A. Jellema

On the contribution of modelling to multifunctional agriculture: Learning from comparisons

In this paper a set of criteria is proposed for the evaluation of the potential contribution of modelling tools to strengthening the multifunctionality of agriculture. The four main areas of evaluation are (1) policy relevance, (2) the temporal resolution and scope, (3) the degree to which spatial and socio-institutional scales and heterogeneity are addressed and (4) the level of integration in the assessment of scientific dimensions and of the multiple functions of agriculture. The evaluative criteria are applied to the portfolio of modelling approaches developed and applied in a joint project of the French research institute INRA and the Dutch Wageningen University & Research Centre. The CLUE-S model focuses on prediction of changes in multifunctional land-use at regional scale, given a set of predetermined scenarios or policy variants, e.g. for ex-ante policy assessment and initiation of discussions on regional development. The two other modelling approaches are complementary and aim to address multifunctional farming activities. The Landscape IMAGES framework generates a range of static images of possible but sometimes distant futures for multifunctional farming activities in a small region or landscape. It supports the exploration of trade-offs between financial returns from agriculture, landscape quality, nature conservation and restoration, and environmental quality. Co-Viability Analysis generates trajectories of states and farming decisions fulfilling a given set of ecological and productive constraints representing a desired and sustainable future. The three modelling approaches differ in their policy relevance, in the ways that spatial and socio-institutional scales are addressed and in their degree of explicitation of interaction between the various functions of agriculture, but jointly cover most of the desired capabilities for assessment of multifunctionality. Caveats were particularly identified in the integration of the socio-institutional dimension and the related heterogeneity. Although the model portfolio did not completely satisfy the demands of the set of evaluative criteria, it is concluded that, due to their complementarities, in combination the three models could significantly contribute to further development and strengthening of multifunctionality.

Landscape character assessment using region growing techniques in geographical information systems.

Landscape character can be defined as the presence, variety and arrangement of landscape features, which give a landscape a specific identity and make it stand out from surrounding landscapes. Landscape character contributes to the esthetical and perceptional value of an area, which is important for the development of non-production functions in the countryside as demanded by society. In this paper we present a new methodology for landscape character assessment using the pattern of landscape features as stored in a GIS to delineate, characterize and evaluate landscapes using a region growing algorithm. We have applied this methodology in a case study area in the north of The Netherlands and compared the results with a series of expert classifications of the study area. The results of the region growing algorithms were good and interpretable in relation to the underlying data. The resemblance between the expert classification and the classification based on the region growing results varied between 34% and 100% for the different landscape types. The differences between the two data sets can be explained in terms of input data and knowledge about the study area. The classification of the region growing algorithm was more consistent than the expert classification throughout the study area. The presented methodology for landscape character assessment is proposed as support for spatial planning processes and policy development for landscape conservation by providing a quantitative tool to analyze landscape patterns, to discriminate between the various landscapes in a study area and by elucidating features that are important for the identity of a region.