Description of the RI

The research infrastructure enables us to create environmental conditions that are projected to happen toward the end of the century. The soil-plant system is monitored with a large number of different type of sensors. A data warehouse is developed from the collected data and a process-based model is created using state-of-the-art ICT tools. Owing to the continuous improvements, the model is capable of simulating the soil-plant system more and more accurately, and supporting climate change mitigation.

Major components of the RI:

1. Free Atmospheric CO2 Enrichment (FACE) experiment: in the 18 m diameter rings, using environmental sensor data, a computer controls the CO2 concentrations and maintains the elevated level (600 ppm) that is expected at the end of the century.
2. Twelve weight-lysimeter columns: 2 m height, undisturbed soil columns with environmental sensors inserted at various depths.
3. Two eddy-covariance stations, designed for the parallel measurement of (CO2, N2O, etc.) gas fluxes between the vegetation and the atmosphere as well as of micro-meteorological variables.
4. A rain shelter with retractable roof and a built-in irrigation system. 5) Data warehouse that integrates the data collected with the RI as well as data from freely accessible data sources (E-OBS, SoilGrids, HCSO, etc.).
5. Simulation agro-ecosystem model and a web-based decision support system.

Activities and Services

With the help of the FACE experiment, the impact of elevated air CO2 on the production of crops is investigated at different fertilization levels. Lysimeters are used for monitoring evapotranspiration and energy and matter flow in the soil in order to better understand the processes leading to water stress during droughts. The eddy-covariance stations are monitoring the ecosystem level energy and matter flows enabling the observation based determination of carbon sequestration of grasslands and croplands. Under the rain shelter, controlled drought stress circumstances are generated and the impact of stress length and intensity on yield quantity and quality is investigated. The continuously expanding data warehouse enables the researchers of remote scientific fields to work on reaching common goals. Using supervised and non-supervised machine learning methods computational modules are developed that describe the various processes of the agro-ecosystem. By connecting the modules, a simulation agro-ecosystem model is created. Using the advantageous features of various programming languages (JavaScript, C, R, Python, Delphi), an interface is also created to facilitate seamless data flow between the modules as well as simple communication with the users. A platform independent decision support system (DSS) is created to promote food security and food safety, the expansion of digital agriculture, the sustainable environment- and climate-smart resource management. The DSS contributes to a more competitive agricultural sector, to the mitigation of environmental pollution as well as of the negative impacts of climate change.