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Conservation contracts for supplying Farm Animal Genetic
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Resources (FAnGR) conservation services in Romania
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1,2WARWICK WAINWRIGHT, 2KLAUS GLENK, 2FAICAL AKAICHI AND 3DOMINIC
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MORAN
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1 Grant Institute, School of Geosciences, University of Edinburgh, Kings Buildings, West Mains
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Road, Edinburgh, EH9 3JW
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2 Land Economy, Environment and Society Group, SRUC, Kings Buildings, West Mains Road,
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Edinburgh, EH9 3JG
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3 Global Academy of Agriculture and Food Security, The Royal (Dick) School of Veterinary
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Studies, The Roslin Institute Easter Bush Campus, Midlothian, EH25 9RG
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Correspondence: Warwick Wainwright, Land Economy, Environment and Society Group, SRUC,
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Kings Buildings, West Mains Road, Edinburgh EH9 3JG
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Email: warwick.wainwright@sruc.ac.uk
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Keywords: conservation contracts; choice experiment; farm animal genetic resources; agri-
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environmental schemes
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Abstract
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This paper describes a choice experiment (CE) administered to explore farmer preferences for
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conservation agreements to conserve rare breeds among a sample of 174 respondents in Transylvania
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(Romania). The study site was chosen due to the prevalence of small-scale and extensive farm systems
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threatened by a changing policy environment that is increasing the scale and intensity of production
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units. Agreement attributes included length of conservation contract (5 or 10 years); scheme structure
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(community or individual managed conservation programme), and scheme support (application
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assistance or farm advisory support). A monetary attribute that reflects compensation for scheme
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participation allows the assessment of farmers’ willingness to accept (WTA) for different contracts.
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Results suggest 89% of respondents would be willing to farm with rare breeds; cattle and sheep being
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the most popular livestock option; 40% of farmers were reportedly farming with endangered breeds.
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However, only 8% were likely to qualify for funding support under current requirements. WTA
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estimates reveal minimum annual compensation values of €167 and € 7 per year respectively, for bovine
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and ovine farmers to consider enrolling in a contract. These values are comparable to Romanian Rural
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Development Programme (RDP) support offered to farmers keeping rare breeds of € 200 and € 10 per
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year for bovine and ovine farmers respectively. Our estimates of scheme uptake, calculated with
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coefficient values derived from the CE, suggest rare breed conservation contracts are considered
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attractive by Romanian farmers. Analysis suggests meeting farmer preferences for non-monetary
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contractual factors will increase participation.
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3
1 Introduction
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Farm Animal Genetic Resources (FAnGR) diversity underpins resilient agricultural systems and
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need to be part of any sustainable intensification (SI) strategy to meet rising demand for livestock
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products (Eisler et al., 2014). However, concentration on elite breeding lines has reduced genetic
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variation in many commercial breeds whilst marginalising traditional breeds whose value is often
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poorly understood (Ahtiainen and Pouta, 2011; FAO, 2015).
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SI strategies should include investments to maintain genetic variation across a range of breeds
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(including rare breeds) to ensure adaptive capacity in livestock systems. This is particularly important
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when considering profound demographic and environmental changes facing the agri-food sector
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including population growth, land scarcity and climate change (FAO, 2017). Equally important, but less
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often articulated in decision making, are the cultural and heritage attributes embodied in rare breeds
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(Gandini and Villa, 2003; Zander et al., 2013). Markets often fail to reflect these values, which can be
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substantial but difficult to measure. Breed genetic diversity is therefore undersupplied by markets and
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there is a need to explore policy interventions to counter market failure.
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While contractual schemes for rare breed conservation are present in Europe, many are often poorly
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targeted (Kompan et al., 2014; Bojkovski et al., 2015). Targeting incentives towards small-holder and
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extensive farm systems may improve scheme efficiency and uptake, given their lower opportunity cost
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of conservation (Naidoo et al., 2006). This paper explores rare breed conservation contracts in
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Transylvania (Romania), where the average farm size is only 3.4 ha and the economic efficiency per
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farm (as measured by standard monetary output of agri-products per holding) is significantly lower
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than the European Union (EU) average (Popescu et al., 2016).
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Traditional farm systems in Transylvania are under pressure from development of more intensive
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farm systems that are changing the scale and nature of practices (Sutcliffe et al., 2013, 2015). A focus
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on improved efficiency is at the expense of the supply of public goods, including breed diversity. Some
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42% of livestock breeds in Romania are classified as ‘at-risk’, according to the United Nations Food
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and Agricultural Organisation (FAO) definition of an ‘at-risk’ breed (Draganescu, 2003). This figure
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may be an underestimate since population estimates for many Romanian breeds are unknown (FAO,
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2018). There is therefore a need to develop targeted policy responses that aid conservation by balancing
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an intensification agenda with incentives for the supply of other non-market goods and services.
