Supplementary Material
An agent-based approach to modeling impacts of agricultural policy
on land use, biodiversity and ecosystem services
Mark Brady, Christoph Sahrbacher, Konrad Kellermann and Kathrin Happe
Table of Contents
Appendix S1. Algorithm for allocating production to plots..................................................................... 2
Appendix S2. Representation of study regions ....................................................................................... 3
Appendix S3. Initialization of virtual landscapes ..................................................................................... 7
Appendix S4. Calibration of species-area relationships ........................................................................ 11
Appendix S5. Calculation of biodiversity at the landscape scale .......................................................... 15
Appendix S6. Details of agricultural policy payment schemes.............................................................. 17
Appendix S7. Validation of dynamic simulation results ........................................................................ 21
References Appendix............................................................................................................................. 25
1
Appendix S1. Algorithm for allocating production to plots
Since production levels generated by AgriPoliS represent profit maximizing levels given a
particular set of model parameters, then a dual of this problem is to allocate optimal cropping
levels across plots of agricultural land in the landscape such that the costs of producing crops
are minimized. Farmers minimize the costs of crop production by growing their various crops,
as far as is possible given crop rotation constraints, on contiguous areas of land. Given that
farmers have maximized profits, the mathematical programme described by the equations
below will allocate the optimal activity levels of a farm across the plots of land managed by
the farm in the abstract landscape so that the costs of production are minimized:
max   (qi aikl ) 2
iI kK
subject to

k
aikl  xil ,  i
a
i
kl
i
aikl  0
 y kl ,  fb kl  FB l
Optimal activitylevels
Contiguous plots ofland
No negative fieldsizes
where aikl is the size of field flikl and represents a contiguous area within farm-block fb kl
allocated to cropping activity i by farm l, xil is the optimal level of activity i provided by the
solution to farm l’s profit maximization problem, and y kl is the size of the farm-block fb kl
managed by the farm (i.e., contiguous plots of a particular soil type). In the problem statement
above, qi reflects the diagonal value of the Q-matrix of the quadratic optimization problem.
The ability to freely set each qi allows us to assign different weights to production activities,
as farmers tend to allocate the most intensive crops to the largest fields (implying high qvalue) and treat set-aside as a residual ( qset aside  0 ) to fill up area not used in crop
production.
The data required to solve this problem are optimal activity levels and the farm-blocks
managed by each farm. The solution is the set of fields { flikl } that maximizes the sum of the
square of field size subject to the constraints that total activity levels cannot exceed optimal
2
levels and the sum of field sizes cannot exceed the area of contiguous plots of land. The
nonlinear form of the objective function implies that larger fields are preferred to smaller. The
quadratic form was chosen because it is straightforward to solve and provides a convenient
way to represent farmers’ preferences for field structure via the Q-matrix.
Appendix S2. Representation of study regions
The study regions are represented in AgriPoliS by the selection and weighting of farms from
the Farm Accountancy Data Network (FADN 2008) which consists of accountancy data from
an annual survey of farms carried out by the Member States of the EU. For details of the
procedure for simultaneously selecting and up-scaling farms see Sahrbacher and Happe
(2008). In this section we compare the representation (i.e. creation and calibration) of the
virtual case-study regions in AgriPoliS with the real regions according to official statistics. By
calibration success we mean, how well is the observed structure of agriculture in the real
region, in the reference year (2001), represented by the virtual region modeled in AgriPoliS.
The quality of the virtual representation of a region in AgriPoliS depends primarily on three
factors; i) the quantity and quality of the regional statistics where the main structural
indicators are the size distributions of farms and livestock herds, ii) consistency of the
regional data, meaning whether it is from the same source, and iii) the number of farms
available in the FADN sample for the region. The quantity, quality and consistency of
regional statistics for Sweden are high on EU standards. However the number of farms from
Jönköping and Västerbotten Counties in the FADN sample is small, but this does not
seriously affect the representation of the regions. These regions are quite homogenous and are
dominated by family farms. Further, the variation in farm size is not excessively large and
dairying is the most important livestock activity. Given this homogeneity, the diversity among
farms in the sample is large enough to represent the regions well.
The results of the calibration procedure for Jönköping County are presented in Table
S1 and those for Västerbotten County in Table S2. At the top of the tables we present general
characteristics such as the total number of farms, total utilized agricultural area (UAA), and
total number of livestock, and after that structural characteristics such as the number of farms
per farm type and legal form, amount of arable land and meadows, number of farms per size
class and number of animals per livestock size class. The second column of these tables
3
contains the regional statistical data. Some minor adjustments to the regional data were
however necessary. Farms smaller than 10 ha have not been considered in the virtual regions
for two reasons. First, the FADN-farm sample does not include such small farms, and
secondly, the behavior of small or hobby farms cannot be represented in a MIP model under
the assumption that they maximize their household income. There were also small
discrepancies in the total number of dairy cows and in the sum of dairy cows by herd size in
the statistics. Finally, only the major agricultural production activities in the regions are
considered (i.e. arable crops, grass fodder, dairying, beef and lamb). The regional data were
adjusted after consultation with the Swedish Board of Agriculture. Adjusted data are in bold
font in the column ‘Considered’ for each region in Table S1 and S2. The fourth column shows
the weighted farm characteristics, i.e. the structure of the virtual region. The fifth column
shows the relative deviations between the individual characteristics of the adjusted real region
and the virtual region. The last column compares data from the virtual region to the regional
data.
