Metapopulations in Agricultural Landscapes: A Spatially Explicit Trade-off Analysis

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Metapopulations in AgriculturalLandscapes: A Spatially Explicit Trade-off AnalysisRolf Groeneveld a , Carla Grashof-Bokdam b & Ekko van Ierland ca Agricultural Economics Research Institute , The Hague, theNetherlandsb Department of Landscape Ecology , Alterra Green WorldResearch , Wageningen, the Netherlandsc Wageningen University, Environmental Economics and NaturalResources Group , Wageningen, the NetherlandsPublished online: 22 Jan 2007.

To cite this article: Rolf Groeneveld , Carla Grashof-Bokdam & Ekko van Ierland (2005)Metapopulations in Agricultural Landscapes: A Spatially Explicit Trade-off Analysis, Journal ofEnvironmental Planning and Management, 48:4, 527-547, DOI: 10.1080/09640560500128442

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Metapopulations in AgriculturalLandscapes: A Spatially ExplicitTrade-off Analysis

ROLF GROENEVELD*, CARLA GRASHOF-BOKDAM** &EKKO VAN IERLAND{

*Agricultural Economics Research Institute, The Hague, the Netherlands,

**Alterra Green World Research, Department of Landscape Ecology, Wageningen, the Netherlands,{Wageningen University, Environmental Economics and Natural Resources Group, Wageningen, the

Netherlands

(Received May 2004; revised January 2005)

ABSTRACT This paper presents a spatially explicit trade-off analysis of species conservation inagricultural areas. A spatially explicit model is presented that integrates an appliedmetapopulation model with a farm management model. The model is used to calculate productionpossibilities frontiers of net monetary benefits from agriculture and conservation of three specieswith different habitats. Simulations of spatial habitat allocation under European agri-environment schemes are compared to these production possibilities frontiers. The resultssuggest that the cost-effectiveness of current conservation policies may be greatly enhanced if thespatial dimension is considered explicitly.

Introduction

Land-use change is recognised as a major driver of biodiversity loss (Swanson,1995). For instance, in the lowlands of northwest Europe only a small fraction ofthe original area of semi-natural grassland is left as a result of fertilisation,drainage, urbanisation and afforestation (Delbaere, 1998). Furthermore, landscapeelements such as hedgerows, cropped willows and cattle ponds were removed asthe scale and intensity of farms increased. These developments have had adramatic impact on wildlife species typical of agricultural landscapes, as many ofthese species depend on extensively managed agricultural land and landscapeelements (Bunce et al., 1993).

To halt or even reverse habitat degradation in agricultural landscapes, agri-environment programmes are implemented through EEC Regulation 2078/92 in theEuropean Union and through the 2002 Farm Bill in the USA. These programmes

Correspondence Address: Rolf Groeneveld, Agricultural Economics Research Institute, PO Box 29703,

NL-2502 LS The Hague, the Netherlands. Telephone: +31 317 477721. E-mail: [email protected]

Journal of Environmental Planning and Management,Vol. 48, No. 4, 527 – 547, July 2005

ISSN 0964-0568 Print/1360-0559 Online/05/040527-21 ª 2005 University of Newcastle upon Tyne

DOI: 10.1080/09640560500128442

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allow for voluntary agreements between landowners (mostly farmers) and thegovernment under which the former take conservation measures and arecompensated for the costs of these measures. In 2002, EU expenditure on agri-environment schemes amounted to nearly e2 billion (Leguen de Lacroix, 2003),whereas in the USA more than $1.8 billion was released for conservation assistancein the 12 months following the introduction of the 2002 Farm Bill (Veneman, 2003).

However, the effectiveness and the efficiency of these programmes have comeunder increasing scrutiny. For instance, Kleijn et al. (2001) found no difference inplant and bird species diversity between fields under an agri-environment schemeand conventionally managed fields in the Netherlands. One of the explanations isthat the spatial structure of fields under an agri-environment scheme providesinsufficient scope for colonisation of newly established habitat. For instance, bothempirical research (Grashof-Bokdam & Geertsema, 1998) and model studies(Geertsema, 2002) suggest that the presence of plant species in agricultural areasincreases with the spatial connectivity of habitat.

Therefore, it is surprising that until recently, agri-environment programmes insome countries have little regard for the spatial dimension. In the Netherlands, forinstance, the spatial dimension plays a role only in the determination of theboundaries of programme areas. Within the area of an agri-environment scheme,compensation payments per hectare are fixed and contracts are granted on a firstcome, first served basis (LNV, 2004). However, this allocation procedure might leadto inefficient and even ineffective habitat configurations.

