Konrad Kellermann

Agent-based Analysis of Agricultural Policies: an Illustration of the Agricultural Policy Simulator AgriPoliS, its Adaptation and Behavior

This paper combines agent-based modeling of structural change with agricultural policy analysis. Using the agent-based model AgriPoliS, we investigate the impact of a regime switch in agricultural policy on structural change under various framework conditions. Instead of first doing a sensitivity analysis to analyze the properties of our model and then examining the introduced policy in an isolated manner, we use a meta-modeling approach in combination with the statistical technique of Design of Experiments to systematically analyze the relationship between policy change and model assumptions regarding key determinants of structural change such as interest rates, managerial abilities, and technical change. As a result, we observe that the effects of policies are quite sensitive to the mentioned properties. We conclude that an isolated analysis of a policy regime switch would be of only minor value for policy advice given the ability of simulation models to examine various potential futures.

On the Dynamics of Structural Change in Agriculture Internalc Frictions, Policy Threats and Vertical Integration

The agricultural sector in Europe faces a number of important challenges, such as developments in biotechnology and biofuel, globalization, vertical integration and policy changes. The important questions are whether and how farms are able to adapt to these changes and what are the potential forces working against adaptation. This paper elaborates on internal frictions regarding structural change in agriculture, namely sunk costs, dependence on subsidies and the challenges of an increasing demand for vertical integration.

An agent-based approach to modeling impacts of agricultural policy on land use, biodiversity and ecosystem services

We present extensions to the agent-based agricultural policy simulator (AgriPoliS) model that make it possible to simulate the consequences of agricultural policy reform on farmers’ land use decisions and concomitant impacts on landscape mosaic, biodiversity and ecosystem services in a real agricultural region. An observed population of farms is modelled as a multi-agent system where individual farm-agent behaviour and their interactions—principally competition for land—are defined through an optimization framework with land use and landscape impacts resulting as emergent properties of the system. The model is calibrated to real data on the farms and the landscape to be studied. We illustrate the utility of the model by evaluating the potential impacts of three alternative frameworks for the European Union Common Agricultural Policy (CAP) on landscape values in two marginal agricultural regions. Mosaic value was found to be sensitive to the choice of policy scheme in one of the landscapes, whereas significant trade-offs were shown to occur in terms of species richness by habitat and species composition at the landscape scale in both regions. The relationship between food production and other ecosystem services was found to be multifaceted. Thus illustrating the difficulty of achieving landscape goals in a particular region with simple or general land management rules (such as the current rules attached to CAPs direct payments). Given the scarcity of funding for conservation, the level and conditions for allocating direct payments are, potentially, of great importance for preserving landscape values in marginal agricultural regions (subject to levels of other support).

Past and future effects of the Common Agricultural Policy in the Czech Republic

This article discusses the impact of the Czech Republics accession to the European Union. Special emphasis is given to effects that have resulted from implementing the Common Agricultural Policy. Two approaches are applied. First, in an ex post analysis we address how accession has thus far influenced structural changes and the income situation. Second, in an ex ante analysis we apply the agent-based model AgriPoliS to simulate the impact of decoupling top-up payments on structural change, farm income and payment redistribution. In the ex post analysis, it is observed that structural change is still influenced by the transformation process. Farm income partially follows the increase of payments, but there is also a capitalisation of payments into factor prices for land, labour and other inputs. Furthermore, simulations support empirical findings that accession slows down structural change compared to a scenario without accession, while decoupling top-ups in 2009 has no significant impact on structural change. However, depending on the type of decoupling, a redistribution of payments among farmers can be observed.

Making More Sense of Agent-Based Simulation for Agricultural Policy Analysis

In the field of agricultural and resource economics there has recently been a growing interest in using agent-based models (ABM) for policy analysis. ABM possess the capability of simulating complex relationships between many interacting agents and their environment. In agricultural economics, ABM offer possibilities for addressing and explaining observable phenomena such as structural change. Many empirical based agent-based models are highly complex and include a multitude of modelled processes as well as a high degree of detail and parameterisation. This inevitably reduces their tractability, and makes it difficult to follow and understand their functioning and interpret results. Because of this, communicating results of complex agent-based models to policy-makers is a challenging task. For ABM to assist in decision-making, policy makers should develop an understanding of the complex processes and assumptions underlying the simulation models based on the provided given information (such as model documentations, model code). Yet, this is hardly a realistic option given policy makers’ varying disciplinary backgrounds and time restrictions. Obviously, models cannot capture the full complexity of a target system and all relevant processes. Inevitably, we need to make guesses and assumptions about the true nature of the target system. However, we do not know what the response will look like if we for different combinations of input parameters, and how these interact with each other. This is particularly important, if we, for example, want to draw relevant policy conclusions based on an analysis of interactions between policy measures and determinants of structural change.