AgroScapeLab Quillow (ZALF) - Germany
AgroScapeLab Quillow (ZALF)
The site covers the whole catchment of the small river “Quillow” and it is called “AgroScapeLab Quillow”. It is located in the north-east lowlands (Uckermark) which is about 90 km North of Berlin and has an area of about 160 km2. It’s a hummocky landscape characterized by gently rolling hills which are the results of glaciation during the Pleistocene. Unconsolidated sediments in a complex setting dominate. The soil pattern of the Quillow catchment is related to topography and the heterogeneity of Pleistocenic deposits. Albic Luvisols, Calcaric Regosols, Calcic Luvisols and Gleyic-Colluvic Regosols can be found. Another result of the ice age forces are the kettle holes (little ponds) which developed in drainless depressions. 74 % of the land is in agricultural use. Small forest patches can be found in the western and southwestern part of the catchment, grassland more in the eastern lowlands. In general the area is only sparsely populated. The ZALF research platform is located in Dedelow
General Characteristics, Purpose, History
Monitoring activities in the Quillow region started in the 1990s. The complex interplay of various landscape elements, integrating methods and expertise from different scientific disciplines are the focus of research in the Quillow region to increase knowledge on various long-term effects. The ZALF activities have been subsequently extended and continue doing so, covering aspects of agronomy, soil science, hydrology, biology and microbiology, socio-economics etc. at different temporal and spatial scales. Four pillars carry the landscape research in the AgroScapeLab Quillow: (1) Monitoring: meteorological, hydrological, and biogeochemical data, land management and yield data, surveys of weeds, plant infestations and mycotoxins, bird surveys. (2) Process studies: Single process studies as well as major projects with numerous partners (e.g., CarboZAF, SoilCan, Landscales, BioMove, BIBS) focusing on single aspects of landscape processes. Results from these studies help to optimize monitoring and modelling activities. (3) Landscape experiments like the CarboZALF project can be planned and interpreted. (4) Modelling and integrated data analysis: Long-term and consistent multivariate data sets are an ideal basis for developing and testing numerical models as well as for application and further development of modern data mining approaches in order to reveal complex interdependencies in landscapes.
Affiliation and Network Specific Information