A novel ecological-economic modeling procedure for the design of cost-effective agrienvironment schemes to conserve grassland biodiversity
DRAFT! Please do not quote without permission of the authors!
In developed countries each year a substantial amount of money is spent on agri-environment
schemes (AES) which compensate farmers for carrying out land use measures which are
costly to them but beneficial for biodiversity. We present an ecological-economic modeling
procedure to design cost-effective AES to conserve grassland biodiversity which is novel in
two ways: First, it comprehensively addresses challenges relevant to AES design. It covers a
wide range of endangered species and grassland types (13 bird species, 14 butterfly species
and 7 grassland types), includes many (altogether 475) different land use measures, takes into
account that the opportunity costs of these measures spatially differ as well as their effects on
the species and grassland types, and can be applied on a large spatial scale. Second, the
modeling procedure explicitly considers the different costs of the timings of the land use
measure as well as the impact of the timings on the different species and grassland types. We
demonstrate the power of the modeling procedure by evaluating an existing grassland AES in
Saxony, Germany, and find substantial improvements can be made in terms of costeffectiveness.
Key words: Biodiversity conservation, cost-effectiveness, ecological-economic modeling,
agri-environment schemes, payments for environmental services
1
1 Introduction
Agri-environment schemes (AES) targeted at the conservation of endangered species and
habitats have become a major policy instrument to protect farmland biodiversity in developed
countries (Khanna and Ando 2009, Kleijn et al. 2011). In the context of such schemes,
farmers are paid for certain types of agricultural land-use which are costly to them but
beneficial to endangered species and habitats. AES have become widespread and each year
several billion Euros are spent in the context of such schemes in Europe as well as in the
United States (e.g. IEEP 2008, Khanna and Ando 2009). However, research (Kleijn and
Sutherland 2003, Kleijn et al. 2011) and farmland biodiversity indicators (Voříšek et al. 2010)
suggest that current AES are – at best – partially successful to conserve farmland biodiversity.
This partial lack of success has also led to demands in the policy arena for a better use of
funds for AES, for example from the European Court of Auditors (2011).
In order to improve AES a key question is how to design payments to farmers in a way that
schemes are cost-effective. Following Wätzold and Schwerdtner (2005) a cost-effective AES
is here understood as a scheme that – depending on the perspective of interest – either
maximizes for a given budget the level of conservation of endangered species and habitats or
achieves desired conservation aims for a minimum budget. The design of cost-effective AES
for biodiversity conservation can be a rather complex problem which requires the integration
of ecological and economic knowledge in an optimization framework (Wätzold et al. 2006,
Cooke et al. 2009). The complexity is particularly high, if (I) a substantial number of species
and habitats are to be conserved, (II) numerous different land use measures exist as potential
conservation alternatives, (III) the opportunity costs of these land use measures in terms of
foregone profits for farmers spatially differ, (IV) the conservation impacts of the different
measures on the species and habitats of interest also spatially differ, and (V) the area in which
the AES is applied is of substantial size.
In this paper, we present an ecological-economic modeling procedure to design cost-effective
AES to conserve grassland biodiversity. Our modeling procedure is novel in two ways: First,
it addresses all the above-mentioned challenges relevant to AES design. (I) It covers a
comprehensive set of endangered grassland species and grassland types (13 bird species, 14
butterfly species and 7 grassland types), (II) includes altogether 475 different types of
mowing regimes, grazing regimes and combinations of mowing and grazing regimes (referred
to in the following as grassland regimes), (III) takes into account spatially different
opportunity costs of these regimes, (IV) considers that the effects of the regimes on the
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species and grassland types also spatially differ, and (V) can be applied to a large area like a
German federal state.
Second, we explicitly consider the timing of the land use measure. The grassland regimes
mainly differ depending on when and how frequent mowing takes place within a year
respectively the starting point and duration of grazing. The modeling procedure is able to
estimate the opportunity costs of the varying timings of grassland regimes as well as their
impacts on the different birds, butterflies and grassland types.
Our work is in the tradition of research that combines ecological and economic knowledge to
improve the cost-effectiveness of AES. Such research has become more frequent recently
although it is still rare. An early paper is by Johst et al. (2002) who develop an optimization
framework to determine cost-effective payments for measures to protect the white stork
(Ciconia ciconia) in a hypothetical landscape. Drechsler et al. (2007a) use the framework to
determine cost-effective payments to conserve an endangered butterfly species (Maculinea
teleius) in a real landscape, and Drechsler et al. (2007b) analyse the design of compensation
payments to protect three species with different habitat requirements. More recent research
explicitly considers species requirements for spatially aggregated habitats (Drechsler et al.
2010, Wätzold and Drechsler in press) and non-aggregated habitats (Bamière et al. 2011,
Bamière et al. 2013), trade-offs between agricultural production and biodiversity conservation
(Mouysset et al. 2011), dynamics aspects of agricultural landscapes (Barraquand and Martinet
2011), and costs and benefits of simplified versus spatially differentiated compensation
payments (Armsworth et al. 2012). Each of these studies addresses some of the challenges
that make AES design a complex task, and thus contributed in different ways to a better
understanding of how to design cost-effective AES. However, none of these papers have
addressed all the above-mentioned challenges in the design of cost-effective AES including
an integration of the timing of land use.
We show the power of the developed ecological-economic modeling procedure for improving
AES in large areas by applying it to the German federal state of Saxony (whose area is
approximately 60% of the size of Belgium). We assess in a first step the conservation impact
of an existing AES for grassland conservation in Saxony using the simulation module of the
procedure. In a second step, we calculate more cost-effective AES using the optimization
module of the procedure and compare the costs and the conservation effects of the proposed
cost-effective AES with the existing AES to assess the potential for improvement.
3
2 Land use and conservation problem
The German federal state of Saxony has a size of 18,342 km² of which approximately 55% is
used for agricultural production. The share of grassland is 186,120 ha which represents 17%
of the overall area used for agricultural production (Sächsisches Staatsministerium für
Umwelt und Landwirtschaft 2011). The existing grassland is either used for mowing, grazing
or a combination of mowing and grazing. In the absence of compensation payments farmers
typically apply the profit maximizing type of land use. For meadows, farmers typically mow
the first time at around 15 May and use the first cut for silage. A second cut is carried out
approximately 6 weeks later which is used for making hay and a third cut another 6 weeks
later which is again used for making hay. If the grassland is used for grazing the stocking rate
of the livestock is selected in a way that the grassland is used at times with optimal energy
content of the grass. Sometimes, farmers also combine mowing and grazing with a first cut in
mid-May for silage generation and a subsequent use of the meadow for grazing. In general,
farmers also apply nitrogen fertilizer.
Until the 1950s in Saxony as in other parts of Western and Central Europe a much larger
variety of mowing and grazing regimes existed which had generated a substantial species and
habitat diversity. Over the past decades, intensification and mechanization of agriculture led
to the current pre-dominant mowing and grazing regimes which results in a rather uniform use
of grasslands threatening many species and grassland types (Gerowitt et al. 2003, Benton et
al. 2003). Table 1 contains a list of grassland bird and butterfly species and grassland types in
Saxony and provides information about their protection status. The species and grassland
types were selected in cooperation with the responsible Saxon authorities for nature
conservation and include species and grassland types that are endangered or likely to become
endangered in the near future. The species and grassland types mentioned in Table 1 are
included in the ecological-economic modeling procedure.
