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Ecological Indicators 61 (2016) 274–281

Contents lists available at ScienceDirect

Ecological Indicators

jo ur nal ho me page: www.elsev ier .com/ locate / ecol ind

evelopment and implementation of a high nature value (HNV)arming indicator for Denmark

ne Kirstine Brunbjerga,b,∗, Jesper Bladta, Martin Brinkc, Jesper Fredshavnd,eter Mikkelsena, Jesper E. Moeslunda, Bettina Nygaarda, Flemming Skova,asmus Ejrnæsa

Department of Bioscience, Aarhus University, Rønde, DenmarkSchool of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UKThe Danish AgriFish Agency, Ministry of Food, Agriculture and Fisheries of Denmark, Copenhagen, DenmarkDanish Center for Environment and Energy, Aarhus University, Denmark

r t i c l e i n f o

rticle history:eceived 20 February 2015eceived in revised form 24 August 2015ccepted 17 September 2015vailable online 11 November 2015

eywords:gricultural subsidiesiodiversity indicatoronservationnvironmental proxiesurope

a b s t r a c t

During the latest decades, conservation awareness have increased and conservation of biodiversity isno longer an agenda restricted to pristine natural areas of high value but is being integrated in culti-vated landscapes characterized by increasing fragmentation and anthropogenic pressures. Conservationof species has become relevant even in agricultural areas especially in countries heavily influenced byintensive farming and ubiquitous infrastructure. Based on the UN biodiversity convention all EU member-ship countries are obliged to develop a high nature value (HNV) farming indicator. The purpose of such anindicator is to provide a basis for informed decisions in prioritizing agricultural subsidies allocating theseto high nature value areas. Here, the development of a national HNV farming indicator for Denmark is pre-sented and its application in the Danish government-controlled agricultural subsidy system is outlined.The Danish HNV farming indicator is based on landscape structural parameters, known occurrences ofnatural and semi-natural habitats, current land use and the distributions of rare and threatened species.It covers all agricultural areas as well as Natura 2000 areas and nationally protected sites in Denmark,ranking them on a 0–13 point scale from low to high nature value according to their estimated value for

threatened and rare biodiversity. The Danish government recognizes areas reaching at least 5 points asHNV farming areas eligible to subsidy and from 2015 and onwards the HNV farming indicator will beimplemented for allocating part of the agricultural subsidies under the Rural Development Program inDenmark. The indicator will be updated annually to include the growing knowledge of species occurrenceand land use changes.

© 2015 Elsevier Ltd. All rights reserved.

. Introduction

Biodiversity is highly important for human well-being andcosystem functioning (Eisenhauer et al., 2013; Fuller et al., 2007;ooper et al., 2012). Halting biodiversity loss has therefore been aolitical target for several years but has by no means been achievedet (Butchart et al., 2010). However, in 2010 world leaders agreed

o halt global biodiversity losses by 2020 at the latest (Unitedations COP 10) – an ambitious agenda calling for conservation

nitiatives across all sectors including farmed areas which are

∗ Corresponding author at: Department of Bioscience, Aarhus University, Rønde,enmark. Tel.: +45 87158865.

E-mail address: akb@bios.au.dk (A.K. Brunbjerg).

ttp://dx.doi.org/10.1016/j.ecolind.2015.09.027470-160X/© 2015 Elsevier Ltd. All rights reserved.

interesting in a conservation context, because they represent a largepart of the European landscape (Clergue et al., 2005). Habitat qual-ity has been found to be positively correlated with species richnesseven in contemporary intensively managed farmland (e.g., Merckxet al., 2012; Pywell et al., 2004, 2005). Particularly fertilization,irrigation, pesticides, drainage and loss of extensively managedareas are factors known to have a negative impact on biodiversity(Stoate et al., 2001) and consequently no species are specificallyrelated to today’s intensive agricultural landscape (Kleijn et al.,2011). Contrary, high nature value (HNV) farming systems char-acterized by low inputs of pesticides, fertilizers, cultivation as well

as low stocking rates can be highly beneficial to biodiversity (e.g.,Albrecht et al., 2007; Bignal and McCracken, 2000; EEA, 2004). Infact, historically many of the important habitats for rare and endan-gered species have developed from a number of the pre-industrial

