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Monitoring and Evaluation Frameworks for the Common Agricultural Policy

Periodic Reporting for period 2 - MEF4CAP (Monitoring and Evaluation Frameworks for the Common Agricultural Policy)

Período documentado: 2022-04-01 hasta 2024-01-31

The last decade has seen a considerable change in society’s awareness of the interaction between agriculture and the environment. This growing concern for environmental sustainability has been reflected in recent European Commission initiatives such as the European Green Deal and the EU Farm to Fork Strategy. This environmental emphasis is also evident in the reform of the Common Agricultural Policy (CAP) for the period 2022-27, which has seen the objectives of the CAP widen to encompass environmental concerns alongside the long standing economic and social objectives.

While historically the CAP relied on compliance with rules as a means to achieve its objectives, the new CAP will place its emphasis on the achievement of results. This creates a new challenge for monitoring and evaluation (M&E) of the CAP.

The overall objective of the project is to make an inventory of future data needs for monitoring and evaluation of the CAP, describe the current developments in information and communications technology (ICT) and data capturing techniques and assess the technological readiness of these solutions. Eventually, we will deliver a roadmap for future monitoring. This will ultimately contribute to a more sustainable agriculture in the EU and contribute towards the goals of the European Green Deal and the EU Farm to Fork Strategy.
Based on literature, workshops, demonstration cases, a Roadmap and an Innovation Agenda for future M&E, the following main messages and actions emerged from MEF4CAP research activities:
• Evolving and broadening policy needs lead to new demands for M&E, with a specific gap when it comes to indicators, data, and capacity in the agri-environmental field.
• There is value in developing an indicator framework with a high level of spatial details, that can be scaled up to provide national and EU aggregate-level information on the policy impacts.
• There are promising technologies that can deliver such data for M&E purposes. At the same time, there is no one-size-fits-all technological approach to provide all the necessary data for monitoring EU agricultural policies.
• Better use of data from farm management information systems and farm accounting systems, while respecting data rights and ensuring data sovereignty, can reduce the administrative burden of farmers to provide a broader set of sustainability indicators for improving farm performance and M&E.
• Digitisation of currently analogue data flows would facilitate automated approaches, such as robotic accounting, for sustainability reporting. Governments have a role to facilitate this.
• The MEF4CAP Roadmap for future M&E of agricultural policies describes how such data delivering technologies can evolve to power the future M&E system.
• Development of new M&E data streams must go hand in hand with improving data interoperability and facilitating reliable and safe data sharing, that protects privacy and data secrecy and creates trust with data holders, especially the farmers, through improved data sovereignty.
• It is a wide-spread idea that integration of new data sources will lead to additional burden for data collection, processing, and validation. Smart automatisation therefore seems, together with trust with data holders, another key element for the adoption of the proposed technologies.
• MEF4CAP proposes an Innovation Agenda from a holistic approach, mixing and matching work on topical themes with cross-cutting challenges in the data ecosystem.
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.
MEF4CAP Work packages
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