--- Table 1 somewhere here
In order to reverse the trend of biodiversity loss in agricultural landscapes AES have been set
up all over the European Union since 1992. In the AES farmers are paid to adapt their land
management to benefit biodiversity, the environment or the landscape (EC 2012). In the
current programming period of the EU structural funds from 2007 to 2013, the main AES to
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conserve endangered grassland species and grassland types in Saxony is the program
‘Extensive Grünlandwirtschaft, Naturschutzgerechte Grünlandbewirtschaftung und Pflege’
(LfULG 2012a). The program contains several mowing and grazing measures which can be
assessed with the developed ecological-economic modeling procedure, but also a few
measures for which the procedure is not suitable (for example the transformation of arable
land to grassland and the impoverishment of grassland soils). We ignore these measures and
focus on the measures which can be assessed with the modeling procedure. Table 2 provides
an overview of these land use measures.
--- Table 2 somewhere here
3 Overview of the ecological-economic modeling procedure
The ecological-economic modeling procedure contains different components which perform
different tasks. A graphical overview of the procedure and how its different components are
connected is presented in Figure 1.
-- Figure 1 somewhere here
The procedure considers the endangered species and grassland types comprised in Table 1
(Fig. 1, box 1). For each species an information folder exists which contains specific
information about the life cycle of the species and its habitat requirements (Fig. 1, box 2).
This information is tailored in a way that it is suitable as input to the ecological model. The
information is based on a literature review and expert knowledge. Similarly, an information
folder exists for each grassland type. Grassland types do not only depend on abiotic
conditions like soil type and altitude but also on the type of land use. Therefore, in the
information folder the grassland types are defined by all possible land use measures (like the
frequency of impact, use of fertilizer) which may lead to their development.
The modeling procedure includes altogether 475 different mowing regimes, grazing regimes
and combinations of mowing and grazing as land use measures (Fig. 1, box 3). Some of these
land use measures also include limitations on the use of N(nitrogen)-fertilizer inputs. The
measures have also been selected together with the responsible Saxon authorities for nature
5
conservation. They comprise those land use measures which have been implemented in the
context of AES (cf. Table 2), those which have been discussed within the administration as
potential measures and those measures which have not yet been considered within the
administration but seem potentially suitable to conserve one or several species. An overview
of the measures is given in Table 3.
Table 3 somewhere here
The procedure considers spatially differentiated landscape and land use information on the
level of grid cells with a resolution of 250mx250m=6.25 ha (Fig. 1, box 4). This information
is required to assess the spatially differentiated opportunity costs of the farmers and impacts
of land use measures on species and grassland types. Land use data from Corine Land Cover
(CLC 2000, cp. European Environment Agency 2004) was used to determine whether a grid
cell contains grassland and whether a grid cell in its direct proximity contains water,
settlement or forest. This information is important to determine the habitat quality for some
species (see section on ecological model). In addition, the Saxon authorities supplied
information on whether mowing, grazing or a combination of mowing and grazing is the
predominant use in a grassland grid cell. There is also information about the soil moisture for
each grassland grid cell (data from BGR 2007) as some species and grassland types require a
certain level of soil moisture. Moreover, data on the soil productivity in each grassland grid
cell (provided by the Saxon authorities) is included which is important in order to spatially
differentiate the opportunity costs of the land use measures and the growth rate of grass which
may be relevant for the impact of measures on species. Data on altitude (from BKG 2008) is
also included for each grid cell which is needed to differentiate between the grassland type of
lowland hay meadow and mountain hay meadow (below/above 500 m height above sea level).
Finally, the Saxon authorities provided data on the occurrence of butterfly species in order to
be able to spatially focus land use measures on those areas which can be reached by the
species (cf. section on ecological model).
Landscape and land use information serve as input to the ecological model (Fig. 1, box 5) and
also partly to the agri-economic cost assessment (Fig. 1, box 6). The ecological model
quantitatively estimates the impact of the different land use measures on the species and
grassland types and is described in more detail in Section 4. The agri-economic cost
6
assessment estimates opportunity costs of the different land use measures and is described in
Section 5. Information from the ecological model and the agri-economic cost assessment is
combined to assess the ecological effectiveness of payment schemes (Fig. 1, box 7) and to
determine the cost-effective compensation payments through numerical optimization (Fig. 1,
box 8).
4 Ecological model
The purpose of the ecological model is to quantitatively estimate the impacts of land use
measures on species and grassland types. In designing the ecological model two major
challenges had to be overcome: First, the model must be general enough to capture all species
and grassland types of relevance for grassland conservation in a common way but at the same
time detailed enough to consider the differences among species. Second, the model has to take
into account that the impact of land use measures does not only depend on the type of
measure (e.g. mowing) but also on the timing of the measure and where it is carried out, i.e.
its spatio-temporal dimension. The ecological model is described in detail in Johst et al.
(submitted), and here only its main features are summarized. We start with an explanation of
how the model assesses the impact of measures on species and then address how the model
estimates the impact of measures on grassland types.
The ecological model assesses the impact of a specific land use measure on grassland species
by estimating its impact on the habitat quality for reproduction of the species. The reason for
this focus is that the type, temporal dimension and location of a land use measure typically
have a strong influence on the grassland in which these species reproduce. The model has a
temporal scale of quarter months, i.e. the year is divided into 48 quarter months with the first
quarter of January denoted as quarter month 1, the second quarter of January as quarter month
2 and so forth. The spatial scale of the model follows the selected spatial differentiation of
land use and landscape information (cf. section 3). We start the description of how the
ecological model functions by explaining how the model estimates for each grid cell the
impact of a land use measure on a species.
As the reproductive success of different species may differ substantially in absolute terms (a
butterfly typically lays much more eggs than a bird) the model uses a relative measure, the
local habitat quality q lj, m (t m ) , to estimate the impact of a land use measure m at timing t m on
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the reproduction of species j for each grid cell l. The timing t m refers to the date of first
application of the measure or to the doublet or triplet of dates in case of two or three mowing
cuts.
The local habitat quality can take values between qlj, m (tm )  0 (which means that land use
measure m leads to such a low habitat quality for species j on grid cell l that reproduction is
not feasible) and qlj, m (tm )  1 (which means that land use measure m maximizes habitat quality
for the reproductive success for species j on grid cell l). The local habitat quality q lj, m (t m )
consists of two components:
 f

qlj, m (tm )  Qlj,0  p wj  S mj, w (tm )  Qlj, m, w (tm ) 
wb

(1)
The first component Q lj,0 includes the local “abiotic” factors of the habitat suitability for
reproduction of species j in grid cell l which exist independent of the egg deposition time of
the species. The second component is the sum in brackets and encompasses features of habitat
suitability which depend on the timing t m of the land use measure m (when and how often it is
carried out) in relation to the egg deposition time of the species.
As species differ in the number of factors determining local ‘abiotic’ habitat suitability, Q lj,0
is calculated by the geometric mean of those factors relevant for a certain species. This
includes predation pressure Pjl which can be important for some bird species and is measured
on a scale from 1 (low predation pressure) to 0 (high predation pressure). If information is
lacking about the spatial allocation of predation pressure, or if predation is considered not
relevant for the design of AES, a value of 1 should be given to each grid cell. The modeling
procedure does not explicitly consider predation for the analysis of AES in Saxony (a value of
1 has been attributed to each grid cell), as information is lacking about the impact of predation
on the species. Fjl describes the suitability of a grid cell for a species with respect to soil
moisture, and is determined by integrating information to what extent species j requires a
certain degree of soil moisture and the availability of this level of soil moisture in the grid cell
8
l (cf. Johst et al. submitted for details). For some bird species, the suitability of an area and
their reproductive success depend on the existence of certain spatial structural elements. In the
modeling procedure we consider water, forest and settlement. E lj describes the suitability of a
grid cell l with respect to the availability of these spatial structural elements in one of its
neighboring grid cells with respect to the requirements of a specific species j.
