Martin - Sediver - Modeling Workshop - Amsterdam_2012-04-23
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Transcript of Martin - Sediver - Modeling Workshop - Amsterdam_2012-04-23
Example of a French farm model: SEDIVER
Guillaume Martin - [email protected]
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.