CAP and Advisory Services: From Farm Advisory Systems to Innovation Support
Abstract
Summary Farm advisory services have become an important topic in the Common Agricultural Policy (CAP) of the European Union (EU). The aim of this article is to draw lessons from the implementation of the regulation and measures of the current 2014–2022 CAP in knowledge transfer and advisory services to contribute to debates about the future of farm advisory policies. Findings are based on two studies commissioned by the European Commission: an evaluation study implemented by a consortium of consultancies and researchers and a comparative analysis implemented in the frame of the H2020 research project AgriLink. The article first provides a historical perspective of the ‘knowledge and advice’ dimension of the CAP since 2007. We show a gradual transition towards greater flexibility of measures supporting innovation and knowledge flows. The professional status of farm advisors across European countries was more strongly acknowledged, offering new life‐long training opportunities to advisors. However, the implementation of CAP’s advisory measures remains complex. As a result, the effects of these measures have been rather limited, with only few farmers reached overall. Fostering an inclusiveness of these measures remains an ongoing challenge for policymakers.
Read the paper
Related studies
- Contribution of social science to large scale biodiversity conservation: A review of research about the Natura 2000 network
- The alignment of agricultural and nature conservation policies in the European Union
- Determination of ecological significance based on geostatistical assessment: a case study from the Slovak Natura 2000 protected area
- Routledge Handbook of the Study of the Commons
- Farmers' acceptance of results-based agri-environmental schemes: A German perspective
- Approaches to Identify the Value of Seminatural Habitats for Conservation Biological Control
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.