NATURAL RESOURCE MODELING Volume 17, Number 4, Winter 2004 THE TRANSFORMATION OF LANDSCAPE: MODELING POLICY AND SOCIAL IMPACTS ON THE AGRICULTURAL LANDSCAPE OF LESVOS THANASIS KIZOS Laboratory of Local and Island Development University of the Aegean Department of Environmental Studies Xenia Building Mitilini 81100, Greece E-mail: [email protected]IOANNIS SPILANIS Laboratory of Local and Island Development University of the Aegean Department of Environmental Studies Xenia Building Mitilini 81100, Greece E-mail: [email protected]ABSTRACT. The term landscape is more and more used as an “umbrella” concept, covering a series of cultural, produc- tive and ecological processes. In order to uncover mechanisms, monitor transformations and predict changes, a complicated set of interacting factors has to be taken into account. This pa- per presents a model for estimating social and policy impacts on agricultural landscapes, based on the assumption that agri- cultural landscapes are shaped at “macro” (landscape) level by “micro” interventions at farm level. The model consists of three parts: an “ecological processes” part, which deals with processes that shape the ecological and aesthetic value of a landscape, a “population dynamics” part, which exam- ines farmer population dynamics and a “policy impact” part, which deals with direct or indirect impacts on farming systems and farmer dynamics and refers to CAP Rural Development Measures. The model is applied for the olive and graze land agricultural landscapes of Lesvos (Greece). Results, apart from revealing landscape change patterns; help to illustrate some mechanisms behind this change and indicate that Rural Development Measures are inherent with minor but impor- tant malfunctions that cannot lead to sustainable landscape management and rural development in the area. Introduction: The agricultural landscape. Agricultural land- scapes can be defined in more than one way depending on the scientific discipline and the goals each definition serves. Most common definitions Copyright c 2004 Rocky Mountain Mathematics Consortium 321
Transcript
NATURAL RESOURCE MODELINGVolume 17, Number 4, Winter 2004
THE TRANSFORMATION OF LANDSCAPE:MODELING POLICY AND SOCIAL IMPACTS
ON THE AGRICULTURAL LANDSCAPE OF LESVOS
THANASIS KIZOSLaboratory of Local and Island Development
University of the AegeanDepartment of Environmental Studies
ABSTRACT. The term landscape is more and more used asan “umbrella” concept, covering a series of cultural, produc-tive and ecological processes. In order to uncover mechanisms,monitor transformations and predict changes, a complicatedset of interacting factors has to be taken into account. This pa-per presents a model for estimating social and policy impactson agricultural landscapes, based on the assumption that agri-cultural landscapes are shaped at “macro” (landscape) levelby “micro” interventions at farm level. The model consistsof three parts: an “ecological processes” part, which dealswith processes that shape the ecological and aesthetic valueof a landscape, a “population dynamics” part, which exam-ines farmer population dynamics and a “policy impact” part,which deals with direct or indirect impacts on farming systemsand farmer dynamics and refers to CAP Rural DevelopmentMeasures. The model is applied for the olive and graze landagricultural landscapes of Lesvos (Greece). Results, apartfrom revealing landscape change patterns; help to illustratesome mechanisms behind this change and indicate that RuralDevelopment Measures are inherent with minor but impor-tant malfunctions that cannot lead to sustainable landscapemanagement and rural development in the area.
Introduction: The agricultural landscape. Agricultural land-scapes can be defined in more than one way depending on the scientificdiscipline and the goals each definition serves. Most common definitions
(Naveh and Lieberman [1984], Forman and Godron [1986], Bourassa[1991], Terkenli [1996], Wacsher [1999], Jongman [1999]) include thevisual element of the landscape, the fact that the most common non-metaphorical use of the word is connected with vision, not necessarilyonly views but images. But a landscape has many more meanings thanjust visual images and is connected with more senses than just vision:it is a production medium; it is the set in which organisms and speciessurvive and reproduce (ECNC [1996], Forman and Godron [1986]) andit is a cultural image, a medium of representing, structuring and sym-bolizing environments through images (Daniels and Cosgrove [1994]).The uniqueness of the landscape experience (Appleton [1996], Muir[1999], Fenton [1988]) for different individuals or groups, stems that alllandscapes are cultural (Terkenli [1996]) and that culture is the ‘key’ tounlock landscape experiences, meanings and symbols (Cosgrove [1998])and unravel the thread of landscape preferences and aesthetics.
A broad definition of the cultural landscape is now more feasible:a landscape is a composed spatial synthesis, whether manmade ormodified by man, used as a place of collective living (Jackson [1994]);or ‘a space out of scale or geography, that people percept, signify andshape according to their means and objectives for each given time’(Terkelni [1996, p. 18]).
Agricultural landscape definitions represent the spatial specializa-tions of the above definitions for agricultural space. In practical terms,agricultural spaces depend on land uses (Forman and Godron [1986])and stand for the land used in agricultural production and animal hus-bandry. Of course this narrow perspective should be expanded to in-clude two vital elements: the ecological dimension, as agricultural land-scapes are the habitats of many species and every change in farmingpractices and systems reflects immediately on biodiversity (Forman andGodron [1986], Turner [1990], Naveh and Lieberman [1984]); and thesymbolic dimension. The ecological dimension is analyzed in the actualdevelopment of the model, but the symbolic dimension is given somespace here.
The symbolic ‘weight’ of agricultural landscapes is very important, asthey represent wider rural societies’ meanings, symbols and ideologies(Phillips [1998], Gray [2000]). The culturally constructed conceptof agricultural landscape concerns groups or individuals that ‘see’and ‘use’ it one way or another (produce in it, consume it, imagine
THE TRANSFORMATION OF LANDSCAPE 323
it, idealize it etc.) for a variety of purposes (as pictures/views, ascomplexes of land uses, as objects of aesthetic interest, as ideology-meaning tanks, as everyday scenery, as nature conservation levels etc.)(Hoggart et al. [1995], Pratt [1996], Phillips [1998], Marsden [1999]).So, a complete understanding of agricultural landscapes should be wideand rich enough in content to carry the symbolic weight they entail fordifferent groups of people.
In this study, a model for the estimation of social and policy impactson the agricultural landscape of Lesvos, a Greek island is developedand used.
Modeling social and policy impacts on agricultural land-scapes. The general assumptions of the model refer to the identifica-tion of the processes that shape and transform agricultural landscapesand the definition of the quality of the landscape, a concept used forthe estimation of positive or negative aspects of this dynamic transfor-mation for nature and societies. The processes that govern agriculturallandscapes formation and change could be distinguished according tothe time scale of their influence on the landscape to: short-term pro-cesses (months or years), such as shifts in cultivation or animals bred,shifts in farming systems or cultivation techniques, fire or other naturaldestruction, etc.; medium term processes (decades and centuries), suchas erosion and deposition, population variation, settlement patterns,technology and transportations change, etc.; and long-term processes(millennium to geological time), such as climate change, evolution, con-tinent formation, etc. (Vos and Meekes [1999], Marcucci [2000]). In themodel presented, all long and medium term processes are consideredinvariable, Table 1.