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Farm scale drivers of diversity loss are often assumed to relate solely to the lower productivity of
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traditional livestock breeds (Cicia et al., 2003). While income forgone is a key factor to establish the
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cost of incentive-based schemes, other factors also motivate farm business decisions, and may be
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particularly relevant in a semi-subsistence farming context. Such non-financial motives may include
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tradition, community relations, professional pride and independence (Gasson, 1973; Ilbery, 1983;
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Burton et al., 2008). It is therefore necessary to identify how such attributes might influence the design
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of conservation programmes and farmer willingness to supply diversity. Other potential technical and
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institutional barriers-to-entry (i.e. requirements for breed genealogical records) also warrant exploration
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in this context.
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We used a choice experiment (CE) survey to elicit farmer preferences for supplying (rare breed)
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conservation under alternative contracts forms. CEs are a stated preference technique where individual
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preferences for attributes of a good or service are elicited using surveys that mimic hypothetical
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scenarios – in this case conservation contracts (Louviere et al., 2000). The paper adds to the literature
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on farmers’ willingness to participate in incentive-based schemes (Ducos et al., 2009; Ruto and Garrod,
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2009; Broch and Vedel, 2010; EspinosaGoded et al., 2010; Greiner, 2015; Lienhoop and Brouwer,
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2015) but focuses on the neglected issue of the cost of conserving FAnGR in small-holder and extensive
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farm systems. The paper aims to investigate farmer preferences for rare breed conservation contracts,
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including the minimum compensation required for enrolment in a conservation scheme. We explore
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whether some of the heterogeneity associated with contractual choices is systematically associated with
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farm or farmer characteristics.
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The paper is structured as follows. Section 2 presents background to the CE design and case study
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site. Section 3 reports the analysis of choice data. Section 4 provides discussion of the design of rare
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breed conservation programmes, and Section 5 provides conclusions.
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2 Methods
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2.1 Case study: Romania
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As an EU member state, Romania’s agricultural policy is structured and supported in an agreed
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Rural Development Programme (RDP 2014-2020), which includes a support measure (M10.2, art 28)
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for rearing endangered livestock breeds under EU Regulation 1305/2013 (MARD, 2014). Uptake for
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this RDP option is anticipated to be low due to farmer difficulties in meeting EU standards to qualify
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for subsidy payments (Page, 2015, personal communication). Data on uptake rates are not yet available,
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but previous work has found that 70% of Romanian farmers experienced difficulties meeting EU
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environmental standards for payments under the Common Agricultural Policy (CAP) (Fischer et al.,
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2012). It is therefore important to explore whether such barriers persist for farmers in small-scale and
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extensive systems, as this could reduce participation. Equally important is to measure whether voluntary
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agri-environmental stewardship (AES) measures, specifically M10.2, match farmer preferences and
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expectations for scheme design and rewards.
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Much of the study site (Figure 1) is situated in the foothills of the Carpathian Mountains and features
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an undulating topography with low nutritional pastures (Mikulcak et al., 2013). Part of the area (Tarnava
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Mare) is classified as high nature value (HNV) farmland. Traditional agricultural practices are common
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in this area, as is the presence of many small scale and semi-subsistence farms (Page et al., 2011).
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Mechanised systems are the mainstay for medium to large farms, though are much less common. The
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site is characterised by high levels of rural poverty, with average household incomes below the national
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average (Gherghinescu, 2008).
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We surveyed livestock keepers across 5 counties (Sibiu, Brasov, Mures, Cluj and Alba). The
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sampling frame was based on local farmer information held by village mayors, with further random
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sampling of farms. The survey was administered from June to August (2015).
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Figure 1: Land cover map of the survey area with inset map of Romania. Sampling locations are shown
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by yellow stars.
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2.2 Questionnaire design and administration
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The survey consisted of four sections. The first asked about the farm business including livestock
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species and breeds, farm size, and traits farmers deem most important when considering choice of breed.
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In the second, respondents were asked if they receive AES payments and whether they were aware of
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financial support for rare breeds and ever considered applying for this support. The third part of the
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questionnaire included the CE. Two CE versions were created - one for ovines and one for bovines.
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Farmers answered either one or both depending on whether they were keeping ovines, bovines, or both.
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After the CE tasks were completed, respondents were asked to state their motivations for their choices
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in the CE, and this information was used to identify genuine choices from protest bids; the latter
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subsequently being removed from the analysis. Respondents were also asked about their preference
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concerning scheme remittance (i.e. individual or community payment). The fourth section collected
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socio-economic information including respondent age, gender, educational attainment and household
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income.
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2.3 Choice experiment design
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In CEs, respondents are asked to repeatedly choose from a number of options that differ in their
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attributes or characteristics following an experimental design. The CE elicited individual preferences
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using hypothetical contract choice sets requiring farmers to upkeep rare breeds from a list of breeds
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proposed by the Romanian Government for support under the 2014-2020 RDP measure (see Appendix
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2 for list of eligible breeds). Farmers were advised that the breeding of animals must be pedigree to
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qualify for further subsides on offspring (i.e. non-random mating). Each choice task consisted of two
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alternative contracts and a ‘none’ option to embody the voluntary nature of the conservation scheme.
