such as Uthes & Matzdorf (2013) reviewed
the literature on agri-environmental measures (AEMs).
However, they did not use a systematic method, thus,
they covered a broad spectrum of AEMs that does not
focus on using the CE methodology to examine farmers’
choices. One recent study by Mamine et al. (2020) did
conduct a meta-analysis of 79 AES studies that use the
CE method to evaluate farmers’ preferences. However,
they did not conduct a systematic review and grouped
the extracted 290 attributes into only two categories –
commitments and incentives.
Mamine et al. (2020) haven’t been the rst to classify
contract attributes into dierent sub-types. Many AES
studies that use the CE methodology classify the choice
attributes as monetary and non-monetary. Usually, AES
studies include a monetary attribute related to payment
level (expressed in currency per hectare per year) to esti-
mate the WTA of the various AES designs (Espinosa-
Goded et al., 2013; Espinosa-Goded et al., 2010; etc.). e
monetary attribute can also be either funding schemes
(e.g., climate premium), international price uctuations,
additional incentives, conditional bonus, etc. (Kuhfuss
et al., 2015; Pröbstl-Haider et al., 2016; etc.). e various
types of non-monetary attributes can either be manage-
ment attributes (like ‘biodiversity’ and ‘carbon seques-
tration’ as environment management attributes used in
the study by Mäntymaa et al. (2018) and ‘cover crops
area size’ as an agriculture-management attribute in
the study by Villanueva et al. (2015a), or policy design
attributes (like ‘collective participation’ and ‘monitor-
ing’ by Villanueva et al. (2015a)), or theory-relevant
attributes (like ‘recommendation’) and policy-relevant
attributes (like ‘share of farm’) (Villamayor-Tomas et al.,
2019), etc. Ruto & Garrod (2009) labeled agri-environ-
mental policy options as their key design attributes (like,
‘duration of AES contract,’ ‘per hectare payment rate,’
etc.), dierentiating them from payment levels. Similarly,
Le Coent et al. (2017) categorized the contract attributes
as: attributes that have a direct eect on farmers’ com-
pliance costs (levels and types of environmental eorts)
and attributes related to contract design (‘length of con-
tract,’ ‘contract cancellation options,’ ‘contract exibil-
ity,’ etc.). ey extended the categorization to introduce
a novel attribute called ‘purpose’ which they tested via
a CE. Dupras et al. (2018) also categorized attributes as
either visual aspects (like ‘crop diversity’) or personal
attributes (like ‘family heritage,’ ‘emotional attach-
ment,’ etc.). Christensen et al. (2011) also categorized
their contract attributes into three categories: exibility
in contract terms (‘contract length’), exibility in prac-
tical management (‘buer zone width’), and economic
incentive (‘subsidy in euro/hectare/year’). ese numer-
ous categorizations can be incoherent for future studies
when selecting attributes, which calls for comprehensible
and practical typologies. One of the ways to do it is by
systematically collating all the attributes from previous
studies and sorting them according to their usage.
us, we aim to conduct a systematic review of AES
studies’ recent literature that uses CEs to reveal the com-
mon attributes they use for testing contract designs and
farmers’ preferences for those contract features. Our
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A systematic review of attributes used in choice experiments for agri-environmental contracts
Bio-based and Applied Economics 10(2): 137-152, 2021 | e-ISSN 2280-6172 | DOI: 10.36253/bae-9678
study also tries to categorize the attributes into broad
typologies and highlight the lesser-used attributes that
can be explored in future AES studies.
A systematic literature review is used to collect and
analyze data from relevant previous studies and identi-
fy empirical evidence to satisfy a specic hypothesis or
research question (Armstrong et al., 2011; Petticrew &
Roberts, 2008; Siddaway et al., 2019). We use a system-
atic review since it has a considerable edge over a narra-
tive review as it is more organized and has reduced bias
(Koutsos et al., 2019). ere have been several proposed
methods for conducting a systematic review, and they
are usually classied by the research discipline. E.g., the
EKLIPSE project report on dierent methodologies sug-
gests using either the Cochrane method (Higgins et al.,
2019), Campbell collaboration protocol (Kugley et al.,
2017), or the Collaboration for Environmental Evidence
(2013) method for conducting a systematic review in the
domain of environmental-related sciences (Dicks et al.,
2017). Another novel approach is the PRISMA (Preferred
Reporting Items for Systematic reviews and Meta-Anal-
yses) methodology that illustrates the ow of informa-
tion in dierent phases of a systematic review. PRISMA
has been widely in dierent research disciplines, and has
been cited more than 25,000 times, and endorsed in over
400 journals (Page et al., 2018). In agricultural sciences,
systematic reviews have been a recent change from the
traditional narrative reviews (Koutsos et al., 2019). Kout-
sos et al. (2019) proposed a framework for conducting
systematic review specically for agricultural sciences
by extending the basic steps of the PRISMA Flowchart
(illustrated by Moher et al., 2010). Hence, we use the
framework by Koutsos et al. (2019) to identify specic
studies that use the CE methodology for AES studies
and shortlist the attributes used in such studies.
is paper is organized as follows: in Section 2, we
give a detailed account of our methodology and how
we shortlisted the studies for the review; in Section 3,
we describe our results and then discuss our outcomes
in Section 4. We conclude our study in Section 4, high-
lighting possible future implications of this review.
2. METHODOLOGY
PRISMA is an evidence-based method for report-
ing in systematic reviews and meta-analyses. It has
been published in several journals to encourage its dis-
semination and citation (like BMJ, Plos, Springer, etc.).
In this study, we use the PRISMA owchart and check-
list downloaded from Moher et al. (2010) and apply it to
our study as explained for agricultural science reviews
by Koutsos et al. (2019). Koutsos et al. (2019) tested the
framework on a simple case to assess the methodology’s
ease and ecacy and thus, promote its adoption among
agro-scientists. eir framework included the following
six steps which we also used in this study:
2.1 Scoping
We set the following research questions to achieve
the objective of this review.
