Environmental Engineering Reference
In-Depth Information
on the same model template as the regional supply models, allowing their
parameterization and exploitation within the same model structure. For each
NUTS 2 region, the most important farm types according to acreage/livestock
units were identified and explicitly modelled, and the rest of the sector was
aggregated into a residual group. An update in 2006 expanded the coverage to
some new Member States and introduced a two-dimensional grouping by farm
size and specialization. An expansion to all EU Member States where farm group
compositions are based on the Farm Structure Survey (EUROSTAT) statistics is
currently under way.
Differently from CAPRI, the most representative farm types in SEAMLESS-IF
are selected from the FADN sample following three criteria: specialization, size and
intensity. Here, only a sub-sample of farm types in a wide range of regions across
the EU-27 is modelled explicitly, without considering any residual farm type
category. In these sample regions, specific surveys have been carried out to obtain
the necessary data on management techniques and biophysical conditions for
running the APES and FSSIM models.
The main advantages of the farm type layers in CAPRI and SEAMLESS-IF
compared to the NUTS 2 regional layer are
•
The ability to capture the policy impacts from a specialized farm perspective;
and
An improved supply response representation as the decision space of farmers is
•
more appropriately depicted.
The main disadvantage is the lack of time series data on farm type production
changes at Pan-European level. Both the CAPRI and SEAMLESS-IF farm type
layers still leave room for further improvement. For instance, the introduction of
regional markets for agricultural intermediates such as nutrients, manure or fodder,
the representation of regional land markets or a better integration of information on
production costs from FADN can be mentioned.
Calibration of Farm Type and Regional Programming Models
A major challenge in using representative or aggregate programming models is the
calibration to observed behaviour
. Calibration to observed behaviour must be
understood as the ability to recover a given situation, but also to react to changes of
determinants in a similar way as observed in the past. The limited number of
constraints which can be motivated from economic theory, agronomic knowledge
and available data, combined with the requirement to cover a larger list of crop and
animal production activities, excluded a classical linear programming (LP) approach
for CAPRI. The methodological development of PMP was, therefore, a cornerstone
in CAPRI as it allowed for perfect calibration in combination with a smooth simula-
tion response based on observed behaviour. Indeed, a rather visible methodological
contribution of the CAPRI team was the step-by-step evolution of the original PMP
