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Integrated assessment of future CAP policies:land use changes, spatial patterns and targeting
Annette Piorr a,*, Fabrizio Ungaro b, Arianna Ciancaglini c, Kathrin Happe d,Amanda Sahrbacher d, Claudia Sattler a, Sandra Uthes a, Peter Zander a
a Leibniz-Centre for Agricultural Landscape Research (ZALF), Institute of Socio-Economics, Eberswalder Straße 84,
15374 Muncheberg, GermanybResearch Institute for Hydrogeological Protection National Research Council (IRPI-CNR) Via Madonna del Piano 10,
50019 Sesto Fiorentino, ItalycUniversity of Florence, Department of Agricultural and Land Economy (DEART-UniFI) Piazzale delle Cascine 18, 50144 Firenze, Italyd Leibniz-Institute of Agricultural Development in Central and Eastern Europe (IAMO), Theodor-Lieser-Street 2, 06120, Halle (Saale), Germany
1. Introduction
The Common Agricultural Policy (CAP) reform 2003 has
aimed at stimulating global markets competitiveness, better
environmental performance, supporting rural viability as
well as better meeting consumer demands. From 2013
further far-reaching policy changes are expected leading to
on-going adaptation processes of European farms that will
change the rural landscapes and their socio-economic
conditions drastically. The FP6 EU-project MEA-Scope
(Micro-economic instruments for impact assessment of
multifunctional agriculture to implement the Model of
European Agriculture) carried out an ex-ante assessment
of scenarios on possible future developments of the CAP.
Special attention is paid towards specific rural development
potentials.
e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 2 ( 2 0 0 9 ) 1 1 2 2 – 1 1 3 6
a r t i c l e i n f o
Published on line 27 February 2009
Keywords:
MEA-Scope
AgriPoliS
MODAM
Structural change
Natura 2000
Semivariance analysis
a b s t r a c t
The recent and upcoming reforms of the Common Agricultural Policies (CAPs) aim at
strengthening the multifunctional role of agriculture, acknowledging the differences in
economic, environmental and social potentials within European regions. This paper pre-
sents results from an integrated assessment of existing and future policies within the
framework set up in the FP6 EU project MEA-Scope. Spatial explicit procedures allow for the
MEA-Scope modelling tools to provide information related to regional, environmental and
socio-economics settings. The impact of different policy scenarios on structural change,
land abandonment and cropping pattern of typical farms has been assessed based on linked
agent-based (ABM) and Linear Programming (LP) models at regional and farm scale for two
study areas. For the German case study area Ostprignitz-Ruppin (OPR), the issue of policy
targeting has been addressed by relating non-commodity outputs (NCOs) to soil quality and
protection status. For the Italian case study area (Mugello), changes in landscape patterns in
terms of increased fragmentation or homogeneity as affected by changes in agricultural
intensity have been analysed using semivariance analysis. The spatial explicit approach
highlighted the relevance of case study research in order to identifying response structures
and explaining policy implementation patterns.
# 2009 Elsevier Ltd. All rights reserved.
* Corresponding author. Tel.: +49 33432 82222.E-mail address: [email protected] (A. Piorr).
avai lable at www.sc iencedi rec t .com
journal homepage: www.elsevier.com/locate/envsci
1462-9011/$ – see front matter # 2009 Elsevier Ltd. All rights reserved.doi:10.1016/j.envsci.2009.01.001
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Within the establishment of rural development policies
and in accordance with sustainability goals, the European
Commission adopted formal impact assessment procedures
for ex-ante policy assessment (Tscherning et al., 2008).
Its core is related to policy driven land use changes and is
mainly concerned with environmental issues mostly
addressed at a pan-European scale (Lambin and Geist, 2006;
Helming et al., 2008; Rienks, 2008). In contrast, this paper
focuses on the regional and farm scale by adopting a
hierarchical model-based approach, which can explicitly
handle the complexity and dynamic change of farms and
structures. Moreover, it accommodates management inten-
sities and the variability of site conditions.
The presented policy impact assessment towards multi-
functionality made use of an indicator framework that
translated the function related concept of multifunctionality
to the concept of non-commodity outputs (Piorr et al., 2006,
2007a; Waarts, 2007). To cover the socio-economic dimension,
indicators such as farm size, farm income, livestock densities
and labour force input have been analysed. To represent the
environmental dimension, various abiotic and biotic indica-
tors have been chosen.
The regional level has been chosen in order to allow for
the analysis of mutual interdependencies of causal chains
and of structural change being observed at regional scale.
Thereby typical processes of policy implementation and
farming practice adaptation are examined. For example
different farms of initially different types in different
environmental settings develop different strategies of
adaptation to new compulsory guidelines and regulations
and to incentives set by voluntary measures as agri-
environmental programmes.
2. Methodology
2.1. The MEA-Scope modelling approach
The applied modelling approach is based on farm-level
models, which are loosely coupled in a hierarchical order
(Happe et al., 2006a,b; Damgaard et al., 2006) (Fig. 1).
AgriPoliS (Happe et al., 2008, 2006a,b) is an agent-based
(ABM) and dynamic model that simulates structural change
based on the individual actions and interactions between
large numbers of individual farms. The model takes account
of simultaneous decisions on production, labour, capital,
land allocation, and investments. MODAM (Zander and
Kachele, 1999) is a linear-programming (LP) model that
simulates cropping and livestock patterns of farms, which
are the basis for a fuzzy-logic-based environmental impact
assessment. It makes use of expert-knowledge that is
processed with the help of fuzzy-logic and results in
Indexes of Goal Attainment (IGA) which are expressed as
dimensionless indexes ranging from zero to one (Sattler
et al., 2006; Sattler, 2008). Farms in both models apply a
profit-maximization strategy on which decisions are based.