For birds, Q lj,0 is therefore calculated by
Qlj,0  3 Pjl  Fjl  E lj
(2)
Butterflies reproduce largely unaffected by predation pressure and the presence of certain
spatial structural elements. They need both appropriate soil moisture Fjl and a certain
grassland type for high reproduction. For example, butterfly larvae may forage on specific
host plants only growing in particular grassland types. The quantity Glj,m (tm ) describes this
requirement by assigning Glj,m (tm )  1 to grassland measures generating the corresponding
grassland type and Glj,m (tm )  0 to measures that do not. Thus, for butterflies Q lj,0 additionally
depends on the measures m and their timings t m resulting in:
Qlj,0  Fjl  G lj,m (tm )
(3)
The second component in eq. 1 describes the impact of the timing t m of the land use measure
m on the habitat suitability of each grid cell in relation to the egg deposition time. Eggs are
deposited in certain quarter months w with certain species specific probabilities p wj . The
index w indicates that the well-being of a cohort depends on which quarter month it is
generated. Egg deposition starts in quarter month b and ends in quarter month f. Some
butterfly and bird species produce a second generation within a year. We refer to Johst et al.
(submitted) for an explanation of how the ecological model takes into account a second
generation.
9
The well-being of each cohort depends on S mj,w (tm ) and Qlj,m,w (tm ) . S mj,w (tm )
𝑆𝑗𝑤 𝑄𝑗𝑤 𝑆𝑗𝑤 describes the direct mortality of a cohort through a land use measure m. This may
happen, for example, by the destruction of bird nests through trampling of livestock or
mowing machines, or the destruction of a plant required for butterfly larvae through mowing
or grazing. In order to determine S mj,w (tm ) Sjw we divide each species’ total reproduction period
(which is the period during which the offspring are unable to leave the grassland) into a
critical reproduction period during which the offspring are immobile and cannot leave the
nest/plant and a mobile period during which they are able to leave the nest/plant and flee with
some probability. If mowing takes place during the critical reproduction period, survival of
the offspring is unlikely and we set S mj, w (tm )  0 , if it takes place during the mobile period it
is likely that some of the young may be able to flee and we set S mj,w (tm )  0.5 , and if mowing
takes place outside the reproduction period we assume no negative impact and set
S mj,w (tm )  1. As empirical studies show that mowing strips have a somewhat positive impact
on the survival of offspring (Broyer 2003) we set for mowing regimes with mowing strips
S mj, w (tm )  0.25 if mowing takes place during the critical reproduction period, and
S mj, w (tm )  0.75 if mowing takes place during the mobile phase. The value of S mj,w (tm ) for
grazing is also between 0 and 1 and depends on livestock type and density (cf. Johst et al.
submitted for details).
Qlj,m,w (tm ) Qw
j describes the impact of grass height on the habitat suitability for a cohort of
young generated in quarter month w. For example, breeding birds benefit from high grass as it
hides their nests, and some butterfly species require a certain blooming plant for egg
deposition which only exists on meadows with a certain grass height. The grass height at a
certain point in time in each grid cell is determined by the beginning of the growing season
(which depends on altitude), soil productivity (which determines the growth rate) and possible
impacts of land use measures, e.g. through mowing (cf. Johst et al. submitted for details).
The formula in eq. (1) is general enough to also assess the impact of land use measures on
grassland types. For this, however, the term in brackets is not required as it refers to the
generation and survival of offspring cohorts which is of no relevance to the generation of
grassland types. Eq. (1) therefore simplifies to
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q lj,m (tm )  Qlj,0  Fjl  G lj,m (tm ) ql,m
= Q0j ∙ Gjm
j
(4)
Q lj,0 here only encompasses soil moisture Fjl as predation and the existence of spatial
structural elements are not relevant for the development of grassland types. For grassland
types, Glj,m (tm ) expresses whether a land use measure m at timing t m generates a certain
grassland type or not (see above).
As described, the local habitat quality q lj, m (t m ) assesses the impact of a land use measure m at
timing t m on a certain species or grassland type j for each grid cell l in the landscape with the
temporal dimension of the land use measure being a key component in this assessment.
Nevertheless, aspects of the spatial dimension of land use (i.e. where a measure is applied) are
already included partly as the quantities Q lj,0 and Qlj,m,w (tm ) depend on the local spatial
conditions in grid cell l.
For evaluating the regional ecological effect of a spatiotemporal pattern of different grassland
measures in a given landscape, another spatial dimension has to be considered: the
connectivity of a grid cell in the landscape with grid cells actually occupied by the species.
This is because a high quality grid cell within the landscape is only beneficial when a species
can actually reach it. Each grid cell therefore contains information about whether it is
occupied by the species of conservation interest (cf. Table 1), and for each species
information is included about its maximum dispersal distance r j .
The overall ecological benefit of land use measure m for species j is assessed by calculating
the effective habitat area Aeff
which is the sum of all grid cells of size, Al in the landscape
j
(Saxony) multiplied with their local habitat quality q lj, m (t m ) :
A eff

j
A
l
 q lj,m (t m )
(5)
l ( r j ; q lj, m ( t m )  q min
j )
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The local habitat quality q lj, m (t m ) calculated by eq. 1 reduces the area of each grid cell to an
‘effective habitat area’ Al q lj,m (t m ) . The connectivity of a grid cell in the landscape with areas
occupied by the species is taken into account in eq. 4 by summing up only grid cells l that
contain cells presently occupied by the species within a certain radius r j around them. A
further restriction in eq. 4 is that only grid cells for which the local habitat quality exceeds a
certain threshold, i.e. for which q lj,m (t m )  q min
are summed up. The reason for this is that a
j
very low local habitat quality q lj, m (t m ) ql,m
cannot be compensated anymore by a larger area
j
and is thus generally not suitable for a species. We set the minimum habitat quality for
butterflies to q min
 0.1 and for birds to q min
 0.3 .
j
j
𝑒𝑓𝑓
In the ecological-economic modeling procedure the effective habitat area Aeff
𝐴𝑗
j
is used to
estimate the impact of a land use measure m or a land use pattern consisting of different
measures m on a species j on the regional scale.
5 Agri-economic cost assessment
The purpose of the agri-economic cost assessment is to estimate the opportunity costs of
farmers if they take part in an AES. Here, we only summarize how the cost assessment works
and refer to Mewes et al. (2013) for a detailed description. Although we speak in the
following of farmers, due to restrictions on data access the ecological-economic modeling
procedure considers only whole grid cells and not individual farmers, i.e. in the modeling
procedure each grid cell is cultivated by one ‘virtual farmer’. We assume that farmers
maximize their profit and that a farmer on grid cell l is willing to take part in a scheme which
supports measure m if the payment 𝑝𝑚 at least covers his costs of participating in the scheme
𝑙
𝑝𝑚 ≥ 𝑐𝑚
(𝑡) + 𝑡𝑐.
(6)
𝑙
Here 𝑐𝑚
(𝑡) represents the opportunity costs of the farmer for not being able to carry out one
of the profit-maximizing forms of grassland use (these are the prevalent grassland uses as
descripted in chapter 2, henceforth referred to as reference scenarios) which depend on the
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timing t of the land use measure m, and tc represents the transaction costs of the farmer which
arise from taking part in the scheme.
