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Agent-based support tool for the development of agriculture policies

Periodic Reporting for period 1 - AGRICORE (Agent-based support tool for the development of agriculture policies)

Reporting period: 2019-09-01 to 2021-02-28

Common Agricultural Policy is one of the EU's most central policies and aims to ensure that food is high quality, safe, and affordable for the consumer. The design of agricultural policies is a long process involving the assessment of various local and global parameters. Therefore, from early on, agricultural models have been used to describe and interpret key aspects of agricultural policy design, assisting in the CAP instruments implementation.
The most widely used models to date compute the equilibrium between aggregate production and aggregate demand to calculate the impact on farmers' profitability and the price paid by consumers. The problem with these models is that, when aggregating, they do not take into account the heterogeneity of European farms, so that some measures that are shown to be beneficial at the aggregate level in the model may be harmful at the particular level for some types of producers or particular regions.
The AGRICORE project aims to offer a solution to this problem through the incorporation of AI, Big Data and Cloud Computing techniques to build an Agent-based Model, by which the real individual farms are simulated in the form of individual agents, allowing the impact of different policy alternatives to be tested at a much higher level of resolution (down to types of farms at a regional level).

Rural areas make up more than 77% of the EU territory, with about half of its population. There are approximately 12 million full-time farmers in the EU and the food industry and agricultural sector account for more than 6% of the EU's total GDP, providing jobs for 46 million people. The CAP currently uses almost 40% of the common budget of the EU (more than 50 billion € per year). It is therefore almost an obligation for the EU to ensure that future CAP reforms include measures whose impact has been modelled in advance, assessing direct benefits for the environment and for farmers' wellbeing, as well as showing efficiency in the use of taxpayers' resources.
The Consortium partners have carried out the first phase of search, detection and characterisation of agricultural data sources that could be used for the generation of synthetic populations of agents.
The Consortium built the AGRICORE ontology, which is an extension of the DCAT-AP 2.0 ontology, allowing a more comprehensive characterisation of the data sources from the highest level (the catalogue to which they belong) to the lowest level (that of their individual variables).
The beta version of Agricultural Research Data Index Tool (ARDIT) is currently undergoing internal testing by the Project's technical partners.

The format required for the storage of the data contained in the different datasets has been defined and the structures of the data warehouse (DWH) that will contain them.
The first steps have been taken so that the required variables can be extracted automatically once selected manually by the user. These variables are processed together, using data fusion techniques, to obtain the statistical distributions corresponding to the real target population.

The Consortium has defined the conceptual map of the agricultural domain to determine the different elements (classes of objects) that will compose each of the agents. The classification of object attributes into states, controllable inputs, disturbances and outputs has been realised, and the definition of equations governing the dynamic variation of agent states have been initiated.
In parallel, a structure of the model that emulates holdings has been proposed in which an economic-financial dimension (associated with long-term planning) and an agronomic dimension (associated with short- and medium-term planning) are distinguished.

For each of the three use cases, it has been identified which attributes of the agents can be generated with existing data sources and for which ones data gaps exist. To fill the latter, tools have been designed that should allow these gaps to be filled through survey campaigns.

The design of the AGRICORE tool has been schematised through its corresponding functional diagram, which includes the different modules that will comprise the suite.

The existing tools have been analysed to present the results of the AGRICORE simulations in an easily interpretable form. This covers both the tools for generating graphs and tables to show consistent results in a large volume of data, as well as the tools for the design of the graphical interface of the AGRICORE tool itself, in which the resulting graphs and tables are to be inserted.

Initially, a complete review of the state of the art was carried out regarding models and tools for analysing the impact of public policies in the different areas covered by AGRICORE: land use, variation in agricultural product prices, impact on the environment and ecosystem services, and impact on rural living conditions. The design of each of the individual impact modules is currently underway. The process of compiling policy instruments and their subsequent translation into mathematical formulation has also been initiated.

Each of the three use cases considered in the project has established its own network of contacts with those actors involved in the life cycle of the instruments that articulate each of the three policy measures analysed.
At the general level of the Project, contacts have been established with the JRC and DG-AGRI to obtain information and advice on the re-use of pre-existing models and data sources.
Finally, the External Experts Advisory Board has been set up.

In this first reporting period, work has started on the design and planning of Ex-Post and Ex-Ante analyses for each of the three use cases.

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.
So far, AGRICORE has already proposed some technical developments and actions that represent progress with respect to the state of the art in the following aspects: (i) characterisation of agricultural data sources down to the level of variables included and the units of analysis they refer to; (ii) micro-data warehousing for automation of data extraction and merging to generate PDFs of attributes of interest of the real population to be synthesised; (iii) agent-based models with state-space feedback; (iv) participatory research design to quantify risk aversion and innovativeness of farmers in regions of interest for the use cases.

At the end of the Project, in addition to consolidating the above advances with concrete implementations and explicit results, the following additional impacts should have been produced: (v) application of advanced Big Data visualisation tools to simulation results; (vi) modularised design allowing interoperability of the suite with different existing or subsequently developed models; (vii) improvement proposals for agricultural statistics regulations and Farm Advisory Systems; (viii) increasing the involvement of farmers in the collection of agro-accounting information by making them see how this reverts into a better-calibrated model that allows to better adjust the design of future measures.
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