To understand how current and new technologies and resulting data streams can prospectively be combined to meet the new needs of the M&E system, MEF4CAP introduced two trajectories of a Roadmap that correspond to “plausible courses of action”, serving different M&E objectives, corresponding indicators and underlying data delivering technologies and data streams and confronted with different barriers and drivers.
In the first trajectory existing technologies already generate data streams and feed indicators at a high spatial resolution scale (farm, parcel and even sub parcel). This responds to the current needs of operational CAP monitoring. The first trajectory is therefore currently very much linked to individual farm performance in relation to CAP objectives, i.e. deriving them from monitoring of compliance with eco-schemes and related agricultural practices. The main data source and data delivering technologies used are, in the first place, the Integrated Administration and Control System, IACS (inclusive of LPIS, GSA, animal-based application systems, farmers’ declarations) and Earth Observations (EO), namely from the Sentinels’ programme. Typically, they deliver data facilitating the provision of indicators on land use/land cover/crop types, individual animals, as well as, in perspective, on carbon sequestration, emissions, nutrients and pesticide use. When EO is not sufficient, further evidence regarding farming activities can be generated through the complementary use of geotagged photos. In the future, additional technologies and data sources are expected to complement existing ones. We are referring to machine data, field or animal sensors, UAVs.
The second trajectory primarily aims at the evaluation of agricultural policies at MS & EU level. In other words, data streams and underlying technologies facilitate the provision of indicators measuring the impact of sectoral policies which is reported at aggregated levels. FADN (and the FSDN in perspective), in combination with other EU level statistical sources (FSS/IFS, Livestock survey, Eurostat, LUCAS, ESDAC, etc.) represent an initial entry point in terms of data sources, underlying delivering technologies and resulting data streams. At a later time, further data will be generated by developments in relevant EU data infrastructures.
Given the limitations of both trajectories, we introduce what we define as “cross-fertilizations” between trajectories implying combinations of current and innovative statistical downscaling techniques and data delivering technologies needed to complement data gaps and improve data quality responding for instance to the know lack of homogeneous data at NUTS 3 level regarding agricultural holdings. Examples are the Digital Farm Books and Farm Management Information Systems (FMIS) as well as financial data which can be handled through robotic accounting. The respective data sources and related supporting technologies can generate information generally not obtainable from the two described trajectories such as, on the one hand, economic and social data and, on the other, on actual agricultural operations and input use.