This paper focuses on two research questions. First, under what circumstancesmay current habitat allocation procedures lead to inefficient spatial habitatconfigurations? Second, what may be the shape of the trade-off curve betweenspecies conservation and agriculture in a specific study area? To provide insight intothese two questions, a spatial ecological model is integrated with an economic land-use model to analyse the spatial cost-effectiveness of agricultural nature manage-ment. This makes it possible to (i) simulate spatial habitat allocation procedures incurrent agri-environment schemes; (ii) optimise spatial allocation of habitat in orderto develop a cost-effective benchmark allocation to which simulation results can becompared, and (iii) calculate trade-off curves of conservation of particular speciesand agricultural income.

The novel contribution of this paper is the application of metapopulation theoryto the economic analysis of spatial habitat allocation in agricultural landscapes. In atypical metapopulation, the habitat consists of a number of patches that are discreteelements in the landscape, but lie sufficiently close to each other to allow formigration between patches. The total population in the area therefore consists of anumber of local populations that go locally extinct as ‘occupied’ habitat patches arevacated, and re-emerge as ‘empty’ habitat patches are colonised. Over the pastdecade, metapopulation theory has greatly improved ecologists’ understanding ofthe survival of wildlife populations in fragmented landscapes (see also Hanski &Gilpin, 1997).

So far, economic applications of metapopulation theory have been restricted tothe optimal exploitation of marine resources (see e.g. Brown & Roughgarden, 1997;Sanchirico & Wilen, 2001). An exception is the study by Bulte & van Kooten (1999),which deals with metapopulations in general, but does not specifically deal with

528 R. Groeneveld et al.

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optimal spatial habitat allocation in agricultural landscapes or other terrestrialecosystems.

Previous economic analyses of spatial habitat allocation used various approachesto relate habitat allocation to some measure of conservation success, for example,linear dispersion models (Hof & Raphael, 1997), ecological network models(Wossink et al., 1999), foraging models (Johst et al., 2002), demographic models(Marshall et al., 2000) and regression models based on simulation data (Calkin et al.,2002). The vast majority of cost-effectiveness analyses of habitat allocationconcentrate on forestry (e.g. Hof & Raphael, 1997; Marshall et al., 2000; Calkinet al., 2002), although arable farming (Wossink et al., 1999) and grasslandagriculture (Johst et al., 2002) are also dealt with.

The analysis relates spatial habitat allocation to the expected area of inhabitedhabitat by means of an applied metapopulation model (cf. Hanski, 1994). Theecological model used has the advantage that it is widely used by ecologists (seee.g. Lawes et al., 2000; Vos et al., 2000; Tyre et al., 2001) and can be estimatedby ‘snapshot’ patch occupancy data. For a demonstration of the model, ananalysis is made of the trade-off between agriculture and conservation of the treefrog (Hyla arborea), the chequered skipper (Carterocephalus palaemon) and thebarn owl (Tyto alba) on land used by dairy farms in a study area in the southernpart of the Netherlands.

This paper is organised as follows. The next section discusses the general structureof the model. The following section discusses the economic and ecological data forthe area chosen to demonstrate the methodology, as well as results from model baseruns as an indication of the model’s performance. There is then a discussion aboutthe results of the analysis, and the final section presents conclusions.

General Model Structure

The objective of the model is to allocate habitat over a number of plots in the area,maximising the size of the metapopulation subject to an agricultural incomeconstraint. The central choice variable in the model is the spatial allocation of landuse, where land use can be intensively managed pasture land or arable land, as wellas woodlots and extensively managed grassland. The choice variable is related to twoobjectives, namely agricultural income and species conservation, where agriculturalincome is determined by an economic land-use model, and metapopulation size isdetermined by an applied metapopulation model (Figure 1).

To identify possible inefficiencies in current habitat allocation procedures in agri-environment schemes, the model is used to calculate two scenarios that differ withregard to how spatial allocation of agri-environment measures takes place. In thefirst scenario, called the ‘grant scenario’, farmers maximise gross margin under afixed grant per hectare for habitat. The second scenario, called the ‘full optimisationscenario’, may be interpreted as such that a social planner allocates habitatmaximising the ecological objective and compensating farmers exactly for theirlosses in gross margin. This scenario serves to calculate a benchmark of cost-effectivespatial habitat allocation to which the results from the grant scenario may becompared to reveal whether current allocation procedures lead to cost-effectivespatial habitat allocations.

Metapopulations in Agricultural Landscapes 529

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To analyse the grant scenario, the economic model maximises gross margin of alldairy farms included, after which the ecological model calculates the ecologicalobjective variable from the resulting habitat allocation. To analyse the fulloptimisation scenario, i.e., to calculate a cost-effective benchmark of spatial habitatallocation, the ecological model maximises the ecological objective while theeconomic model sets economic welfare at the same level as in the grant scenario.

The indicator of economic welfare used in the full optimisation scenario issomewhat different from gross margin as maximised in the grant scenario. This isbecause gross margin includes agri-environment grants, which are a benefit tofarmers, but a fiscal burden on society. To analyse a trade-off between economicwelfare and species conservation, ‘net monetary benefits from agriculture’ areintroduced, which include revenues from sale of milk and cattle minus transportcosts and factor inputs, but not agri-environment grants.