Transaction costs occur in particular due to administrative work like filling out forms and
communication with the responsible authorities, for example, in the case of monitoring and
enforcement activities. Following Mettepennigen et al. (2009) who empirically estimated
transaction costs of farmers arising from participation in AES we assume that t is € 40 per ha
per year.
𝑙
The calculation of 𝑐𝑚
(𝑡) is done in line with EU requirements (cf. Regulation (EC) No
1698/2005), and follows, in principle, the way payments are calculated by the Saxon
authorities. The calculations are done for each grid cell l and measure m and contain three
elements: First, changes in the quantity and quality of the yield (fresh grass, silage, hay)
generated with land use measure m, second changes in labor input from the farmer if measure
m is implemented, and third changes in other inputs required for grassland production. All
three changes are calculated relative to the profit-maximizing reference scenarios.
The calculation of the costs resulting from a change in yield is not trivial. One reason is that
not only the quantity but also the quality of silage and hay (in the case of mowing) and grass
(in the case of grazing) differ depending on the timing t of mowing or grazing (see Mewes et
al. 2013 for details). Furthermore, grass, silage, and hay are typically used as input by the
farmer himself for his livestock production. This implies that market prices for silage and hay
of different qualities do not exist, which makes it difficult to directly calculate the costs
resulting from changes in yield.
In line with common practice of how payments are calculated in Saxony, we therefore assume
that farmers buy concentrated feed to compensate the loss in the quality and quantity of yield
due to applying land use measure m. In order to quantify the loss we use as a proxy the
changes in net energy content in the yield (measured in mega joule net energy content for
lactation) resulting from applying land use measure m. To calculate this indicator, the net
𝑙
energy content of the yield of land use measure m, 𝑦𝑚
(𝑡), is estimated and subtracted from
the estimated net energy content of the yield from the profit-maximising reference scenario
𝑙
𝑦𝑟𝑒𝑓
. We assume that the farmer has to be compensated for the price of concentrated feed that
contains the net energy content which he has lost due to participation in land use measure m.
A detailed description of the calculations of the net energy content of the yield in the
reference scenario and for the various land use measures can be found in Mewes et al. (2013).
13
The agri-economic cost assessment includes as input goods for grassland production seeds,
pest management products, fertilizer, hail insurance, use of machines, hired labor, machine
𝑙
rental, ensilage and other inputs. In line with the approach in Saxony, the labor input 𝑙𝑎𝑚
from the farmer is considered separately. To calculate changes in the different inputs the
𝑙,𝑖
modeling procedure estimates the input quantity 𝑣𝑚
for each input good i (excluding use of
𝑙
machinery) and the labor input 𝑙𝑎𝑚
from the farmer of grid cell l for land use measure m and
𝑙,𝑖
subtracts it from the input quantity 𝑣𝑟𝑒𝑓
for each input i (excluding use of machinery) and the
𝑙
labor input 𝑙𝑎𝑟𝑒𝑓
from the farmer for the profit-maximizing reference scenario for each grid
cell l multiplied with the respective prices. Costs for changes in the use of machines (uref-um)
are calculated differently by considering each machine type used taking into account its
service time, fuel price and maintenance costs and the corresponding changes if the land use
measure m is applied. Overall, the opportunity costs of the farmer for not being able to carry
out the profit-maximizing form of grassland use are calculated as
𝑙,𝑖
𝑙,𝑖
𝑙
𝑙
𝑙
𝑐𝑚
= (𝑦𝑟𝑒𝑓
− 𝑦𝑚
)𝑝𝑓 − (𝑙𝑎𝑟𝑒𝑓 − 𝑙𝑎𝑚 )𝑝𝑙 − ∑𝑛𝑖=1(𝑣𝑟𝑒𝑓
− 𝑣𝑚
) 𝑝𝑣𝑖 − (𝑢𝑟𝑒𝑓 − 𝑢𝑚 )
(7)
with 𝑝𝑣𝑖 representing the market price of input 𝑣𝑖 , 𝑝𝑙 the farmer’s wage rate, and 𝑝𝑓 the market
price of concentrated feed per mega joule net energy content for lactation.
6. Simulation of AES
The ecological-economic modeling procedure is able to simulate the effects of existing and
planned AES on one, several or all species and grassland types that are considered in the
modeling procedure.
In the context of the modeling procedure a specific AES is determined by one or several land
use measure(s) m, a payment 𝑝𝑚 for each measure, and a maximum area 𝐴𝑚𝑎𝑥
for each
𝑚
measure on which it can be applied. Selecting a maximum area 𝐴𝑚𝑎𝑥
allows to simulate
𝑚
existing schemes for which the size of the area on which a particular measure is applied is
known. Furthermore, setting a maximum area may be necessary to avoid that the modeling
procedure allocates all grid cells to the same measure if taking part in this measure is most
profitable for farmers (see below how the modeling procedure allocates grid cells to measures
14
and Ohl (2008) in general for potential problems if farmers have to select between different
payment schemes).
For the simulation of an AES the modeling procedure now adopts the following process. For
each measure m all grid cells are identified on which the payment exceeds participation costs
𝑙
of farmers 𝑝𝑚 ≥ 𝑐𝑚
+ 𝑡𝑐 and on which for at least one species the minimum habitat quality
(cf. description of ecological model) is exceeded. The procedure generates for each measure a
list which includes all identified grid cells. The grid cells on these lists are ranked according
to the positive differences between payments and participation costs with the grid cell with
the largest difference ranked first.
The modeling procedure now considers the first grid cell of each list and attaches the measure
𝑙
to the grid cell for which the difference between payment and participation cost 𝑝𝑚 − (𝑐𝑚
+
𝑡𝑐) is highest. The assumption behind this selection criterion is that in reality it is likely that
those farmers with the highest difference between payment and participation costs undertake
the most efforts to participate in a specific measure. The grid cell with the attached measure is
then taken from the list with the former second grid cell now becoming the first grid cell on
this list. This process continues until either a measure is attached to each grid cell on all lists
or the maximum area for each measure 𝐴𝑚𝑎𝑥
is reached. Thus, the simulation of an AES
𝑚
results in a certain land use pattern.
The ecological effect of this land use pattern is determined by calculating the effective habitat
𝑒𝑓𝑓
area 𝐴𝑗
for each species and grassland type as described in the chapter on the ecological
𝑒𝑓𝑓
model 𝐴𝑗
. The budget required for the AES which generates this land use pattern is
calculated by multiplying the payments for each measure with the number of grid cells on
which this measure is applied.
𝐵 = ∑𝑚 𝑝𝑚 𝑙𝑚
(8)
7. Optimization of AES
The ecological-economic modeling procedure can also design cost-effective AES with two
different optimization alternatives being available. The first alternative is to maximize the
ecological benefits of an AES for selected species and grassland types for a given Budget 𝐵0
15
𝑒𝑓𝑓
𝐴 = ∑𝑗 𝑤𝑗 𝐴𝑗
→ 𝑚𝑎𝑥 subject to 𝐵 ≤ 𝐵0
(9)
with 𝑤𝑗 representing weights to express the relative importance of each species to the
regulator. As we have no information about these weights we assume for the cost-effective
assessment of the Saxon AES that the regulator has equal preferences for all species and
grassland types. For this alternative, the modeling procedure determines the cost-effective
measure or set of measures and the corresponding payment(s) as well as the maximizing sum
of effective habitat areas A.