For Jönköping, the virtual region covers only 57% of the farms in reality, but they use
94% of the total UAA (due to the elimination of small farms). The deviation between the
number of farms in the virtual region and the number of farms considered for the
representation is only 2%. The maximum deviation of real to virtual characteristics for
Jönköping is only 4%, which occurred for arable land and meadow. For all other
characteristics, the deviation is smaller. If less structural indicators are available, the data are
not consistent, and it is difficult to achieve a perfect representation of a region. This is the
case for Västerbotten where the small number of FADN farms (32) affected the calibration
somewhat negatively due to a lack of farms with meadow, farms between 50–100 ha, and
dairy cows in herds of 10–24. Consequently, one can say that the agricultural structure of
Jönköping is very well represented in AgriPoliS whereas that for Västerbotten is less well
represented but adequate.
4
Table S1 Results of representation of Jönköping County in AgriPoliS
Regional Considered Virtual
General characteristics
and
(1)
Data
(2)
(3)
region
(4)
1)
adjusted
Number of farms
3 824
2 216
2 165
data
Utilised agricultural area (UAA; ha)
134 216
125
2042) 126 704
Number of beef cattle older than 1 year
20 403
20 403
20 605
Number of dairy cows
33 158
33 158
33 322
Number of suckler cows
12 173
12 173
12 262
Number of ewes and rams
8 548
8 548
8 580
Sows after the first mating
4 826
Fattening pigs
14 325
Structural characteristics
Area (ha)
Arable land
91 369
82 3572)
85 606
Meadows
42 847
42 847
41 098
Total
134 216
125 2042) 126 704
Number of farms specialised in4)
Field crop farms (13, 14, 60)
1 166
Grazing livestock (41, 42, 43, 44)
2 054
Pig and poultry (50)
19
Mixed farms (71, 72, 81, 82)
931
Total
4 170
Number of farms in different size classes
2-10 ha
1 608
10-20 ha
779
779
758
20-30 ha
438
438
433
30-50 ha
506
506
493
50-100 ha
400
400
389
More than 100 ha
93
93
92
Total
3 824
2 216
2 165
Number of dairy cows per herd size class
1-9
474
4783)
472
3)
10-24
5 332
5 374
5 394
25-49
14 717
14 8323)
14 976
More than 50
12 377
12 4743)
12 480
Total
32 900
33 158
32 322
Deviation Coverage
to
of(6)
the
(5)
considered
regional
-2%
57%
data
data
1%
94%
[1-(4)/(3)]
[(4)/(2)]
1%
101%
0%
100%
1%
101%
0%
100%
4%
-4%
94%
96%
-3%
-1%
-3%
-3%
-1%
97%
99%
97%
97%
99%
-1%
0%
1%
0%
100%
101%
102%
101%
Notes: 1) Farms with less than 10 ha arable land are not considered; a total of 1 608 farms.
2) The total UAA was reduced by 5.6 ha for each of the 1 608 farms not considered (or 7% of the UAA).
3) There is a small difference in the total number of dairy cows and in the sum of dairy cows by herd size, thus
the number of each herd size is adjusted to the total number of dairy cows. Sources: Statistics Sweden (SCB
2003); 4) Swedish Board of Agriculture (SJV 2002).
5
Table S2 Results of the representation of Västerbotten County in AgriPoliS
Regional Considered Virtual Deviation Coverage
General characteristics
and
to
of(6)
the
(1)
Data
(2)
(3)
region
(4)
(5)
1)
adjusted
regional
Number of farms
2 506
1 500
1 469 considered
-2%
59%
data
data
data
Utilised agricultural area (UAA; ha)
74 414
68
0322)
69 740
3%
94%
[1-(4)/(3)]
[(4)/(2)]
Number of beef cattle older than 1 year
7 297
7 297
7 199
-1%
47%
Number of dairy cows
15 526
15 526
16 519
6%
106%
Number of suckler cows
1 130
1 130
1 140
1%
101%
Number of ewes and rams
3 857
Sows after the first mating
2 322
Fattening pigs
15 039
Structural characteristics
Area (ha)
Arable land
70 269
64 4233)
66 950
4%
95%
3)
Meadows
4 145
3 609
2 790
-23%
67%
Total
74 414
68 0323)
69 740
Number of farms specialised in5)
Field crop farms (13, 14, 60)
1 807
Grazing livestock (41, 42, 43, 44)
745
Pig and poultry (50)
21
Mixed farms (71, 72, 81, 82)
544
Total
3 117
Number of farms per size class
2-10 ha
1 006
10-20 ha
516
516
527
2%
102%
20-30 ha
250
250
248
-1%
99%
30-50 ha
283
283
289
2%
102%
50-100 ha
328
328
278
-15%
85%
More than 100 ha
123
123
127
3%
103%
Total
2 506
1 500
1 469
Number of dairy cows per herd size class
1-9
299
3324)
329
-1%
110%
4)
10-24
3 593
3 992
4 640
16%
129%
25-49
6 926
7 6964)
8 049
5%
116%
4)
50-74
2 240
2 489
2 500
0%
112%
More than 74
915
1 0174)
1 001
-2%
109%
Total
13 973
15 526
16 519
Note: 1) Farms with less than 10 ha are not considered. 2) The total UAA was reduced by 6.3 ha for
each of the 1 006 farms not considered. 3) The area of arable and meadow is reduced according to
the total UAA by keeping the relative proportions of arable and meadow.
4) There is a small difference in the total number of dairy cows and in the sum of dairy cows by herd
size, thus the number of each herd size is adjusted to the total number of dairy cows. Sources:
Statistics Sweden (SCB 2003); 5) Swedish Board of Agriculture (SJV 2002).
6
Appendix S3. Initialization of virtual landscapes
The values of the landscape initialization parameters used to create the virtual landscapes for
Jönköping and Västerbotten Counties are specified in Table S3 (explanations provided in text
associated with Table 1 in the main article). Two different types of arable land are initialized
in Västerbotten to reflect the larger variation in yields in this region. Average and median
block size are considerably smaller in Jönköping than Västerbotten (Table S4), hence the
choice of smaller pixel size for Jönköping. The OVERSIZE is similar in both regions and
creates more plots of each land type than are actually found in the region according to the
formula
OVERSIZE  Area_Land_Typei .