However, excluding other agricultural subsidies such as the EU premium formaize production from net monetary benefits may complicate interpretation of theresults. After all, it would be difficult to distinguish efficiency gains due to thedifference in habitat allocation from those due to a difference in the definition ofgross margin and net monetary benefits from agriculture. This does not hold foragri-environment grants as these grants differ by definition between the grantscenario and the full optimisation. Therefore, all other agricultural subsidies thanagri-environment grants are included in net monetary benefits from agriculture.

The Economic Model

The economic land-use model is based on the farm management model developed byBerentsen & Giesen (1995). For a set of dairy farms and the plots used by thesefarms, the economic model maximises gross margin or calculates net monetarybenefits taking the following restrictions into consideration: (i) enough fodder mustbe supplied through purchase and production; (ii) enough land must be available forgrazing; and (iii) input and output of nitrogen must be in balance.

Fodder Supply. Fodder supply must be sufficient to feed both young cattle and dairycattle:

Figure 1. General structure of the model.


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Xv2Dc

nivScfv5dcikcf 8 c; f; i; ð1Þ

whereDc is the set of all fodder types v suitable for cattle type c, niv denotes the nutritivevalue of fodder type v with respect to feeding criterion i; Scfv denotes supply of foddertype v to cattle type c on farm f; dci denotes fodder demand per cow of cattle type cwithrespect to criterion i; and kcf denotes the number of cattle of type c on farm f. Becausehay from extensivelymanaged grasslands is not suitable for dairy cattle (deHaan et al.,1996), Dc includes this fodder type for young cattle, but not for dairy cattle.

Current Dutch agri-environment programmes are designed under the assumptionthat they must fit in the regular management of the farm without drasticallychanging farm strategy, and that cattle numbers are currently restricted by theavailable milk quota. Therefore, investigations of the economic effects of agri-environment contracts, such as those by de Haan et al. (1996), assume a fixedlivestock and grazing system. It is therefore assumed that the number of young anddairy cattle (kcf) is fixed. Each cattle category has its own feed intake capacity:

Xv

zv Scfv 4cckcf 8 c; f; ð2Þ

where zv denotes the extent to which fodder type v occupies the feed intake capacitycc of cattle type c (Zom et al., 2002).

Available fodder types are concentrates, grazed grass, freshly mown grass and grasssilage from conventionally managed plots, freshly mown hay and hay silage fromextensively managed plots, and maize. Concentrates are bought on the market, andfresh grass and hay, as well as grass and hay silage, are produced on the farm. Maizecan be produced, bought and sold. Fodder production per farm is determined by

Xc

Qcfv ¼Xp2Of

Xl2Lv

dlvAlp 8 f; v; ð3Þ

where Qcfv denotes production of fodder type v for cattle type c on farm f; Of denotesthe set of all plots p belonging to farm f; Lv denotes the set of all land-use types lproducing fodder type v; dlv denotes production per hectare of fodder type v by land-use type l, and Alp denotes the area of land-use type l on plot p. The sum of the areasof all land-use types is smaller than or equal to the size of the plot:

Xl

Alp4ap 8 p; ð4Þ

where ap denotes the size of plot p. The difference between production and supply iseither purchased or sold:

Xc

ðScfv �QcfvÞ ¼ Pfv 8 f; v; ð5Þ


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where Pfv denotes the net purchase of fodder type v by farm f.

Grazing. To enable daily milking, dairy cattle can graze only on plots close to thefarm building (Nijssen & van Scheppingen, 1995):

Q‘dairy cattle’; f ‘grazed grass’4Xp2Hf

d‘grazing’; ‘grazed grass’A‘grazing’; p 8 f; ð6Þ

where Hf denotes the set of home plots of farm f. Furthermore, sufficient grazingland must be available on home plots to graze dairy cattle:

Xp2Hf

uA‘grazing’; p5k‘dairy cattle’; f 8 f; ð7Þ

where u denotes the maximum number of dairy cows per hectare grazing land.Finally, to take other cattle types into consideration, sufficient land must be availableto graze all cattle:

Xp2Of

tA‘grazing’; p5Xc

kcf 8 f; ð8Þ

where t denotes the maximum number of cattle per hectare grazing land. Farms witha number of dairy cows per hectare far beyond the maximum number suggested byde Haan et al. (1996) and by Nijssen & van Scheppingen (1995) presumably keepcattle indoors all year, and are treated accordingly in the analysis.

Manure balance. Input of nitrogen from on-farm manure production and fertiliserpurchase is equal to output of nitrogen by removal and application of manure andfertilisers:

Rf þXp2Ot

Xl

klAlp ¼ Ff þXc

mckcf 8 f; ð9Þ

where kl denotes nitrogen supply to land-use type l; Rf denotes removal of nitrogen inthe form of animal manure from farm f; mc denotes nitrogen output through manureproduction per cow; and Ff denotes nitrogen fertiliser purchase by farm f.