The second alternative is to minimize the budget for an AES under the condition that for each
𝑒𝑓𝑓𝑚𝑖𝑛
species and/or habitat of interest an effective habitat area of a certain minimum size 𝐴𝑗
is reached
𝑒𝑓𝑓
W𝐵 → 𝑚𝑖𝑛 subject to 𝐴𝑗
𝑒𝑓𝑓𝑚𝑖𝑛
≥ 𝐴𝑗
for all j
(10)
For the second option, preferences for the conservation of different species and grassland
𝑒𝑓𝑓𝑚𝑖𝑛
types are included through the selection of the minimum size 𝐴𝑗
for each species.
We use simulated annealing (Kirkpatrick et al. 1983), a heuristic numerical optimization
method, for identifying the cost-effective alternatives. Simulated annealing quasi-randomly
explores the decision space. To start the process, an initial solution is compared with a
randomly created neighboring solution and the cost-effective alternative is selected. If in the
beginning of the process the neighboring solution is worse than the previous solution it can be
still selected with some probability. This is to prevent the algorithm to get stuck in some local
optimum. The selected solution is in a next step then compared with a new randomly created
neighboring solution. This is repeated many times where towards the end of the process only
better solutions are accepted. As a result, simulated annealing determines a near-optimal
solution to the optimization problem.
16
8 Estimating the ecological effectiveness and cost-effectiveness of the Saxon AES
We applied the ecological-economic modeling procedure to estimate the ecological
effectiveness (through simulation) and the cost-effectiveness (through optimization) of the
Saxon AES ‘Extensive Grünlandwirtschaft, Naturschutzgerechte Grünlandbewirtschaftung
und Pflege’ (LfULG 2012a) targeted at biodiversity conservation in grasslands.
Simulation of the Saxon AES
For the simulation of the Saxon grassland AES, the ecological effects of the different land use
measures contained in this AES on species and grassland types are calculated. The calculation
considers for each individual measure the sub-budgets allocated to this measure and the
payment (cf. Table 2). The effects on all species and grassland types included in Table 1 are
𝑒𝑓𝑓
estimated. The calculated effective habitat areas 𝐴𝑗
for each species and grassland type are
shown in Table 4.
--- Table 4 somewhere here
The simulation indicates that the Saxon AES is able to conserve endangered grassland birds
(all birds with the exception of the crested lark are to some extent protected as their effective
areas are larger than zero), but is much less successful at the protection of grassland types and
butterflies. Only four of seven grassland types and four of 14 butterflies are conserved – with
only very minor protection for the marsh fritillary.
Optimization of the Saxon AES
In order to analyze the cost-effectiveness of the Saxon AES the optimization is carried out in
the two alternative ways introduced above.
Alternative (I):
17
We take the overall budget of 11,127,357 € spent for the existing Saxon AES (Table 2) as
given and maximize the conservation of species and grassland types under consideration
(each of which is given equal weight 𝑤𝑗 , cf. eq. 8), and
Alternative (II):
We take the results from simulating the impact of the existing AES on species and grassland
types (Table 4) as the conservation aims and minimize the budget (cf. eq. 9).
As the optimization with all 475 land use measures would require too much computation time
we carry it out for both alternatives in two steps. In a first step we identify land use measures
that are strong candidates to be included in the cost-effective AES. We do this by dividing for
each species and grassland type the ecological benefits of each measure by its cost and then
select for each species the two measures with the highest benefit/cost ratio as candidates to be
included in the optimization. In a second step, we carry out the optimization with the
candidate measures plus the land use measures from the existing Saxon AES, resulting
altogether in 68 measures.
For both alternatives the results of the optimization analysis comprise a proposed list of landuse measures m (with mowing respectively grazing dates t m ) to be included in the costeffective AES, the compensation payment for each measure, the estimated area to be covered
with each land use measure, the budgets for the individual measures, and the overall budget
for AES as well as the total area covered with measures from the proposed cost-effective
scheme (Tables 5a and 5b). The results also contain the impacts of the proposed cost-effective
AES on birds, butterflies, and grassland types for the alternatives I (Figures 2a-c) and II
(Figures 3a-c).
--- Tables 5a and 5b and Figures. 2a-c and 3a-c somewhere here
We find that for a budget of approximately the same size as the budget for the existing AES,
substantial improvements can be made in terms of conservation (alternative I). As estimated
above the current AES conserves only 4 butterfly species, 12 bird species, and 4 grassland
types whereas the proposed cost-effective scheme covers 7 butterfly species, 13 bird species,
and 6 grassland types. Moreover, for all species and grassland types (with the exception of
18
lowland hay meadows and alluvial meadows) the proposed scheme reaches a higher level of
conservation. The increases in effective habitat area range from 40.6% for the meadow pipit
up to a factor of 9.1 for the garganey and more than 62 for the marsh fritillary. However,
many more different individual measures are required for these improvements. The existing
Saxon AES contains only 8 different land use measures (cf. Table 2) whereas the proposed
cost-effective AES includes 35 measures (cf. Table 5a), 4 of which, however, are also
included in the existing Saxon AES.
We also find that all species and grassland types that are conserved with the existing AES and
a budget of € 11,127,357 million can also be conserved with an alternative AES and a budget
of € 7,973,502 million (alternative II). The proposed alternative cost-effective AES even leads
to more conservation than the existing AES (Figs. 3a-c). Whereas most birds only perform
moderately better under the proposed alternative, some grassland types, and in particular
butterflies, perform substantially better. The proposed alternative AES contains 16 different
land use measures which are 8 more measures than the current AES. Two of the 16 measures
are also included in the existing Saxon AES (cf. Table 5b).
9. Discussion of results
Our results indicate that the Saxon grassland AES to conserve endangered grassland
biodiversity can be substantially improved in terms of cost-effectiveness.
One key reason for the lack of cost-effectiveness is that the Saxon scheme contains only
relatively few land use measures (eight measures) which are unable to provide the variety of
grazing and mowing regimes necessary to conserve many different endangered species and
grassland types. Therefore, not surprisingly, especially the cost-effective alternative I, which
aims to conserve a wide spectrum of species and grassland types, encompasses many more
land use measures.
Many different land use measures are needed because in particular individual butterfly species
and grassland types require specific land use regimes for their conservation. This is reflected
in the ecological model by Glj,m (tm ) which is either one or zero and thus has a strong impact
on the resulting habitat quality (eqs. (2) and (3)). In contrast, bird species do not depend on
Glj,m (tm ) (eq. 2). For them it is important that they are not disturbed by grazing or mowing
during their reproduction in the grassland (depending on the bird species roughly until early
19
June/July). Some of the measures in the existing Saxon AES fulfill this condition (e.g.
measures G3a and 3b, cf. Table 2) which is the reason why this AES nearly protects all bird
species. However, our analysis also indicates that improvements in terms of how well they are
conserved can be made: the effective habitat area is considerably increased in the costeffective AES.
Another important reason for the lack of cost-effectiveness of the existing Saxon AES is that
the ecological-economic modelling procedure proposes lower compensation payments for the
same measures than the payments in the existing Saxon AES (compare Table 2 with Tables 5a
and 5b respectively). Then, obviously, it is less expensive to cover a given area with the same
measure (eq. 7) which increases the cost-effectiveness of the proposed two alternative AES.
