Martin Damgaard

Multifunctional farming, multifunctional landscapes and rural development

The Common European Agricultural Policy (CAP) is under transformation towards a Common Agricultural and Rural Policy of Europe (CARPE). During this transformation, substantial parts of the previously direct support for agricultural production (1st pillar measures) are now decoupled from production. Moreover, a share of direct payments is modulated to the rural development programme, which is the 2nd pillar of the CARPE (Table 1).

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.

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.

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.

Recreating Context in Spatial Modelling of Agricultural Landscapes

This study uses spatial location of farms as a case of recreating context in spatial modelling of agricultural landscapes. When working with generally available agricultural structural data such as FADN data, spatial reference on farm location is not available. This means that methods to reliable recreations of spatial context must be developed. This study recreates spatial location of farms within a German and a Danish agricultural landscape where real farm locations are known, using an approach based on indexation of structural heterogeneity. The approach can be used generally since it is based on the utilisation of generally available data across the EU countries. Based on the Danish case, it is concluded that the method leads to a close to random location of farms. An additional case study carried out in Italy concludes that initial spatial location of farms is important in an area with great spatial heterogeneity like Italy, but might mean less in spatial homogenous areas like Denmark and Germany.

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.