The results presented in this paper refer to two case study
regions: Ostprignitz-Ruppin (OPR) (Germany) and Mugello
(Italy). The specific advantage of MEA-Scope lies in its dynamic
perspective. In this light, MEA-Scope considers the dynamic
interactions of farms on the local land and product markets as
well as in simultaneous decision making on factor allocation
(Happe et al., 2006a,b; Osuch et al., 2007). For example, when
deciding on renting-in additional land, farms simultaneously
take into account a change in the farm’s production structure
or the allocation of labour, as well as off-farm activities. The
Fig. 1 – MEA-Scope modelling approach (www.mea-scope.eu).
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simulation results considered in this paper refer to the four
policy scenarios listed in Table 1. All policy scenario runs, start
from the Agenda 2000 situation (BAS00) and cover a time span
of 10 periods. A switch to the other policies takes place after 4
periods. The presented simulation results reflect medium
term developments—as simulated for period 9, which is 5
years after the implementation of the new policy options.
The reference scenario (REF) has been set up as an idealized
decoupled single farm payment, based on historical payments
3 years prior to policy change. The payment is completely
decoupled from land and is granted to the farm operator. It is
conditional on further running the farm and keeping land in
good agricultural condition (GAEC). The payment is not
distributed anymore to farmers who quit farming and is not
tradable, differing to that extent from a BOND scheme as
suggested by Swinbank and Tangermann (2001). As the
payment is not linked to land anymore, it can not be fully
compared to single farm payment schemes implemented in
some EU Member States as well. For second pillar measures
minimum grassland care was implemented as a payment of
130 s/ha and 200 s/ha respectively for OPR and Mugello. For
Natura 2000 areas the simulation by MODAM applied the
cross-compliance obligation of minimum care with a payment
of 200 s/ha and 450 s/ha respectively for OPR and Mugello.
The scenarios S01 and S02 refer to conditions in absence of any
direct payments.
In terms of grassland management practice, minimum care
means one mulching cut per year for grassland maintenance
without use for fodder production (GAEC obligation in Natura
2000 areas), while extensive grassland management means
low intensity management for fodder production (low live-
stock densities, low fertilizer input). In the following text the
term ‘‘land abandonment’’ refers to the decrease of the total
utilized agricultural area (UAA) in the region within the
observed time steps, e.g. if farms cease to operate but the land
is not taken over by other farms. The term ‘‘idle land’’ refers to
land which is not in use, and does not receive any payments,
but still belongs to an active farm.
For the localization of the farms in the case study region, a
spatial distribution approach was chosen that allows for a
spatial explicit analysis of structural changes and their
impacts on multifunctionality (Kjeldsen et al., 2006; Damgaard
et al., 2007; Ungaro et al., in press). Different modelling
strategies have been implemented for the different regions in
order to take regional peculiarities and data availability into
account. Further applications of the approach involve, i.e.
Happe et al., 2006a,b; Uthes et al., 2008; Damgaard, 2008.
2.2. Case study area descriptions and analytical approach
The German case study region Ostprignitz-Ruppin (OPR)
covers a utilized agricultural area (UAA) of about 125,000 ha
and is situated in North-Eastern Germany (Fig. 2). The region is
comparatively rich in extensive grassland, forests and wood-
land. The overall landscape structure is versatile including
water bodies, heath land and swamp areas. In 2003, the region
counted 585 farms with an average farms size of 200 ha and an
average livestock density of 0.5 LU/ha. There is a broad range
of farm sizes and cropping conditions, though yield expecta-
tion and soil productivity are comparably low on average. The
cropping pattern is dominated by winter rye, winter wheat,
maize, and rape production. 30% of the UAA is designated as
Natura 2000 areas.
The model is initialized with a set of 585 individual farms of
different initial types. They have been derived from farm
accountancy data of the European Farm Accountancy Data
Network (FADN). The database set up for the modelling
procedure comprises simulations on 1246 production prac-
tices for 35 different crops in 2 intensities for 6 site qualities
and taking into account 7 livestock branches. For the analysis
of impact assessment results, farms in the sample were
grouped in sub samples representing two target groups of the
policies. Target groups were identified based on farm
specialization and the share of land in protected areas. The
selection criteria were protection status of the site (Natura
2000), and specific site conditions (soil quality class), which
Table 1 – The MEA-Scope policy scenarios; results are always considered at year 0 (BAS00 = initial state) and at year 9(medium term). AEP: agri-environmental programme. GAEC: good agricultural and environmental condition.
Scenario First pillar Second pillar
BAS Agenda 2000 - Full implementation of Agenda 2000 at the
end of 2002
AEP - Agri-environmental
programme on extensive
grazing land- No cross-compliance Natura 2000
REF Reference - Idealized decoupled single farm payment (SFP) AEP - Agri-environmental
programme on extensive
grazing land
- Historical payment (3 year average) paid to the
farm operator
Natura 2000
- Conditional on running the farm
- Cross-compliance: GAEC minimum care
(all farmland has to be kept in good agricultural
condition (at least cutting once a year))
S01 Liberalization +
Environment
- Removal of direct payments AEP - Agri-environmental
programme on extensive
grazing land
- Cross-compliance: GAEC minimum care
(all farmland has to be kept in good agricultural
condition (at least cutting once a year))
Natura 2000
S02 Liberalization - Removal of direct payments No AEP
- No Agri-environmental programme No Natura 2000
- No cross-compliance
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were translated into rules and thresholds for the analysis of
the two target groups (Table 2).
The extremely heterogeneous Mugello territory (elevation
160–1241 m a.s.l.) covers a UAA of about 26,000 ha in Central
Italy (Tuscany) and is characterized by small mixed crop-
livestock farms (total number 1237, average farm size 22 ha),
mostly engaged in the total cow-calf line mixed farming. The
beef sector is made of traditional farms with forage crops or
grassland for grazing. Livestock density is 0.3 LU/ha. Mountain
pastures and permanent grasslands dominate the land-use,
followed by fodder crops such as alfalfa and forage sorghum.
