Biosphere Reserve Rhön)

Home , Sinn (river), Thuringian forest, Ulster (river)

IRON CURTAIN Project Reference No. QLK5-2001-01401 Deliverable 4.1 Integrated multilayer data base for the reference areas and interpreted maps and time series

REFERENCE AREA 2 GERMANY - GERMANY

(Biosphere Reserve Rhön)

PARTNER: Institute for Geography – Geoinformatics, Friedrich-Schiller-Universität Jena (UNIJENA)

compiled by UNIJENA

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Table of Contents

1 Area Description...... 4 1.1 General Description of the Area...... 4 1.1.1 Location...... 4 1.1.2 Geology and Soils ...... 5 1.1.3 Climate: ...... 5 1.1.4 Flora ...... 5 1.1.5 Fauna ...... 6 1.1.6 Ecosystems...... 6 1.1.7 Land cover ...... 6 1.1.8 Major streams...... 6 1.1.9 Socio-Economic Conditions ...... 6 1.1.10 Legal Status ...... 7 1.2 Historical Development ...... 9 1.3 Main Administrative Actors ...... 9 1.4 Steering Committee, Working Group, End Users ...... 10 2 Summary of Problem Definition...... 11 2.1 Vision...... 12 2.2 Strengths-Weaknesses-Opportunities-Threats (SWOT)...... 12 2.3 Specific Problems ...... 17 2.3.1 Population Density ...... 17 2.3.2 Agricultural Land use...... 18 2.3.3 Tourism ...... 20 2.4 Development Scenarios...... 20 2.5 Indicators ...... 21 3 Regional Database...... 23 3.1 Data Acquisition and Management ...... 23 3.1.1 GIS Data ...... 23 3.1.2 Remote Sensing Data ...... 23 3.1.3 Statistical Data ...... 24 3.2 Data Quality ...... 25 3.2.1 GIS and Remote Sensing Data ...... 25 3.2.2 Statistical Data ...... 25 4 Conclusions and Recommendations ...... 27 5 Annexes ...... 29 5.1 Basic Documentation of the Area ...... 29 5.1.1 Position of the Area...... 29 5.1.2 Population Density ...... 29 5.1.3 Population: Age Structure ...... 30 5.1.4 Population: Migration...... 30 5.1.5 Communities ...... 31 5.1.6 Protected Areas...... 31 5.1.7 Infrastructure ...... 32 5.1.8 Topography ...... 32 5.1.9 Geology...... 33 5.1.10 Soils ...... 33

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5.1.11 Land cover ...... 34 5.1.12 Structure of Agriculture (2 maps) ...... 34 5.2 Results of GIS Analyses ...... 35 5.2.1 Transportation ...... 35 5.2.2 Land-cover Change...... 36 5.3 Breakdown of indicators on territorial components ...... 37 5.4 Indicator descriptions...... 39 5.4.1 Net Migration Rate ...... 39 5.4.2 Total Population (male/female)...... 41 5.4.3 Age Distribution...... 42 5.4.4 Unemployment Rate...... 43 5.4.5 Nutrition Balance Surplusses ...... 44 5.4.6 Potential for Erosion...... 45 5.4.7 Spatial Share of Organic Farming ...... 47 5.4.8 Spatial Share of Fallow Land...... 48 5.4.9 Spatial Share of Grassland ...... 49 5.4.10 Spatial Share of Forest ...... 50 5.4.11 Spatial Share of Agricultural Areas ...... 51 5.4.12 Spatial Share of Extensively used Areas ...... 52 5.4.13 Landscape Metrics...... 53 5.4.14 Development in Number of Holdings...... 54 6 References ...... 55

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1 Area Description

1.1 General Description of the Area

1.1.1 Location The Biosphere Reserve Rhön is located at the border triangle of the three German federal states Thuringia (former German Democratic Republic, GDR), Bavaria and Hessen (Federal Republic of Germany, FRG). In order to use the statistical data that are available on a community basis, all communities lying at least partly in the biosphere reserve have been incorporated into the studies (see annex 5.1.12 agriculture).

Figure 1: Position of the reference area

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1.1.2 Geology and Soils Basement of the Rhön mountains are Triassic sediments (sandstone and shell limestone) which have been penetrated by volcanic activities resulting in basalt covering of today’s higher areas. The valleys are filled with quarternary sediments. Predominating soils are several types of cambisols, partly podsolic (sandstone areas), rendzina soils (limestone areas) and gleysoils in valleys or peatsoils in the bogs. The high plateau rises to 950 meters above sea level thus the Rhön mountains have not been glaciated during the ice ages. See also annexes 5.1.9 geology and 5.1.10 soils.

1.1.3 Climate: Generally speaking the climate in the Rhön can be described as pretty harsh with high precipitation and low temperatures. Due to the North-South direction of the main mountainous range relatively high amounts of precipitation can be expected on the western slopes because of predominating western winds. Table 1: some climatic parameters (source: Grebe & Bauernschmitt, 1995) Place Elevation Mean Precipitaion Vegetation [m asl.] temperature [mm/a] period [d] [°C] Oberelsbach 410 7-8 700-750 200-210 Kaltensundheim 460 6-7 700-750 190-200 Frankenheim 750 5-5,5 950-1000 170-180 Bad Neustadt 240 8-9 500-550 210-220 Fulda 250 7-8 600-650 220-230 Eisenach 240 7-8 900-950 200-210

1.1.4 Flora The potential natural vegetation of the area is deciduous forest with different beech- forest communities except for moist areas in the valleys where alders would be the prevailing species and peat bogs and rocky slopes both being naturally woodless. Because many areas in the Rhön mountains are only extensively used, 40% of the forest area consists of semi natural deciduous forest (beech) whereas the rest is made up of conifers introduced in the area for forestal use. Any grassland is of anthropogenic origin however large parts of it have only been used extensively. Depending on the underlying rock (either basalt or limestone) and their use as either meadow or pasture, different grassland communities have been established during the last centuries that used to be typical in many mountainous areas of middle Europe. Grassland is visually dominating the area. More than 100 species listed in the German Red Book have been detected in the area of the biosphere reserve. The biosphere reserve contains raised bogs in its higher areas, an ecosystem type seldomly found nowadays with a typical and protected flora.

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Figure 2: position of the climate stations in Table 1

1.1.5 Fauna Amongst the animals found in the area are several protected bird species, e.g. black storck and kingfisher. Largest predator is the wildcat; several rare species of bats, butterflies, dragonflies, grasshoppers and others have been observed.

1.1.6 Ecosystems Dominating Ecosystems are forests, grassland communities, and arable land. Important for conservation are local ecosystems as block areas (basalt) and peat bogs.

1.1.7 Land cover Forests cover about 40%, meadows and pastures some 30%, arable land some 20% and all other uses less than 10% of the area (see annex 5.1.11 land cover).

1.1.8 Major streams The northern part is drained by the Ulster, a tributary to the Werra (Weser)), in the southern part the Sinn (a tributary to the Main (Rhine)) has the largest catchment area.

1.1.9 Socio-Economic Conditions Main economic activities are light industry, construction, tourism, agriculture (partly combined with direct marketing) and forestry. Agriculture in the Thuringian part is

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© IRON CURTAIN Consortium Issued by: GEO Date: 25.2.04 done by highly professional large-scale farms of several thousand hectares, the successors of the German Democratic Republic’s collective farms (LPGs). The agricultural cooperative in Kaltensundheim is dedicated to extensive use and ecological production in accordance with needs of the biosphere reserve since the early 1990s, made possible by support from the European Union and the state of Thuringia.

The structure in agriculture, especially as can be found in Bavaria, fostered the development of fallow land: Most of the farms are small-scale family enterprises, half of them below 10 hectares (figures from 1999 for the Bavarian districts Bad Kissingen and Rhön-Grabfeld). The better part of all farms is operated by part-time or hobby farmers whose main source of income is industrial or other work outside agriculture.

1.1.10 Legal Status The Rhön was approved as a biosphere reserve by the UNESCO in 1991 after more than one year of preparation starting as soon as December 1989.

In 1995 the framework concept for the biosphere reserve (Grebe & Bauernschmitt, 1995) was handed over to the authorities proposing three zones of graded protection among other things. The establishment of three zones is according to the German guidelines for biosphere reserves.

Zone I – Core Zone: In this zone ecosystems are allowed to develop naturally without human interference. The core zone consists of deciduous forest, bogs, watercourses and block areas and accounts for 2% of the reserve’s area The whole core zone is protected as nature conservation areas.

Zone II – Buffer Zone: This is the zone of the main efforts in nature conservation. Its goal is to keep and protect the area’s typical ecosystems developed through human use with their biocenosis. It consists of all types of ecosystems and covers some 67 000 hectares or 36% of the area. Because of its large area it is divided into two parts containing the sensitive and damageable areas on the one hand and the rest on the other hand. Parts of the buffer zone are protected as nature conservation areas.

Zone III – Zone in Transition: This is the largest zone accounting for 62% of the area and is dedicated to human-nature relations. It is supposed to be used for agriculture, production and recreation in a sustainable way (see annex 5.1.6 protected areas).

