RP1:
The Consortium built the AGRICORE ontology, which is an extension of the DCAT-AP 2.0 ontology, allowing a more comprehensive characterisation of agricultural data sources from the highest level (the catalogue to which they belong) to the lowest level (that of their individual variables).
The Consortium built the Agricultural Research Data Index Tool (ARDIT), which is a web-based application (with APIs to allow external calls) that allows the semantic search and indexing of useful datasets for agricultural research in general, and specifically for the building of AGRICORE synthetic populations.
Almost 300 datasets were characterised and included in ARDIT.
The format required for the storage of the data contained in the different datasets and the structures of the data warehouse (DWH) have been defined. The DWH stores all the data extracted from the datasets previously identified by ARDIT, as well as the rest of the derived datasets generated by AGRICORE, will be stored. The data extraction module (DEM) and data fusion module (DFM) have also been completed, including the algorithm that allows the construction of the Bayesian network (BN) used to assign values to the attributes of each agent during the synthetic population generation (SPG).
The architecture of the AGRICORE agents has been determined, including the skeleton of attributes of interest (classified in states, inputs, outputs and disturbances), and the optimization structures for the dynamic modelling of the agent are being finalised. These optimization structures determine the actions of each agent in the short term (agro-economic dimension) using Positive Mathematical Programming (PMP) and in the long term (financial structural dimension) using Model Predictive Control (MPC).
For each of the three use cases, those attributes of the agents that can be generated with existing data sources and those ones for which data gaps exist were identified. To fill the latter, participatory research tools were designed that should allow these gaps to be filled through survey campaigns.
The design of the AGRICORE graphical user interface (GUI) was validated by experts. The libraries needed for the visualisation of results have also been identified and packaged. The implementation of the Land Market Module (LMM) have been completed, which allows the auction-based local exchange of land between agents. The basis for obtaining sales prices for each of the commodity products produced by the agents has also been established through the Product Market Module (PMM). The Key Performance Indicators (KPIs) by which the environmental and ecosystem services impact will be measured have been selected and the equations necessary for their calculation have been implemented in the corresponding Impact Assessment Modules. The communication mechanisms that allow the exchange of information between the different modules using a customisable DAPR sidecar and the module that allows the connection of the ABM engine with external biophysical models have also been implemented.
RP2:
In this second reporting period, the design and planning of Ex-Post and Ex-Ante analyses for each of the three use cases continued. Open-sourcing of the software has been enabled through the choice of the collaborative development tool (GitLab) and the creation of modular repositories corresponding to each of the modules of the future tool.
The development of each of the elements composing the AGRICORE suite has advanced as planned (though with some delay).
RP3:
In the last stage of the project, the complexities arisen from the integration process lead to delays in the implementation. However, by the end of the project, the AGRICORE project managed to demonstrate the AGRICORE approach, and the individual developments completed within the project.