Important arable crops are grain maize, barley and durum
wheat. The model was initialized with 1237 farms of different
types, which have been derived from FADN data. The database
set up for the modelling procedure comprises simulations on
188 production practices for 25 different crops in 2 intensities
for 12 site types and taking into account 4 livestock branches.
The extreme heterogeneity of the Italian study region
claimed for a spatially explicit assignment: based on produc-
tion techniques farms were allocated in specific ‘‘field types’’
based on a multi-criterion approach in a GIS-environment
(Ungaro et al., 2006, in press) (Fig. 3). A farmstead ID number
identified each individual farm allocated randomly in the
region. Each field type (Table 3) resulted from a combination of
soil capability class, terrain morphology, and elevation class
and is characterized by different intensity of land use. In each
field type, given a specific set of environmental constraints the
typical crop rotations and associated production techniques
were allocated. Crop allocation and associated production
techniques resulted from direct surveys and interviews,
statistical data from the agricultural census (ISTAT, 2002)
and revised Corine Land Cover (APAT, 2004).
The effects of the changes in land use intensity on the
environmental indicators can be analysed considering an
indicator directly related to crop management practices such
as risk of nitrate leaching or risk of soil loss due to water
erosion. Each production system is characterized by a certain
level of inputs and field operations which determine land use
intensity at a given site, i.e. at 1 ha plot level, providing the
basis for the environmental impact assessment (EIA) within
MODAM (Sattler et al., 2006; Sattler, 2008). Since MODAM-EIA
results for a given area reflect the underlying production
system(s) and the specific crop rotations associated with it,
then the spatial pattern of a selected indicator reflects the
Fig. 2 – Case study area, land cover type map and site type map Ostprignitz-Ruppin (OPR) (Germany).
Table 2 – Selection criteria for the analysis of target farmgroups for agricultural development targets for the OPR(DE) study area.
Development target environment
E–G: ‘‘Extensive grassland farms’’
� >80% of the farm UAA in Natura 2000 and
� >40% of the farm UAA on low productivity grassland
E–A: ‘‘Arable farms’’
� >80% of the farm UAA in Natura 2000 and
� >40% of the farm UAA in soil quality class 25
Development target competitiveness
C–A: ‘‘Arable farms’’
� 100% of the farm UAA in soil quality class 38
� Only farms remaining in production in the liberalization
scenario on the medium term
C–G: ‘‘Intensive grassland farms’’
� >40% of the farm UAA on intensive grassland
� No extensive grassland
� Only farms remaining in production in the liberalization
scenario on the medium term
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patterns of the different crops combinations in terms of
agricultural intensity. Its variations over time under the
different scenarios are due to correspondent variations in
cropping patterns. Such variations in agricultural intensities at
landscape scale can be analysed in a spatially explicit context
resorting to semivariograms calculation and modelling (Ernoult
et al., 2003). Variograms are increasingly being used to
investigate spatial pattern of raster data providing information
about the spatial variability structure of the variable of interest,
including land use (Dendoncker et al., 2007).
3. Results
The analysis focuses on environmental and land use impacts
for different development targets. The reasoning behind
structural adjustments in terms of farm size, farm numbers,
and the allocation of labour is discussed in detail in Osuch
et al. (2007).
3.1. Land use change: development target environment
The maintenance of permanent grassland belongs to the
preferential environmental objectives of agriculture in the
region OPR and in the Federal State of Brandenburg in general.
Herewith, especially grassland in wetland areas, often
characterized by a comparably low productivity, should be
preserved due to biodiversity, habitat and landscape amenity
reasons. Amongst farms that are supposed to contribute the
most to environmental objectives, those located in Natura
2000 areas are of particular interest. The research question for
the CAP policy assessment is to analyse the environmental
impact of structural change and management shifts in
designated areas.
Fig. 3 – Case study area and field type map Mugello (Italy).
Table 3 – Field types classification for the Mugello (IT) study area.
Plant production system Site Soil land capability class
Use Altitude m a.s.l. Intensity Field type (share %) Morphology First Second Third
Arable Valley <300 High VL (7.7%) Plain (slope <5%) 3 3/4
VH (54.4%) Terraces (slope >5%) 2 3
Hills 300–700 Medium HL (28.9%) Low (<500 m) 3/4 4 4/6
HH (8.4%) High (>500 m) 4 6
Mountain >700 Low ML (0.6%) Low (<900 m) 6 4
Grassland Valley <300 Low VL-G (0.14%) Plain (slope <5%) 3 3/4
VH-G (50.7%) Terraces (slope >5%) 2 3
Hills 300–700 Low HL-G (21.2%) Low (<500 m) 3/4 4
HH-G (50.7%) High (>500 m) 4 6 4/6
Mountain >700 Low ML-G (21.2%) Low (<900 m) 6 4
MH-G (8.7%) High (<900 m) 6 4/6
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Farms with a high share of extensive grassland and Natura
2000 area (E–G) show adaptation behaviour (Fig. 4a). The area
in use (initially 10,408 ha) undergoes an extreme reduction in
the Agenda 2000 (�76%) and idealized single farm payment
(REF) scenarios (�27%) coupled with an ever higher reduction
in the number of farms (�85% and �51%). Minimum care
already takes a high share of grassland use in the initial state.
Under both scenarios, green fodder production with 2 cuts is
completely given up and turned into minimum care. In the REF
scenario, this takes place despite the complete abolishment of
livestock husbandry (suckler cows). In the scenario without
direct payments (S01) there is a dramatic increase of
abandoned land (+97%) (Table 4). The reason is that minimum
grassland care is not practiced due to the lack of incentives for
this measure in the ‘‘liberalization’’ settings.