RQ1: How many and what are the common contract
attributes used by studies while designing a CE for
eliciting farmers’ preferences for AESs?
RQ2: What are the different typologies that the
attributes can be classied into?
RQ3: How can the lesser-used attributes inuence
farmers’ WTA?
2.2 Planning
We conducted an extensive search to identify the
studies relevant to our RQs. For that, we shortlisted key-
words (and Boolean operators) and selected the digital
database for the search. We tested a range of keywords
before nalizing on the following: ‘choice experiment’,
‘agri-environmental’, ‘contracts’, ‘schemes’, ‘measures.’
We chose two digital databases for our search: Scopus
Database (https://www.scopus.com/) and Web of Science
(WOS) (https://apps.webonowledge.com/).
2.3 Identication
We performed the search (query execution) using
various combinations of keywords. We also decided to
use no additional lters (like year, subject area, docu-
ment type, document language, etc.) for the search. We
executed the query in May 2020. In total, we found 110
documents (from Scopus and WOS).
Scopus search
Search string: choice AND experiment AND agr*-
environmental AND contracts OR schemes OR
measures
Outputs: 56 documents from 2006–2020; included
55 Articles and 1 Conference Paper.
WOS search
Search string: “choice experiment” AND agr*-envi-
ronmental schemes OR “choice experiment” AND
agr*-environmental contracts OR “choice experi-
ment” AND agr*-environmental measures.
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Nidhi Raina et al.
Outputs: 54 documents from the years 2006 – 2020;
included 51 Articles, 2 Reviews, and 1 Conference
Paper.
2.4 Screening
We assessed the quality of the resulting documents
from the search query by rst deleting 40 duplicated
documents, and then conducting initial screening of the
remaining 70 documents by skimming through titles
and abstracts. Out of the 70 studies, we excluded 12 pub-
lications. Some of the common reasons for exclusion
were either the document was completely unrelated to
the search query, or the study did not use the CE meth-
od. E.g., two studies (Bartkowski & Bartke, 2018 and
Dessart et al., 2019) are reviews of other empirical stud-
ies related to AESs, but not related to AESs that use CE
methodology, hence were excluded.
2.5 Eligibility
We applied content-based quality checks of the full
paper for the remaining 58 documents to make sure the
selected studies aligned with our objectives. For that, we
set inclusion/exclusion criteria for eective checks, as
suggested by Khan et al. (2003). e inclusion/exclusion
criteria we applied to the studies were as follows:
a) e study should have used a CE to explore farmers’
willingness to participate in or accept an AES
b) e survey respondents should be specically farm-
ers
c) The study should have recorded AES for public
goods, not private benets
Based on our criteria, 34 studies were nally selected
for review with specic reasons for exclusion, like, study
design, study measures, type of survey respondents or
sample-type, etc., with specic reasons for the exclusion
provided in Appendix 1.
2.6 Presentation
We concluded the review by presenting the evi-
dence, summarizing it, and interpreting it to answer
our research questions. Using the PRISMA owchart
(extracted from Moher et al., 2010), we mapped out the
number of articles identied included or excluded (Fig-
ure 1). We tabulated the study characteristics and choice
attributes and their levels found in each study for data
synthesis to answer the research questions (Appendix
2). Similar attributes were grouped and the frequency
of their occurrence was noted using MS Excel (Appen-
dix 3). We then classied the attributes on basis of dif-
ferent typologies, which are discussed in the following
sections.
3. RESULTS
We derived 177 attributes in total from the 34
reviewed studies (Appendix 3). e duplicated attrib-
utes are collated together, and the resulting 32 unique
attributes are depicted in Table 1. By categorizing simi-
lar attributes, ve main typologies emerge: ‘monetary
attributes,’ that can be used as a means to calculate
potential monetary trade-os among attributes (‘pay-
ment,’ ‘bonus,’ ‘ne,’ etc.); ‘general attributes,’ that out-
line the general preferences of a contract (‘area,’ ‘dura-
tion,’ etc.), ‘exibility attributes,’ that indicate contract
exibilities (over a duration, over an area, over prescrip-
tions, etc.), ‘prescription attributes,’ that include manage-
ment, technical, and policy-related specications across
alternative contracts (‘communal participation,’ ‘risk,’
‘farmer recommendation,’ ‘eco-label,’ ‘monitoring,’ etc.),
and ‘purpose attributes’ that dene the purpose of the
AESs and have a direct eect on farmers’ compliance
costs (either through chemical reductions or through
PRISM A 2009 Flo w D iag ra m
Records identified through Scopus
database searching
(n = 56)
Screening
Included
Eligibility
Identification
Additional records identified
through Web of Science database
(n = 54)
Records after duplicates removed
(n = 70)
Records screened by
titles and abstracts
(n = 70)
Records excluded
(n = 12)
Full-text articles assessed
for eligibility
(n = 58)
Articles excluded (based
on the set criteria)
(n = 24)
Final studies for review
(n = 34)
Studies included in
qualitative analysis
(n = 34)
Figure 1. Prisma Flowchart lled with study results. Source: Moher
et al. (2010).
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other environmental eorts like ‘biodiversity conserva-
tion’). We found 7 monetary attributes, 5 general attrib-
utes, and 5 attributes related to exibilities in contracts.
We also found 11 prescription attributes and 3 purpose
attributes that are specic to the purpose of the AES.
We discuss the attributes below in detail and how they
Table 1. Attributes found in the review.