Economic objective variables. Total gross margin G is defined as the sale of milk,culled cows, and calves plus agri-environment payments, minus transport costs andfactor inputs:

G ¼Xf

�Xc

rckcf þXp2Of

Xl

�gl � otp

�Atp �

Xv

tvPfv � wFf � rRf

�8 f; ð10Þ

where rc denotes revenues per cattle type c; gl denotes grants paid for land-use type l;olp denotes general land-use costs of land-use type l on plot p; iv denotes the price offodder type v; w denotes the price of fertiliser, and r denotes the costs of removing


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manure from the farm. Net monetary benefits from agriculture are equal to grossmargin minus agri-environment grants.

The Ecological Model

The ecological submodel is an applied metapopulation model based on Hanski’s(1994) incidence function model (IFM). The ecological model assumes each plot cancontain one habitat patch for each species, the size of which is equal to the cover areaof all land-use types that can serve as habitat:

Hps ¼Xl2FS

Alp 8 p; s; ð11Þ

where Hps denotes the size of the habitat patch of species s on plot p, and Fs denotesthe set of all land-use types that may serve as habitat for species s. The size of thehabitat patch determines the extinction probability of the local population as well asthe immigration rate of the habitat patch:

Eps ¼ min

�1;

msH

bsps

�8 p; s; ð12Þ

Mps ¼Xq6¼p

JqsbpqsHssqs 8 p; s; ð13Þ

where Eps denotes the extinction probability of species s on plot p; ms and bs arecoefficients;Mps denotes the immigration rate of species s in the habitat patch on plotp; q denotes all other plots than p; Jps denotes the probability that the habitat patchof species s on plot p is inhabited by species s; bpqs denotes the distance between p andq corrected for ecological barriers to migration of individuals of species s, and ss is acoefficient. Variable Jps is generally called the incidence in the ecological literature.The immigration rate Mps determines the probability that an empty habitat patch onplot p is colonised:

Cps ¼Mzs

ps

Mzsps þ gs

8 p; s; ð14Þ

where Cps denotes the colonisation probability of the habitat patch on plot p byspecies s, and zs and gs are coefficients. The extinction probability Eps and thecolonisation probability Cps determine the incidence Jps by what is generally referredto as the incidence function:

Jps ¼ Cps

Cps þ Eps � CpsEps8 p; s: ð15Þ

Finally, we express the ecological objective as the expected area of occupied habitat,denoted by Bs, and defined as the sum of all products of habitat patch size andincidence:


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Bs ¼Xp

JpsHps 8 s: ð16Þ

Data Used and Model Base Run

This section explains the economic and ecological data used to calibrate the modeland compares results of a model base run to empirical data. The study area is part ofDe Leijen, a region of roughly 125 km2 located between Tilburg and ‘s-Hertogenbosch in the province of Noord-Brabant (Figure 2), which is dominatedby dairy agriculture. The focus is on the eastern part of De Leijen, which coversroughly 66 km2 (Schmitz, 1996) and consists mainly of sandy soil, earthy soil andhumic podzol (Schrijver, 1991).

Target Species in the Study Area

The analysis concentrates on three species present in De Leijen: the chequeredskipper (Carterocephalus palaemon), the tree frog (Hyla arborea) and the barn owl(Tyto alba). These species were selected because of their presence in the study area,their conservation status and the availability of IFM parameter values or otherconservation criteria to be included in the model. The exact parameter values usedare presented in the Appendix to this paper.

The chequered skipper (Carterocephalus palaemon) depends mainly on semi-natural grasslands, swamps and shrubs and is very sensitive to early mowing andgrazing regimes. Because, to the authors’ knowledge, IFM parameters have not yetbeen estimated for the chequered skipper, Hanski’s (1994) IFM parameter estimatesfor the silver-spotted skipper (Hesperia comma), which has similar requirements asregards habitat size and connectivity (Bink, 1992), were used. In the model,chequered skipper habitat consists of extensively managed grassland, where bothgrazing and the use of fertiliser and biocides are prohibited, and mowing dates arepostponed.

Figure 2. Location of the study area in the Netherlands.


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The tree frog (Hyla arborea) is on the IUCN Red List of Threatened Species(IUCN 2002). Its habitat consists of an aquatic part (e.g. a pond) and a terrestrialpart (e.g. shrubs and bushes) (Vos et al., 2000). Because the incidence function modelassumes homogeneous habitat patches, tree frog habitat patches are treated ashomogenous in the model. Furthermore, because the costs and the compensationpayments per ha of the aquatic part are negligible compared to those of theterrestrial part, only the costs of and the compensation payments for the terrestrialpart are included. The calculations use the IFM parameter estimates of ter Braak &Etienne (2003), which offer improved parameter estimations with the data used byVos et al. (2000).