The reason why the existing Saxon AES has higher payments than our proposed cost-effective
alternatives is as follows. The calculations for the existing payment scheme are done on
average values for opportunity costs of grassland regimes for the whole of Saxony. This
implies that also areas with high opportunity costs are taken into account in the calculations.
This is different in the ecological-economic modelling procedure. The payment here is
selected in a way that it just covers the opportunity cost of the grassland scheme for the
farmer with the highest opportunity cost among all the farmers who participate in the scheme.
The opportunity costs of the farmers with high opportunity costs have no influence on the
payment.
One possible reason for the comparatively low level of cost-effectiveness of the existing
Saxon AES could also be that one of the individual measures has little conservation impact. In
order to find out whether this reason played a role we individually simulated all eight existing
measures. However, all measures performed reasonably well.
Interestingly, when comparing the simulated conservation effects of the individual measures
with the simulated effects of the whole Saxon AES we found that the effects of the measure
G2 (cf. Table 2) on the woodland ringlet, five-spot burnet and alluvial meadows when
simulated individually was higher than the effects of the whole Saxon AES (all eight
measures including G2).
The reason for this somewhat surprising result is that in the individual simulation grid cells
for the measure G2 can be selected from the whole landscape, i.e. the whole of Saxony. This
is different when all measures are simulated together. Then – depending on the profits of
farmers from the different measures (cf. Section 6) – grid cells are also allocated to other
20
measures. This leads to a different location of grid cells allocated to the measure G2 resulting
in a different impact on the two butterfly species and the alluvial meadows.
On a general level, this finding draws attention to the fact that if in the context of AES
farmers are presented with a choice between different land use measures (as with the Saxon
AES, see Table 2) this choice may have important repercussions of the effectiveness and costeffectiveness of AES. This aspect has not been given much attention yet in the literature (see
Ohl (2008) for an exemption).
Note that the two proposed cost-effective alternative AES do not include all species and
grassland types included in the ecological-economic modelling procedure; not even
alternative 1 where we give equal weight to the conservation of all species and grassland
types (eq. 8). The reason for this result is that the protection of these species and grassland
types requires either very specific measures or they exist currently in Saxony only on very
few small areas. This implies that if they shall be protected with an AES for which farmers
from the whole of Saxony can apply and no spatial discrimination is possible conservation
becomes so costly that – given equal weight for the protection of all species and grassland
types – the modelling procedure ignores measures to protect these species and grassland
types.
10 Summary and conclusions
We developed an ecological-economic modeling procedure which is able to estimate the
effects of AES in grasslands on endangered species and grassland types and to assess the costeffectiveness of existing schemes and identify more cost-effective alternatives. The novelty of
our modeling procedure is that it considers a wide range of species (birds and butterflies) and
grassland types as well as alternative land use measures, i.e. mowing and grazing regimes and
combinations of mowing and grazing. It also takes into account the spatial variation of the
costs of these different land use measures and also the spatial variation of the effects of the
measures on species and grassland types, and can be applied to a large area like a German
federal state. Moreover, we explicitly consider the timing of the land use measures as the
modeling procedure is able to estimate the opportunity costs of the varying timings of
measures as well as the impact of the timing on the different birds, butterflies and grassland
types.
21
In terms of policy relevance, the modeling procedure is also novel as it is suitable for
improving existing AES on large spatial scales. We demonstrate this power by assessing the
conservation impact of existing grassland schemes in the German federal state of Saxony and
by designing cost-effective alternatives. Our results indicate that substantial improvements
can be made in terms of cost-effectiveness. In order to enable the use of the modeling
procedure for policy design we used it as a basis for developing the decision support software
DSS-Ecopay. DSS-Ecopay has been developed in cooperation with potential users from the
Saxon authorities responsible for the design of AES (Sächsisches Landesamt für
Landwirtschaft, Umwelt und Geologie). Like the modeling procedure the software can be
used to improve AES in terms of effectiveness and cost-effectiveness. It is also flexible in a
way that a user is able to adapt it to changing ecological and economic data and information
(Mewes et al. 2012).
It has been criticized that there is an insufficient availability of suitable methodology to
evaluate the success of AES (Höjgård and Rabinowicz (in press)). In our opinion, ecologicaleconomic modeling procedures as the one presented in this paper, and, based on them,
decision support software are suitable approaches for a better design of real-world AES. In
order to hold the decline in farmland biodiversity, and to ensure that society’s scarce
resources devoted to AES are spend cost-effectively it is highly important to further develop
such approaches.
Our results indicate that cost-effective grassland schemes which comprehensively conserve
species and grassland types include many different land use measures. The ecological reason
for this result is that different grassland species and grassland types require for their
conservation also different mowing and grazing regimes (Johst et al. submitted). This finding
is in line with the call from ecologists for generating habitat heterogeneity in agricultural
landscapes to conserve farmland biodiversity (Benton et al. 2003) and supports the demand
from conservation practitioners in many European countries to have more diverse agrienvironment schemes (Wätzold et al. 2010). However, agri-environment schemes with many
different land use measures tend to lead to higher transaction costs for the administration
compared to rather uniform schemes, and are often rejected on these grounds (Wätzold et al.
2010, Armsworth et al. 2012). We do not have a quantitative assessment of administrative
costs of the existing AES as well as of the proposed cost-effective alternatives, but it is
plausible that the “marginal administrative cost” of adding one additional land use measure in
the portfolio of land use measures included in an AES is not very high.
22
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25
Tables
Table 1: Species and grassland types of Saxony included in the modeling procedure,
information about protection status according to LfULG (2012b).
Latin name
Red List
Saxony1
English name
Butterflies
Coenonympha glycerion
Cupido minimus
Erebia medusa
Erynnis tages
Euphydryas aurinia
Hesperia comma
Lasiommata maera
Lycaena hippothoe
Maculinea nausithous
Maculinea teleius
Melitaea cinxia
Polyommatus amandus
Polyommatus semiargus
Zygaena trifolii
3
G
2
V
1
2
3
2
*
1
2
*
2
-
Chestnut Heath
Small Blue
Woodland Ringlet
Dingy Skipper
Marsh Fritillary
Silver-spotted Skipper
Large Wall Brown
Purple-edged Copper
Dusky Large Blue
Scarce Large Blue
Glanville Fritillary
Amanda´s Blue
Mazarine Blue
Five-spot Burnet
Grassland types
Festuco-Brometalia with
Bromion erecti
Nardetalia
Molinion caeruleae
Cnidion dubii
Arrhenatheretalia,
Arrhenatherion elatioris
Polygono-Trisetum
Calthion
BNat
SchG4
§
§
Annex II
§
Annex II, IV
Annex II, IV
§
§§
§§
§
§
§
Birds Directive3
Birds
Alauda arvensis
Anas querquedula
Anthus pratensis
Crex crex
Galerida cristata
Gallinago gallinago
Numenius arquata
Perdix perdix
Saxicola rubetra
Tetrao tetrix
Tringa totanus
Upupa epops
Vanellus vanellus
Legal Protection
Grassland types
Directive2
(V)
1
1
2
2
1
2
3
1
1
1
2
Skylark
Garganey
Meadow Pipit
Corncrake
Crested Lark
Snipe
Curlew
Partridge
Whinchat
Black Grouse
Redshank
Hoopoe
Lapwing
Annex I
Annex I
§
§§
§
§§
§§
§§
§§
§
§
§§
§§
§§
§§
Grassland types
Directive2-Code
Semi-natural dry grasslands + scrubland facies on calcareous substrates
Species-rich Nardus grasslands on
siliceous substrates
Molinia meadows on calcareous,
peaty or clayey-silt-laden soils
Alluvial meadows of river valleys of
the Cnidion dubii
Lowland hay meadows
Mountain hay meadows
Wet meadows
1
6210
6230
6410
6440
6510
6520
-
Red list of threatened species: 1: critically endangered - extremely high risk of extinction; 2:
endangered - high risk of extinction; 3: vulnerable - high risk of endangerment, V: near
26
threatened - likely to become endangered in the near future; G: endangerment is assumed, *:
least concern
2
Grassland types Directive: Council Directive 92/43/EEC on the Conservation of natural
grassland types and of wild fauna and flora adopted in 1992; it aims to protect some 220
grassland types and approximately 1,000 species listed in the directive's Annexes. Annex II
species require designation of Special Areas of Conservation, Annex IV species are in need of
strict protection.
