Amanda Sahrbacher

The MEA-Scope Modelling Approach

The MEA-Scope project developed, and applied a modelling approach that allows for the ex ante assessment of sustainability impacts of new policies, technologies and market changes. Thereby, the agricultural production at farm level and its effect on social, economic and environmental assets under changing circumstances is examined. The MEA-Scope modelling approach simulates the development of regional agricultural production structures over time. Within the same analysis, the approach considers details of individual farms and soils. During the project duration, three pre-existing models were further completed and interlinked with each other. The modelling approach was applied at two different levels of detail in seven different European regions to examine the effects of five agricultural policy scenarios. The core models involved were AgriPoliS, MODAM and FASSET/Farm-N. In this chapter, the modelling approach, characteristics of the models involved and the policy scenarios are introduced while results as well as details on the different modelling applications can be found in subsequent chapters of this book.

Analysing Exemplary Policy Issues Using the MEA-Scope Framework

This contribution links the activity of farming in rural areas in response to policy changes to the achievement of economic, social, and environmental policy objectives. The focus here is on a model-based evaluation of policy impacts on the supply of multifunctional activities using an indicator framework. We apply the MEA-Scope modelling approach.

Validation of an Agent-Based, Spatio-Temporal Model for Farming in the River Gudenå Landscape. Results from the MEA-Scope Case Study in Denmark

A validation of the agent-based model AgriPoliS by back casting is presented. The agent-based model AgriPoliS is calibrated to a Danish agricultural catchment. The model was supplied with empirical data on the exact location of individual plots as well as farm characteristics of 2,383 individual farms covering the period 1998–2004. Validation was carried out comparing the results of the simulation to the empirical data. The comparison shows that the model simulates development on the most aggregated level (the number of farms in the catchment) relatively well. There are some variations to the degree of precisions on less aggregated levels of analysis. The overall conclusion is that the agent-based model approach utilized here was effective in regard to prediction.

Parameterisation of AgriPoliS: A Model of Agricultural Structural Change

This model description follows the ODD protocol (Overview, Design concepts and Details) as proposed and described in Grimm et al. (Ecol Model 198:115–126, 2006) and Grimm et al. (Ecol Model 221:2760–2768, 2010). The ODD protocol allows a standardized description of agent-based models what improves the clarity and the comparability of models. The following ODD protocol has already been published by Sahrbacher et al. (ODD-protocol of AgriPoliS. Technical report. IAMO. Halle, 2012).

Scaling from Farm to Landscape

This chapter presents upscaling methodologies, implemented in the MEA-Scope strategic research project. MEA-scope is based on a bottom-up approach, where farm information are collected for landscapes in Germany, Slovakia, Poland, France, Hungary, Italy and Denmark. This chapter is about the upscaling from farm to landscape level, and focuses on the modelling of Nitrogen surplus from agriculture as an indicator for water pollution. It is demonstrated in detail how farm information from the Danish landscape is upscaled for such landscape level analyses, using the EU Integrated Area Control System (IACS) and GIS. Subsequently, farm N-surpluses, upscaled for each of the other the landscapes, are also presented, and different upscaling pathways are reviewed. Based on the results, advantages in the bottom-up approaches applied are emphasized. It is concluded, that bottom-up methods for upscaling are needed to convey information from research to decision-makers, and that it is important to specifically address the scale issue within the cycle of strategic research, where an iterative interaction between researchers and decision-makers is carried out. MEA-Scope is an example of a project where such interactions have been practised.

A Scenario-Wise Analysis of Economic and Environmental Impacts in the MEA-Scope Case Study Regions

This chapter summarises economic and environmental impacts of five CAP scenarios in seven European case study regions located in Germany, Denmark, Italy, Slovakia, Hungary, Poland and France. The spatially explicit MEA-Scope modelling approach based on three farm-level models – AgriPoliS (agent-based), MODAM (bio-economic), FASSET (bio-physical) – is used to dynamically simulate the behaviour of single farms. At regional level, the change in a range of economic (i.a. number of farms, farm size, farm income) and environmental indicators (related to soil, water and habitat quality) is analysed to compare the scenarios.

Impacts of Three Direct Payment Options on Farm Structure, Economic Performance and Production Pattern: Results from the MEA-Scope Case Study in Italy

The Mugello area in the heart of Tuscany, Italy is a traditional region in which the existence of the characteristic cultural landscape is closely linked to quality beef and dairy production. This chapter uses the MEA-Scope modelling approach that is based on the micro-economic models AgriPoliS, MODAM and FASSET, to analyse how different EU policy options affect farm structure, farm profits, and agricultural production activities in Mugello. Simulated scenarios include an assumed continuation of the Agenda 2000 policy, an introduction of a decoupled single farm payment, and a scenario without direct payments.