Predicting adoption of agri-environmental schemes by farmers in the European Union

Much of the land across the European Union (EU) is threatened by unsustainable land-use through intensive farming. To help combat this, Agri-Environmental Schemes (AESs) are provided by the EU to encourage farmers to use a portion of their land to aid with environmental goals such as sustainable farming, bio-diversity or landscape recovery. Farmers in the EU are given the opportunity to take on an AES for a monetary payment that is based on the choice of scheme and the amount of land dedicated to it. If we know or can accurately predict which farmers adopt which AES, we can then predict if the intended benefits to the environment according to the given scheme are likely to be achieved. As a preliminary step, we develop a generalised linear model coupled with a microsimulation that is fed with data from the Farm Accountancy Data Network to predict AES uptake. We find the model is able to accurately predict approximately 70% of farmers’ decisions on whether to adopt an AES across 27 countries in the EU. In the future, this model can be used to predict, for example, if the chosen schemes adopted will lead to their intended benefits, and if changes in the offered AES payment may affect AES adoption.Author summary: The intensity of farming practices differs greatly across the European Union (EU) and post-Brexit United Kingdom. More intensive farming (e.g. high use of fertilisers, pesticides and herbicides) has a negative effect on wildlife populations, air quality and natural flood management. Since 1992, the EU has offered Agri-Environmental Schemes (AES) which encourage farmers to adopt practices that reduce their farming intensity in environmentally sensitive areas in exchange for monetary compensation for the subsequent loss of income. This system has come under scrutiny amid widespread European farmer protests in 2024, with demonstrators demanding more flexibility in environmental rules and better compensation for green farming practices. Meanwhile, the UK has replaced EU policies with its Environmental Land Management Scheme (ELMS). It is important to be able to predict if farmers are likely to use these environmental schemes so the potential benefits can be understood. This paper develops a model predicting AES adoption using data on farm characteristics across the EU (e.g. economic size, crop/livestock types) and subsidy receipt history. The model achieves high accuracy and will help forecast how policy changes might affect scheme participation and subsequent environmental outcomes, while also evaluating if increasing advisory support for farmers could boost uptake.