3. Household Modelling
For the sustainable intensification of agriculture
Human dimensions
Household surveys and typologies, Understanding vulnerabilities:
interviews, and discussion groups identifying priorities and
options
Uncertainties (unknowns)
Strategic: Sensitivity and scenario Increased preparedness,
analyses, use of downscaled GCMs increased resilience and
profitability
Risks (known unknowns)
Tactical: What if? Improved risk management
through the relevant use of
forecasting tools
4. Participatory whole farm
modelling
Diagnosis and evaluation
Describe, current Explain, current farmer’s
Supporting implementation
production systems and decision on resource
their problems allocation and their
consequences
Social desirability
Design, new
Explore, options for
management systems
agro-technological
that contribute to
improvement in face of
increasing resilience and
possible future scenarios
profitability in agriculture
Ex-ante understanding, impacts, performances & trade offs
5. Representing complex systems
Acceptance State contingent
Functional Dynamic
Scenario and Linear
sensitivity programing
analyses
Deterministic Stochastic Variable
environment
Evolutionary
approaches What if?
Empirical questions
Static
Data availability / level of ignorance
6. How optimum is optimum enough?
“…In my judgment, farmers' decisions made in this ad hoc
way are usually very good. They are not perfect (farmers
are human!) but they are usually near enough…”
David Pannell
So, what is the fuss about optimization?
7. Trade-offs
Alternative farming systems • Practices
• Tillage & ground cover
• Moisture seeking
Objective 1
• Tactics
More of 1 More of • Planting rules
both • Soil water thresholds
• Crop sequences & intensity
• Long fallowing
Less of 2
• Forage conservation
• Strategies
• Crop selection (winter / summer)
• Water allocations
• Land allocations
• Cropping / grazing mix
Objective 2
• Farmers’ preference
• Risk preference & its trade offs
• Plastic vs rigid
Increase our (both researchers and the farming community) understanding (...we are all learning...) on what is changing and what are the likely consequences if those changes would persistWork with our farmers and agronomists towards reducing their exposure to change (now and the next 5-10 years), by increasing our understanding on what farming systems are more profitable and less riskySmall number of representative future climates
The past 10 years brought a flurry of farm scale models to understand resource management at this level, which is seen as relevant compared to a sole focus on the field scale, certainly in mixed or specialized animal production systems. Different types of models have been developed: static versus dynamic, summary versus detailed (mechanistic?), goal-oriented versus process-based, geared to evaluation of a single system versus assessment of multiple alternative systems to assess trade-offs among goals. Questions would include: relation between choice of modelling method and specific setting (data availability, goals of study), types of results generated (scenarios, trade-offs, blueprints), which kinds of system delimitations and interrelations among components, how much detail is required, etc.