The Rhön area is a low-mountain landscape with a mix of land uses and ecosystems as it was typical for similar areas in middle Europe. Therefore this landscape can be interpreted as the last survivor regarding its size and characteristic mix of (semi- )natural and anthropogenic landscape elements. It was created by a type of land use that followed former generations‘ technical possibilities and economic needs and that would nowadays be called sustainable. So not the natural but the man-made ecosystems depending on a certain type of agricultural land use can be considered

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© IRON CURTAIN Consortium Issued by: GEO Date: 25.2.04 typical for such areas. This type of land use is now endangered by change in use intensity (either more intensive or fallow) caused by agricultural politics focussing on production amounts. The designation as a UNESCO biosphere reserve honours the efforts to establish a sustainable use in the area. Parts of the biosphere reserve are protected under different national nature protection categories and form part of the EU’s nature conservation network NATURA2000 (some 40 areas have been reported to the EU) stressing the fact that many rare and endangered species adapted to the described type of agricultural landscape are still living here. Table 2: comparable parameters Former FRG Former GDR Total area of all included 1434 676 communities [km2] Total population (2000) 147477 50499 Population density [inh/km2] 102,84 74,7 Number of communities 35 45 Land-use structure Forest: 40%, grassland: 30%, arable land: 20%, (approx.) others: 10% GDP per capita (2002) 31496 (Hessen) 16929 (Thuringia) [EUR]* 29858 (Bavaria) Gross wages and salaries 28427 (Hessen) (workplace) per employee 20980 (Thuringia) 27708 (Bavaria) (2002) [EUR]* Unemployment on district level (mean 2002) [% 7,5 13,2 civilian labour force]** Life expectancy men [a]*** Between 74 and 76 Below 73 Life expectancy women > 81 < 80 [a]*** % of area with special 100 100 protection status Number of hotel beds Not available (see section 3.2.2) Total length of Not applicable (as various roads of various categories roads/railroads [km/km2] can not be considered equal) Price of internet connection (30 hrs/month) [% of 0,7 0,9 average disposable income 2001*] *source: http://www.statistik.baden-wuerttemberg.de/Arbeitskreis_VGR/, Jan 21st 2004 **source: http://www.pub.arbeitsamt.de/hst/services/statistik/detail/d.html, Jan 21st 2004 ***source: http://www.bbr.bund.de/index.html?/raumordnung/bevoelkerung/lebenserwartung.htm, Jan. 21st 2004

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1.2 Historical Development The earliest human traces date back to Neolithic people (4000 to 2000 B.C.) that are supposed to have only visited the area, settlements were established during the Bronze Age and especially during the Iron Age’s Hallstatt and La Tène periods (starting approximately 700 B.C.). The Celtic La Tène people were driven away by the ancient Germans in the first A.D. years. During the following centuries the area was basically uninhabited until Frankian settlers moved in. The large clearing period in the early middle ages (800 till 1000 A.D) brought settlers even in the higher regions of the Rhön mountains. Since around 1350 A.D. settlements were abandoned but resettled in the late middle ages. It is assumed that today’s ratio between wood and agricultural area was laid during this period. The ratio between arable land and grassland was 2:1 then. Most important livestock were sheep; the importance of dairy cattle grew with industrialization leading to higher proportions of grassland. Industrial centres existed around but not within the Rhön so many people left the area due to poverty.

Since the early middle ages there were some important roads crossing the area: Frankfurt-Erfurt (E-W), Grabfeld-Hessen (SE-NW) and Fulda-Bad Salzungen (SW- NE).

Recently published pollen-analysis data from a peat bog in the Thuringian Rhön (Lange & Gringmuth-Dallmer, 2001) showed that the Bronze Age settlers found an oak dominated forest whereas the beech provided two thirds or more of the tree pollen from around 500 B.C. until the late middle ages. The centuries until around 1800 were characterised by declining forests due to heavy use. Afterwards afforestations with fir and pine are reported for the area. After the Second World War and especially during the 1970s and 1980s the higher areas were afforested as a result of fallow agricultural land because of the structural change in agriculture. This is basically true only for the parts west of the Iron Curtain.

1.3 Main Administrative Actors Each of the three federal states having a fraction of the reserve has established an administrative post. The reason for this is that no state authority may pursue administrative tasks within the area of another state on a regular basis. Nevertheless the three authorities are working closely together and have divided the optional tasks between them. However the administrative hierarchy is different in all three states. The Bavarian administration is a subdivision of the regional district of Unterfranken, the Hessian administration is a subdivision of the district of Fulda and the Thuringian administration is a subdivision of the Thuringian Ministry of Agriculture, Conservation and Environment. A treaty between the three states is planned that is going to assign common rights and duties to the reserve administration.

The districts of Bad Kissingen and Rhön-Grabfeld (Bavaria), Schmalkalden- Meiningen and Wartburgkreis (Thuringia) and Fulda (Hessen) are partly within the biosphere reserve. The districts as corporate bodies of the communities play an

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© IRON CURTAIN Consortium Issued by: GEO Date: 25.2.04 important role in local planning and conservation. They are among others responsible for local planning and supervision of the communities in the fields of building or nature protection. However the respective reserve administration has to be informed about (Bavaria) or must agree with (Thuringia) any plans. In Hessen the biosphere reserve administration and the administration of the district of Fulda are identical

1.4 Steering Committee, Working Group, End Users As the situation in this reference area is different from that in the other five areas (the whole area lies within one country but within three federal states) and cooperation is already daily business, focus was set more on the establishment of a working group than of a steering committee. All three administrative units delegated one or two of their staff to form the working group. This group is also considered the end user because it is set up of experts for data handling/GIS, agriculture and nature conservation.

Members of the Steering Committee are: Dr. Martin Bucerius (Bavarian Environmental Protection Agency, Augsburg), Mr. Michael Geier (Biosphere Reserve Rhön, Bavarian Division, Oberelsbach), Mr. Heinrich Hess (Biosphere Reserve Rhön, Hessian Division, Fulda) and Mr. Karl-Friedrich Abé (Biosphere Reserve Rhön, Thuringian Division, Kaltensundheim).

Members of the Working Group/Endusers are (Dr. Doris Pokorny and Mr. Karl-Heinz Kolb (Biosphere Reserve Rhön, Bavarian Division, Oberelsbach), Mr. Eugen Sauer (Biosphere Reserve Rhön, Hessian Division, Fulda) and Mr. Reinhard Braun (Biosphere Reserve Rhön, Thuringian Division, Kaltensundheim).

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2 Summary of Problem Definition To identify the main problems and goals of the area a series of workshops has been held with the working group following the bottom-up approach as agreed on by the IRON CURTAIN consortium to be used in all reference areas. The goals of the workshops were to identify regional problems and state development goals (figure 3), to formulate indicators (annexes 5.3 and 5.4), to state a vision and analyse the area’s current position (SWOT, figure 4) and to give input for the formulation of scenarios. Some of the workshops were done in close cooperation with project partner DGGS.

biodiversity working-age touristic attraction (= preservation of population stays of the landscape characteristic/rare in the area is guaranteed species) is guaranteed

visual properties of the landscape are protected

habitats of a minimal structural different characteristic/rare landscape abiotic habitat species are configuration conditions are protected and is conserved conserved interconnected

fraction of forest unwanted neophytes and use of fertilizer conservation erosion protection remains more or succession "problem species„ according to of hedgerows is established less constant decreases are repelled requirements

new buildings are built extensification of new opportunities saving ground and no further loss of agriculture; to earn money respecting the landscape; extensively used promotion of in tourism no road construction areas organic farming beyond local needs

persisting agriculture creation of jobs in the whole area; no outside the further segregation of agricultural sector landuse figure 3: Goal System; result of a workshop at June 24th 2002

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2.1 Vision The members of the working group agreed on a vision for the year 2020: The Rhön should be an open, manifold and highly structured cultural landscape with high biodiversity, sound material cycles and living villages that are characterised by a sufficient population density, typical crafts, sustainable agriculture and cultural autonomy. The vision underlines the main goals as they have already been expressed in the goal system for the reference area (figure 3).

They are to keep the typical character of the landscape which is the base for the high biodiversity of the region. This landscape is a man-made landscape so its preservation can only be done together with the people living in the area expressed in the goal of sufficient population density. Typical crafts and sustainable agriculture shall offer these people a professional perspective. Sustainable agriculture is characterized by low-input high-recycling rate for nutrients which is expressed as sound material cycles.

2.2 Strengths-Weaknesses-Opportunities-Threats (SWOT)

figure 4 SWOT analysis, result of a workshop at July 16th 2003; red boxed elements are equally important The elements of the SWOT analysis (figure 4) were collected during a workshop with the working group and then positioned in a plane defined by four half-axes (S, W, O,

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T). The stronger the element was perceived within one category the farther away from the axes interception. Position in quadrants aside of axes reflect the fact of percepting the factor as e.g. strength and opportunity at the same time.

In the following session the members of the working group were asked to rate the direct influence of each SWOT element on each of the others in the following scale: 0 no influence at all, 1 slight influence, 2 strong influence, 3 very strong influence. The aim of this procedure is to reduce the complexity of the underlying system and to sort which of the elements are very sensible to change and which of them influence the system as a whole. The approach was developed by VESTER (1992, see also http://www.frederic-vester.de). The result was then analysed and some adjustments were made ending up with 21 elements or factors. All factors are complex systems themselves that can be categorized in one of four groups: group 1 comprises all factors that cannot be influenced on the regional level (so called Global Change elements). They are the EU agricultural policy (NAP), the Internet and the Climate Change. These factors are thus per definition without any direct influence from other (regional) factors. Group 2 holds the objectives (compare to figure 3). They are regional systems themselves which cannot be directly influenced, e.g. there exist means to increase the regional identity, however it cannot be directly increased as it is the feeling of the people living in the area. Group 3 holds the regional factors that can be directly influenced by the biosphere-reserve administration, whereas group 4 contains those that cannot be influenced by the biosphere-reserve administration. Some of the elements of the original SWOT (figure 4) were not included, e.g. the division in three parts, as their influence in the system is none existing and they are not influenced by other factors either. Some others were combined into one factor (e.g. tourism) and the objectives of the vision (see above) were added.

For interpretation of the matrix, sums were calculated in two directions: the row sum or active sum AS indicates the total influence of this factor in the system whereas the column sum or passive sum PS shows the total influenceability of the respective factor. The AS and PS values were plotted (figure 6) to identify those factors that can influence the system (active factors) while not being influenced too much themselves. As figure 6 shows there are no active factors in the system, this can be interpreted as there is no single lever to influence the system. Most active is tourism, which, however, is also very highly influenced by the other factors, thus it can be interpreted as a critical element, indicating a very high involvement in the system. Changes of a critical factor strongly influence the whole system. Factors with a high PS and a low AS are called passive factors and should always be carefully monitored if doing any changes. Most resistant to changes are buffering factors, they are also not very active either. At the bottom of the plot (not being influenced by the other factors, PS = 0) are, of course, the factors 1, 4 and 12 that have been introduced as Global Change elements in the preceding. From the factors that are influanceable on a regional level the factors 15/20 (number of farms/structure of agriculture) and 8/3 (agricultural land use/regional products) seem to be the most active. From those four only the latter can be influenced by the biosphere-reserve administration. The highest influenceability has factor 9 (identity) which is also located in the critical quadrangle (figure 6). The outside recognition (13) and the local market (14) also depend more

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© IRON CURTAIN Consortium Issued by: GEO Date: 25.2.04 on the others than they depend on them. Half of the factors can be interpreted as buffering, although some (1/4, 15/20) are very close to being active.