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Attributes and their levels used to describe the conservation contract were determined in a multi-stage
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process involving literature review, expert consultations and pilot testing.
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Each contract option consisted of four attributes (Table 1). The first three attributes described
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contract length (CL); scheme support (SS); and structure of scheme (SOS). Choice of attributes drew
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on empirical work suggesting their importance in AES scheme design (Ruto and Garrod, 2009;
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Christensen et al., 2011; Greiner, 2015). A final monetary attribute (COS) represented an annual
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payment to farmers (per animal) and took four different levels. The monetary attribute in local currency
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(Lei per year) was based on a percentage (10%, 30%, 60% and 100%) of the proposed monetary reward
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outlined in the RDP; the premise being that some farmers may be willing to accept (WTA) a lower
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reward, depending on contract design. The choice tasks were differentiated based on the livestock
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species. For bovine (cattle, horses and buffalo) and ovine farmers (sheep and goats) the choice tasks
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were similar except for the value of the monetary attribute, which reflected the relative support normally
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given to different species under current RDP conditions.
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Table 1: Attributes and attribute levels used in the CE including relevant coding and a prioir
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expectations
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Contract
attributes
No. of
levels
Coding
Attribute levels
Expected
sign
Contract duration
2
Effects
- 5 years
+ 10 years
-
Scheme support
2
Effects
- Basic assistance to complete the scheme
application form
+ Additional advisory support throughout the
scheme (e.g. additional training for animal
breeding)
+
Structure of scheme
2
Effects
- Individually managed conservation scheme
+ Community managed conservation scheme
-
Subsidy
4
Discrete
- Bovines = 90; 270; 530; 890 Lei / year
+
Discrete
- Ovines = 5; 15; 25; 45 Lei / year
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Choice set design was optimised according to prior information on the distribution of random
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parameters to improve statistical efficiency - i.e. reduction in sample size needed to achieve statistical
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significance (Crabbe and Vandebroek, 2011). Prior information concerning the parameter coefficients
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was estimated from results of the pilot data that was collected in situ to ensure the attributes were
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relevant to participants. A D-efficient experimental design optimised for the random parameter logit
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(RPL) model was formulated using NGene (Metrics, 2012). The final CE comprised 16 choice sets
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which were blocked into 4 blocks of four choice tasks each in a bid to reduce the cognitive burden for
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respondents (Hensher, 2006). Figure 2 shows a typical choice task presented to respondents.
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Option A
Option B
No contract
Contract Length
5 years
10 years
--
Scheme support
Basic application
assistance only
Additional advisory
support (e.g. extra
training)
--
Structure of
conservation scheme
Community managed
conservation
programme
Individually managed
conservation
programme
--
Subsidy
(per animal / per year)
Lei 90
Lei 270
Lei 0
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I prefer: Option A Option B Nothing
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❑ ❑ ❑
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Figure 2: A typical choice task shown to respondents
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2.4 Econometric specification of choice models
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Respondent choices were modelled with reference to Lancaster's theory of value (Lancaster, 1966)
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and Random Utility Theory (McFadden, 1973; Luce, 2005). For a general description see (Holmes et
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al., 2017). The multinomial logit (MNL) model (McFadden, 1973) was used in the first iteration of this
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analysis. This assumes the random component of the utility of the alternatives is independent and
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identically distributed (i.i.d.). A key limitation of the MNL is that preferences for attributes of different
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alternatives are assumed to be homogenous across individuals. Subsequently, the RPL model was
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employed in the second iteration because the approach is more advanced and takes into account
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heterogeneity of the parameter values among respondents. The RPL relaxes key assumptions that
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constrain the use of conditional logit models, namely independence of irrelevant alternatives - iia
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(Hensher et al., 2005). Under a RPL specification, the utility a respondent i derives from an alternative
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j in each choice situation t is given by:
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( 1)
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Where Uijt is a utility maximising individual, Xijt is a vector of observed attributes associated with
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each contract option (i.e. contract length, scheme support, structure of scheme and price) plus the socio-
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economic characteristics of respondents, and εijt is the random component of the utility that is assumed
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to have an iid value distribution. Conditional on the individual specific parameters βi and error
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components εi the probability that individual i chooses alternative j in a particular choice task n is
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represented as:
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( 2)
Note, choices for bovine and ovine farmers were modelled separately to explore preference
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heterogeneity between both groups. The empirical model was estimated using the econometric software
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NLOGIT 5.0. For a full description of the model specification, see Appendix 3.