Attr ibutes Frequency Relation with WTA
MONETARY ATTRIBUTES
1Payments (€/ha/year) or compensation; for animals (€/animal/year) 34 Same as WTA
2Conditional Bonus/Incentive 6 +
3Potential price uctuation 2 -
4Cost ceiling for compensation 1 +
5Gross margin (€/ha/year) or (%) 1 +
6Compost price per trolley (in currency) 1 +
7Fine (in case of infringement) 1 -
GENERAL ATTRIBUTES
8Duration of contract 17 -
9Area enrolled in contract (%) 15 -
10 Availability of technical training/ scheme support/assistance 8 +
11 Average time spent on paperwork/ administration 2 -
FLEXIBILITY ATTRIBUTES
12 Flexibility over adherence to scheme prescriptions 11 +
13 Flexibility over what areas of the farm are entered into the scheme 7 +
14 Flexibility of duration or cancellation of contract 6 +
15 Flexibility to change agricultural practice (fertilizers, pesticides, manure) 6 +
16 Non-participation: exibility to opt-out 2 +
17 Flexibility of dates for working on elds 3 +
PRESCRIPTION ATTRIBUTES
18 Monitoring 9Not signicant
19 Communal participation or compensation 7 +/-
20 Maximum grazing (stocking density) 4 +
21 More labor days for work 2 -
22 Coordination with neighbors 1 +
23 Recommendation 1 +
24 Likelihood of complete crop failure (time in years) 1 -
25 Data provision type 1 +/-
26 Process optimization 1 +
27 Input risk 1 -
28 Conservation Outcome risk 1 -
29 Eco-label 1 +
PURPOSE ATTRIBUTES
30 Allocation of land to some environmental activity(s) 15 +/-
31 Ecological focus areas (%) 5 -
32 Reduction of chemicals (%) 4 -
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Nidhi Raina et al.
impact farmers’ participation and WTA. e lesser-used
attributes have been used in only one study, and we also
discuss their potential for future studies.
3.1 Monetary attributes
Monetary attributes signify those contract features
that are specied in monetary terms. ese include con-
tract payments that promote farmers’ participation and
keep agricultural policy budgets under control (Vil-
lamayor-Tomas et al., 2019), or economic incentives that
motivate farmers’ adherence to the terms of the contract
(like ‘ne’) (Alló et al., 2015). We observed 7 attributes
that could fall under this typology.
3.1.1 Payment
A typical contract dictates farmers modify their
farming practices for per-hectare (annual) payments.
So, every CE includes a monetary cost/benet attribute
called ‘payment’ that allows for evaluating welfare esti-
mates, i.e., willingness to pay (WTP) or willingness to
accept (WTA) compensation, for changes in the levels
of contract attributes (Birol, 2012). e monetary attrib-
ute can not only evaluate the farmer preferences, but it
can also help estimate the public expenditure needed
for each new design of a contract. us, ‘payment’ is
an essential attribute for informing AES policy design
(Espinosa-Goded et al., 2010). We observed that all
reviewed studies used this attribute, and it is generally
depicted in currency per hectare per annum. ough
farmers’ WTA for changes in dierent attribute values
can be calculated using payment as an attribute in CEs,
payment can also cover the combination of opportunity
costs, management costs, monitoring cost, risk premi-
um, and prot margin in an AES (Greiner et al., 2014)
which a respondent needs to be aware of while choosing
AES options.
However, the impact of other attributes aects pay-
ment amounts hugely. e trade-os the farmers are
willing to make in exchange for dierent levels of pay-
ments are interesting to analyze. E.g., Ruto & Gar-
rod (2009) observed that farmers would easily trade-
off approximately 10% of their current payments in
exchange for increased exibility over what lands to
enter in the contract or what measures to enroll in the
AES. Similar results have been noticed in other studies.
Espinosa-Goded et al. (2010) also observed that relaxing
the restriction on grazing areas could increase farmer
participation and decrease the budget of the contract.
Similarly, Santos et al. (2016) noted that technical sup-
port is more important for the farmers than subsidy
amounts. e same study estimated that farmers would
give up around 400€ per hectare per year for increas-
ing the cattle density by one livestock unit per hectare,
reecting the high opportunity costs of extensication
of grazing in Portuguese montados (Santos et al., 2015).
Furthermore, Wainwright et al. (2019) observed a non-
linear relationship between payment values and farm-
ers’ participation, which indicates the high signicance
of other contract attributes. Villanueva et al. (2017) also
observed that farmers required higher compensation for
programs with very high levels of demand and low ex-
ibilities. Pröbstl-Haider et al. (2016) also observed that,
farmers are not ready to sway from intensive cropping
even with higher compensations. us, even though pay-
ment is the only monetary attribute in most of the AES
studies, farmers’ preferences depend on a wider set of
factors than just the monetary factors.
3.1.2 Conditional Bonus/Incentives (one-time only)
A conditional bonus is paid in addition to the annu-
al compensation payments per hectare as an incentive
to farmers to favor higher participation rates and land
enrolment in AESs and achieve higher targets of con-
tract purposes. Kuhfuss et al. (2015) and Roussel et al.
(2019) used the attribute on the condition of additional
chemical reductions per year. Similarly, Villanueva et
al. (2017) oered a xed incentive at the end of the con-
tract period (aer 5 years) on the condition of improve-
ments in the provision of biodiversity and soil function-
ality. is attribute should theoretically positively aect
farmers’ participation; however, it is highly inuenced
by other contract features. Since the bonus is condition-
al, farmers may not agree to stringent conditions of the
contract. Roussel et al. (2019) observed a high preference
of the farmers towards the bonus. In contrast, Kuhfuss
et al. (2015) observed higher initial participation but,
the bonus had no eect on the individual area enrolled
in the scheme. Only if the bonus would be used as in
collective performance, it eciently increased the total
area enrolled, which signies the use of this attribute for
analyzing collective contract types. Some studies also
showed that additional bonus was insignicant for farm-
ers, and they would instead prefer higher exibilities in
contracts than additional payments. E.g., the attribute
‘premium for results’ used by Villanueva et al. (2017)
had no significance on farmers’ participation. Also,
Chang et al. (2017) observed that farmers are reluctant
to reduce fertilizer consumption even when incentivized
with additional payments.