Impact of on 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 AS Q P 1 NAP 0 0 0 0 0 0 0 3 2 0 0 0 2 3 3 0 0 0 0 2 0 15 error 0 2 Biosphere Reserve 0 0 3 0 3 3 3 2 3 0 1 0 3 3 0 0 0 1 0 0 0 25 1,19 525 3 Regional Products 0 0 0 0 2 3 3 2 3 0 0 0 3 2 0 2 0 3 0 0 1 24 1,20 480 4 Internet 0 3 1 0 0 3 0 0 1 1 0 0 3 1 0 1 0 1 0 0 1 16 error 0 5 Biodiversity 0 3 0 0 0 3 0 2 2 0 3 0 3 0 0 0 0 0 0 0 0 16 0,67 384 6 Tourism 0 3 3 0 3 0 3 1 3 3 1 0 3 2 0 3 0 2 0 1 3 34 1,21 952 7 open & manifold Landscape 0 3 0 0 3 3 0 0 3 0 0 0 3 1 0 0 1 0 1 0 0 18 0,69 468 8 agricultural Landuse 0 3 2 0 3 0 3 0 2 0 0 0 1 1 0 2 0 1 3 0 0 21 1,05 420 9 Identity 0 3 3 0 1 2 2 2 0 0 1 0 1 1 0 0 2 2 0 0 0 20 0,65 620 10 Public Transport 0 1 0 0 0 2 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 5 0,71 35 11 Highway construction 0 2 0 0 1 3 0 0 1 0 0 0 2 1 0 1 1 0 0 0 1 13 1,44 117 12 Climate Change 0 0 0 0 2 1 2 1 0 1 0 0 0 0 0 0 0 0 1 0 0 8 error 0 13 Recognition 0 0 0 0 2 3 2 0 3 0 1 0 0 3 0 0 0 0 0 0 0 14 0,48 406 14 Market 0 0 3 0 0 1 0 0 1 0 0 0 2 0 0 3 0 2 0 0 0 12 0,44 324 15 Structure of Agric. 0 0 0 0 0 0 3 3 0 0 0 0 0 1 0 2 2 1 2 1 0 15 2,50 90 16 Income 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 3 0 0 0 0 6 0,29 126 17 Population Density 0 0 1 0 1 0 1 0 1 2 2 0 1 2 0 0 0 1 0 0 1 13 1,00 169 18 Typical Crafts 0 0 3 0 0 1 0 0 3 0 0 0 1 3 0 2 0 0 0 0 1 14 1,00 196 19 Material Cycles 0 0 0 0 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0,50 32 20 Number of Farms 0 0 1 0 1 0 3 3 2 0 0 0 0 0 2 2 1 0 1 0 0 16 2,67 96 21 Labour Market 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 3 3 0 0 2 0 9 1,13 72 PS 0 21 20 0 24 28 26 20 31 7 9 0 29 27 6 21 13 14 8 6 8 figure 5: Impact matrix of the elements of the SWOT; orange elements are elements of Global Change, green elements are objectives, yellow elements are elements that can be influenced within the area by the biosphere-reserve administration, white elements cannot be influenced by the biosphere-reserve administration; PS = passive sum, AS = active sum, Q = quotient (AS/PS), P = product (AS x PS); further explanations see text In addition to the absolute values the relative impact and relative influenceability were calculated as quotient of AS/PS and product of AS*PS respectively (figure 5). Both values are plotted in figure 7. The pair number of farms/structure of agriculture again shows the highest impact, whereas tourism and identity are the most critical factors. The Global-Change factors do not show results here.

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Absolute influence by other factors (PS) 10 15

Absolute impact of the factor (AS) figure 6: Scatter plot of the AS and PS values from the matrix in figure 5; the highest value is set to 100 and the other values are scaled accordingly. The red quadrangle holds the active elements, the green quadrangle holds the buffering, the blue the passive and the yellow the critical elements.

Relative impactQ of the factor: Q 2,75 2,50

2,25 acti ve 2,00 1,75 1,50 1,25 1,00 0,75 0,50 0,25 0,00 1 NAP 2 3 4 5 6 7 open 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Biosphe Region Internet Biodiver Tourism & agricult Identity Public Highwa Climate Recogni Market Structur Income Populati Typical Material Number Labour passi ve re al sity manifol ural Transpo y Change tion e of on Crafts Cycles of Market

Relative influenceP by other factors: P 1000 900 800 cr itical 700 600 500 400 300 200 100 0 1 NAP 2 3 4 5 6 7 open 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Biosphe Region Internet Biodiver Tourism & agricult Identity Public Highwa Climate Recogni Market Structur Income Populati Typical Material Number Labour re al sity manifol ural Transpo y Change tion e of on Crafts Cycles of Market buffering figure 7: graphical representation of the Q and P values of the impact matrix in figure 5, note that for factors 1, 4 and 12 Q is not defined and P = 0

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As a conclusion it can be stated that there is no single factor included in the SWOT on which the system is depending on. This can have two reasons: either it has not been identified or it does not exist. Assuming the latter would mean that the region has only limited means to reach its objectives biodiversity, landscape, identity, population density and material cycles. The objectives biodiversity (5), landscape (7) and identity (9) can be influenced by other factors. Population density (17) and especially material cycles (19) are buffering factors, i.e. their influanceability is low. Amongst those with some impact are agricultural land use, regional products and the biosphere reserve itself. All of them can be influenced by the biosphere-reserve administration. They are, however, also critical factors, meaning that any changes can change the system as a whole. The most promising factors to influence the system are the structure of agriculture/number of farms (15/20), which behave very similar and thus suggest the conclusion to consider the latter as part of the first and last but not least the agricultural policy (1) and the internet (4). The internet is a communication technology and therefore connected to many other factors in the impact matrix (figure 5). However, at the current state its real impact on the regional level is difficult to determine as e.g. no studies exist on the share of internet booking in the area. It definitely offers many opportunities especially for scenic remote areas like the reference area (telejobs, touristic marketing) and therefore it is rated very high in the opportunity direction (figure 4). As with any new technology where hopes rise sky-high in the beginning a settling process will start (and has already started) to reduce hopes to a more realistic level, as examples of web-based warehouses show. Therefore it is questionable if the impact on the regional system in the next decade is really as strong as it seems in figures 6 and 7 which could also be an expression of the hope that the members of the working group link with this factor.

The relative large impact of the factors connected with agriculture (15/20 and 1) and both the SWOT and the goal system are somewhat biased by the working group’s main topics of landscape and biodiversity. Thus factors connected with these two hold the majority of factors whereas socio-economic aspects are in the minority leading to less impact of the latter in the system’s analysis. Only tourism is connected to both aspects and thus holds the top critical position. Considering this it seems unlikely that a single active factor does exist. It therefore cannot be identified even with a more detailed analysis and it was not within the scope of this project to identify all relevant factors in the regional system and their impact on each other, either. So both aspects landscape/biodiversity and population density should be considered separately. These findings underline the fact that the above shown impact matrix is more a tool to understand dependencies between factors and their relative position than to produce absolute results. Instead it helps structuring the unstructured and thus can be used in political discussions. This showed to be true when presenting the results to the working group during the scenario workshop on Sept. 10th 2004 (see section 2.4).

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2.3 Specific Problems

2.3.1 Population Density The economic structure being weak the area is a traditional source of emigration, nowadays especially of young better educated people. Emigration is a particular problem in the eastern part of the reference area and in the more remote central areas (annex 5.1.4 population: migration). Comparing the migration figures to the general trend as expressed by the migration figures of the federal states concerned (see figure 8) shows that the emigration in the Thuringian part is even lower than the Thuringian average, except in 2000, instead there has been a slight immigration in some years. This number can be interpreted as an effect of the German reunification in 1990 which was followed by a huge loss in employment and emigration to the western part of the country. The lower regional figure might be caused by the relative closeness to the former border that makes commuting possible. Further investigations are needed to verify this hypothesis.

The federal states of Hessen and Bavaria show a higher rate of immigration than the respective part of the reference area. This high rate is imposed by Bavaria, that has constant immigration into its strong centres like Munich. The reference area can not profit from this development, on the contrary it is the Hessian part that has higher immigration rates than the Bavarian part, which in 1998 to 2000 showed negative rates (see also annex 5.1.4 population: migration).

Reference Area Federal States 7 7 Hessian & Bavarian part 6 Thuringian part 6 5 5 4 4 3 2 3 2 1 Hessen & Bavaria 0 1 Thuringia -1 0 -2 -1 -3 -2 -4 -5 -3 [persons per 1000 inhabitants]

-6 -4 -7 -5 1996 1997 1998 1999 2000 2001 1996 1997 1998 1999 2000 2001 Figure 8: migration between 1996 and 2001 for the reference area and the federal states the reference area lies in (note: data for Hessen & Bavaria 1999 are missing) Sources: Bayerisches Landesamt für Statistik und Datenverarbeitung, Hessisches Statistisches Landesamt, Thüringer Landesamt für Statistik

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Natural Development Reference Area Federal States 3 3 Hessian & Bavarian part 2 Thuringian part 2

1 1

0 0

-1 -1 Hessen & Bavaria Thuringia -2 -2

-3 -3

-4 -4

-5 -5

[birth/death excess per 1000 inhabitants] -6 -6 1996 1997 1998 1999 2000 2001 1996 1997 1998 1999 2000 2001 Figure 9: natural population development between 1996 and 2001 for the reference area and the federal states the reference area lies in; negative values: there are more deaths than births (note: data for Hessen & Bavaria 1999 are missing) Sources: Bayerisches Landesamt für Statistik und Datenverarbeitung, Hessisches Statistisches Landesamt, Thüringer Landesamt für Statistik As for the natural population development the birth excess in the reference area is on a slightly higher level than the respective federal states’ average (figure 9). In Thuringia the deaths exceed the births leading to a negative population development, this again being a state-wide trend having started in 1990 with the German reunification. Related to this is the higher percentage of people too old for reproduction in the Thuringian part of the reference area (45 and older, see annex 5.1.3 population: age structure). This can be an indication for younger people (and their families) having moved away, however at this stage this is just a hypothesis.

