Skip Navigation LinksResearch Concept

​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​Research Concept

​Landscape research is based on numerous studies of single processes at single sites, scales, and during certain periods depending​​ from each other. These findings are condensed in terms of general inferences that could be transferred to other regions, scales, and time periods, often via implementation in respective models. However, that approach systematically falls short of grasping unexpected relationships and feedbacks between the realms of different disciplines.

​Revealing complex feedbacks between different "landscape elements" requires comprehe​nsive data sets that describe various aspects of the same region in a coordinated way. This applies even for data acquisition of very different disciplines in order to enable detecting unexpected relationships. Only then the study region can be characterized in a high-dimensional phase-space (cf., Lischeid et al. 2016), i.e., yielding a comprehensive data cube, that could be analysed with powerful modern data mining approaches.

​To that end, landscape research in the AgroScapeLab Quillow is based on four major pillars:

I. MonitoringShow text

II. Process studiesShow text

III. ​Landscape experimentsShow text

IV. ​Modelling and integrated data analysisShow text

​Key components of the current ASLQ strategy include 

  • (i) manipulative landscape experiments, 
  • (ii) a platform to develop and test Agriculture 4.0 technologies (e.g., robotics, tractor-based sensor networks, geophysics, etc.), and 
  • (iii) the development of a landscape research approach by bringing together comprehensive datasets from multiple disciplines. 

Large heterogeneous datasets (e.g., from satellite and drone remote sensing, tractor-mounted sensors, bat recorders, isotopes, etc.) will be combined to estimate, with high spatial coverage and resolution, the effects of pesticide-free management in the surrounding landscape. Analysis of such datasets requires advanced data mining approaches that consider nonlinearity, nonstationarity, multi-causality, and the spatial dependence in landscape processes. In this way, biodiversity, agricultural, and biogeochemical research can be better interconnected. The ASLQ provides an ideal platform for integrated model development and validation. Urgent and fundamental topics, such as agricultural production in times of climate change and corresponding impacts on the environment, can be addressed following a holistic research approach.

Two women are sampling phytopathogenic fungi in the field
Sampling of phytopa​thogenic fungi in the field. © ​Marina Müller
Technical equipment of experimental site of project Volcorn.
Experimental site of project Volcorn.​​ © Gernot Verch
Gas measurement hoods at the Dedelow research station
Gas measurement hoods at the Dedelow research station. © Marten Schmidt

References used in text