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Beschreibung:
Agricultural ecosystems are anthropogenically highly transformed ecosystems, mainly designed to maximise the delivery of provisioning ecosystem services (ES) such as food, material and fuel, often at the expense of other ES. Especially, conventional agriculture and agricultural landscape simplification have become major causes of climate change, ecosystem degradation and biodiversity loss. At the same time, the production of provisioning services depends on other, mainly regulating, ES. In the long-term, the viability of agricultural ecosystems and the delivery of provisioning ES rely on more sustainable farming practices and the conservation of ES and biodiversity. This calls for a shift in the agricultural production paradigm, towards more multifunctional and sustainable agricultural landscapes. Spatially explicit assessments of ES are key components in supporting the shift towards sustainable land use management: they inform on how and where land use decisions can affect ecosystems, on potential trade-offs between the delivery of different ES and help to design targeted ES conservation measures. Understanding the distribution patterns and the main drivers influencing the delivery of ES is needed to determine where land use management measures can be improved to maximise the delivery of (specific) ES. Specifically, spatial information on ES can assist economical decisions underlying agricultural practices: for instance, higher pollination and natural pest control ES potentials can increase crop yields and save resources. The central question of this thesis is to assess how different ES assessment methods influence the predictions of ES supply potential, aiming to find the adapted level of information needed for an ES assessment at the local scale, in an agricultural landscape. To address this research question, several ES mapping and assessment methods, using simple (tier 1) to more complex (tiers 2 and 3) approaches, were developed and applied to a case study area in northern Germany. Additionally, this work ...