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A.K. Brunbjerg et al. / Ecolog

xtensive agricultural practices (Bignal and McCracken, 2000)hich are now increasingly abandoned. HNV areas have recently

ecome part of the nature conservation strategy within the EUAnon., 2009) obliging all EU membership countries to develop aNV farmland indicator for mapping areas holding high nature val-es. The aim is a focused effort within the agricultural landscapey subsidizing these HNV farmland areas depending on politicalecisions to maintain and attract favorable farming practices andereby preserve biodiversity as well as ecosystem services pro-ided by e.g. extensive farming (O’Rourke and Kramm, 2012). Aood HNV farmland indicator should represent the variation iniodiversity and natural structures in the agricultural landscapehile being easy to understand and readily applicable to policyaking. Furthermore, a HNV farmland indicator could acknowl-

dge areas enhancing ecosystem services like pollination. Whileo rules have been specified on which sub-indicators to use to

dentify and monitor HNV farmland areas the EU member statesre addressing the development of national HNV farmland indi-ators in different ways (Lomba et al., 2014, 2015; van Doorn andlbersen, 2012). Andersen (2003) proposes three categories of HNVarmland: (1) farmland with a high proportion of semi-natural veg-tation, (2) farmland dominated by low-intensity agriculture or aosaic of semi-natural and cultivated land and small-scale fea-

ures, and (3) farmland supporting rare species or a high proportionf European or world populations. Although the third HNV farm-and category may include intensively managed farming systemshe vast majority of habitats for rare or significant species rely onxtensive farming practices and further the intensive farmed areasarely need focused subsidies. Three of the components suggestedor HNV farmland (extensive farming, semi-natural areas and

osaic landscapes) reflect the positive correlation between exten-ive farming and diverse landscapes and biodiversity as revealedy Duelli and Obrist (2003) who found more than 63% of all animalpecies in agricultural areas to depend on semi-natural habitats.ikewise, extensively or organically managed areas do not neces-arily hold rare or endangered species but have been acknowledgeds potential HNV farmland areas (Andersen, 2003) and have beenocumented to benefit species diversity (e.g., Tuck et al., 2014).lso, presence of rare or threatened species in an area is a valu-ble indicator of high nature value and known occurrences of rarend threatened species should therefore be emphasized in the HNVarmland indicator (Anon., 2009). Taxonomic and geographic biasn the mapping of localities of endangered species is inevitable,s knowledge of distribution of species is biased and limited inost countries. Proxies for biodiversity are therefore widely usedithin conservation biology (Lewandowski et al., 2010). In an agri-

ultural landscape, biodiversity is expected to be highest in theeast intensively cultivated and most heterogeneous areas (e.g.,onthier et al., 2014). Indicators of fertilization, drainage, cultiva-

ion as well as measures of habitat diversity, share of semi-naturallements, etc. may therefore reflect and consequently act as prox-es for the biodiversity in a given area (Billeter et al., 2008; Ejrnæst al., 2002). Obvious HNV farmland areas are alpine mountainousreas or steppe areas in southern and eastern parts of Europe (EEA,004) still being used for extensive grazing. However, countries

ike Denmark are often dominated by areas which are economi-ally and technically feasible to cultivate with high intensity. Apartrom the EU obligation to develop a HNV farmland indicator, the

ain reason for the Danish AgriFish Agency to initiate the HNVarmland project has been the increased awareness of and demandor ensuring direct effect of area based subsidies under the Ruralevelopment Program (RDP). This way, the purpose of subsidies

or grazing and mowing has changed from environmental ben-fits such as N-reduction and groundwater protection toward aiodiversity purpose exclusively. However, the positive effect ofubsidies on biodiversity has been questioned due to the fact that

dicators 61 (2016) 274–281 275

ordinary grasslands prone to more intensive farming e.g. tilling andfertilization were subsidized. As the subsidy scheme only com-mits the farmer to apply an extensive grazing or cutting regimefor a 5-year period, more intensive farming could be applied afterthis period, thereby resetting the biodiversity effect of subsidy. Onsome of these areas even many years of extensive farming wouldonly slowly enhance their conservation value. Developing a Dan-ish HNV farmland indicator should therefore help in identifyingareas of high value both in terms of landscape structure and biodi-versity to subsidize management in less productive areas creatingan incentive for farmers to value these areas and maintain currentagricultural practices or practices favorable for nature. In the fol-lowing we therefore consequently use the term HNV farming asopposed to HNV farmland in order to emphasize that also semi-natural habitats in need of extensive grazing or hay cutting may beconsidered HNV farming areas despite that they are not tradition-ally considered farmland.

