1. Optimisation of rural land health: integrating multiple functions Burghard C. Meyer (1), R. Grabaum (2), D. Watson (3), D. Adams (3), A. Hood (3), A. Phillips (3) (1) Dortmund University of Technology, School of Spatial Planning, August-Schmidt-Straβe 10, 44227 Dortmund, Germany; (2) Olanis GmbH, Leipzig, Germany; (3) Department of Sustainability and Environment, Melbourne, Victoria, Australia; Contact: burghard.meyer@tu-dortmund.de Results UFZ - CENTRE FOR ENVIRONMENTAL RESEARCH LEIPZIG-HALLE Conclusion Optimisation could be done by using selected assessment results. We also state that it would be better to have a higher differentiation in the assessment data for a detailed and site specific optimisation. In our case results are suitable on a regional scale level. The second scenario shows better improvement for all the 3 functions compared with the first scenario. Therefore it makes sense to “play” with the optimisation restrictions, goals and weights (e.g. the amount of land use changes needed) to get a better solution. Introduction As the results of the Millennium Ecosystems Assessment has shown, there is an obvious and urgent need for integrated land use planning to help provide optimal outcomes for the health of rural landscapes. Land use problems in rural landscapes involve complex tradeoffs between multiple, and sometimes competing, objectives. These land use decision problems require new kinds of decision methodologies which can integrate these multiple objectives and provide optimal solutions. Method The MULBO (Multi-criteria Landscape Assessment and Optimisation) framework has been specifically developed to help guide these complex multiple objective decisions. The MULBO process includes the following steps (a) goal determination across multiple management criteria, (b) function analysis on the basis of GIS, (c) function assessment, (d) scenario formulation and (e) land use compromise optimisation to calculate land use scenarios. Investigation area In this current project MULBO has been trialed in the Lake Tyrrell Basin of Northern Victoria, Australia. Lake Tyrrell is situated within the Mallee region and covers an area of around 400,000 hectares. The Mallee is a semi-arid region which supports broad-scale cropping on sandy low nutrient soils, where traditional agriculture has had a high impact on native fauna and flora, and where naturally saline groundwater and wind erosion present significant threats to both agricultural production and biodiversity, when adapted farm management should be placed best. References Meyer, B.C. & R. Grabaum, 2008. MULBO - Model framework for multicritieria landscape assessment and optimisation – A support system for spatial land use decision. Landscape Research 33 No. 2: 155-179. Meyer, B.C., Phillips, A. & S. Annett, 2008. Optimising rural land health: from landscape policy to community land use decision making. Landscape Research 33 No. 2: 181-196. Grabaum, R. & B.C. Meyer, 1998. Multicriteria optimization of landscapes using GIS-based functional assessments. Landscape and Urban Planning 43 (1): 21-34. MULBO – Multi-criteria Assessment and Optimisation Optimal compromise (Scenario 2) (Scenario 2) Maximising the management practice Minimising the wind erosion risk Maximising the production output Optimal compromise (Scenario 1) Maximising the management practice Minimising the wind erosion risk Maximising the production output areasland use typetype of value restriction (% of area) allbest practiceSum value0 - 50 % allNative Vegetation Sum value10.0 - 10.1 % allsaltbushSum value4.9 - 5.0 % Optimisation scenarios For the optimisation two scenarios were selected. Both are dealing with a change of 10 % of current farming into natural vegetation and 5 % of current farming into saltbush. They differ in the change to best practice farming. Assessment of Landscape Functions Restrictions for scenario 1 Optimisation with scenario 1 areasland use typetype of value restriction (% of area) allbest practiceSum value9.9 - 20 % allNative vegetation Sum value10.0 - 10.1 % allsaltbushSum value4.9 - 5.0 % Optimisation with scenario 2 Restrictions for scenario 2 Goal function values scenario1 Goal function values scenario 2 Biodiversity function Management function Wind erosion risk Production function