3
Birds Directive: Council Directive 2009/147/EC on the conservation of wild birds adopted in
2009 in replacement of Council Directive 79/409/EEC of 2 April 1979. It aims to protect all
European wild birds and the grassland types of listed species.
4
BNatSchG= Federal Nature Conservation Act: §= specially protected, §§= strictly protected.
27
Table 2: Measures according to Richtlinie (Directive) AuW/2007, part A, section G
‚Extensive Grünlandwirtschaft, Naturschutzgerechte Grünlandbewirtschaftung und Pflege‘
Name of measure and main requirements
1
G1a (extensive grassland management pasture)
Paym
ent
per
ha in
€1
Size of area
for this
measure in
2011 in ha2
Overall
expenses
for this
measure
2011 in €2
108
24,425
2,637,900
108
6,379
688,932
312
2,998
935,376
373
11,275
4,205,575
394
3,200
1,260,800
392
782
306,544
190
4,733
899,270
536
360
192,960
use of pasture or of pasture with early mowing, minimum
(maximum) stocking rate of 0.3 (1.4) grazing livestock unit per ha
(GLU)/ha), maximum input of liquid manure not to exceed 1.4
LU/ha per annum, N fertilizer restriction according to EC 834/2007
G1b (extensive grassland management meadow)
extensive meadow, use of pasture allowed after 15 August
(maximum stocking rate 1.4 GLU/ha), maximum input of liquid
manure not to exceed 1.4 LU/ha per annum, N fertilizer restriction
according to EC 834/2007
G2 (conservation-enhancing meadow use; no fertiliser before
mowing, 15 June)
first mowing not allowed before 15 June (grazing only allowed after
1 August), no application of N fertilizer before first mowing
G 3a (conservation-enhancing meadow use; general ban on
fertiliser, 15 June)
first mowing not allowed before 15 June (grazing only allowed after
1 August), complete ban on application of N fertilizer
G 3b (conservation-enhancing meadow use; general ban on
fertiliser, 15 July)
first mowing not allowed before 15 July (grazing only allowed after
1 September), complete ban on application of N fertilizer
G 5 (conservation-enhancing meadow use; ban on fertilizer,
temporary halt of utilisation)
minimum two mowings per year, completion of first mowing not
after 10 June, second mowing not before 15 September, complete
ban on application of N fertilizer
G 6 (conservation-enhancing grazing, late beginning)
minimum period of grazing each year with minimum stocking rate
0.3 GLU/ha, beginning of grazing not before 1 June, complete ban
on application of N fertilizer
G 9 (establishment of fallow land/strips on grassland)
mowing and clearing of cut grass between 15 August and 15
November at least every two years, measure is only supported if
(agriculturally used) grassland is adjacent, minimum size of 0.1 ha,
maximum size of 2 ha, complete ban on application of N fertilizer
Overall amount of money available for measures in Table 2: 11,127,357 €
1
Information and data from LfULG (2012a)
2
Data from Sächsisches Staatsministerium für Umwelt und Landwirtschaft (2011)
28
Table 3: Overview of the 475 measures differentiated according to land use regimes and
parameters (QM=quarter month, year divided in 48 consecutively numbered QM, e.g. QM 19
= 15th to 22th of May)
Land use
regime
Characteristics
Number of measures
Mowing
Time of first cut (QM 19-30): 12
Interval from first to second cut (0,4,6,8,10 QM): 5
N-Fertilizer (reduced/no): 2
Only one cut after QM 30, time (QM 31-40): 10
N-Fertilizer (reduced/no): 2
Time of first cut (QM 19/20): 2
Interval from first to second cut (0,4,6,8,10 QM): 5
N-Fertilizer (reduced/no): 2
Stocking rate (Ø 0.5 GV/ha): 1
Mix of livestock type: 1
N-Fertilizer (no): 1
Start of grazing period (QM 13,15,17,…,29): 9
stocking rate (1.5, 3, 4 GV/ha): 3
type of livestock (lively/quiet): 2
N-Fertilizer (no): 1
First time of grazing (QM 19-30): 12
Interval from first to second grazing (0,4,6,8,10 QM): 5
N-Fertilizer (reduced/no): 2
Only one grazing time after QM 30, time (QM 31-40): 10
N-Fertilizer (reduced/no): 2
Time of first cut (QM 19-28): 10
Interval from first cut to grazing (6 QM): 1
N-Fertilizer (reduced/no): 2
Type of livestock (lively/quiet): 2
Stocking rate (1.5, 3, 4 GV/ha): 3
12*5*2=120
Mowing strips
All-year grazing
Seasonal
grazing
Rotational
grazing
Combination of
mowing and
pasture
29
2*10=20
2*5*2=20
1
9*3*2*1=54
12*5*2=120
2*10=20
10*1*2*2*3=120
Table 4: Results from simulating the ecological effectiveness of the Saxon AES from Table 2
Species and grassland type
Effective habitat area
𝑒𝑓𝑓
𝐴𝑗 in ha
Butterflies
Chestnut Heath
Small Blue
Woodland Ringlet
Dingy Skipper
Marsh Fritillary
Silver-spotted Skipper
Large Wall Brown
Purple-edged Copper
Dusky Large Blue
Scarce Large Blue
Glanville Fritillary
Amanda’s Blue
Mazarine Blue
Five-spot Burnet
15.27
0.00
17.60
0.00
0.65
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
12.51
Birds
Skylark
Garganey
Meadow Pipit
Corncrake
Crested Lark
Snipe
Curlew
Partridge
Whinchat
Black Grouse
Redshank
Hoopoe
Lapwing
8704.89
468.94
47638.05
4712.25
0.00
3044.25
7077.94
16924.50
31981.10
12170.10
11371.37
791.33
11720.38
Grassland types
Semi-natural dry grassland + scrubland facies on calcareous substrates
Species-rich Nardus grasslands on siliceous substrates
Molinia meadows on calcareous, peaty or clayey-silt-laden soils
Alluvial meadows of river valleys of the Cnidion dubii
Lowland hay meadows
Mountain hay meadows
Wet meadows
30
0.00
0.00
0.00
607.38
1656.57
945.05
584.38
Table 5a: Results from optimizing the Saxon AES (alternative I; maximizing conservation
aims for a given budget)
Land use measure
Code1
Payment
per ha in €
Participating
area in ha
Overall cost of
measure in €
Mowing (1)*
19/6/6.0 D
58.39
1,518.75
88,679.81
Mowing
19/10/6.0 D
107.43
1,850.00
198,745.50
Mowing
20/6/0.0 D
277.08
18.75
5,195.25
Mowing
21/10/0.0 D
201.96
312.50
63,112.50
Mowing
21/6/6.0 D
101.12
2,606.25
263,544.00
Mowing
22/6/6.0 D
72.93
1,343.75
97,999.68
Mowing (2)*
23/6/6.0 D
90.27
2,331.25
210,441.93
Mowing
24/6/0.0 D
219.12
4,018.75
880,588.50
Mowing
26/4/0.0 D
407.74
356.25
145,257.37
Mowing
26/8/0.0 D
213.10
493.75
105,218.12
Mowing
26/6/6.0 D
123.38
8,981.25
1,108,106.62
Mowing (3)*
27/6/6.0 D
142.52
3,681.25
524,651.75
Mowing strips
19/8/0.0 D
426.61
12.50
5,332.62
Mowing strips (4)*
19/6/6.1 D
53.66
10,018.75
537,606.12
Mowing strips
20/6/6.0 D
109.14
4,512.50
492,494.25
Mowing strips
20/10/0.0 D
263.72
606.25
159,880.25
Seasonal grazing
15/0/0.1.5 GLU
401.96
31.25
12,561.25
Seasonal grazing
21/0/0.1.5 GLU
283.42
25.00
7,085.50
Seasonal grazing
23/0/0.3 GLU
505.54
25.00
12,638.50
Seasonal grazing
25/0/0.3 GLU
305.93
6.25
1,912.06
Seasonal grazing
29/0/0.3 GLU
576.81
106.25
61,286.06
Rotational grazing
25/6/6.0 D
98.09
5,281.25
518,037.81
Rotational grazing
26/4/4.0 D
109.80
6,562.50
720,562.50
Rotational grazing
26/6/6.0 D
111.14
2,993.75
332,725.37
Rotational grazing
30/6/4.0 D
127.65
4,518.75
576,818.43
Rotational grazing
30/4/6.0 D
133.41
268.75
35,853.93
Mowing & pasture comb.