The objective of this study is to develop a HNV farming indica-tor for a highly intensified agricultural country (e.g. Denmark) thatcan be applied for subsidizing land use promoting the conservationand integration of high quality habitats. The Danish HNV farmingindicator presented here is a map consisting of 14 added layersrepresenting a combination of proxies for high nature value (e.g.landscape structure, land use and habitat based sub-indicators) andknown occurrences of endangered species in the landscape. In thispaper we (1) present and describe the Danish HNV farming map,(2) outline the current application of the HNV farming indicator indistributing agricultural subsidies in Denmark and (3) discuss theDanish approach in a European Union context.

2. Methods

2.1. Developing a HNV farming indicator

When developing a HNV farming indicator the EU recom-mends including data on land cover, farming systems and species(Andersen, 2003). Here, we developed 14 sub-indicators represent-ing either proxies for high nature value or observation data on thedistribution of rare and threatened species throughout Denmark.These sub-indicators fell into the following four categories inagreement with the aforementioned EU recommendations: (1)landscape structure, (2) natural habitats, (3) management practice,and (4) distribution of species. We used a national grid with a spa-tial resolution of 10 m × 10 m as HNV unit of analyses in order tosecure maximum resolution and maximum flexibility with respectto applications for subsidies.

In the Danish HNV farming indicator it was decided to empha-size nationally rare and threatened as well as internationallytargeted species although species of lesser concern (e.g. speciesknown as “farmland dependent species”) may also be targeted inthe management of the agricultural landscape. Available data onoccurrence of rare and threatened species are far from completeand many areas may be home to such species without any reportedrecords. A HNV farming indicator based solely on occurrence of rareand endangered species of conservation interest is therefore likelyto cause many false negatives (type II errors, i.e., that high qual-ity natural areas receive a low score). This issue is internationallyrelevant and can be circumvented by increasing the scale to e.g.10 km × 10 km with loss of general applicability at farm level, or,alternatively increasing the mapping effort of species of conserva-tion interest. Furthermore, our knowledge is taxonomically biasedin favor of well-known groups such as plants and vertebrates. This

applies to the number of records of redlisted species, but also tothe redlists themselves where species in lesser known groups havenot been assigned a redlist category due to lack of knowledgeeven though they may actually be threatened. Approximately one

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ourth of all known species occurring in Denmark has been assessedWind and Pihl, 2004), whereas the number is 47% for FinlandRassi et al., 2010), 52% for Norway (Kålås et al., 2010) and 43%or Sweden (Gärdenfors, 2010). Thus, the use-only-known-species-ccurrences approach is insufficient for developing a trustworthynd rigid HNV farming indicator. We consider false negatives muchore critical than false positives (type I errors, i.e., that low quality

atural areas receive a high score) as false negatives imply a risk ofiscarding valuable habitats in the prioritizing process.

For all 10 m × 10 m pixels in the map, each of the 14 selectedub-indicators contributed equally with 1 point in case of positivevaluation and the resulting pixel score equaled the sum of sub-ndicator scores. Using simple addition of sub-indicators in a HNVarming context is desirable (cf. Occam’s Razor, Domingos, 1998) toase the implementation and comprehensibility for both scientistss well as local farmers. Transparency, operability as well as reliabil-ty in reflecting the actual conditions are highly important for theNV farming indicator to become an acknowledged and respected

ool to bridge extensive agricultural production and biodiversityonservation. The 14 sub-indicators making up the HNV farmingndicator are described in detail in Appendix A.I (see Table 1 andig. 1 for an overview). Two of the sub-indicators were mutuallyxclusive (both cannot be true at the same time) hence yielding

final score between 0 and 13. All sub-indicator were produced

n a 10 m × 10 m resolution (see details in Appendix A.I). Detailsbout which GIS tools were used for each sub-indicator are foundn Appendix B.