The specific analysis for arable farms within the target
group with high share of UAA in Natura 2000 areas (E–A)
resulted in the findings reported in Table 4. Changing the
support scheme to an idealized single farm payment (REF)
leads to a significant shift in land use away from E–A and E–G
farms towards farms with more market-oriented production
(C–A and C–G). The total area in use (initially a total UAA of
2358 ha) undergoes a pronounced reduction, which in the
Agenda 2000 and idealized single farm payment (REF)
scenarios is less extreme (�23% to �30%) than the reduction
in number of farms. In the single farm payment scenario 40%
of the arable farms on poor soils survive (Fig. 4b). Without
direct payments (S-scenarios) less than 15% of the UAA of the
region remains in agricultural use of arable farms. Farms of the
target group are selected as they use land as well inside as
outside of Natura 2000 areas what allows for distinguishing
policy responses accordingly. Natura 2000 areas underlie land
abandonment in a similar way as non-designated sites, but less
fertile soils are more likely to be abandoned than fertile soils
(Fig. 5). The preference for set aside leads to a reduction of
initially diverse and extensive arable cropping by 80% (Fig. 5a).
Especially the traditional winter rye production is given up.
Fig. 6 shows the environmental impacts of the land use change
compared to the initial situation and to the average of all farms
in OPR. The idealized single farm payment leads to a clear
improvement of all biotic and abiotic indicators (see also Uthes
et al., 2008). Only groundwater recharge potential is markedly
reduced due to the further extensification respectively abol-
ishment of grassland use. The overall average of OPR farms,
with less land abandonment but a diverse low input cropping
pattern prove the tendency to a better environmental perfor-
mance than the farms selected as target group environment
(Fig. 6a). The baseline scenario shows opposite results but a less
distinct improvement of the environmental situation (Fig. 6b).
3.2. Land use change: development target competitiveness
The region OPR is characterized by a comparably low
productivity in terms of soil fertility classes and yields on
Fig. 4 – Land abandonment related to site qualities in farms with high Natura 2000 share in response to idealized SFP
compared to Agenda 2000, simulated for year 9.
a. Total area in use in farms >40% of UAA extensive grassland, b. total area in use in farms >40% arable land in site class 25.
Table 4 – Absolute (in ha) and relative (difference in % to initial situation) changes in utilized agricultural area in responseto different policy scenarios after 9 years of implementation.
Scenario E–G E–A C–A C–G
BAS00 10.408 Change (%) 2.358 Change (%) 19.996 Change (%) 100 Change (%)
BAS09 2.494 �76 1.641 �30 22.601 13 188 88
REF09 6.723 �35 1.680 �29 20.186 1 146 46
S0109 0 �100 0 �100 6.409 �68 211 111
E–G (Environment–Grassland): farms >40% extensive grassland, >80% Natura 2000; E–A (Environment–Arable land): farms >40% arable land in
site class 25, >80% Natura 2000; C–A (Competitiveness–Arable land): farms with 100% arable land, soil class 38, remaining in liberalization
scenario; C–G (Competitiveness–Grassland): farms >40% grassland, only intensively managed, remaining in liberalization scenario.
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both arable land (dominance of sandy soils) and grassland
(high share of wetlands). The MODAM modelling system
distinguishes soil quality classes based on a soil rating
index ranging from 7 to 100 (Stremme, 1951). In the case
study region OPR, most sites only belong to the classes I
(<25) and II (25–>38), with very low to medium fertility. For
the target group ‘‘competitiveness’’, only farms in class II
sites have been selected. The simulation results show
that the area in production remains stable under the
conditions of scenario REF (Table 4), with 92 farms of an
average size of 172 ha. The cropping pattern of those
comparatively ‘‘competitive’’ arable farms (C–A), shifts into
a pure cash crop rotation with low diversity (winter wheat,
winter rye and winter rape) (Fig. 5b). Formerly set aside area
is completely returned into production. If soil conditions do
not allow for extension of cash crop area, land is rather
abandoned. Mixed farms keep dairy cows numbers
unchanged and use grassland at relatively high intensity.
Fig. 5 – Change in cropping pattern in response to policy scenarios: (a) farms at low soil quality sites with high Natura 2000
share and (b) farms at medium soil quality sites.
Fig. 6 – Index of Goal Achievement (IGA) performance relative to initial state: comparison of farms in the E–A target group
and average of all farms in the German case study region OPR, (a) Agenda 2000 (BAS), (b) idealized decoupled single farm
payment (REF).
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Without direct payments however the area in production is
reduced markedly (�68%) (Table 4). At the same time the
number of farms shows a drastic reduction. In the S01
scenario, only 25% of the farms remain active in the
medium term.
3.3. Land use change: spatial patterns related to field type
As land use is one of the primary determinants of ecosystem
vulnerability, the assessment of changes in land use pattern
for the different scenarios is crucial to understand how the
different policy scenarios affect environmental services
provided by agriculture. Land use patterns nearly always
exhibit a certain degree of spatial autocorrelation, resulting
from the interaction between landscape features and gradi-
ents and farming technologies (Verburg et al., 2006). Spatial
autocorrelation, measuring the level of interdependence
between variables, provide a mean to elucidate and describe
spatial patterns in the landscape. The changes in autocorrela-
tion can be then detected, modelled and used to support the
analysis of CAP policy scenarios.
The land use share (% total UAA) for the whole Italian case
study area at the initial reference state (BAS00) is illustrated by
Table 7. This general cropping pattern is highly differentiated
in terms of occurrence of the different crops in the different
field types (Table 5).
