Information on funding phase II (2025–2028)

DAKIS (Digital Agricultural Knowledge and Information System) is one of Europe’s most important research projects for the development of a digital decision support system for knowledge-based and sustainable agriculture. Based on models and new monitoring systems such as drones and satellites, data was generated and combined in software that digitally supports farmers in their daily decisions and in the implementation of agri-environmental measures. The current DAKIS prototype from the first funding phase (2019-2024) is an innovation because it combines the complex and diverse goals of agriculture. The focus is on environmental and climate protection, in particular the improvement of ecosystem services such as climate resilience, yield, water, soil and flood protection, and the preservation of biodiversity, while food security and stable incomes are to be ensured through resource efficiency and financial security for farms under practical conditions. The aim of DAKIS2, the planned second funding phase starting in 2025, is to develop an automated decision support system that integrates ecosystem services and biodiversity as management objectives into agricultural land use at the landscape level for the federal states of Brandenburg and Bavaria. Advisory and political actors will play a key role here alongside agricultural businesses.

Software development and its integration into the scientific process is carried out through close networking between five sub-projects in which nine project partners from various institutes work closely together, including the Leibniz Institute for Agricultural Engineering and Bioeconomy, the University of Bonn, the Jülich Research Centre, the Bavarian State Research Centre for Agriculture, the German Research Centre for Artificial Intelligence, Osnabrück University of Applied Sciences, Eberswalde University for Sustainable Development and the Fraunhofer Institute for Systems and Innovation Research. In the first sub-project, so-called “use cases” are being developed that combine different objectives. These are concrete applications for agri-environmental measures that promote a small-scale, diverse and thus resilient agricultural landscape. Measures currently in focus include hedges, flower strips and beetle banks. By collecting and combining various data, suitable locations for such measures are to be identified and integrated into agricultural operations. At the same time, ecological and economic synergies are to be generated, measured and remunerated in a results-oriented manner. An important and new goal in the development of use cases is their scalability. Calculation models that were only implemented in the landscape window of four pilot regions in Brandenburg and Bavaria in the first project phase are to be applicable across regions. This is crucial in order to communicate theoretical approaches to practitioners in an understandable way and to consolidate the results of the project in the long term and sustainably. In the second sub-project, the use cases will therefore be scaled across the entire state. In addition to farmers as the original users of the software, this will generate recommendations for action at various levels, e.g. for water catchment areas and flood protection measures, or at the state level for political recommendations for action or advisory bodies such as landscape conservation associations and nature conservation associations. In the third sub-project, the use cases will be linked in a robust software architecture to the DAKIS2 application, in which data and recommendations for action can be determined, recorded and updated in real time in order to make them available to the user via an intuitive user interface. The user interface will be developed in the fourth sub-project together with decision-makers from agriculture, consulting and politics in order to find a direct path to practical implementation. Agriculture Incentive systems will be determined by new evaluation approaches in the fifth sub-project. Among other things, technical solutions for monitoring biodiversity will be developed here. Sensors and artificial intelligence will be integrated to record indicators and analyse data automatically.

The aim of SP1 is to develop a comprehensive concept for data collection and analysis that enables a holistic understanding of agricultural systems by incorporating data from different spatial and temporal scales. By integrating different data sources, such as remote sensing data, field observations and socio-economic data, a holistic view of the landscape and its interactions is made possible at the landscape window. To this end, SP1.1 identifies information and data flows based on use cases, and SP1.2 defines indicators for ecosystem services and biodiversity at the landscape level.