ig. 1. Model showing the 14 layers of the HNV farming indicator as exemplified by theoastal areas (coast), nationally protected open terrestrial natural and semi-natural habitxtensive farming, organic farming, high-habitat-quality plant sub-indicator 1, 2, and 3 (pategorized into structures related to landscape, habitats, land use and species. Colored andicator is colored according to the sum of the 14 layers (warmer colors = higher HNV fa

dicators 61 (2016) 274–281

2.2. Use of expert judgment in species distribution maps

For selected groups of rare and threatened species we decidedto use expert drawn maps of habitats as input to the HNV farm-ing sub-indicators (see sub-indicators 12–14 described in AppendixA.I). Existing data for these species comprised both precisely geore-ferenced observations (<50 m uncertainty) and imprecise data (upto 10 km uncertainty). Hence, many observations were not preciseenough for direct employment in the HNV farming map. In addition,most of the selected species are very mobile (e.g. birds and butter-flies) implying that an observation does not necessarily correspondto a habitat. Experts based their maps on all existing data fromnational databases (Naturbasen, the Danish Ornithological Society,etc.), both precise and imprecise observations.

The task for each expert involved in this work was to judgethe validity of each observation and if found trustworthy then todelimit the habitat(s) for rare and threatened species (see sub-indicators 12–14 described in Appendix A.I) of vascular plants,butterflies, moths, scarabs and birds. In this way, a number ofacknowledged experts drew polygons in Google Earth by combin-ing all known occurrences of the species of interest and expertknowledge of the species’ habitat requirements in a specific area.Each polygon was assigned an uncertainty level: (A) highly certainthat the species lives in the specific area (>95% sure), (B) highly

likely that the species lives in the specific area (75–95% sure),(C) likely that the species lives in the specific area (25–75% sure).We only used areas with uncertainty levels A and B for the HNV

island of Læsø, Denmark: Low-lying areas, areas with steep slopes (steep slopes),ats (protected areas), proximity to protected habitats, proximity to small biotopes,lant 1, 2 and 3), red listed/EU annex species sub-indicator 1, 2 and 4. The layers arereas indicate grid cells with 1 point, gray indicate 0 points. The final HNV farming

rming value; white = areas where the HNV farming indicator is not defined).

A.K. Brunbjerg et al. / Ecological Indicators 61 (2016) 274–281 277

Table 1Overview of the 14 HNV farming sub-indicators. Description, rationale, resolution and source are given. See Appendix A.I for a detailed description.

Sub-indicator Description Rationale Resolution Source

Landscape basedLow-lying areas A historical map of Danish

wetlands excluding areas currentlycharacterized by intensiveagricultural practices

These areas have historically beencharacterized by wet conditions andpeat formation. Nowadays some ofthese areas are cultivated and drainagehas founded the way for intensiveagricultural practices. At a Europeanscale many wetland areas have beenlost (Klimkowska et al., 2007) andrestoration efforts have increasedbecause of the and high conservationvalue in these areas e.g. regardingcharacteristic species and potential forhigh diversity given the application ofthe right management (Prajs andAntkowiak, 2010; Tallowin and Smith,2001). However, many areas are stillcharacterized by wet conditions andlong continuity of extensiveagriculture.

10 m × 10 m Greve et al.(2014)

Steep slopes All areas in the country slopingmore than 15◦ based on a nationalLight Detection and Ranging 1.6 mDigital Elevation Model (DEM).

There is an increased probability of along history of extensive agriculturalpractices on steep slopes (Odgaardet al., 2013). Furthermore, a Danishstudy showed a relationship betweensteep slopes and high plant speciesrichness (Moeslund et al., 2013)justifying the use of this sub-indicatorin the HNV farming indicator.

10 m × 10 m

Coastal areas A 1 km wide zone fringing themarine coastline based on a bufferpolygon inside a national coastlinemap marking these coastal zones.

The Danish coastal zone contains arelatively large proportion of valuablenatural and semi-natural areas partlybecause coastal erosion, sand drift,periodic inundation and steep slopeshave impeded intensive cultivation,partly because of restrictive Danishlegislation protecting the coastal zoneand its natural and seminaturalhabitats against development andexcessive exploitation (Danish NatureLegislation Act; Danish Planning Act).Coastal areas provide habitat for manycharacteristic and disturbance adaptedspecies (e.g., Brunbjerg et al., 2015;Ejrnæs et al., 2011).