The higher the OVERSIZE the greater the chance of
a farm-agent being allocated contiguous plots close to the farm centre. The parameter
NON_AG_LAND is set higher in Jönköping to achieve the greater fragmentation of blocks in
this landscape (see Fig. 3). The overall size of the virtual landscape created by AgriPoliS is
Landscape_Size_ha  OVERSIZE  i Area_Land_Typei  1  NON _ AG _ LAND  .
After the allocation of agricultural land to farm-agents according to their resource
endowments the area of non-agricultural land finally initialized in the virtual landscape will
be the residual area, i.e., Landscape_Size_ha  i Area_Land_Typei . The landscape
initialization procedure is exemplified in pseudo code in Fig. S1.
Table S3 Landscape initialization parameters used to create virtual case-study regions
Parameter
Description
Jönköping Västerbotten
NO_OF_SOILS
Different soil types defined
2a
3b
PLOT_SIZE
Standard pixel size
0.5 ha
1.0 ha
OVERSIZE
Additional land initialized in region
1.15
1.12
NON_AG_LAND Share of non-agricultural land
0.90
0.7
Notes: This table follows from Table 1 in the main article. a) The two soil types initialized for
Jönköping are Arable_Land and Meadow_Land; b) The three soil types initialized for Västerbotten are
Arable_Land_low, Arable_Land_high and Meadow_Land.
7
Fig. S1 Initialization of the virtual landscape in AgriPoliS in pseudo code
To provide an indication of the accuracy of the landscape initialization procedure we compare
the distributions of arable land blocks (contiguous areas of arable land type that are separated
from other arable plots by either non-agricultural land or land type meadow) in the real
landscapes with those of the virtual landscapes. In Table S4 we compare descriptive statistics
of the distributions of block size for the real and virtual landscapes. As can be seen the mean
and standard deviation of block size are almost identical for the real and virtual landscapes in
both regions. Median block size is significantly lower in the virtual landscapes due to the
minimum pixel size in the model landscape being set lower than the median block size in the
8
real landscapes. Here a trade-off must be made and our landscape initialization then creates
too many blocks comprising a single pixel.
Table S4 Comparison of real and modelled landscapes based on size distribution of arable
land blocks
Statistic
Reala
2001
89 239
48 383
25%
64%
Jönköping
Modelb
2001
89 331
48 460
22%
66%
Diffc
<1%
<1%
-12%
<1%
Västerbotten
Reala Modelb
2001
2001
Diffc
66 900 66 805
<1%
28 235 28 425
<1%
39%
27%
-30%
74%
62%
-16%
Total arable area
ha
Blocks
nr
- number > 2 ha
%
- total area > 2
%
had
Block size - mean ha
1.84
1.84
<1%
2.37
2.35
-1%
- median
ha
1.04
0.50
-52%
1.60
1.00
-38%
- standard dev.
ha
2.53
2.79
10%
2.51
2.53
1%
- minimum
ha
0.30
0.50
67%
0.30
1.00
233%
37.50
-46% 34.30
36.00
5%
- maximum
ha 69.51
a
Real landscape statistics for blocks ≥ 0.3 ha in calibration year, i.e. 2001 (SJV 2003). b Model
landscape generated by AgriPoliS for 2001. c Percentage difference between real and modeled
characteristic. d Total area of blocks greater than 2 ha in size.
This effect can be seen more clearly in the histograms in Fig. S1 which show the frequency of
occurrence of blocks in different size classes in the real and virtual landscapes. Both the real
and modeled distributions are heavily skewed to the left with a concentration of blocks about
the mean and larger fields being represented by a flat tail (note the similarity of the frequency
of large blocks in the real and virtual landscapes). Although AgriPoliS hasn’t been able to
reproduce the exact distribution of blocks by size class it does represent the overall structure
of the landscape in terms of large and small blocks well (which is important because of
potential size economies in production). To demonstrate this we compare also in Table S4 the
proportion of the arable area comprising blocks ≥ 2 ha (i.e. the size category 3 and above). As
can be seen these are well represented for Jönköping with only a minor deviation between the
real and virtual landscapes (i.e. < 1 %). A larger deviation occurs for Västerbotten with the
number and total area of larger blocks being underrepresented. What is important in the
context of this study is that the model landscapes provide an adequate representation of the
real landscape for the purpose of analyzing the regional impacts of changes in agricultural
policy. Overall the comparison of descriptive statistics and histograms indicate that the model
9
landscapes are representative of the real landscapes, since they capture the general
characteristics of each landscape.
(a) Jönköping
4000
3500
Frequency
3000
2500
2000
Real
1500
Virtual
1000
500
0
2
3
4
5 6 7 8
Block size (ha)
9
10 11
(b) Västerbotten
4500
4000
Frequency
3500
3000
2500
2000
Real
1500
Virtual
1000
500
0
2
3
4
5 6 7 8
Block size (ha)
9
10 11
Fig. S2 Frequency of arable blocks by size in ha for the real and virtual landscapes a)
Jönköping b) Västerbotten
10
Finally in Table S5 we list the land use categories used in the evaluation of landscape mosaic
and calculations of mosaic indices for the baseline year 2004 according to the ShannonWeiner Index, Eq. 2, (where
pi is the proportion of land use i in the virtual landscape in
2004).