Grassland in Mugello is exclusively run under extensive
grassland use, i.e. grassland areas (3–10 years) that receive
minimum grassland care of one cut per year. Common to all
scenarios is an increase of arable land which is coupled with
an almost complete or complete (under S0209) disappearance
of grasslands. The initial grassland share of 47% is reduced to
29%, 31% and 6% respectively for the Agenda 2000 (BAS09), the
idealized single farm payment (REF09) and the partial ‘‘liberal-
ization’’ (S0109) scenario. Under all scenarios there is a
dramatic abandonment of the mountain grassland field types
(MH-G and ML-G): �62% at BAS09, �51% at REF09 and �91% at
S0109.
The change in the share of set aside land provides a clear
picture of the structural changes under decoupled subsidies
that result in a relevant increase of uncultivated land (from
2215–5951 ha). Under the condition of ‘‘no subsidies’’ (i.e.
under the S01 and S02 scenarios) on the contrary, there is a
(nearly) complete disappearance of set aside land and an
increase in cultivated areas. In hilly field types (HH and HL) the
structural changes under decoupled subsidies lead to an
increase of arable lands and at the same time result in a
relevant decrease of cultivated areas. In the valley field types
(VH and VL) the structural changes under absence of subsidies
result in an increase of arable lands and at the same time lead
to the abandonment of set aside practices with a relevant
increase of cultivated areas under cereals, with maize and
barley dominating the more productive valley field types.
Typical crops such as alfalfa and spelt take over set aside and
fava bean in the less productive hilly field types.
3.4. Land use change: spatial pattern related toagricultural intensity
In order to assess how agricultural landscape patterns are
affected by policy driven land use change, an index of goal
attainment (IGA) was calculated for various indicators and
accounted environmental goals such as the reduction of soil
water erosion risks. The closer the index is to 1 the lower the
land use related risks are assumed. MODAM does not
simulate single crops but crop rotations within a given
Table 5 – Crop share in the arable field types for the main crops of the area: relative differences (%) at year 9 with respect toinitial state; in italics crops whose share drops down to 0%. HH: high hills field type; HL: low hills field type; VL: valleyplain field type; VH: valley terraces field type.
Scenario Field type Set aside Maize Barley Durum Spelt Alfalfa Fava bean
Initial HH 26.3 0.0 14.9 0.0 16.4 9.0 26.3
BAS09 HH 2.4 0.0 12.5 0.0 2.6 2.1 �15.6
REF09 HH 27.5 0.0 5.3 0.0 �0.5 �0.1 �26.3S0109 HH �18.9 0.0 14.0 0.0 24.8 13.4 �26.3S0209 HH �26.3 0.0 10.5 0.0 37.2 11.9 �26.3
Initial HL 26.8 0.0 15.9 14.0 13.3 8.2 17.4
BAS09 HL �2.6 0.0 9.9 �5.1 4.3 2.4 �7.3
REF09 HL 29.5 0.0 2.9 �14.0 1.9 0.7 �17.4S0109 HL �18.5 0.0 10.1 �14.0 30.7 13.6 �17.4S0209 HL �26.8 0.0 11.9 �14.0 36.5 14.2 �17.4
Initial VH 10.9 24.7 27.6 17.0 0.0 9.0 10.7
BAS09 VH �3.9 1.0 3.0 �0.1 0.0 0.5 �0.7
REF09 VH 23.2 �11.1 0.7 �17.0 0.0 �0.3 �10.7S0109 VH �10.9 24.7 0.3 �17.0 0.0 0.8 �10.7S0209 VH �10.9 29.3 �3.2 �17.0 0.0 0.5 �10.7
Initial VL 2.8 24.2 25.9 27.0 0.0 10.2 9.2
BAS09 VL 2.6 1.2 �0.9 �1.5 0.0 0.8 �1.9
REF09 VL 32.2 �10.5 1.2 �27.0 0.0 �1.5 �9.2S0109 VL �2.8 17.3 9.7 �27.0 0.0 �0.4 �9.2S0209 VL �2.8 20.2 6.7 �27.0 0.0 �0.4 �9.2
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production system since usually more than one crop is
allotted to one plot. Hence, it is not possible to localize a single
specific crop in each 1 ha plot at the different time steps but
only the share of each single crop and hence the assessment
in terms of IGA, is averaged for the whole plot. For this specific
indicator its spatial pattern reflects then the patterns of the
different crops combinations in terms of soil protection and
input intensity, and its variations over time under the
different scenarios are due to correspondent variations in
cropping patterns.
Fig. 7 – Case study Mugello, IGA Water Erosion: spatial distribution at initial state and at year 9 under the different policy
scenarios.
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In order to evaluate the different crop production practices
regarding their potential to contribute to water erosion, the
following parameters are considered in MODAM:
� the degree of soil-coverage,
� the cultivation method (i.e. zero/no tillage, under-sowing,
catch crops, sod seeding etc.),
� soil compaction in the winter half year (due to machinery
passages and field operations).
All the production practices of all crops defined for the
model have been rated by their Index of Goal Attainment (IGA)
using a fuzzy tool with the optimum (IGA WaEro = 1) being a
zero risk of soil erosion. Best rated among those evaluated are
set aside, grassland and alfalfa. Row crops like potatoes,
maize, and sunflowers are given the lower ratings while the
intermediate score goes to cereals depending on the time of
seeding.