2.3.2 Agricultural Land use In order to make the area attractive for younger people to stay, sustainable tourism and agriculture based on the strong regional identity were mentioned by the working group. They have to be in compliance with the objectives landscape and biodiversity, i.e. to protect and develop a landscape of outstanding beauty with a high density of protected species. Main danger for this landscape are changing use patterns in agriculture (abandonment of poor sites on the one hand and/or intensification of use on the other hand). Sustainable agriculture therefore would be an agriculture that protects the landscape and thus the habitats. The working group clearly stated that area-wide organic farming would foster this (see figure 3). Another development path would follow the idea of putting the conventional farms into a position to keep their production (and thus the landscape) alive. For further investigations into this direction

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© IRON CURTAIN Consortium Issued by: GEO Date: 25.2.04 a closer look on the regional structure of agriculture is useful: Agricultural land use in the higher areas is more traditional-style grassland farming with cattle and dairy cows, although the link between grassland and cattle raising has gotten weak because of imported fodder. The lower areas to the east and the west of the reference area show higher shares of both arable land and pigs (see also annex 5.1.11 land cover). Sheep, in former times being a predominant animal for the Rhön (with the local breed Rhönschaf), nowadays play only a minor role. The livestock density is generally low (below 1 per hectare of agriculturally used area), except in the western communities where it is between 1 and 2. In Thuringia large farms are predominant, the successors of the socialist LPGs, whereas the farms in the western federal states are a lot smaller, again showing the same pattern as could already be observed: the areas favourable for farming in the low parts show larger farms. (see annex 5.1.12 structure of agriculture).

figure 10: land cover change in the reference area between 1990 and 2001 as derived from Landsat images for two land cover classes (forest (forst) and grassland (grld)) in relation to the elevation The typical grassland landscape of the Rhön, habitat for many protected species, however, is depending on the continued use of the grassland. Especially this land use type is endangered by the weak structure of agriculture (small farms, part-time farming) in combination with national and European agricultural policies.

A particular danger is afforestation in the area. As figure 10 shows, the rate of afforestation (or scrubbing if the land is abandoned) derived from Landsat satellite images is around 10% in almost all elevation classes. We can assume that almost no forest is cut for further grassland use in the area, so figure 10 indicates that the error in detection might be not more than 10% of the calculated afforestation area. The higher increase of grassland on former forest areas above 800 meters is at least partly caused by clear cutting for conservation reasons as the grassland dominated nature protection areas are located in higher elevations. The correlation between

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2.3.3 Tourism Tourism has been a source of income for the last century, though on a low intensity. Main attraction is the landscape for its scenery and good air. Especially in the last decade nature based leisure activities like biking, skiing and paragliding became more important alongside the traditional hiking and soring. However this type of tourism has only a limited demand, especially in high-revenue overnight tourism. The share of day tourists from centres located within a driving distance of some two hours (Würzburg, Frankfurt, Erfurt to name a few) is fairly high. Touristic development concentrates more on quality and local production chains (marketing of local products) than on quantity. As for sustainable tourism the accessibility of the area by public transportation is difficult (see annex 5.2.1 transportation).

2.4 Development Scenarios During a workshop on September 10th 2003 the members of the working group were asked for options how to reach the vision’s objectives landscape, biodiversity, material cycles and living villages. The method used was the problem-analysis scheme (PAS): The working group was first asked to name the problems related to each of the objectives and their causes. This basically repeated what had already been entered into the problem analysis at the first workshop on June 24th 2002, although some new aspects were mentioned. Then the participants were asked to name the options to solve the respective problems and the obstacles to overcome.

A scenario in general is a combination of options that cover all relevant fields of decision. A scenario in this sense is a strategy to reach the regional vision as a whole. This strategy comprises of options in all relevant fields. The relevant fields are the number of agricultural enterprises, the split-up land use, the standards for produce and production, the land-use pattern, the programmes for landscape conservation and the situation of income and jobs. These fields were derived from the PAS. The objectives to investigate are the visual landscape properties, (objective landscape) the population (refers to objective living villages), biodiversity and material cycles (see table 3).

All options gained at the workshop were analysed and underlying storylines, i.e. basic ideas of the direction of development were extracted. All options in one storyline form a scenario. The main storylines identified are the large-scale ecological farming and the market-oriented land use. The first one is the optimum the stakeholders would like to follow in order to reach the objectives. The analysis with

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© IRON CURTAIN Consortium Issued by: GEO Date: 25.2.04 the IRON CURTAIN tool set will show them if this is feasible. The second is more a crash scenario, i.e. a scenario to test the system’s edge and to see what happens, if a very different approach to land-use rules. The third one is status quo, the extrapolation of the status quo into the future for comparison reasons.

A matrix was created from the storylines, the relevant fields and objectives (table 3). Whenever options for a relevant field were mentioned in the PAS they were entered, empty cells were filled with assumptions matching the respective storyline. table 3: development scenarios with assumptions for the relevant fields

Scenario 1: Scenario 2: Scenario 3: Status-Quo-Scenario Market-oriented landuse large-scale ecological farming Fields Number of agricultural results from the below options (how many decreasing, rate as today heavily decreasing enterprises are needed?) split-up landuse to be maintained combination of large and easily usable units common use => combination of parcels new standards and labels Standards for produce and no standards only in the frame of EU regulations (cross no pesticides production eco farming as today compliance) increased eco-farming Preservation of grassland areas by large- Afforestation of all parcels that seem to be scale grazing unattractive for agricultural use give-up of landuse as today Preservation of arable land Landuse pattern Grassland only as far as needed for production change of landuse as today No increase of forest area Landuse (and thus nutrient input) is oriented at Preservation of structures within arable land microeconomic optimisation landuse is oriented to the needs of the site Programs for Landscape flexible, oriented towards the needs of the as today declining? (check for EU regulations) Conservation farmers Increase by increase in green tourism Development of industry Income/Jobs as today (stagnation?) Increase by increased regional marketing of Development of touristic centres goods and produce objectives visual landscape properties result of modelling result of modelling result of modelling Population result of modelling result of modelling result of modelling Biodiversity result of modelling result of modelling result of modelling nutrient flows result of modelling result of modelling result of modelling

2.5 Indicators A set of indicators for the elements of the Goal System and the SWOT was created. The indicators were divided according to the components of Territorial Competitiveness (LEADER concept). These indicators are continuously reviewed and updated in the further course of the project according to data availability and with respect to requirements of detailed indicator models being developed for the reference areas. This update is performed using the tool for indicator management (NEIC system), which was built by partner DGGS. This system includes a indicator database for each reference area with data references.

For the actual list of indicators for the reference area see annex 5.3, which is an output from the local indicator database stored at UNIJENA. Each indicator has a description according to the CSD standard (Commission on Sustainable Development, http://www.un.org/esa/sustdev/natlinfo/indicators/isd.htm) which was agreed on to be used in the project (examples see annex 5.4).

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For each of the fields and objectives of the scenarios (table 3) appropriate indicators were chosen from this list of indicators after data availability had been checked (table 4). The CSD-compliant description of these indicators is included in annex 5.4. All further analyses will be performed on this basic set of indicators derived for the development scenarios. table 4: indicators and their units for the development scenarios (table 3); indicator numbers refer to the id of the indicator in UNIJENA’s indicator database Fields Indicator [unit] Number of agricultural development in number of holdings [number] -- Ind. enterprises #28 average size of agricultural parcels [ha] OR median split-up land use size of agricultural parcels [ha] Standards for produce and use of pesticides [kg/ha] spatial share of organic production farming [%] -- Ind. #12 spatial share of agricultural areas [%] -- Ind. #33 spatial share of grassland [%] -- Ind. #24 spatial share of forest [%] -- Ind. #48 Land-use pattern spatial share of fallow land [%] -- ind. #23 spatial share of extensively used areas [%] -- Ind. #34 structures: landscape metrics Programs for Landscape ? Conservation relative income [EUR/person] – Ind. #67 Income/Jobs unemployment rate [%] -- Ind. #44 Objectives visual landscape properties landscape metrics (several spatial indicators) net migration rate [number per 1000] -- Ind. #2 Population total population [number] -- Ind. #3 age distribution [%] -- Ind. #4 Biodiversity landscape metrics (several spatial indicators) Nutrition Balance Surpluses [kg/ha] -- Ind. #10 nutrient flows Potential for Erosion [kg/ha year] -- Ind. #11

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3 Regional Database

3.1 Data Acquisition and Management

3.1.1 GIS Data The administration of the biosphere reserve owns data concerning almost all relevant aspects. Most of the GIS data (see table below) had been handed over to the Prime Mover at the start of the project. These data have been imported into GIS software (Arc/INFO, ArcView GIS) and described in the IC metadata database. All of the data belong to the biosphere reserve administration thus the prime mover is prohibited to distribute them to third parties. The GIS database was set up on a central network file server at the department of geoinformatics at the Friedrich-Schiller-Universität Jena (UNIJENA) so that all project members within this institution can easily access the data. Table 5: GIS datasets for the reference area (excerpt from metadata database) Title Abstract geologic formation beneath the soil, 117 Geology Reference Area 2 classes Soils Reference Area 2 soil types Digital Elevation Model (DEM) Reference Digital Elevation Model as Floating Point Area 2 Grid, cell size 10x10 meters River Network Reference Area 2 river network as derived from the DEM Biotope types as manually derived from Biotope types of reference area 2 CIR aerial photographs taken in 1993 Polygon Shapefile of the communities in Communities reference area 2 Reference Area 2 (Biosphere Reserve Rhön) Division of the Biosphere Reserve into Zonation of the Biosphere Reserve Rhön core zone, development zone and transition zone

3.1.2 Remote Sensing Data In order to detect land-cover changes three remote sensing datasets were either purchased or downloaded free of charge. The datasets were imported into ERDAS Imagine software and processed there (coregistration, georeferencing, classification). Change detection was conducted after classification (see annex 5.2.2). The classification results are provided on the same file server as the other spatial data.

