1. Example of a French farm model:
SEDIVER
Guillaume Martin - guillaume.martin@toulouse.inra.fr
INRA (France)
AGIR Group (Toulouse)

2. Short model description
• Goal of model development: evaluation of
adaptation options against climate variability for
grassland-based beef farms
• Typical research questions addressed:
– Area allocation (mechanized harvest vs. grazing) or tactical
management to reach self-sufficiency for forage?
– To which extent can self-sufficiency for forage be improved
when revising grazing management?
– Are these revisions feasible for farmers?
– What is the impact of changing indicators upon which
decisions rely?

3. Short model description
Manager (Decision system) Operating system
The ontology DIESE (Martin-
Strategy Decision
Implementation of Clouaire and Rellier, 2008)
activities
defines concepts such as the
system entities and their
causal relationships.
Biophysical system
Food stocks
A set of concepts specific to
grassland-based beef farms
Grassland plots Herd batches
has been developed such as
the entity field, the process
herbage growth, etc.
System Information Events Matter / Energy
boundaries
Two main originalities: explicit representation of
(i) management strategies as the planning and coordination of activities in time and
space through which the farmer controls the biophysical processes
(ii) the diversity in plant, animals, grassland and farmland, and its consequences for
management

4. Developments needed to
better deal with this
attribute
Attribute Covered in If ‘yes’, which Which indicators For your For
previous indicators were would you like to use model household
analyses? used? in future to deal with level models
attribute? in general
Economic Yes Forage and animal Forage and animal Focus on Interactions
performance production (kg production (kg forage economic between
forage DM, kg meat) DM, kg meat) in total, indicators (e.g. economic and
in total, per ha or per ha or per animal gross margin) agronomic
per animal decisions
Food self- Food self- Ratio of forage Ratio of forage Response of Response of
sufficiency sufficiency produced to produced to plants and plants and
of animals consumed consumed animals to animals to
 for extreme extreme
forage climatic events climatic
events
Food security No None Not relevant ?

5. Developments needed to
better deal with this
attribute
Attribute Covered in If ‘yes’, which Which indicators would For your model For household
previous indicators were used? you like to use in future to level models in
analyses? deal with attribute? general
Climate Yes Ratio of forage Indicators reflecting Simulation of Response of
produced to consumed exposure, sensitivity and strategic biophysical
variability Ratio of herbage adaptive capacity of farms adaptations entities to
produced to harvested
(currently extreme events
tactical and Simulation of
operational) adaptation
decisions and
actions
Risk No Risk aversion indicators Decision under Interactions
risk between
economic and
agronomic risks
Mitigation No None Not relevant ?
Adaptation Yes Forage and animal Indicators reflecting Capturing the Adaptation
production adaptive capacity of farms diversity of decision-making
Self-sufficiency for adaptation
forage options and the
conditions for
their
implementation

6. Why modelling decision-making?
Martin, G., Duru, M., Schellberg, J., Ewert, F., 2012. Simulations of plant productivity are affected by
modelling approaches of farm management. Agricultural Systems 109, 25-34.

7. Final remarks
• Authors (e.g. Cox, 1996; McCown, 2002) regularly flag the need for
concepts and methodologies to support the development of decision-
making models  PhD J. Dury
• Cross-disciplinary research with social science and artificial intelligence
• Imbalance scientific / empirical knowledge in our models: cross-
fertilization with participatory approaches  Forage rummy
• Computer models rarely support the development of exploratory
innovations despite the acknowledged limitations of exploitative
innovations (Ash et al. 2008; Howden et al. 2007) to cope with the
changing world.
• New issues with old models?  Efficiency and Substitution vs. Redesign

8. Connected references
• Dury, J., 2011. The cropping-plan decision-making: A farm level modelling and simulation
approach. PhD Thesis, Toulouse Univ., Available at: http://ethesis.inp-
toulouse.fr/archive/00001788/01/dury.pdf
• Mérot, A., Bergez, J.E., 2010. IRRIGATE: A dynamic integrated model combining a knowledge-
based model and mechanistic biophysical models for border irrigation management.
Environmental Modelling & Software 25, 421-432.
• Martin, G., Martin-Clouaire, R., Duru, M., 2012. Farming system design to feed the changing
world. A review. Agronomy for Sustainable Development, in press, doi: 10.1007/s13593-011-
0075-4.
• Martin, G., Felten, B., Duru, M., 2011. Forage rummy: A game to support the participatory
design of adapted livestock systems. Environmental Modelling & Software 26, 1442-1453.
• Martin, G., Theau, J.P., Therond, O., Martin-Clouaire, R., Duru, M., 2011. Diagnosis and
Simulation: a suitable combination to support farming systems design. Crop & Pasture
Science 62, 328-336.
• Martin, G., Martin-Clouaire, R., Rellier, J.P., Duru, M., 2011. A simulation framework for the
design of grassland-based beef-cattle farms. Environmental Modelling & Software 26, 371-
385.