For the Mugello region, the average IGA for water erosion
risk (IGA WaEro) exhibits a dramatic decrease under the two
scenarios without direct payments (�31 and �35% respec-
tively under S01 and S02 with respect to the initial state), a
relevant increase under the idealized single farm payment
scenario (+13%) and a weak decrease under Agenda 2000
(�2%). Nevertheless these overall figures are quite differen-
tiated in the different field types, with the hilly field types
exhibiting a positive trend under all the policy scenarios and
the valley field type exhibiting opposite trends. For the VH field
type the trend is positive under REF (+27%), negative under BAS
(�3%), S01 (�32%) and S02 (�33%), while for the VL field type
the trend is positive under REF (+29%) and BAS (+7%), negative
under S01 and S02 (�17%). These trends are made clear in the
raster maps in Fig. 7 (pixel size 1 ha) which show the spatial
distribution of the IGA for risk of soil erosion at the initial state
and at year 9 under the different policy scenarios. The
differences in the spatial patterns of the indicator (Fig. 7)
result from:
� reduction or disappearance of grasslands in parts of the
area,
� changes in set aside land under the different scenarios,
� changes in land use intensities related to different crop
patterns.
In order to quantify such differences in the spatial
distribution of the IGA for water erosion, the standardized
semivariograms for this indicator at year 9 under the different
policy scenarios were calculated and interpolated with
authorized models (Goovaerts, 1997). The semivariogram is
a function describing the degree of spatial dependence of a
variable Z(x) which is assumed to result from a stochastic
process. The semivariogram g(h) is computed as half the
expected squared increment of the values between locations x
and x + h:
gðhÞ ¼ 12n
Xn
i¼1
fZðxÞ � Zðxþ hÞg2
where n is the number of pairs of sample points separated by
the distance h; g(h) is calculated for all possible lag distance
classes in a data set. It is commonly represented as a graph
showing the semivariance as function of increasing distance
between all pairs of sampled locations. Such a graph is helpful
to build a mathematical model that describes the variability of
the measure with location. The omnidirectional standardized
semivariograms and their models are shown in Fig. 8. In all
cases a double nested spherical model proved to provide the
most suitable model; Table 6 shows the parameter for the
model used to interpolate the semivariograms.
The changes in the values of the structural component of the
variograms result from substantial changes in land use pattern,
with clear and constant modifications with respect to the initial
state. This trend is characterized by an increase of the nugget
effect C0 (i.e. the spatially uncorrelated variance), whose ratio to
the total sill (i.e. the spatially correlated variance) increases
under all scenarios with respect to the initial state. This is
coupled with a decrease of the ranges of the variograms (i.e. the
distance at which the observations are no longer spatially
correlated), particularly evident for the long range component
A2 of the nested model. The increasing spatially uncorrelated
variance suggests an increase of the spatial randomness. This
implies a decrease of spatially structured variability, and a
higher degree of fragmentation. The reduction of the range
indicates a decrease in the size of patches with similar land use
intensity, which under the scenarios without direct payments is
more likely to be surrounded by smaller patches of contrasting
land use intensity with respect to the other scenarios and to the
initial state.
Table 6 – Standardized omnidirectional semivariograms for IGA WaEro at time 9. Sph.: spherical model = 1.5 T {[(distance/range)] S 0.5 T [(distance/range)3]}.
Scenario Nugget C0 Model 1 Sill C1 Range A1, m Model 2 Sill C2 Range A2, m Total sill Nugget/sill
BAS00 0.12 Sph. 0.23 1650 Sph. 0.60 11000 0.83 0.14
BAS09 0.18 Sph. 0.29 2530 Sph. 0.53 9680 0.82 0.22
REF09 0.19 Sph. 0.24 2640 Sph. 0.59 9680 0.83 0.23
S0109 0.48 Sph. 0.37 1440 Sph. 0.12 6000 0.49 0.98
S0209 0.59 Sph. 0.32 1560 Sph. 0.12 5132 0.44 1.34
Table 7 – The land use share (% total UAA) for the wholeItalian case study area at the initial reference state(BAS00).
Land use Percentage
Barley (Hordeum vulgare) 23
Set aside 17
Durum wheat (Triticum durum) 16
Maize (Zea mays) 15
Field bean (Vicia faba var. minor) 14
Alfalfa (Medicago sativa) 9
Spelt (Triticum spelta) 5
Sunflower (Heliantus annus) 1
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4. Discussion
4.1. Policy implications: targeting of policies
Targeting, understood as appropriate objective setting and
instrument provision, has so far been mainly directed at
geophysical conditions, and at the limitation of environmental
threats to sensitive areas. A spatially explicit approach is
required in order to properly evaluate the impacts of the
different scenarios on the environmental services provided by
agriculture. This approach will equally provide sound indica-
tions to policy makers and stakeholders. Different from other
policy impact assessment tools that work on highly aggre-
gated scales, the hierarchically linked MEA-Scope modelling
approach connects spatially explicit analysis with actions and
interactions of individual farms. As regards policy impact
assessment, the resulting aggregate effects are thus the
results of individual adjustment reactions to the policy at
Fig. 8 – Experimental standardized semivariograms (ominidirectional) and semivariogram models for IGA Water Erosion at
initial state and at year 9 under the different policy scenarios. Black dots: experimental semivariogram; continuous line:
semivariogram model.
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the farm level given specific spatial characteristics. Results
can thus be, linked to specific target groups of farms or to
spatial characteristics that may correspond with development
targets. The first chosen approach to do so is a criteria and rule
based selection of farm groups that are identified as the typical
implementers of environment related strategies and mea-
sures, respectively related to the development target competi-
tiveness.
