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Contribution to ZALF research

For a sustainable use of landscapes, a profound understanding of the multitude of processes that interact in the landscape is a central prerequisite. These landscape processes are studied at ZALF in a variety of networked research activities, which generate large amounts of empirical data with varying degrees of uncertainty. These require extensive documentation as well as powerful methods of statistical analysis. The research platform "Data Analysis & Simulation" ensures the collection of meta-data, long-term storage of well-documented data and the creation of user-friendly interfaces for research. In addition, powerful methods for the analysis of high-dimensional, heterogeneous data sets of different temporal and spatial coverage are further developed and applied, with special focus on typical characteristics of such data sets, such as non-linearity, instationarity, spatial correlations and memory effects.

Hypotheses derived from the data analysis will be tested by means of models. Modelling is an established tool in many disciplines and the number and complexity of available models is constantly increasing. Last but not least, the complexity of landscape processes has led to a multitude of model approaches which have to be combined in newly developed model platforms to support integrated landscape research. But also beyond model development challenges arise which require a more in-depth scientific consideration: Methods of model calibration and validation, data assimilation techniques, data-driven modelling approaches as well as the behaviour of models and model ensembles at the limits of their application. In particular, models that act at the landscape level and integrate processes of different instances in order to describe interactions at the interface of nature, economy and society have not yet been fully researched. The research platform "Data Analysis & Simulation" closes these gaps and provides a framework for the integration of different disciplines and modelling approaches for the understanding of the agricultural landscape function.


Working groups


Dimensionality Assessment and Reduction

Image of the WG Data Dimensionality 

Despite the large number of potential observables, landscapes are regarded as highly constrained systems with rather low effective degrees of freedom. The working group will quantify the number of effective degrees of freedom of large environmental datasets (e.g. via correlation dimension) and subsequently determine the dominating processes using methods of dimensionality reduction (e.g. principal component analysis, isometric feature mapping, etc.). To this end, the working group will use methods that explicitly consider the challenges of high-dimensional, heterogeneous datasets with different temporal and spatial coverage and typical characteristics like non-linearity, non-stationarity, spatial correlation, and memory effects. To date, these methods are rarely applied in most disciplines of environmental or related socio-economic science.

Contact: Prof. Dr. Gunnar Lischeid


Landscape Modelling

Image of the WG Integrated Landscape Modelling

This newly established working group develops the framework for the coupling of fundamentally different modelling approaches into an integrate landscape simulation. Initially, the working group will concentrate on a concept for the integration of process-based simulation models for cropland, grassland and forest ecosystems into a single modelling framework, with a special focus on transition zones between these ecosystems and the observed abiotic processes in these transition zones. In addition, remote sensing data is to be used to parameterise, initialise and drive one-dimensional process-based and other types of two- to three-dimensional models at the landscape scale. This includes models for socioeconomic processes.

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Contact: Prof. Dr. Claas Nendel


Ecosystem Modelling

Image of the WG Ecosystem Modelling 

The working group deals with point- and grid-based simulations with respect to topics like food security, climate protection and resource management. A focus lies on the numerous process interactions between soil and plants in managed agricultural systems and the effects their representation in models has on simulations. The working group builds on existing process-based agroecosystem, forest ecosystem and hydrological models, which form the core of future model development. Model improvement will focus on the reduction of uncertainties associated with model structure and parameters as well as on the expansion of the represented processes. To this end, the working group cooperates with different national and international networks and projects, such as e.g. MACSUR and AgMIP.

Contact: Prof. Dr. Claas Nendel


Simulation Methods and Data-driven Models

Image of the WG Simulation Methods 

This methodologically oriented working group deals with the analysis of the data-model-data loop in agricultural landscape research. The overarching task is to test the suitability of established methods from neighbouring disciplines and sectors for the simulation of interrelations and patterns in landscapes and the deduction of new insights from their application. To this end, the working group uses methods such as machine learning, deep learning, cluster computing, fuzzy models as well as Bayesian modelling.

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Contact: Dr. Ralf Wieland


Artificial Intelligence

Image of the WG Simulation Methods 

The working group is committed to developing and applying cutting-edge data-driven techniques such as interpretable machine learning and deep learning for a broad range of agriculture-related problems. We are interested in exploring the potential of artificial intelligence for improving agricultural management, testing novel hypotheses that cannot be tested with conventional statistical methods, and discovering unexpected patterns from uniquely combined datasets across scales and sectors. We wish to offer AI-powered, Nature-based Solutions for global sustainability challenges.

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Contact: Prof. Dr. Masahiro Ryo


Data Infrastructures (Service)

Image of the WG Data Infrastructures (Service)  

This service working group develops and operates the technical infrastructure for the provision and publication of (research) data. Together with the working group “Research Data Management”, various services are offered to meet the needs of users for research data management at ZALF. Among other things, the working group operates the infrastructures necessary for data publication (e.g. BonaRes repository, tools for data transfer and metadata maintenance). In addition, solutions for ZALF-internal (geo)data provision (Portal for ArcGIS) are offered, but also web services required for various work steps in the context of data provision (e.g.helpdesk ticket system, CRM, DOI registration).​

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Contact: Dr. Xenia Specka


Research Data Management (Service)

Image of the WG Research Data Management (Service)  

This service working group, together with the working group "Data Infrastructures" (DIS), supports the research data management of ZALF. It specializes in the acquisition and publication of soil and agricultural science (geo)data and increases their (added) value with individual solutions. The working group is the contact partner for all ZALF researchers, and also offers its services to the scientific community of soil and agricult​ural sciences. WG RDM supports data management plans, offers training and consulting, and processes (geo)data requests. In doing so, services provided by WG DIS are utilized. The wishes and requirements of researchers regarding research data management are incorporated into the further development of both service groups. WG RDM serves as a link between the technical infrastructure (WG DIS) and the respective users.​

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Contact: Dr. Nikolai Svoboda


Model & Simulation Infrastructure (Service)

Image of the WG Model & Simulation Infrastructure (Service) 

This working group is engaged in the development of a sustainable base for the modelling and simulation activities of the ZALF working groups, particularly the Research Platform “Data Analysis & Simulation”. Initially, the working group is concerned with the consolidation of the different models used at ZALF. The goal is to create a common IT infrastructure, which can run these models quickly and efficiently, but at the same time is easily useable by the ZALF scientists. A special focus is on automated machine-to-machine communication for the models. Building upon this work, the WG will implement the technological foundation to integrate inter-methodological and inter-disciplinary models, aiming at having socio-economic and biophysical models running side by side – and interacting with each other – within the ZALF infrastructure.

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Contact: Michael Berg-Mohnicke


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