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3.1.3 Other monetary attributes
Pröbstl-Haider et al. (2016) studied the inuence
of the attribute ‘potential price uctuations’ in their
CE since international market prices are of increasing
importance in their study region (March–aya ood-
plains, Vienna). eir study observed that farmers chose
an AES on the basis of price uctuations play rather
than on the value of the environmental premium.
Another attribute that is important for exploring in
future studies is ‘Fine.’ Alló et al. (2015) used it in their
study to analyze the farmer’s moral hazard and free-rid-
ing behavior under an AES. ough the ‘ne’ attribute
is similar to ‘monitoring’; however, unlike monitoring,
it is a monetary attribute, and will expectedly increase
the WTA, but has not been tested suciently in AES
studies.
Other market-based monetary attributes like ‘gross
margin’ and ‘cost ceiling’ have been used in individual
studies as an addition to the payment attribute to test
the eect of additional monetary factors on WTA. ey
also positively impact WTA; however, they are contract-
specic attributes that reect farmers’ prots rather than
the policy design of an AES.
3.2 General attributes
We found 4 general attributes including the basic
contract design elements (such as ‘contract length,’ ‘con-
tract area,’ etc.). Every contract has at least one general
attribute, which denes the basic contract regulations.
Even though theoretically, monetary attributes are of the
highest importance to farmers while choosing an AES,
many studies have observed the general attributes could
sway farmers’ preferences for an AES (Christensen et
al., 2011; Greiner, 2016; Hasler et al., 2019; Lienhoop &
Brouwer, 2015). e basic design elements of a contract
can thus inuence farmers’ WTA signicantly.
3.2.1 Duration of contract
e contract duration is an important attribute to
determine farmers’ WTA. We observed 17 out of select-
ed 34 studies used this attribute in their CEs. In almost
all the studies, the farmers preferred shorter contracts,
except in the study by Franzén et al. (2016), wherein it
was insignificant. Most studies show that increasing
contract duration requires higher compensation by the
farmers. E.g., Ruto & Garrod (2009) observed that farm-
ers demand an increase of 1% of the current payments
for a year’s increase in the contract duration.
3.2.2 Area enrolled in contract (%)
We observed 15 studies used this attribute to test
its impacts on contract design, and 8 out of those
showed that farmers prefer to enroll shorter areas into
the contracts, while 6 showed no role of significance.
Studies have indicated this as a conflict between agri-
cultural intensification and conservation (De Salvo
et al., 2018; Espinosa-Goded et al., 2010; Villamayor-
Tomas et al., 2019; Villanueva et al., 2015a). Farm-
ers also have a high reluctance and strong disutil-
ity for larger conservation areas or larger forest sizes.
E.g., Lienhoop & Brouwer (2015) noted that farmers
do not find large-scale afforestation projects attrac-
tive and demand very high costs for such a contract.
Other studies also proved that farmers are willing to
accept smaller subsidies for smaller areas enrolled
in the contracts. Hasler et al. (2019) observed that
the Danish farmers required an increase of 1% in
their payments for every additional 1% of arable land
enrolled in the contract, thus making this attribute
important for considering payment amounts. Simi-
larly, Villanueva et al. (2015) also reported that only
44% of the farmers surveyed would accept a low-to-
medium increase in compensation amounts for 1% of
the increase in cover crops area, while the rest would
either not enroll more areas or ask for higher com-
pensation amounts. Enrolling larger areas into the
contract increases the probability of adopting more
restrictive measures, so farmers prefer to enroll small-
er areas (Roussel et al., 2019).
3.2.3 Availability of technical training/ scheme support
We found 8 studies that used ‘technical training/
scheme support’ as an attribute for analyzing farm-
ers’ WTA. e majority of the studies observed that
technical support is welcomed by farmers and can
lead to higher participation and lower compensation
payments (Christensen et al., 2011; Espinosa-Goded
et al., 2010; Hasler et al., 2019; Kuhfuss et al., 2015;
Ruto & Garrod, 2009). However, farmers did not con-
sider scheme support important for a conservation
program in some studies (Franzén et al., 2016; Wain-
wright et al., 2019). Furthermore, the attribute is high-
ly preferred when it is provided free of cost (Chris-
tensen et al., 2011; Kuhfuss et al., 2015). Santos et al.
(2016) attributed technical support as the second most
observed factor inuencing farmers’ participation in
future AESs, though it was not included as an attrib-
ute in their CE.
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Nidhi Raina et al.
3.3 Flexibility attributes
e exibility in a contract is one of the key fac-
tors that facilitate its adoption. Flexibility can be in plot
selection, prescription selection, or withdrawal from the
contract (Christensen et al., 2011; Kuhfuss et al., 2015;
Ruto & Garrod, 2009; etc.), which can inuence com-
pensation amounts immensely. Usually, studies have
noted that higher exibilities in contracts can lead to
lower WTA. E.g., Lienhoop & Brouwer (2015) observed
that only a smaller percentage of farmers were inu-
enced by the payment levels of the AESs, as compared to
more farmers preferring to have the option to return to
agriculture aer the contract ends.
3.3.1 Flexibility over adherence to scheme prescriptions
Flexibility in scheme prescription measures or the
choice of choosing management type is another attrib-
ute many studies deem as important for their CEs. It
generally has a positive correlation with farmer partici-
pation. e 11 studies that use this attribute observed
that farmers preferred higher exibility. Latacz-Lohm-
ann & Breustedt (2019) observed that oering exibil-
ity to farmers like allowing organic fertilizer to be used
(compared to no fertilization) reduced the compensa-
tion requirement by 127.40€. Even the studies not using
this attribute have reported farmers’ preferences for
higher exibility in measures and management practices
(Christensen et al., 2011; Espinosa-Goded et al., 2010;
Villanueva et al., 2015b, etc.)