19/6/0.1.5 GLU
330.45
218.75
72,285.93
Mowing & pasture comb.
22/6/0.2 GLU
303.12
3,543.75
1,074,181.50
Mowing & pasture comb.
25/6/0.3 GLU
400.97
2,562.50
1,027,485.62
Mowing & pasture comb.
26/6/0.3 GLU
298.63
3,906.25
1,166,523.43
Mowing & pasture comb.
19/4/6.0 D
124.42
6.25
777.62
Mowing & pasture comb.
19/10/6.0 D
143.33
1,075.00
154,079.75
Mowing & pasture comb.
21/6/6.0 D
135.66
350.00
47,481.00
Mowing & pasture comb.
22/6/6.0 D
122.13
268.75
32,822.43
Mowing & pasture comb.
27/6/6.0 D
158.95
2,406.25
382,473.43
Overall cost
11,124,493,58
*(1) corresponds to measure G 1b in Table 2, (2) to G 3a, (3) to G3b, and (4) to G 9.
1
the first number in the code is the QM of the first cut/beginning of grazing, the second
(third) number indicates the interval between the first (second) cut and second (third) cut in
31
QM. 0 D (1 D) indicates that N-fertilizer is not (only after the first cut) allowed. GLU
indicates the maximum grazing livestock unit permitted. For example, ‘mowing 20/6/0.0 D’
means that the first cut is not allowed before the 20 QM, a second cut is allowed six weeks
later and the 0 means there is no difference between the second and third cut, i.e. there is no
third cut, and the use of N fertilizer is permitted after the 20 QM. Consider as another
example ‘seasonal grazing 23/0/0.3 GLU’ which means grazing can start at 23 QM with no
restriction afterwards except that the maximum grazing livestock units shall not exceed 3
GLU.
32
Table 5b: Results from optimizing the Saxon AES (alternative II; minimizing budget for
given conservation aims)
Land use measure
Code1
Payment per ha
in €
Overall cost of
measure in €
Mowing (1)*
19/6/6.0 (D)
87.60
12,350.00
1,081,860.00
Mowing
19/10/6.0 (D)
113.03
2,025.00
228,885.75
Mowing
20/6/6.0 (D)
87.62
9.875.00
865,247.50
Mowing
21/6/6.0 (D)
96.10
1,362.50
130,936.25
Mowing
22/6/6.0 (D)
107.54
1,825.00
196,260.50
Mowing
23/6/0.1 (D)
124.21
14,231.25
1,767,663.56
Mowing
26/6/6.0 (D)
137.63
50.00
6,881,50
Mowing strips (2)*
19/6/6.1 (D)
72.32
10,968.75
793,260.00
Mowing strips
20/10/0.0 (D)
220.00
81.25
17,875.00
Seasonal grazing
25/0/0.3 (GV)
321.79
368.75
118,660.06
Seasonal grazing
29/0/0.3 (GV)
420.00
2,425.00
1,018,500.00
Rotational grazing
19/6/6.0 (D)
87.93
4,662.50
409,973.62
Rotational grazing
21/6/6.0 (D)
93.66
5,587.50
523,325.25
Rotational grazing
20/6/6.0 (D)
88.71
156.25
13,860.93
Rotational grazing
25/6/6.0 (D)
105.89
268.75
28,457.93
Rotational grazing
26/4/4.0 (D)
118.19
6,531.25
771,928,43
72,768.75
7,973,502.00
*(1) corresponds to measure G 1b in Table 2, and (2) to G 9.
1
Participating
area in ha
see explanations in Table 5a.
33
Figures
Figure 1: Overview of the ecological-economic modeling procedure
1. Species and habitats of
conservation interest
2. Species & habitats characteristics
(information folder)
3. Biodiversity-enhancing land use measures
4. Landscape information
5. Ecological
model
7. Simulation and optimization
8. Output: Effectiveness and cost-effectiveness
analysis
34
6. Agri-economic cost
assessment
Figures 2: Results of the cost-effective analysis for alternative I (maximisation of
conservation for a given budget for birds (a), butterflies (b) and grassland types (c)). The yaxis shows the effective habitat area Aeff
j (in ha) resulting from simulating the existing Saxon
AES (AES Saxony, yellow) and for the cost-effective alternative (CE-AES_maxcons, blue)
2a: Birds
80000
70000
60000
50000
40000
AES Saxony
AES-CE_maxcons
30000
20000
10000
0
35
2b: Butterflies.
300
250
200
150
AES Saxony
100
AES-CE_maxcons
50
0
36
2c: Grassland types
4000
3500
3000
2500
2000
1500
AES Saxony
1000
500
AES-CE_maxcons
0
37
Figure 3a-c: Results of the cost-effective analysis for alternative II (minimization of budget
for given conservation aims for birds (a), butterflies (b) and grassland types (c)). The y-axis
shows the effective habitat area Aeff
j (in ha) resulting from simulating the existing Saxon AES
(AES Saxony, yellow) and for the cost-effective alternative (CE-AES_minbudget, blue).
Figure 3a: Birds
70000
60000
50000
40000
AES Saxony
30000
CE-AES_minbudget
20000
10000
0
38
Figure 3b: Butterflies
200
180
160
140
120
100
AES Saxony
CE-AES_minbudget
80
60
40
20
0
Chestnut
Heath
Woodland
Ringlet
Marsh
Fritillary
Five-spot
Burnet
39
Figure 3c: Grassland types
10000
9000
8000
7000
6000
5000
AES Saxony
4000
CE-AES_minbudget
3000
2000
1000
0
alluvial
meadows
lowland hay mountain hay
wet
meadows
meadows
meadows
40