Table S5 Shannon-Wiener Index in 2004
Jönköping
Land use
pi
 pi ln  pi 
Västerbotten
pi
 pi ln  pi 
0.02
0.08
0.14
0.28
Silage - intensive
0.16
0.29
0.11
0.24
Silage - extensive
0.06
0.16
0.12
0.26
Arable Pasture
0.06
0.17
0.07
0.19
Arable Crops
0.15
0.28
0.02
0.08
Meadow
Forest
0.55
0.33
0.53
0.34
a
Baseline Shannon-Weiner
1.00
1.32
1.00
1.39a
Index Calculations according to habitat areas in 2004. a Maximum possible index value 1.79
Source:
Appendix S4. Calibration of species-area relationships
The indicator used to evaluate the impact of policy reform on biodiversity is based on the
number of unique species associated with particular agricultural habitat. Threatened species
are by definition “unique” in some way. The Swedish Species Information Centre’s species
database (ArtDataBanken 2005) contains information about the state of almost 20 000 multicellular organisms found in Sweden. The status of each species has been assessed using the
internationally accepted criteria for red listing established by the IUCN (2001). Of the species
analyzed in Sweden some 3 771 have been red-listed and 1 735 classified as threatened; of
these 488 reproduce in Jönköping and 505 in Västerbotten. Clearly a large number of
threatened species are supported in these regions.
The red list is of course incomplete since it only considers species that have been
studied in some detail. There are thousands of insects, beetles, etc. that have never been
studied and hence lack the information required to red-list them. The red list has therefore
been formulated in a fairly arbitrary manner but this is quickly changing thanks to the work of
the Swedish Species Information Centre. Despite these limitations this is the best knowledge
we have and is based on a systematic categorization that ensures lists for different habitats and
11
regions are comparable. An important criterion is the geographic extent of the species which
considers its occurrence in other regions or countries and hence its risk of extinction. Another
criterion is a quantitative analysis of the risk of extinction within a specific timeframe. Overall
the red-list not only provides an indication of a species local uniqueness but also, to a certain
extent, on a global scale. Assuming that the red listdespite its incompletenessprovides a
measure of the relative importance of various habitat for biodiversity value then our
biodiversity indicator (Eq. 2) should provide a reliable measure of the relative change in
biodiversity value, because the species-area relationship is based on a homothetic production
function.
Almost 46% of threatened species in Sweden reproduce in agricultural landscapes and
50% in forest landscapes. Generally speaking, biodiversity in Sweden increases from north to
south. However, the competition for land is higher in the South and the proportion of
protected land is lower, which partly explains why local extinction has been greater in the
South. Jönköping, in the South, is one of the counties where local extinction has been highest
(21%) and Västerbotten, in the North, lowest (9%) (ArtDataBanken, 2005). Table S6 and S7
provide an overview of threatened species by group and agricultural habitat in each county.
As can be seen meadow is the single most important habitat for conservation of biodiversity
in these landscapes (which is also the case for Swedish agricultural landscapes generally).
Unfortunately it was not possible for us to evaluate the impact of changes in the length of
arable field edges explicitly (but represents potential for future development of AgriPoliS).
12
Table S6 Red-listed species associated with agricultural habitat: Jönköping
Group description
Intensive
silage
Fungi
Hymenoptera
Beetles
Bryophytes
Lichens
Vascular plants
Crustaceans
Orthopterans
Bugs
Amphibians and reptiles
Birds
Butterflies and moths
Mammals
Molluscs
Algae
Total species
0
1
1
0
0
1
0
0
0
0
2
0
0
0
0
5
Extensive
silage
Arable
pasture
0
2
0
1
0
7
0
0
0
0
3
1
0
0
0
14
0
1
3
1
0
1
0
0
0
0
2
0
0
0
0
8
Arable
crops
0
0
0
0
0
15
0
0
0
0
4
0
0
0
0
19
Meadow
30
3
30
7
15
35
1
1
1
2
6
11
2
1
1
146
Source: ArtDataBanken (2005)
Table S7 Red-listed species associated with agricultural habitat: Västerbotten
Group description
Intensive
silage
Fungi
Hymenoptera
Beetles
Bryophytes
Lichens
Vascular plants
Orthopterans
Bugs
Birds
Butterflies and moths
Molluscs
Algae
Total species
0
1
0
0
0
0
0
0
8
0
0
0
9
Extensive
silage
Arable
pasture
0
1
0
0
0
1
0
0
10
2
0
0
14
0
1
5
3
0
0
0
0
8
0
0
0
17
Arable
crops
0
0
0
0
0
1
0
0
12
0
0
0
13
Meadow
15
2
6
6
5
20
1
1
13
11
2
1
83
Source: ArtDataBanken (2005)
In Table S8 we summarize the data for observed species  Si  and habitat area  Ai  in each
region that were used to calibrate the unknown parameter ci of the SAR for each habitat
using the transformation shown in Eq. 3. The implicit assumption in this regard is that the
observed number of red-listed species and area of habitat represent an equilibrium, e.g. the
13
46 110 ha of meadow observed in Jönköping County in 2001contributes to the survival of 146
red-listed species: consequently ci is obtained by plugging 46 110 and 146 into Eq. 3. The
resulting functions are steeply increasing with small areas of habitat, after which species
production is asymptotically increasing (in accordance with the underlying Species-Area
Relationship). Consequently the marginal biodiversity value of habitat  dSi dAi  zci Aiz 1  is
decreasing in habitat area and approaches zero when habitat area is relatively large (NB:
Marginal biodiversity value of a habitat is the implied reduction in species if 1 ha of that
habitat is lost). It can be seen in Table S8 that meadow has the highest marginal value in both
regions—primarily because it is highly productive habitat rather than being scarce—followed
by arable crops which is relatively scarce habitat in both regions (compared to arable grass
land). The marginal biodiversity value of a particular habitat is therefore affected by the
relative scarcity of the habitat (in economics scarcity value is synonymous with the concept of
price; hence the calculated marginal biodiversity value can be interpreted as a valuation or
relative price). Since there is a relatively large area of extensive arable grass in both regions
and a relatively small area of arable crops, we can expect changes in the area of arable crops
to have a relatively greater impact on diversity value than extensive grass. Similarly, it would
require a relatively large reduction in the area of meadow in Jönköping from its current 46
110 ha to cause a significant reduction in biodiversity value (according to this indicator).