3.3.2 Flexibility over what areas of the farm are entered
into the scheme
e exibility of the area under contract has a pos-
itive signicance in most studies (e.g., Alló et al., 2015;
Christensen et al., 2011; Greiner, 2016; Ruto & Garrod,
2009). ough 7 studies have mainly used this attrib-
ute, other studies have analyzed it through the attrib-
ute ‘area size under contract.’ However, this attribute
is dierent from contract area size enrolled as it allows
the farmers to choose the area size, conservation activ-
ity on that area, and the duration of being enrolled for
that area. us, this attribute is an integration of dier-
ent exibility options which can lead to higher partici-
pation and lower compensation amounts. E.g., Chris-
tensen et al. (2011) observed that an average farmer
could give up 43€/ha/year for flexible buffer zone
width.
3.3.3 Flexibility of duration or cancellation of contract
Many farmers consider the opportunity to terminate
the contract at any time to be an important pre-condi-
tion for participation (as shown in studies by Broch &
Vedel, 2012; Christensen et al., 2011; Hasler et al., 2019,
etc.). Generally, this attribute has a positive correlation
with farmer participation. Farmers prefer this possibly
because canceling the contract at will would allow them
to switch to more intensive farming when market prices
increase (Mariel & Meyerho, 2018). is attribute can
also be used as an incentive for participation (Greiner,
2016; Hasler et al., 2019).
3.3.4 Flexibility to change agricultural practices (fertiliz-
ers, pesticides, manure)
Studies have shown that exibility in contract regu-
lations is more important for farmer participation than
pre-determined changes in agricultural practices. Stud-
ies provide this choice of changing agricultural practices
at will in their CEs to determine the trade-os between
compensation amounts and conservation eorts. E.g.,
Kuhfuss et al., (2015) observed that farmers would not
include their whole vineyard in the contract unless they
have the exibility to use chemicals in some farm areas.
Similarly, Latacz-Lohmann & Breustedt (2019) observed
that allowing organic fertilizers, instead of prohibiting
all fertilization, reduced the compensation amount by
127.40€. Likewise, Villanueva et al. (2017) also observed
that compensation amounts were reduced with increas-
ing levels of insecticidal treatments allowed in the con-
tracts. eir study showed that farmers’ WTA is lowest
for limited treatment and highest for non-treatment,
indicating that farmers are reluctant to give up chemical
treatments altogether. However, only 6 studies used this
attribute; thus, there is a greater scope of experimenting
with dierent conservation options.
3.3.5 Non-participation: exibility to opt-out
ough all the studies (like Broch & Vedel, 2012;
Christensen et al., 2011; Espinosa-Goded et al., 2010;
Kuhfuss et al., 2015; Ruto & Garrod, 2009; etc.) use it
in their choice cards when conducting a CE; however,
only 2 studies used it specically as an attribute for the
CE (Le Coent et al., 2017 and Roussel et al., 2019). Not
including it as an attribute could be because the cod-
ing of variables in the CE testing model with an opt-out
option poses several challenges (Le Coent et al., 2017).
e opt-out option is generally used in CE to give the
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farmers the voluntary choice of choosing an AES. Vil-
lamayor-Tomas et al. (2019) noted that 37% of farmers
chose the opt-out option; however, he suggested explor-
ing further whether this would aect the main ndings.
Roussel et al. (2019) studied the attribute to understand
farmers’ preference to keep their current practices. is
attribute generally positively correlates to farmers’ pref-
erences since it avoids them facing a forced choice.
3.4 Prescription attributes
Most of the attributes in the reviewed studies were
prescription attributes that dened the technical and
management aspects of the contracts. We found 16 such
attributes used in 5 or less than 5 studies; however, most
are uniquely used (only in one study) and are also dis-
cussed under lesser-used attributes in the Discussions
section. Researchers use attributes like ‘monitoring’ are
to check for non-compliance among farmers (9 studies
use this attribute). However, monitoring is costly, and the
balance between non-compliance and monitoring is oen
ignored (Vedel et al., 2015). We observed that the moni-
toring attribute was insignicant in most of the studies
indicating that it plays a minor role in farmers’ choice of
participating in an AES (Greiner, 2016; Rodríguez-Entre-
na et al., 2019; Villanueva et al., 2015b, 2015a; Villanueva
et al., 2017). However, only Broch & Vedel (2012) and
Vedel et al. (2015) observed that monitoring had a sig-
nicantly negative impact on respondents’ utility and led
to increased WTA. e reason for the negative attitude
towards monitoring could be the farmer’s mistrust of the
system or the farmer’s perception of the system control-
ling him (Broch & Vedel, 2012).
‘Communal participation’ or ‘communal schemes’
are also attractive to farmers since they induce a ‘neigh-
bor-eect’ among farmers, leading to increased partici-
pation in the AES. Communal management can have
mixed results on farmers’ WTA. Studies such as Hope
et al. (2008) and Villanueva et al. (2017) reported a
positive correlation to farmers’ preferences. Hope et al.
(2008) reported that farmers prefer working as a group
rather than as individuals. Villanueva et al. (2017)
reported that older farmers (> 60 years) show a higher
willingness for collective participation than younger
farmers in olive groves of plain areas. Even though only
7 studies have used this attribute, many other studies
mention similar factors that indicate that farmers’ have
high utility for community participation and manage-
ment. E.g., Aslam et al. (2017) observed that social pres-
sure and social networks could increase farmers’ accept-
ance for contracts. Similarly, Alló et al. (2015) tested
the variable ‘social trust’ to evaluate whether farm-
ers believe their neighbors fully comply with the con-
tract terms, and observed that majority of respondents
think that their neighbors will comply. is compliance
indicates that the attribute should be tested in CEs for
collective contract types. However, some studies also
observed that farmers prefer individual management
and discrete compensation, like Rodríguez-Entrena et
al. (2019) noted that collective participation leads to a
higher degree of uncertainty among the farmers. Simi-
larly, Villanueva et al. (2015a) suggested that most farm-
ers showed medium to high WTA for collective partici-
pation because they anticipated loss of freedom of their
farm management due to community participation.
