Lesson 6.1: Typologies

Icône de l'outil pédagogique Authors

Erling Andersen and Berien Elbersen


Icône de l'outil pédagogique Introduction

Typologies are needed for classification, simplification and for making integration with other datasets possible. A typology is the same as a classification of one of more variables into meaningful classes. This implies that the threshold values between two classes have been chosen from a certain perspective (environmental, social, economic, etc.). Typologies can be expressed in spatial and in tabular format. Typologies can be one and more dimensional. Examples of the main typologies included in the SEAMLESS database are discussed below.


Icône de l'outil pédagogique Farm typology

In SEAMLESS the data on farming stemming from the EU dataset Farm Accountancy Data Network (FADN) have been aggregated to farm types. This is based on a farm typology elaborated in earlier projects and adapted to SEAMLESS. The typology is based on a combination of three different dimensions, size, combined specialisation and land use and intensity. An example of a SEAMLESS farm type is thus large scale-medium intensity-arable/cereal farm – the most dominant type managing 15% of the utilised agricultural area in EU15 in 2004. One of the main reasons that the single farms included in FADN are aggregated to farm types is the disclosure rules that specify that FADN information can only be displayed if it is representing a minimal of 15 or more sample farms (Andersen et al., 2006). The different discriminating variables and the specific threshold values determining the classes in the 4 dimensions of the typology build on earlier work and include consultations with Member State experts as well as statistical analysis. In SEAMLESS further consultations with experts have been used to improve the typology. The typology is now used as the basis for linking environmental and economic models on both the input and the output side of the model chains to do the integrated impact assessments.


Icône de l'outil pédagogique Agri-environmental typology

The Agri-Environmental Zonation (AEnZ) is a framework which is needed to assess the impacts of agricultural policies covering the wide biophysical variation in which agricultural activities take place in Europe (Hazeu et al., 2006). The main objective of building this AEnZ was therefore to stratify Europe on the main biophysical factors that determine the agronomic production capacity in Europe. The agri-environmental zones are based on a combination of biophysical characteristics and aiming to identify regions where the biophysical conditions for farming are relatively homogenous. At the same time the link to the marked level modelling was ensured by the inclusion of the administrative regions (NUTS regions). The combination of agri-environmental zones with the administrative NUTS boundaries resulted in spatial units called SeamZones. To these SeamZones the SEAMLESS farm-type information is linked enabling the combined information to be used for the model chain assessments. The SeamZones have been used as a framework for selection of sample regions. Sample regions are used to collect detailed information on farm management not available in the European level statistical sources. This again enables detailed modelling at crop and farm type level within these regions.


Icône de l'outil pédagogique Socio-economic typologies

The SEAMLESS database includes regional typologies based on socio-economic indicators in the EU25 that can serve as contextual information for assessments in SEAMLESS. In the current version of the database, we only included typologies on the share of agriculture in total employment, rurality derived from population density, leading and lagging regions derived from employment growth and livestock density. Further typologies have been developed and are under consideration to be included in the final version of the database such as population density (i.e. rurality), population growth plus rurality, employment growth plus rurality, share agriculture in total employment plus rurality, unemployment rates plus rurality, GDP/capita plus rurality, share of LFAs plus rurality, size of farms in hectare plus rurality, European Size Units per hectare and per farm plus rurality, share of farm holders >65 years plus rurality, share of part time farm holders plus rurality, share of female farm holders plus rurality.


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