Bezlepkina, I.

Dominguez, I.P.

Heckelei, T.

Kanellopoulos, A.

Oude Lansink, A.G.J.M.

Romstad, E.

English

Wageningen Yield 

  
AgSAP Conference 2009, Egmond aan Zee, The Netherlands 
58 
 
 
EXPAMOD – connecting the farm and market levels 
 
I. Pérez Dominguez, I. Bezlepkina, A. Kanellopoulos, T. Heckelei,  
A. Oude Lansink, E. Romstad 
Joint Research Center of the EC, 
Institute for Prospective Technological Studies, Sevilla, Spain 
Contact: Ignacio.perez-dominguez@ec.europa.eu  
 
Introduction 
Farm management models help quantifying changes in land use patterns and agronomic 
practices for a given set of prices and policies. These changes impact indicators of multi-
functionality like the visual character of agricultural landscapes, biological diversity, and 
pollution levels. However, prices are unlikely to remain constant as policies change. There-
fore, farm level optimization results based on exogenous prices may no longer be valid. 
Market level models, on the contrary, are able to capture the supply and price impacts derived 
by policy shocks at the farm and regional level, but are generally not sufficiently detailed for 
the calculation of environmental impacts, since they lack differentiated agronomic practices. 
 By linking farm level and market level models through a newly developed econometric 
model, EXPAMOD, we seek to mitigate this weakness and endogenize the price-quantity 
response in farm management models. This paper explains how EXPAMOD works, and 
applies it to a number of policy scenarios as used in SEAMLESS (Van Ittersum et al., 2008). 
 The main modelling benefit of our approach is that it enables combining the strong points 
of farm management (the detailed linkages to agronomic and natural science models), and 
market level models (endogenous prices and closure of the economy). From a policy analysis 
perspective the resulting farm type and acreage responses provide a much improved base for 
various environmental and landscape modelling exercises. 
 
Methods  
Our modelling chain consists of three models. First, FSSIM, a normative mathematical pro-
gramming model that is specifically fitted for model farm types and regions across Europe. 
Second, CAPRI, a comparative static partial equilibrium model of the European agricultural 
sector. Third, EXPAMOD, an econometric model that estimates changes in supply responses, 
and statistically propagates these responses to out of sample farm-region combinations.  
 The first step in our model chain is a collection of farm models, FSSIM, for several repre-
sentative farm types with different exogenous price sets for a baseline and a policy scenario. 
Next, the econometric model, EXPAMOD, is used to estimate changes in supply responses 
for the exogenous price sets. Changes in relative farm level profits are then used to assign 
new weights to the farm types covered by the analysis. The supply changes at the micro level 
and the revised weights for the farm types are then used to adjust supply in the market model 
CAPRI, so that revised prices are obtained. Finally, these prices are fed back to FSSIM. 
 A major challenge for connecting the farm and market level is that the number of model 
runs at the farm level is limited due to the input data requirements of farm level model, 
FSSIM. Therefore, FSSIM is run for a stratified sample of model farms and regions to cover 
the main variation in the EU-27 of farm types and agro-climatic zones. Following Andersen et 
al. (2007), the selection of sample regions was made at the NUTS2 level, as this is the 
minimum disaggregation level for the market models in SEAMLESS. Nevertheless, the major 
source of farm type data is only available for FADN regions, i.e. regional classification used 
by Farm Accountancy Data Network, so that a mapping between both classifications is 
necessary (Janssen et al., 2008). Due to laborious requirements on the data collection, 16 
sample regions were targeted as this was judged to be feasible for data collection and 
modelling purposes. 
  
Session A2: Methods for up- and downscaling in integrated assessments 
59 
 
 
 In FSSIM regional supply at NUTS2 is recovered by aggregating farm type supply using 
farm weights from the FADN (see, Wieck & Heckelei (2007) for further details). How well 
the farm types selected represent the farm composition within a NUT2 region is an important 
issue for EXPAMOD. The weights derived from the observed data are only suitable for the 
calibration of the model in the base year, since they refer to FSSIM results. Since 
SEAMLESS targets the ex-ante impact assessment of agricultural policies, the projection of 
agricultural markets to a baseline period in the future, requires additional assumptions on 
technological development, changes in consumer demand, inflation, GDP growth, etc. These 
effects are explicitly handled by the CAPRI model and fed back into FSSIM. 
 The price impacts from supply changes in the farm optimization models generate 
information interpreted as ‘pseudo-observations’ for the econometric estimation of 
EXPAMOD. The current simulation design implements varying 'one-price-at-a-time'. The 
price vector, for each scenario, is kept at the 100% level of the initial price vector obtained 
from CAPRI and additional price-quantity vectors for four different price shocks in FSSIM 
are considered (–40%, –20%, +20%, and +40% from the initial price). These scenarios 
generate information on own and cross price-quantity effects that are reintroduced in the 
extrapolation routine of EXPAMOD. In most cases, price changes are likely to be far smaller. 
However, sufficient variation of prices is needed to stabilize the estimates of the price-related 
coefficients. 
 
Results and discussion  
The tests, performed with a flexible functional form, show plausible results and a high 
statistical explanatory power. Nevertheless, some poor predictions have been observed for 
estimations with a low number of observations and high number of parameters. This should 
be easily solved by generating a higher number of pseudo-observations. Additionally, a higher 
variance has been observed compared to the data (especially for products under a quota 
regime, such as sugar beet) and a closer link of results to the biophysical and farm 
management variables would be desirable. Out of sample tests are envisaged to provide 
relevant validation of strengths and weaknesses of the statistical extrapolation. 
Our approach may also be applicable to scale up non-economic results, such as environmental 
impacts. However, further research is needed to refine the method employed for up-scaling 
such impacts, in particular where the spatial distribution of impacts matter. 
 
References 
Andersen, E., et al., 2007. The environmental component, the farming systems component 
and the socio-economic component of the SEAMLESS database for the Prototype 2, 
D4.3.5-D4.4-D4.5.4. SEAMLESS integrated project, 195 pp. 
Janssen, S., et al., 2008. A European database for policy evaluation and assessment of 
agricultural systems. Environmental Science & Policy. (forthcoming) 
Van Ittersum, M.K., et al., 2008. Agricultural Systems 96: 150-165. 
Wieck, C. & T. Heckelei, 2007. Agricultural Economics 36: 203-220. 
  


A European database for policy evaluation and assessment of agricultural systems.

The environmental component, the farming systems component and the socio-economic component of the SEAMLESS database for the Prototype 2, D4.3.5-D4.4-D4.5.4. SEAMLESS integrated project,

EXPAMOD connecting the farm and market levels

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