Example of a French farm model: SEDIVER

Guillaume Martin - guillaume.martin@toulouse.inra.fr

INRA (France)

AGIR Group (Toulouse)

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?

Short model description

Events

Manager (Decision system) Operating system

Biophysical system

Strategy DecisionImplementation of

activities

Matter / EnergyInformation

Food stocks

System

boundaries

Grassland plots Herd batches

Events

Manager (Decision system) Operating system

Biophysical system

Strategy DecisionImplementation of

activities

Matter / EnergyMatter / EnergyInformation

Food stocks

System

boundaries

Grassland plots Herd batches

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

The ontology DIESE (Martin-Clouaire and Rellier, 2008) defines concepts such as the system entities and their causal relationships. A set of concepts specific to grassland-based beef farms has been developed such as the entity field, the process herbage growth, etc.

Developments needed to

better deal with this

attribute

Attribute Covered in

previous

analyses?

If ‘yes’, which

indicators were

used?

Which indicators

would you like to use

in future to deal with

attribute?

For your

model

For

household

level models

in general

Economic

performance

Yes Forage and animal

production (kg

forage DM, kg meat)

in total, per ha or

per animal

Forage and animal

production (kg forage

DM, kg meat) in total,

per ha or per animal

Focus on

economic

indicators (e.g.

gross margin)

Interactions

between

economic and

agronomic

decisions

Food self-

sufficiency

Food self-

sufficiency

of animals

for

forage

Ratio of forage

produced to

consumed

Ratio of forage

produced to

consumed

Response of

plants and

animals to

extreme

climatic events

Response of

plants and

animals to

extreme

climatic

events

Food security

No None

Not relevant ?

Developments needed to

better deal with this

attribute

Attribute Covered in

previous

analyses?

If ‘yes’, which

indicators were used?

Which indicators would

you like to use in future to

deal with attribute?

For your model For household

level models in

general

Climate

variability

Yes Ratio of forage produced to consumed Ratio of herbage produced to harvested

Indicators reflecting

exposure, sensitivity and

adaptive capacity of farms

Simulation of

strategic

adaptations

(currently

tactical and

operational)

Response of

biophysical

entities to

extreme events

Simulation of

adaptation

decisions and

actions

Risk

No Risk aversion indicators Decision under

risk

Interactions

between

economic and

agronomic risks

Mitigation No None Not relevant ?

Adaptation

Yes Forage and animal

production

Self-sufficiency for

forage

Indicators reflecting

adaptive capacity of farms

Capturing the

diversity of

adaptation

options and the

conditions for

their

implementation

Adaptation

decision-making

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.

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

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.