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Table 6: remote sensing datasets (excerpt from metadata database) Title Abstract Landsat TM5 image, 7 bands; Path 194, Row 25; needs to be georeferenced for Landsat TM5 Image Germany further use; only NE part of the scene; (Wuerzburg/Thuringian Forest) 1984 covering the reference area "Biosphere Reserve Rhön" Landsat TM5 image, 7 bands; Path 194, Landsat TM5 Image Germany (South Row 25; needs to be georeferenced for Thuringia/Bavaria) 1990 further use Landsat 7 ETM+ Image Germany Landsat 7 ETM+ image, 8 bands; Path (Rhoen) 2001 195, Row 25

Metadata on all spatial data were input in the central metadata database provided by partner DGGS.

3.1.3 Statistical Data Statistical data were either received from the biosphere reserve administration (same restriction as above applies) or downloaded or purchased from the respective state statistical offices. All data were harmonized (time, id of the legal entity the data refer to) and imported into a MS Access database. This database now holds areawide data on several topics (table 7). Table 7: statistical data Topic Spatial Timeframe Aggregation Population (total, age community 1995-1999 (no age groups), 2000, groups, births, deaths) 2001 (complete) Tourism (no of beds, community 1995-2000 (very incomplete), 2001 nights, arrivals, (complete) enterprises) Agriculture (farmland with community 1999 (complete), 2001 (incomplete) crops [ha], no of farms, livestock) Mean annual district 1998-2002 unemployment [%]

All data acquired for the project or being a result of the project work will be provided to the end user at the end of the project. This includes e.g. classified remote sensing datasets, results of GIS analyses and a MS Access database with selected statistical data.

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3.2 Data Quality

3.2.1 GIS and Remote Sensing Data All of the GIS data received from the biosphere-reserve administration were already harmonized, and the area coverage is in almost all cases 100%. As some of the data come from different sources the quality is different even after harmonization (e.g. the soils were not available in Bavaria, so a synthetic soil map had been produced by the biosphere-reserve administration). All processing steps are documented, so the quality can be easily accessed and taken into account for the following work.

Table 8: Confusion Matrix for the ETM7+ 2001 classification Classification result Coniferous Decidous Grass- Arable Subur- Continuous Wet- producer's 1 Water Sum forest forest² land land ban urban³ land accuracy R Coniferous forest 4888 123 51 0 0 17 0 27 5106 95,73 e Decidous forest 379 5363 138 45 0 0 0 0 5925 90,51 f Grassland 0 309 5853 181 0 17 0 0 6360 92,03 e Arable land 0 6 303 4647 0 381 13 0 5350 86,86 r Water 0 0 0 0 556 0 0 0 556 100,00 e Suburban 7 0 24 381 0 2613 35 0 3060 85,39 n c Continuous urban 0 0 0 13 0 52 683 0 748 91,31

e Wetland 8 0 0 0 0 0 0 216 224 96,43 Sum 5282 5801 6369 5267 556 3080 731 243 27329

user's accuracy 92,54 92,45 91,90 88,23 100 84,84 93,43 88,89 90,82 1 includes also coniferous dominated mixed forest 2 includes also decidous dominated mixed forest 3 includes basalt quarries

As classification of remote sensing data will at no time reach 100% accuracy (depending on remote sensing data and demanded scale), the user's and producer's accuracy for all classifications are provided. Because of the similar signatures it was especially delicate to distinguish settlement areas and large-scale bare-soil partitions in the space of arable land (see confusion matrix, table 8). This problem could be diminished with the aid of the DEM. The overall accuracy of the land cover maps derived by classification in case of the 2001 ETM7+ image is about 90%. Considering the following work step of change detection nine different land cover classes were considered. These are coniferous and deciduous forest, water, suburban and continuous urban built-up areas, rich and poor grassland and arable land (two classes).

3.2.2 Statistical Data The statistical data are basically comparable because the table definitions originate from the German federal bureau of statistics. Most data are on a community basis which causes certain problems: Some communities are only partly in the biosphere reserve so the data were disaggregated with an area weight for these cases. Some data are secret because of personal rights protection so they are only available for larger parts of the reserve (e.g. one figure for all communities within one district). And

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© IRON CURTAIN Consortium Issued by: GEO Date: 25.2.04 last but not least the data may be incomplete. This is true for the data on tourism: First of all the data count only overnight tourists but the area is heavily frequented by one-day tourists from the agglomerations of Frankfurt and Würzburg/Schweinfurt. Second only touristic enterprises with more than eight beds are considered, thus small-scale farm tourism is underrepresented.

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4 Conclusions and Recommendations The project applied a bottom-up process of problem analysis following the Logical Framework Approach (TEMPUS handbook) which proved to be suitable to identify problems and their causes and to name objectives of development (figure 3). Together with the stakeholders a vision for the year 2020 was formulated, a SWOT analysis was performed and options for problem solutions were identified. The analysis of these elements resulted in two development scenarios which form the basis for further analyses within the IRON CURTAIN project. Using a traditional type SWOT analysis together with an approach from systems theory helped in structuring the input and in communicating it (section 2.2).

Although located in the middle of the reunified Germany the area can be characterized as periphery. Population density is far below the national average of around 230 inh/sqkm (see table 2). There is only one centre (Fulda) in the vicinity which has some impact on the west of the reference area. The accessibility of the area by car is satisfying, especially by public transportation it is low (annexes 5.1.7 infrastructure and 5.2.1 transportation). The area is mountainous and had been poor and a source of emigration during the last centuries. The border situation since the Second World War further maintained the remoteness of the area. This situation on the other hand allowed a once common type of landscape to survive the times as an anachronism.

Two facts eased the stakeholder work in this reference area: As the former Iron Curtain survived as a sub national border only, functioning cross-border working contacts had already been established more than ten years ago and there is no language barrier either. On the other hand the prime mover UNIJENA had no connections to stakeholders in the reference area prior to the project. Due to time constraints no stakeholder analysis could be performed, so only those stakeholders were contacted that had been recommended by partners from other projects. This resulted in a somewhat biased mixture of the stakeholder group with a strong concentration on environmental topics (landscape, biodiversity).

Nevertheless emigration and aging of the population were identified as the main socio-economic problems which the reference area has in common with many other peripheral areas throughout Europe. The causes are manifold but centred around the lack of job and education opportunities in the area and the weak agricultural structure, especially in the western part, offering no alternatives in this field. Quality tourism by marketing the strengths of the region (local products, landscape) is the strategy to keep the share of income from this sector more or less constant; increase is not expected.

The connection between (agricultural) landuse and the main objectives of the biosphere reserve to preserve the unique grassland landscape with its typical biodiversity could be compiled in detail. Causal relationships were determined and development options named. This allowed the formulation of detailed development scenarios focussing on these topics.

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As these questions have a strong spatial implication spatial analysis and modelling techniques are needed in order to work on them. Therefore mainly GIS data covering the complex of landuse/landcover change, agriculture and nature protection were collected and some 15 indicators determined to either parametrize the model or evaluate modelling results. The scenarios contain the population development as the main socio-economic component which is reflected by two indicators on the input and three indicators on the results side (table 4).

It is recommended to concentrate further project work on the question of landuse/landcover change in connection with its impact on the landscape and biodiversity by applying GIS technology and on investigating the population development with dynamic modelling.

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5 Annexes

5.1 Basic Documentation of the Area

5.1.1 Position of the Area

5.1.2 Population Density The map illustrates the population distribution and density.

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5.1.3 Population: Age Structure The map shows the share of persons older than 45 on the community level and the age structure both the east and west part of the reference area.

5.1.4 Population: Migration The map shows the spatial distribution of migration on the community level and the figure illustrates the migration development for the eastern and western part of the reference area from 1996 to 2001.

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5.1.5 Communities The map shows the administrative division of the reference area

5.1.6 Protected Areas The large map shows the outline of all protected areas (nature protection areas) with the Landsat ETM7 true colour scene in the background, the small map shows the proposed zonation of the biosphere reserve.

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5.1.7 Infrastructure The large map shows the main road and railroad connections in the area, the small map is a GIS analysis illustrating the estimated driving time to the respective district capital.

5.1.8 Topography The map shows the elevation and the river network in the area on a shaded relief map, the picture in the upper right corner is a 3D view of the central part of the reference area.

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5.1.9 Geology The large map illustrates the main geologic ages in the area, the small map shows the main types of rocks in a shaded relief illustration. The central basalt plateau is clearly visible in both illustrations.

5.1.10 Soils The map shows the disposition for a soil type (artificial soil map as result from a GIS analysis).

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5.1.11 Land cover The land-cover map shows the land-cover classes as they result from the classification of the Landsat ETM7+ scene, the pie chart illustrates the class distribution for the area.

5.1.12 Structure of Agriculture (2 maps) The first map gives an indication on the farm size (livestock units per farm) in the large map and the land-use structure (agricultural/non-agricultural) in the small map, both on the base of community figures.

The second map shows the amount of livestock and the share of the main animals in the large map and the land-use structure (arable land/grassland) in the small map, both on the base of community figures.

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5.2 Results of GIS Analyses

5.2.1 Transportation The large map illustrates the frequency and median speed of the public transportation network in the area. The small map shows the expected speed for the same trip by car for comparison.

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5.2.2 Land-cover Change The maps show the result of two land-cover classifications (1984, 1990) using Landsat optical datasets, the bars indicate the class distribution for the main classes, for the two datasets and the 2001 classification (see appendix Fehler! Verweisquelle konnte nicht gefunden werden. land cover). Detailed analyses on land-cover change have been worked on continuously (see also section 2.3.2).