For short-term processes, agricultural landscapes are regarded asa system resulting out of centuries of interactions between the envi-ronment, the flora and fauna and anthropogenic actions (Naveh andLieberman [1984], Solon [1995], Van Mansvelt [1997], Marcucci [2000],Baudry et al. [2000]). This general conceptual model is depicted inmore detail in Figure 1. The elaboration of the complex interactionsdescribed in Figure 1 starts with the identification of the functionalunits of landscape formation, through which all anthropogenic actionsare channeled. Considering that a landscape is the macro result ofthe sum of interventions in micro and macro levels (CEC [2000]), then
324 T. KIZOS AND I. SPILANIS
TABLE 1. General assumptions made for developing landscape transformation.
Level Content
Landscape Dynamics Long and medium term processes invariable
Landscape Dynamics Short-term processes are result of the sum of micro
(farm) level and macro (area) level interventions
realized in macro (landscape) level
Landscape Quality Landscape quality comprised of ecological quality,
productive potential and symbolic-
aesthetic value
Landscape Quality Landscape quality estimated with hierarchical system of
presence and quality of landscape characteristics that
increase or reduce one or more of three dimensions
(ecological, productive, aesthetic). Definition of
characteristics spatial dependent (different for each
landscape examined).
Landscape Quality Farmer interventions (farming system) affect and shape
characteristics’ quality. Correspondence is landscape
and spatial dependent.
Landscape Dynamics Policies for agricultural and rural development affect
and Quality farmers’ decisions about the interventions they
undertake.
the micro level unit is the agricultural holding, through which most,if not all, micro level interventions are realized (Deffontaines et al.[1995], CEC [1999], CEC [2000], Baudry et al. [2000]). Macro levelinterventions for agricultural landscapes usually represent major works,infrastructure, etc.
This more elaborated conceptual model is depicted in Figure 2,where all general factors shown in Figure 1 fall into two categories:factors that shape the landscape through the holdings’ interventionsand thus have indirect influence on the landscape; and factors that formagricultural landscapes through macro interventions and have directinfluence on them. Rural development measures examined here canbe considered as a typical case of indirect influence. These micro andmacro level interventions are presented in Table 2.
THE TRANSFORMATION OF LANDSCAPE 325
System Environment
ExogenousParameters
Policies(National, EU)
Endogenous parameters
Landscape
Natural System
Biotic Parameters Flora Fauna.
AbioticParameters
Climate Geology Hydrology Soil
Socioeconomic System
EconomicFactors
ProductivestructureDistribution ofgoods
Politicalfactors
Local powerstructures
Social Factors
PopulationAge structureSocialorganization
Culturalfactors
Tradition Local values
Source: Adapted form Messerli and Messerli in Naveh Z, Lieberman A. (1984), p. (S2-13).
Internationalinfluences
FIGURE 1. Endogenous and exogenous parameters of agricultural landscapeformation.
Agricultural Landscape
Agricultural Landscape
Natural Parameters
Anthropogenic parameters
Exogenous characteristics
Endogenous characteristics
CAP
Other Policies
Farms
Microinterventions
Macrointerventions
Macro results
Time
Change in Policies
FIGURE 2. Agricultural landscape dynamics.
326 T. KIZOS AND I. SPILANIS
TABLE 2. Interventions that shape agricultural landscapes.
Micro interventions
Farms
Alteration of local soil and hydrological conditions1. Greenhouse construction2. Creation of local irrigation and/or drainage networks
3. Drilling
4. Terracing
5. Warehouse, stable, etc., construction
6. Fencing
Alteration of flora and fauna communities1. Variation change
2. Cultivation shift (annuals, perennials)
3. Shift from farming to animal husbandry or vice versa
Energy input1. Irrigation and change in irrigation technique
2. Fertilizers
3. Plant protection products
4. Tractor and all equipment usage
5. Animal feeding stuff quantity and quality
Land use changesHousing
Abandonment
Macro interventions
Alteration of local soil and hydrological conditionsMajor reclamation works
1. Land redistribution
2. Provincial and national road network
3. Creation of irrigation and/or drainage networks
The quality of the agricultural landscape. The above model has pro-ceeded far enough in determining the formation process and dynamicsof agricultural landscapes. But in order to evaluate social and policyimpacts, another concept is introduced, a concept used for evaluating ifchanges have positive or negative impact: the quality of the agriculturallandscape. Its definition is based on the definition of the agriculturallandscape as a cultural hyperspace, offering three main dimensions inwhich the concept of quality can be sought: ecological quality, pro-ductive potential and aesthetic/symbolic value (Wascher, Mugica andGulinck [1999], ECNC [1996], Wacsher [1999], Van Mansvelt [1997]).
Although the definition of quality through these three parametersis a concept immediately applicable to all agricultural landscapes,the ‘breaking down’ to specific characteristics and the developmentof measurable indicators, is a landscape-dependent procedure, withdifferent characteristics for different landscapes. Thus, a careful andstep-by-step definition of the spatial and symbolic characteristics ofeach landscape is necessary when using the above conceptual model,especially for the symbolic characteristics; as for ecological and pro-ductive/economic ones current literature offers significant assistance(ECNC [1996], Wacsher [1999], Wascher, Mugica and Gulinck [1999]).For Lesvos agricultural landscapes the characteristics used in the modelare presented in Table 4, along with the designation of value for eachfeature. It must be stressed here that this classification is schematicand does not imply any ‘real’ distinction between causes and effects.A landscape is a complex system that should be treated as a whole.Thus, one must always bear in mind that such classifications are onlyuseful as tools to facilitate easier calculation and do not correspond toactual differences between the characteristics. So, when we argue thatan x feature of a characteristic increases or decreases ecological qualityor aesthetic value, it is not implied that this effect is confined to thisaspect, but that the effect is more important for this aspect and throughthis effect the remaining aspects are affected, as an effect on ecologicalquality obviously affects productive potential and aesthetic/symbolicvalue.
Landscape characteristics and model development. The model con-sists of three parts: an “ecological processes” part, a “population dy-namics” part and a “policy impact” part and is developed using the
THE TRANSFORMATION OF LANDSCAPE 329
TABLE 4. Classification of landscapes in Lesvos.
Zone Definition Criteria Used
1st Zone: Includes the West and part of the 1. Graze land > 50%
Graze lands Northwest island (Map 1). Soils lie of the total
on recent lava and other compressed settlement area.
volcanic residuals (tuff) and are very
recent compared to the rest of the 2. Cultivated land
island (Higgins and Higgins [1996]) < 30% of the total
and are poor with limited nutrient settlement area.
availability. Mainly barren graze lands.