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3 Results
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3.1 Respondent characteristics
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A total 174 respondents were surveyed - 116 were bovine farmers and 81 were ovine farmers (note
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45 respondents kept both ovines and bovines). The means and standard deviation of multiple individual
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specific variables is outlined in Table 2. There were later used as interaction terms in the choice model
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to determine significant covariates that help to explain respondent choice. The mean age of participants
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was from 40-49 years, with highest education levels of either secondary school or college. Fewer female
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respondents featured in our sample as more males are generally employed in agriculture (European
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Commission, 2012). Average monthly household income was reported to be in the range of €181 to
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€362; lower than the national average but anticipated at the sample site (Page et al., 2011). The primary
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income for most farmers was EU subsides, while sale of milk and meat products were generally
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secondary and tertiary sources, respectively. Some 40% of farmers claimed to be farming with a rare
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breed from a list of ‘at risk’ breeds, while 32% were enrolled in AES measures. Only 21% of respondents
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were aware of RDP support for rare breeds whilst only 8% actually met the EU’s criteria to qualify for
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payments.
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Table 2: Summary of individual specific variables (with means) and relevant interpretation
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Variable
Interpretation
Mean
Std. Dev
National mean
Gender
1, if male, 0 otherwise
0.83
0.91
49% malea
Age
Categorical (1=<20, 2=20-29, 3=30-39, 4=40-49, 5=50-
59, 6=60-69, 7=over 70 years)
4.23
1.44
55.7% (25-64
years)a
EDU
Categorical (1=secondary, 2=college, 3=degree &
professional)
1.58
0.61
85.6% (secondary
or college)a
Income
Categorical (1=<€45, 2=€45-€90, 3=€91-€181, 4=€181-
€362, 5=€362-€678, 6=>€679)
3.8
1.45
€ 566b
Size
Categorical (1=1-2 ha, 2=3-6 ha, 3=7-20 ha, 4=>20 ha)
2.59
1.05
3.6 hac
FRB
1, if farming with rare breeds, 0 otherwise
0.4
0.49
-
CON
1, if farmer would consider farming with rare breed in the
future, 0 otherwise
0.89
0.32
-
AES
1, if farmer is currently enrolled in an agri-environment
scheme (AES), 0 otherwise
0.32
0.47
-
RDP
1, if farmer aware of RDP support for rare breeds, 0
otherwise
0.21
0.41
-
BEN
Categorical (1=if farmer prefers 100% individual cash
benefits from a conservation programme, 2=50% cash
benefit, 50% community in-kind benefit, 3=100%
community in-kind benefit)
1.39
0.71
-
REG
1, if farmer is registering livestock in a genealogic
register, 0 otherwise
0.08
0.27
-
Yield
1, if farmer is keeping cross breeds for yield
improvement, 0 otherwise
0.47
0.5
-
References: a(National Institute of Statistics, 2013) b(National Institute of Statistics, 2015) c(Popescu et al., 2016)
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3.2 Farm characteristics
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To determine how intensification may threaten traditional farming systems and breed diversity,
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respondents were asked to detail how their farming practices have changed over the preceding 10 years
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(Figure 3). Increases to dairy cattle herd size were reported by 52% of respondents. Of the 20% of our
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sample that reported manual hay cutting, 74% reported this to be either stable or increasing; a clear
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response to EU incentives that reward small-holders for the activity. Mechanical hay cutting was
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reported to be increasing (67% of respondents) and some 54% of farmers also stated their sheep herd
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size was increasing.
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Figure 3: Reported change in farming practices over the last 10 years from respondents.
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To investigate whether willingness to participate in a (rare breed) conservation programme was
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linked to preferences for farm animal species, respondents were asked both livestock species kept and
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their interest in joining a conservation scheme. Pigs were the most frequently kept farm animal followed
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by cattle and sheep (Table 3). The highest number of breeds reported was for pigs, while buffalo had
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the least. The prevalence of breed diversity varied across species. For instance, the main breed kept for
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each farm species ranged from 83% (Romanian Buffalo) to 37% (Large White pig). Across the sample,
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89% of farmers registered interest in joining a rare breed conservation programme, of which cattle
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(52%) and sheep (39%) were the most popular species. Least popular species were goats (11%); horses
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(13%) and buffalo (14%). Of interest is the low preference for conserving rare horse breeds given their
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popularity in the Romanian farming context. This may suggest rare horse breeds do not match farmer
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preferences for horse breed characteristics and hence are undersupplied.
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Table 3: Sample summary of farm animal characteristics, breed abundance and farmer interest in
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farming with a rare breed
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Species
Incidence of farm
animal in sample
(%)
Total no.
breeds reported
Most popular breed
(% abundance)*
Farmers stating interest
in farming with rare
breed (%)
Sheep
61
8
Tsurcana (47%)
39
Goats
24
4
Unknown (56%)
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Pigs
84
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Large White (37%)
-
Buffalo
10
3
Romanian Buffalo (83%)
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Cattle
73
9
Baltata Romanesca (61%)
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Horses
51
8
Unknown mix (51%)
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* Percentage abundance was calculated as the number of farm animals in our sample that correspond to a specific breed
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Livestock-keepers in different countries prefer different breed attributes. Respondents were
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asked to rank livestock attributes by importance for breed selection. In Figure 4 radar charts indicate
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different preferences between rare breed and commercial breed keepers for some attributes. Here,
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farmers were asked to rank multiple breed attributes in terms of importance on a 1-8 scale (1 being most
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important, 8 being least). The proportion of farmers selecting each attribute (for ranks 1, 2 and 3) is
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shown. Yield was the most important attribute for both rare breed and commercial breed keepers.