The barn owl (Tyto alba) is far more mobile than the tree frog and the chequeredskipper. Therefore, the study adopts de Bruijn’s (1994) recommendation that onaverage at least 5 km of hedgerow per km2 be present in the area. Assuming the totalarea where barn owl habitat needs to be established has a size of 72 km2 thisamounts to a required 360 km of hedgerow for the entire study area.

Economic Data Used

The total surface area of agricultural plots in the Eastern part of De Leijen is4032 ha, which is used by 337 farms. About 36% of these farms have dairy cows,with an average of 46 head of dairy cattle per farm (Schmitz, 1996). Otheragricultural activities in the area include beef cattle farming, horticulture andarable farming.

Using the classification applied by Schmitz (1996), those farms were includedwhere (i) livestock agriculture takes up more than 60% of total farm size; (ii) noother type of agriculture takes up more than 20% of total farm size; and (iii) wherethe number of dairy cattle units is positive. This selection includes 96 farms that use atotal number of 521 plots, covering about 44% of the original area of agriculturalplots (see Figure 3). The agricultural area not included in the analysis belongs toother cattle farms (1095 ha, 27% of the study area), breeding stations of pigs, poultryor calves (534 ha, 13%), horticultural enterprises (437 ha, 11%), arable farms (116ha, 3%) and farms of unknown or undefined types (61 ha, 2%).

Detailed farm and plot data such as plot size, plot perimeter, plot ownership,farm-plot distance, and the number of dairy cows come from Schmitz (1996). Datanot specifically available for De Leijen were taken from nationwide sources (de Haanet al., 1996; PV 2001; Zom et al., 2002). Appendix A presents the values of theparameters in the model.

Economic properties of habitat types. Chequered skipper habitat is less productivethan intensively managed grassland, and hay produced on this land-use type is oflow quality and unsuitable for dairy cattle. The area of chequered skipper habitat perfarm is therefore restricted by the area of land needed for grazing, and by the amountof hay that can be fed to young cattle. Direct costs are lower than those of intensivelymanaged grassland as a result of reduced agro-chemical use (de Haan et al., 1996). Itis assumed that the effect of a postponed mowing date is sufficiently visible in thereduced quantity and quality of fodder production. The compensation payment forthis type of agricultural nature management is e1122 per ha (LASER, 2001).


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Treefrog habitat requires maintenance of both the aquatic and the terrestrial part.Based on the assumptions underlying the official compensation payments (LBL,1997; LASER, 2001), it is assumed that the direct maintenance costs are e524 per haplus average transport costs of e33 per km per ha. As the barn owl also depends onhedgerows, the costs of its habitat are comparable to those of tree frog habitat.

Model performance in base runs. As an indication of the performance of the modelbase run results were compared to empirical data on gross margin, cover area ofmaize and the uptake rate of agri-environment schemes.

Average gross margin is e88 475 per farm in base runs of the model, and aboute1773 per dairy cow. According to LEI (2003), average gross margin of dairy farmsin the sand regions (which includes De Leijen) was e112 810 per farm and e2169 perdairy cow in 1999/2000. This implies that the model tends to underestimate grossmargin both per farm and per cow. This may be due to the fact that the empiricalgross margin includes revenues from and costs of non-dairy activities, such as pig,poultry and arable farming, or that farmers annually sell a larger fraction of the herdthan the 25% assumed in the model. However, it should be noted that the figuresfrom LEI (2003) refer to a larger area than just the study area, so that thesedifferences may also be due to regional variation.

With regard to the relative area of maize, the base run predicts that 30% of landused by the farms in the dataset is devoted to maize production. The average area offodder crops per farm in the sand areas was 24% in 1999/2000 (LEI, 2003), andSchrijver (1991) suggests that the area of fodder crops in De Leijen was about 33%in 1989. The model results fit well within this range.

Figure 3. Plots in De Leijen included in the analysis, the N65 motorway from Tilburg to ‘s-Hertogenbosch and the area counted as the area within which an average of 5 km of hedgerowper km2 is required for conservation of the barn owl. The shading of the plots indicates theirdistance (corrected for road quality according to Schmitz, 1996) from the farm.


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With regard to uptake of agri-environment contracts, empirical data show that44% of the area where agri-environment contracts can be concluded is under such acontract (Dienst Landelijk Gebied, pers. comm., 2004; Landelijke Service BijRegelingen, pers. comm., 2004). However, this figure is difficult to compare to themodel results because it includes more types of agri-environment contracts than areincluded in the model. Furthermore, many of these contracts were concluded underthe old RBON scheme (LNV, 1975), which was replaced by the newer ProgrammaBeheer scheme in 2000 (LNV, 2004). The model base run predicts that about 9% ofthe area will be under an agri-environment contract. Because the measures assumedin the model are quite restrictive, an idea of the accuracy of the model can be gainedby comparing the modelling results to the uptake of the most restrictive contractsunder RBON and Programma Beheer. These contracts cover about 12% of the area(Dienst Landelijk Gebied, pers. comm., 2004; Landelijke Service Bij Regelingen,pers. comm., 2004). The model may therefore underestimate uptake of agri-environment contracts, but it should also be noted that the empirical figures refer tosomewhat different contracts than assumed in the model.