Results from the application of the MEA-Scope approach to
two different regional settings indicated that for extensive
sites the shares of land abandonment was highest in any kind
of policy configuration. The maintenance of permanent
grassland on less productive sites, mainly wetlands in the
OPR landscape, belongs to the main environmental objectives
of the region. Especially grassland farms on those sites turned
out to quit farming to a significantly higher share than the
average farm in OPR. While differing in magnitude between
policy scenarios, the general direction of this effect cannot be
reversed by providing additional incentives to use grassland as
part of an agri-environmental scheme. While under Agenda
2000 and without direct payments especially those low
productive grassland sites are abandoned drastically, the
idealized single farm payment allows for a certain buffering of
the reduction. Mixed farms undergo a less dramatic structural
change, as they make use of the set aside option on arable land
and the minimum care measure on grassland. As regards the
latter, it is applied whenever support payments to farms are
decoupled from keeping livestock on grassland. In this case,
grassland use is decoupled completely from fodder produc-
tion, and thus has the character of a pure NCO production. Yet,
as soon as payments are phased out, farms will abandon the
land completely due to a lack of incentive. Interesting is the
finding that land abandonment on low fertility sites takes
place slightly more in non Natura 2000 when compared with
other sites. To a certain degree this means payments related to
Natura 2000 as an obligation in the idealized single farm
payment scenario save land from abandonment, especially
low quality grassland. These results strengthen support
towards targeting measures as far as the maintenance of
low productive grassland in use is the objective. With regard to
the environmental impacts, the outcomes of the simulations
are less distinct from the average than expected. Though the
single farm payment results in a clear improvement of most
environmental indicators, farms with a high share of area
located in Natura 2000 sites show no better environmental
performance than the average low intensity farming of the
region. Particularly for arable farming, the value of the set
aside measure (connected with payments on arable land
without yielding a crop) implemented at such a high share is
more than questionable in terms of cultural landscape
preservation and identity. Traditional winter rye production
and grassland use are turned into set aside and minimum
care. Farmers apply a profit maximization strategy with full
exploitation of agri-environmental payments on poor soils as
far as possible if it is profitable. If not, land is left idle. In order
to reach a higher added value of arable sites with Natura 2000
designation, clearer restrictions should be set, in terms of
maintenance of a certain diversity within crop rotation, as
they e.g. inherently exist within the system of organic
farming.
The site class under consideration in the target group
competitiveness (C–A), although constituting the ‘‘high’’
quality soils of the region OPR, in general terms only posses
an average potential yield capacity. Accordingly, the low share
of arable land kept in production and able to compete in
absence of direct payments (S-scenario) is a consequence of
insufficient yield potentials due medium soil fertility condi-
tions. The highly above average results for the intensive
grassland farms in OPR that markedly increase area, have to be
interpreted from this background too, as they are a result of
the comparatively low competitive capacity of the arable
farms with regard to cash crop production. The plots become
interesting for fodder cropping according to the comparatively
lower value on the land market, resulting finally in a shift
towards bigger farms with animal husbandry in general and
hence a shift in farm type distribution. Cropping pattern
results support this development. The cropping pattern of
arable farms on medium/good soils is characterized by low
diversity and cash crop orientation. The more the policy
presses towards market-orientation, the less different crops
are cultivated. Under the condition of profit maximization, if
soil conditions do not allow for extension of cash crop area,
land is rather abandoned than used for less attractive crops.
Amongst other animal husbandry sectors, only dairy cows are
kept in production if payments are phased out (Piorr et al.,
2007b). The policy settings for farms that turn out to work
competitively and successfully throughout all years, are
targeted in so far as the single farm payment can be assessed
clearly advantageous compared to the Agenda 2000 scenario.
4.2. Policies implications: land use patterns
The outcome for the Mugello scenarios in terms of land use
controlled environmental services can be distinguished in two
groups. Idealized decoupled single farm payment (REF) results
in an extensification of the region land use (increase of arable
lands in hilly field types, relevant decrease of cultivated areas,
increase of typical crops such as alfalfa and spelt replacing
cereals). Phasing out of direct payments (S01, S02) results in an
intensification of the regions’ land use (increase of arable
lands in valley field types, abandonment of set aside practices
and increase of cultivated cereal such as maize and barley).
All scenarios show an evolutionary trend characterized by
the disappearance of open areas, which is complete under the
scenarios without direct payments. This is coherent with the
historical data for the mountainous areas of the central
Apennine (�18.6% between 1990 and 2000) (ISTAT, 2002).
The indicator of soil erosion is highly affected by policy
induced land use changes and crop shares. An idealized
decoupled single farm payment with AEP results in a marked
reduction of average soil erosion risk with respect to the
reference situation, while phasing out of direct payments
leads to a marked increase in soil erosion risk with respect to
the initial state. The effects on erosion risk are highly
differentiated across the landscape, with the soils of the
valley terraces, characterized by a higher capability and
productivity, to be considered the most vulnerable to policy
driven changes. On the other side, all scenarios result in
reduced erosion losses from the lower capability hilly field
types, where the losses of set aside under scenarios (without
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direct payments) are counterbalanced by an increase of
alfalfa.
The changes in land use intensity highlighted by the
different spatial autocorrelation functions observed for the
different scenarios indicate that the scenarios induced land
use changes at medium term are likely to results in radical
changes of landscape patterns. The indicator of soil erosion
risk assumed here as indicator of land use intensity suggests a
strong homogenization with low correlation under the two
scenarios with phased out payments as opposed to a complex
and well structured pattern with a high degree of spatial
dependence under single farm payments and Agenda 2000.
For the scenarios without direct payments the spatially
uncorrelated variance C0 is >50% and the range of auto-
correlation A is dramatically reduced (short range continuity),
indicating an increasing homogeneity (randomness) in the
land use intensity patterns. These, with respect to the initial
state, appear to be characterized by progressively smaller and
weakly aggregated spatial structures characterized by a higher
degree of fragmentation.