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Adaptability was ranked 2nd for farmers keeping rare breeds, while disease and parasitic resistance was
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ranked 3rd. For commercial breed keepers, yield was also ranked 2nd and adaptability 3rd. This suggests
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productive traits are considered most important by both farmer groups, but they differ in perceived
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importance of non-productive traits. This supports work suggesting rare breed adaptability
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characteristics play an important role within the livestock sector not matched by commercial breeds
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(Leroy et al., 2018).
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Figure 4: Radar charts showing ranked importance of livestock attributes according to farmer preference.
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The charts reveal the proportion (%)of farmers who chose each attribute in 1st 2nd and 3rd rank. Key, CT =
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cultural tradition; DPR = disease and parasitic resistance; VB = veterinary bills; MH = management and
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handling; PQ = product quality
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3.3 Choice Models
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The choice models investigate whether some of the heterogeneity associated with contractual
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choices is systematically associated with farm or farmer characteristics. Initial results from the MNL
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are provided in Appendix 3 to provide an overview of the basic model estimation. Results from the
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more sophisticated RPL model for bovine and ovine farmers are reported separately in Table 4. Both
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models delivered a good statistical fit (i.e. the model is a good estimator of respondent choice) as
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indicated by McFadden pseudo R2 values
1
of 0.33 (bovines) and 0.38 (ovines).
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Table 4: RPL model output of estimated marginal utilities for both ovine and bovine farmers for all
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CE attributes and significant covariate interaction terms
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Attribute
Bovines
Ovines
Coefficient
SE
Coefficient
SE
Random parameters
[CL] Contract Length
-0.829***
0.175
-0.984***
0.213
[SS] Scheme Support
0.147
0.230
0.618
0.259
[SOS] Structure of Scheme
-0.554**
0.221
1.499***
0.466
[COS] Subsidy
0.022***
0.003
0.594***
0.108
[N0] Nothing option
1.90***
0.516
2.301***
0.492
Standard deviations of random parameters
[CL] Contract Length
0.501
0.311
0.652**
0.291
[SS] Scheme Support
1.022***
0.261
0.297
0.495
[SOS] Structure of Scheme
1.689***
0.324
1.223***
0.279
[COS] Subsidy
0.006
0.012
0.018
0.282
[N0] Nothing option
1.675***
0.358
1.112***
0.378
Covariates (socio-economic variables)
COS:AES
-0.981***
0.374
COS:BEN
0.016***
0.006
1
Note the McFadden pseudo R2 can be interpreted very much like a regression R2 value but the goodness of
fit will always be much lower in CE modelling (typically between 0.2 to 0.4).
16
N0:AES
1.681***
0.509
SOS:BEN
-2.506***
0.565
COS:AES
-0.110*
0.062
COS:BEN
-0.188**
0.077
Model summary
No of observations
464
324
Log likelihood
-344.089
-222.246
Chi squared
331.345
267.409
Prob > Chi square
0.000
0.000
McFadden Pseudo R2
0.325
0.376
Note: ***; ** indicates significance at 1% and 5% respectively. SE=standard error.
Socio-economic parameter definitions: AES (whether the respondent is enrolled in an
agri-environment scheme), BEN (farmers with preferences for receiving either
individual or community benefits from the scheme).
290
The N0 (non-contract option) is positive and significant in both models meaning most farmers have
291
preferences for the status quo option which follows economic theory (Greiner, 2015). This is perhaps
292
because there are some variables, not included in the model, which induce farmers to prefer to not join
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the offered contract alternatives. The subsidy attribute is positive in both models meaning higher
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conservation payments increased likelihood of enrolment. Contract length (bovines and ovines) is
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significant and negative meaning respondents prefer a shorter contract. Scheme support was not
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significant for both bovine and ovine farmers. Structure of scheme was negative and significant for
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bovine farmers meaning they prefer individually managed conservation schemes. For ovine farmers
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structure of scheme is positive and significant, suggesting they prefer community managed conservation
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programmes.
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Significant standard deviations of the normally distributed coefficients indicate there is
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heterogeneity in farmers’ preferences for some attributes. The standard deviations were significant for
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all attributes accept contract length and subsidy (bovines only) and scheme support and subsidy (ovines
304
only).
