Projects also applied interdisciplinarity at the data level, by modelling social, economic, ecological, environmental and hydrological factors from input data representing different factors or perspectives to a problem.

In some instances this was done by modelling a combination of social and environmental data, either using linear or tabular modelling, or spatial modelling in a geographical information system. Other projects incorporated local knowledge - qualitative or quantitative - as input data into models.

Local or stakeholder knowledge was often used to fine-tune models and make them better fit the reality.


Modelling the impacts of the Water Framework Directive

This project brings together hydrology and economics to examine the physical impacts of the EU Water Framework Directive upon rivers and how the changes in land use needed to achieve a reduction in pollutants in water are likely to impact upon already fragile farming communities.

The project developed a methodology for integrated modelling of the relationship between rural land use (and consequent farm incomes) and water quality (including diffuse and point sources of nutrients, pesticides and faecal matter and consequent ecological status). This methodology combines econometric statistical and linear programming analysis of a large cross-section and time series panel database of farm activity with hydrological models linking land use with consequent water quality.

The model is being used to provide policy guidance on strategies for implementing the Water Framework Directive within the context of ongoing Common Agricultural Policy reforms. Particular attention is given to the impact on land use, farm incomes and the rural economy of alternate policy options.

The project is also assessing economic values for the social benefits that may be generated by implementation of the Water Framework Directive and testing the transferability of these benefits assessments.

The project shows the impact of Water Framework Directive policy changes that aim to reduce diffuse pollution on farm activities, farm income and water quality. A hydrological model at catchment level simulates how pollutants from fertilisers or pesticides leach into water and how this affects aquatic biology. An econometric shows land use changes, farm activities and incomes for farms in water catchment areas, as a result of policy changes that aim to reduce diffuse pollution by reducing inorganic fertiliser application, livestock rates or conversion from arable to ungrazed grassland.

These models can be used to predict land use changes in response to shifts in environmental, policy, or market forces; and to assess how such changes in agricultural land use are likely to affect levels of diffuse pollution to rivers.

Modelling the impacts of the Water Framework Directive
Ian Bateman, University of East Anglia



Simulating the present from the past

The threat to biodiversity and rural landscapes from tree disease epidemics is greater today than ever before.

Expanding international trade in plants, together with increased passenger movements, has led to the entry of various invasive pathogens into the UK in recent years. Many of these have the capacity to kill native trees in very large numbers. Tree disease management is complicated by scientific uncertainty, the presence of large numbers of stakeholders, the difficulty of establishing clear lines of institutional responsibility and a clash of public and private interests over who should pay.

However, tree disease epidemics are not new and policymakers have various historical precedents on which they can draw in seeking to avoid past mistakes and lengthen institutional memory. In the UK, the Dutch Elm Disease epidemic of the 1970 killed over 30 million trees. For scientists and public alike, it is probably one of the most dramatic domestic environmental events in their lifetimes. This project has sought to integrate historical analysis into the heart of the current biosecurity debate by comparing the Dutch Elm Disease epidemic with the Sudden Oak Death outbreak that is currently unfolding in the UK.

For Dutch Elm Disease to be a rich source of interdisciplinary and policy-relevant knowledge to understand present day threats like Sudden Oak Death, there needs to be an integrated understanding of the biological and socio-economic aspects of these different disease problems.

This was achieved through a linked historical and contemporary analysis that began with a biophysical and socio-economic reconstruction of the Dutch Elm outbreak. Modelling work, directly informed by insights from archival research and interviews with key actors involved in the attempted management of the outbreak at the time, was undertaken to simulate the origins, spread and eventual trajectory of the disease. This allowed the researchers to identify key events and phases of the outbreak, drawing on an interdisciplinary understanding of the interaction between the biology, epidemiology and economics of the epidemic.

Further work explored the sensitivity of the outbreak to different courses of action. Different disciplines were brought together in order to arrive at a full understanding of the way in which interacting biological and institutional factors shaped the course of the disease and its outcomes.

This fresh analysis of Dutch Elm Disease sheds important light on the current Sudden Oak Death outbreak and explains what policymakers are encountering in their attempts to contain it. Despite biological differences between the two disease systems, the research demonstrates the difficulties in both cases of early detection and the speed with which outbreaks become uncontainable once established in the wider environment. But it also points to an enduring lack of public awareness of the underlying drivers of disease risk and need for a broader debate amongst stakeholders of the conflicts between freer trade in horticultural products and effective biosecurity.

Lessons from Dutch Elm Disease in assessing the threat from Sudden Oak Death
Clive Potter, Imperial College London



Modelling and measuring rural inequality

Achieving sustainable rural development depends on the distribution of social, economic and environmental goods and services that are needed to maintain and reinforce the vitality of rural areas.

Inequality in such goods and services has important implications for individuals or groups of people experiencing it, but also for society as a whole. In urban areas, poor environments are associated frequently with deprivation and social exclusion, but the relationship between environment and deprivation in rural areas is less well understood.

Research that can inform evidence-based rural policy-making requires readily accessible data, from both social and natural scientific disciplines, about the distribution and inequality of social, economic and environmental conditions. The researchers quantified and measured such inequalities throughout rural England by developing a high resolution spatial dataset containing: the natural and constructed physical components of rural areas; the qualities and character of places and people in the countryside; information about living and working there; and the political and economic context. After identifying those areas where inequalities were greatest they investigated how rural residents experience the kinds of inequalities identified and which inequalities they perceive as inequitable.

One of the challenges was the apparent incompatibility of spatial data collected by different academic disciplines, due to the differing scale and nature of data collection and the phenomena studied. This requires a critical understanding of data form and distribution.

Social data typically correspond to administrative or political areas, which are often subject to temporal change and not related to the landscape. Environmental data such as land cover or biodiversity correspond to ecological zones and are frequently organised as grids. For farmed areas, the farmers to whom socio-economic data are attached are located spatially at points that may be some distance from their land holdings, the areas associated with environmental data. Also underlying distributions pose challenges, like irregular distributions of land cover, land use or settlement, or continuous distributions for air pollution. Variation also depends on how data are collected and organised. The combined expertise of researchers with different disciplinary backgrounds was essential in getting to grips with such challenges.

The team selected as its basic spatial unit the Lower Super Output Areas. These are areas with consistent population size (average 1,500 residents) but highly variable in size, designed for the collection and publication of small area statistics for the 2001 Census of Population in the UK. They mapped onto those areas a range of data related to economic activity, income and wealth, health and well-being, and ecology, land and the environment.

Social and environmental inequalities in rural areas
Meg Huby, University of York