2nd Zone: Includes the East and Southeast part 1. Cultivated land
Olives of the island and Gera gulf (Map 1). > 40% of the total
The “population dynamics” part examines farmer population dynam-ics. In the model, age was considered an important factor that shapesattitudes and beliefs inside the framework of a typical economic model,where the actor decides rationally according to the information avail-able at the time (Popkin [1979], Mooney [1988], Damianos and Skuras
[1996], Beedell and Rehman [2000]) and three age groups were used:farmers 18 to 39, farmers 40 to 65 and farmers older than 65 that cor-respond to the farmer census age data. Other factors apart from agethat also influence attitudes and decision formation are education, in-formation on policies and markets, family income, survival strategiesetc. (Deffuant [2001], Samaras et al. [1994], Beedell and Rehman [2000],Damianos and Skouras [1996], Samaras et al. [1995], Kalantaridis andLabrianidis [1999]).
For each age group 3 outputs and 1 input are used: outputs are ageing(not available in the older than 65 years old group), abandonment offarming and death. Assuming that age groups are homogenous, eachyear 1/21, or 1/25, farmers go to group 40 to 65, or older than 65,respectively. The input refers to new farmers (from Young Farmersinstallation scheme or not) and ageing from a previous age group. The
332 T. KIZOS AND I. SPILANIS
equations used are:
(1)dF18−39
dt= 0.8∗
(N(t) +
Nmax + 0.3∗N(t)N(t)
)− Ag1 − D18−39(t)
− Ab18−39(t)
(2)dF40−65
dt= 0.2∗
(N(t) +
Nmax + 0.3∗N(t)N(t)
)+ Ag1 − Ag2
− D40−65(t) − Ab40−65(t)
(3)dF65
dt= Ag2 − D65(t) − Ab65(t)
where F18−39, F40−65 and F65 stand for farmers in classes 18 to 39, 40 to65, and older than 65 respectively, N stands for Young farmers installedwith the Young Farmers Scheme (the scheme was introduced in 1988in Greece but significant initiation began after 1994, so for applicationbefore 1994 equations have to be modified), NNA stands for the rest ofnew farmers each year, Nmax stands for the maximum value of YoungFarmers in the zone the model is applied, 0.8 stands for the percentageof new farmers that are below 40 years old and 0.2 is respectively thepercentage of new farmers 40 to 65 years old. The system of equations(1) (3) is continuous, but it is used as discrete (dt = 1 year) due toinsufficient data availability for most of the coefficients. N is calculatedwith the equation:
(4) Nt = N + 0.5∗Succession∗(Ab40−65 + Ab65 + D65)
where Succession stands for the percentage of the farmers that abandonor die and are replaced by new farmers that are not Young Farmers andis calculated by the research, and finally 0.5 stands as an approximationof the ratio Young Farmers/new farmers.
Ag1 and Ag2 stand for farmers that are age 18 to 39, and 40 to 65,respectively. The values are the 1/21 and 1/25 of the total farmers’number. D18−39, D40−65 and D65 stand for farmers that die each yearfor the three groups respectively. The equation used is:
(5) Di = Fi ∗ di,
THE TRANSFORMATION OF LANDSCAPE 333
TABLE 5. Lesvos landscape characteristics, features measured
Boundary Shannon Increasing value Nodiversity index increases boundary
diversity
Landscape Landscape Boundary Increasing value No
Diversity(2) hetero- effect increases landscape
geneity heterogeneity
Spatial Index of Increasing value Nocomplexity spatial increases spatial
complexity complexity
Spatial Distribution Higher distribution Nodistribution along increases ecological
relief lines quality
Presence Yes/No Presence increases Yes
ecological and
Terraces(3) aesthetic value
Quality m3 of preserved Quality increases Yesmasonry/total aesthetic value
masonry
Presence Yes/no Presence increases Yes
aesthetic value
Fences(3) Building Stone, stone Stone increases Yes
material with else, else aesthetic value
Quality M2 of pre- Quality increases Yesserved mason- aesthetic value Yesry/total
masonry
334 T. KIZOS AND I. SPILANIS
TABLE 5. (Continued)
Criteria Features Measure- Designation Measured
Measured ment Unit of Value in Lesvos
Presence Yes/No Presence increases No
aesthetic value
Building Stone, else Stone increases No
material aesthetic value
Paths(3) Quality M of pre- Quality increases No
served pathway/ aesthetic value
total path
Recreation Vistas Presence increases No
offered aesthetic value
Ecosystem More ecosystems No
types greater
aesthetic value
Presence Yes/No Presence increases Yes
aesthetic value
Construc- Building Stone, stone Stone increases Yes
tions(3) material with else, else aesthetic value
Quality M3 of preserved Quality increases Yes
masonry/total aesthetic value
masonry
Presence Yes/No Neutral Yes
Density Buildings/ha Decreasing density No
Buildings, increases
housing aesthetic quality
Local Yes/No Increases No
character aesthetic quality
Major Presence Yes/No Neutral Yes
works Impact Positive/Negative as measurement Yes
where Di stands for deaths for each group, Fi stands for the totalnumbers of farmers in the group and di stands for the death percentagefor the group. Ab18−39, Ab40−65 and Ab65 stand for farmers thatabandon farming for each group. The number is calculated by theformula:
(6) Abi = Fi ∗ abi,
THE TRANSFORMATION OF LANDSCAPE 335
TABLE 6. Classification of Lesvos farmers in groups.
Name Description Definition
Professionals Farmers with no or very small • Age <65 years old and
incomes from out of • Income from agriculture/
agriculture income from out of
agriculture = 3, 4 or
5 degrees of difference (∗)Hobby/retired Retired farmers, retired 1. Age > 65 years
professionals and owners of 2. Age < 65 years and pension
agricultural holdings with 3. Age < 65 years and income
major out of agriculture from agriculture/income from
incomes and very small out of agriculture = 3, 4 or 5
agricultural ones degrees of difference (∗)Semi- “Intermediate” farmers, with • Age < 65 years and
professionals important out of agriculture • Income from agriculture/
incomes income from out of
agriculture = 0, 1 or
2 degrees of difference (∗)
where abi is the abandon percentage for each group, with the notableexception of the 40 65 age group, to whom the Early RetirementScheme was addressed and the equation is modified thus:
(7) Ab40−65 = F40−65 ∗ ab40−65 + ER
where ER stands for the number of farmers that participated in thescheme.