5. Conclusions
The demand on ex-ante policy impact assessment to assist the
knowledge creation process among decision makers is
increasing. Especially the fundamental changes of the CAP
accompanied by increased accountability requirements for
policy makers underline that more specific knowledge on
policy implementation is essential. This applies as well to
policies and measures targeted on specific objectives such as
the provision of public benefits, as to responses of farms
related to their specific socioeconomic and geophysical
framework conditions. Simulation models that work at highly
disaggregated scale, combining the farm level and regional
scale, may facilitate the development of knowledge on
potential adjustment reactions and their impact. Neverthe-
less, the results presented in this paper have to be qualified
from the point of view that decisions was made based on
economic concerns. All three models applied in the modelling
cascade run the simulations from the assumption of profit
maximization being the main driver for decisions of change.
Whereas there may be some grounds for this assumption,
with regard to a better targeting of policies promoting the
multiple functions of agriculture other behavioural drivers
should be taken into account. Hence, farmer activities are also
related to their individual value canon. They are determined
by the environment and the resulting perception of needs as
well as social factors (e.g. imitation, normative influences,
comparison processes). Further research should therefore
integrate such elements, also in enlarging modelling capabil-
ities.
The tool approach used provides insights regarding the
distributional effects among single farm agents, showing their
individual development over time as result of differences in
resource endowments (labour, capital, and land), and the
farms’ land market and investment activities. Because each
farm agent is spatially localized in a regional area, the
approach is capable of addressing policy induced farm
adaptation strategies, and spatially explicit impacts resulting
from on-farm structural change and changes in cropping
pattern or management practices.
The results from the application of the approach to
Ostprignitz-Ruppin, Germany and Mugello, Italy show that
the impacts of the simulated CAP scenarios differ across
different soil and climate characteristics, a result of different
site and farm type specific management decisions that go
from changes in cropping pattern to complete abandonment
of production branches (husbandry) or of the whole farm.
Farms located at rather favourable site conditions were the
beneficiaries of the idealized single farm payment scenario
(REF) while in marginal areas, particularly in NATURA 2000
areas, a high share was turned into set aside land. Without
direct payments (S02 scenario) arable farming in these areas
could not be maintained at all. Moreover, give the simulation
results support that on favourable site conditions, the
acceptance of extensification measures, e.g. set aside or
minimum care on grassland, is lower. Such an increased
abandonment of arable land connected with a loss of diversity
and habitats would run contrary to the objectives of NATURA
2000 area designated for the purpose of environmental
protection and preservation of traditional landscapes.
The spatially explicit hierarchical analysis applied to the
Italian case study region Mugello allowed for the analysis of
the interdependencies of policy driven land use changes and
impacts on the landscape pattern. The phasing out of direct
payments (S02) leads to distinct structural changes resulting
in a more homogeneous cropping pattern. An originally
diverse landscape developed towards a more uniform one
not only from the visual diversity value but also with regards
to biodiversity and environmental impacts.
Both case studies show that site-specific settings within
regions have different and marked impacts on the particular
policy adaptation strategies of farms, and that this results in
clearly different landscape patterns. The results underline the
necessity to take the heterogeneity of regions and farm
structures into account for ex-ante policy assessment,
especially if implementation impacts on landscape fragmen-
tation are considered.
Acknowledgements
This work was carried out as part of the EU funded 6th
framework project MEA-Scope (Micro-economic instruments
for impact assessment of multifunctional agriculture to
implement the Model of European Agriculture, SSPE-CT-
2004-501516). The authors wish to thank all the members in
the consortium who contributed to the discussion that helped
shape this work and Tim Hycenth Ndah for his helpful
assistance in revising the manuscript, as well as the
anonymous referees whose comments and suggestions
improved both clarity and precision of the paper.
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Annette Piorr is a senior researcher and head of research groups atZALF. She holds a PhD in Agronomy from the University of Bonn.Her scientific expertise covers sustainable land use, multifunc-tional agriculture, organic farming, indicator development, impactassessment and evaluation, including policy makers and stake-holder involvement. She carried out the mid-term evaluation ofthe Rural Development Programme in Brandenburg/Berlin andrecent research on the CAP 2003 reform. She co-ordinated theFP6 EU-research project MEA-Scope.
Fabrizio Ungaro is a research scientist who graduated in TropicalAgriculture with a PhD in Soil Science. He is a specialist in geospatialmodelling, applied environmental soil physics and pedometricswith long years experience in the national and international con-text. He is senior researcher at the CNR IRPI in Florence.
Arianna Ciancaglini holds a Bachelor degree in AgriculturalScience from the University of Italy. From 2006 to 2007 she colla-borated with the Department of Agricultural and Land Economy,University of Florence involved in the MEA-Scope project. She isPhD student in Rural Development at the University of Florence.
Kathrin Happe holds a PhD in agricultural economics and is work-ing on structural change and policy analysis. Currently, she is asenior researcher at the Leibniz Institute of Agricultural Develop-ment in Central and Eastern Europe in Halle (Saale), Germany.
Amanda Sahrbacher has studied Agricultural and LifeSciences and Engineering at INA P-G in Paris (now AgroParis-Tech). She got her MS in Environmental Economics there andjoined the IAMO in 2004, where she was involved in the 6thFramework EU project MEA-Scope. She is currently studyingfor her PhD on distributive impacts of CAP using an agent-basedapproach.
Claudia Sattler is a scientist at the ZALF Institute of Socio-Eco-nomics. Her research deals with the ecological assessment ofcropping practices and modelling as well as the acceptance byfarmers of the implementation of more environmental friendlycrop management and land use practices.
Sandra Uthes is an agricultural economist at ZALF. Her mainresearch interests are in policies for sustainable and multifunc-tional agricultural land use, whole farm modelling, and trade-offanalyses between economic and ecological objectives of agricul-tural land use practices.
Peter Zander holds a PhD in Production Ecology and ResourceConservation from Wageningen University and is a Senior Scien-tist at the Institute of Socio-Economics at the Centre for Agricul-tural Landscape Research (ZALF). His research interest is themodelling of farm level decision making related to environmentalimpact of crop production systems.
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