Table S8 Species-area data for agricultural habitat in the study landscapes in reference year
Jönköping County
ci
Si
Ai
(nr)
(ha)
Silage - intensive
5
18 885
0.77
Silage - extensive
14
24 390
Arable Pasture
17
19
Habitat
Arable grass
Arable crops
dSi dAi
146
ci
dSi dAi
Si
Ai
(nr)
(ha)
0.00005
9
18 612
1.39
0.00009
2.05
0.00011
14
18 903
2.16
0.00014
24 162
2.50
0.00013
17
15 628
2.71
0.00021
21 894
2.85
0.00016
13
13 662
2.13
0.00018
17.80
0.00477
89 331
Total arable area
Meadow
Västerbotten County
46 110
66 805
18.98
0.00060
83
3 305
Sources: Si is the number of red-listed species per Table S6 for Jönköping and Table S7 for
Västerbotten; Ai is habitat area in calibration year, 2001 (SCB 2005); ci is calculated using the
transformation shown in Eq. 3; and marginal species value dSi dAi  zci Aiz 1 is based on the values
of Si , Ai and ci shown in the table above , and assuming z  0.19.
14
Note also that any reduction in the area of a particular habitat results in reduced biodiversity
value according to our indicator. This follows directly from the species-area relationship,
which implies that the probability of species survival will fall if habitat is reduced. This
property is consistent with the view that the current area of agricultural habitat in Jönköping
should be maintained in its entirety: the Swedish National Environmental Goal is to preserve
100% of the meadow area. Since even a small reduction in the area of meadow will result in a
loss in species, if only slight, the goal is consistent with a goal to conserve biodiversity. The
objective of our analysis is not so much to question this goal but to estimate how large the
effect of a policy change and consequent change in agricultural habitat might have on
biodiversity.
Appendix S5. Calculation of biodiversity at the landscape
scale
To calculate biodiversity at the landscape scale we assume that the habitat types; arable grass
(i.e. intensive silage, extensive silage, fallow grass and arable pasture), arable crops and
meadow, are additive at the landscape scale because they support ostensibly different species
pools. Total species in the landscape, Slandscape , is therefore calculated by summing the species
given by each SAR
Slandscape  Sarable grass  Sarable crops  Smeadow .
(S1)
To simply sum species across the different arable grass habitats would, though, result in
double counting of shared species. To exclude species common too the various arable grass
habitats we use the following method. To begin with we assume that the more species rich
arable grass habitats also support the species found in the less rich habitats (i.e. all species
found in intensive silage are assumed to be found in extensive silage/grass and pasture as
well). Accordingly the entire area of arable grass can be considered habitat of the least
productive type (intensive silage), which provides an initial estimate of species richness as
S1  c1  A1  A2  A3 
z
(S2)
where the subscripts i 1, 2,3 refer respectively to {Intensive silage, Extensive silage/grass,
arable pasture}. The additional species generated by the area of extensive silage is given by
15
S2  c2  A2  A3   c1  A2  A3 
z
  c2  c1  A2  A3 
z
(S3)
z
where c1  A2  A3  eliminates the species already counted in Eq. S2. Similarly, those added by
z
arable pasture are
S3  c3  A3   c2  A3 
z
  c3  c2  A3 
z
z
(S4)
where c2  A3  eliminates species already counted in Eq. S3. It follows that species richness
z
generated by the total area of arable grass habitats is Sarable grass  S1  S2  S3 which after
insertion of Eq. S2–4 yields
Sarable grass  c1  A1  A2  A3    c2  c1  A2  A3    c3  c2  A3  .
z
z
z
(S5)
In Table S9 and S10 we present the resulting calculations of species in 2004 and for each
policy scheme in 2013 for both counties.
Table S9 Calculations for aggregation of arable grassland species to landscape scale:
Jönköping
Arable grass habit
Intensive Silage
Arable Pasture
Extensive Silage/Grass
Aggregated species
All arable grassa
Arable silage/grassb
Arable pasturec
i
Si
ci
1 5 0.77
2 8 1.18
3 14 2.05
Baseline AGENDA DECOUP FUTURE
2004
2013
2013
2013
Ai
Ai
Ai
Ai
6 145
17 276
47 147
nr
16.53
16.01
7.51
7 219
20 245
50 166
nr
16.79
16.21
7.74
2 253
12 253
71 559
nr
17.51
17.21
7.03
557
11 483
57 661
nr
16.83
16.50
6.95
Notes: a calculated according to Eq. S5; b calculated using Ssilage  c1  A1  A3    c3  c1  A3  ;
z
c
calculated based on area of pasture alone Spasture  c2 A2 .
z
16
z
Table S10 Calculations for aggregation of arable grassland species to landscape scale:
Västerbotten
Arable grass habit
i
Intensive Silage
Extensive Silage/Grass
Arable Pasture
Aggregated species
All arable grassa
Arable silage/grassb
Arable pasturec
1
2
3
Si
9.0
14.0
17.0
Baseline AGENDA
2004
2013
ci
Ai
Ai
1.39
20 920
23 378
2.16
15 933
9 017
2.71
18 475
25 033
nr
nr
20.25
20.53
15.06
14.32
17.55
18.59
DECOUP
2013
Ai
17 043
29 566
12 692
nr
20.37
16.13
16.34
FUTURE
2013
Ai
18 396
26 379
13 345
nr
20.29
15.93
16.50
Notes: a Calculated according to Eq. S5; b Calculated using Ssilage  c1  A1  A2    c2  c1  A2  ;
z
c
z
Calculated based on SAR for pasture alone Spasture  c3 A3 .