The modeling of the differences in attitudes and finally on interven-tions and/or farming practices that change landscape quality was de-veloped with the use of a farm typology in terms of farmer age andhouseholds’ income dependence on agriculture. This typology drawson Damianos and Skuras [1996] and classifies farmers according to theratio of farm to off-farm income, the source of off-farm income andthe age of the owner. Three farmers’ groups emerge: ‘professionals,’‘semi-professionals’ and ‘hobby/retired,’ Table 6. The equations usedfor calculating the numbers are very simple and run as follows:
where E1, E2 and E3 stand for the numbers of farmers in the threegroups, respectively, p118 and p318 stand for the percentages of profes-sionals and hobby farmers in 18 to 39 farmers, respectively, p140 andp340 stand for the percentages of professionals and hobby farmers in 40to 65 farmers, respectively.
The “ecological processes” part deals with processes that shape theecological, productive and aesthetic value of the landscape. The inter-ventions on the characteristics of the landscape are generally three:maintenance, negligence and removal.
Maintenance refers to the upgrading of the characteristics’ quality,defined as the quantity of the characteristic that is maintained to thetotal quantity of the characteristic for a specified area in a land use(e.g., in m3 of maintained masonry/m3 of total masonry in the case ofterraces). The formula used is:
(11) M = M0 + Cm − W
where M stands for the quality of the characteristic in the maintenancearea, M0 stands for the initial quality of the characteristic in themaintenance area, W stands for the deterioration of quality rate andCm stands for the maintenance coefficient.
Negligence refers to the gradual downgrading of the characteristics’quality, defined as the quantity of the characteristic that isn’t main-tained to the total quantity of the characteristic of a specified area ina land use. Negligence is generally connected to intensification farmingpractices that do not require immediate removal of the characteristic(mild intensification) and abandonment of farming or mild abandon-ment, which is the case where farming continues, but only to collectthe end product, e.g. olives, with no other farming practices (case quitecommon in land uses such as olive groves). Thus two negligence coef-ficients are used, defining the quantity not maintained by mild intensi-fication and abandonment at each time step. The formula used is:
(12) N = N0 − (Can + Cni)
where N stands for the quality of the characteristic in the negligencearea, N0 stands for the initial quality of the characteristic in the
THE TRANSFORMATION OF LANDSCAPE 337
negligence area and Cni and Can stand for the negligence coefficients formild intensification and abandonment, respectively. Removal refers tothe rapid downgrading of the characteristics’ quality, by its destruction.It is defined as the quantity of the characteristic that is removed to thetotal quantity of the characteristic in a specified area in a land use.The formula used is:
(13) R = R0 − Cr
where R stands for the quality of the characteristic in the removal area,R0 stands for the initial quality of the characteristic in the removal areaand Cr stands for the removal coefficient. These three processes takeplace randomly in the land uses of the landscape and therefore theexact areas of their effect have to be defined, according to the farmingpractices that are connected with each process (for Lesvos farmingpractices the correspondence is presented in Table 3). So the areasof each land use have to be calculated for each time step of the model,and for all land uses the areas for each farming practice have to bedefined. In these areas for each land use the processes that changethe quality of the characteristics take place. The actual calculation isperformed with the following formulas:
(14) L = New − Aban
(15)Aban = ((Ab18+Ab40+Ab65) + (D18+D40+D65)) ∗ P ∗ MFA
+ (F18 + F40 + F65) ∗ PC ∗ MFA
(16) MFA = L/(F18 + F40 + F65)
where L stands for the land use area, New stands for land input eachyear, Aban stands for land output each year, P stands for abandonedpercentage from farmers that die or abandon farming permanently,(D18 + D40 + D65 and Ab18 + Ab40 + Ab65), PC stands for abandonedpercentage from farmers that continue farming (F18 + F40 + F65) andMFA stands for the mean farm area for the specific land use.
We use the technique of assuming equivalent areas in which theprocesses that change the quality of the landscape characteristics take
338 T. KIZOS AND I. SPILANIS
place and of assuming that the effect of each process is separate and‘happens’ in an area the total size of the areas this process uses for eachland use. So, for each land use three equivalent areas are defined, eachwith the size of the corresponding process of the three characteristics’quality change.
The equations used are simple and are based on the assumptions ofcorrespondence between certain farming practices and processes thatchange landscape quality. The equations used are:
Ext = L ∗ Cext,(17)Int = L ∗ Cint,(18)Mild = L ∗ Cmild,(19)
where
Cmild = 1 − (Cext + Cint),(20)Hint = Int ∗ Chint,(21)Lint = Int − Hint,(22)Rem = AbPer ∗ Ccon,(23)
where Ext and Cext stand for the maintenance equivalent area andthe maintenance proportion respectively, Int and Cint stand for theintensification equivalent area and the intensification proportion, re-spectively, Mild and Cmild stand for the mild abandonment equiva-lent area and the mild abandonment proportion, respectively, Hint andChint stand for the intense intensification equivalent area and the in-tense intensification proportion, respectively, Lint, stands for the mildintensification equivalent area and Rem and Ccon stand for the re-moval equivalent area and the removal proportion, respectively, causedby housing, while AbPer stands for the abandonment equivalent area.
The next step is the spatial integration of the equivalent areas atlandscape level by summing the separate results of the processes. Thissumming up is formulated for the quality values of each traditionalcharacteristic individually only and not at landscape level, leaving atthe end of each calculation cycle three values of quality for each landuse and each characteristic. The equations used are:
where Sm, Sr and Sn stand for the quality for maintenance, removaland negligence equivalent areas, respectively, wm, wr and wn stand forthe weights for maintenance, removal and negligence, respectively, Totstands for the size of the land use and if all new areas are abandonedareas formerly occupied by the same land use, then:
The “policy impact” part deals with direct or indirect impacts onfarming systems and farmer dynamics from the environment of thelandscape system, as presented in Figures 1 and 2. The main parame-ter assumed to play the key role in farming systems and farmers’ inter-ventions, and therefore on Lesvos’ landscape transformation, is CAP(Common Agricultural Policy) rural development measures, a set ofschemes and programs aiming either at the improvement of the incomesof rural households (directly on farm incomes, or indirectly by diver-sifying family incomes and helping farmers acquire out-of-agriculturalincomes), or at the improvement of the area’s attractiveness. The mea-sures and their expected impact on farms and farming systems arepresented in Table 7.
Introduction of the rural development measures in the model is madein a qualitative and indirect way. This is mainly due to the fact thatthey are targeted at professionals and some of the semi-professionalsonly, as other farmers do not meet age or out-of-agriculture incomestandards, which presents interesting results on landscapes that willbe discussed later. Another reason lies in the voluntary nature of themeasures. They are not implemented for all farmers as, for example,the Common Market Organization (CMO) does for olive oil, but arearranged and intended for farmers that want to participate and meetcertain criteria.
340 T. KIZOS AND I. SPILANIS
TABLE 7. Rural development measures and expected impact on farmers and farms.