Other attributes that span under the umbrella of
‘neighbor-eect’ include ‘coordination with neighbors’
and ‘recommendation.’ Neighbor-eect generally posi-
tively correlates with farmers’ participation. Villanueva,
et al. (2017) noted that farmers are more willing to par-
ticipate at lower transaction costs if the neighbors also
participate. Also, the attribute ‘farmers’ recommenda-
tion’ used by Villamayor-Tomas et al. (2019) exhibited
a positive signicance to farmers’ acceptance, whereas
the attribute ‘scientist recommendation’ had no signi-
cant impact. e attribute is also similar to ‘communal
participation’, with the only dierence being that this
tests the farmer’s preferences to his immediate neigh-
bor’s preferences, whereas the latter is on the level of the
whole community. De Salvo et al. (2018) suggested that
neighbor-eect can improve acceptability of AESs and
achieve cost-eectiveness of contracts, and hence farm-
ers’ preferences for ‘local context’ should be considered
by policymakers.
‘Grazing intensity’ or ‘Stocking density’ is another
attribute that 4 studies included for testing farmers’ pref-
erences for a reduction in grazing intensity or the num-
ber of animals per hectare. e studies (Breustedt et al.,
2013b; Latacz-Lohmann & Breustedt, 2019; Santos et al.,
2015) show that stricter prescriptions for lesser grazing
lead to higher compensations, thus higher WTA.
Non-monetary incentives have also been overlooked
by all the studies except one. Chang et al. (2017) used
the ‘eco-label’ attribute to incentivize farmers who suc-
cessfully complied with the AES standards and observed
that farmers would readily exchange an eco-label for
lower compensation amounts. So, including non-mon-
etary incentives like eco-label can also help lower the
farmers’ WTA.
‘Risk’ is another attribute that has been used in the
study by Star et al. (2019) that explored how input or
outcome risk limits the farmers’ willingness to imple-
ment environmental measures. eir study reported that
higher levels of either risk would reduce participation
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Nidhi Raina et al.
and increase the compensation amount. is attribute
should be extensively tested especially in the face of cli-
mate and socio-economic uncertainties.
3.5 Purpose attributes
These attributes are different from the contract
design attributes as they specically iterate the purpose
for which the farmer will accept the contract prescrip-
tions. e purpose of an AES could be a conservation
activity, aorestation, land allocation for environmental
activity, chemical reduction, etc., which is what these
attributes oer.
3.5.1 Allocation of land to some environmental activity(s)
Attributes like ‘maintenance of soil organic mat-
ter,’ ‘protection of soil from water erosion,’ ‘recreational
access,’ ‘biodiversity improvements,’ ‘forest co-benets,’
‘aorestation,’ etc. are dierent types of environmen-
tal and conservation activities that dene the contract
motives. Some studies like Broch & Vedel (2012) used
the attribute ‘purpose’ specically to combine dierent
conservation activities into one choice for their CE (bio-
diversity, water protection, or recreation). Le Coent et al.
(2017) also used ‘purpose’ as a separate attribute to high-
light farmers’ preferences between dierent conservation
activities. One of the signicant inferences from testing
this attribute has been that most farmers prefer conser-
vation over compensation (according to the studies by
Le Coent et al., 2017; Lienhoop & Brouwer, 2015; Santos
et al., 2015; Vedel et al., 2015). Greiner (2016) also used
this attribute to understand the signicance of dierent
conservation activities; however, he observed that 33% of
farmers found the choice insignicant, rather focused on
payment values and contract duration.
3.5.2 Ecological focus area
ough 5 out of 34 reviewed studies used this attrib-
ute in their CE; however, 4 of these studies use the same
set of choice attributes in their CE (Rodríguez-Entrena
et al., 2019; Villanueva et al., 2015b, 2015a; Villanueva
et al., 2017). According to Villanueva et al. (2017, p6),
this attribute was included in the CE to “explore a hypo-
thetical future implementation of the EFA requisite of
the Common Agricultural Policy (CAP) ‘green payment’
in permanent crops such as olive groves”. Some previous
studies have also mentioned EFA in their articles but do
not test it in their CEs; like Breustedt et al. (2013) and
Villamayor-Tomas et al. (2019).
Overall, studies using this attribute observed that
farmers have a negative preference for EFAs, since agree-
ing to it would mean dedicating additional areas to eco-
logical functions than stated in the contract. A similar
attribute called ‘Naturalization’ used by Rocchi et al.
(2017) denes conversion of agricultural areas to pas-
tures, using particular species with a high natural value.
However, farmers in their study show the least interest
in this attribute.
3.5.3 Reduction of chemicals (%)
is attribute is typically used to study the com-
pensation payments that would be required for a higher
reduction in chemicals. Kuhfuss et al. (2015) found that
higher chemical reduction can lead farmers to enroll
more farm areas in the AES because chemical reduc-
tion needs higher investment in equipment that becomes
more cost-ecient if used on the whole farm rather than
just small areas. Chang et al. (2017) observed that aer
a point, farmers show high reluctance to further reduc-
tions of chemical fertilizer use even when additional
payments are oered. Similarly, Kanchanaroek & Aslam
(2018) also observed that shorter contract lengths and a
lower reduction in chemical input together lowered the
WTA substantially. 3 out of 4 studies using this attrib-
ute reported that chemical reduction negatively impacts
farmers’ participation and increases their WTA. Only
Rocchi et al. (2017) observed that most of their respond-
ents are interested in reducing nitrates. Chang et al.
(2017) suggested that farmers should be incentivized if
they agree to an additional reduction of chemical ferti-
lizers.