10 m × 10 m

Habitat basedNationally protected

open terrestrialnatural andsemi-naturalhabitats

Nationally protected openterrestrial natural and semi-naturalhabitats are mapped nation-wideas a consequence of the DanishNature Legislation Act. They cover8.8 percent of Denmark andinclude dry grassland, heathland,mires, fens, meadows, saltmarshes

Complexity of the landscape, i.e. themix of semi-natural and natural areasin between farmed areas has beenfound to be important for biodiversity(Gonthier et al., 2014; Lindborg et al.,2014).

10 m × 10 m

Proximity toprotected habitats

This sub-indicator consisted of azone within 50 m from theaforementioned nationallyprotected habitats including theseprotected habitats themselves.

Assuming that areas close to protectednature are higher in nature value thanareas further apart because of thespill-over effect as well as the logic inacknowledging the value of bufferzones nearby a protected site,landscapes in close proximity toprotected areas constituted asub-indicator.

10 m × 10 m

Proximity to smallbiotopes

This sub-indicator was based onpolygon layers representing knownlocations of small biotopes andwoodland (e.g. woodland, hedges,wind-breaks and field boundaries).A 50 m buffer around a merge ofthese layers was calculated andthis buffer zone together with thewoodland layer represented theproximity to small biotopes

In general, there is a higher naturevalue in mosaic landscapes comparedto plain agricultural areas (Anon.,2009). Restored agricultural areas withconstructed small biotopes like hedgeshave been found to promote not onlygeneralist species but also providehabitat for specialist and less mobilespecies (Kremen and M’Gonigle, 2015).

10 m × 10 m

278 A.K. Brunbjerg et al. / Ecological Indicators 61 (2016) 274–281

Table 1 (Continued)

Sub-indicator Description Rationale Resolution Source

Land use basedExtensive farming This sub-indicator comprised a

map of extensively farmed fieldsthroughout the country i.e.permanent low-yield grass areas,set-aside fields, uncultivated fields,nature-like areas, riparian zones,open areas in forests, cultivation ofwillow, poplar, alder, etc.

Extensive and low-input farming havebeen shown to have a positive effect onbiodiversity (reviewed in Gonthieret al., 2014) which justifies theinclusion in the HNV farming indicator:

10 m × 10 m

Organic farming A layer representing all organicallygrown fields in the countryconstituted the eighthsub-indicator.

Extensive and low-input farming havebeen shown to have a positive effect onbiodiversity (reviewed in Gonthieret al., 2014) which justifies theinclusion in the HNV farming indicator:

10 m × 10 m

Species basedHigh-habitat-quality

plant sub-indicator1, 2, and 3

This sub-indicator was based on anofficial high-habitat-quality plantindicator value system assigning ahabitat quality score rangingbetween −1 and 7 (high is best)based on the plant speciescomposition in a given site. Thefirst sub-indicator identifiedlocalities having a meanhabitat-quality plant indicatorvalue of at least 2.5, the secondrepresented localities having amean value of at least 3.25, and thethird covered localities with amean value of at least 3.75.

This sub-indicator adds extra value tothe areas with positive indication ofHNV based on plant species lists as weconsider these of particularly highcredibility.

Fredshavn andEjrnæs (2007)

Red listed/EU annexspeciessub-indicator 1, 2and 4

Occurrence of at least 1, 2 or 4species respectively which areeither nationally red listed(categories: regionally extinct,critically endangered, endangered,vulnerable and near threatened;(Wind and Pihl, 2004) or listed inthe EU habitats directive annex II

Wind and Pihl(2004), CouncilDirective92/43/EEC(1992)

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or IV (Council Directive 92/43/EEC,1992).

arming sub-indicators. For birds we only used polygons represent-ng breeding habitat (not migrating or foraging).

.3. Exclusion of areas from the map

Since the HNV farming indicator was originally suggested byhe EU as a management tool for farmed areas, the indicator onlyovered terrestrial environments and only landscapes relevant forarming i.e. fields and nationally protected areas that are highlyependent on grazing, mowing and other extensive farming prac-ices (Halada et al., 2011) and therefore eligible to agriculturalubsidies. Therefore, the HNV farming indicator included tem-orarily abandoned areas whereas forests, white dunes, the ocean,ities, roads, railways, gardens, parks, streams, lakes, and the likesere not included in the indicator map unless they had been des-

gnated as protected habitat with management targets such asrazing or mowing.