Measures Expected Impact
Farm Incomes Area Attractiveness
Investments in equipment �(including agrotourism and
manufacture)
Young Farmers Installation Scheme �Early Retirement Scheme �Education �Less Favored Areas (LFA) measures �Agri-environmental measures �Assistance in manufacture and trade �
of agricultural products
Assistance in adjustment and �development of rural areas
As already mentioned earlier, information about available choices iscrucial for farmers (Deffuant [2001], Samaras et al. [1994], Damianosand Skuras [1996]). In the model, information on rural developmentmeasures is disseminated through formal (Prefectures’ Bureau for Agri-culture and farmers cooperatives mainly) and informal channels (otherfarmers that have participated, newspapers, internet, private compa-nies, etc.). The typical information spread equations are used, assum-ing that the first farmers receiving the information will inform v morefarmers, each one v more, etc. If k farmers are receiving each time theinitial information from official channels, then if time t = 1 week foreach farmer each one informs, there is no cross-information, all farm-ers are willing to spread information they receive accurately and thewhole process stops when everybody receives the information, then theequation used runs (Terzidis [1983]):
(28) P (t) =n∑
i=0
Pt
1 + ((Pt/k) − 1) ∗ e−Pta(t−bT )
where P stands for the number of informed farmers at time t, namelyin every step of information spread, Pt stands for total farmer number,a stands for the coefficient determining the accuracy, willingness andcross-information (here a = 1), n stands for the total number of
THE TRANSFORMATION OF LANDSCAPE 341
information waves through formal channels and b stands for eachinformation wave, taking place after time T and including always thesame initial number k.
The case of Lesvos’ agricultural landscapes
The island of Lesvos. The implementation and realization of themodel is conducted in Lesvos, a Greek island in North Aegean. Lesvosis one of the biggest Greek islands (1632.8 km2), isolated from conti-nental Greece. In 2001, 89,935 inhabitants lived in it (increase of 4%since 1991, while Greece presented a 6.6% increase for the same pe-riod). It has suffered intense depopulation and out-migration from the1940’s to the 1980’s when the population stabilized at about today stan-dards. The current population is aged (ageing indicator at 119.9, whilefor Greece was 71.1), as the economically active young inhabitants mi-grated. It is a relatively poor island with the Prefecture’s GDP for 1999standing at 84% of the country’s average, where agriculture still playsa major role in production (12% of the Prefecture’s GDP in 1998, whenGreece was at 8% for the same year), incomes and occupation. On theother hand, the number of agricultural holdings is decreasing (almost30% decrease between 1971 and 1991, from 22,799 to 15,696 holdings).In the spirit of the structural measures of the Common AgriculturalPolicy (CAP), the island is designated as a Less Favored Area (LFA)with 40 settlements classified as mountainous (Directive 85/148/EEC).
Research method and model development. The method used to arriveat the model implemented in Lesvos, corresponds to the general processdescribed above: first, the landscape types of Lesvos are defined and thespecific landscape characteristics for each type are identified and finallydata for the application of the model are collected.
Landscape types. A first approach for the definition of landscape typesin Lesvos arrives at two main types: olive groves and graze lands. Theactual classification used defines one more zone, which is intermediate,meaning that it is the zone where both graze lands and olives arefound, along with arable land and forests. The specific criteria forthat classification use climatic and geological backgrounds, but really
342 T. KIZOS AND I. SPILANIS
rely on the land uses that predominate in each zone. These criteria are:
1. Climate. The climatic classification of Lesvos (Thornthwaite fromTheodorakakis [1995]) presents three well-defined zones that correspondroughly to the classification proposed here.
2. Geology. The geology of the island corresponds to the abovezoning pattern and subsequently to the landscape types that are usedhere (details and maps in Higgins and Higgins [1996]).
3. Agriculture and land uses. The actual landscapes’ classification isbased on the land uses of the island (data from the 1991 census of landuses for settlement areas or the 1991 census of agricultural land) andarrives at the following land uses (Table 4 and Map 1): graze lands,cultivated land (expressed as percentages of the total administrativeunit area), and groves (expressed as percentages of Utilized Agricul-tural Land UAA) as main criteria; and forest (expressed as percent-ages of the total administrative unit area), arable land (expressed aspercentages of UAA) as complimentary criteria.
4. Spatial uniformity. As already mentioned, the limits of the areasused in the classification are the administrative units of each settlementof the island, the smallest spatial level for which data are available.
Data collection and landscape characteristics used. The data used inthe model involved some official published data but the majority wascollected from Lesvos’ farmers through research on their agriculturalholdings. Land use changes, presence and quality of the traditionalcharacteristics of the landscape, interventions and investments thatchange landscape characteristics and farming systems were acquired byinterviewing the owners of the holdings. Structured questionnaires wereused. Apart from the farming systems questions, the owners were askedabout household survival strategies and agriculture attractiveness.
The research was conducted by stratified random sampling, withlandscape zone and age of the owners serving as the main stratifications.The owners were divided in three zones and three age groups (18to 39, 40 to 65 and over 65 years old). The sample size was takenfrom the Farmer’s Record in order to have at least 300 questionnaires(100 in each landscape zone, out of 18,768 holdings). Zone and agestratifications were described earlier. The individual owners of each ageclass were interviewed randomly, while specific numbers of interviews
THE TRANSFORMATION OF LANDSCAPE 343
per settlement were taken, based on the total number of the holdingsin the settlement. Finally, 304 valid questionnaires were completed.
For aesthetic/symbolic quality, the identification of the individualcharacteristics derives from the “traditional” landscape characteristics.These are the characteristics that shaped the landscapes that are re-garded today as ‘traditional’ and are valued both ideologically (as cul-tural heritage of the island and as a collective memory tank for the‘rural past’ of islanders and tourists, Cosgrove [1998]) and aestheti-cally (as an element of beauty in landscape images). The identificationof such characteristics involves the identification of certain productivesystems and their ‘marks’ on the landscape. Terraces, fences, construc-tions (small storage constructions, barns, stables, wells, water tanks,churches, etc.) and paths can be regarded as such characteristics, asis evident from travelers’ accounts, current landscape structures, mate-rial from Greek literature and agrarian studies etc. (Kizos [2003]) whichtend to focus on these groups of characteristics.
The same groups of characteristics appear to affect a major part ofthe productive potential of Lesvos landscapes as well. This contributionis owed to the fact that these characteristics served relatively extensiveproductive systems in the time they were still functional (Rackhamand Moody [1996]). Industrialization of agriculture and dependenceon exogenous sources for inputs (energy, irrigation, fertilization, plantand animal protection) has marginalized traditional productive systemsand the landscape characteristics that evolved around them. Islandresources (soil fertility, water for irrigation, relief) and isolation fromcontinental land does not allow competition on a par with continentalagriculture, resulting in intensification that has created environmentaland productive problems, as resources are depleting and production issmaller in quantity, while inputs are more expensive. This vicious circlecan be untied only through extensification and focusing on quality,which can be achieved easily through productive systems that utilizeat least some of the traditional characteristics.