z
Appendix S6. Details of agricultural policy payment
schemes
Given that some agricultural production activities are region specific and of direct importance
for landscape and conservation, we begin by explaining their characteristics before
proceeding with the description of the payment schemes. Agriculture in Jönköping and
Västerbotten is dominated by highly subsidized cattle husbandry. Livestock are essential to
maintaining land in Sweden because the biological values associated with particularly
meadows can only be maintained by ruminants and not machinery (Lindborg et al. 2008). In
these regions the vegetation period is short and crop yields low (e.g. the average yield of
spring barley in Jönköping is 3 t/ha and in Västerbotten 2.3 t/ha (SCB 2011), hence they are
referred to as marginal agricultural regions. Around 60-80% of arable land is used for
producing grass silage or as pasture, or kept fallow. Grains are primarily planted to maintain a
crop rotation that avoids a decline in grass yields; otherwise farms would use their land only
for grass silage and pasture, and import grain fodder requirements. Since cattle husbandry
plays an important role in Jönköping and Västerbotten, and the choice of fattening activity is
crucial for the landscape, it is modeled in detail. Consequently three different forms of beef
fattening are modelled that differ in both the intensity and duration of fattening. Intensive and
extensive forms of grass silage and pasture are also modeled with contrasting environmental
17
characteristics. Cereals used as protein fodder for livestock can either be produced by the farm
or purchased (i.e. imported to the region).
Table S11 and S12 show the levels of CAP payments for the DECOUP scheme by
production activity for Jönköping and Västerbotten counties. The payment levels shown for
2004 are those that applied in that year and are used in all periods of the AGENDA scheme
simulations. Prior to 2005 farmers received various types of payments coupled to the type of
livestock production, of which we only present the sum for 2004. This sum includes: direct
payments that are fully decoupled in 2005; a slaughter premium that is partially decoupled
(25%) in 2005 and fully decoupled in 2009; and extensification payments that are fully
decoupled in 2005; for details of each payment see Sahrbacher (2011, Tables A-51 to 53). The
full amounts of decoupled support are added to the regional payments for arable land and
meadow in each region; hence payments for grass crops and meadow increase significantly
after decoupling. For example the additional payment to meadow resulting from reallocation
of the former livestock payments amounts to 117 €/ha in 2005 and an additional 33 €/ha in
2009.
Similar to livestock, farmers received several different payments coupled to specific
plant production activities in 2004; direct payments, environmental payments, compensation
payments, etc. Total payments are higher in Västerbotten due to higher compensation
payments and a drying aid for cereals in the far North of Sweden. Dairy farmers in
Västerbotten also receive a large national payment (Nordic Aid) of €701 per dairy cow.
Consequently the overall level of support is significantly higher in Västerbotten despite both
regions being marginal. In 2005 only direct payments for plant production and the drying aid
are decoupled. In Jönköping the regional payment is higher for arable land (133 €/ha) than for
meadow (117 €/ha) whereas in Västerbotten it is identical for both arable land and meadow
(117 €/ha). Note that existing environmental and compensation payments are kept constant in
both regions in all schemes and are added to the relevant arable or meadow payments (which
explains the different payment levels for different plant production activities in the DECOUP
and FUTURE payment schemes).
18
Table S11 DECOUP scenario payments for the Jönköping region
2004a 2005b
186
133
192
232
Cereals, set
aside silage
Grass
Unit
€/ha
€/ha
2006
133
232
2007
133
232
2008
133
232
2009
166
265
2010
166
265
2011
166
265
2012
166
265
2013
166
265
Arable pasture
€/ha
99
216
216
216
216
249
249
249
249
249
Meadow
€/ha
165
282
282
282
282
315
315
315
315
315
Bullock dairy
€/head
295
113
113
113
113
0
0
0
0
0
Bull dairy
€/head
200
79
79
79
79
0
0
0
0
0
Bull suckler
€/head
400
158
158
158
158
0
0
0
0
0
Heifer suckler
€/head
200
0
0
0
0
0
0
0
0
0
Suckler cow
€/head
300
0
0
0
0
0
0
0
0
0
Dairy cow
€/head
109
0
0
0
0
0
0
0
0
0
Ewe
€/head
21
0
0
0
0
0
0
0
0
0
Source: Payments for 2004 from AGRIWISE (2006). Notes: a Payments for 2004 identical to
payments for the AGENDA scheme 2004–13. b Payments after 2004 are calculated based on
AgriPoliS results for the years 2002–4.
Table S12 DECOUP scenario payments for the Västerbotten region
2004a 2005b
273
172
168
117
Cereals
Set aside
Unit
€/ha
€/ha
2006
172
117
2007
172
117
2008
172
117
2009
198
143
2010
198
143
2011
198
143
2012
198
143
2013
198
143
Grass silage
€/ha
405
438
438
438
438
464
464
464
464
464
Arable pasture
€/ha
321
438
438
438
438
464
464
464
464
464
Meadow
€/ha
206
323
323
323
323
349
349
349
349
349
Bullock dairy
€/head
295
113
113
113
113
0
0
0
0
0
Bull dairy
€/head
200
79
79
79
79
0
0
0
0
0
Bull suckler
€/head
400
158
158
158
158
0
0
0
0
0
Heifer suckler
€/head
200
0
0
0
0
0
0
0
0
0
Suckler cow
€/head
300
0
0
0
0
0
0
0
0
0
Dairy cow
€/head
701
701
701
701
701
701
701
701
701
701
Ewe
€/head
21
0
0
0
0
0
0
0
0
0
a
Source: Payments for 2004 from AGRIWISE (2006). Notes: Payments for 2004 identical to
payments for the AGENDA scheme 2004–13. b Payments after 2004 are calculated based on
AgriPoliS results for the years 2002–4.