.4. Application of the HNV farming indicator

In 2015 the budget for the subsidy measure for grazing andutting in Denmark was around 33.5 million D . Within the Natura000 network of designated areas known to hold threatened orulnerable species or habitats according to the Habitats Directive

Council Directive 92/43/EEC, 1992) a fixed area for subsidy wasppointed by the Danish Ministry of Environment and based on theurrent Natura 2000 plans. The remaining non-Natura 2000 areaspplicable for subsidy were prioritized based on the HNV farming

map. The Danish government recognized areas reaching at least5 points as areas potentially eligible to subsidy. A score of 5 didnot necessarily trigger a subsidy; rather subsidies were allocatedto the applicants with the highest scoring fields first. Because ofthe fine resolution, the HNV farming score usually varied within afield and in these cases, the maximum HNV farming score coveringminimum 0.1 ha was used for evaluation as conserving the bestareas will be most beneficial for biodiversity conservation. Thisway, subsidy was granted for the entire field applied for even if thearea of the highest HNV farming score only covered a minor partof the total field area (minimum 0.1 ha).

3. Results

3.1. Final HNV farming indicator

The final HNV farming map for Denmark covered approximately3,000,000 ha of fields, other agricultural areas (borders betweenfields, access paths, etc.) and protected natural areas highly depen-dent on grazing, mowing and other extensive farming practices.An example of the map in 10 m resolution with HNV farmingscores ranging in this specific case from 0 to 11 is shown in Fig. 2.The full map is shown in Appendix C. The map is implemented in

the official Danish Internet portal of land use and environmentaldata and can be reached via www.arealinfo.dk. In the Danish HNVfarming map, the majority (90%) of the total number of grid cellsfell below a threshold of 5 points, meaning that only about 10%

A.K. Brunbjerg et al. / Ecological Indicators 61 (2016) 274–281 279

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Table 2Allocated HNV farming subsidy in 2015. The number of fields, area and amount ofEuros for each HNV score and Natura 2000 areas are given. A total of ∼33.5 millionD were allocated in 2015 of which ∼24 million D were allocated to N2000 areas.The remaining amount was prioritized based on the HNV farming map (HNV 5–12).

No. fields Area (ha) Subsidy (D )

Natura 2000 5101 19,686.24 24,088,267.76HNV 12 5 29.76 50,294.24HNV 11 21 118.98 185,574.40HNV 10 63 295.00 415,904.16HNV 9 189 665.63 870,798.93HNV 8 385 1254.27 1,576,313.00

ig. 2. Local example of the HNV farming indicator map showing 10 m × 10 m gridells with HNV farming scores ranging from 0 to 11. The colored band reflects theNV farming values in a river valley while the zero point areas are intensive fields orreas where the HNV farming indicator is not defined such as urban areas or roads.

288,000 ha) of the areas covered by the map will be considered ofigh nature value (i.e., score 5–13) and hence eligible for subsidies.he majority of HNV farming areas with more than 4 points wereationally protected natural areas mainly consisting of wetlands,eaths and meadows (Fig. 3). Only a small number of intensively

armed areas had a score higher than 4. The relatively high scoreor these areas is most likely an artifact created by edge effects inhe ArcGIS calculations.

New and updated versions of relevant layers will be downloadednd processed for each annual update of the HNV farming indicator.he newest version of the HNV farming indicator map will be pub-ished online at The Danish Natural Environment Portal after eachpdate. A simpler version used for applications by farmers can beound at the Land Parcel Information System by the Danish AgriFishgency.

.2. Application of the final HNV farming indicator

In more than 74% of the areas having a HNV farming score of or more at least one species based sub-indicator was involved,nd this was interpreted as a strong indication of high nature value

ig. 3. Total area (kHa) (light gray) and areas of fields having a HNV farming scoref 5 and above (dark gray) for different management regimes included in this study.ote the break on the y-axis.