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5.3 Breakdown of indicators on territorial components

Table 9: breakdown of indicators on territorial components, a “-“ indicates the lack of a goal or SWOT element for the respective component (compare figures 3 and 4)

Goals from Goal System (GS) and status quo from Indicators (currently mainly available for GS); SWOT multiple entries possible Environmental Competitiveness share of threatened/extinct species as a percent of Physical Resources Biodiversity is guaranteed (GS) total native species (includes utilisation abundance of selected key species practices) a minimal structural landscape configuration is biotopes in the landscape conserved (GS) habitats of characteristic/rare species are protected spatial share of biotope interconnection and interconnected (GS) Extent and Condition of Native Vegetation Development in the Size of Endangered Biotopes Protected Area as Percent of Total Area Average Size of non-fragmented Land Parcels Share of Endangered/Extinct Biotopes in total of all Occurring Types of Biotopes different abiotic habitat conditions are conserved (GS) Percentage of Meagre/Mesothrophic Grassland Arable Land on Shell Limestone

neophytes and "problem species" are repelled (GS) introduced species: dominance of such like species no further loss of extensively used areas (GS) Spatial Share of Extensively used Areas use of fertilizer according to requirements (GS) use of fertilizer nutrition balance surpluses

unwanted succession decreases (GS) spatial share of fallow land erosion protection is established (GS) potential for erosion traditional land use (SWOT) Human resources high regional identity (SWOT) split-up landownership (SWOT) development in number of holdings extensification of agriculture; promotion of organic Markets / external relations farming (GS) development in number of livestock spatial share of organic farming low levels of pesticide in regional produce (SWOT) perception / image visual properties of the landscape are protected (GS) spatial distribution of land use spatial share of grassland change in quality and spatial distribution of landscape units fraction of forest remains more or less constant (GS) spatial share of forest new buildings are built saving ground and respecting the landscape (GS) landscape-conserving building activities

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Goals from Goal System (GS) and status quo from Indicators (currently mainly available for GS); SWOT multiple entries possible persisting agriculture in the whole area; no further segregation of land use (GS) spatial share of agricultural areas conservation of hedgerows condition and denseness of hedgererows Social Competitiveness Human resources - Culture / identity high regional identity SWOT) Governance / financial region is divided in three parts (SWOT) resources Know-how / skills - Economic Competitiveness Physical Resources touristic attraction of the landscape is guaranteed (GS) number of overnight stays number of visitors in natural areas low prices for building suites (SWOT) open/manifold landscape and unique natural features (biodiversity) (SWOT) Activities / business firms creation of jobs outside the agricultural sector (GS) share of wage earners in sectors rate of commuting

working-age population stays in the area (GS) net migration rate total population (male/female) age distribution no road construction beyond local needs (GS) fraction of transit traffic poor chances to create an income in the region (SWOT) pronounced niche economy (SWOT) income of agriculture as share of total income in all structure of agriculture (SWOT) sectors development in number of holdings

development in number of livestock age of manager insufficient public transport (SWOT) Improve marketing structures for products, net processes (SWOT) no education in agriculture/forestry with concentration Know-how / skills of low-production soils (SWOT) income of tourism as share of total income in all Markets / external relations new opportunities to earn money in tourism (GS) sectors Lack of market for high-quality regional products (SWOT) Low quality of touristic services, high-quality tourism is underdeveloped (SWOT) Quality tourism with a young tradition (SWOT) regional products (SWOT)

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Goals from Goal System (GS) and status quo from Indicators (currently mainly available for GS); SWOT multiple entries possible internet marketing (tourism, shopping) (SWOT) Governance / financial - resources Positioning in the Global context Governance / financial - resources Markets / external relations - Perception / image Biosphere Reserve (SWOT) recognition of the region outside is low (SWOT) Activities / business firms -

5.4 Indicator descriptions The following indicator descriptions follow the CSD (UN’s Comission on Sustainable Development) standard. The descriptions are formatted in html-language. All indicators relevant for the scenarios are described here. Description shows the current status and will undergo some changes in the course of indicator application.

5.4.1 Net Migration Rate

Net Migration Rate

Social Population Population Change

1. Indicator (a) Name: Net Migration Rate (b) Brief Definition: Ratio of the difference between the number of in-migrants and out-migrants from a particular area during a specified period to the average population of that area during the period considered. (c) Unit of Measurement: The indicator is usually expressed as per thousand population. (d) Placement in the CSD Indicator Set: Social/Population/Population Change

2. Policy Relevance (a) Purpose: The net migration rate measures the geographical mobility of population. Migration is one of the basic demographic events -- birth and death are the others -- that directly influence the size of a population in an area. (b) Relevance to Sustainable/Unsustainable Development (Themes/Subthemes): Migration is often seen as an economic phenomenon e.g. in discussions of labour migration from rural to urban areas. The significance of migration does not rest only in its size, but also in its composition. Such migrant characteristics as age, sex, fertility level, educational background, occupation, and skill levels have profound implications for development in both the sending and the receiving areas or countries. On a worldwide view there are other reasons for migration like ecological disasters, poverty or wars, to name a few. (c) International Conventions and Agreements: (d) International Targets/Recommended Standards:

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(e) Linkages to Other Indicators: Within the context of Iron Curtain the migration to and from one of the reference areas should be considered as one phenomenon whether it can be categorised as national or international migration. High out-migration indicates that people are leaving the area due to economic decline, high in-migration indicates economic (or natural?) attractiveness. The indicator is linked with other indicators namely the unemployment rate and the rate of commuting. Total population (male/female) can show the results of migration as well as the age distribution because normally only younger people tend to migrate while old-age pensioners stay in the area.

3. Methodological Description and Underlying Definitions (a) Underlying Definitions and Concepts: (b) Measurement Methods: Migration is normally not counted on a regional level however as indicated above (section 2(a)) migration can be calculated on the basis of the natural population development (birth and death rates). (c) Limitations of the Indicator: (d) Status of the Methodology: (e) Alternative Definitions:

4. Assessment of Data (a) Data Needed to Compile the Indicator: birth rate, death rate, total population for at least two points in time (b) National and International Data Availability and Sources: All countries have data on this topic however the spatial resolution can be a problem. (c) Data References:

5. Agencies Involved in the Development of the Indicator (a) Lead Agency: United Nations DESIPA (b) Other Organizations:

6. References (a) Readings: (b) Internet site: UN CSD Indicators Chapter5

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5.4.2 Total Population (male/female)

Total Population (male/female)

Social Population Population Change

1. Indicator (a) Name: Total Population (male/female) (b) Brief Definition: total number of persons living in an area separated by gender (c) Unit of Measurement: absolute number (d) Placement in the CSD Indicator Set: Social/Population/Population Change

2. Policy Relevance (a) Purpose: The purpose of this indicator is to show the development in population over time. (b) Relevance to Sustainable/Unsustainable Development (Themes/Subthemes): The number of population can indicate the pressure on natural resources caused by people living in an area but in the context of Iron Curtain the indication is more towards how many people can make a living an a certain area especially in connection with other indicators (see section (e)). (c) International Conventions and Agreements: (d) International Targets/Recommended Standards: (e) Linkages to Other Indicators: The indicator is linked with other Social/Population/Population Dynamic indicators in particular with net migration rate and age distribution. Together with these two a picture of the possibilities to make an income in the area can be drawn, especially when considering the unemployment rate.

3. Methodological Description and Underlying Definitions (a) Underlying Definitions and Concepts: All people living in a certain area are this area's population. (b) Measurement Methods: (c) Limitations of the Indicator: The indicator gives only absolute numbers, therefore being somewhat limited for the understanding of underlying reasons of population changes. In order to gain insight into these topics other indicators like e.g. birth rates or child mortality rates have to be investigated. However these can not be influenced by Iron Curtain. Therefore only those indicators relevant for the understanding of the underlying socio-economic processes are listed in section 2.(e). (d) Status of the Methodology: (e) Alternative Definitions:

4. Assessment of Data (a) Data Needed to Compile the Indicator: population figures by gender (b) National and International Data Availability and Sources: All countries have data on this topic however the spatial resolution can be a problem. (c) Data References:

5. Agencies Involved in the Development of the Indicator (a) Lead Agency: German Secretary for Conservation (Bundesumweltministerium) (b) Other Organizations:

6. References (a) Readings: (b) Internet site: the German report on the test of the CSD indicators (PDF, in German) can be downloaded at the Bundesumweltministerium (search for "Indikatoren CSD")

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5.4.3 Age Distribution

Age Distribution

Social Population Population Change

1. Indicator (a) Name: Age Distribution (b) Brief Definition: share of the population below 20 and above 60 and 80 years of age (c) Unit of Measurement: % (d) Placement in the CSD Indicator Set: Social/Population/Population Change

2. Policy Relevance (a) Purpose: This indicator shows how many economically inactive persons live in an area. (b) Relevance to Sustainable/Unsustainable Development (Themes/Subthemes): In most industrialized countries the population is getting older and older putting stress on the social insurance systems. (c) International Conventions and Agreements: (d) International Targets/Recommended Standards: (e) Linkages to Other Indicators: In the context of Iron Curtain the indicator may indicate an excess of age if the share of persons older than 60 is higher within a reference area than the outside average. If the economically active age groups show a higher share than the average the area might be attractive for persons to go there for work. The indicator is therefore linked with the net migration rate and the unemployment rate. Together with the rate of commuting conclusions could be drawn, whether the area is a place to live in while economic activity is taking place outside.

3. Methodological Description and Underlying Definitions (a) Underlying Definitions and Concepts: It is assumed that persons between 20 and 60 years of age are economically active, whereas the other age groups depend on them either direct (children) or indirect (old-age pensioners). (b) Measurement Methods: (c) Limitations of the Indicator: (d) Status of the Methodology: (e) Alternative Definitions:

4. Assessment of Data (a) Data Needed to Compile the Indicator: population figures by three age classes: 0-20, 20-60/65, above 60/65 (b) National and International Data Availability and Sources: All countries have data on this topic however the spatial resolution can be a problem. (c) Data References:

5. Agencies Involved in the Development of the Indicator (a) Lead Agency: German Secretary for Conservation (Bundesumweltministerium) (b) Other Organizations:

6. References (a) Readings: (b) Internet site: the German report on the test of the CSD indicators (PDF, in German) can be downloaded at the Bundesumweltministerium (search for "Indikatoren CSD")

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5.4.4 Unemployment Rate

Unemployment Rate

Social Population Labour Market

NOTE: See the related UN CSD website for detailed information (Section 6b)

1. Indicator (a) Name: Unemployment Rate (b) Brief Definition: Unemployment rate is the ratio of unemployed people to the labour force. (c) Unit of Measurement: % (d) Placement in the CSD Indicator Set: Social/Population/Labour Market

2. Policy Relevance (a) Purpose: (b) Relevance to Sustainable/Unsustainable Development (Themes/Subthemes): (c) International Conventions and Agreements: (d) International Targets/Recommended Standards: (e) Linkages to Other Indicators: Within the context of Iron Curtain the indicator in connection with the net migration rate, the commuting rate and the age distribution can show, if it is possible to make an income in the reference area.

