Environmental Engineering Reference
approach to an econometrically estimated primal-dual approach (Britz and Heckelei
2000a ; Heckelei and Wolff 2003 ; Heckelei and Britz 2005 ; Jansson 2007) .
In SEAMLESS-IF, the FSSIM farm type models are comparable to the regional
supply models in CAPRI. They originally had been designed as pure mixed integer
linear programming models. However, PMP calibration was introduced at a later
stage to allow for more realistic simulation behaviour. The standard version of the
models used for upscaling are based on a variation of the standard PMP approach
(Kanellopoulos et al. 2007) which allows recovery of observed average returns to
land, but the FSSIM models are currently not calibrated to empirically estimated
supply responses. Furthermore, the survey data used for FSSIM do not necessarily
perfectly match regional statistics, so that only relative responsiveness to prices
and policies is extrapolated by EXPAMOD, and the SEAMCAP supply model
continues to be calibrated to a given price-quantity framework in the base year or
baseline (ex-post or ex-ante).
Technology and Externalities
In CAPRI, an average technology for each agricultural production activity in a
specific region is defined based on regional data on crop areas, herd sizes and yields,
along with engineering data on fertilization and feeding practices. Moreover, other
data such as Standard Gross Margins or statistical estimates from the FADN are
taken into account. The technology is expressed as a vector of Input-Output (IO)
coefficients per activity. To perform simulations to a future point in time, the output
coefficients of each activity are trend projected and input coefficients adjusted in a
proportional fashion adjusted by assumptions on input saving technical progress.
Environmental or bio-physical indicators in CAPRI are currently calculated
based on two approaches. The more standard one is to borrow emission factors
from the literature, such as those delivered by the Intergovernmental Panel on
Climate Change (IPCC), and combine them with production activity coefficients.
tion processes is linked to cattle production intensity and specific engineering
parameters such as digestibility or energy consumption. The second approach
consists of a formal link to the biophysical model DNDC (Li et al. 1994) . It allows
a far more detailed description of water and nutrient flows compared to linear
activity approach and it is further detailed below.
In SEAMLESS-IF agricultural technologies and externalities are jointly
modelled by the APES-FSSIM modelling chain. This approach potentially provides
much more detailed information and the ability to represent complex dynamic
processes at very low scale and allows for flexible aggregation possibilities. However,
the approach is also very demanding regarding data and engineering knowledge.
The data requirements and work load to parameterize and calibrate the bio-physical
and bio-economic models were the major reasons for the system developers to
implement a representative instead of a fully covering database and APES-FSSIM