HNV 7 694 1893.69 2,369,944.37HNV 6 1499 3924.49 4,488,611.60HNV 5 2369 5434.72 5,955,911.19

within these areas. This was supported by the fact that 97% of HNVfarming areas were protected semi-natural areas (meadow, heath,wetland, dry grassland or salt meadow) while only ∼3% were exten-sively, semi-intensively or intensively used (Fig. 3). Contrary toextensively, semi-intensively and intensively managed areas themajority of protected natural areas were HNV farming areas (HNVscore > 5, Fig. 3). Approximately 3000 farmers applied for subsidyin 2015. 33.5 million D were allocated as subsidies, 24 million toNatura 2000 and 9.5 million to HNV farming areas outside Natura2000 with HNV values from 6 to 12 (Table 2). All applications involv-ing areas with HNV score > 5 were accepted, but because of limitedresources subsidy could only be granted to fields with HNV score5 that were larger than 12 ha (size of area was used as secondarycriterion).

4. Discussion

4.1. HNV farming indicators

In this paper we have reported the development of a nationalHNV farming indicator for Denmark in 10 m resolution forstraightforward prioritization of HNV farming subsidies and com-munication of nature values to farmers and other decision makers.Depending on the specific conditions, the threats to HNV farm-ing, and available data, different approaches have been taken bydifferent EU member states when developing their HNV farmingindicator (van Doorn and Elbersen, 2012). We applied a combinedapproach (Lomba et al., 2014) in which information on land cover,farming system and species occurrences constituted the final HNVfarming indicator. To our knowledge this is the first field-scaleHNV farming indicator documented with national coverage that isready for implementation (but see van Doorn and Elbersen, 2012).At the European scale a HNV farming indicator in 1 km resolu-tion was recently developed based on species occurrence data in50 km × 50 km grid cells (Overmars et al., 2014). However, in anumber of countries (including Denmark) many high-value natureareas are much smaller and would consequently not be appro-priately represented in coarse scale (>100 m) maps. Furthermore,actual farming management often takes place at relatively fine scale(i.e. field sizes much smaller than 1 km × 1 km (Herzog et al., 2006))and hence a HNV farming map in 1 km resolution is suitable forfocusing subsidies at landscape scale, but not at the scale of a man-aged field.

Data on occurrences of species dependent on extensive agri-cultural practices are often scarce and of variable quality regardingspatial and temporal resolution as well as geographic extent. There-fore, such data are of limited use in HNV farming indicators (Lomba

et al., 2014). Using occurrence of redlisted species and EU annexspecies as indicators of high nature value may be a better and moreobjective approach than choosing target species for a certain typeof management. More data are available for redlisted and EU annex

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pecies e.g. because of ongoing monitoring programs. Furthermore,he process of deciding which species should be used as indicatorpecies in a HNV farming indicator could be both time consuming,ubjective and specific for each EU member state. Since we onlynclude species occurrences on fields and semi-natural areas wessume that most of these species benefit from extensive farmingractices. We accept that this may not be the case for a few of thepecies. It would be valuable in future work to evaluate if some ofhe threatened species included in the HNV indicator are actuallyarmed by extensive agricultural management and hence shoulde excluded from or interpreted differently in the indicator.

Although collecting species occurrence data may be costly, anffort can be made to improve existing biodiversity data by usingxpert knowledge and geo-referencing if EU member states chooseo emphasize occurrence of species in their HNV farming indica-or. Furthermore, expert geo-referencing may be more efficienthan using observation data on rare and threatened species occur-ences, especially for mobile organisms, where the observationsay include both false absences due to poor cover and false pres-

nces due to observations outside habitats. It would be relevanto apply this approach of expert judgment also for other speciesroups as many species observation data have been excluded fromhe HNV farming map based on a combination of poor precisionnd poor relationship between the position of the habitat and theosition of the observation. For example we were hitherto unableo include bats in the sub-indicators as linking the mobile specieso specific habitat areas would be too inaccurate without an expertssessment. As taxonomic groups differ in their response to landse according to their mobility and habitat niches (Gonthier et al.,014) it is highly important to include both local farm practiceainly affecting sessile organisms (e.g. low input, extensive or

rganic farming) as well as more landscape based factors affectingobile organism (e.g. landscape density of semi-natural and natu-

al habitats). The Danish HNV farming indicator was based on bothiodiversity proxies and actual knowledge of occurrence of habitatsnd rare and threatened species; a necessary approach consideringhe incomplete data on the distribution of targeted species. Fur-hermore, the sub-indicators used allow for the necessary frequentpdates of the system at high spatial precision to catch spatiotem-oral farmland changes in accordance with the Rural Developmentrogram (Lomba et al., 2014).