For ecological quality, the characteristics will be sought among thosethat affect biodiversity in habitat and species level (Wascher [1999]).Landscape diversity is a function of existing land uses, their changesover time, their spatial differentiation in the landscape, the size ofeach parcel and their spatial complexity (Jongman [1999], Forman andGodron [1986], Baudry [1993], Sauget and Balent [1993]). Traditional
344 T. KIZOS AND I. SPILANIS
characteristics as defined above are of course part of these parametersthat shape landscape diversity, especially terraces and fences. InTable 5 the characteristics measured are presented along with thedesignation of value for each characteristic.
Results of the model. The results of the implementation of themodel are presented in four parts corresponding to the parts earlierdiscussed, namely land use changes abandonment, landscape quality,farmers’ numbers and age and rural development measures. A smallcomment should be made for the time periods the model simulationsran. From the brief discussion on rural development measures, the timeto start the simulations is 1994, when the 2nd Programming period(1994 1999) began. This offers a finishing point as well, 1999. Due tothe nondynamic character of most coefficients (namely the coefficientson Young Farmers introduction and the separation in farmer groups),the simulation period was not run for long time periods (10 to 15 years),although a certain number of simulations was run for greater timeperiods for indicative reasons only. Therefore, the results presentedwill use two time periods: one from 1994 to 1999 and one from 1994 to2004.
Land use changes Abandonment. The most common land usechange is by far abandonment, which represents 96% of the olive landuse changes, 67% of the arable land use changes and 52% of the totalland use changes (49 out of 95 cases). Another significant change ishousing (24% of the total land use changes) and reuse of abandonedland (13% of the total). The rest of the land use changes refer mainly toconversions from cultivated land to graze lands and changes from oneagricultural land use to another. What follows is the slight decrease ofcultivated land (by almost 5% after 10 years simulation which is greaterbut comparable to the official 3% decrease in 1961 1991), mainly causedby the abandonment of olive groves and arable land from professionalfarmers (Table 8), while semi-professionals and hobby/retired farmersseem more attached to their land and do not easily abandon it, probablybecause they don’t expect to make a living out of the agriculturalincomes. What semi-professional farmers abandon is their graze lands,as they cannot or don’t want to keep up with animal husbandry.
THE TRANSFORMATION OF LANDSCAPE 345
TABLE 8. Abandonment of main Lesvos land uses for two
simulation periods, farmer groups and landscape zones.
Farmer Groups/ Land use abandoned %
Landscape Zones Olive Arable Cultivated Graze UAAgroves land land land
Professionals -0.9% -1.9% -1.0% -0.9% -0.8%
6 years Semi-professionals -1.0% -0.1% -0.9% -8.9% -22.6%
Hobby/retired -1.1% -0.2% -1.0% -0.4% -0.2%
Total -1.0% -0.8% -1.0% -2.1% -2.6%
Professionals -1.8% -3.8% -2.1% -1.8% -1.7%
10 years Semi-professionals -2.0% -0.1% -1.8% -15.9% -40.1%
Hobby/retired -2.3% -0.5% -2.0% -0.9% -0.5%
Total -2.0% -1.5% -1.9% -3.9% -4.8%
Graze land -2.3% -0.2% -2.0% -0.3% -0.2%
6 years Olives -2.1% -1.8% -1.9% -2.2% -2.8%
Intermediate zone -0.3% 0.0% -0.2% -2.4% -3.2%
Total -1.0% -0.8% -1.0% -2.1% -2.6%
Graze land -4.5% -0.4% -3.9% -0.7% -0.3%
10 years Olives -4.0% -3.5% -3.7% -3.7% -3.7%
Intermediate zone -0.5% 0.0% -0.4% -3.9% -5.3%
Total -2.0% -1.5% -1.9% -3.9% -4.8%
The fields that are abandoned are the most remote and barren ones,located on steep slopes, which were in the past turned into terraced olivegroves (at great pains and expense for the time), as olive oil had verygood market value and population to work in the fields was available.Nowadays, with active population dropping rapidly and olive oil losingits value, those marginal fields are once again turned to forests or shrublands.
The average size of the holdings is increasing, although UAA isdecreasing, due to the rapid decrease of farmer numbers. Professionalsin the intermediate landscape zone provide the most extreme case,as they are the group more rapidly reducing and consequently theolive groves average area in the zone increases by 77% for 10 yearssimulation (from 4.5 ha to 8.0 ha). This has probably more to do withthe constant coefficients used in the model, rather than with actualnumbers, though official data reveal that the tendency is undoubtedly
346 T. KIZOS AND I. SPILANIS
correct (increase in average farm size in Greece by 16% in 1971 1991).Sensitivity analysis of the coefficients used in this part of the modelreveals that, as expected, the abandonment rates for each land use andfarmer group are the key points in simulating land use change.
Landscape quality results. Before presenting the actual results ofquality change, the presence of the characteristics used in Lesvos isgiven, to provide a clear idea of the complete picture of the Lesvos’landscape zones. Terraces are present in olive groves mainly in alllandscape zones (80%, 92% and 86% of olives lie in terraces for Zones1 (graze land), 2 (olives) and 3 (intermediate), respectively). Terracesare also present in graze lands in Zone 1 (14% of the total graze landarea), as graze lands in the zone were tilled and sowed with cerealsor legumes used for animal feed until the 1960’s and terraces are thedying marks of that practice. The rest of the uses are mainly situatedon non-terraced fields, with the exception of some fruit and chestnuttrees in the mountains of Zone 2 (olives).
The abundance of the rest of the characteristics (fences and con-structions, Table 9) is greater in olive groves and graze lands, reflectingtheir dominance in size; and therefore most constructions were builtin them, especially in a time when going to/from the fields had to bedone on foot. The results that refer to the characteristics’ quality shedlight upon the differentiations in farmer’s groups’ practices. Indeed,it appears that in the terraces’ case where results are clearer, onlyhobby/retired farmers maintain their terraces to some extent. As aresult, simulations depict a slow but steady decrease in terrace quality(Table 10) for other groups, a decrease that is greater in areas that aregrazed. This quality decrease in terraces is quite important, as theystill serve an important functional role in everyday farming practiceswhile the other traditional characteristics serve mainly as decorative,and in any case, they do not serve the same functions they used towhen they were built. The functional characteristics that are builtnow for contemporary farming practices (stables, storehouses, fencesand drills mainly) are just not ‘traditional’ anymore (different build-ing styles and different materials used, easier and cheaper to build andmaintain). This is obvious from the simulation results as well, wherethe quality of all traditional characteristics is dropping with different
THE TRANSFORMATION OF LANDSCAPE 347
TABLE 9. Fences and constructions in Lesvos landscape zones.