Table S13 and S14 show the levels of CAP payments for the FUTURE scheme. Recall that
existing environmental, compensation and national payments are also kept constant in this
scheme. In 2005 direct payments are decoupled according to the DECOUP scheme payment
19
levels in 2005 in both regions. Starting in 2006 the decoupled direct payments are phased out
to 2013. To alleviate the expected radical environmental consequences of the elimination of
direct payments we introduce an additional environmental payment to arable land of 99 €/ha
that is phased in as direct payments are phased out; and an additional environmental payment
of 65 €/ha to meadow that starts immediately in 2006 given the acute biological value of
meadows (Lindborg et al. 2008).
Table S13 FUTURE scenario payments for the Jönköping region
2004a 2005a 2006b
186
176
167
192
232
228
Cereals, set
aside
Grass silage
Unit
€/ha
€/ha
2007
157
224
2008
147
219
2009
138
215
2010
128
211
2011
118
207
2012
109
202
2013
99
198
Arable pasture
€/ha
99
216
214
212
209
207
205
203
200
198
Meadow
€/ha
165
282
330
330
330
330
330
330
330
330
Bullock dairy
€/head
295
113
98
84
70
56
42
28
14
0
Bull dairy
€/head
200
79
69
59
49
39
30
20
10
0
Bull suckler
€/head
400
158
138
118
98
79
59
39
20
0
Heifer suckler
€/head
200
0
0
0
0
0
0
0
0
0
Suckler cow
€/head
300
0
0
0
0
0
0
0
0
0
Dairy cow
€/head
109
0
0
0
0
0
0
0
0
0
Ewe
€/head
21
0
0
0
0
0
0
0
0
0
a
Notes: Payments in 2004 identical to AGENDA payments in 2004 and payments in 2005
identical to payments in DECOUP scheme in 2005 (i.e. as shown in Table S11). b Payments
after 2005 are hypothetical and based on a 50% cut in direct payments and strengthening of
environmental payments.
20
Table S14 FUTURE scenario payments for the Västerbotten region
Cereals
Set aside
Unit
€/ha
€/ha
2004
273
168
2005
172
117
2006
170
115
2007
168
113
2008
165
110
2009
163
108
2010
161
106
2011
159
104
2012
156
101
2013
154
99
Grass silage
€/ha
405
438
436
434
431
429
427
425
422
420
Arable pasture
€/ha
321
438
436
434
431
429
427
425
422
420
Meadow
€/ha
206
323
330
330
330
330
330
330
330
330
Bullock dairy
€/head
113
98
84
70
56
42
28
14
0
113
Bull dairy
€/head
79
69
59
49
39
30
20
10
0
79
Bull suckler
€/head
158
138
118
98
79
59
39
20
0
158
Heifer suckler
€/head
200
0
0
0
0
0
0
0
0
0
Suckler cow
€/head
300
0
0
0
0
0
0
0
0
0
Dairy cow
€/head
810
701
701
701
701
701
701
701
701
701
Ewe
€/head
21
0
0
0
0
0
0
0
0
0
a
Notes: Payments in 2004 identical to AGENDA payments in 2004 and payments in 2005
identical to payments in DECOUP scheme in 2005 (i.e. as shown in Table S12). b Payments
after 2005 are hypothetical and based on a 50% cut in direct payments and strengthening of
environmental payments.
Appendix S7. Validation of dynamic simulation results
Validating the dynamic simulation results is less straightforward than validating the
representation of the region as done above. To begin with, the purpose of the AgriPoliS
model—and prescriptive policy analysis in general—is to determine the possible effects of
alternative policy options on variables relevant to decision makers, e.g. economic welfare
(Nagel 1999). In other words, our aim is to evaluate the potential or ex ante effects of an
anticipated policy change on agricultural structure and landscape variables all other things
equal. Accordingly it is not our goal to predict the future with AgriPoliS but to identify the
potential impacts of possible policy options, given current socio-economic conditions. In this
context simulation experiments have the advantage that one can simulate a situation with and
without a policy change and compare the results; such experiments are obviously not
plausible in reality. In this way alternative policy options can be tested in policy evaluation
models and the results fed to policymakers to provide decision support. Evaluation of
historical effects of policy or ex post analysis is consequently not the purpose of the current
model; potentially the model could be and has been used for counterfactual analysis, e.g.
Sahrbacher et al. (2009).
21
Naturally there will always be uncertainty surrounding the simulated results of the
model because of unexpected events, as for example the food price spikes of 2007−08. Such
events might indeed outweigh policy effects in the long-run, though foreseeing such events is
not the goal of policy modeling. Rather it is to determine the likely implications of a change
in the status-quo brought about by a political decision. It is however possible to test the
consequences of alternative assumptions about the future in conjunction with a policy change
but this is likely to confound the results, as well as being of secondary, if any importance, to
policymakers. For example, if politicians tried to market cuts in the CAP budget today on the
pretext that they expected food prices to increase in the future—for reasons unrelated to
CAP—they would clearly face a difficult battle. As such the primary goal to isolate the
impacts of the policy change ceteris paribus.
To validate the landscape and biodiversity indicators they have to be compared with
empirical data regarding structural change and land use. The average field size as an indicator
of landscape mosaic/fragmentation is closely related to farm growth. It can be assumed that
larger farms might realign the boundaries of their fields and thereby maximize field size. Thus
a stronger rate of farm growth could negatively affect landscape mosaic if small fields are
amalgamated or abandoned. The Shannon-Wiener Index (Eq. 1) and our biodiversity indicator
(Eq. 2) are influenced by farmers’ production decisions, i.e. whether they reduce the area of a
scarce habitat or not.
