Book of Abstract (incl. addendum) - IFSA symposium 2012

Workshop 6.4 Integrated simulation modelling in farming systems research
Combining farm simulation with frontier efficiency analysis
David Berre, Jonathan Vayssières, Jean-Philippe Boussemart, Hervé Leleu and Emmanuel Tillard
University of Lille, France
D.Berre@ieseg.fr
The model used, Gamede, is a “whole-farm” dynamic model composed of 6 biophysical modules and a
decisional system (Vayssières et al, 2009). This simulation model gives accurate predictions for
various sustainability indicators (labor, energy consumption, nitrogen leaks to the environement…) to
characterize different observed or hypothetical farms. As Gamede is based on a stock-flow approach,
we can monitor the farm stocks (slurry, fodders…) over time. Gamede also gives a full description of
management operations of the production system. The Gamede model is randomly parameterized with
the objective to cover the feasible domain of production systems by simulation. Key issues of the
method are selecting variable parameters and defining lower and upper bounds to these parameters.
Using observed ranges and expert knowledge are the tested hypothesis. Even though the simulation
approach constitutes a relevant tool for describing the production system, it can not provide a global
efficiency analysis taking into account multiple parameters. We suggest combining Gamede with a
linear programming model using the “Data Envelopment Analysis” method to assess the efficiency of a
large variety of simulated farms. Each farm is characterized by different structural and decisional
parameters, inflows and sustainability indicators. The last two types of variables are respectively inputs
and outputs of the optimization model. The DEA method implemented in this paper considers both the
production flows (good outputs) and the undesirable outflows (bad outputs). The calculated frontier
corresponds to the farms which minimize both pollution and inputs while optimizing their production.
A distance between actual farms and the frontier is calculated to evaluate their potential efficiency
progress. The main advantage of combining simulation and optimization method is that it supplies
large data sets and describes accurately the parameters correlated to the efficiency.
RECORD: an open platform to build, evaluate and simulate integrated
models of farming and agro-ecosystems
J. Bergez, H. Raynal, F. Garcia
INRA, France
Jbergez@toulouse.inra.fr
The rapid change in the agricultural industry requires the development of new methods of production
to guarantee sustainable agriculture. In silico approaches offer the possibility to identify more quickly
new systems to tackle current social, political and environmental concerns. Numerous agrosystem
functioning models already exist. Nowadays the main issue is more to couple and use them at different
spatial and temporal scales rather than developing new ones.
INRA set up an integrated modelling framework to gather, link and provide models and
companions tools to answer new society questions regarding agriculture. Functional specifications
were drawn during a two-day meeting in January 2007. Requirements are: to develop new models as
modular components, to re-use and combine them to represent cropping and farming systems at
different time and spatial integration steps; to allow the modelling and simulation of farmer's decision
making process; to link with statistical packages to perform model calibration or in silico experiment
analyses; with economics and optimization software and risk analysis; with databases and GIS; to
include random weather generators to work on climate change and uncertainty.
After different tests, the RECORD platform was built under the VLE environment, allowing the
design of atomic and coupled models. It integrates different time steps and spatial scales and proposes
standard formalisms to model agro-ecosystems (e.g. difference equations, differential equations, state
charts). A graphic user-interface was designed to simplify coding tasks.
A variety of research projects already use RECORD. Examples are given showing the ability to
recode simple models, encapsulate more complex ones, link with GIS and databases, and use the R
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