3. Methodological Description and Underlying Definitions (a) Underlying Definitions and Concepts: (b) Measurement Methods: (c) Limitations of the Indicator: (d) Status of the Methodology: (e) Alternative Definitions:

4. Assessment of Data (a) Data Needed to Compile the Indicator: unemployment rate (b) National and International Data Availability and Sources: All countries have data on this topic however comparability may be a problem. (c) Data References:

5. Agencies Involved in the Development of the Indicator (a) Lead Agency: (b) Other Organizations:

6. References (a) Readings: (b) Internet site: UN CSD Indicator "Unemployment Rate"

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5.4.5 Nutrition Balance Surplusses

Nutrition Balance Surpluses

Environmental Land use Agriculture

1. Indicator (a) Name: Nutrition Balance Surpluses (b) Brief Definition: Saldo of nutrient input and output (c) Unit of Measurement: kg per hectare agricultural area (d) Placement in the CSD Indicator Set: Environmental/Land use/Agriculture

2. Policy Relevance (a) Purpose: To give an impression of the pressure on the biosphere and resources by nutrition inputs. (b) Relevance to Sustainable/Unsustainable Development (Themes/Subthemes): Inputs of nutrients, such as nitrogen and phosphorus, are essential to agricultural production, and integral to raising productivity. At the same time, a surplus of nutrients in excess of immediate crop needs can be a source of potential environmental damage to surface and ground water (eutrophication), air quality (acidification) and contribute to global warming (greenhouse effect). If soils are farmed and nutrients not replenished, this can lead to declining soil fertility and may impair agricultural sustainability through "soil mining" of nutrients. (Source: 6a, p. 117) (c) International Conventions and Agreements: (d) International Targets/Recommended Standards: are in discussion (see 6a, p. 126) (e) Linkages to Other Indicators: directly related to use of fertilizer and nutrient losses from agricultural land; development in number of livestock

3. Methodological Description and Underlying Definitions (a) Underlying Definitions and Concepts: main nutrients are N and P, if this indicator is used within the IC project it might be necessary to concentrate on one nutrient (b) Measurement Methods: The nitrogen balance indicator is measured by the soil surface balance, which is calculated as the difference between the total quantity of nitrogen inputs entering, and the quantity of nitrogen outputs leaving, the soil over one year. Calculation of a soil surface balance for other nutrients, e.g. phosphorous or potassium, is similar (Source: 6a p. 120f) (c) Limitations of the Indicator: (d) Status of the Methodology: available (see 6a) (e) Alternative Definitions:

4. Assessment of Data (a) Data Needed to Compile the Indicator: Nutrition input (fertilizer, manure...) and output in produce (b) National and International Data Availability and Sources: (c) Data References:

5. Agencies Involved in the Development of the Indicator (a) Lead Agency: OECD (b) Other Organizations:

6. References (a) Readings: OECD, Organisation for Economic Co-Operation and Development (Ed.) (2001): Environmental Indicators for Agriculture. Vol. 3 : Methods and Results. Paris : OECD Publications

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5.4.6 Potential for Erosion

Potential for Erosion

Environmental Land use Land Condition

1. Indicator (a) Name: Potential for Erosion (b) Brief Definition: The agricultural area subject to water erosion, that is the area for which there is a risk of degradation by water erosion above a certain reference level. (Source: OECD, p. 200 (see section 6a)) (c) Unit of Measurement: tonnes per hectare (d) Placement in the CSD Indicator Set: Environmental/Land use/Land Condition

2. Policy Relevance (a) Purpose: indicate the potential sites and amount of soil loss through erosion (b) Relevance to Sustainable/Unsustainable Development (Themes/Subthemes): soils are an endable resource, water erosion endangers this resource (c) International Conventions and Agreements: (d) International Targets/Recommended Standards: (e) Linkages to Other Indicators: spatial share of agricultural areas

3. Methodological Description and Underlying Definitions (a) Underlying Definitions and Concepts: (b) Measurement Methods: potential can be estimated with the Universal Soil Loss Equation USLE (Wischmeyer and Smith 1978) or a national adaption (c) Limitations of the Indicator: USLE only accounts for sheet and not gully erosion; and does not indicate the destination of the eroded material, either on or off-farm (Source: OECD, p. 207 (see section 6a)) (d) Status of the Methodology: available (e) Alternative Definitions:

4. Assessment of Data (a) Data Needed to Compile the Indicator: climate, soil, topography, crop management, protection management, land cover: agriculture, open soil (b) National and International Data Availability and Sources: (c) Data References:

5. Agencies Involved in the Development of the Indicator (a) Lead Agency: OECD (b) Other Organizations: ANZECC

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Australian and New Zealand Environment and Conservation Council, State of the Environment Reporting Task Force (2000) Core environmental indicators for reporting on the state of the environment. Environment Australia, Canberra. download coreindicators.pdf (438 KB) (b) Internet site:

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5.4.7 Spatial Share of Organic Farming

Spatial Share of Organic Farming

Environmental Land use Agriculture

1. Indicator (a) Name: Spatial Share of Organic Farming (b) Brief Definition: Area with organic farming as percent of total agricultural area. (c) Unit of Measurement: % (d) Placement in the CSD Indicator Set: Environmental/Land use/Agriculture

2. Policy Relevance (a) Purpose: shows the extent of organic farming in a certain area (b) Relevance to Sustainable/Unsustainable Development (Themes/Subthemes): Organic farming can be of benefit to the environment. In particular, it can help to create habitats in which biodiversity is encouraged by management practices. (Source: 6a [1]) (c) International Conventions and Agreements: A number of EC Regulations apply to the promotion of organic farming, including 2328/91, 2078/92, 866/90 and 3669/93, 2081/93 and 2088/93. Regulation 2092/91 sets out a harmonised framework for labelling, production and control of agricultural products bearing or intended to bear indications referring to organic production methods. The new Regulation 1804/99 extends this to livestock products. (Source: 6a [1]) (d) International Targets/Recommended Standards: (e) Linkages to Other Indicators: use of fertilizer, nutrition balance surpluses, spatial share of agricultural areas, abundance of selected key species

4. Assessment of Data (a) Data Needed to Compile the Indicator: organically farmed area, land cover: total agricultural area (b) National and International Data Availability and Sources: (c) Data References:

5. Agencies Involved in the Development of the Indicator (a) Lead Agency: EEA (European Environment Agency) (b) Other Organizations: OECD

6. References (a) Readings: [1] EAA Indicator Fact Sheet: Organic Farming (pdf, 58K) [2] OECD, Organisation for Economic Co-Operation and Development (Ed.) (2001): Environmental Indicators for Agriculture. Vol. 3 : Methods and Results. Paris : OECD Publications download as pdf (3808K, read-only) (b) Internet site: EEA indicator Organic farming

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5.4.8 Spatial Share of Fallow Land

Spatial Share of Fallow Land

Environmental Land use Land-use Change

1. Indicator (a) Name: Spatial Share of Fallow Land (b) Brief Definition: agricultural area not in use for production as a percentage of the whole land surface area (c) Unit of Measurement: % (d) Placement in the CSD Indicator Set: Environmental/Land use/Land-use Change

2. Policy Relevance (a) Purpose: indicates abandonment of land (b) Relevance to Sustainable/Unsustainable Development (Themes/Subthemes): Fallow land is an ambivalent indicator: In intensively used agricultural areas fallow land can provide habitats for species that have otherwise no chance to inhabit this landscape. On the other hand land can become permanently fallow because of a decline in agriculture or a concentration and intensification process. In this case fallow land can provide space for uncontrolled succession (scrubbing) leading to a serious threat to species adapted to the former extensive type of land use. (c) International Conventions and Agreements: (d) International Targets/Recommended Standards: (e) Linkages to Other Indicators: The indicator is linked with others that indicate intensification and segregation in the agricultural sector: age of manager, development in number of holdings, spatial share of extensively used areas, spatial share of agricultural areas, subsidies for agriculture

3. Methodological Description and Underlying Definitions (a) Underlying Definitions and Concepts: land should be permanently fallow and not set aside for a short period of time e.g. for supply control (b) Measurement Methods: (c) Limitations of the Indicator: (d) Status of the Methodology: (e) Alternative Definitions:

4. Assessment of Data (a) Data Needed to Compile the Indicator: land cover: fallow land (b) National and International Data Availability and Sources: (c) Data References:

5. Agencies Involved in the Development of the Indicator (a) Lead Agency: UNIJENA (b) Other Organizations:

6. References (a) Readings: (b) Internet site:

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5.4.9 Spatial Share of Grassland

Spatial Share of Grassland

Environmental Land use Land-use Change

1. Indicator (a) Name: Spatial Share of Grassland (b) Brief Definition: Area covered by grassland as a percentage of the whole agricultural area. (c) Unit of Measurement: % (d) Placement in the CSD Indicator Set: Environmental/Land use/Land-use Change

2. Policy Relevance (a) Purpose: Within Iron Curtain's German-German reference area this indicator indicates the continuation of a traditional type of land use. (b) Relevance to Sustainable/Unsustainable Development (Themes/Subthemes): In grassland dominated landscapes the continued grassland farming can help in preserving the landscape character and therefore the recreational potential of the region. (c) International Conventions and Agreements: (d) International Targets/Recommended Standards: (e) Linkages to Other Indicators: The indicator is very closely linked with the development in number of livestock as grassland depends on livestock farming. However if the number of livestock gets too high there exists a danger of manure disposal on grassland areas. This could be monitored with nutrition balance surpluses. The grassland itself can be extensively used and therefore included in the spatial share of extensively used areas.