Validation of these indicators can be done in two ways. First, since the objective is to
simulate the future, simulated developments in farm structure (i.e. the rate of decline in the
number of farmers and growth in average farm size) and simulated land use for the baseline
policy framework can be compared to observed historical rates. Second, after the passage of
time one can compare simulated results with those revealed by unfolding events. In practice
this step implies the possibility for continual model improvement as more data becomes
available. Given small changes in socio-economic conditions we would hope to find close
agreement between simulated developments in the virtual region and observed developments
in reality. On the other hand if significant changes eventuated in socio-economic conditions
since initial model calibration (such as the price spikes of 2007−08) we could not expect close
agreement between simulated and actual developments. Nonetheless, if the assumptions of the
model are subsequently changed to match these changes in conditions we would once again
hope to find close agreement between simulated and actual developments.
22
For the purposes of this paper we have carried out an ex post evaluation of the
simulation results in two ways. In Table S15 we compare the proportional decline in farms
over the period 2005 to 2010 according to official statistics with the simulated decline in
farms under the DECOUP scheme in AgriPoliS (NB: DECOUP is the relevant policy scheme
for this purpose since it has been in force over the period). As can be seen the decline in farms
simulated by AgriPoliS is stronger than occurred in reality. But recall that our simulations are
based on conditions prevailing in 2005 (for future policy work we need to recalibrate the
model to the most recently available data i.e. 2010). Nevertheless in 2005 the outlook for
farming in these regions was more pessimistic than it is today because of the expectation that
the historical decline in relative commodity prices would continue. However in 2007–08 there
was a radical change in market conditions brought about by the food crisis which saw prices
increase three fold compared to those in 2005.
Even today prices are significantly higher than was generally expected in 2005. With
the benefit of hind sight we therefore performed a dynamic re-calibration of AgriPoliS for
experimental purposes to reflect actual price developments over the period 2005-10.
Implementation of the higher output prices was sufficient to slow the exit of farms in
AgriPoliS dramatically, which can be seen in the third column of Table S15. We have
however not revised investment costs and a number of other cost parameters that have also
risen over the period, which if implemented should cause more farms to exit. Nevertheless we
are confident that a full dynamic re-calibration would result in closer agreement between
observed and simulated structural change (since the model is sufficiently flexible and detailed
to permit this—as demonstrated in the representation of the study regions in Appendix S2).
Such an exercise is however a research project in itself and indeed is the subject of ongoing
work.
More importantly—given that the future is unknown—is that AgriPoliS captures the
qualitative or relative differences between policy schemes. Real data (SJV 2007) shows that
decoupling slowed structural change which is correctly predicted by AgriPoliS (i.e. our
DECOUP scheme compared to the AGENDA scheme). FUTURE should increase exits
compared to AGENDA because of reduced profitability, which it also does.
Table S15 Comparison of real versus simulated declines in farms 2005−10
Reala AgriPoliS AgriPoliS
23
Jönköping
-9%
Västerbotten -10%
DECOUP Ex post
-17%
-1%
-22%
-3%
Notes: a Farms having > 10 ha arable land (SCB 2006, 2011).
Finally, we wish to validate simulated changes in land use. In Fig. S3 it can been seen that the
area of arable crops has been declining in the real regions over the evaluation period, which is
consistent with our simulation results (Fig. 4). Notice that a clear effect of the decoupling
reform in 2005 can also be seen in Fig. S3, with a greater reduction in the area of crops than
the historical trend. This is consistent with our result that the area of arable crops declines
more under the DECOUP and FUTURE schemes than AGENDA. Further a temporary break
in the downward trend appears in 2007–08 as a result of the global food price spikes, an affect
that is not captured in our schemes. However, now that prices have fallen the historical
downward trend has been restored.
30
Area ('000 ha)
25
20
15
Jönköping
10
Västerbotten
5
0
1995
2000
2005
2010
Fig. S3 Actual developments in the areas of arable crops in Jönköping and Västerbotten
Counties 1995–2010
Source: Yearbook of agricultural statistics 1995–2011 (SCB 2011).
We also analyzed changes in the total area of arable land in the real regions over the period
2005–10. In the case of Jönköping the total arable area has declined by 1.7% and in
24
Västerbotten by 1.3% (SCB 2007, 2011) which is consistent with our simulation results that
the total arable area has remained largely unchanged (i.e. DECOUP scheme) thanks to the
minimum GAEC obligation (i.e. the increase in area of arable grassland compensates for the
reduction in area of arable crops). In our hypothetical scheme FUTURE the arable area is
shown to decrease which is consistent with the reduction in payments to maintain arable land.
In conclusion, the simulated land use changes are consistent with the observed developments
in land use over the period 2005–10.
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Agricultural Sciences (SLU), Uppsala
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IUCN (2001) IUCN Red List Categories and Criteria version 3.1. Species Survival Commission, Gland,
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Lindborg R, Bengtsson J, Berg A et al (2008) A landscape perspective on conservation of semi-natural
grasslands. Agriculture, Ecosystems & Environment 125(1-4):213-222
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(IAMO)
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[http://www.agripolis.de/documentation/adaptation_v1.pdf], Halle (Germany),
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SCB (2003) Yearbook of agricultural statistics 2003. Statistics Sweden, Örebro,
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SCB (2006) Jordbrukstatistik årsbok 2006 (Yearbook of agricultural statistics 2006). Statistics Sweden,
Örebro,
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SCB (2011) Jordbrukstatistik årsbok 2011 (Yearbook of agricultural statistics 2011). Statistics Sweden,
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26