The aim of SP2 is to integrate comprehensive data and model results and to generate management proposals (with a focus on selected use cases from SP1) at the level of the federal states of Brandenburg and Bavaria. Results and data sets (SP1) are aggregated from the field and sub-area level to the farm and municipal levels and adapted to the requirements of interest groups (farmers, consultants, politicians). The integration of the various ÖSL layers (SP1) is carried out with the aim of identifying stakeholder-tailored decision support. Furthermore, methods are being developed to facilitate the prioritisation of measures in the event of conflicting objectives. For this purpose, a rule-based weighting system is being developed that takes into account the ranking of individual ÖSL and biodiversity indicators (from SP1) by individual user groups. The use cases are validated by cooperation partners at two test sites in Brandenburg and Bavaria under real conditions with regard to their practical suitability and effectiveness.

Sub-project 3 aims to develop a robust software architecture for DAKIS-pro that enables the implementation of various use cases. The architecture is designed to be highly adaptable and to allow for flexible extensions and integration with existing farm management and information systems (FMIS). Figure 1 shows a visual representation of the architecture.

SP4 combines the design, technical development and validation of the DAKIS2 user interface with a particular focus on the intuitive usability of the system for different user groups. To this end, possible operating concepts are discussed and adapted with the respective technical experts of the consortium in order to be subsequently integrated into the prototypes of the DAKIS2 application. This process is pursued iteratively for each of the use cases planned in SP1 as well as for the interaction of the components in an overall system.

In order to validate the usability of the system, the prototype user interface will be presented to interested farmers and agricultural advisors in a workshop. Here, comprehensibility, completeness, handling and integrability into operational processes will be examined and evaluated by the participants. The data collected will be directly incorporated into further development. In addition to the primary stakeholders, additional actors from the upstream and downstream value chain will be involved in the further course of the project. These stakeholders from the agricultural and food sector will also be invited to test DAKIS2 in a workshop format and identify possible additional functionalities. The aim here is to make DAKIS2 usable for a larger group of users. Finally, DAKIS2 will be evaluated together with the stakeholders with regard to its potential impact on sustainability criteria (erosion control, water protection, biodiversity, etc.).

The implemented and validated operating concepts and user interfaces are intended to serve as a best practice concept for seamless integration into an FMIS and will be made available together with the technical components of the system.

Sub-project 5 aims to integrate information on incentive systems for the provision of public goods into DAKIS2. This includes both existing support measures (including the CAP) and newly developed payment systems. This includes the integration of public support programmes and private initiatives into economic optimisation, as well as the integration of control mechanisms, e.g. through the evaluation of new sensor technology, to justify payments. To this end, gaps in data usage, for example, are identified and proposed solutions are developed. Based on the need to record ecosystem services and biodiversity, adjustments are made to sensors and their data is recorded and evaluated.

The project has high innovation potential and economic relevance. The DAKIS2 decision support system contributes to more sustainable land use by highlighting new perspectives and opportunities for capitalising on ecosystem services and biodiversity. Specifically, the system provides location-specific and ecologically and economically evaluated agri-environmental measures as well as data on their implementation, which can be integrated into existing land use on a location-specific basis. In some cases, interfaces to the digital application system for obtaining subsidies are also possible. This has corresponding positive effects for agriculture, e.g. the reduction of bureaucracy and planning effort, but also for environmental and agricultural policy. At the same time, the efficiency of measures is ensured, which can be implemented precisely with the support of digital technologies such as modern guidance systems or field robots. By regularly collecting user feedback, the system is to be continuously developed and established among the potential target groups. The modular structure of DAKIS2 enables it to be integrated into private-sector tools such as farm management systems on the one hand, and into publicly available digital applications on the other, where it can be used seamlessly. Location-specific management measures that increase both productivity and ecosystem services can be identified and made available. In the medium term, agri-environmental and climate measures as well as market-based measures can be developed from DAKIS2 to enhance ecosystem services and biodiversity. Linking data into a complex decision support system is the biggest challenge for the project. The anticipated knowledge gain is of great importance both from a scientific perspective and for practical application. The project will be sustained through a large number of scientific contributions, innovative concepts, successful integration into established tools (FMIS) and positive effects on the sustainable transformation of the agricultural landscape and policy with added value for the environment and society.