3. Methodological Description and Underlying Definitions (a) Underlying Definitions and Concepts: (b) Measurement Methods: (c) Limitations of the Indicator: This indicator makes only sense in areas where grassland farming has a tradition. (d) Status of the Methodology: (e) Alternative Definitions:

4. Assessment of Data (a) Data Needed to Compile the Indicator: land cover: grassland (b) National and International Data Availability and Sources: (c) Data References:

5. Agencies Involved in the Development of the Indicator (a) Lead Agency: UNIJENA (b) Other Organizations:

6. References (a) Readings: (b) Internet site:

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5.4.10 Spatial Share of Forest

Spatial Share of Forest

Environmental Land use Land-use Change

NOTE: See the related UN CSD website for detailed information (Section 6b)

1. Indicator (a) Name: Spatial Share of Forest (b) Brief Definition: forest area as a percentage of the whole land surface area (c) Unit of Measurement: % (d) Placement in the CSD Indicator Set: Environmental/Land use/Land-use Change

2. Policy Relevance (a) Purpose: indicates how much of an area is covered with forest (b) Relevance to Sustainable/Unsustainable Development (Themes/Subthemes): Forest is recently discussed as a carbon-dioxide sink and (depending on its management) can provide habitats for certain species. In the IC context the share of forest indicates afforestation that normally takes place on abandoned farmland (most likely formerly with extensive use) thus destroying habitats for species adapted to this type of land use. Moreover heavy afforestation can change the visual character of a landscape. (c) International Conventions and Agreements: (d) International Targets/Recommended Standards: (e) Linkages to Other Indicators: These indicate areas in danger of afforestation: spatial share of fallow land, spatial share of extensively used areas

3. Methodological Description and Underlying Definitions (a) Underlying Definitions and Concepts: includes even afforested areas (b) Measurement Methods: (c) Limitations of the Indicator: Time series are needed to permit a statement on the development. (d) Status of the Methodology: (e) Alternative Definitions:

4. Assessment of Data (a) Data Needed to Compile the Indicator: land cover: forest (b) National and International Data Availability and Sources: (c) Data References:

5. Agencies Involved in the Development of the Indicator (a) Lead Agency: UNIJENA (b) Other Organizations: UN (see section 6b)

6. References (a) Readings: (b) Internet site: UN CSD indicator Forest Area as a Percent of Land Area aims at the deforestation problematics (tropical areas of the world), however the indicator is more or less the same.

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5.4.11 Spatial Share of Agricultural Areas

Spatial Share of Agricultural Areas

Environmental Land use Agriculture

1. Indicator (a) Name: Spatial Share of Agricultural Areas (b) Brief Definition: Area with agricultural use as percentage of the whole area. (c) Unit of Measurement: % (d) Placement in the CSD Indicator Set: Environmental/Land use/Agriculture

2. Policy Relevance (a) Purpose: shows the spatial dimension of the agricultural sector (b) Relevance to Sustainable/Unsustainable Development (Themes/Subthemes): Agriculture provides a certain type of landscape used not only for production but for recreation. Agriculture provides food and income. (c) International Conventions and Agreements: (d) International Targets/Recommended Standards: (e) Linkages to Other Indicators: share of wage earners in agriculture, income from agriculture as share of total income from all sectors, development in number of holdings, development in number of livestock

3. Methodological Description and Underlying Definitions (a) Underlying Definitions and Concepts: agricultural use means arable land or grassland. (b) Measurement Methods: (c) Limitations of the Indicator: (d) Status of the Methodology: (e) Alternative Definitions:

4. Assessment of Data (a) Data Needed to Compile the Indicator: land cover: agriculture (grassland, arable land) (b) National and International Data Availability and Sources: (c) Data References:

5. Agencies Involved in the Development of the Indicator (a) Lead Agency: UNIJENA (b) Other Organizations:

6. References (a) Readings: (b) Internet site:

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5.4.12 Spatial Share of Extensively used Areas

Spatial Share of Extensively used Areas

Environmental Land use Agriculture

1. Indicator (a) Name: Spatial Share of Extensively used Areas (b) Brief Definition: Agricultural area with extensive use as percentage of all agricultural area. (c) Unit of Measurement: % (d) Placement in the CSD Indicator Set: Environmental/Land use/Agriculture

2. Policy Relevance (a) Purpose: shows the extent of a less burdening land use within an area (b) Relevance to Sustainable/Unsustainable Development (Themes/Subthemes): Extensive land use can be of benefit to the environment. In particular, it can help to conserve habitats in which biodiversity is higher than in comparable but intensely used areas. (c) International Conventions and Agreements: (d) International Targets/Recommended Standards: (e) Linkages to Other Indicators: use of fertilizer, development in number of livestock and nutrition balance surpluses show the intensity of agricultural land use, spatial share of organic farming and spatial share of grassland indicate other categories of less intensive use arable land on shell limestone and percentage of meagre/mesotrophic grassland are types of land use that are extensive

3. Methodological Description and Underlying Definitions (a) Underlying Definitions and Concepts: extensive use means low or no inputs (e.g. fertilizer, pesticides) and no or low tillage (b) Measurement Methods: (c) Limitations of the Indicator: (d) Status of the Methodology: (e) Alternative Definitions:

4. Assessment of Data (a) Data Needed to Compile the Indicator: land cover: agriculture (grassland, arable land), amount of fertilizer used, stocking numbers, participation in extensification programmes TO BE CONTINUED (b) National and International Data Availability and Sources: (c) Data References:

5. Agencies Involved in the Development of the Indicator (a) Lead Agency: UNIJENA (b) Other Organizations:

6. References (a) Readings: (b) Internet site:

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5.4.13 Landscape Metrics

Change in the Quality and Spatial Distribution of Landscape Units

Environmental Landscape Visual Landscape Properties

NOTE: This indicator needs further definition and could develop into a group of indicators.

1. Indicator (a) Name: Change in the Quality and Spatial Distribution of Landscape Units (b) Brief Definition: (c) Unit of Measurement: To be developed (d) Placement in the CSD Indicator Set: Environmental/Landscape/Visual Landscape Properties

3. Methodological Description and Underlying Definitions (a) Underlying Definitions and Concepts: Landscape Metrics as implemented in FRAGSTATS software. (b) Measurement Methods: (c) Limitations of the Indicator: (d) Status of the Methodology: (e) Alternative Definitions:

4. Assessment of Data (a) Data Needed to Compile the Indicator: landscape units (b) National and International Data Availability and Sources: (c) Data References:

5. Agencies Involved in the Development of the Indicator (a) Lead Agency: UNIJENA (b) Other Organizations:

6. References (a) Readings: (b) Internet site: FRAGSTATS homepage

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5.4.14 Development in Number of Holdings

Development in Number of Holdings

Environmental Land Agriculture

1. Indicator (a) Name: Development in Number of Holdings (b) Brief Definition: The total number of farms in a certain area over time. (c) Unit of Measurement: number (d) Placement in the CSD Indicator Set: Environmental/Land/Agriculture

2. Policy Relevance (a) Purpose: The purpose of this indicator is to show the changes in the number of farms in a certain area over a period of time. In the EU the number of farms has been decreasing over the last decades due to a concentration and intensification process. (source: 6.(b)) (b) Relevance to Sustainable/Unsustainable Development (Themes/Subthemes): The indicator can show how far the concentration and intensification process has developed in a region. Intensification, greater specialisation and unit-enlargement can have environmental consequences, which need to be controlled to ensure the sustainability of agriculture. High-yield fodder crops reduce the amount of land needed for grazing animals, which can result in the loss of permanent pastures. At the same time, agricultural marginalisation can occur, from the field to the regional scale: difficult areas within a farm or whole farms may be abandoned. Regions with extensive systems or small-scale agriculture are especially vulnerable. Abandonment can have serious consequences for the natural environment including the growth of scrub and then forest, and loss of the species associated with agricultural land. (Source: 6b) (c) International Conventions and Agreements: (d) International Targets/Recommended Standards: (e) Linkages to Other Indicators: The indicator is closely related to development in number of livestock also indicating intensification of production. Concerning the abandonment of land the indicators spatial share of agricultural areas, spatial share of extensively used areas, spatial share of fallow land and spatial share of grassland should be used in addition.

3. Methodological Description and Underlying Definitions (a) Underlying Definitions and Concepts: German statistics defines a farm as an agricultural production unit with an agriculturally used area of at least two hectares or a certain amount of livestock. If the forestal used area is more than ten times the size of the agricultural area the unit is classified as a forest unit. It is assumed that every country uses a similar definition for farms. (b) Measurement Methods: The indicator measures the total number of farms within a certain area. (c) Limitations of the Indicator: Main limitation of this indicator is that it assumes a certain type of family-style agriculture to be the rule. This type of agriculture (small enterprises with at their best mixed production i.e. field-crop production and livestock farming) is declining as a result of intensification and cost reduction within the agricultural sector. Along with this process goes the abandonment of unproductive sites. However if the agricultural structure is completely different namly in the former socialist countries with huge agricultural enterprises with dozens of employees the indicator itself is useless and has to be seen in conjunction with the other indicators given above in sector 2e. (d) Status of the Methodology: (e) Alternative Definitions:

4. Assessment of Data (a) Data Needed to Compile the Indicator: Statistics on the number of farms per statistical unit (which

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© IRON CURTAIN Consortium Issued by: GEO Date: 25.2.04 should be as small as possible in order to indicate differences within one reference area). (b) National and International Data Availability and Sources: (c) Data References:

5. Agencies Involved in the Development of the Indicator (a) Lead Agency: European Environment Agency (EEA) (b) Other Organizations: OECD

6. References (a) Readings: [1] EEA Indicator Fact Sheet Intensification of agriculture (pdf, 146 K) [2] OECD, Organisation for Economic Co-Operation and Development (Ed.) (2001): Environmental Indicators for Agriculture. Vol. 3 : Methods and Results. Paris : OECD Publications download as pdf (3808 K, read-only) (b) Internet site: EEA indicator Agricultural intensity

6 References

GREBE, R. ; BAUERNSCHMITT, G. (1995): Biosphärenreservat Rhön : Rahmenkonzept für Schutz, Pflege und Entwicklung. Radebeul : Neumann. - Bearbeiter: Planungsbüro Grebe, Landschafts- und Ortsplanung, Nürnberg

HUNDTH, R. (1998): Vegetationskundliche Modelluntersuchung am Grünland der Vorderen Rhön als Grundlage für eine umweltgerechte Nutzung und deren ökologisch fundierte Förderung. Kaltensundheim : Biosphärenreservat Rhön/Verwaltung Thüringen (Mitteilungen aus dem Biosphärenreservat Rhön, Monographie 1)

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