4. DISCUSSIONS
Our study used a systematic review for a reliable and
transparent method of reviewing previous AES literature
that uses CE to elicit farmers’ preferences to alterna-
tive AESs. We set three specic research questions that
this review hoped to answer and discuss. We listed out
32 attributes used by studies as shown in Appendix 3
and dened and analyzed in the Results section, which
answers our rst research question. e most common
attribute used by all studies is the payment attribute that
can help estimate the monetary value of other attributes.
However, AES studies aim to nd incentives other than
monetary payments for estimating farmers’ WTA (Vil-
lamayor-Tomas et al., 2019). e contract purpose is pre-
sent in all studies, which could include either ‘allocation
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of land to environmental activity,’ or ‘chemical reduc-
tions,’ or ‘changing the land to an ecological focus area.’
e purpose of the contract helps in deducting the con-
servation versus compensation behavior of the farmers.
e general contract design is shaped by attributes such
as ‘duration of contract,’ ‘area enrolled under contract,’
and ‘availability of scheme support/additional training,’
which are usually the rst few attributes in the choice
cards of AES studies. Attributes indicating exibility
in overall contract terms and environmental goals have
shown to increase farmers’ acceptance and participa-
tion (like Christensen et al., 2011; Espinosa-Goded et al.,
2010; Ruto & Garrod, 2009; etc.); thus, most studies also
include a exibility attribute. e management, techni-
cal, and policy prescriptions can also be tested for an
eective policy design through a CE. ese can include
attributes such as ‘collective participation,’ ‘monitoring,’
‘farmer recommendation,’ etc. ey can also be a novel
attribute that has not been tested before, like ‘risk.’
Upon surveying the common attributes, ve main
typologies were established under which all the extract-
ed attributes could be classied, which answers our sec-
ond research question. At least one attribute under each
typology must be used in the AES study for an eective
outcome and to remove subjectivity bias among research-
ers designing CE. Our classication includes monetary
attributes, general attributes, exibility attributes, pre-
scription attributes, and purpose attributes, which have
been discussed in detail in the Results section.
Economic factors of farmers’ WTA has been well-
understood and widely discussed by many studies (like
Christensen et al., 2011; Lastra-Bravo et al., 2015; San-
tos et al., 2016, etc.) which can include the level of com-
pensation, transaction costs, duration, and exibility of
contracts, availability of scheme support, etc. However,
equally important cognitive, behavioral, and societal
factors have not been discussed enough in AES studies.
Farmers’ attitudes and values, perceptions about conser-
vation and compensation, and social norms like collec-
tive participation can inuence farmer participation in
an AES (Kuhfuss et al., 2015; Villamayor-Tomas et al.,
2019). Many studies have also inferred upon the farm-
ers’ dilemma between compensation and conservation.
Le Coent et al. (2017) conducted their CE with two types
of contracts: compensation and conservation contracts.
ey reported that farmers preferred to participate in a
contract with a biodiversity conservation objective than
with a biodiversity compensation objective and exhibit
higher WTA for enrolling into the compensation con-
tract. On the contrary, a study by Villamayor-Tomas et
al. (2019) showed conservation programs tend to harm
farmers’ utility and were not preferred by the farm-
ers. Studies have also noted that when the conservation
options restrict the land-use options for the farmers,
their WTA for conservation measures increases (Aslam
et al., 2017; Hope et al., 2008; Pröbstl-Haider et al.,
2016). is disparity between what has been tested and
what can be tested prompted us to discuss the lesser-
used attributes.
Our review also found 12 uniquely used attributes,
and we explored their utility for further studies, as per
our research question 3. Most of these attributes are pre-
scription attributes, that are specic to the contract area
and type and might not be replicable over other AESs.
However, some of the attributes can be studied over dif-
ferent contract types and must be explored more. One
such novel monetary attribute is ‘ne’ used by Alló et al.
(2015), which could be applied for any law infringement.
Even though other studies have also tried to test compli-
ance through economic incentives (Kuhfuss et al., 2015)
or monitoring (Broch & Vedel, 2012); however, ne is the
only attribute that enforces an economic penalty on non-
compliance to the contract, and thus should be tested in
more studies. Attributes such as ‘coordination’ and ‘rec-
ommendation’ are prescription attributes that play on
social psychology and behavioral economics to positively
inuence the choice of farmers to participate in an AES if
there is already a high level of participation (Kuhfuss et
al., 2015). is indicates that farmers care not only about
the economic incentives of the contracts but also of their
“reputation” (Villamayor-Tomas et al., 2019), which can
be tested through attributes exhibiting neighbor-eect.
‘Risk’ is another prescription attribute that has only been
used in one study (in Star et al., 2019). ough many
other AES studies talk about farmers’ perceptions of risk
and uncertainty as core reasons for non-participation
(e.g., Hellerstein et al., 2015; Schilizzi & Latacz-Lohmann,
2016; Whitten et al., 2013; etc.); however it has not been
studied in their CEs. ough Star et al. (2019) studied
the input and output risks endured by landholders, their
study did not consider institutional, production, or mar-
ket risks that are also critical in designing ecient agri-
environmental policies. Another interesting attribute is
an ‘eco-label’ that has been tested in one study (by Chang
et al., 2017) that farmers appreciated more than higher
compensation amounts. However, such non-monetary
incentives are not usually tested in EU studies, but with
the rise in local certication schemes, more AESs could
have such attributes.