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Additionally, we also tested for significant relationships between respondent preferences for
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different contract attributes and various individual specific covariates. The significant covariate
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interactions for both models are listed in Table 4. For both models, a negative, significant relationship
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was obtained by interacting farmers currently enrolled in AES schemes (AES) with subsidy (COS)
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suggesting farmers enrolled in AES measures typically require less subsidy support. Conversely,
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farmers not enrolled in AES schemes demanded higher subsidy payments. The N0 interacted with AES
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was positive and significant suggesting farmers currently enrolled in AES schemes were more likely to
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select the non-contract option. Education level did not influence likelihood of enrolling into a contract
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and farmer age did not affect preferences for contract length (both non-significant).
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For bovine farmers, interacting respondents wishing to receive community benefits from the scheme
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(BEN) with COS was significant and positive, indicating farmers looking to receive community based
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(in-kind) rewards require a higher equivalent subsidy reward. For ovine farmers, interacting BEN with
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structure of scheme (SOS) is negative and significant meaning farmers preferring individual benefit
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schemes also prefer individually managed conservation programmes (i.e. consistency in our results).
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Interacting BEN with COS was also negative and significant suggesting ovine farmers preferring
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individual payment schemes are WTA lower subsidy premiums.
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3.4 Willingness to accept estimates
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For WTA estimates (Table 5) the positive value for the N0 of €167 year-1 and €7 year-1 for bovine and
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ovine farmers, respectively, can be interpreted as the starting value needed for farmer participation in
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the contractual scheme relative to the baseline contract (Christensen et al., 2011); where baseline refers
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to a shorter contract length, scheme application support only and an individually managed conservation
328
breeding programme. Changing from a 5 to 10 year contract would cost around €72.8 year-1 and €3.3 year-
329
1 for bovines and ovines respectively. To move from an individual to a community managed
330
18
conservation scheme would cost an additional €48.6 year-1 for bovine farmers while conversely for ovine
331
farmers it would cost an additional €5 year-1 to enrol them in an individual scheme.
332
333
Table 5: WTA results (€ year-1) derived from the RPL model for both ovine and bovine farmers
334
Attribute
Bovines
Ovines
Coefficient
95%
confidence
interval
Coefficient
95%
confidence
interval
[CL] Contract Length
-72.8***
-33.1 to -144.7
-3.3***
-1.4 to -7.3
[SS] Scheme Support
12.9
40.7 to -37.6
-0.2
1.4 to -2.3
[SOS] Structure of Scheme
-48.6**
-8.3 to -121.8
5.0***
6.0 to 3.1
[COS] Subsidy
-
-
-
-
[N0] Nothing option
166.9***
198.3 to 109.8
7.0***
67.6 to 5.9
Note, ***; ** indicates significance at 1% and 5% respectively
335
3.5 Estimating contract participation
336
Contract participation was estimated according to different payment and contract scenarios to
337
determine how projected uptake by farmers varied according to contract attributes. Coefficient means
338
from the RPL model were used for calculating probabilities under two alternative scenarios; optimal
339
and non-optimal contracts, where optimal refers to contract attributes that meet farmer preferences
340
elicited in the CE while ‘non-optimal’ contracts do not. For instance, for bovines this would be a 5 year
341
contract that is individually managed. The subsidy premium took consistent values across both
342
scenarios, ranging from 10% to 100% of remuneration offered in the RDP scheme option. This allowed
343
exploration of how scheme uptake might vary with different contract options to gauge the importance
344
of monetary and non-monetary attributes in farmer decision making.
345
346
As expected, non-optimal contracts were estimated to receive lower participation relative to optimal
347
contracts (Figure 5). Participation estimates ranged from 4% (€20 year-1) to 70% (€200 year-1) for bovines
348
and 2% (€1 year-1) to 78% (€10 year-1) for ovine farmers under the non-optimal scenario. Conversely, in
349
the optimal scenario participation estimates ranged from 38% (€20 year-1) to 97% (€200 year-1) for bovines
350
19
and 71% (€1 year-1) to 99% (€10 year-1) for ovine farmers. Recalling that subsidy premiums are comparable
351
across both contract scenarios, our estimates show the difference in participation (between the two
352
contract scenarios) ranges from 27% to 58% for bovine farmers and 22% to 84% for ovine farmers.
353
354
We find a non-linear relationship between participation and financial reward, suggesting a one unit
355
change in subsidy does not necessarily equate to a mirrored change in participation (i.e. there are other
356
factors exogenous to our model influencing farmers willingness to participate). Respondents presented
357
with optimal contract designs were much more likely to enrol in a conservation programme even at
358
lower premiums. Ovine farmers were less likely to enrol in a contract that did not match their
359
preferences for non-monetary attributes at lower subsidy premiums (though this was not the case with
360
higher premiums). For both farmers groups (non-optimal contracts) there appears to be a tipping point,
361
before which contract enrolment is relatively static.
362
363
Figure 5: Probability of contract participation according to ‘non-optimal’ and ‘optimal’ contract
364
scenarios for different subsidy premiums (bovine and ovine farmers). ‘Optimal’ refers to contract
365
attributes that meet the preferences of agents.
