Urbanization leads to a continuous loss of agricultural land, both directly under the form of land take, and indirectly through the use of agricultural land for non-productive rural activities like recreation, horse keeping or hobby farming. These urbanization processes put pressure on farmers, making farming activities harder through reduced agricultural land, negative externalities and the competition for land. To estimate possible future changes, an agricultural agent-based model was combined with spatially explicit urbanization scenarios and with different agricultural subsidizing policies for the case study of Belgium. As a result of an aging farming population and low succession rates, simulations until 2035 showed a continuous decline of farmers in all scenarios; a trend that will continue for as long as not every farmer that quits his activities is replaced. The results also showed that these declines are expected to be higher in the rural-urban fringe.

The ongoing economic pressure on farmers has resulted in lower gross margins, lower income, and a continuous decrease in the number of farmers in large parts of the world. Most remaining farmers upscale their activities by taking over the land of their former competitors, resulting in a decrease in agricultural employment and an increase in average farm size, accompanied by specialisation and new management techniques. Understanding these significant trends and their impact on the land use and environment requires a deeper knowledge of the mechanisms involved and the impacts of different policy measures. These processes are ideally represented through agent-based modelling. Currently, agent-based models are rarely for larger regions. This paper presents ADAM (Agricultural Dynamics through Agent-based Modelling), using it for the case study of Belgium. ADAM was created to obtain insights in past and current agricultural trends and to explore possible effects of policy measures. ADAM simulates the evolution of a farmer population and their farms at a fine scale on the country level. It produces yearly outputs on the number of farms, their size, and the type of farming activity on every parcel. Results show that ADAM is capable of adequately modelling a farmer population according to past trends and that it can be used to explore the results of a business-as-usual scenario, therefore showing the possibility of creating agent-based models for larger scale real-world applications.