Another variable of interest that hasn’t been tested
in any study but has shown to lower farmers’ WTA and
increase participation (Breustedt et al., 2013; Latacz-
Lohmann & Breustedt, 2019) which is ‘farmers’ previ-
ous participation in an AES contract.’ However, Wain-
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Nidhi Raina et al.
wright et al. (2019) noted that farmers already enrolled
in an AES scheme were more likely not to select a con-
tract option. us, this is a possible attribute that can be
explored through future CE studies.
is study can thus be used as a reference for other
AES studies that use literature review for selecting the
attributes for their CE from various categories. It also
provides a systematic framework for organizing literature
that can be applied to newer AES studies. is study can
also help shortlist attributes for future CE testing that
can evaluate specic aims of CAP post-2020 like penal-
ties to non-compliance (like fine) and alternatives to
greening through certication schemes (like eco-label).
5. CONCLUSIONS
Our study aimed to highlight the common attrib-
utes that are used in a CE for studying farmers’ accept-
ance of choice of agri-environmental contracts using a
systematic review of literature while also categorizing
the attributes into denitive typologies and glancing at
the utility of lesser-used attributes. In conclusion, we
found 32 attributes that could t in ve distinct typolo-
gies: 7 monetary attributes, 4 general attributes, 6 ex-
ibility attributes, 12 prescription attributes, and 3 pur-
pose attributes. Contract design attributes can impact
compensation amounts hugely; e.g., general contract
attributes (like smaller area and shorter duration) and
exibility attributes (such as higher exibility of par-
ticipation, contract area, contract duration, manage-
ment, etc.) are highly preferred by the farmers and
can lower their WTA and increase their participation.
Technical support and scheme assistance are also posi-
tively welcomed by the farmers. Overall, the commonly
used attributes are an indicator of those contract fea-
tures that previous studies have tested repeatedly with
CEs, and have shown consistent outcomes, e.g., shorter
contract duration and the lesser enrolled area is pre-
ferred by farmers in most of the studies. However, some
attributes also show varied results, e.g., monitoring has
been insignificant for farmer acceptance in most of
the studies and was found to be negatively related to
farmer acceptance in two studies. Moreover, attributes
that can directly address some of the emerging issues
in EU’s CAP reform features, such as result-based con-
tracts (e.g., ‘conditional monetary bonus’ attribute used
by Roussel et al., 2019) and collective contracts (like
‘collective participation’ and ‘communal management’
attribute used by Villanueva et al., 2017) have not been
tested in many studies. ey can be comprehensively
analyzed in future AES studies.
We also found attributes that have theoretically been
shown to be critical for AES selection but have been over-
looked by most of the studies. ese are non-monetary
incentives, ne, recommendation, risk, coordination with
neighbors, etc. e reasons for this exclusion could prob-
ably be a lack of literature to support their importance, or
maybe these attributes require exhaustive coding in mod-
els. Market-based and value-chain attributes such as crop
failure, price uctuations, climate risks, etc. have also not
been explored much which can become important under
uncertain future scenarios (like climate change, socio-
economic change, etc.). us, the lesser-used attributes are
also an important indicator of farmers’ acceptance of a
contract and should be studied intensively.
Our review indicates that CEs should take more
advantage of the virtual environment they are set to test
and should experiment on a broader range of attributes
across dierent areas and contract types. We hope that
our systematic review can be used as a repository for
choosing choice attributes for future studies and our
typologies can be used to make a choice bundle that can
fully explain both the farmer perceptions and value of a
particular landscape.
FUNDING
This work was supported by Research Executive
Agency (REA), under the powers delegated by the Euro-
pean Commission, under the European Union’s Horizon
2020 Research and Innovation Program through the
project CONSOLE, Grant Agreement No. 817949. is
work does not necessarily reect the view of the EU and
in no way anticipates the Commission’s future policy.
ACKNOWLEDGEMENTS
We thank the anonymous reviewers for their
insightful comments and suggestions that signicantly
improved the manuscript.
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La réduction de l’usage de produits phytosanitaires est devenu un défi majeur en France et dans la plupart des pays développés. L’essor récent du Bio en France (+20% de ventes en 2016, BioBaromètre 2017) montre une réelle prise de conscience du grand public et de nouvelles exigences des consommateurs. Plusieurs pratiques permettant de réduire le recours aux produits phytosanitaires tout en maintenant les profits semblent avoir émergé dans des réseaux pilotes (Lerchenet et al 2017) mais demeurent peu appliquées par la majorité des agriculteurs. Les politiques publiques mises en place jusqu’à présent ont abouti à des résultats décevants et l’utilisation de produits phytosanitaires continue d’augmenter (hausse d’environ 12% en France entre 2009 et 2014). Notre étude s’interroge sur les principaux freins et principales motivations des agriculteurs français à réduire leur utilisation de pesticides. Elle repose sur la méthode d'expérimentation par les choix (Discrete Choice Experiment) et permet de mesurer le poids relatif de différents facteurs de décision dans les choix de pratiques des agriculteurs. Elle permet également d’estimer des consentements et payer / à recevoir pour les éléments non monétaires associés à ces choix de pratiques agricoles. Nous montrons notamment que le rôle du risque-récolte est prépondérant dans la réticence des agriculteurs à réduire leur usage de produits phytosanitaires.
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The revised edition of the Handbook offers the only guide on how to conduct, report and maintain a Cochrane Review ? The second edition of The Cochrane Handbook for Systematic Reviews of Interventions contains essential guidance for preparing and maintaining Cochrane Reviews of the effects of health interventions. Designed to be an accessible resource, the Handbook will also be of interest to anyone undertaking systematic reviews of interventions outside Cochrane, and many of the principles and methods presented are appropriate for systematic reviews addressing research questions other than effects of interventions. This fully updated edition contains extensive new material on systematic review methods addressing a wide-range of topics including network meta-analysis, equity, complex interventions, narrative synthesis, and automation. Also new to this edition, integrated throughout the Handbook, is the set of standards Cochrane expects its reviews to meet. Written for review authors, editors, trainers and others with an interest in Cochrane Reviews, the second edition of The Cochrane Handbook for Systematic Reviews of Interventions continues to offer an invaluable resource for understanding the role of systematic reviews, critically appraising health research studies and conducting reviews.