366
367
20
4 Discussion
368
4.1 Contract preferences
369
Results suggest farmers demonstrate a clear willingness to participate in conservation programmes
370
for rare breeds. Participation may be reduced by up to 84% if farmer preferences for non-financial
371
attributes are not taken into consideration Within the model, the N0 may capture the dis-utility of
372
enrolling in a voluntary subsidy scheme that is not linked to contract attributes, but potentially other
373
factors not included in our model (e.g. family tradition or mistrust in authorities). It may also reflect a
374
general reluctance to join a voluntary incentive scheme (Christensen et al., 2011). However,
375
heterogeneity across farmers in our sample (as shown by significant standard deviation of non-random
376
parameters) complicates interpretation of the N0.
377
378
Farmers revealed a tendency to value flexibility in contracts as demonstrated through a
379
preference for shorter contract durations, a common finding in similar studies (Christensen et al., 2011;
380
Tesfaye and Brouwer, 2012; Santos et al., 2015). While bovine farmers preferred individually managed
381
conservation programmes ovine farmers preferred community managed schemes. This seems logical in
382
post-communist Romania, which has seen a shift from collective to individual ownership rights across
383
agriculture (Tudor and Alexandri, 2015). On the other hand an enduring communal herd grazing regime
384
among sheep farmers may explain the alternative preference. The significance of the standard deviation
385
for this attribute further complicates interpretation. Although scheme support for a conservation
386
programme was not considered important by both farmer groups similar attributes were significant in
387
other studies (Ruto and Garrod, 2009). For instance, work by Christensen et al. (2011) has shown
388
farmers are able to place a monetary value on being released from certain administrative burdens and
389
that the use of farm advisors for schemes might make farmers willing to accept a lower payment for
390
enrolling in a scheme. In developing countries like Romania, where rural populations are generally less
391
21
educated than the wider population (FAO, 2001) application support for schemes may in-fact be
392
paramount to securing farmer participation.
393
394
A number of covariates help explain heterogeneity in both models. We did not find that farmers
395
keeping rare breeds were WTA less for supplying conservation services, perhaps suggesting other non-
396
monetary motives were driving their decisions regarding the contract options. Both farmer groups
397
enrolled in AES schemes were WTA less compensation for supplying conservation services, thus
398
providing a means for conservation agencies to target least cost service providers. However, farmers
399
enrolled in AES schemes were also more likely not to select a contract option, suggesting overlap with
400
existing contractual schemes may deter farmers from participating. In addition, farmers already enrolled
401
on AES programmes are more likely to harbour pro-environmental attitudes (Heyman and Ariely, 2004)
402
that may improve compliance with contractual schemes.
403
404
In both models community (in-kind) based support is associated with higher cost than those
405
preferring cash based payments; implying the use of in-kind rewards will increase overall scheme cost.
406
However, in-kind payments have been shown to be more effective than cash payments in stimulating
407
conservation effort (Gorton et al., 2009) and may provide longer term infrastructure benefits to
408
communities supplying public goods. In addition, Narloch et al. (2017) argue collective payments to
409
community groups may effectively ‘crowd-in’ compliance, thus reducing monitoring costs and
410
improving conservation outcomes. The additional costs of community schemes must therefore be
411
weighed against (potentially) improved social and farm animal diversity outcomes.
412
413
4.2 Contract participation
414
Contract participation estimates reveal a trade-off between non-monetary attributes and financial
415
incentives. For instance, if RDP subsidies paid € 120/ animal year-1 and € 6/ animal year-1 for bovine and
416
22
ovine farmers in an ‘optimal’ contract scenario then uptake rates could be as high as 86% and 98%,
417
respectively. This contrasts with enrolment of just 28% and 25% for identical price premiums but with
418
‘non-optimal’ contracts for bovine and ovine farmers, respectively. The higher uptake rates associated
419
with ovine farmers in optimal contracts may reflect that performance differences between rare and
420
commercial breeds are larger for bovines than ovines, though this supposition requires further evidence.
421
422
These participation estimates are still well above actual participation rates of 15% for an AES
423
scheme in Northern Italy (Defrancesco et al., 2008). Empirical work by Wossink and van Wenum,
424
(2003) suggests participation of up to 60% might be achieved in a hypothetical Dutch field margin
425
programme, suggesting the scheme proposed here is indeed considered attractive by farmers. However,
426
while strategies were employed to prevent hypothetical bias (e.g. cheap talk statement) it nonetheless
427
must be considered that the high participation rates found in our work may be exaggerated by such bias
428
(i.e. the hypothetical nature of a CE may induce respondents to overstate their desire to enrol in a
429
contract option). That said, farmers in our sample were generally poorer than the national average which
430
may be an underlying factor driving an increased desire to participate.