Fences
Zone 1 Zone2 Zone 3 Total(Graze (Olives) (Inter-land) mediate)
No fence 62.0% 23.0% 17.0% 25.2%Rest Wire 38.0% 65.0% 72.0% 64.0%uses Stone 4.0% 5.5% 4.3%
Stone and wire 8.0% 5.5% 6.5%N 13 34 23 70
No fence 5.3% 66.7% 32.4% 25.4%Arable Wire 63.2% 33.3% 59.5% 59.3%land Stone 21.1% 5.4% 10.2%
Stone and wire 10.5% 2.7% 5.1%N 19 3 37 59
No fence 7.1% 16.8% 23.4% 16.1%Olive Wire 54.8% 68.3% 46.8% 57.0%groves Stone 16.7% 5.9% 18.1% 13.3%
Stone and wire 15.5% 7.9% 11.7% 11.5%Stone and wood 6.0% 1.0% 0.0% 2.2%
N 84 101 94 279No fence 5.6% 18.3% 10.6%
Graze Wire 43.7% 90.0% 31.7% 41.8%lands Stone 25.4% 35.0% 27.7%
Stone and wire 21.1% 10.0% 15.0% 17.7%Stone and wood 4.2% 0.0% 2.1%
N 71 10 60 141
348 T. KIZOS AND I. SPILANIS
TABLE 9. (Continued)
Constructions built with stone
Zone 1 Zone2 Zone 3 Total Total(Graze (Olives) (Inter- noland) mediate) stone
No constructions 100% 100% 100% 100% 94.3%Stable 1.4%Drill 2.9%House 1.4%
TABLE 10. Change of terraces’ quality in olive groves for 6 and 10
years’ simulation per landscape zone and farmer group.
Farmer Groups
Profes- Semi- Hobby/ Total
sionals profes- retired
sionals
Initial quality 0.710 0.810 0.700 0.740
6 years simulation 0.670 0.800 0.770 0.747
Graze land 10 years simulation 0.640 0.760 0.740 0.713
Change % 6 years -5.6% -1.2% 10.0% 0.9%
Change % 10 years -9.9% -6.2% 5.7% -3.6%
Initial quality 0.500 0.600 0.610 0.570
6 years simulation 0.500 0.570 0.600 0.557
Olives 10 years simulation 0.490 0.540 0.580 0.537
Change % 6 years 0.0% -5.0% -1.6% -2.3%
Change % 10 years -2.0% -10.0% -4.9% -5.8%
Initial quality 1.000 0.670 0.570 0.747
6 years simulation 1.000 0.640 0.540 0.727
Intermediate 10 years simulation 0.990 0.610 0.510 0.703
Change % 6 years 0.0% -4.5% -5.3% -2.7%
Change % 10 years -1.0% -9.0% -10.5% -5.8%
Initial quality 0.620 0.710 0.650 0.660
6 years simulation 0.590 0.680 0.670 0.647
Total 10 years simulation 0.560 0.650 0.650 0.620
Change % 6 years -4.8% -4.2% -3.1% -2.0%
Change % 10 years -9.7% -8.5% 0.0% -6.1%
degrees for different farmer groups, but dropping all the same. Thefact that the hobby/retired farmers try to maintain the traditionalcharacteristics is very important, especially when connected to theirreluctance to abandon their lands.
The sensitivity analysis of this part of the model reveals that thecoefficients that measure ‘how much’ of the quality of each character-istic is to be reduced or increased for each dt are determining results.The different time scales involved also affect the model’s credibilityand predictive power. Rural development measures, farmer dynamics,and even land use changes ‘occur’ in different temporal scales than the
350 T. KIZOS AND I. SPILANIS
change of the quality of the traditional characteristics of Lesvos land-scapes. If a decade is a more than adequate scale for evaluating ruraldevelopment measures and a fair scale for farmer and farm changes,it is limiting the estimation of landscape quality change. This scaleproblem is not inherent in the model presented here only, as it reflectsa greater problem of scale compatibility between human actions andnatural processes, but it has to be noted as a weakness of the model.
Farmers. As described earlier, the farmer dynamics part of themodel is its most important part, as the main assumptions made inthe model’s development phase involved farmers and their practicesas central actors in landscape transformations. Results from thesimulation indicate that the decline in the number of holdings andfarmers’ numbers will continue in the next decade, as the number offarmers that are installed through the Young Farmers Scheme and newfarmers are less than the ones that abandon farming. The differencesfor the landscape zones are important, with the smallest decrease forthe graze land zone (-13%), close to the olives zone (-17%) whilefor the intermediate zone the percentage escalates (-30%, Table 11).The number of middle-aged farmers (40 to 65 years old) suffers thegreatest decrease; old farmers decrease as well, while young farmersincrease slightly, due to the Young Farmers Scheme. The distributionof the three cohorts is changing and, after a decade, the simulationsshow that although middle-aged farmers still predominate, differencesfrom older farmers are reduced significantly, while in the case of theintermediate zone, older farmers predominate after 10 years (Table 11).Simulations longer than a decade indicate that the trend is continuedand, after a while, the number of farmers reaches an equilibrium, newfarmers stabilize, middle-aged ones reduce even more and older farmerspredominate in all zones, a fact that is of great significance.
If this ageing that the simulations indicate is compared to the resultsof the farmer groups, then it produces a future image of farmers thatis focused around two important groups: older retired/hobby farmersthat consist of the majority of the total farmer population, and youngerprofessionals and semi-professionals. This is evident, as although pro-fessional farmers decrease with higher rates than semi-professionals andhobby/retired farmers (Table 11), a significant percentage of profession-als will still remain active (almost 14%).
TA
BLE
11.
Changes
infa
rmer
s’age
cohort
sand
gro
ups’
per
centa
ges
for
6and
10
yea
rs’si
mula
tions.
Farm
ers
%Farm
ers
%
18
to40
40
to65
>65
NP
rofe
ssio
nals
Sem
i-H
obby/
Tota
l
pro
fess
ional
Ret
ired
1994
14.0
52.0
34.0
2.9
08
Init
ialPer
cent
24.3
30.7
47.1
100.0
Graze
1999
15.2
49.4
35.4
2.7
04
Change
in6
y.∗
-7.2
-9.9
-5.2
-7.0
land
2004
16.4
47.5
36.1
2.5
13
Change
in10
y.∗
-13.2
-18.0
-11.3
-13.6
1994
14.0
52.0
34.0
10.1
12
Init
ialPer
cent
8.4
29.2
62.4
100.0
Olives
1999
15.8
48.9
35.2
9.3
02
Change
in6
y.∗
-11.5
-7.3
-7.9
-8.0
2004
16.3
47.2
36.5
8.3
58
Change
in10
y.∗
-22.6
-17.5
-16.6
-17.3
1994
14.0
52.0
34.0
5.7
49
Init
ialPer
cent
25.5
31.2
43.3
100.0
Inte
rm
ed-
1999
16.8
43.8
39.4
4.7
21
Change
in6
y.∗
-26.2
-22.6
-9.6
-17.9
iate
2004
19.4
38.0
42.6
3.9
01
Change
in10
y.∗
-43.2
-37.9
-21.5
-32.1
1994
14.0
52.0
34.0
18.7
69
Init
ialPer
cent
16.1
30.1
54.2
100.0
Tota
l1999
16.0
47.5
36.5
16.7
27
Change
in6
y.∗
-17.6
-12.6
-7.9
-10.9
2004
17.2
44.8
38.0
14.7
73
Change
in10
y.∗
-30.4
-24.0
-17.1
-21.3
∗R
efer
sto
%ch
ange
infa
rmer
s’num
ber
sper
gro
up
and
not
change
inper
centa
ges
.