Constructing Production Possibilities Frontiers of Conservation and Agriculture in De

Leijen

To simulate current spatial habitat allocation procedures the grant scenario was runassuming the government sets a fixed grant per hectare for extensively managedgrassland (habitat for the chequered skipper) or agricultural timber combined with acattle pond (habitat for the tree frog). To find possible improvements to the spatialallocation of habitat found in the grant scenario, the full optimisation scenario wasrun at the same level of net monetary benefits. If the current neglect of habitatconnectivity in the spatial allocation of agricultural nature management contractsleads to inefficient spatial configurations, the full optimisation scenario should yieldhigher ecological objective values than the grant scenario at the same level of netmonetary benefits from agriculture.

To construct production possibilities frontiers of species conservation andagriculture this procedure was repeated for different values of the agri-environmentgrants.

Net Monetary Benefits from Agriculture Versus Single Species Conservation

Figures 4 and 5 show the results from the grant scenario (triangles) and the fulloptimisation scenario (dots) for the chequered skipper and the tree frog. As Figure 4shows, little improvement seems possible of the spatial allocation of chequeredskipper habitat that results from the grant scenario. However, tree frog habitat maybe allocated more cost-effectively as the expected area of occupied habitat issomewhat higher in the full optimisation scenario than in the grant scenario (Figure5).

Table 1 shows the differences between the scenarios in more detail at a grant ofe1330 per ha of tree frog habitat. In the grant scenario, the expected area of occupiedhabitat is 60 ha at net monetary benefits from agriculture of e8.4 million. However,the full optimisation scenario suggests that at the same level of net monetary benefits


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80 ha of expected occupied habitat can be achieved with a different spatial habitatallocation. Furthermore, in the cost-effective allocation calculated in the fulloptimisation scenario, the actual number and area of habitat patches is smaller than

Figure 5. Production possibilities frontier of net monetary benefits from agriculture and theexpected area of occupied tree frog habitat.

Figure 4. Production possibilities frontier of net monetary benefits from agriculture and theexpected area of occupied chequered skipper habitat.


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that in the grant scenario. This indicates that in order to improve habitatconnectivity, ‘cheap’, poorly connected plots are substituted by ‘expensive’, wellconnected plots, thereby increasing expected area of occupied habitat by more than34%.

This can also be seen at Figures 6 and 7, which show that many habitat patches inthe grant scenario are considerably isolated. Under the full optimisation scenario,these habitat patches are left out, whereas well connected habitat patches areincreased in size.

The reason why the grant scenario yields such inefficiencies for the tree frog andnot for the chequered skipper lies in the difference in local extinction probability

Table 1. Economic and ecological characteristics of the grant scenario at a grant of e1330 perha of tree frog habitat, and of the full optimisation scenario at the same level of net monetarybenefits from agriculture

Grant scenario Full optimisation

Net monetary benefits from agriculture (1000 e) 8368 8368Compensations paid (1000 e) 235 214Gross margin (1000 e) 8604 8582Actual number of habitat patches 80 58Expected number of occupied patches 22 24Actual habitat area (ha) 177 169Expected area of occupied habitat (ha) 60 80

The difference in compensation payments is a result of the assumption that agriculturalincome losses are offset exactly in the full optimisation scenario, whereas in the grant scenarioincome losses are overcompensated for in some cases.

Figure 6. Spatial configuration of tree frog habitat in the grant scenario at a grant level ofe1330 per ha.


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between the two species. Figure 8 shows the increase in local incidence due to anincrease in the immigration rate Mps, as a function of Mps for a habitat patch of 5 ha.The figure shows that an increase in the immigration rate has a much stronger effecton the incidence Jps for the tree frog than for the chequered skipper. This, in turn, is aresult of the higher extinction probability of the tree frog at virtually all values of

Figure 8. Marginal incidence with respect to the migration rate of a habitat patch of 5 ha. Asthe extinction probability of the tree frog is much higher in a 5 ha habitat patch than that ofthe chequered skipper, an increase in migration rate contributes more to the incidence of a treefrog habitat patch than it would contribute to the incidence of a chequered skipper habitatpatch.

Figure 7. Spatial configuration of tree frog habitat in the full optimisation scenario at the samelevel of net monetary benefits from agriculture as in Figure 6.


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habitat patch size. For instance, the extinction probability Eps of a given patch p of 5ha is 0.25 for the tree frog, whereas it is only 0.0014 for the chequered skipper at thesame patch size. Therefore, as local tree frog populations are more prone toextinction, the tree frog metapopulation depends more strongly on habitatconnectivity.