431
432
Contrary to expectations, farm size, education level and age did not have a significant effect on
433
participation. These findings confirm conflicting results found in the literature concerning the influence
434
of education (Dupraz et al., 2002; Defrancesco et al., 2008; Greiner, 2015), age (Wossink and van
435
Wenum, 2003) and farm size (Christensen et al., 2011; Adams et al., 2014) on participation in
436
contractual conservation schemes. The hypothesis that farmers keeping rare breeds would be more
437
likely to participate in a conservation scheme was not supported. This may be because a high number
438
of farmers were keen to participate in the scheme, irrespective of whether they were currently farming
439
with a rare breed. Although few studies have directly assessed farmer willingness to participate in rare
440
breed conservation programmes, work by Pattison et al. (2007) suggests that farmers keeping rare breed
441
23
pigs in Mexico were willing to participate in a community conservation breeding programme even
442
without financial incentives.
443
4.3 Barriers to uptake
444
Some have been critical of RDP approaches to rural policy (Shortall, 2008; Milcu et al., 2014). This
445
study suggests there are clear barriers to entry for smallholder farmers wishing to participate in some
446
RDP options. This is apparent where RDP eligibility requires a minimum parcel size of 0.3 ha to be
447
entered into agreements and a cumulative field size of 1 ha or more (Mikulcak et al., 2013). The average
448
farm size in our sample was 3-6 ha and discussion by Page et al. (2011) stresses this is a major obstacle
449
for small-scale farmers in Eastern Europe wishing to enrol land into incentive schemes (Gorton et al.,
450
2009). Herd or flock-book registration of livestock is a requirement to qualify for RDP support for
451
rearing local livestock breeds in danger of extinction (MARD, 2014) yet only 8% of farmers in our
452
sample reported having animals registered in this way revealing a major barrier-to-uptake.
453
Implementing alternative mechanisms, or proxies, to identify the genetic merit of farm animals has been
454
identified as an important consideration by Pattison et al. (2007) and novel approaches developed by
455
Bhatia et al. (2010) may serve as a way to surpass such barriers through phenotypic identification of
456
breeds.
457
458
EU rural development policy needs be more clearly communicated. In our sample, only 21% of
459
farmers were aware of RDP funding support for farmers rearing endangered breeds. Surveys by
460
Mikulcak et al. (2013) suggest funding measures are often poorly communicated to small-scale farmers
461
and local mayors in Transylvania, emphasising the importance of using local communication channels.
462
In Transylvania, Fundatia ADEPT (a local conservation NGO) are meeting this need by helping small
463
scale farmers through workshops on the CAP and RDP measures; developing milk collection points in
464
local villages and facilitating cooperative bids for farm applications to AES options where, individually,
465
farmers would be ineligible to apply (Fundatia ADEPT, 2014). These factors have culminated in better
466
24
support for small-scale farm incomes in Transylvania while maintaining the high levels of public goods
467
that arise from these production systems.
468
5 Conclusion
469
Farm intensification is a trend across Romania and Central and Eastern Europe (Henle et al., 2008;
470
Popescu et al., 2016) threatening breed diversity. Sustaining this diversity makes an important
471
contribution to the delivery of SI objectives given the high option value that arises from breed diversity,
472
through greater adaptive capacity (Hoffmann et al., 2014). This adaptability, in addition to breed
473
cultural heritage, is considered important by farmers in Transylvania, particularly those keeping rare
474
breeds.
475
476
This analysis supports the findings of other work (e.g. Greiner, 2015; Permadi et al., 2018) that
477
suggest contract length and the structure of schemes, in addition to monetary rewards, are important
478
determinants of participation rates in conservation programmes. But we also acknowledge that the
479
monetary values farmers place on accepting specific contractual schemes are case specific (Christensen
480
et al., 2011). As a consequence, the robustness of these results needs to be addressed in further work
481
exploring cost-effectiveness of FAnGR conservation programmes in similar contexts. Moreover, this
482
work has not explored how farmer WTA a contract might vary depending on breed options as part of
483
the scheme. Indeed, work by Zander and Drucker, (2008) suggests farmer do possess heterogeneous
484
preferences for breed attributes and breeds themselves. Exploring the importance of alternative breed
485
and attribute combinations in contracts appears warranted and may further affect farmer willingness to
486
participate in schemes and their WTA a conservation contract.
487
488
We found that the average bovine farmer (in Transylvania) needs to be paid €122 per annum per
489
animal extra in order to enrol in a 10 year community managed conservation contract. For ovines, an
490
additional price incentive of €8.3 would be required for farmers to enrol in a 10 year individually
491
25
managed conservation contract. A key question is whether the conservation and genetic diversity benefit
492
of a longer contract that either includes a collectively or individually managed conservation breeding
493
scheme will exceed the additional costs.
494
Acknowledgements
495
We acknowledge NERC E3 DTP studentship (NE/L002558/1) and the support offered by
496
Operation Wallacea throughout the project and funding that made fieldwork possible. We thank
497
Fundatia ADEPT for advice on fieldwork planning and Marcela Man for her work in survey
498
implementation. Finally, thanks are extended to Frazer Christie for GIS mapping.
499
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