352 T. KIZOS AND I. SPILANIS
The sensitivity analysis of the farmers’ dynamics model reveals thatthe succession procedure and the coefficients involved in it (new farmersnumber, succession rate and age) are the factors that alter the results ofthe simulation significantly. The coefficients used for the classificationof farmers into groups are also affecting the results. What is moreimportant, they need refreshment with empirical data after relativelyshort periods, although they are considered constant here and so placerestraints on the number of years the simulations last.
Rural development measures. The evaluation of the impact of ruraldevelopment measures was one of the main targets in developing themodel presented. Although a ‘straightforward’ evaluation isn’t pro-duced by the model, the descriptives of farmers’ and farming systemsdynamics that were produced is telling and could serve as qualitativeevaluation of the measures, some of which namely Young Farm-ers Scheme, Early Retirement Scheme and Investment in AgriculturalHoldings Scheme played an extensive part in the model.
The main result of the simulations appears to be that the targeting ofthe measures should be reexamined, as their addressing to professionalfarmers only raises two important issues: First, the simulations revealthat the professionals are the group of farmers expected to decrease inactual numbers more rapidly than other groups and so the tank of pos-sible beneficiaries will be reduced significantly, causing the measuresto fall short on expected participation. It should be noted here thatprofessionals that did participate in one or more of the Measures de-clare that they are satisfied with their incomes or investments that theycould not undertake otherwise (semi-professionals and hobby farmersdeclared that they would participate if they were allowed to). Sec-ond, farming practices reveal that semi-professionals and retired/hobbyfarmers are the ones that use more extensive practices than the profes-sionals, as a certain amount of money is guaranteed from off-agricultureincomes and extensification is preferred over intensification (for foodsafety and cultural heritage reasons). Moreover, hobby/retired farm-ers and semi-professionals are ‘here to stay’ and the simulations revealthat their proportion in the total farmers’ population is expected toincrease, leaving matters of justice unanswered, as Rural DevelopmentMeasures addressed to farmers will eventually ‘ignore’ a significant partof the population they are supposed to serve. Moreover, hobby/retired
THE TRANSFORMATION OF LANDSCAPE 353
and semi-professionals are the ones that undertake investment with-out subsidies that actually maintain or improve landscape quality insymbolic/aesthetic and ecological terms, while the professionals sub-sidized by Rural Development Measures choose to invest on intensifi-cation only. This does not imply of course that Rural DevelopmentMeasures should ‘open’ to all farmers in their present form, but thatsome alterations are necessary in order to keep up with current trans-formations in rural societies, farmer attitudes and farming practices tomake the Measures work better towards sustainable rural developmentand landscape maintenance.
Conclusion. This paper had two overall targets: to present thegeneral concepts behind a model for estimating social and policyimpacts on the agricultural landscape and test its applicability in thecase of CAP’s Rural Development Measures in Lesvos, an LFA island,and second to shed some light on the current change of rural societiesand their implications to landscape quality in light of the contemporaryEuropean rural policy setting.
For the first target, it appears that the conceptual model developedcan be used for the understanding of farming systems transformationand the interactions between societies, nature and public policies up toa certain extent. Its non-dynamic ‘nature’ in the areas of landscapequality processes, land use changes and farmer succession requirefurther elaboration before it can be regarded as a complete dynamictool (along with systematic data collection). But the results of thesimulations seem to indicate that the model works to a certain degreeand the application in Lesvos’ agricultural landscapes proves it.
One thing that should be noted though is that, although the gen-eral concept of using agricultural landscapes as the proper spatial unitfor the estimation of rural development policy impacts through farmerpractices is applicable, different temporal scales between the changeof quality of landscape characteristics and rural development measuresmake the actual ‘translation’ of the general concept to numerical ap-plication difficult. This scale difference raises compatibility issues inmore than one aspect: First between the interactions and the assump-tions for farmer population and landscape quality that are developedhere and make up the ‘core’ of the landscape transformations model.Second, they raise a more general issue of the possibility of producing
354 T. KIZOS AND I. SPILANIS
a model with enough predictive potential to capture landscape qualitychange at its ‘proper’ temporal scale while at the same time describethe complex procedures that form farmer succession and farmers dy-namics while evaluating rural development measures. Although theinteractions between the three dimensions are obvious and undeniable,landscape quality change refers to decades, farmer dynamics to sev-eral years and rural development measures to a few years. Even if thetemporal scales between the two former could easily be matched, theoverall temporal scales differ significantly. Nevertheless, the model usedhere proves that up to a point this bridging is manageable but requiresfurther elaboration.
Regarding the second target, the extensive empirical work conductedwith Lesvos’ farmers provided some valid information on their prac-tices, information that cannot be acquired through official data. Thatinformation has proved useful in interpreting the simulation resultsand arriving at some important conclusions about farmers’ attitudesand practices. The main interpretive tool used here, farmers’ groups,has proved valuable and managed to shed some light on the complextransformation of current rural societies and the ways they respond topolicy initiatives, and also it has provided significant information onthe mechanisms of Policy Schemes implementation and how they couldbe improved in the EU policy setting.
ENDNOTES
1. Forman and Godron [1986], Pignatti [1983], Poudevigne and Alard [1997]
2. Wascher [1999], Turner [1990], Haber [1990], Jongman [1999], Forman andGodron [1986], Forman [1998], Duhme and Pauleit [1998], Farina [1995], Alardand Poudevigne [1999], MacGarigal and Marks [1995], ECNC [1996], CEC [2000],Hoffman [2000], EEA [2001], Wascher [2000], Wascher, Mugica and Gulinck [1999],Lee, Elton and Thompson [1999], Bird, Taylor and Brewer [2000], Palmer andLankhorst [1998], Deffontaines, Thenail and Baudry [1995], Ihse [1995], Solon [1995],Baudry et al. [2000].
3. Rackham and Moody [1996], Papadimitriou [1998], Jongman [1999], Hooper
[1977], Forman [1998], Gropalli [1993].
THE TRANSFORMATION OF LANDSCAPE 355
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