These results indicate that when connectivity of habitat patches plays a minor rolein spatial habitat allocation, as is currently the case in Dutch agricultural naturemanagement policy, the resulting spatial habitat allocation may for some species beinefficient.

Interspecies Trade-offs

Interspecies trade-offs were calculated by maximising the expected area of occupiedchequered skipper habitat under the restriction that (i) the expected area of occupiedtree frog habitat is at least as high as some minimum value; and that (ii) netmonetary benefits from agriculture are at least as high as some minimum value. Byrepeating this procedure for varying minimum values for the expected area ofoccupied tree frog habitat and net monetary benefits from agriculture, a set of pointson the production possibilities frontier was calculated. Following this procedure, athree-dimensional production possibilities frontier was calculated with and withoutan additional restriction that 360 km of hedgerow must be present for the barn owl.

Figure 9. Production possibilities frontier of net monetary benefits from agriculture, expectedarea of occupied tree frog habitat and expected area of chequered skipper habitat.


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Results without 360 km of hedgerow. As shown in Figure 9, the productionpossibilities frontier of the chequered skipper and the tree frog without hedgerowrestrictions is a three-dimensional plane spanned between three points. The firstpoint is where net monetary benefits from agriculture in the area are maximal (e8.6million) and the expected area of occupied habitat of both species is zero. At thesecond point, the expected area of occupied habitat is maximal for the chequeredskipper (761 ha), net monetary benefits are e7.1 million and the expected area ofoccupied tree frog habitat is zero. At the third point the expected area of occupiedtree frog habitat is maximal (674 ha), net monetary benefits are e6.2 million and theexpected area of occupied chequered skipper habitat is zero.

Theoretically, if social preferences were known, for instance, as indifference curvesof tree frog conservation, chequered skipper conservation and net monetary benefitsfrom agriculture, the optimal conservation strategy could be determined. However,even without such information a frontier as presented in Figure 9 can assist policymakers in the development of conservation strategies in the study area.

Results with 360 km of hedgerow to conserve the barn owl (Tyto alba). The model wasalso run under the additional restriction that 360 km hedgerows must be present inthe study area. The results show that because hedgerows also serve as tree froghabitat, this restriction has the effect that the expected area of occupied tree froghabitat is above roughly 8 ha in all solutions. As hedgerows are not included assuitable habitat for the chequered skipper, barn owl conservation reduces the scopefor chequered skipper conservation. However, the overall production possibilitiesfrontier remains the same.

Discussion and Conclusions

This paper aims to identify possible inefficiencies in current habitat allocationprocedures in agri-environment schemes, and to gain insight into the shape of thetrade-off curve of species conservation and agriculture. To this objective, the studycalculated production possibilities frontiers of net monetary benefits fromagriculture and conservation of three different species, and simulation results ofcurrent policy were compared to these production possibilities frontiers. This wasdone using a spatially explicit farm management model and a spatial metapopulationmodel.

The results suggest that in some cases current allocation procedures, which hardlyconsider the spatial dimension, may indeed lead to inefficient spatial habitatconfigurations. However, this result depends strongly on the target species and thetotal actual habitat area. In the analysis, this effect was found only for the tree frogand only when total actual habitat area was small. The main reason was that the treefrog has a relatively high probability of going locally extinct.

Therefore, when the achievable habitat area is small and the target species issensitive to spatial habitat configuration, it seems wise to adopt a habitat allocationprocedure that allows for spatial co-ordination. The ecological achievements ofagricultural nature management schemes might be improved if, instead of applyingthe current first come, first served selection process, nature management agencieswere first to consider all contract requests, and then grant those that together


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constitute the optimal spatial habitat configuration. Cost-effectiveness of spatialhabitat allocation can also be improved by allowing compensation payments to varyper farm, for instance, by auctioning nature management contracts (see e.g. Muller& Weikard, 2002). Another option is to establish collective nature managementagreements with environmental cooperatives, and to let the cooperatives determinethe cost-effective spatial habitat allocation (see also Franks, 2003).

Before coming to the conclusions, some limitations of study should be notedthat ask for some caution when interpreting the quantitative results. Comparisonof IFM parameter estimates for the tree frog in Vos et al. (2000) and ter Braak &Etienne (2003), and of those for butterfly species in, for instance, Hanski (1994),Hanski et al. (1996), and Moilanen & Cabeza (2002), reveal that IFM parameterestimates vary considerably depending on the estimation method used. Likewise,the agricultural economic data might vary, as many of the data used are averageson a national scale.

To preserve the applicability of the model some simplifying assumptions had to bemade. In accordance with the assumptions in Dutch agri-environment policy, cattlestock were kept exogenous in the analysis. Furthermore, all species were assumed tobe independent of populations or habitat areas outside the study area, as these areaswere ignored in the analysis. This might have led to the underestimation of incidence.As discontinuous variables are problematic in the optimisation algorithm applied,small contract areas were allowed for, whereas in reality only contracts larger than0.5 ha are granted. Costs of postponing mowing dates and establishment costs ofhabitat were neglected, as these data were not available. Finally, the analysisexcludes non-agricultural land, possible habitat outside the study area, andagricultural land of other categories than the dairy farms included. Thereby, thepotential habitat area of the target species is probably underestimated.