.2. Application of the HNV farming indicator

The Danish government recognizes areas reaching at least 5oints as areas potentially eligible to subsidy. Choosing this thresh-ld assigns weight to areas in which rare or threatened species areikely present. The rationale is that an area has a higher expectedature value if rare species are known to be present than if the valuef the area is only estimated based on proxies.

Besides the immediate objective of focused subsidies, the HNVarming indicator also introduces an incentive for local farmerso improve the conditions for biodiversity and to spend resources

apping threatened species as this will improve the HNV farmingcore and potentially pay off via subsidy. This is a radical changerom a previous situation, where farmers were reluctant to reportare species based on concern for economic loss following conser-ation claims.

The development of a new HNV farming indicator has triggeredumerous comments from both farmers, agricultural advisers,unicipalities and other interest groups. Some have been nega-

ive, especially from those having ordinary permanent grasslands

ow excluded from subsidy. Others because they disagree withhe HNV farming score set for their parcels. In some cases this isaused by a lack of species occurrence data which has triggeredany municipalities to launch new inventories of plant species

dicators 61 (2016) 274–281

on their best semi-natural areas. On the positive side, there hasbeen a general acknowledgment of the need for more focus onbiodiversity effects when spending public money for this purpose.Furthermore, addressing HNV farming areas has triggered discus-sion and more awareness of the value of biodiversity. Finally, theinclusion of citizen science data and the acknowledgment of workdone by non-professionals have strengthened this trend of publicinvolvement.

4.3. Recommendations and perspectives

As no specific rules have been given from the EU as to how tochoose sub-indicators many types of indicators have been devel-oped (Lomba et al., 2014, 2015; van Doorn and Elbersen, 2012).Several HNV farming indicators include only a subset of the threecategories of HNV farming (semi-natural vegetation, low-intensityagriculture, rare species) or are only implemented in a limited geo-graphical area (e.g. Natura 2000 sites) (van Doorn and Elbersen,2012). The indicators are subject to ongoing refinement and fur-ther development (Peppiette, 2011). Farm or even field specificdetails related to cultivation intensity like crop diversity, irrigationlevels, stocking density etc. could have been used in the presentHNV farming indicator (Lomba et al., 2015). However, we argue thatmore permanent yet field specific variables, indicating e.g. continu-ity, species richness, soil infertility, natural hydrology or vulnerablespecies are more informative in a HNV farming indicator context.Future HNV farming indicators might benefit from incorporatingmore sophisticated information on structural variation e.g. basedon LiDAR data and remote sensing data (Lefsky et al., 2002). Whilethis kind of data is yet expensive and complex to use the inheritedhigh spatial resolution may improve the resolution of HNV farm-ing indicators considerably in many parts of the EU and herebysubsidies can be targeted toward specific areas.

We believe the expert based geo-referencing method presentedhere may serve as an inspiration for other EU member states toimprove the spatial resolution and reliability of existing speciesoccurrence data in a cost-efficient way. In theory the method canbe applied to all species groups, but it is developed for and probablymost useful for mobile organisms.

Biodiversity levels are not only determined by local conditions;nearby high quality natural habitats are also crucial to obtain pos-itive effects of agricultural subsidies on biodiversity (Kleijn et al.,2009). This way the HNV farming indicator can also be applied toidentify potentially valuable areas for restoration by consideringthe HNV farming score of the surrounding landscape and herebypoint out special target areas of conservation interest. Future appli-cations of the HNV farming map may also include studies of thecost-effectiveness of rural land planning for conservation. A HNV-farming map must prove its usefulness in real life and from 2015the presented map is being tested for the prioritization of Danishsubsidies.

Acknowledgements

We thank Flemming Helsing, Karsten Laursen, Lykke Pedersen,Anders Tøttrup, Palle Jørum, and Peter Wind for expert drawn mapsof butterflies, moths, birds, scarabs and vascular plants. AKB wassupported by the Carlsberg Foundation.

Appendix A. Supplementary data

Supplementary data associated with this article can be found,in the online version, at http://dx.doi.org/10.1016/j.ecolind.2015.09.027.

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