Introduction
Valuation/evaluation of rural social-ecological systems involving landscape-scale feedbacks between institutions, socio-economic aspects and the biophysical background requires a holistic approach that embraces several scientific disciplines (ecology, economics, social sciences, agronomy). Nevertheless, evaluation methods have been developed in specific disciplinary contexts and thus respond to different disciplinary research objectives. Consequently, it is difficult to rally consistent results from a wide range of fragmented studies based on different research traditions and analytical processes.
Available evaluation/valuation tools have been selected and tested in the case study areas according to their applicability to the analytical framework developed in the CLAIM project. The following figure outlines the relevance of the case studies in connection to the CLAIM framework.
Based on the empirical experience the following categories of methodological approaches have been applied:
- Landscape indicators
- Valuation methods for societal preferences and performances
- Simulation models
- System description, relationships and impact pathways
Landscape indicators
Indicators are measures or synthesis of measures that aims to interpret a complex phenomenon. Landscape indicators may focus the composition of land use patterns (e.g. richness/ diversity of crops or landscape elements) or man-made objects resulting from human activities (e.g. stone walls, shelterbelts, etc.). This group of indicators are usable for monitoring changes in land use planning, but they usually do not contain information about peoples’ preferences and societal demand.
Also, indicators of landscape may focus the natural potential of landscape to provide ES (e.g. m3 of water provision, ha of vegetation cover, N. of pollinating insects, etc.). This group can be related to societal preferences as long as indicators are explicitly recognised to represent landscape quality.
Finally, indicators may be related to Cultural services and the preference and attitudes of consumers toward a certain landscape (e.g. N. of elements with cultural heritage importance, km of trails for hiking, N. of beds in agritourisms, or indicators of stakeholders’ values etc.). Usually, this type of indicators are locally referred and difficult "upscale" because they are linked to local-scale culture and traditions.
Valuation methods for societal preferences and performances
Valuation methods for societal preferences and performances focus the societal preference for landscape services. This group includes monetary and non-monetary based methods depending on the output of the methods; and statistical/econometric techniques methods aimed at the description and measurement of socio-economic features through the explanation of behaviours and preferences or through landscape variables.
Monetary-based studies estimate monetary values by observing the actual behaviour of or estimate the willingness to pay based on answers to hypothetical market questions. Also, monetary valuations can be based on estimating the cost for substitutes (or avoid loss) of services (e.g. cost of water purification facilities can estimate the value of buffering areas like wetlands).
Non-monetary techniques include Multiple Criteria Analysis (MCA) which is used as a tool for the assessment of the relevance and the trade-offs between different objectives. MCA cover a wide range of techniques that may also provide the opportunity for stakeholders and community participation at different levels of the decision-making process.
Statistical and econometric techniques use observed data based on secondary sources or surveys. These can be used to predict quantities or to estimate relationships among variables. These methods can also include a spatio-temporal component that can account for specific correlations among data observed at different sites and/or at different times or be based on indicators. Values from a particular study site can also be extrapolated to a larger geographical extent (value transfer). However, this technique is not easily applicable for assessing landscape services, due to the local specificity of these services.
Simulation models
Modelling scope and approaches are very wide and include a huge variety of option and disciplinary perspectives, among which environmental modelling, bio-economic modelling, and the mix of the two. A widely used application of simulation models aims at understanding the consequences of agricultural policy and other drivers on agricultural economy and land uses.
In general, the representation of the mechanisms through which policy action (or other drivers) affect rural landscapes and economic values may be too complex to be presented by means of “hard” economic models only. In this case, mixed methods approaches, based on the mixed use of indicators, economic data and expert consultation can be particular appropriate to strike a balance between effective evaluation of complex systems and the high workload needed.
Economic approaches distinguish econometric-oriented models used for simulation, mathematical programming models, and agent-based models. Also, chain modelling instruments like the Bayesian Belief networks and the Analytical Network Process can simulate the cascade effect between landscape and competitiveness including different mechanism that influence the system.
System description, relationships and impact pathways
The description of the agricultural landscape system may require an overview of the structure of the relationships between actors (e.g. local institutions, farmers, etc.) and/or the trade-offs between different ecosystem services in relation to different land uses.
Similarly to the simulation models, these methods aim to reproduce a system by focusing on specific features and structures. While simulation models are usually grounded on economic or ecological theory, the main aim of this group of methods is to reach a deeper understanding of the system by means of a description of the system components and relationships (e.g. Bayesian Belief networks and agent-based models). This description may also be based on collection of local information and opinions through a participative approach (e.g. social network analysis).