The ecological model used is applicable only to species that can be characterisedby the incidence function model. Furthermore, it is imperative that the populationcan be approached as a metapopulation, in other words, that its habitat isdistributed over a number of discrete, small patches. In linearly shaped habitats suchas field margins, patch-based models such as the ecological model applied in thisanalysis are therefore less suitable. Furthermore, as more habitat is allocated in thearea and more patches are established possibly adjacent to other habitat patches, themetapopulation approach becomes less applicable. The same holds for the economicmodel: farmers will probably change their cattle stock or grazing system if much landneeds to be converted to habitat, but these changes were not included in the model ascattle stock and grazing system are assumed to be fixed. Therefore, the model resultsare most relevant when total habitat area is small. The habitat area realised at thecurrent compensation payment per hectare is such that both models are wellapplicable.

Despite these limitations, it is concluded that the methodology presented in thispaper can serve as a tool to explore the possibilities of species conservation in dairyagriculture in specific areas and to estimate the cost-effectiveness of spatially explicitconservation scenarios. It can also be used to provide the spatial coordination ofhabitat allocation by agricultural nature management agencies or environmental co-operatives, as recommended in this section. The methodology can be extended toinclude metapopulations of other species and to other agricultural sectors. An


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important condition for the application of the methodology is that spatially explicitdata of the plots and their users, as well as parameter estimates of the incidencefunction model, are available for the target species, and that the target species can betreated as a metapopulation.

Acknowledgements

The authors gratefully acknowledge useful comments made by Hans-Peter Weikardand Paul Berentsen on the analysis and on previous versions of this paper, and theprovision of data on uptake of agri-environment contracts by Dienst LandelijkGebied and Landelijke Service Bij Regelingen.

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Appendix: Parameter Values

This appendix provides a brief overview of the parameter values included in themodel. A more detailed description including all data sources is available with theauthors of this paper.

Table A1. Parameter values of land use types

Grant

(e ha7 1)

Transport costs

(e km7 1 ha7 1)

General

costs (e

ha7 1)

Nitrogen

input

Production

(ton ha7 1)

Mowed hay 1122 55 583 0 5.9Hay silage 1122 55 501 0 6.3Grazed grass - 55 212 300 10.3Mowed grass - 55 1224 300 11.0Grass silage - 55 1069 300 11.7Maize - 145 772 200 14.8Tree frog habitat 550 33 524 0 -

Table A2. Parameter values of fodder types

Nutritive value

Market price

(e ton7 1)

Energy (MJ

NEL kg7 1)

Protein

(gDVE kg7 1) Fiber (7 ) Satiety value

Mowed hay - 5.4 70 3.75 1.45Hay silage - 4.8 53 3.75 1.45Grazed grass - 6.9 102 1.7 0.9Mowed grass - 6.8 107 1.7 0.9Grass silage - 6.2 73 2.75 1Maize 121 6.4 47 1.5 0.8Concentrates 160 6.5 90 0.24 0.4


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Table A4. Incidence function model parameter values of the target species

Parameter

Carterocephaluspalaemon Hyla arborea

a 2 2Incidence y2 10.1761 4.81function b 1.103 0.1876model s 1 0.5parameters z 1.636 0.4087

e 0.008 0.3341Critical habitat patchsize

0.0126 0.002898

Barrier indicator 1 0.5

Table A5. Parameter values of hedgerows

Hedgerow maintenance costs (e ha7 1) 524Hedgerow transport costs (e km7 1 ha7 1) 33Hedgerow width (m) 3Minimum total hedgerow length (km) 360

Table A6. Some key figures about the dataset used

Number of farms 96Number of plots 521Average farm size 18.6+ 10.3 haAverage number of plots per farm 5.4+ 2.8Average plot distance 1.7+ 2.3 kmAverage number of dairy cows 48+ 22.3Average plot size 3.4+ 3.6 ha

Table A3. Parameter values of cattle

Calves Yearlings Dairy

Milk production (kg per year) - - 7000Net revenues (e per dairy cow) - - 2617Maximum number of cows per hectare home plot - - 7Maximum number of cows per hectare farm area - - 3.5Fodder intake capacity (ton per season) Summer 0.8 1.5 2.7

Winter 0.7 1.4 2.6Energy demand (MJ NEL per season) Summer 7.5 7.5 21.1

Winter 6.5 6.5 20.5Protein demand (kg DVE per season) Summer 53 53 240

Winter 53 53 242Nitrogen input through animal manure (kg per year) 18 37 54


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Metapopulations in Agricultural Landscapes: A Spatially Explicit Trade-off Analysis

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